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b/dataset_info.html new file mode 100644 index 0000000..0a489df --- /dev/null +++ b/dataset_info.html @@ -0,0 +1,706 @@ + + + + + + + + + +fastMONAI - Dataset information + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Dataset information

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

MedDataset

+
+
 MedDataset (path=None, postfix:str='', img_list:list=None,
+             reorder:bool=False, dtype:(<class'fastMONAI.vision_core.MedIm
+             age'>,<class'fastMONAI.vision_core.MedMask'>)=<class
+             'fastMONAI.vision_core.MedImage'>, max_workers:int=1)
+
+

A class to extract and present information about the dataset.

+
+

source

+
+
+

get_class_weights

+
+
 get_class_weights (labels:(<built-infunctionarray>,<class'list'>),
+                    class_weight:str='balanced')
+
+

*Calculates and returns the class weights.

+

Args: labels: An array or list of class labels for each instance in the dataset. class_weight: Defaults to ‘balanced’.

+

Returns: A tensor of class weights.*

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/external_data.html b/external_data.html new file mode 100644 index 0000000..2e3e74c --- /dev/null +++ b/external_data.html @@ -0,0 +1,801 @@ + + + + + + + + + +fastMONAI - External data + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

External data

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

MURLs

+
+
 MURLs ()
+
+

A class with external medical dataset URLs.

+
+
+

IXI

+
+

source

+
+

download_ixi_data

+
+
 download_ixi_data (path:(<class'str'>,<class'pathlib.Path'>)='../data')
+
+

*Download T1 scans and demographic information from the IXI dataset.

+

Args: path: Path to the directory where the data will be stored. Defaults to ‘../data’.

+

Returns: The path to the stored CSV file.*

+
+
+
+

IXITiny

+
+

source

+
+

download_ixi_tiny

+
+
 download_ixi_tiny (path:(<class'str'>,<class'pathlib.Path'>)='../data')
+
+

*Download the tiny version of the IXI dataset provided by TorchIO.

+

Args: path: The directory where the data will be stored. If not provided, defaults to ‘../data’.

+

Returns: The path to the directory where the data is stored.*

+
+
+
+

Lower spine data

+
+

source

+
+

download_spine_test_data

+
+
 download_spine_test_data
+                           (path:(<class'str'>,<class'pathlib.Path'>)='../
+                           data')
+
+

*Downloads T2w scans from the study ‘Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method’ by Chu et. al. 

+

Args: path: Directory where the downloaded data will be stored and extracted. Defaults to ‘../data’.

+

Returns: Processed dataframe containing image paths, label paths, and subject IDs.*

+
+

source

+
+
+

download_example_spine_data

+
+
 download_example_spine_data
+                              (path:(<class'str'>,<class'pathlib.Path'>)='
+                              ../data')
+
+

*Downloads example T2w scan and corresponding predicted mask.

+

Args: path: Directory where the downloaded data will be stored and extracted. Defaults to ‘../data’.

+

Returns: Path to the directory where the example data has been extracted.*

+
+
+
+

MedMNIST3D

+
+

source

+
+

download_medmnist3d_dataset

+
+
 download_medmnist3d_dataset (study:str,
+                              path:(<class'str'>,<class'pathlib.Path'>)='.
+                              ./data', max_workers:int=1,
+                              skip_download:bool=True)
+
+

*Downloads and processes a particular MedMNIST3D dataset.

+

Args: study: MedMNIST dataset (‘OrganMNIST3D’, ‘NoduleMNIST3D’, ‘AdrenalMNIST3D’, ‘FractureMNIST3D’, ‘VesselMNIST3D’, ‘SynapseMNIST3D’) path: Directory to store and extract downloaded data. Defaults to ‘../data’. max_workers: Maximum number of worker processes for data processing. Defaults to 1. skip_download: Flag to indicate if the dataset should be re-downloaded if the dataset exists locally. Default set to False.

+

Returns: Two pandas DataFrames. The first DataFrame combines training and validation data, and the second DataFrame contains the testing data.*

+
+
+
+

Endometrical cancer

+
+

source

+
+

download_example_endometrial_cancer_data

+
+
 download_example_endometrial_cancer_data
+                                           (path:(<class'str'>,<class'path
+                                           lib.Path'>)='../data',
+                                           multi_channel:bool=True)
+
+

*Download example data for endometrial cancer.

+

Args: path (str or Path): Path to save the downloaded data. multi_channel (bool): If True, download multi-channel data (Vibe, T2, and ADC). Otherwise, download only Vibe.*

+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..53eb811 --- /dev/null +++ b/index.html @@ -0,0 +1,758 @@ + + + + + + + + + +fastMONAI - Overview + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Overview

+
+ + + +
+ + + + +
+ + + +
+ + + +

+

CI Docs PyPI

+

A low-code Python-based open source deep learning library built on top of fastai, MONAI, TorchIO, and Imagedata.

+

fastMONAI simplifies the use of state-of-the-art deep learning techniques in 3D medical image analysis for solving classification, regression, and segmentation tasks. fastMONAI provides the users with functionalities to step through data loading, preprocessing, training, and result interpretations.

+

Note: This documentation is also available as interactive notebooks.

+
+

Installing

+
+

From PyPI

+

pip install fastMONAI

+
+
+

From GitHub

+

If you want to install an editable version of fastMONAI run:

+
    +
  • git clone https://github.com/MMIV-ML/fastMONAI
    • +
  • pip install -e 'fastMONAI[dev]'
    • +
+
+
+
+

Getting started

+

The best way to get started using fastMONAI is to read our paper and dive into our beginner-friendly video tutorial. For a deeper understanding and hands-on experience, our comprehensive instructional notebooks will walk you through model training for various tasks like classification, regression, and segmentation. See the docs at https://fastmonai.no for more information.

+ + + + + + + + + + + + + + + + + + + + + + + + + +
Notebook1-Click Notebook
10a_tutorial_classification.ipynb
shows how to construct a binary classification model based on MRI data.		Google Colab
10b_tutorial_regression.ipynb
shows how to construct a model to predict the age of a subject from MRI scans (“brain age”).		Google Colab
10c_tutorial_binary_segmentation.ipynb
shows how to do binary segmentation (extract the left atrium from monomodal cardiac MRI).		Google Colab
10d_tutorial_multiclass_segmentation.ipynb
shows how to perform segmentation from multimodal MRI (brain tumor segmentation).		Google Colab
+
+
+

How to contribute

+

See CONTRIBUTING.md

+
+
+

Citing fastMONAI

+

If you are using fastMONAI in your research, please use the following citation:

+
@article{KALIYUGARASAN2023100583,
+title = {fastMONAI: A low-code deep learning library for medical image analysis},
+journal = {Software Impacts},
+pages = {100583},
+year = {2023},
+issn = {2665-9638},
+doi = {https://doi.org/10.1016/j.simpa.2023.100583},
+url = {https://www.sciencedirect.com/science/article/pii/S2665963823001203},
+author = {Satheshkumar Kaliyugarasan and Alexander S. Lundervold},
+keywords = {Deep learning, Medical imaging, Radiology},
+abstract = {We introduce fastMONAI, an open-source Python-based deep learning library for 3D medical imaging. Drawing upon the strengths of fastai, MONAI, and TorchIO, fastMONAI simplifies the use of advanced techniques for tasks like classification, regression, and segmentation. The library's design addresses domain-specific demands while promoting best practices, facilitating efficient model development. It offers newcomers an easier entry into the field while keeping the option to make advanced, lower-level customizations if needed. This paper describes the library's design, impact, limitations, and plans for future work.}
+}
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/robots.txt b/robots.txt new file mode 100644 index 0000000..490a6d9 --- /dev/null +++ b/robots.txt @@ -0,0 +1 @@ +Sitemap: https://fastmonai.no/sitemap.xml diff --git a/search.json b/search.json new file mode 100644 index 0000000..4ff2cb0 --- /dev/null +++ b/search.json @@ -0,0 +1,361 @@ +[ + { + "objectID": "vision_metrics.html", + "href": "vision_metrics.html", + "title": "Vision metrics", + "section": "", + "text": "source\n\ncalculate_dsc\n\n calculate_dsc (pred:torch.Tensor, targ:torch.Tensor)\n\nMONAI compute_meandice\n\nsource\n\n\ncalculate_haus\n\n calculate_haus (pred:torch.Tensor, targ:torch.Tensor)\n\nMONAI compute_hausdorff_distance\n\nsource\n\n\nbinary_dice_score\n\n binary_dice_score (act:<built-\n inmethodtensoroftypeobjectat0x7ff050730e20>,\n targ:torch.Tensor)\n\n*Calculates the mean Dice score for binary semantic segmentation tasks.\nArgs: act: Activation tensor with dimensions [B, C, W, H, D]. targ: Target masks with dimensions [B, C, W, H, D].\nReturns: Mean Dice score.*\n\nsource\n\n\nmulti_dice_score\n\n multi_dice_score (act:torch.Tensor, targ:torch.Tensor)\n\n*Calculate the mean Dice score for each class in multi-class semantic segmentation tasks.\nArgs: act: Activation tensor with dimensions [B, C, W, H, D]. targ: Target masks with dimensions [B, C, W, H, D].\nReturns: Mean Dice score for each class.*\n\nsource\n\n\nbinary_hausdorff_distance\n\n binary_hausdorff_distance (act:torch.Tensor, targ:torch.Tensor)\n\n*Calculate the mean Hausdorff distance for binary semantic segmentation tasks.\nArgs: act: Activation tensor with dimensions [B, C, W, H, D]. targ: Target masks with dimensions [B, C, W, H, D].\nReturns: Mean Hausdorff distance.*\n\nsource\n\n\nmulti_hausdorff_distance\n\n multi_hausdorff_distance (act:torch.Tensor, targ:torch.Tensor)\n\n*Calculate the mean Hausdorff distance for each class in multi-class semantic segmentation tasks.\nArgs: act: Activation tensor with dimensions [B, C, W, H, D]. targ: Target masks with dimensions [B, C, W, H, D].\nReturns: Mean Hausdorff distance for each class.*", + "crumbs": [ + "Vision metrics" + ] + }, + { + "objectID": "tutorial_regression.html", + "href": "tutorial_regression.html", + "title": "Regression", + "section": "", + "text": "Google Colab\n\n\n\nfrom fastMONAI.vision_all import *\n\n\npath = Path('../data')\npath.mkdir(exist_ok=True)\n\n\nSTUDY_DIR = download_ixi_data(path=path)\n\nImages already downloaded and extracted to ../data/IXI/T1_images\n2022-12-22 16:47:36,114 - INFO - Expected md5 is None, skip md5 check for file ../data/IXI/IXI.xls.\n2022-12-22 16:47:36,115 - INFO - File exists: ../data/IXI/IXI.xls, skipped downloading.\nPreprocessing ../data/IXI/IXI.xls\n\n\n\nLooking at the data\n\ndf = pd.read_csv(STUDY_DIR/'dataset.csv')\ndf['age'] = np.around(df.age_at_scan.tolist(), decimals=0)\n\n\ndf.head()\n\n\n\n\n\n\n\n\nt1_path\nsubject_id\ngender\nage_at_scan\nage\n\n\n\n\n0\n../data/IXI/T1_images/IXI002-Guys-0828-T1.nii.gz\nIXI002\nF\n35.80\n36.0\n\n\n1\n../data/IXI/T1_images/IXI012-HH-1211-T1.nii.gz\nIXI012\nM\n38.78\n39.0\n\n\n2\n../data/IXI/T1_images/IXI013-HH-1212-T1.nii.gz\nIXI013\nM\n46.71\n47.0\n\n\n3\n../data/IXI/T1_images/IXI014-HH-1236-T1.nii.gz\nIXI014\nF\n34.24\n34.0\n\n\n4\n../data/IXI/T1_images/IXI015-HH-1258-T1.nii.gz\nIXI015\nM\n24.28\n24.0\n\n\n\n\n\n\n\n\ndf.age.min(), df.age.max()\n\n(20.0, 86.0)\n\n\n\nmed_dataset = MedDataset(path=STUDY_DIR/'T1_images', max_workers=12)\n\n\ndata_info_df = med_dataset.summary()\n\n\ndata_info_df.head()\n\n\n\n\n\n\n\n\ndim_0\ndim_1\ndim_2\nvoxel_0\nvoxel_1\nvoxel_2\norientation\nexample_path\ntotal\n\n\n\n\n3\n256\n256\n150\n0.9375\n0.9375\n1.2\nPSR+\n../data/IXI/T1_images/IXI002-Guys-0828-T1.nii.gz\n498\n\n\n2\n256\n256\n146\n0.9375\n0.9375\n1.2\nPSR+\n../data/IXI/T1_images/IXI035-IOP-0873-T1.nii.gz\n74\n\n\n4\n256\n256\n150\n0.9766\n0.9766\n1.2\nPSR+\n../data/IXI/T1_images/IXI297-Guys-0886-T1.nii.gz\n5\n\n\n0\n256\n256\n130\n0.9375\n0.9375\n1.2\nPSR+\n../data/IXI/T1_images/IXI023-Guys-0699-T1.nii.gz\n2\n\n\n1\n256\n256\n140\n0.9375\n0.9375\n1.2\nPSR+\n../data/IXI/T1_images/IXI020-Guys-0700-T1.nii.gz\n2\n\n\n\n\n\n\n\n\nresample, reorder = med_dataset.suggestion()\n\n\nbs=4\nin_shape = [1, 256, 256, 160]\n\n\nitem_tfms = [ZNormalization(), PadOrCrop(in_shape[1:]), RandomAffine(scales=0, degrees=5, isotropic=False)]\n\n\ndblock = MedDataBlock(blocks=(ImageBlock(cls=MedImage), RegressionBlock), \n splitter=RandomSplitter(seed=32),\n get_x=ColReader('t1_path'),\n get_y=ColReader('age'),\n item_tfms=item_tfms,\n reorder=reorder,\n resample=resample)\n\n\ndls = dblock.dataloaders(df, bs=bs)\n\n\nlen(dls.train_ds.items), len(dls.valid_ds.items)\n\n(449, 112)\n\n\n\ndls.show_batch(anatomical_plane=2)\n\n\n\n\n\n\n\n\n\n\nCreate and train a 3D model\nImport a network from MONAI that can be used for regression tasks, and define the input image size, the output size, channels, etc.\n\nfrom monai.networks.nets import Regressor\nmodel = Regressor(in_shape=[1, 256, 256, 160], out_shape=1, channels=(16, 32, 64, 128, 256),strides=(2, 2, 2, 2), kernel_size=3, num_res_units=2)\n\n\nloss_func = L1LossFlat()\n\n\nlearn = Learner(dls, model, loss_func=loss_func, metrics=[mae])\n\n\nlearn.summary()\n\n\n\n\n\n\n\n\nRegressor (Input shape: 4 x 1 x 256 x 256 x 160)\n============================================================================\nLayer (type) Output Shape Param # Trainable \n============================================================================\n 4 x 16 x 128 x 128 \nConv3d 448 True \nInstanceNorm3d 0 False \nPReLU 1 True \nConv3d 6928 True \nInstanceNorm3d 0 False \nPReLU 1 True \nConv3d 448 True \n____________________________________________________________________________\n 4 x 32 x 64 x 64 x \nConv3d 13856 True \nInstanceNorm3d 0 False \nPReLU 1 True \nConv3d 27680 True \nInstanceNorm3d 0 False \nPReLU 1 True \nConv3d 13856 True \n____________________________________________________________________________\n 4 x 64 x 32 x 32 x \nConv3d 55360 True \nInstanceNorm3d 0 False \nPReLU 1 True \nConv3d 110656 True \nInstanceNorm3d 0 False \nPReLU 1 True \nConv3d 55360 True \n____________________________________________________________________________\n 4 x 128 x 16 x 16 x \nConv3d 221312 True \nInstanceNorm3d 0 False \nPReLU 1 True \nConv3d 442496 True \nInstanceNorm3d 0 False \nPReLU 1 True \nConv3d 221312 True \nReshape \n____________________________________________________________________________\n 4 x 327680 \nFlatten \n____________________________________________________________________________\n 4 x 1 \nLinear 327681 True \n____________________________________________________________________________\n\nTotal params: 1,497,401\nTotal trainable params: 1,497,401\nTotal non-trainable params: 0\n\nOptimizer used: <function Adam at 0x7f6ed28f1e50>\nLoss function: FlattenedLoss of L1Loss()\n\nCallbacks:\n - TrainEvalCallback\n - CastToTensor\n - Recorder\n - ProgressCallback\n\n\n\nlearn.lr_find()\n\n\n\n\n\n\n\n\nSuggestedLRs(valley=5.248074739938602e-05)\n\n\n\n\n\n\n\n\n\n\nlr = 1e-4\n\n\nlearn.fit_one_cycle(4)\n\n\n\n\n\n\n\n\nepoch\ntrain_loss\nvalid_loss\nmae\ntime\n\n\n\n\n0\n22.412138\n47.074982\n47.074982\n01:14\n\n\n1\n26.331188\n12.824456\n12.824456\n01:16\n\n\n2\n13.703990\n15.106963\n15.106963\n01:10\n\n\n3\n9.790899\n10.356420\n10.356420\n01:13\n\n\n\n\n\n\nlearn.save('model-brainage');\n\n\n\nInference\n\nlearn.load('model-brainage');\n\n\ninterp = Interpretation.from_learner(learn)\n\n\n\n\n\n\n\n\n\ninterp.plot_top_losses(k=9, anatomical_plane=2)", + "crumbs": [ + "Tutorials", + "Regression" + ] + }, + { + "objectID": "vision_augment.html", + "href": "vision_augment.html", + "title": "Data augmentation", + "section": "", + "text": "source\n\n\n\n CustomDictTransform (aug)\n\nA class that serves as a wrapper to perform an identical transformation on both the image and the target (if it’s a mask).", + "crumbs": [ + "Data augmentation" + ] + }, + { + "objectID": "vision_augment.html#transforms-wrapper", + "href": "vision_augment.html#transforms-wrapper", + "title": "Data augmentation", + "section": "", + "text": "source\n\n\n\n CustomDictTransform (aug)\n\nA class that serves as a wrapper to perform an identical transformation on both the image and the target (if it’s a mask).", + "crumbs": [ + "Data augmentation" + ] + }, + { + "objectID": "vision_augment.html#vanilla-transforms", + "href": "vision_augment.html#vanilla-transforms", + "title": "Data augmentation", + "section": "Vanilla transforms", + "text": "Vanilla transforms\n\nsource\n\ndo_pad_or_crop\n\n do_pad_or_crop (o, target_shape, padding_mode, mask_name, dtype=<class\n 'torch.Tensor'>)\n\n\nsource\n\n\nPadOrCrop\n\n PadOrCrop (size, padding_mode=0, mask_name=None)\n\nResize image using TorchIO CropOrPad.\n\nsource\n\n\nZNormalization\n\n ZNormalization (masking_method=None, channel_wise=True)\n\nApply TorchIO ZNormalization.\n\nsource\n\n\nBraTSMaskConverter\n\n BraTSMaskConverter (enc=None, dec=None, split_idx=None, order=None)\n\nConvert BraTS masks.\n\nsource\n\n\nBinaryConverter\n\n BinaryConverter (enc=None, dec=None, split_idx=None, order=None)\n\nConvert to binary mask.\n\nsource\n\n\nRandomGhosting\n\n RandomGhosting (intensity=(0.5, 1), p=0.5)\n\nApply TorchIO RandomGhosting.\n\nsource\n\n\nRandomSpike\n\n RandomSpike (num_spikes=1, intensity=(1, 3), p=0.5)\n\nApply TorchIO RandomSpike.\n\nsource\n\n\nRandomNoise\n\n RandomNoise (mean=0, std=(0, 0.25), p=0.5)\n\nApply TorchIO RandomNoise.\n\nsource\n\n\nRandomBiasField\n\n RandomBiasField (coefficients=0.5, order=3, p=0.5)\n\nApply TorchIO RandomBiasField.\n\nsource\n\n\nRandomBlur\n\n RandomBlur (std=(0, 2), p=0.5)\n\nApply TorchIO RandomBiasField.\n\nsource\n\n\nRandomGamma\n\n RandomGamma (log_gamma=(-0.3, 0.3), p=0.5)\n\nApply TorchIO RandomGamma.\n\nsource\n\n\nRandomMotion\n\n RandomMotion (degrees=10, translation=10, num_transforms=2,\n image_interpolation='linear', p=0.5)\n\nApply TorchIO RandomMotion.", + "crumbs": [ + "Data augmentation" + ] + }, + { + "objectID": "vision_augment.html#dictionary-transforms", + "href": "vision_augment.html#dictionary-transforms", + "title": "Data augmentation", + "section": "Dictionary transforms", + "text": "Dictionary transforms\n\nsource\n\nRandomElasticDeformation\n\n RandomElasticDeformation (num_control_points=7, max_displacement=7.5,\n image_interpolation='linear', p=0.5)\n\nApply TorchIO RandomElasticDeformation.\n\nsource\n\n\nRandomAffine\n\n RandomAffine (scales=0, degrees=10, translation=0, isotropic=False,\n image_interpolation='linear', default_pad_value=0.0, p=0.5)\n\nApply TorchIO RandomAffine.\n\nsource\n\n\nRandomFlip\n\n RandomFlip (axes='LR', p=0.5)\n\nApply TorchIO RandomFlip.\n\nsource\n\n\nOneOf\n\n OneOf (transform_dict, p=1)\n\nApply only one of the given transforms using TorchIO OneOf.", + "crumbs": [ + "Data augmentation" + ] + }, + { + "objectID": "tutorial_beginner_video.html", + "href": "tutorial_beginner_video.html", + "title": "Getting started video", + "section": "", + "text": "Video tutorial detailing the classification notebook and explaining key functionalities within fastMONAI:", + "crumbs": [ + "Tutorials", + "Getting started video" + ] + }, + { + "objectID": "tutorial_binary_segmentation.html", + "href": "tutorial_binary_segmentation.html", + "title": "Binary semantic segmentation", + "section": "", + "text": "Google Colab\n\n\n\nfrom fastMONAI.vision_all import *\n\nfrom monai.apps import DecathlonDataset\nfrom sklearn.model_selection import train_test_split\n\n\nDownload external data\nWe use the MONAI function DecathlonDataset to download the data and generate items for training.\n\npath = Path('../data')\npath.mkdir(exist_ok=True)\n\n\ntraining_data = DecathlonDataset(root_dir=path, task=\"Task02_Heart\", section=\"training\", \n download=True, cache_num=0, num_workers=3)\n\nTask02_Heart.tar: 435MB [05:11, 1.46MB/s] \n\n\n2023-01-18 08:33:46,957 - INFO - Downloaded: ../data/Task02_Heart.tar\n2023-01-18 08:33:47,618 - INFO - Verified 'Task02_Heart.tar', md5: 06ee59366e1e5124267b774dbd654057.\n2023-01-18 08:33:47,618 - INFO - Non-empty folder exists in ../data/Task02_Heart, skipped extracting.\n\n\n\ndf = pd.DataFrame(training_data.data)\ndf.shape\n\n(16, 2)\n\n\nSplit the labled data into training and test\n\ntrain_df, test_df = train_test_split(df, test_size=0.1, random_state=42)\ntrain_df.shape, test_df.shape\n\n((14, 2), (2, 2))\n\n\n\n\nLook at training data\nEach class label will be counted for the masks (including the background) if you pass in the mask path and define the dtype to be MedMask in MedDataset\n\nmed_dataset = MedDataset(img_list=train_df.label.tolist(), dtype=MedMask, max_workers=12)\n\n\nmed_dataset.df.head()\n\n\n\n\n\n\n\n\npath\ndim_0\ndim_1\ndim_2\nvoxel_0\nvoxel_1\nvoxel_2\norientation\nvoxel_count_0\nvoxel_count_1\n\n\n\n\n0\n../data/Task02_Heart/labelsTr/la_023.nii.gz\n320\n320\n110\n1.25\n1.25\n1.37\nRAS+\n11220796\n43204\n\n\n1\n../data/Task02_Heart/labelsTr/la_004.nii.gz\n320\n320\n110\n1.25\n1.25\n1.37\nRAS+\n11205525\n58475\n\n\n2\n../data/Task02_Heart/labelsTr/la_007.nii.gz\n320\n320\n130\n1.25\n1.25\n1.37\nRAS+\n13256556\n55444\n\n\n3\n../data/Task02_Heart/labelsTr/la_022.nii.gz\n320\n320\n110\n1.25\n1.25\n1.37\nRAS+\n11230449\n33551\n\n\n4\n../data/Task02_Heart/labelsTr/la_011.nii.gz\n320\n320\n120\n1.25\n1.25\n1.37\nRAS+\n12229545\n58455\n\n\n\n\n\n\n\n\ndata_info_df = med_dataset.summary()\n\n\ndata_info_df.head()\n\n\n\n\n\n\n\n\ndim_0\ndim_1\ndim_2\nvoxel_0\nvoxel_1\nvoxel_2\norientation\nexample_path\ntotal\n\n\n\n\n3\n320\n320\n110\n1.25\n1.25\n1.37\nRAS+\n../data/Task02_Heart/labelsTr/la_004.nii.gz\n4\n\n\n4\n320\n320\n120\n1.25\n1.25\n1.37\nRAS+\n../data/Task02_Heart/labelsTr/la_005.nii.gz\n4\n\n\n1\n320\n320\n100\n1.25\n1.25\n1.37\nRAS+\n../data/Task02_Heart/labelsTr/la_009.nii.gz\n2\n\n\n0\n320\n320\n90\n1.25\n1.25\n1.37\nRAS+\n../data/Task02_Heart/labelsTr/la_016.nii.gz\n1\n\n\n2\n320\n320\n109\n1.25\n1.25\n1.37\nRAS+\n../data/Task02_Heart/labelsTr/la_029.nii.gz\n1\n\n\n\n\n\n\n\n\nresample, reorder = med_dataset.suggestion()\nresample, reorder\n\n([1.25, 1.25, 1.37], False)\n\n\n\nbs=4\n\n\nimg_size = med_dataset.get_largest_img_size(resample=resample)\nimg_size\n\n[320.0, 320.0, 130.0]\n\n\n\nsize = [160,160,128]\n\n\nitem_tfms = [ZNormalization(), PadOrCrop(size), RandomAffine(scales=0, degrees=5)] # RandomMotion()\n\n\ndblock = MedDataBlock(blocks=(ImageBlock(cls=MedImage), MedMaskBlock), \n splitter=RandomSplitter(valid_pct=0.1, seed=42), # By passing valid_pct=0.1, we tell it to get a random 10% of the training set for validation.\n get_x=ColReader('image'),\n get_y=ColReader('label'),\n item_tfms=item_tfms,\n reorder=reorder,\n resample=resample)\n\n\ndls = dblock.dataloaders(train_df, bs=bs)\n\n\n# training and validation\nlen(dls.train_ds.items), len(dls.valid_ds.items)\n\n(13, 1)\n\n\n\ndls.show_batch(anatomical_plane=2)\n\n\n\n\n\n\n\n\n\n\nCreate and train a 3D model\nThe current state-of-the-art CNN models for medical image segmentation tasks are based on encoder-decoder architectures like the U-Net. In the following line, we import an enhanced version of 3D UNet from MONAI. In addition, we import the Dice coefficient (Dice) loss, a commonly used loss function in medical image segmentation tasks: \\[\\begin{equation*}\n\\begin{aligned}\nloss = 1- \\frac{y \\cap \\hat{y}}{\\left | y \\right |+\\left | \\hat{y} \\right |}\n\\end{aligned}\n\\end{equation*}\\]\n\nfrom monai.networks.nets import UNet\nfrom monai.losses import DiceLoss\n\n\nmodel = UNet(spatial_dims=3, in_channels=1, out_channels=1, channels=(16, 32, 64, 128, 256),strides=(2, 2, 2, 2), num_res_units=2)\nmodel = model.model\n\n\nloss_func = CustomLoss(loss_func=DiceLoss(sigmoid=True))\n\nWe use a built-in ranger optimizer from fastai, that combines (RAdam + Lookahead) into a single optimizer. Ranger21 tested with fastai available: https://github.com/lessw2020/Ranger21\n\nlearn = Learner(dls, model, loss_func=loss_func, opt_func=ranger, metrics=binary_dice_score)\n\n\nlearn.lr_find()\n\n\n\n\n\n\n\n\nSuggestedLRs(valley=0.009120108559727669)\n\n\n\n\n\n\n\n\n\n\nlr = 1e-2\n\nAs shown in https://walkwithfastai.com/Segmentation, with the ranger optimizer, we want to use a different fit function, called fit_flat_cos\n\nlearn.fit_flat_cos(200 ,lr)\n\n\nlearn.recorder.plot_loss()\n\n\n\n\n\n\n\n\n\nlearn.save('heart-model')\n\nPath('models/heart-model.pth')\n\n\n\nlearn.show_results(anatomical_plane=1)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTest data (with labels to evaulate the model)\nEvaluate the performance of the selected model on unseen data. It’s important to not touch this data until you have fine tuned your model to get an unbiased evaluation!\n\nlearn.load('heart-model');\n\n\ntest_dl = learn.dls.test_dl(test_df,with_labels=True)\n\n\ntest_dl.show_batch(anatomical_plane=0, figsize=(10,10))\n\n\n\n\n\n\n\n\n\npred_acts, labels = learn.get_preds(dl=test_dl)\npred_acts.shape, labels.shape\n\n\n\n\n\n\n\n\n(torch.Size([2, 1, 160, 160, 128]), torch.Size([2, 1, 160, 160, 128]))\n\n\n\nbinary_dice_score(pred_acts, labels)\n\ntensor(0.8893)\n\n\n\nlearn.show_results(anatomical_plane=0, dl=test_dl)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExport learner\n\nstore_variables(pkl_fn='vars.pkl', size=size, reorder=reorder, resample=resample)\n\n\nlearn.export('heart_model.pkl')", + "crumbs": [ + "Tutorials", + "Binary semantic segmentation" + ] + }, + { + "objectID": "tutorial_inference.html", + "href": "tutorial_inference.html", + "title": "Inference with exported learner", + "section": "", + "text": "#all_skip\nfrom fastMONAI.vision_all import *\n\nfrom monai.apps import DecathlonDataset\n\n\n#task = 'Task09_Spleen'\ntask = 'Task01_BrainTumour'\n\n\npath = Path('../data')\npath.mkdir(exist_ok=True)\n\n\ntest_data = DecathlonDataset(root_dir=path, task=task, section=\"test\", download=True,\n cache_num=0, num_workers=3)\n\n\ntest_imgs = [data['image'] for data in test_data.data]\n\n\nlearn_inf = load_learner('braintumor_model.pkl', cpu=False);\n\n\n_, reorder, resample = load_variables(pkl_fn='vars.pkl')\nreorder, resample\n\n(False, [1.0, 1.0, 1.0])\n\n\n\nsave_path = Path('../data/results/braintumor')\npath.mkdir(parents=True, exist_ok=True)\n\n\nidx = 3\nimg_fn = test_imgs[idx]\n\n\npred_fn = inference(learn_inf, reorder, resample, fn, save_path=save_path)\n\n\n\n\n\n\n\n\n\nfrom torchio import Subject, ScalarImage, LabelMap\n\nsubject = Subject(image=ScalarImage(img_fn), mask=LabelMap(pred_fn))\nsubject.plot(figsize=(10,5))", + "crumbs": [ + "Tutorials", + "Inference with exported learner" + ] + }, + { + "objectID": "utils.html", + "href": "utils.html", + "title": "Utils", + "section": "", + "text": "source\n\nstore_variables\n\n store_variables (pkl_fn:(<class'str'>,<class'pathlib.Path'>), size:list,\n reorder:bool, resample:(<class'int'>,<class'list'>))\n\nSave variable values in a pickle file.\n\nsource\n\n\nload_variables\n\n load_variables (pkl_fn:(<class'str'>,<class'pathlib.Path'>))\n\n*Loads stored variable values from a pickle file.\nArgs: pkl_fn: File path of the pickle file to be loaded.\nReturns: The deserialized value of the pickled data.*\n\nsource\n\n\nprint_colab_gpu_info\n\n print_colab_gpu_info ()\n\nCheck if we have a GPU attached to the runtime.", + "crumbs": [ + "Utils" + ] + }, + { + "objectID": "tutorial_multiclass_segmentation.html", + "href": "tutorial_multiclass_segmentation.html", + "title": "Multi-class semantic segmentation", + "section": "", + "text": "Google Colab\n\n\n\nfrom fastMONAI.vision_all import *\n\nfrom monai.apps import DecathlonDataset\nfrom sklearn.model_selection import train_test_split\n\n\nDownload external data\nWe use the MONAI function DecathlonDataset to download the data and generate items for training.\n\npath = Path('../data')\npath.mkdir(exist_ok=True)\n\n\ntraining_data = DecathlonDataset(root_dir=path, task=\"Task01_BrainTumour\", section=\"training\", download=True,\n cache_num=0, num_workers=3)\n\n2022-09-01 17:36:43,099 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n2022-09-01 17:36:43,100 - INFO - File exists: ../data/Task01_BrainTumour.tar, skipped downloading.\n2022-09-01 17:36:43,101 - INFO - Non-empty folder exists in ../data/Task01_BrainTumour, skipped extracting.\n\n\n\ndf = pd.DataFrame(training_data.data)\ndf.shape\n\n(388, 2)\n\n\nSplit the labled data into training and test\n\ntrain_df, test_df = train_test_split(df, test_size=0.1, random_state=42)\ntrain_df.shape, test_df.shape\n\n((349, 2), (39, 2))\n\n\n\n\nLook at training data\n\nmed_dataset = MedDataset(img_list=train_df.label.tolist(), dtype=MedMask, max_workers=12)\n\n\nmed_dataset.df.head()\n\n\n\n\n\n\n\n\npath\ndim_0\ndim_1\ndim_2\nvoxel_0\nvoxel_1\nvoxel_2\norientation\nvoxel_count_0\nvoxel_count_1\nvoxel_count_2\nvoxel_count_3\n\n\n\n\n0\n/home/sathiesh/lib_dev/fastMONAI/data/Task01_BrainTumour/labelsTr/BRATS_477.nii.gz\n240\n240\n155\n1.0\n1.0\n1.0\nRAS+\n8765377\n83088\n15826\n63709.0\n\n\n1\n/home/sathiesh/lib_dev/fastMONAI/data/Task01_BrainTumour/labelsTr/BRATS_350.nii.gz\n240\n240\n155\n1.0\n1.0\n1.0\nRAS+\n8872636\n21364\n8872\n25128.0\n\n\n2\n/home/sathiesh/lib_dev/fastMONAI/data/Task01_BrainTumour/labelsTr/BRATS_266.nii.gz\n240\n240\n155\n1.0\n1.0\n1.0\nRAS+\n8725071\n83276\n69784\n49869.0\n\n\n3\n/home/sathiesh/lib_dev/fastMONAI/data/Task01_BrainTumour/labelsTr/BRATS_294.nii.gz\n240\n240\n155\n1.0\n1.0\n1.0\nRAS+\n8790699\n90806\n20231\n26264.0\n\n\n4\n/home/sathiesh/lib_dev/fastMONAI/data/Task01_BrainTumour/labelsTr/BRATS_466.nii.gz\n240\n240\n155\n1.0\n1.0\n1.0\nRAS+\n8911252\n14046\n60\n2642.0\n\n\n\n\n\n\n\n\nsummary_df = med_dataset.summary()\n\n\nsummary_df.head()\n\n\n\n\n\n\n\n\ndim_0\ndim_1\ndim_2\nvoxel_0\nvoxel_1\nvoxel_2\norientation\nexample_path\ntotal\n\n\n\n\n0\n240\n240\n155\n1.0\n1.0\n1.0\nRAS+\n/home/sathiesh/lib_dev/fastMONAI/data/Task01_BrainTumour/labelsTr/BRATS_002.nii.gz\n349\n\n\n\n\n\n\n\n\nresample, reorder = med_dataset.suggestion()\nresample, reorder\n\n([1.0, 1.0, 1.0], False)\n\n\n\nimg_size = med_dataset.get_largest_img_size(resample=resample)\nimg_size\n\n[240.0, 240.0, 155.0]\n\n\n\nbs=4\nsize=[224,224,128]\n\n\nitem_tfms = [ZNormalization(), PadOrCrop(size), RandomAffine(scales=0, degrees=5, isotropic=True)]\n\n\ndblock = MedDataBlock(blocks=(ImageBlock(cls=MedImage), MedMaskBlock), \n splitter=RandomSplitter(seed=42),\n get_x=ColReader('image'),\n get_y=ColReader('label'),\n item_tfms=item_tfms,\n reorder=reorder,\n resample=resample)\n\n\ndls = dblock.dataloaders(train_df, bs=bs)\n\n\n# training and validation\nlen(dls.train_ds.items), len(dls.valid_ds.items)\n\n(280, 69)\n\n\n\ndls.show_batch(anatomical_plane=0)\n\n\n\n\n\n\n\n\n\n\nCreate and train a 3D model\nAs in the binary segmentation task, we import an enhanced version of UNet from MONAI. This time instead of using Dice loss, we import a loss function that combines Dice loss and Cross Entropy loss and returns the weighted sum of these two losses.\n\nfrom monai.losses import DiceCELoss\nfrom monai.networks.nets import UNet\n\n\ncodes = np.unique(med_img_reader(train_df.label.tolist()[0]))\nn_classes = len(codes)\ncodes, n_classes\n\n(array([0., 1., 2., 3.], dtype=float32), 4)\n\n\n\nmodel = UNet(spatial_dims=3, in_channels=4, out_channels=n_classes, channels=(16, 32, 64, 128, 256),strides=(2, 2, 2, 2), num_res_units=2)\nmodel = model\n\n\nloss_func = CustomLoss(loss_func=DiceCELoss(to_onehot_y=True, include_background=True, softmax=True))\n\n\nlearn = Learner(dls, model, loss_func=loss_func, opt_func=ranger, metrics=multi_dice_score)#.to_fp16()\n\n\nlearn.lr_find()\n\n\n\n\n\n\n\n\nSuggestedLRs(valley=0.0020892962347716093)\n\n\n\n\n\n\n\n\n\n\nlr = 1e-1\n\n\nlearn.fit_flat_cos(20 ,lr)\n\n\n\n\n\n\n\n\nepoch\ntrain_loss\nvalid_loss\nmulti_dice_score\ntime\n\n\n\n\n0\n0.731845\n0.639536\ntensor([0.4518, 0.0668, 0.2126])\n01:35\n\n\n1\n0.607809\n0.513107\ntensor([0.4640, 0.1777, 0.5614])\n01:38\n\n\n2\n0.519589\n0.469945\ntensor([0.5452, 0.3205, 0.5655])\n01:42\n\n\n3\n0.491277\n0.432317\ntensor([0.6120, 0.2937, 0.6087])\n01:43\n\n\n4\n0.447122\n0.436939\ntensor([0.6344, 0.2947, 0.5832])\n01:40\n\n\n5\n0.438858\n0.399160\ntensor([0.6423, 0.3719, 0.6272])\n01:37\n\n\n6\n0.428492\n0.395152\ntensor([0.6066, 0.4034, 0.6307])\n01:38\n\n\n7\n0.430274\n0.439361\ntensor([0.5118, 0.3754, 0.6161])\n01:42\n\n\n8\n0.430529\n0.396985\ntensor([0.6117, 0.4036, 0.6407])\n01:37\n\n\n9\n0.426335\n0.397388\ntensor([0.5862, 0.4063, 0.6515])\n01:43\n\n\n10\n0.405544\n0.410180\ntensor([0.5997, 0.3905, 0.6501])\n01:38\n\n\n11\n0.404089\n0.375698\ntensor([0.6476, 0.4064, 0.6567])\n01:38\n\n\n12\n0.410570\n0.397038\ntensor([0.6492, 0.3614, 0.6325])\n01:36\n\n\n13\n0.398087\n0.422734\ntensor([0.5770, 0.4029, 0.5979])\n01:38\n\n\n14\n0.410939\n0.380410\ntensor([0.6152, 0.4226, 0.6542])\n01:41\n\n\n15\n0.400566\n0.413136\ntensor([0.5845, 0.4104, 0.6108])\n01:37\n\n\n16\n0.395092\n0.360473\ntensor([0.6729, 0.4272, 0.6761])\n01:38\n\n\n17\n0.368555\n0.350397\ntensor([0.6723, 0.4393, 0.6917])\n01:40\n\n\n18\n0.348801\n0.352961\ntensor([0.6729, 0.4536, 0.6669])\n01:36\n\n\n19\n0.338820\n0.342739\ntensor([0.6730, 0.4586, 0.7052])\n01:40\n\n\n\n\n\n\nlearn.save('braintumor-model')\n\nPath('models/braintumor-model.pth')\n\n\n\nlearn.show_results(anatomical_plane=0, ds_idx=1)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nInference on test data\n\nlearn.load('braintumor-model');\n\n\ntest_dl = learn.dls.test_dl(test_df[:10],with_labels=True)\n\n\ntest_dl.show_batch(anatomical_plane=0, figsize=(10,10))\n\n\n\n\n\n\n\n\n\npred_acts, labels = learn.get_preds(dl=test_dl)\npred_acts.shape, labels.shape\n\n\n\n\n\n\n\n\n(torch.Size([10, 4, 224, 224, 128]), torch.Size([10, 1, 224, 224, 128]))\n\n\nDice score for labels 1,2 and 3:\n\nmulti_dice_score(pred_acts, labels)\n\ntensor([0.5708, 0.4186, 0.6994])\n\n\n\nlearn.show_results(anatomical_plane=0, dl=test_dl)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExport learner\n\nstore_variables(pkl_fn='vars.pkl', size=size, reorder=reorder, resample=resample)\n\n\nlearn.export('braintumor_model.pkl')", + "crumbs": [ + "Tutorials", + "Multi-class semantic segmentation" + ] + }, + { + "objectID": "vision_plot.html", + "href": "vision_plot.html", + "title": "Vision plot", + "section": "", + "text": "source\n\nvalidate_anatomical_plane\n\n validate_anatomical_plane (anatomical_plane)\n\nEnsure anatomical_plane is either 0, 1, or 2.\n\nsource\n\n\nshow_med_img\n\n show_med_img (im, ctx, channel:int, slice_index:int,\n anatomical_plane:int,\n voxel_size:(<class'int'>,<class'list'>), ax=None,\n figsize=None, title=None, cmap=None, norm=None,\n aspect=None, interpolation=None, alpha=None, vmin=None,\n vmax=None, origin=None, extent=None,\n interpolation_stage=None, filternorm=True, filterrad=4.0,\n resample=None, url=None, data=None, **kwargs)\n\n*Show an image on ax. This is a modified code from the fastai function show_image.\nArgs: im: The input image. ctx: The context. channel: Channel of the image. slice_index: Index of the 2D slice. anatomical_plane: Anatomical plane of the image. voxel_size: Voxel size for the image. ax: Axis for the plot. figsize: Figure size for the plot. title: Title for the plot. kwargs: Additional parameters for plt.Axes.imshow method.\nReturns: Axis with the plot.*\n\nsource\n\n\nfind_max_slice\n\n find_max_slice (mask_data, anatomical_plane)\n\nFind slice index based on mask", + "crumbs": [ + "Vision plot" + ] + }, + { + "objectID": "vision_inference.html", + "href": "vision_inference.html", + "title": "Vision inference", + "section": "", + "text": "source", + "crumbs": [ + "Vision inference" + ] + }, + { + "objectID": "vision_inference.html#post-processing", + "href": "vision_inference.html#post-processing", + "title": "Vision inference", + "section": "Post-processing", + "text": "Post-processing\n\nsource\n\nrefine_binary_pred_mask\n\n refine_binary_pred_mask (pred_mask,\n remove_size:(<class'int'>,<class'float'>)=None,\n percentage:float=0.2, verbose:bool=False)\n\n*Removes small objects from the predicted binary mask.\nArgs: pred_mask: The predicted mask from which small objects are to be removed. remove_size: The size under which objects are considered ‘small’. percentage: The percentage of the remove_size to be used as threshold. Defaults to 0.2. verbose: If True, print the number of components. Defaults to False.\nReturns: The processed mask with small objects removed.*", + "crumbs": [ + "Vision inference" + ] + }, + { + "objectID": "vision_inference.html#gradio", + "href": "vision_inference.html#gradio", + "title": "Vision inference", + "section": "Gradio", + "text": "Gradio\n\nsource\n\ngradio_image_classifier\n\n gradio_image_classifier (file_obj, learn, reorder, resample)\n\nPredict on images using exported learner and return the result as a dictionary.", + "crumbs": [ + "Vision inference" + ] + }, + { + "objectID": "vision_loss_functions.html", + "href": "vision_loss_functions.html", + "title": "Custom loss functions", + "section": "", + "text": "source\n\nCustomLoss\n\n CustomLoss (loss_func)\n\nA custom loss wrapper class for loss functions to allow them to work with the ‘show_results’ method in fastai.\n\nsource\n\n\nTverskyFocalLoss\n\n TverskyFocalLoss (include_background:bool=True, to_onehot_y:bool=False,\n sigmoid:bool=False, softmax:bool=False, gamma:float=2,\n alpha:float=0.5, beta:float=0.99)\n\nCompute Tversky loss with a focus parameter, gamma, applied. 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Then we classify all values greater than or equal to 0.5 to 1, and the values below 0.5 to 0.\nArgs: pred: [B, C, W, H, D] or [C, W, H, D] activation tensor\nReturns: Predicted binary mask(s).*\n\nsource\n\n\n\n\n MedDataBlock (blocks:list=None, dl_type:fastai.data.core.TfmdDL=None,\n getters:list=None, n_inp:int=None, item_tfms:list=None,\n batch_tfms:list=None, reorder:bool=False,\n resample:(<class'int'>,<class'list'>)=None, **kwargs)\n\nContainer to quickly build dataloaders.", + "crumbs": [ + "Vision data" + ] + }, + { + "objectID": "vision_data.html#prediction-to-mask", + "href": "vision_data.html#prediction-to-mask", + "title": "Vision data", + "section": "", + "text": "source\n\n\n\n pred_to_multiclass_mask (pred:torch.Tensor)\n\n*Apply Softmax on the predicted tensor to rescale the values in the range [0, 1] and sum to 1. Then apply argmax to get the indices of the maximum value of all elements in the predicted Tensor.\nArgs: pred: [C,W,H,D] activation tensor.\nReturns: Predicted mask.*\n\nsource\n\n\n\n\n batch_pred_to_multiclass_mask (pred:torch.Tensor)\n\n*Convert a batch of predicted activation tensors to masks.\nArgs: pred: [B, C, W, H, D] batch of activations.\nReturns: Tuple of batch of predicted masks and number of classes.*\n\nsource\n\n\n\n\n pred_to_binary_mask (pred:torch.Tensor)\n\n*Apply Sigmoid function that squishes activations into a range between 0 and 1. Then we classify all values greater than or equal to 0.5 to 1, and the values below 0.5 to 0.\nArgs: pred: [B, C, W, H, D] or [C, W, H, D] activation tensor\nReturns: Predicted binary mask(s).*\n\nsource\n\n\n\n\n MedDataBlock (blocks:list=None, dl_type:fastai.data.core.TfmdDL=None,\n getters:list=None, n_inp:int=None, item_tfms:list=None,\n batch_tfms:list=None, reorder:bool=False,\n resample:(<class'int'>,<class'list'>)=None, **kwargs)\n\nContainer to quickly build dataloaders.", + "crumbs": [ + "Vision data" + ] + }, + { + "objectID": "vision_data.html#transformblock-for-segmentation", + "href": "vision_data.html#transformblock-for-segmentation", + "title": "Vision data", + "section": "TransformBlock for segmentation", + "text": "TransformBlock for segmentation\n\nsource\n\nMedMaskBlock\n\n MedMaskBlock ()\n\nCreate a TransformBlock for medical masks.\n\nsource\n\n\nMedImageDataLoaders\n\n MedImageDataLoaders (*loaders, path:str|pathlib.Path='.', device=None)\n\nHigher-level MedDataBlock API.", + "crumbs": [ + "Vision data" + ] + }, + { + "objectID": "dataset_info.html", + "href": "dataset_info.html", + "title": "Dataset information", + "section": "", + "text": "source\n\nMedDataset\n\n MedDataset (path=None, postfix:str='', img_list:list=None,\n reorder:bool=False, dtype:(<class'fastMONAI.vision_core.MedIm\n age'>,<class'fastMONAI.vision_core.MedMask'>)=<class\n 'fastMONAI.vision_core.MedImage'>, max_workers:int=1)\n\nA class to extract and present information about the dataset.\n\nsource\n\n\nget_class_weights\n\n get_class_weights (labels:(<built-infunctionarray>,<class'list'>),\n class_weight:str='balanced')\n\n*Calculates and returns the class weights.\nArgs: labels: An array or list of class labels for each instance in the dataset. class_weight: Defaults to ‘balanced’.\nReturns: A tensor of class weights.*", + "crumbs": [ + "Dataset information" + ] + }, + { + "objectID": "tutorial_classification.html", + "href": "tutorial_classification.html", + "title": "Classification", + "section": "", + "text": "Google Colab\nThe following line imports all of the functions and classes from the fastMONAI library:\nfrom fastMONAI.vision_all import *", + "crumbs": [ + "Tutorials", + "Classification" + ] + }, + { + "objectID": "tutorial_classification.html#inference-on-test-data", + "href": "tutorial_classification.html#inference-on-test-data", + "title": "Classification", + "section": "Inference on test data", + "text": "Inference on test data\n\nlearn.load('model-2');\n\n\ndls.valid_ds.items = df_test\n\n\npreds, targs = learn.get_preds();\n\n\n\n\n\n\n\n\n\naccuracy(preds, targs)\n\nTensorBase(0.8194)\n\n\n\nTest-time augmentation\nTest-time augmentation (TTA) is a technique where you apply data augmentation transforms when making predictions to produce average output. In addition to often yielding better performance, the variation in the output of the TTA runs can provide some measure of its robustness and sensitivity to augmentations.\n\npreds, targs = learn.tta();\n\n\n\n\n\n\n \n \n \n \n \n\n\n\n\n\n\n\n\n\n\naccuracy(preds, targs)\n\nTensorBase(0.8387)", + "crumbs": [ + "Tutorials", + "Classification" + ] + }, + { + "objectID": "tutorial_classification.html#export-learner", + "href": "tutorial_classification.html#export-learner", + "title": "Classification", + "section": "Export learner", + "text": "Export learner\n\nstore_variables(pkl_fn='vars.pkl', size=in_shape, reorder=reorder, resample=resample)\n\n\nlearn.export('learner.pkl')", + "crumbs": [ + "Tutorials", + "Classification" + ] + }, + { + "objectID": "tutorial_classification.html#make-a-simple-web-app", + "href": "tutorial_classification.html#make-a-simple-web-app", + "title": "Classification", + "section": "Make a simple web app", + "text": "Make a simple web app\nMake a simple web application with Gradio and host it on Hugging Face Spaces.\n\nlearn = load_learner('learner.pkl', cpu=True)\n_, reorder, resample = load_variables(pkl_fn='vars.pkl')\n\n\nimport gradio as gr\n\ngr.Interface(fn=lambda fileobj: gradio_image_classifier(fileobj, learn,\n reorder, resample),\n inputs=['file'],\n outputs=gr.Label(num_top_classes=2),\n examples=[df_test.img_path[0], df_test.img_path[200]],\n title='Example app').launch()\n\nRunning on local URL: http://127.0.0.1:7860\n\nTo create a public link, set `share=True` in `launch()`.", + "crumbs": [ + "Tutorials", + "Classification" + ] + }, + { + "objectID": "vision_core.html", + "href": "vision_core.html", + "title": "Vision core", + "section": "", + "text": "source\n\n\n\n med_img_reader (file_path:(<class'str'>,<class'pathlib.Path'>,<class'fast\n core.foundation.L'>,<class'list'>), dtype=<class\n 'torch.Tensor'>, reorder:bool=False, resample:list=None,\n only_tensor:bool=True)\n\n*Loads and preprocesses a medical image.\nArgs: file_path: Path to the image. 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0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #3fb618;--bs-btn-disabled-border-color: #3fb618}.btn-info{--bs-btn-color: #fff;--bs-btn-bg: #9954bb;--bs-btn-border-color: #9954bb;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #82479f;--bs-btn-hover-border-color: #7a4396;--bs-btn-focus-shadow-rgb: 168, 110, 197;--bs-btn-active-color: #fff;--bs-btn-active-bg: #7a4396;--bs-btn-active-border-color: #733f8c;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #9954bb;--bs-btn-disabled-border-color: #9954bb}.btn-warning{--bs-btn-color: #fff;--bs-btn-bg: #ff7518;--bs-btn-border-color: #ff7518;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #d96314;--bs-btn-hover-border-color: #cc5e13;--bs-btn-focus-shadow-rgb: 255, 138, 59;--bs-btn-active-color: #fff;--bs-btn-active-bg: #cc5e13;--bs-btn-active-border-color: #bf5812;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: 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none}.btn-outline-primary{--bs-btn-color: #2780e3;--bs-btn-border-color: #2780e3;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2780e3;--bs-btn-hover-border-color: #2780e3;--bs-btn-focus-shadow-rgb: 39, 128, 227;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2780e3;--bs-btn-active-border-color: #2780e3;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #2780e3;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #2780e3;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-secondary{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #343a40;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #343a40;--bs-btn-bg: 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#ff0039;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #ff0039;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #f8f9fa;--bs-btn-border-color: #f8f9fa;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #f8f9fa;--bs-btn-hover-border-color: #f8f9fa;--bs-btn-focus-shadow-rgb: 248, 249, 250;--bs-btn-active-color: #000;--bs-btn-active-bg: #f8f9fa;--bs-btn-active-border-color: #f8f9fa;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #f8f9fa;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #f8f9fa;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 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reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 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0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static 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0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end 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var(--bs-popover-border-width))}.bs-popover-top>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::before,.bs-popover-top>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::after{border-width:var(--bs-popover-arrow-height) calc(var(--bs-popover-arrow-width)*.5) 0}.bs-popover-top>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::before{bottom:0;border-top-color:var(--bs-popover-arrow-border)}.bs-popover-top>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::after{bottom:var(--bs-popover-border-width);border-top-color:var(--bs-popover-bg)}.bs-popover-end>.popover-arrow,.bs-popover-auto[data-popper-placement^=right]>.popover-arrow{left:calc(-1*(var(--bs-popover-arrow-height)) - 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this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const 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Object.keys(f).sort((function(t,e){return f[t]-f[e]}))}const si={name:"flip",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name;if(!e.modifiersData[n]._skip){for(var s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0===r||r,l=i.fallbackPlacements,c=i.padding,h=i.boundary,d=i.rootBoundary,u=i.altBoundary,f=i.flipVariations,p=void 0===f||f,m=i.allowedAutoPlacements,g=e.options.placement,_=be(g),b=l||(_!==g&&p?function(t){if(be(t)===Kt)return[];var e=Ve(t);return[Qe(t),e,Qe(e)]}(g):[Ve(g)]),v=[g].concat(b).reduce((function(t,i){return t.concat(be(i)===Kt?ni(e,{placement:i,boundary:h,rootBoundary:d,padding:c,flipVariations:p,allowedAutoPlacements:m}):i)}),[]),y=e.rects.reference,w=e.rects.popper,A=new Map,E=!0,T=v[0],C=0;C=0,S=L?"width":"height",D=ii(e,{placement:O,boundary:h,rootBoundary:d,altBoundary:u,padding:c}),$=L?k?qt:Vt:k?Rt:zt;y[S]>w[S]&&($=Ve($));var I=Ve($),N=[];if(o&&N.push(D[x]<=0),a&&N.push(D[$]<=0,D[I]<=0),N.every((function(t){return t}))){T=O,E=!1;break}A.set(O,N)}if(E)for(var P=function(t){var e=v.find((function(e){var i=A.get(e);if(i)return i.slice(0,t).every((function(t){return t}))}));if(e)return T=e,"break"},M=p?3:1;M>0&&"break"!==P(M);M--);e.placement!==T&&(e.modifiersData[n]._skip=!0,e.placement=T,e.reset=!0)}},requiresIfExists:["offset"],data:{_skip:!1}};function oi(t,e,i){return void 0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function ri(t){return[zt,qt,Rt,Vt].some((function(e){return t[e]>=0}))}const ai={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var 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m?m(Object.assign({},e.rects,{placement:e.placement})):m,O="number"==typeof C?{mainAxis:C,altAxis:C}:Object.assign({mainAxis:0,altAxis:0},C),x=e.modifiersData.offset?e.modifiersData.offset[e.placement]:null,k={x:0,y:0};if(A){if(o){var L,S="y"===y?zt:Vt,D="y"===y?Rt:qt,$="y"===y?"height":"width",I=A[y],N=I+g[S],P=I-g[D],M=f?-T[$]/2:0,j=b===Xt?E[$]:T[$],F=b===Xt?-T[$]:-E[$],H=e.elements.arrow,W=f&&H?Ce(H):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},z=B[S],R=B[D],q=Ne(0,E[$],W[$]),V=v?E[$]/2-M-q-z-O.mainAxis:j-q-z-O.mainAxis,K=v?-E[$]/2+M+q+R+O.mainAxis:F+q+R+O.mainAxis,Q=e.elements.arrow&&$e(e.elements.arrow),X=Q?"y"===y?Q.clientTop||0:Q.clientLeft||0:0,Y=null!=(L=null==x?void 0:x[y])?L:0,U=I+K-Y,G=Ne(f?ye(N,I+V-Y-X):N,I,f?ve(P,U):P);A[y]=G,k[y]=G-I}if(a){var J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"

"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const 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t=e.state,n=e.name,r=t.rects.reference,o=t.rects.popper,i=t.modifiersData.preventOverflow,a=J(t,{elementContext:"reference"}),s=J(t,{altBoundary:!0}),f=ve(a,r),c=ve(s,o,i),p=ye(f),u=ye(c);t.modifiersData[n]={referenceClippingOffsets:f,popperEscapeOffsets:c,isReferenceHidden:p,hasPopperEscaped:u},t.attributes.popper=Object.assign({},t.attributes.popper,{"data-popper-reference-hidden":p,"data-popper-escaped":u})}},be=Z({defaultModifiers:[ee,te,oe,ie]}),xe=[ee,te,oe,ie,ae,le,he,me,ge],we=Z({defaultModifiers:xe});e.applyStyles=ie,e.arrow=me,e.computeStyles=oe,e.createPopper=we,e.createPopperLite=be,e.defaultModifiers=xe,e.detectOverflow=J,e.eventListeners=ee,e.flip=le,e.hide=ge,e.offset=ae,e.popperGenerator=Z,e.popperOffsets=te,e.preventOverflow=he,Object.defineProperty(e,"__esModule",{value:!0})})); + diff --git a/site_libs/quarto-html/quarto-syntax-highlighting.css b/site_libs/quarto-html/quarto-syntax-highlighting.css new file mode 100644 index 0000000..d9fd98f --- /dev/null +++ b/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of 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b/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,289 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + 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function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 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0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return fe(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),ge(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=re(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(le(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..5f723d7 --- /dev/null +++ b/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1286 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if (now - then > kFuseMaxWait && subSearchFuse === undefined) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..e7a12a6 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,71 @@ + + + + https://fastmonai.no/vision_metrics.html + 2024-07-01T11:45:16.894Z + + + https://fastmonai.no/tutorial_regression.html + 2024-07-01T11:45:16.046Z + + + https://fastmonai.no/vision_augment.html + 2024-07-01T11:45:16.066Z + + + https://fastmonai.no/tutorial_beginner_video.html + 2024-07-01T11:45:15.930Z + + + https://fastmonai.no/tutorial_binary_segmentation.html + 2024-07-01T11:45:15.894Z + + + https://fastmonai.no/tutorial_inference.html + 2024-07-01T11:45:15.790Z + + + https://fastmonai.no/utils.html + 2024-07-01T11:45:13.822Z + + + https://fastmonai.no/tutorial_multiclass_segmentation.html + 2024-07-01T11:45:11.986Z + + + https://fastmonai.no/vision_plot.html + 2024-07-01T11:45:15.986Z + + + https://fastmonai.no/vision_inference.html + 2024-07-01T11:45:15.834Z + + + https://fastmonai.no/vision_loss_functions.html + 2024-07-01T11:45:16.674Z + + + https://fastmonai.no/vision_data.html + 2024-07-01T11:45:15.750Z + + + https://fastmonai.no/dataset_info.html + 2024-07-01T11:45:15.850Z + + + https://fastmonai.no/tutorial_classification.html + 2024-07-01T11:45:15.906Z + + + https://fastmonai.no/vision_core.html + 2024-07-01T11:45:15.978Z + + + https://fastmonai.no/external_data.html + 2024-07-01T11:45:16.902Z + + + https://fastmonai.no/index.html + 2024-07-01T11:45:16.082Z + + diff --git a/styles.css b/styles.css new file mode 100644 index 0000000..97e6444 --- /dev/null +++ b/styles.css @@ -0,0 +1,22 @@ +div.description { + font-style: italic; +} + +.cell-output pre { + margin-left: 0.8rem; + margin-top: 0; + background: none; + border-left: 2px solid lightsalmon; + border-top-left-radius: 0; + border-top-right-radius: 0; + } + + .cell-output .sourceCode { + background: none; + margin-top: 0; + } + + .cell > .sourceCode { + margin-bottom: 0; + } + \ No newline at end of file diff --git a/tutorial_beginner_video.html b/tutorial_beginner_video.html new file mode 100644 index 0000000..bbe8f8b --- /dev/null +++ b/tutorial_beginner_video.html @@ -0,0 +1,685 @@ + + + + + + + + + +fastMONAI - Getting started video + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + +
+ + + +
+ +
+
+

Getting started video

+
+ + + +
+ + + + +
+ + + +
+ + + +

Video tutorial detailing the classification notebook and explaining key functionalities within fastMONAI:

+
+
+ + +
+
+ + + +
+ +
+ + + + + \ No newline at end of file diff --git a/tutorial_binary_segmentation.html b/tutorial_binary_segmentation.html new file mode 100644 index 0000000..798facb --- /dev/null +++ b/tutorial_binary_segmentation.html @@ -0,0 +1,1241 @@ + + + + + + + + + + +fastMONAI - Binary semantic segmentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Binary semantic segmentation

+
+ +
+
+ Image segmentation, or semantic segmentation, is the task of classifying each pixel within an image with a corresponding class. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
+

Google Colab

+
Google Colab
+
+
+
+
from fastMONAI.vision_all import *
+
+from monai.apps import DecathlonDataset
+from sklearn.model_selection import train_test_split
+
+
+

Download external data

+

We use the MONAI function DecathlonDataset to download the data and generate items for training.

+
+
path = Path('../data')
+path.mkdir(exist_ok=True)
+
+
+
training_data = DecathlonDataset(root_dir=path, task="Task02_Heart", section="training", 
+    download=True, cache_num=0, num_workers=3)
+
+
Task02_Heart.tar: 435MB [05:11, 1.46MB/s]
+
+
+
2023-01-18 08:33:46,957 - INFO - Downloaded: ../data/Task02_Heart.tar
+2023-01-18 08:33:47,618 - INFO - Verified 'Task02_Heart.tar', md5: 06ee59366e1e5124267b774dbd654057.
+2023-01-18 08:33:47,618 - INFO - Non-empty folder exists in ../data/Task02_Heart, skipped extracting.
+
+
+
+
df = pd.DataFrame(training_data.data)
+df.shape
+
+
(16, 2)
+
+
+

Split the labled data into training and test

+
+
train_df, test_df = train_test_split(df, test_size=0.1, random_state=42)
+train_df.shape, test_df.shape
+
+
((14, 2), (2, 2))
+
+
+
+
+

Look at training data

+

Each class label will be counted for the masks (including the background) if you pass in the mask path and define the dtype to be MedMask in MedDataset

+
+
med_dataset = MedDataset(img_list=train_df.label.tolist(), dtype=MedMask, max_workers=12)
+
+
+
med_dataset.df.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
pathdim_0dim_1dim_2voxel_0voxel_1voxel_2orientationvoxel_count_0voxel_count_1
0../data/Task02_Heart/labelsTr/la_023.nii.gz3203201101.251.251.37RAS+1122079643204
1../data/Task02_Heart/labelsTr/la_004.nii.gz3203201101.251.251.37RAS+1120552558475
2../data/Task02_Heart/labelsTr/la_007.nii.gz3203201301.251.251.37RAS+1325655655444
3../data/Task02_Heart/labelsTr/la_022.nii.gz3203201101.251.251.37RAS+1123044933551
4../data/Task02_Heart/labelsTr/la_011.nii.gz3203201201.251.251.37RAS+1222954558455
+ +
+
+
+
+
data_info_df = med_dataset.summary()
+
+
+
data_info_df.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
dim_0dim_1dim_2voxel_0voxel_1voxel_2orientationexample_pathtotal
33203201101.251.251.37RAS+../data/Task02_Heart/labelsTr/la_004.nii.gz4
43203201201.251.251.37RAS+../data/Task02_Heart/labelsTr/la_005.nii.gz4
13203201001.251.251.37RAS+../data/Task02_Heart/labelsTr/la_009.nii.gz2
0320320901.251.251.37RAS+../data/Task02_Heart/labelsTr/la_016.nii.gz1
23203201091.251.251.37RAS+../data/Task02_Heart/labelsTr/la_029.nii.gz1
+ +
+
+
+
+
resample, reorder = med_dataset.suggestion()
+resample, reorder
+
+
([1.25, 1.25, 1.37], False)
+
+
+
+
bs=4
+
+
+
img_size = med_dataset.get_largest_img_size(resample=resample)
+img_size
+
+
[320.0, 320.0, 130.0]
+
+
+
+
size = [160,160,128]
+
+
+
item_tfms = [ZNormalization(), PadOrCrop(size), RandomAffine(scales=0, degrees=5)]  # RandomMotion()
+
+
+
dblock = MedDataBlock(blocks=(ImageBlock(cls=MedImage), MedMaskBlock), 
+                      splitter=RandomSplitter(valid_pct=0.1, seed=42), # By passing valid_pct=0.1, we tell it to get a random 10% of the training set for validation.
+                      get_x=ColReader('image'),
+                      get_y=ColReader('label'),
+                      item_tfms=item_tfms,
+                      reorder=reorder,
+                      resample=resample)
+
+
+
dls = dblock.dataloaders(train_df, bs=bs)
+
+
+
# training and validation
+len(dls.train_ds.items), len(dls.valid_ds.items)
+
+
(13, 1)
+
+
+
+
dls.show_batch(anatomical_plane=2)
+
+
+
+

+
+
+
+
+
+
+

Create and train a 3D model

+

The current state-of-the-art CNN models for medical image segmentation tasks are based on encoder-decoder architectures like the U-Net. In the following line, we import an enhanced version of 3D UNet from MONAI. In addition, we import the Dice coefficient (Dice) loss, a commonly used loss function in medical image segmentation tasks: \[\begin{equation*} +\begin{aligned} +loss = 1- \frac{y \cap \hat{y}}{\left | y \right |+\left | \hat{y} \right |} +\end{aligned} +\end{equation*}\]

+
+
from monai.networks.nets import UNet
+from monai.losses import DiceLoss
+
+
+
model = UNet(spatial_dims=3, in_channels=1, out_channels=1, channels=(16, 32, 64, 128, 256),strides=(2, 2, 2, 2), num_res_units=2)
+model = model.model
+
+
+
loss_func = CustomLoss(loss_func=DiceLoss(sigmoid=True))
+
+

We use a built-in ranger optimizer from fastai, that combines (RAdam + Lookahead) into a single optimizer. Ranger21 tested with fastai available: https://github.com/lessw2020/Ranger21

+
+
learn = Learner(dls, model, loss_func=loss_func, opt_func=ranger, metrics=binary_dice_score)
+
+
+
learn.lr_find()
+
+ + +
+
+ +
+
+
SuggestedLRs(valley=0.009120108559727669)
+
+
+
+
+

+
+
+
+
+
+
lr = 1e-2
+
+

As shown in https://walkwithfastai.com/Segmentation, with the ranger optimizer, we want to use a different fit function, called fit_flat_cos

+
+
learn.fit_flat_cos(200 ,lr)
+
+
+
learn.recorder.plot_loss()
+
+
+
+

+
+
+
+
+
+
learn.save('heart-model')
+
+
Path('models/heart-model.pth')
+
+
+
+
learn.show_results(anatomical_plane=1)
+
+ + +
+
+ +
+
+
+
+

+
+
+
+
+
+
+

Test data (with labels to evaulate the model)

+

Evaluate the performance of the selected model on unseen data. It’s important to not touch this data until you have fine tuned your model to get an unbiased evaluation!

+
+
learn.load('heart-model');
+
+
+
test_dl = learn.dls.test_dl(test_df,with_labels=True)
+
+
+
test_dl.show_batch(anatomical_plane=0, figsize=(10,10))
+
+
+
+

+
+
+
+
+
+
pred_acts, labels = learn.get_preds(dl=test_dl)
+pred_acts.shape, labels.shape
+
+ + +
+
+ +
+
+
(torch.Size([2, 1, 160, 160, 128]), torch.Size([2, 1, 160, 160, 128]))
+
+
+
+
binary_dice_score(pred_acts, labels)
+
+
tensor(0.8893)
+
+
+
+
learn.show_results(anatomical_plane=0, dl=test_dl)
+
+ + +
+
+ +
+
+
+
+

+
+
+
+
+
+
+

Export learner

+
+
store_variables(pkl_fn='vars.pkl', size=size, reorder=reorder,  resample=resample)
+
+
+
learn.export('heart_model.pkl')
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/tutorial_classification.html b/tutorial_classification.html new file mode 100644 index 0000000..71e19e0 --- /dev/null +++ b/tutorial_classification.html @@ -0,0 +1,1550 @@ + + + + + + + + + +fastMONAI - Classification + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Classification

+
+ + + +
+ + + + +
+ + + +
+ + + +
+
+

Google Colab

+
Google Colab
+
+
+

The following line imports all of the functions and classes from the fastMONAI library:

+
+
from fastMONAI.vision_all import *
+
+
+

Downloading external data

+

To demonstrate the use of fastMONAI, we download the NoduleMNIST3D dataset from MedMNIST v2, a dataset containing lung nodules with labels indicating whether the nodules are benign (0) or malignant (1):

+
+
df,_ = download_medmnist3d_dataset(study='NoduleMNIST3D', max_workers=2)
+
+
+
+

Inspecting the data

+

Let’s look at how the processed DataFrame is formatted:

+
+
df.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
img_pathlabelsis_val
0../data/NoduleMNIST3D/train_images/0_nodule.nii.gz0False
1../data/NoduleMNIST3D/train_images/1_nodule.nii.gz1False
2../data/NoduleMNIST3D/train_images/2_nodule.nii.gz1False
3../data/NoduleMNIST3D/train_images/3_nodule.nii.gz0False
4../data/NoduleMNIST3D/train_images/4_nodule.nii.gz0False
+ +
+
+
+

In fastMONAI, various data augmentation techniques are available for training vision models, and they can also optionally be applied during inference. The following code cell specifies a list of transformations to be applied to the items in the training set. The complete list of available transformations in the library can be found at https://fastmonai.no/vision_augment.

+
+
item_tfms = [ZNormalization(), PadOrCrop(size=28), RandomAffine(degrees=35, isotropic=True)]
+
+

Before feeding the data into a model, we must create a DataLoaders object for our dataset. There are several ways to get the data in DataLoaders. In the following line, we call the ImageDataLoaders.from_df factory method, which is the most basic way of building a DataLoaders.

+

Here, we pass the processed DataFrame, define the columns for the images fn_col and the labels label_col, some transforms item_tfms, voxel spacing resample, and the batch size bs.

+
+
dls = MedImageDataLoaders.from_df(df, fn_col='img_path', label_col='labels',
+                                  splitter=ColSplitter('is_val'), item_tfms=item_tfms,
+                                  resample=1, bs=64)
+
+

We can now take a look at a batch of images in the training set using show_batch :

+
+
dls.show_batch(max_n = 2, anatomical_plane = 2)
+
+
+
+

+
+
+
+
+

We’re now ready to construct a deep learning classification model.

+
+
+

Create and train a 3D deep learning model

+

We import a classification network from MONAI and configure it based on our task, including defining the input image size, the number of classes to predict, channels, etc.

+
+
from monai.networks.nets import Classifier
+
+model = Classifier(in_shape=[1, 28, 28, 28], classes=2, 
+                   channels=(8, 16, 32, 64), strides=(2, 2, 2))
+
+

Then we create a Learner, which is a fastai object that combines the data and our defined model for training.

+
+
learn = Learner(dls, model, metrics=accuracy)
+
+
+
learn.fit_one_cycle(4)
+
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
epochtrain_lossvalid_lossaccuracytime
00.5748310.4093100.83712100:04
10.4777870.3833740.84090900:03
20.4089930.3456900.85984800:03
30.3656730.3577120.84090900:03
+
+
+

Note: Small random variations are involved in training CNN models. Hence, when running the notebook, you may see different results.

+

With the model trained, let’s look at some predictions on the validation data. The show_results method plots instances, their target values, and their corresponding predictions from the model.

+
+
learn.show_results(max_n=2, anatomical_plane=2)
+
+ + +
+
+ +
+
+
+
+

+
+
+
+
+
+
+

Model evaluation and interpretation

+

Let’s look at how often and for what instances our trained model becomes confused while making predictions on the validation data:

+
+
interp = ClassificationInterpretation.from_learner(learn)
+
+ + +
+
+ +
+
+
+
cm = interp.plot_confusion_matrix()
+
+ + +
+
+ +
+
+
+
+

+
+
+
+
+

Class imbalance is a common challenge in medical datasets, and it is something we’re facing in our example dataset. When dealing with a classification task on such imbalanced datasets, specific sampling techniques may be necessary. See the Advanced) section for further details.

+
+
interp.plot_top_losses(k=4, anatomical_plane=2)
+
+ + +
+
+ +
+
+
+
+

+
+
+
+
+

Here are the instances our model was most confused about (in other words, most confident but wrong)

+

## Advanced

+

The following line imports only the needed components for our classification task:

+
+
from fastai.vision.learner import Learner
+from fastai.losses import CrossEntropyLossFlat
+from fastai.vision.data import ImageBlock, CategoryBlock
+from fastai.data.transforms import ColReader, ColSplitter
+from fastai.metrics import accuracy
+from fastai.callback.schedule import lr_find
+
+from monai.networks.nets import Classifier
+
+from fastMONAI.vision_augmentation import PadOrCrop, RandomAffine, ZNormalization
+from fastMONAI.external_data import download_medmnist3d_dataset
+from fastMONAI.dataset_info import MedDataset, get_class_weights
+from fastMONAI.vision_core import MedImage
+from fastMONAI.vision_data import MedDataBlock
+from fastMONAI.utils import store_variables
+
+
+
df_train_val, df_test = download_medmnist3d_dataset(study='NoduleMNIST3D', max_workers = 8)
+
+
+
df_train_val.head(1)
+
+
+ + + + + + + + + + + + + + + + + + + +
img_pathlabelsis_val
0../data/NoduleMNIST3D/train_images/0_nodule.nii.gz0False
+ +
+
+
+
+
med_dataset = MedDataset(img_list=df_train_val.img_path.tolist(), max_workers=12)
+
+
+
data_info_df = med_dataset.summary()
+
+
+
data_info_df.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
dim_0dim_1dim_2voxel_0voxel_1voxel_2orientationexample_pathtotal
02828281.01.01.0RAS+../data/NoduleMNIST3D/train_images/0_nodule.nii.gz1323
+ +
+
+
+
+
resample, reorder = med_dataset.suggestion()
+resample, reorder
+
+
([1.0, 1.0, 1.0], False)
+
+
+
+
img_size = med_dataset.get_largest_img_size(resample=resample)
+img_size
+
+
[28.0, 28.0, 28.0]
+
+
+
+
bs=64
+in_shape = [1, 28, 28, 28]
+
+
+
item_tfms = [ZNormalization(), PadOrCrop(size=28), RandomAffine(degrees=35, isotropic=True)]
+
+

As we mentioned earlier, there are several ways to get the data in DataLoaders. In this section, let’s rebuild using DataBlock. Here we need to define what our input and target should be (MedImage and CategoryBlock for classification), how to get the images and the labels, how to split the data, item transforms that should be applied during training, reorder voxel orientations, and voxel spacing. Take a look at fastai’s documentation for DataBlock for further information: https://docs.fast.ai/data.block.html#DataBlock.

+
+
dblock = MedDataBlock(blocks=(ImageBlock(cls=MedImage), CategoryBlock), 
+                      splitter=ColSplitter('is_val'),
+                      get_x=ColReader('img_path'),
+                      get_y=ColReader('labels'),
+                      item_tfms=item_tfms, 
+                      reorder=reorder,
+                      resample=resample)
+
+

Now we pass our processed DataFrame and the bath size to create a DataLoaders object.

+
+
dls = dblock.dataloaders(df_train_val, bs=bs)
+len(dls.train_ds.items), len(dls.valid_ds.items)
+
+
(1158, 165)
+
+
+
+
dls.show_batch(max_n=6, figsize=(5, 5), anatomical_plane=2)
+
+
+
+

+
+
+
+
+
+
model = Classifier(in_shape=in_shape, classes=2, 
+                   channels=(8, 16, 32, 64), strides=(2, 2, 2))
+
+
+
+

Choosing a loss function

+

Class imbalance is a common challenge in medical datasets, and it is something we’re facing in our example dataset:

+
+
print(df_train_val.labels.value_counts())
+
+
labels
+0    986
+1    337
+Name: count, dtype: int64
+
+
+

There are multiple ways to deal with class imbalance. A straightforward technique is to use balancing weights in the model’s loss function, i.e., penalizing misclassifications for instances belonging to the minority class more heavily than those of the majority class.

+
+
train_labels = df_train_val.loc[~df_train_val.is_val]['labels']
+class_weights = get_class_weights(train_labels)
+print(class_weights)
+
+
tensor([0.6709, 1.9627])
+
+
+
+
loss_func = CrossEntropyLossFlat(weight=class_weights)
+
+
+
learn = Learner(dls, model, loss_func=loss_func, metrics=accuracy)
+
+
+
learn.summary()
+
+ + +
+
+ +
+
+
Classifier (Input shape: 64 x 1 x 28 x 28 x 28)
+============================================================================
+Layer (type)         Output Shape         Param #    Trainable 
+============================================================================
+                     64 x 8 x 14 x 14 x  
+Conv3d                                    224        True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    1736       True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    224        True      
+____________________________________________________________________________
+                     64 x 16 x 7 x 7 x 7 
+Conv3d                                    3472       True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    6928       True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    3472       True      
+____________________________________________________________________________
+                     64 x 32 x 4 x 4 x 4 
+Conv3d                                    13856      True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    27680      True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    13856      True      
+Reshape                                                        
+____________________________________________________________________________
+                     64 x 2048           
+Flatten                                                        
+____________________________________________________________________________
+                     64 x 2              
+Linear                                    4098       True      
+____________________________________________________________________________
+
+Total params: 75,552
+Total trainable params: 75,552
+Total non-trainable params: 0
+
+Optimizer used: <function Adam at 0x7f368fbefaf0>
+Loss function: FlattenedLoss of CrossEntropyLoss()
+
+Callbacks:
+  - TrainEvalCallback
+  - CastToTensor
+  - Recorder
+  - ProgressCallback
+
+
+
+
lr = learn.lr_find()
+
+ + +
+
+ +
+
+
+
+

+
+
+
+
+
+
learn.fit_one_cycle(4, lr.valley)
+
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
epochtrain_lossvalid_lossaccuracytime
00.6315020.4764120.84242400:03
10.5523070.4555220.82424200:03
20.5054920.4429220.86060600:03
30.4745270.4421310.86060600:03
+
+
+
+
learn.show_results(anatomical_plane=2)
+
+ + +
+
+ +
+
+
+
+

+
+
+
+
+
+
learn.save('model-2')
+
+
Path('models/model-2.pth')
+
+
+
+
+

Inference on test data

+
+
learn.load('model-2');
+
+
+
dls.valid_ds.items = df_test
+
+
+
preds, targs = learn.get_preds();
+
+ + +
+
+ +
+
+
+
accuracy(preds, targs)
+
+
TensorBase(0.8194)
+
+
+
+

Test-time augmentation

+

Test-time augmentation (TTA) is a technique where you apply data augmentation transforms when making predictions to produce average output. In addition to often yielding better performance, the variation in the output of the TTA runs can provide some measure of its robustness and sensitivity to augmentations.

+
+
preds, targs = learn.tta();
+
+ + +
+
+ +
+ + +
+ +
+
+ + +
+
+ +
+
+
+
accuracy(preds, targs)
+
+
TensorBase(0.8387)
+
+
+
+
+
+

Export learner

+
+
store_variables(pkl_fn='vars.pkl', size=in_shape, reorder=reorder,  resample=resample)
+
+
+
learn.export('learner.pkl')
+
+
+
+

Make a simple web app

+

Make a simple web application with Gradio and host it on Hugging Face Spaces.

+
+
learn = load_learner('learner.pkl', cpu=True)
+_, reorder, resample = load_variables(pkl_fn='vars.pkl')
+
+
+
import gradio as gr
+
+gr.Interface(fn=lambda fileobj: gradio_image_classifier(fileobj, learn,
+                                                        reorder, resample),
+             inputs=['file'],
+             outputs=gr.Label(num_top_classes=2),
+             examples=[df_test.img_path[0], df_test.img_path[200]],
+             title='Example app').launch()
+
+
Running on local URL:  http://127.0.0.1:7860
+
+To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/tutorial_inference.html b/tutorial_inference.html new file mode 100644 index 0000000..ae277d0 --- /dev/null +++ b/tutorial_inference.html @@ -0,0 +1,779 @@ + + + + + + + + + +fastMONAI - Inference with exported learner + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Inference with exported learner

+
+ + + +
+ + + + +
+ + + +
+ + + +
+
#all_skip
+from fastMONAI.vision_all import *
+
+from monai.apps import DecathlonDataset
+
+
+
#task = 'Task09_Spleen'
+task = 'Task01_BrainTumour'
+
+
+
path = Path('../data')
+path.mkdir(exist_ok=True)
+
+
+
test_data = DecathlonDataset(root_dir=path, task=task, section="test", download=True,
+                                 cache_num=0, num_workers=3)
+
+
+
test_imgs = [data['image'] for data in test_data.data]
+
+
+
learn_inf = load_learner('braintumor_model.pkl', cpu=False);
+
+
+
_, reorder, resample = load_variables(pkl_fn='vars.pkl')
+reorder, resample
+
+
(False, [1.0, 1.0, 1.0])
+
+
+
+
save_path = Path('../data/results/braintumor')
+path.mkdir(parents=True, exist_ok=True)
+
+
+
idx = 3
+img_fn = test_imgs[idx]
+
+
+
pred_fn = inference(learn_inf, reorder, resample, fn, save_path=save_path)
+
+ + +
+
+ +
+
+
+
from torchio import Subject, ScalarImage, LabelMap
+
+subject = Subject(image=ScalarImage(img_fn), mask=LabelMap(pred_fn))
+subject.plot(figsize=(10,5))
+
+ + + +
+ +
+ + + + + \ No newline at end of file diff --git a/tutorial_multiclass_segmentation.html b/tutorial_multiclass_segmentation.html new file mode 100644 index 0000000..bdfd03a --- /dev/null +++ b/tutorial_multiclass_segmentation.html @@ -0,0 +1,1347 @@ + + + + + + + + + + +fastMONAI - Multi-class semantic segmentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Multi-class semantic segmentation

+
+ +
+
+ For the multi-class semantic segmentation task, we will use the brain tumors dataset from the Medical Segmentation Decathlon challenge (http://medicaldecathlon.com/). The data is collected from the Multimodal Brain Tumor Image Segmentation Benchmark Challenge (BraTS) dataset from 2016 and 2017. The task is to segment tumors into three different subregions (active tumor (AT), necrotic core (NCR), and peritumoral edematous/infiltrated tissue (ED)) from multimodal multisite MRI data (T1w, T1ce, T2w, and FLAIR). The challenge with this dataset is the brain tumors’ highly heterogeneous appearance and shape. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
+

Google Colab

+
Google Colab
+
+
+
+
from fastMONAI.vision_all import *
+
+from monai.apps import DecathlonDataset
+from sklearn.model_selection import train_test_split
+
+
+

Download external data

+

We use the MONAI function DecathlonDataset to download the data and generate items for training.

+
+
path = Path('../data')
+path.mkdir(exist_ok=True)
+
+
+
training_data = DecathlonDataset(root_dir=path, task="Task01_BrainTumour", section="training", download=True,
+                                 cache_num=0, num_workers=3)
+
+
2022-09-01 17:36:43,099 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.
+2022-09-01 17:36:43,100 - INFO - File exists: ../data/Task01_BrainTumour.tar, skipped downloading.
+2022-09-01 17:36:43,101 - INFO - Non-empty folder exists in ../data/Task01_BrainTumour, skipped extracting.
+
+
+
+
df = pd.DataFrame(training_data.data)
+df.shape
+
+
(388, 2)
+
+
+

Split the labled data into training and test

+
+
train_df, test_df = train_test_split(df, test_size=0.1, random_state=42)
+train_df.shape, test_df.shape
+
+
((349, 2), (39, 2))
+
+
+
+
+

Look at training data

+
+
med_dataset = MedDataset(img_list=train_df.label.tolist(), dtype=MedMask, max_workers=12)
+
+
+
med_dataset.df.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
pathdim_0dim_1dim_2voxel_0voxel_1voxel_2orientationvoxel_count_0voxel_count_1voxel_count_2voxel_count_3
0/home/sathiesh/lib_dev/fastMONAI/data/Task01_BrainTumour/labelsTr/BRATS_477.nii.gz2402401551.01.01.0RAS+8765377830881582663709.0
1/home/sathiesh/lib_dev/fastMONAI/data/Task01_BrainTumour/labelsTr/BRATS_350.nii.gz2402401551.01.01.0RAS+887263621364887225128.0
2/home/sathiesh/lib_dev/fastMONAI/data/Task01_BrainTumour/labelsTr/BRATS_266.nii.gz2402401551.01.01.0RAS+8725071832766978449869.0
3/home/sathiesh/lib_dev/fastMONAI/data/Task01_BrainTumour/labelsTr/BRATS_294.nii.gz2402401551.01.01.0RAS+8790699908062023126264.0
4/home/sathiesh/lib_dev/fastMONAI/data/Task01_BrainTumour/labelsTr/BRATS_466.nii.gz2402401551.01.01.0RAS+891125214046602642.0
+ +
+
+
+
+
summary_df = med_dataset.summary()
+
+
+
summary_df.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
dim_0dim_1dim_2voxel_0voxel_1voxel_2orientationexample_pathtotal
02402401551.01.01.0RAS+/home/sathiesh/lib_dev/fastMONAI/data/Task01_BrainTumour/labelsTr/BRATS_002.nii.gz349
+ +
+
+
+
+
resample, reorder = med_dataset.suggestion()
+resample, reorder
+
+
([1.0, 1.0, 1.0], False)
+
+
+
+
img_size = med_dataset.get_largest_img_size(resample=resample)
+img_size
+
+
[240.0, 240.0, 155.0]
+
+
+
+
bs=4
+size=[224,224,128]
+
+
+
item_tfms = [ZNormalization(), PadOrCrop(size), RandomAffine(scales=0, degrees=5, isotropic=True)]
+
+
+
dblock = MedDataBlock(blocks=(ImageBlock(cls=MedImage), MedMaskBlock), 
+                      splitter=RandomSplitter(seed=42),
+                      get_x=ColReader('image'),
+                      get_y=ColReader('label'),
+                      item_tfms=item_tfms,
+                      reorder=reorder,
+                      resample=resample)
+
+
+
dls = dblock.dataloaders(train_df, bs=bs)
+
+
+
# training and validation
+len(dls.train_ds.items), len(dls.valid_ds.items)
+
+
(280, 69)
+
+
+
+
dls.show_batch(anatomical_plane=0)
+
+
+
+

+
+
+
+
+
+
+

Create and train a 3D model

+

As in the binary segmentation task, we import an enhanced version of UNet from MONAI. This time instead of using Dice loss, we import a loss function that combines Dice loss and Cross Entropy loss and returns the weighted sum of these two losses.

+
+
from monai.losses import DiceCELoss
+from monai.networks.nets import UNet
+
+
+
codes = np.unique(med_img_reader(train_df.label.tolist()[0]))
+n_classes = len(codes)
+codes, n_classes
+
+
(array([0., 1., 2., 3.], dtype=float32), 4)
+
+
+
+
model = UNet(spatial_dims=3, in_channels=4, out_channels=n_classes, channels=(16, 32, 64, 128, 256),strides=(2, 2, 2, 2), num_res_units=2)
+model = model
+
+
+
loss_func = CustomLoss(loss_func=DiceCELoss(to_onehot_y=True, include_background=True, softmax=True))
+
+
+
learn = Learner(dls, model, loss_func=loss_func, opt_func=ranger, metrics=multi_dice_score)#.to_fp16()
+
+
+
learn.lr_find()
+
+ + +
+
+ +
+
+
SuggestedLRs(valley=0.0020892962347716093)
+
+
+
+
+

+
+
+
+
+
+
lr = 1e-1
+
+
+
learn.fit_flat_cos(20 ,lr)
+
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
epochtrain_lossvalid_lossmulti_dice_scoretime
00.7318450.639536tensor([0.4518, 0.0668, 0.2126])01:35
10.6078090.513107tensor([0.4640, 0.1777, 0.5614])01:38
20.5195890.469945tensor([0.5452, 0.3205, 0.5655])01:42
30.4912770.432317tensor([0.6120, 0.2937, 0.6087])01:43
40.4471220.436939tensor([0.6344, 0.2947, 0.5832])01:40
50.4388580.399160tensor([0.6423, 0.3719, 0.6272])01:37
60.4284920.395152tensor([0.6066, 0.4034, 0.6307])01:38
70.4302740.439361tensor([0.5118, 0.3754, 0.6161])01:42
80.4305290.396985tensor([0.6117, 0.4036, 0.6407])01:37
90.4263350.397388tensor([0.5862, 0.4063, 0.6515])01:43
100.4055440.410180tensor([0.5997, 0.3905, 0.6501])01:38
110.4040890.375698tensor([0.6476, 0.4064, 0.6567])01:38
120.4105700.397038tensor([0.6492, 0.3614, 0.6325])01:36
130.3980870.422734tensor([0.5770, 0.4029, 0.5979])01:38
140.4109390.380410tensor([0.6152, 0.4226, 0.6542])01:41
150.4005660.413136tensor([0.5845, 0.4104, 0.6108])01:37
160.3950920.360473tensor([0.6729, 0.4272, 0.6761])01:38
170.3685550.350397tensor([0.6723, 0.4393, 0.6917])01:40
180.3488010.352961tensor([0.6729, 0.4536, 0.6669])01:36
190.3388200.342739tensor([0.6730, 0.4586, 0.7052])01:40
+
+
+
+
learn.save('braintumor-model')
+
+
Path('models/braintumor-model.pth')
+
+
+
+
learn.show_results(anatomical_plane=0, ds_idx=1)
+
+ + +
+
+ +
+
+
+
+

+
+
+
+
+
+
+

Inference on test data

+
+
learn.load('braintumor-model');
+
+
+
test_dl = learn.dls.test_dl(test_df[:10],with_labels=True)
+
+
+
test_dl.show_batch(anatomical_plane=0, figsize=(10,10))
+
+
+
+

+
+
+
+
+
+
pred_acts, labels = learn.get_preds(dl=test_dl)
+pred_acts.shape, labels.shape
+
+ + +
+
+ +
+
+
(torch.Size([10, 4, 224, 224, 128]), torch.Size([10, 1, 224, 224, 128]))
+
+
+

Dice score for labels 1,2 and 3:

+
+
multi_dice_score(pred_acts, labels)
+
+
tensor([0.5708, 0.4186, 0.6994])
+
+
+
+
learn.show_results(anatomical_plane=0, dl=test_dl)
+
+ + +
+
+ +
+
+
+
+

+
+
+
+
+
+
+

Export learner

+
+
store_variables(pkl_fn='vars.pkl', size=size, reorder=reorder,  resample=resample)
+
+
+
learn.export('braintumor_model.pkl')
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/tutorial_regression.html b/tutorial_regression.html new file mode 100644 index 0000000..34673f4 --- /dev/null +++ b/tutorial_regression.html @@ -0,0 +1,1222 @@ + + + + + + + + + + +fastMONAI - Regression + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Regression

+
+ +
+
+ We will use the same data used in the classification tutorial for this task (the IXI Dataset). The approach for regression tasks is nearly identical. Therefore, take a look at the classification tutorial for explanations of the various cells. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
+

Google Colab

+
Google Colab
+
+
+
+
from fastMONAI.vision_all import *
+
+
+
path = Path('../data')
+path.mkdir(exist_ok=True)
+
+
+
STUDY_DIR = download_ixi_data(path=path)
+
+
Images already downloaded and extracted to ../data/IXI/T1_images
+2022-12-22 16:47:36,114 - INFO - Expected md5 is None, skip md5 check for file ../data/IXI/IXI.xls.
+2022-12-22 16:47:36,115 - INFO - File exists: ../data/IXI/IXI.xls, skipped downloading.
+Preprocessing ../data/IXI/IXI.xls
+
+
+
+

Looking at the data

+
+
df = pd.read_csv(STUDY_DIR/'dataset.csv')
+df['age'] = np.around(df.age_at_scan.tolist(), decimals=0)
+
+
+
df.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
t1_pathsubject_idgenderage_at_scanage
0../data/IXI/T1_images/IXI002-Guys-0828-T1.nii.gzIXI002F35.8036.0
1../data/IXI/T1_images/IXI012-HH-1211-T1.nii.gzIXI012M38.7839.0
2../data/IXI/T1_images/IXI013-HH-1212-T1.nii.gzIXI013M46.7147.0
3../data/IXI/T1_images/IXI014-HH-1236-T1.nii.gzIXI014F34.2434.0
4../data/IXI/T1_images/IXI015-HH-1258-T1.nii.gzIXI015M24.2824.0
+ +
+
+
+
+
df.age.min(), df.age.max()
+
+
(20.0, 86.0)
+
+
+
+
med_dataset = MedDataset(path=STUDY_DIR/'T1_images', max_workers=12)
+
+
+
data_info_df = med_dataset.summary()
+
+
+
data_info_df.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
dim_0dim_1dim_2voxel_0voxel_1voxel_2orientationexample_pathtotal
32562561500.93750.93751.2PSR+../data/IXI/T1_images/IXI002-Guys-0828-T1.nii.gz498
22562561460.93750.93751.2PSR+../data/IXI/T1_images/IXI035-IOP-0873-T1.nii.gz74
42562561500.97660.97661.2PSR+../data/IXI/T1_images/IXI297-Guys-0886-T1.nii.gz5
02562561300.93750.93751.2PSR+../data/IXI/T1_images/IXI023-Guys-0699-T1.nii.gz2
12562561400.93750.93751.2PSR+../data/IXI/T1_images/IXI020-Guys-0700-T1.nii.gz2
+ +
+
+
+
+
resample, reorder = med_dataset.suggestion()
+
+
+
bs=4
+in_shape = [1, 256, 256, 160]
+
+
+
item_tfms = [ZNormalization(), PadOrCrop(in_shape[1:]), RandomAffine(scales=0, degrees=5, isotropic=False)]
+
+
+
dblock = MedDataBlock(blocks=(ImageBlock(cls=MedImage), RegressionBlock), 
+                      splitter=RandomSplitter(seed=32),
+                      get_x=ColReader('t1_path'),
+                      get_y=ColReader('age'),
+                      item_tfms=item_tfms,
+                      reorder=reorder,
+                      resample=resample)
+
+
+
dls = dblock.dataloaders(df, bs=bs)
+
+
+
len(dls.train_ds.items), len(dls.valid_ds.items)
+
+
(449, 112)
+
+
+
+
dls.show_batch(anatomical_plane=2)
+
+
+
+

+
+
+
+
+
+
+

Create and train a 3D model

+

Import a network from MONAI that can be used for regression tasks, and define the input image size, the output size, channels, etc.

+
+
from monai.networks.nets import Regressor
+model = Regressor(in_shape=[1, 256, 256, 160], out_shape=1, channels=(16, 32, 64, 128, 256),strides=(2, 2, 2, 2), kernel_size=3, num_res_units=2)
+
+
+
loss_func = L1LossFlat()
+
+
+
learn = Learner(dls, model, loss_func=loss_func, metrics=[mae])
+
+
+
learn.summary()
+
+ + +
+
+ +
+
+
Regressor (Input shape: 4 x 1 x 256 x 256 x 160)
+============================================================================
+Layer (type)         Output Shape         Param #    Trainable 
+============================================================================
+                     4 x 16 x 128 x 128  
+Conv3d                                    448        True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    6928       True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    448        True      
+____________________________________________________________________________
+                     4 x 32 x 64 x 64 x  
+Conv3d                                    13856      True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    27680      True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    13856      True      
+____________________________________________________________________________
+                     4 x 64 x 32 x 32 x  
+Conv3d                                    55360      True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    110656     True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    55360      True      
+____________________________________________________________________________
+                     4 x 128 x 16 x 16 x 
+Conv3d                                    221312     True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    442496     True      
+InstanceNorm3d                            0          False     
+PReLU                                     1          True      
+Conv3d                                    221312     True      
+Reshape                                                        
+____________________________________________________________________________
+                     4 x 327680          
+Flatten                                                        
+____________________________________________________________________________
+                     4 x 1               
+Linear                                    327681     True      
+____________________________________________________________________________
+
+Total params: 1,497,401
+Total trainable params: 1,497,401
+Total non-trainable params: 0
+
+Optimizer used: <function Adam at 0x7f6ed28f1e50>
+Loss function: FlattenedLoss of L1Loss()
+
+Callbacks:
+  - TrainEvalCallback
+  - CastToTensor
+  - Recorder
+  - ProgressCallback
+
+
+
+
learn.lr_find()
+
+ + +
+
+ +
+
+
SuggestedLRs(valley=5.248074739938602e-05)
+
+
+
+
+

+
+
+
+
+
+
lr = 1e-4
+
+
+
learn.fit_one_cycle(4)
+
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
epochtrain_lossvalid_lossmaetime
022.41213847.07498247.07498201:14
126.33118812.82445612.82445601:16
213.70399015.10696315.10696301:10
39.79089910.35642010.35642001:13
+
+
+
+
learn.save('model-brainage');
+
+
+
+

Inference

+
+
learn.load('model-brainage');
+
+
+
interp = Interpretation.from_learner(learn)
+
+ + +
+
+ +
+
+
+
interp.plot_top_losses(k=9, anatomical_plane=2)
+
+ + +
+
+ +
+
+
+
+

+
+
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/utils.html b/utils.html new file mode 100644 index 0000000..358eb58 --- /dev/null +++ b/utils.html @@ -0,0 +1,713 @@ + + + + + + + + + +fastMONAI - Utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Utils

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

store_variables

+
+
 store_variables (pkl_fn:(<class'str'>,<class'pathlib.Path'>), size:list,
+                  reorder:bool, resample:(<class'int'>,<class'list'>))
+
+

Save variable values in a pickle file.

+
+

source

+
+
+

load_variables

+
+
 load_variables (pkl_fn:(<class'str'>,<class'pathlib.Path'>))
+
+

*Loads stored variable values from a pickle file.

+

Args: pkl_fn: File path of the pickle file to be loaded.

+

Returns: The deserialized value of the pickled data.*

+
+

source

+
+ + +
+ +
+ + + + + \ No newline at end of file diff --git a/vision_augment.html b/vision_augment.html new file mode 100644 index 0000000..2c8a520 --- /dev/null +++ b/vision_augment.html @@ -0,0 +1,871 @@ + + + + + + + + + +fastMONAI - Data augmentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Data augmentation

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

Transforms wrapper

+
+

source

+
+

CustomDictTransform

+
+
 CustomDictTransform (aug)
+
+

A class that serves as a wrapper to perform an identical transformation on both the image and the target (if it’s a mask).

+
+
+
+

Vanilla transforms

+
+

source

+
+

do_pad_or_crop

+
+
 do_pad_or_crop (o, target_shape, padding_mode, mask_name, dtype=<class
+                 'torch.Tensor'>)
+
+
+

source

+
+
+

PadOrCrop

+
+
 PadOrCrop (size, padding_mode=0, mask_name=None)
+
+

Resize image using TorchIO CropOrPad.

+
+

source

+
+
+

ZNormalization

+
+
 ZNormalization (masking_method=None, channel_wise=True)
+
+

Apply TorchIO ZNormalization.

+
+

source

+
+
+

BraTSMaskConverter

+
+
 BraTSMaskConverter (enc=None, dec=None, split_idx=None, order=None)
+
+

Convert BraTS masks.

+
+

source

+
+
+

BinaryConverter

+
+
 BinaryConverter (enc=None, dec=None, split_idx=None, order=None)
+
+

Convert to binary mask.

+
+

source

+
+
+

RandomGhosting

+
+
 RandomGhosting (intensity=(0.5, 1), p=0.5)
+
+

Apply TorchIO RandomGhosting.

+
+

source

+
+
+

RandomSpike

+
+
 RandomSpike (num_spikes=1, intensity=(1, 3), p=0.5)
+
+

Apply TorchIO RandomSpike.

+
+

source

+
+
+

RandomNoise

+
+
 RandomNoise (mean=0, std=(0, 0.25), p=0.5)
+
+

Apply TorchIO RandomNoise.

+
+

source

+
+
+

RandomBiasField

+
+
 RandomBiasField (coefficients=0.5, order=3, p=0.5)
+
+

Apply TorchIO RandomBiasField.

+
+

source

+
+
+

RandomBlur

+
+
 RandomBlur (std=(0, 2), p=0.5)
+
+

Apply TorchIO RandomBiasField.

+
+

source

+
+
+

RandomGamma

+
+
 RandomGamma (log_gamma=(-0.3, 0.3), p=0.5)
+
+

Apply TorchIO RandomGamma.

+
+

source

+
+
+

RandomMotion

+
+
 RandomMotion (degrees=10, translation=10, num_transforms=2,
+               image_interpolation='linear', p=0.5)
+
+

Apply TorchIO RandomMotion.

+
+
+
+

Dictionary transforms

+
+

source

+
+

RandomElasticDeformation

+
+
 RandomElasticDeformation (num_control_points=7, max_displacement=7.5,
+                           image_interpolation='linear', p=0.5)
+
+

Apply TorchIO RandomElasticDeformation.

+
+

source

+
+
+

RandomAffine

+
+
 RandomAffine (scales=0, degrees=10, translation=0, isotropic=False,
+               image_interpolation='linear', default_pad_value=0.0, p=0.5)
+
+

Apply TorchIO RandomAffine.

+
+

source

+
+
+

RandomFlip

+
+
 RandomFlip (axes='LR', p=0.5)
+
+

Apply TorchIO RandomFlip.

+
+

source

+
+
+

OneOf

+
+
 OneOf (transform_dict, p=1)
+
+

Apply only one of the given transforms using TorchIO OneOf.

+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/vision_core.html b/vision_core.html new file mode 100644 index 0000000..96823f1 --- /dev/null +++ b/vision_core.html @@ -0,0 +1,790 @@ + + + + + + + + + +fastMONAI - Vision core + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Vision core

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

Load images

+
+

source

+
+

med_img_reader

+
+
 med_img_reader (file_path:(<class'str'>,<class'pathlib.Path'>,<class'fast
+                 core.foundation.L'>,<class'list'>), dtype=<class
+                 'torch.Tensor'>, reorder:bool=False, resample:list=None,
+                 only_tensor:bool=True)
+
+

*Loads and preprocesses a medical image.

+

Args: file_path: Path to the image. Can be a string, Path object or a list. dtype: Datatype for the return value. Defaults to torch.Tensor. reorder: Whether to reorder the data to be closest to canonical (RAS+) orientation. Defaults to False. resample: Whether to resample image to different voxel sizes and image dimensions. Defaults to None. only_tensor: Whether to return only image tensor. Defaults to True.

+

Returns: The preprocessed image. Returns only the image tensor if only_tensor is True, otherwise returns original image, preprocessed image, and original size.*

+
+

source

+
+
+

MetaResolver

+
+
 MetaResolver (*args, **kwargs)
+
+

*A class to bypass metaclass conflict: https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/data/batch.html*

+
+

source

+
+
+

MedBase

+
+
 MedBase (*args, **kwargs)
+
+

A class that represents an image object. Metaclass casts x to this class if it is of type cls._bypass_type.

+
+

source

+
+
+

MedImage

+
+
 MedImage (*args, **kwargs)
+
+

Subclass of MedBase that represents an image object.

+
+

source

+
+
+

MedMask

+
+
 MedMask (*args, **kwargs)
+
+

Subclass of MedBase that represents an mask object.

+
+
MedBase.item_preprocessing(resample=[1,1,1], reorder=True)
+im = MedImage.create('images/IXI002-Guys-0828-T1.nii.gz')
+test_eq(type(im), MedImage)
+
+
+
ax = im.show(anatomical_plane=0)
+
+
+
+

+
+
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/vision_data.html b/vision_data.html new file mode 100644 index 0000000..4c45b0f --- /dev/null +++ b/vision_data.html @@ -0,0 +1,761 @@ + + + + + + + + + +fastMONAI - Vision data + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Vision data

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

Prediction to mask

+
+

source

+
+

pred_to_multiclass_mask

+
+
 pred_to_multiclass_mask (pred:torch.Tensor)
+
+

*Apply Softmax on the predicted tensor to rescale the values in the range [0, 1] and sum to 1. Then apply argmax to get the indices of the maximum value of all elements in the predicted Tensor.

+

Args: pred: [C,W,H,D] activation tensor.

+

Returns: Predicted mask.*

+
+

source

+
+
+

batch_pred_to_multiclass_mask

+
+
 batch_pred_to_multiclass_mask (pred:torch.Tensor)
+
+

*Convert a batch of predicted activation tensors to masks.

+

Args: pred: [B, C, W, H, D] batch of activations.

+

Returns: Tuple of batch of predicted masks and number of classes.*

+
+

source

+
+
+

pred_to_binary_mask

+
+
 pred_to_binary_mask (pred:torch.Tensor)
+
+

*Apply Sigmoid function that squishes activations into a range between 0 and 1. Then we classify all values greater than or equal to 0.5 to 1, and the values below 0.5 to 0.

+

Args: pred: [B, C, W, H, D] or [C, W, H, D] activation tensor

+

Returns: Predicted binary mask(s).*

+
+

source

+
+
+

MedDataBlock

+
+
 MedDataBlock (blocks:list=None, dl_type:fastai.data.core.TfmdDL=None,
+               getters:list=None, n_inp:int=None, item_tfms:list=None,
+               batch_tfms:list=None, reorder:bool=False,
+               resample:(<class'int'>,<class'list'>)=None, **kwargs)
+
+

Container to quickly build dataloaders.

+
+
+
+

TransformBlock for segmentation

+
+

source

+
+

MedMaskBlock

+
+
 MedMaskBlock ()
+
+

Create a TransformBlock for medical masks.

+
+

source

+
+
+

MedImageDataLoaders

+
+
 MedImageDataLoaders (*loaders, path:str|pathlib.Path='.', device=None)
+
+

Higher-level MedDataBlock API.

+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/vision_inference.html b/vision_inference.html new file mode 100644 index 0000000..b22f011 --- /dev/null +++ b/vision_inference.html @@ -0,0 +1,759 @@ + + + + + + + + + +fastMONAI - Vision inference + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Vision inference

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

save_series_pred

+
+
 save_series_pred (series_obj, save_dir, val='1234')
+
+

Saves series prediction with updated DICOM UIDs.

+
+

source

+
+
+

load_system_resources

+
+
 load_system_resources (models_path, learner_fn, variables_fn)
+
+

Load necessary resources like learner and variables.

+
+

source

+
+
+

inference

+
+
 inference (learn_inf, reorder, resample,
+            fn:(<class'str'>,<class'pathlib.Path'>)='',
+            save_path:(<class'str'>,<class'pathlib.Path'>)=None,
+            org_img=None, input_img=None, org_size=None)
+
+

Predict on new data using exported model.

+
+

source

+
+
+

compute_binary_tumor_volume

+
+
 compute_binary_tumor_volume (mask_data:torchio.data.image.Image)
+
+

Compute the volume of the tumor in milliliters (ml).

+
+
+

Post-processing

+
+

source

+
+

refine_binary_pred_mask

+
+
 refine_binary_pred_mask (pred_mask,
+                          remove_size:(<class'int'>,<class'float'>)=None,
+                          percentage:float=0.2, verbose:bool=False)
+
+

*Removes small objects from the predicted binary mask.

+

Args: pred_mask: The predicted mask from which small objects are to be removed. remove_size: The size under which objects are considered ‘small’. percentage: The percentage of the remove_size to be used as threshold. Defaults to 0.2. verbose: If True, print the number of components. Defaults to False.

+

Returns: The processed mask with small objects removed.*

+
+
+
+

Gradio

+
+

source

+
+

gradio_image_classifier

+
+
 gradio_image_classifier (file_obj, learn, reorder, resample)
+
+

Predict on images using exported learner and return the result as a dictionary.

+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/vision_loss_functions.html b/vision_loss_functions.html new file mode 100644 index 0000000..2c6f249 --- /dev/null +++ b/vision_loss_functions.html @@ -0,0 +1,702 @@ + + + + + + + + + +fastMONAI - Custom loss functions + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Custom loss functions

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

CustomLoss

+
+
 CustomLoss (loss_func)
+
+

A custom loss wrapper class for loss functions to allow them to work with the ‘show_results’ method in fastai.

+
+

source

+
+
+

TverskyFocalLoss

+
+
 TverskyFocalLoss (include_background:bool=True, to_onehot_y:bool=False,
+                   sigmoid:bool=False, softmax:bool=False, gamma:float=2,
+                   alpha:float=0.5, beta:float=0.99)
+
+

Compute Tversky loss with a focus parameter, gamma, applied. The details of Tversky loss is shown in monai.losses.TverskyLoss.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/vision_metrics.html b/vision_metrics.html new file mode 100644 index 0000000..c97917b --- /dev/null +++ b/vision_metrics.html @@ -0,0 +1,750 @@ + + + + + + + + + +fastMONAI - Vision metrics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Vision metrics

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

calculate_dsc

+
+
 calculate_dsc (pred:torch.Tensor, targ:torch.Tensor)
+
+

MONAI compute_meandice

+
+

source

+
+
+

calculate_haus

+
+
 calculate_haus (pred:torch.Tensor, targ:torch.Tensor)
+
+

MONAI compute_hausdorff_distance

+
+

source

+
+
+

binary_dice_score

+
+
 binary_dice_score (act:<built-
+                    inmethodtensoroftypeobjectat0x7ff050730e20>,
+                    targ:torch.Tensor)
+
+

*Calculates the mean Dice score for binary semantic segmentation tasks.

+

Args: act: Activation tensor with dimensions [B, C, W, H, D]. targ: Target masks with dimensions [B, C, W, H, D].

+

Returns: Mean Dice score.*

+
+

source

+
+
+

multi_dice_score

+
+
 multi_dice_score (act:torch.Tensor, targ:torch.Tensor)
+
+

*Calculate the mean Dice score for each class in multi-class semantic segmentation tasks.

+

Args: act: Activation tensor with dimensions [B, C, W, H, D]. targ: Target masks with dimensions [B, C, W, H, D].

+

Returns: Mean Dice score for each class.*

+
+

source

+
+
+

binary_hausdorff_distance

+
+
 binary_hausdorff_distance (act:torch.Tensor, targ:torch.Tensor)
+
+

*Calculate the mean Hausdorff distance for binary semantic segmentation tasks.

+

Args: act: Activation tensor with dimensions [B, C, W, H, D]. targ: Target masks with dimensions [B, C, W, H, D].

+

Returns: Mean Hausdorff distance.*

+
+

source

+
+
+

multi_hausdorff_distance

+
+
 multi_hausdorff_distance (act:torch.Tensor, targ:torch.Tensor)
+
+

*Calculate the mean Hausdorff distance for each class in multi-class semantic segmentation tasks.

+

Args: act: Activation tensor with dimensions [B, C, W, H, D]. targ: Target masks with dimensions [B, C, W, H, D].

+

Returns: Mean Hausdorff distance for each class.*

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/vision_plot.html b/vision_plot.html new file mode 100644 index 0000000..b172554 --- /dev/null +++ b/vision_plot.html @@ -0,0 +1,719 @@ + + + + + + + + + +fastMONAI - Vision plot + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Vision plot

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

validate_anatomical_plane

+
+
 validate_anatomical_plane (anatomical_plane)
+
+

Ensure anatomical_plane is either 0, 1, or 2.

+
+

source

+
+
+

show_med_img

+
+
 show_med_img (im, ctx, channel:int, slice_index:int,
+               anatomical_plane:int,
+               voxel_size:(<class'int'>,<class'list'>), ax=None,
+               figsize=None, title=None, cmap=None, norm=None,
+               aspect=None, interpolation=None, alpha=None, vmin=None,
+               vmax=None, origin=None, extent=None,
+               interpolation_stage=None, filternorm=True, filterrad=4.0,
+               resample=None, url=None, data=None, **kwargs)
+
+

*Show an image on ax. This is a modified code from the fastai function show_image.

+

Args: im: The input image. ctx: The context. channel: Channel of the image. slice_index: Index of the 2D slice. anatomical_plane: Anatomical plane of the image. voxel_size: Voxel size for the image. ax: Axis for the plot. figsize: Figure size for the plot. title: Title for the plot. kwargs: Additional parameters for plt.Axes.imshow method.

+

Returns: Axis with the plot.*

+
+

source

+
+
+

find_max_slice

+
+
 find_max_slice (mask_data, anatomical_plane)
+
+

Find slice index based on mask

+ + +
+ +
+ +
+ + + + + \ No newline at end of file
+ + +