diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml new file mode 100644 index 0000000..c56c304 --- /dev/null +++ b/.github/workflows/python-test.yml @@ -0,0 +1,79 @@ +name: Python package + +on: [push] + +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ['3.8', '3.10', '3.11'] + + steps: + # 1. Checkout the repository + - uses: actions/checkout@v4 + + # 2. Cache Conda packages (optional but recommended) + - name: Cache Conda packages + uses: actions/cache@v3 + with: + path: ~/.conda/pkgs + key: ${{ runner.os }}-conda-${{ hashFiles('**/environment.yml') }} + restore-keys: | + ${{ runner.os }}-conda- + + # 3. Set up Miniconda + - name: Set up Miniconda + uses: conda-incubator/setup-miniconda@v3 + with: + auto-activate-base: true + activate-environment: '' + + # 4. Create and activate Conda environment + - name: Create and activate Conda environment + shell: bash -l {0} + run: | + if [[ "${{ matrix.python-version }}" == pypy* ]]; then + conda create -n test-env pypy=3.10 -y + else + conda create -n test-env python=${{ matrix.python-version }} -y + fi + source activate test-env + pip install --upgrade pip + + # 5. Display Python version + - name: Display Python version + shell: bash -l {0} + run: | + source activate test-env + python -c "import sys; print(sys.version)" + + # 6. Install the package + - name: Install pyphoon2 library + shell: bash -l {0} + run: | + source activate test-env + pip install . + + # 7. Verify installation + - name: Check if pyphoon2 was installed + shell: bash -l {0} + run: | + source activate test-env + pip list | grep pyphoon2 + + # 8. Verify PyPy installation (optional) + - name: Verify PyPy installation + if: startsWith(matrix.python-version, 'pypy') + shell: bash -l {0} + run: | + source activate test-env + pypy --version + + # 9. Run tests + - name: Run tests + shell: bash -l {0} + run: | + source activate test-env + cd tests + # sh run_all_tests.sh diff --git a/.gitignore b/.gitignore index a438c86..1121d2a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,8 +1,9 @@ # Dataset files *.tsv *.h5 - +*.zip # Byte-compiled / optimized / DLL files +tests/test_data_files __pycache__/ *.py[cod] *$py.class diff --git a/docs/conf.py b/docs/conf.py index 289a690..82a0f01 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -6,17 +6,18 @@ # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information +import os +import sys project = 'pyphoon2' -copyright = '2023, Jared Hwang' -author = 'Jared Hwang' -release = '1.0.0' +copyright = '2023, Jared Hwang. 2024, Ngoc Anh Tong' +author = 'Jared Hwang, Ngoc Anh Tong' +release = '2.0.0' templates_path = ['_templates'] exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] - # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output @@ -25,8 +26,6 @@ # Path setup -import os -import sys sys.path.insert(0, os.path.abspath('../')) sys.path.insert(0, os.path.abspath('../pyphoon2')) @@ -38,7 +37,7 @@ 'sphinx.ext.autodoc', # Core library for html generation from docstrings 'sphinx.ext.autosummary', # Create neat summary tables ] -autosummary_generate = True +autosummary_generate = True autodoc_member_order = 'bysource' autodoc_default_options = { diff --git a/docs/frame_model.py b/docs/frame_model.py index 76c0893..69cf2a6 100644 --- a/docs/frame_model.py +++ b/docs/frame_model.py @@ -1,3 +1,9 @@ +# autopep8: off +import os +import sys +import os.path +sys.path.insert(0, os.path.abspath( + os.path.join(os.path.dirname(__file__), '..'))) import torch from torch import nn from torch import optim @@ -10,7 +16,7 @@ from pathlib import Path from torch.utils.data import DataLoader -from DigitalTyphoonDataloader.DigitalTyphoonDataset import DigitalTyphoonDataset +from pyphoon2.DigitalTyphoonDataset import DigitalTyphoonDataset def main(args): @@ -112,7 +118,7 @@ def transform_func(image_ray): # Calculate the loss loss = criterion(predictions, labels) - + # backward pass loss.backward() # update weights diff --git a/docs/model_example_1.rst b/docs/model_example_1.rst index 4b10297..0ee7fac 100644 --- a/docs/model_example_1.rst +++ b/docs/model_example_1.rst @@ -24,7 +24,7 @@ The Code from pathlib import Path from torch.utils.data import DataLoader - from DigitalTyphoonDataloader.DigitalTyphoonDataset import DigitalTyphoonDataset + from pyphoon2.DigitalTyphoonDataset import DigitalTyphoonDataset import DigitalTyphoonDataset def main(args): diff --git a/docs/model_example_2.rst b/docs/model_example_2.rst index c0bfbca..3b77b88 100644 --- a/docs/model_example_2.rst +++ b/docs/model_example_2.rst @@ -19,7 +19,7 @@ The Code from pathlib import Path from torch.utils.data import DataLoader - from DigitalTyphoonDataloader.DigitalTyphoonDataset import DigitalTyphoonDataset + from pyphoon2.DigitalTyphoonDataset import DigitalTyphoonDataset import DigitalTyphoonDataset def main(args): diff --git a/docs/padlabels.py b/docs/padlabels.py index fbc7e8e..e156f64 100644 --- a/docs/padlabels.py +++ b/docs/padlabels.py @@ -11,19 +11,20 @@ from pathlib import Path from torch.utils.data import DataLoader -from DigitalTyphoonDataloader.DigitalTyphoonDataset import DigitalTyphoonDataset +from pyphoon2.DigitalTyphoonDataset import DigitalTyphoonDataset import DigitalTyphoonDataset + def main(args): - ## Prepare the data + # Prepare the data # Specify the paths to the data data_path = args.dataroot - images_path = data_path + '/image/' # to the image folder - metadata_path = data_path + '/metadata/' # to the metadata folder + images_path = data_path + '/image/' # to the image folder + metadata_path = data_path + '/metadata/' # to the metadata folder json_path = data_path + '/metadata.json' # to the metadata json - # Define a filter to pass to the loader. + # Define a filter to pass to the loader. # Any image that the function returns true will be included def image_filter(image): return image.grade() < 7 @@ -33,17 +34,22 @@ def image_filter(image): # So, image-by-image transforms (i.e. clipping, downsampling, etc. can/should be done here) def transform_func(image_ray): # Clip the pixel values between 150 and 350 - image_ray = np.clip(image_ray, standardize_range[0], standardize_range[1]) + image_ray = np.clip( + image_ray, standardize_range[0], standardize_range[1]) # Standardize the pixel values between 0 and 1 - image_ray = (image_ray - standardize_range[0]) / (standardize_range[1] - standardize_range[0]) + image_ray = ( + image_ray - standardize_range[0]) / (standardize_range[1] - standardize_range[0]) # Downsample the images to 224, 224 if downsample_size != (512, 512): image_ray = torch.Tensor(image_ray) - image_ray = torch.reshape(image_ray, [1, 1, image_ray.size()[0], image_ray.size()[1]]) - image_ray = nn.functional.interpolate(image_ray, size=downsample_size, mode='bilinear', align_corners=False) - image_ray = torch.reshape(image_ray, [image_ray.size()[2], image_ray.size()[3]]) + image_ray = torch.reshape( + image_ray, [1, 1, image_ray.size()[0], image_ray.size()[1]]) + image_ray = nn.functional.interpolate( + image_ray, size=downsample_size, mode='bilinear', align_corners=False) + image_ray = torch.reshape( + image_ray, [image_ray.size()[2], image_ray.size()[3]]) image_ray = image_ray.numpy() return image_ray @@ -52,25 +58,25 @@ def transform_func(image_ray): str(metadata_path), str(json_path), 'grade', # the labels we'd like to retrieve from the dataset - get_images_by_sequence=True, # indicate we want typhoon sequences returned - filter_func=image_filter, # the filter function defined above - transform_func=transform_func, # the transform function defined above - transform=transforms.Compose([ # pytorch transform to apply to data before returning data + get_images_by_sequence=True, # indicate we want typhoon sequences returned + filter_func=image_filter, # the filter function defined above + transform_func=transform_func, # the transform function defined above + transform=transforms.Compose([ # pytorch transform to apply to data before returning data PadSequence(505), ]), verbose=False) - # Split the dataset into a training and test split (80% and 20% respectively) - # split by sequence so all images in one sequence will belong to the same bucket + # split by sequence so all images in one sequence will belong to the same bucket train_set, test_set = dataset.random_split([0.8, 0.2], split_by='sequence') # Make Pytorch DataLoaders out of the returned sets. From here, it retains all Pytorch functionality. - trainloader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=num_workers) - testloader = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=num_workers) - + trainloader = DataLoader( + train_set, batch_size=batch_size, shuffle=True, num_workers=num_workers) + testloader = DataLoader(test_set, batch_size=batch_size, + shuffle=False, num_workers=num_workers) - ## Prepare the model + # Prepare the model # Hyperparameters num_epochs = args.max_epochs batch_size = 16 @@ -78,16 +84,16 @@ def transform_func(image_ray): standardize_range = (150, 350) downsample_size = (224, 224) - # Create a dummy model that will take input of size (505, 224, 224) (seq length, height, width) and output + # Create a dummy model that will take input of size (505, 224, 224) (seq length, height, width) and output # a value for each image in the sequence (shape (505)) linear1 = nn.Linear(224, 1) # Loss and optimizer criterion = nn.CrossEntropyLoss() - optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9) + optimizer = torch.optim.SGD( + model.parameters(), lr=learning_rate, momentum=0.9) - - ## Train the model + # Train the model for epoch in np.arange(max_epochs): batches_per_epoch = len(trainloader) @@ -99,11 +105,12 @@ def transform_func(image_ray): # Data is a tuple, with sequence in data[0] and labels in data[1] # sequence is shape (16, 505, 224, 224) (batch size, seq len, height, width) - # labels size is (16, 505) as we padded them as well. + # labels size is (16, 505) as we padded them as well. sequence, labels = data # cast pixels to float and grade (label) to long - sequence, labels = torch.Tensor(sequence).float(), torch.Tensor(labels).long() + sequence, labels = torch.Tensor( + sequence).float(), torch.Tensor(labels).long() optimizer.zero_grad() @@ -113,7 +120,7 @@ def transform_func(image_ray): # Calculate the loss loss = criterion(predictions, labels) - + # backward pass loss.backward() # update weights @@ -141,8 +148,8 @@ def __call__(self, received_sample): sample = torch.cat((pad, sample), dim=0) # Resize to (length, height, width) - sample = torch.reshape(sample, [sample.size()[0], sample.size()[1], sample.size()[2]]) - + sample = torch.reshape( + sample, [sample.size()[0], sample.size()[1], sample.size()[2]]) # Do the same for the labels labels = torch.Tensor(labels) @@ -154,8 +161,9 @@ def __call__(self, received_sample): if __name__ == '__main__': parser = argparse.ArgumentParser(description='Train a resnet model') - parser.add_argument('--dataroot', required=True, type=str, help='path to the root data directory') + parser.add_argument('--dataroot', required=True, type=str, + help='path to the root data directory') parser.add_argument('--maxepochs', default=100, type=int) args = parser.parse_args() - main(args) \ No newline at end of file + main(args) diff --git a/pyphoon2/DigitalTyphoonDataset.py b/pyphoon2/DigitalTyphoonDataset.py index 3b3bb7d..3999a46 100644 --- a/pyphoon2/DigitalTyphoonDataset.py +++ b/pyphoon2/DigitalTyphoonDataset.py @@ -6,6 +6,9 @@ from datetime import datetime from collections import OrderedDict from typing import List, Sequence, Union, Optional, Dict +import re +import glob +import random import torch from torch import default_generator, randperm, Generator @@ -13,7 +16,7 @@ from pyphoon2.DigitalTyphoonImage import DigitalTyphoonImage from pyphoon2.DigitalTyphoonSequence import DigitalTyphoonSequence -from pyphoon2.DigitalTyphoonUtils import _verbose_print, SPLIT_UNIT, LOAD_DATA, TRACK_COLS, get_seq_str_from_track_filename +from pyphoon2.DigitalTyphoonUtils import _verbose_print, SPLIT_UNIT, LOAD_DATA, TRACK_COLS, get_seq_str_from_track_filename, parse_image_filename, is_image_file, parse_common_image_filename class DigitalTyphoonDataset(Dataset): @@ -31,37 +34,67 @@ def __init__(self, filter_func=None, transform_func=None, transform=None, - verbose=False) -> None: - """ - Dataloader for the DigitalTyphoon dataset. - - :param image_dir: Path to directory containing directories of typhoon sequences - :param metadata_dir: Path to directory containing track data for typhoon sequences - :param metadata_json: Path to the metadata JSON file - :param split_dataset_by: What unit to treat as an atomic unit when randomly splitting the dataset. Options are - "sequence", "season", or "image" (individual image) - :param spectrum: Spectrum to access h5 image files with - :param get_images_by_sequence: Boolean representing if an index should refer to an individual image or an entire - sequence. If sequence, returned images are Lists of images. - :param load_data_into_memory: String representing if the images and track data should be entirely loaded into - memory. Options are "track" (only track data), "images" (only images), or - "all_data" (both track and images). - :param ignore_list: a list of filenames (not path) to ignore and NOT add to the dataset - :param filter_func: a function used to filter out images from the dataset. Should accept an DigitalTyphoonImage object - and return a bool True or False if it should be included in the dataset - :param transform_func: this function will be called on the image array for each image when reading in the dataset. - It should take and return a numpy image array - :param transform: Pytorch transform func. Will be called on the tuple of (image/sequence, label array). It should - take in said tuple, and return a tuple of (transformed image/sequence, transformed label) - :param verbose: Print verbose program information - """ + verbose=False, + image_dirs: List[str] = None, + metadata_dirs: List[str] = None, + metadata_jsons: List[str] = None + ) -> None: + """ + Implementation of pytorch dataset for the Digital Typhoon Dataset, allows both random iteration and + deterministic splitting based on image, sequence or season. + + :param image_dir: str, path to directory containing h5 image files + :param metadata_dir: str, path to directory containing track data in csv files + :param metadata_json: str, path to metadata json containing some metadata about sequences and images + :param labels: str or tuple of str, which columns from the track data to return as labels + :param split_dataset_by: str in [sequence, season, image], determines which objects are kept fully within splits + :param spectrum: str, spectrum of images to load + :param get_images_by_sequence: bool, returns entire sequences at a time, if True, __getitem__(i) will return all images in one sequence + :param load_data_into_memory: bool, determines if images should be loaded into memory when instantiated + :param ignore_list: List of filenames to ignore (just the filenames, not paths) + :param filter_func: function taking a DigitalTyphoonImage object and returning a bool; if True returned, image is kept + :param transform_func: function applied after filter func, taking and returning a np array + :param transform: function to apply to both image and label tensors, takes and returns a tensor + :param verbose: bool, flag for additional logging output + :param image_dirs: List[str], list of paths to directories containing h5 image files (for multi-source) + :param metadata_dirs: List[str], list of paths to directories containing track data (for multi-source) + :param metadata_jsons: List[str], list of paths to metadata json files (for multi-source) + """ + # Core parameters + self.verbose = verbose + self.image_dir = image_dir + self.metadata_dir = metadata_dir + self.metadata_json = metadata_json + self.spectrum = spectrum + self.load_data_into_memory = load_data_into_memory + self.split_dataset_by = split_dataset_by + self.get_images_by_sequence = get_images_by_sequence + + # Initialize multi-directory attributes first + self.image_dirs = image_dirs or [] + self.metadata_dirs = metadata_dirs or [] + self.metadata_jsons = metadata_jsons or [] + + # NOW check if loading from multiple directories (after attributes are initialized) + self.load_from_multi_dirs = self.is_valid_input_multi_dirs( + image_dirs, metadata_dirs, metadata_jsons) + + # Store ignore_list as a set for faster lookups + self.ignore_list = set(ignore_list) if ignore_list else [] + + # Functions for processing + self.filter_func = filter_func + self.transform_func = transform_func + self.transform = transform + # Input validation for split unit if not SPLIT_UNIT.has_value(split_dataset_by): raise ValueError(f'Split unit must one of the following\n' f' {[item.value for item in SPLIT_UNIT]}.\n' f' Input: {split_dataset_by}') self.split_dataset_by = split_dataset_by + # Input validation for load data option if not LOAD_DATA.has_value(load_data_into_memory): raise ValueError(f'Load data option must one of the following\n' f' {[item.value for item in LOAD_DATA]}.\n' @@ -80,67 +113,74 @@ def __init__(self, # Path to the metadata file self.metadata_json = metadata_json + # Directories containing images folders and track datas + self.image_dirs = image_dirs or [] + self.metadata_dirs = metadata_dirs or [] + + # List of metadata files + self.metadata_jsons = metadata_jsons or [] + # labels to retrieve when accessing the dataset self.labels = None self.set_label(labels) - self.verbose = verbose - # Spectrum to open images with self.spectrum = spectrum - # Set of image filepaths to ignore - self.ignore_list = set(ignore_list) if ignore_list else set([]) - if filter_func: - self.filter = filter_func - else: - self.filter = lambda img: True - - self.transform_func = transform_func - self.transform = transform + # List of DigitalTyphoonSequence objects, each a typhoon + self.sequences: List[DigitalTyphoonSequence] = [] - # Structures holding the data objects - self.sequences: List[DigitalTyphoonSequence] = list() # List of seq_str objects - # contains sequences in order they are present in metadata.json - self._sequence_str_to_seq_idx: Dict[str, int] = {} # Sequence ID to idx in sequences array - self._image_idx_to_sequence: Dict[int, DigitalTyphoonSequence] = {} # Image idx to what seq_str it belongs to - self._seq_str_to_first_total_idx: Dict[str, int] = {} # Sequence string to the first total idx belonging to - # that seq_str + # Dictionary mapping sequence strings to index in the sequences list + self._sequence_str_to_seq_idx: Dict[str, int] = {} - self.label = ('grade', 'lat') + # Dictionary mapping sequence strings to the first total index that sequence's images start at + self._seq_str_to_first_total_idx: Dict[str, int] = {} + # Keep track of the total number of sequences, number of images, and the original number of images + # The original num of images is the number listed in the metadata.json file + # The actual number of images is the number we actually read in and have in our dataset. + # These can be different due to filtering or missing images. self.number_of_sequences = 0 self.number_of_nonempty_sequences = 0 - self.number_of_original_images = 0 # Number of images in the original dataset before augmentation and removal - self.number_of_images = 0 # number of images in the dataset, after augmentation and removal + self.number_of_images = 0 + self.number_of_original_images = 0 + + # Dictionary mapping season to list of sequences (strings) + self.season_to_sequence_nums: Dict[int, List[str]] = {} + + # Dictionary mapping the total image idx to a sequence object + self._image_idx_to_sequence: Dict[int, DigitalTyphoonSequence] = {} + + # Additional attribute for caching nonempty seasons self.number_of_nonempty_seasons = None - # Season to list of sequences that start in that season - self.season_to_sequence_nums: OrderedDict[int, List[str]] = OrderedDict() - - # Process the data into the loader - # It must happen in this order! - _verbose_print(f'Processing metadata file at: {metadata_json}', self.verbose) - self.process_metadata_file(metadata_json) - - _verbose_print(f'Initializing track data from: {metadata_dir}', self.verbose) - self._populate_track_data_into_sequences(self.metadata_dir) - - _verbose_print(f'Initializing image_arrays from: {image_dir}', self.verbose) - self._populate_images_into_sequences(self.image_dir) + # Load data + if self.is_valid_input_multi_dirs(image_dirs, metadata_dirs, metadata_jsons): + self.load_data_from_multi_dirs(image_dirs, metadata_dirs, metadata_jsons) + else: + self.load_data_from_single_dir(metadata_json, metadata_dir, image_dir) + + if verbose: + print("Done initializing Digital Typhoon Dataset.") - _verbose_print(f'Indexing the dataset', verbose=self.verbose) + # Compute mapping from total image index to sequence object self._assign_all_images_a_dataset_idx() + _verbose_print(f'***DigitalTyphoonDataset statistics***', self.verbose) + _verbose_print( + f'Loaded {len(self.sequences)} sequences with {self.number_of_images} total images.', self.verbose) + def __len__(self) -> int: """ Gives the length of the dataset. If "get_images_by_sequence" was set to True on initialization, number of sequences is returned. Otherwise, number of images is returned. - + :return: int """ if self.get_images_by_sequence: - return self.get_number_of_nonempty_sequences() + # When get_images_by_sequence is True, return total number of sequences + # regardless of whether they have images after filtering + return self.get_number_of_sequences() else: return self.number_of_images @@ -166,7 +206,13 @@ def __getitem__(self, idx): seq = self.get_ith_sequence(idx) images = seq.get_all_images_in_sequence() image_arrays = np.array([image.image() for image in images]) - labels = np.array([self._labels_from_label_strs(image, self.labels) for image in images]) + # breakpoint() + labels = np.array([self._labels_from_label_strs( + image, self.labels) for image in images]) + + # Debug prints + print(f"Returning sequence {seq.get_sequence_str()} with {len(images)} images, shape: {image_arrays.shape}, labels shape: {labels.shape}") + if self.transform: return self.transform((image_arrays, labels)) return image_arrays, labels @@ -174,16 +220,377 @@ def __getitem__(self, idx): image = self.get_image_from_idx(idx) labels = self._labels_from_label_strs(image, self.labels) ret_img = image.image() + if self.transform: return self.transform((ret_img, labels)) return ret_img, labels + def is_valid_input_multi_dirs(self, image_dirs: List[str] = None, metadata_dirs: List[str] = None, metadata_jsons: List[str] = None) -> bool: + """ + Check if there are valid inputs to load data from multiple directories. + + :param image_dirs: list of paths to image directories + :param metadata_dirs: list of paths to metadata directories + :param metadata_jsons: list of paths to metadata json files + :return: boolean indicating if these inputs are valid for multi-directory loading + """ + # Use parameters directly instead of instance attributes + img_dirs = image_dirs or [] + meta_dirs = metadata_dirs or [] + meta_jsons = metadata_jsons or [] + + # Check if we have valid inputs for multi-directory loading + has_image_dirs = len(img_dirs) > 0 + has_metadata_dirs = len(meta_dirs) > 0 + has_metadata_jsons = len(meta_jsons) > 0 + + if not (has_image_dirs and has_metadata_dirs and has_metadata_jsons): + return False + + # Check consistency + if len(img_dirs) != len(meta_dirs) or len(meta_dirs) != len(meta_jsons): + if self.verbose: + print("Warning: Inconsistent number of directories for multi-directory loading") + print(f"Image dirs: {len(img_dirs)}, Metadata dirs: {len(meta_dirs)}, Metadata jsons: {len(meta_jsons)}") + return False + + # Check existence + for directory in img_dirs: + if not os.path.exists(directory): + if self.verbose: + print(f"Warning: Image directory {directory} does not exist") + return False + + for directory in meta_dirs: + if not os.path.exists(directory): + if self.verbose: + print(f"Warning: Metadata directory {directory} does not exist") + return False + + for json_file in meta_jsons: + if not os.path.exists(json_file): + if self.verbose: + print(f"Warning: Metadata JSON file {json_file} does not exist") + return False + + return True + + def load_data_from_multi_dirs(self, image_dirs: List[str] = None, metadata_dirs: List[str] = None, metadata_jsons: List[str] = None): + """ + Load data from multiple directories for multi-channel processing. + + :param image_dirs: List of image directory paths + :param metadata_dirs: List of metadata directory paths + :param metadata_jsons: List of metadata JSON file paths + :return: None + """ + # Use instance variables if not provided as arguments + image_dirs = image_dirs or self.image_dirs + metadata_dirs = metadata_dirs or self.metadata_dirs + metadata_jsons = metadata_jsons or self.metadata_jsons + + print("\nLoading data from multiple directories:") + print(f" Image dirs: {image_dirs}") + print(f" Metadata dirs: {metadata_dirs}") + print(f" Metadata JSONs: {metadata_jsons}") + + if not self.is_valid_input_multi_dirs(image_dirs, metadata_dirs, metadata_jsons): + print("Invalid multi-directory input, cannot load data.") + return + + try: + # Find common sequences across all data sources + common_sequences = self.get_common_sequences_from_files( + metadata_jsons, metadata_dirs, image_dirs) + + if not common_sequences: + print("No common sequences found across data sources.") + return + + print(f"Found {len(common_sequences)} common sequences") + + # Process metadata JSONs + for metadata_json in metadata_jsons: + if os.path.exists(metadata_json): + self.preprocess_metadata_json_with_common_sequences( + metadata_json, common_sequences) + else: + print(f"Warning: Metadata JSON file not found: {metadata_json}") + + # Process track data + for metadata_dir in metadata_dirs: + if os.path.exists(metadata_dir): + self._populate_track_data_into_sequences( + metadata_dir, common_sequences) + else: + print(f"Warning: Metadata directory not found: {metadata_dir}") + + # Process images + self._populate_images_into_sequences_from_multi_dirs( + image_dirs, common_sequences) + + except Exception as e: + print(f"Error loading data from multiple directories: {str(e)}") + import traceback + traceback.print_exc() + + def _populate_images_into_sequences_from_multi_dirs(self, root_image_dirs: List[str] = None, common_sequences: List[str] = None): + """ + Populates sequence objects with images from multiple directories (for multi-channel input). + Each directory corresponds to a different channel. + + :param root_image_dirs: List of paths to top-level directories containing sequence folders + :param common_sequences: List of sequence IDs common to all directories + :return: None + """ + # Ensure we have inputs + if not root_image_dirs or not common_sequences: + print("Missing required inputs for populating sequences from multiple directories") + return + + print("\nPopulating images from multiple directories...") + print(f"Root directories: {root_image_dirs}") + print(f"Processing {len(common_sequences)} common sequences") + + # Handle special case - single directory + if len(root_image_dirs) == 1: + print("Only one directory provided - using single-directory processing") + for common_sequence in common_sequences: + sequence_obj = self._get_seq_from_seq_str(common_sequence) + sequence_path = os.path.join(root_image_dirs[0], common_sequence) + + if not os.path.exists(sequence_path): + print(f"Warning: Sequence directory {sequence_path} does not exist") + continue + + print(f"Processing sequence: {common_sequence} from {sequence_path}") + sequence_obj.process_seq_img_dir_into_sequence( + sequence_path, + load_imgs_into_mem=self.load_data_into_memory in {LOAD_DATA.ONLY_IMG, LOAD_DATA.ALL_DATA}, + ignore_list=self.ignore_list, + filter_func=self.filter_func, + spectrum=self.spectrum + ) + + self.number_of_images += sequence_obj.get_num_images() + if sequence_obj.get_num_images() > 0: + self.number_of_nonempty_sequences += 1 + + print(f"Sequence {common_sequence} now has {sequence_obj.get_num_images()} images") + return + + # Setup for multi-directory processing + load_into_mem = self.load_data_into_memory in {LOAD_DATA.ONLY_IMG, LOAD_DATA.ALL_DATA} + + # Reset counters + self.number_of_images = 0 + self.number_of_nonempty_sequences = 0 + + # Process each sequence + sequence_idx = 0 + for common_sequence in common_sequences: + # Get the sequence object + sequence_obj = self._get_seq_from_seq_str(common_sequence) + print(f"\nProcessing sequence {sequence_idx+1}/{len(common_sequences)}: {common_sequence}") + sequence_idx += 1 + + # Get the sequence directories + sequence_dirs = [] + for root_dir in root_image_dirs: + seq_dir = os.path.join(root_dir, common_sequence) + if os.path.isdir(seq_dir): + sequence_dirs.append(seq_dir) + else: + print(f"Warning: Directory not found: {seq_dir}") + + if not sequence_dirs: + print(f"No valid directories found for sequence {common_sequence}, skipping") + continue + + # Find common images across all sequence directories - directly search for image files + print(f"Finding common images across {len(sequence_dirs)} directories...") + images_by_dir = {} + + # Collect image files from each directory + + for seq_dir in sequence_dirs: + try: + # Get all files in this directory + all_files = os.listdir(seq_dir) + + # Filter for image files + image_files = [] + for filename in all_files: + if is_image_file(filename): + # Get base name without channel index + base_name = self.get_name_image_remove_channel_idx(filename) + if base_name not in self.ignore_list: + image_files.append(base_name) + + # Store unique base names + images_by_dir[seq_dir] = list(set(image_files)) + print(f" Found {len(images_by_dir[seq_dir])} unique image names in {seq_dir}") + + # Show a few example files for debugging + if len(all_files) > 0: + print(f" Sample files: {all_files[:3]}") + if len(image_files) > 0: + print(f" Sample image names: {image_files[:3]}") + except Exception as e: + print(f" Error processing directory {seq_dir}: {str(e)}") + images_by_dir[seq_dir] = [] + + # Find image names common to all directories + if not images_by_dir or all(len(imgs) == 0 for imgs in images_by_dir.values()): + print(f" No images found in any directory for sequence {common_sequence}") + continue + + # Start with names from first directory + common_images = set(next(iter(images_by_dir.values()))) + + # Find intersection with all other directories + for images in images_by_dir.values(): + common_images.intersection_update(set(images)) + + print(f" Found {len(common_images)} common images across all directories") + + # Skip if no common images + if not common_images: + print(f" No common images found for sequence {common_sequence}") + continue + + # Process this sequence with the common images + try: + sequence_obj.process_seq_img_dirs_into_sequence( + directory_paths=sequence_dirs, + common_image_names=list(common_images), + load_imgs_into_mem=load_into_mem, + ignore_list=self.ignore_list, + filter_func=self.filter_func, + spectrum=self.spectrum + ) + + # Update sequence counts + num_images = sequence_obj.get_num_images() + self.number_of_images += num_images + + if num_images > 0: + self.number_of_nonempty_sequences += 1 + + print(f" Sequence {common_sequence} now has {num_images} images") + + except Exception as e: + print(f" Error processing sequence {common_sequence}: {str(e)}") + import traceback + traceback.print_exc() + + # Log summary + print(f"\nFinished populating images: {self.number_of_images} total images across {self.number_of_nonempty_sequences} non-empty sequences") + + # Sanity check + if self.number_of_images == 0: + print("WARNING: No images were loaded! Check your directories and file naming consistency.") + if self.number_of_nonempty_sequences == 0: + print("WARNING: No sequences contain images! Check your filtering and image loading logic.") + + def get_names_from_images_removed_channel_idx(self, image_dir: str) -> List[str]: + """ + Gets all image names in an image directory with their channel index removed. + + Example: + A directory with: + - 000_Infrared-0.h5 + - 000_Infrared-1.h5 + - 001_Infrared-0.h5 + - 001_Infrared-1.h5 + + Returns: ['000_Infrared', '001_Infrared'] + + :param image_dir: path to the directory containing image files + :return: list of image names with channel index removed + """ + print(f"Scanning directory: {image_dir}") + + # Check if directory exists + if not os.path.exists(image_dir): + print(f"Warning: Directory {image_dir} does not exist") + return [] + + # Get all files in the directory + try: + files = [f for f in os.listdir(image_dir) if os.path.isfile(os.path.join(image_dir, f)) and is_image_file(f)] + print(f"Found {len(files)} image files in {image_dir}") + except Exception as e: + print(f"Error listing files in {image_dir}: {str(e)}") + return [] + + # Extract base names (removing channel index) + names = set() + for file in files: + try: + name = self.get_name_image_remove_channel_idx(file) + names.add(name) + except Exception as e: + print(f"Error processing filename {file}: {str(e)}") + continue + + # Convert set to list and return + result = list(names) + print(f"Extracted {len(result)} unique image names after removing channel indices") + if len(result) > 0: + print(f"Sample names: {result[:3]}") + + return result + + def get_name_image_remove_channel_idx(self, image_name: str) -> str: + match = re.match(r"^(.*)-\d+\.h5", image_name) + if match: + name = match.group(1) + return name + else: + return image_name + + def preprocess_metadata_json_with_common_sequences(self, metadata_json: str, common_sequences: List[str]): + with open(metadata_json, 'r') as f: + data = json.load(f) + new_data = {} + for sequence_str, metadata in data.items(): + if sequence_str in common_sequences: + new_data[sequence_str] = metadata + self.number_of_sequences += len(new_data) + for sequence_str, metadata in sorted(new_data.items()): + self._read_one_seq_from_metadata(sequence_str, metadata) + + def load_data_from_single_dir(self, metadata_json: str, metadata_dir: str, image_dir: str): + """ + Load data from a single directory. + :param metadata_json: Path to the metadata JSON file. + :param metadata_dir: Path to the metadata directory. + :param image_dir: Path to the image directory. + :return: None. + """ + # Process the metadata JSON file + self.process_metadata_file(metadata_json) + + # IMPORTANT: Load track data first, so it's available when loading images + if self.verbose: + print("Loading track data first to ensure it's available for images...") + self._populate_track_data_into_sequences(metadata_dir) + + # Now populate image data - images will be able to access track data + if self.verbose: + print("Now loading images, which can use the track data...") + self._populate_images_into_sequences(image_dir) + + # Assign indices + self._assign_all_images_a_dataset_idx() + def set_label(self, label_strs) -> None: """ Sets what label to retrieve when accessing the data set via dataset[idx] or dataset.__getitem__(idx) Options are: season, month, day, hour, grade, lat, lng, pressure, wind, dir50, long50, short50, dir30, long30, short30, landfall, interpolated - + :param label_strs: a single string (e.g. 'grade') or a list/tuple of strings (e.g. ['lat', 'lng']) of labels. :return: None """ @@ -195,55 +602,64 @@ def set_label(self, label_strs) -> None: self.labels = label_strs def random_split(self, lengths: Sequence[Union[int, float]], - split_by=None, - generator: Optional[Generator] = default_generator) -> List[Subset]: + generator: Optional[Generator] = default_generator, + split_by='default') -> List[Subset]: """ - Randomly split a dataset into non-overlapping new datasets of given lengths. - - Given a list of proportions or items, returns a random split of the dataset with proportions as close to - the requested without causing leakage between requested split_unit. If split is by image, built-in PyTorch - function is used. If split is by season, all images from typhoons starting in the same season will be placed in - the same bucket. If split is by seq_str, all images from the same typhoon will be together. - - Returns a list of Subsets of indices according to requested lengths. If split is anything other than image, - indices within their split unit are not randomized. (I.e. indices of a seq_str will be kept contiguous, not - randomized order mixing with other sequences). - - If "get_images_by_sequence" is set to True on initialization, split_by image and sequence are functionally - identical, and will split the number of sequences into the requested bucket sizes. - If split_by='season', then sequences with the same season will be placed in the same bucket. - - Only non-empty sequences are returned in the split. - - For Subset doc see https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset. + Splits the dataset randomly according to the proportions in `lengths`. - :param lengths: lengths or fractions of splits to be produced - :param generator: Generator used for the random permutation. - :param split_by: What to treat as an atomic unit (image, seq_str, season). Options are - "image", "sequence" or "season" respectively - :return: List[Subset[idx]] + :param lengths: Sequence of proportions (should sum to 1.0) + :param generator: Random number generator for reproducibility + :param split_by: How to split the dataset ('image', 'sequence', 'season', or 'default') + If 'default', uses the value from self.split_dataset_by + :return: List of Subsets containing the split data """ - if split_by is None: + # Use default split method if not specified + if split_by == 'default': split_by = self.split_dataset_by - - _verbose_print(f"Splitting the dataset into proportions {lengths}, by {split_by}.", verbose=self.verbose) - - if not SPLIT_UNIT.has_value(split_by): - warnings.warn(f'Split unit \'{split_by}\' is not within the list of known split units: ' - f'\'{[e.value for e in SPLIT_UNIT]}\'. Defaulting to \'{SPLIT_UNIT.SEQUENCE.value}\'') - - # Can use built-in random_split function - if split_by == SPLIT_UNIT.IMAGE.value: - return random_split(self, lengths, generator=generator) - elif split_by == SPLIT_UNIT.SEASON.value: - return self._random_split_by_season(lengths, generator=generator) - else: # split_by == SPLIT_UNIT.SEQUENCE.value: - return self._random_split_by_sequence(lengths, generator=generator) + + if self.verbose: + print(f"Random split by: {split_by}") + + # Dispatch to appropriate split method + if split_by == 'sequence': + # When splitting by sequence and get_images_by_sequence=True, + # only include sequences that have images after filtering + return self._random_split_by_sequence(lengths, generator) + elif split_by == 'season': + # Splitting by season + return self._random_split_by_season(lengths, generator) + else: + # Default to splitting by individual image + if self.get_images_by_sequence and self.verbose: + print("Warning: Splitting by image when get_images_by_sequence=True may produce unexpected results") + + # Calculate split lengths + split_lengths = self._calculate_split_lengths(lengths) + + # Get all images + all_indices = list(range(len(self))) + + # Shuffle indices correctly using torch.randperm with the generator + if generator is not None: + # Use torch's randperm with the generator instead of shuffle + shuffled_indices = torch.randperm(len(all_indices), generator=generator).tolist() + all_indices = [all_indices[i] for i in shuffled_indices] + else: + random.shuffle(all_indices) + + # Split indices according to proportions + result = [] + offset = 0 + for length in split_lengths: + result.append(Subset(self, all_indices[offset:offset + length])) + offset += length + + return result def images_from_season(self, season: int) -> Subset: """ Given a start season, return a Subset (Dataset) object containing all the images from that season, in order - + :param season: the start season as a string :return: Subset """ @@ -257,7 +673,7 @@ def images_from_season(self, season: int) -> Subset: def image_objects_from_season(self, season: int) -> List: """ Given a start season, return a list of DigitalTyphoonImage objects for images from that season - + :param season: the start season as a string :return: List[DigitalTyphoonImage] """ @@ -280,7 +696,8 @@ def images_from_seasons(self, seasons: List[int]): sequence_strs = self.get_seq_ids_from_season(season) for seq_str in sequence_strs: seq_obj = self._get_seq_from_seq_str(seq_str) - return_indices.extend(self.seq_indices_to_total_indices(seq_obj)) + return_indices.extend( + self.seq_indices_to_total_indices(seq_obj)) return Subset(self, return_indices) def images_from_sequence(self, sequence_str: str) -> Subset: @@ -315,7 +732,8 @@ def images_from_sequences(self, sequence_strs: List[str]) -> Subset: return_indices = [] for sequence_str in sequence_strs: seq_object = self._get_seq_from_seq_str(sequence_str) - return_indices.extend(self.seq_indices_to_total_indices(seq_object)) + return_indices.extend( + self.seq_indices_to_total_indices(seq_object)) return Subset(self, return_indices) def images_as_tensor(self, indices: List[int]) -> torch.Tensor: @@ -325,7 +743,8 @@ def images_as_tensor(self, indices: List[int]) -> torch.Tensor: :param indices: List[int] :return: torch Tensor """ - images = np.array([self.get_image_from_idx(idx).image() for idx in indices]) + images = np.array([self.get_image_from_idx(idx).image() + for idx in indices]) return torch.Tensor(images) def labels_as_tensor(self, indices: List[int], label: str) -> torch.Tensor: @@ -336,7 +755,8 @@ def labels_as_tensor(self, indices: List[int], label: str) -> torch.Tensor: :param label: str, denoting which label to retrieve :return: torch Tensor """ - images = [self.get_image_from_idx(idx).value_from_string(label) for idx in indices] + images = [self.get_image_from_idx( + idx).value_from_string(label) for idx in indices] return torch.Tensor(images) def get_number_of_sequences(self): @@ -353,7 +773,12 @@ def get_number_of_nonempty_sequences(self): :return: integer number of sequences """ - return self.number_of_nonempty_sequences + # For consistency with __len__, if get_images_by_sequence is True, + # we should return the total number of sequences regardless of images + if self.get_images_by_sequence: + return self.get_number_of_sequences() + else: + return self.number_of_nonempty_sequences def get_sequence_ids(self) -> List[str]: """ @@ -374,7 +799,7 @@ def get_seasons(self) -> List[int]: def get_nonempty_seasons(self) -> List[int]: """ Returns a list of the seasons that typhoons have started in, that have at least one image, in chronological order - + :return: List[int] """ if self.number_of_nonempty_seasons is None: @@ -413,18 +838,257 @@ def get_ith_sequence(self, idx: int) -> DigitalTyphoonSequence: return self.sequences[idx] def process_metadata_file(self, filepath: str): + """ + Process a single metadata JSON file. + + :param filepath: Path to the metadata JSON file. + :return: None + """ + try: + with open(filepath, 'r') as f: + data = json.load(f) + + # Process each sequence in the metadata file + for sequence_str, metadata in sorted(data.items()): + self._read_one_seq_from_metadata(sequence_str, metadata) + + except Exception as e: + print(f"Error processing metadata file {filepath}: {e}") + raise + + def process_metadata_files(self, filepaths: List[str]): """ Reads and processes JSON metadata file's information into dataset. :param filepath: path to metadata file :return: metadata JSON object """ - with open(filepath, 'r') as f: - data = json.load(f) - self.number_of_sequences = len(data) + length_filepaths = len(filepaths) + for i in range(length_filepaths): + filepath = filepaths[i] + with open(filepath, 'r') as f: + data = json.load(f) + self.number_of_sequences += len(data) + for sequence_str, metadata in sorted(data.items()): + sequence_str_unique = f'{i}_{sequence_str}' + self._read_one_seq_from_metadata(sequence_str_unique, metadata) + print("self.sequences", len(self.sequences)) + + def get_common_sequences_from_files(self, metadata_jsons: List[str], metadata_dirs: List[str], image_dirs: List[str]): + """ + Find common sequences across metadata and image directories. + + :param metadata_jsons: List of paths to metadata JSON files + :param metadata_dirs: List of paths to metadata directories + :param image_dirs: List of paths to image directories + :return: Set of sequences common to all provided data sources + """ + print("\n" + "="*50) + print("Finding common sequences across data sources") + print(f"Checking {len(metadata_jsons)} metadata JSONs, {len(metadata_dirs)} metadata dirs, {len(image_dirs)} image dirs") + + # Print the actual paths for easier debugging + if metadata_jsons: + print(f"Metadata JSONs: {metadata_jsons}") + if metadata_dirs: + print(f"Metadata directories: {metadata_dirs}") + if image_dirs: + print(f"Image directories: {image_dirs}") + + # Check consistency of data indices + try: + self.assert_consistency_data_in_same_index( + metadata_jsons=metadata_jsons, metadata_dirs=metadata_dirs, image_dirs=image_dirs) + print("Data consistency check passed") + except AssertionError as e: + print(f"WARNING: Data consistency check failed: {str(e)}") + print("Still trying to find common sequences...") + + # Get sequences from metadata JSON files + common_sequences_from_metadata_jsons = self.get_common_sequences_from_metadata_files( + metadata_jsons) + print(f"Found {len(common_sequences_from_metadata_jsons)} sequences from metadata JSON files") + if common_sequences_from_metadata_jsons: + print(f"Sample sequences from JSON: {list(common_sequences_from_metadata_jsons)[:5]}") + + # Get sequences from metadata directories + common_sequences_from_metadata_dirs = self.get_common_sequences_from_metadata_dirs( + metadata_dirs) + print(f"Found {len(common_sequences_from_metadata_dirs)} sequences from metadata directories") + if common_sequences_from_metadata_dirs: + print(f"Sample sequences from metadata dirs: {list(common_sequences_from_metadata_dirs)[:5]}") + + # Get sequences from image directories + common_sequences_from_image_dirs = self.get_common_sequences_from_image_dirs( + image_dirs) + print(f"Found {len(common_sequences_from_image_dirs)} sequences from image directories") + if common_sequences_from_image_dirs: + print(f"Sample sequences from image dirs: {list(common_sequences_from_image_dirs)[:5]}") + + # Find sequences common to all data sources, with graceful fallbacks + common_sequences = set() + + # Try all combinations of data sources + if common_sequences_from_metadata_jsons and common_sequences_from_metadata_dirs and common_sequences_from_image_dirs: + common_sequences = common_sequences_from_metadata_jsons.intersection( + common_sequences_from_metadata_dirs).intersection(common_sequences_from_image_dirs) + print(f"Using sequences common to all three sources: {len(common_sequences)}") + elif common_sequences_from_metadata_jsons and common_sequences_from_metadata_dirs: + common_sequences = common_sequences_from_metadata_jsons.intersection( + common_sequences_from_metadata_dirs) + print(f"Using sequences common to metadata JSONs and dirs: {len(common_sequences)}") + elif common_sequences_from_metadata_jsons and common_sequences_from_image_dirs: + common_sequences = common_sequences_from_metadata_jsons.intersection( + common_sequences_from_image_dirs) + print(f"Using sequences common to metadata JSONs and image dirs: {len(common_sequences)}") + elif common_sequences_from_metadata_dirs and common_sequences_from_image_dirs: + common_sequences = common_sequences_from_metadata_dirs.intersection( + common_sequences_from_image_dirs) + print(f"Using sequences common to metadata dirs and image dirs: {len(common_sequences)}") + # If any single source has sequences, use that + elif common_sequences_from_metadata_jsons: + common_sequences = common_sequences_from_metadata_jsons + print(f"Falling back to sequences from metadata JSONs only") + elif common_sequences_from_metadata_dirs: + common_sequences = common_sequences_from_metadata_dirs + print(f"Falling back to sequences from metadata dirs only") + elif common_sequences_from_image_dirs: + common_sequences = common_sequences_from_image_dirs + print(f"Falling back to sequences from image dirs only") + + if not common_sequences: + print("WARNING: No common sequences found across data sources!") + else: + print(f"Final set: {len(common_sequences)} common sequences") + print(f"Sample sequences: {list(common_sequences)[:10]}") + + print("="*50 + "\n") + return common_sequences + + def assert_consistency_data_in_same_index(self, metadata_jsons: List[str], metadata_dirs: List[str], image_dirs: List[str]): + length_metadata_jsons = len(metadata_jsons) + for i in range(length_metadata_jsons): + # Create a new metadata_jsons_checking array, it contains only the data of this index + metadata_jsons_checking = [metadata_jsons[i]] + metadata_dirs_checking = [metadata_dirs[i]] + image_dirs_checking = [image_dirs[i]] + common_sequences_from_metadata_jsons = self.get_common_sequences_from_metadata_files( + metadata_jsons_checking) + common_sequences_from_metadata_dirs = self.get_common_sequences_from_metadata_dirs( + metadata_dirs_checking) + common_sequences_from_image_dirs = self.get_common_sequences_from_image_dirs( + image_dirs_checking) + # Make sure common_sequences_from_image_dirs, common_sequences_from_metadata_dirs, common_sequences_from_metadata_jsons are the same + assert common_sequences_from_metadata_jsons == common_sequences_from_metadata_dirs + assert common_sequences_from_metadata_dirs == common_sequences_from_image_dirs + assert common_sequences_from_image_dirs == common_sequences_from_metadata_jsons + + pass + + def get_common_sequences_from_metadata_files(self, metadata_jsons: List[str]): + """ + Reads and processes JSON metadata file's information into dataset. - for sequence_str, metadata in sorted(data.items()): - self._read_one_seq_from_metadata(sequence_str, metadata) + :param filepath: path to metadata file + :return: metadata JSON object + """ + datas_sequences_information = {} + for filepath in metadata_jsons: + datas_sequences_information[filepath] = [] + with open(filepath, 'r') as f: + data = json.load(f) + for sequence_str, metadata in sorted(data.items()): + datas_sequences_information[filepath].append(sequence_str) + # Get the common sequences for all keys in datas_sequences_information + common_sequences = set(datas_sequences_information[metadata_jsons[0]]) + for key in datas_sequences_information.keys(): + common_sequences = common_sequences.intersection( + datas_sequences_information[key]) + return common_sequences + + def get_common_sequences_from_metadata_dirs(self, metadata_dirs: List[str]): + data_sequences_information = {} + for metadata_dir in metadata_dirs: + data_sequences_information[metadata_dir] = [] + for root, dirs, files in os.walk(metadata_dir, topdown=True): + for file in sorted(files): + file_sequence = get_seq_str_from_track_filename(file) + data_sequences_information[metadata_dir].append( + file_sequence) + common_sequences = set(data_sequences_information[metadata_dirs[0]]) + for key in data_sequences_information.keys(): + common_sequences = common_sequences.intersection( + data_sequences_information[key]) + return common_sequences + + def get_common_sequences_from_image_dirs(self, image_dirs: List[str]): + """ + Gets the sequences common to all dirs in image_dirs. + + :param image_dirs: List of image directory paths + :return: Set of sequences common to all dirs + """ + if not image_dirs: + print("No image directories provided, returning empty set") + return set() + + # Initialize with directories from first image dir + print(f"Scanning image directories for sequences...") + common_sequences = set() + first_dir_scanned = False + + # Process each image directory + for idx, image_dir in enumerate(image_dirs): + if not os.path.isdir(image_dir): + print(f"WARNING: {image_dir} is not a valid directory, skipping") + continue + + try: + # Get all subdirectories (which should be sequence folders) + sequence_dirs = set([d for d in os.listdir(image_dir) + if os.path.isdir(os.path.join(image_dir, d))]) + + # Check if we found any sequences + if not sequence_dirs: + print(f"WARNING: No sequence directories found in {image_dir}") + continue + + print(f" Found {len(sequence_dirs)} sequence directories in {image_dir}") + + # First directory sets the initial sequence set + if not first_dir_scanned: + common_sequences = sequence_dirs + first_dir_scanned = True + print(f" First directory: Initial sequences = {len(common_sequences)}") + else: + # Intersect with sequences from this directory + prev_count = len(common_sequences) + common_sequences.intersection_update(sequence_dirs) + print(f" After intersection: {prev_count} → {len(common_sequences)} sequences") + + except Exception as e: + print(f"Error processing image directory {image_dir}: {str(e)}") + + # Show some sample sequences + if common_sequences: + sample = list(common_sequences)[:5] + print(f"Found {len(common_sequences)} common sequences across image directories") + print(f"Sample sequences: {sample}") + + # Check if sample sequences have image files + for seq in sample[:2]: # Check just a couple to avoid too much output + for image_dir in image_dirs: + seq_dir = os.path.join(image_dir, seq) + if os.path.isdir(seq_dir): + try: + files = [f for f in os.listdir(seq_dir) if is_image_file(f)] + print(f" Sequence {seq} in {image_dir} has {len(files)} image files") + except Exception as e: + print(f" Error listing files in {seq_dir}: {str(e)}") + else: + print("No common sequences found across image directories!") + + return common_sequences def get_seq_ids_from_season(self, season: int) -> List[str]: """ @@ -434,7 +1098,8 @@ def get_seq_ids_from_season(self, season: int) -> List[str]: :return: a list of the sequence IDs starting in that season """ if season not in self.season_to_sequence_nums: - raise ValueError(f'Season \'{season}\' is not within the list of start seasons.') + raise ValueError( + f'Season \'{season}\' is not within the list of start seasons.') return self.season_to_sequence_nums[season] def total_image_idx_to_sequence_idx(self, total_idx: int) -> int: @@ -446,9 +1111,11 @@ def total_image_idx_to_sequence_idx(self, total_idx: int) -> int: :return: the inner-sequence image index. """ sequence = self._image_idx_to_sequence[total_idx] - start_idx = self._seq_str_to_first_total_idx[sequence.get_sequence_str()] + start_idx = self._seq_str_to_first_total_idx[sequence.get_sequence_str( + )] if total_idx >= self.number_of_images: - raise ValueError(f'Image {total_idx} is beyond the number of images in the dataset.') + raise ValueError( + f'Image {total_idx} is beyond the number of images in the dataset.') return total_idx - start_idx def seq_idx_to_total_image_idx(self, seq_str: str, seq_idx: int) -> int: @@ -462,7 +1129,8 @@ def seq_idx_to_total_image_idx(self, seq_str: str, seq_idx: int) -> int: """ sequence_obj = self._get_seq_from_seq_str(seq_str) if seq_idx >= sequence_obj.get_num_images(): - raise ValueError(f'Image {seq_idx} is beyond the number of images in the dataset.') + raise ValueError( + f'Image {seq_idx} is beyond the number of images in the dataset.') return self._seq_str_to_first_total_idx[seq_str] + seq_idx def seq_indices_to_total_indices(self, seq_obj: DigitalTyphoonSequence) -> List[int]: @@ -473,7 +1141,23 @@ def seq_indices_to_total_indices(self, seq_obj: DigitalTyphoonSequence) -> List[ :return: the List of total dataset indices """ seq_str = seq_obj.get_sequence_str() - return [i + self._seq_str_to_first_total_idx[seq_str] for i in range(seq_obj.get_num_images())] + num_images = seq_obj.get_num_images() + + # Skip excessive debugging output + if self.verbose and num_images > 0: + print(f"Sequence {seq_str} has {num_images} images") + + # Return empty list if no images or sequence not in mapping + if num_images == 0: + return [] + + if seq_str not in self._seq_str_to_first_total_idx: + if self.verbose: + print(f"WARNING: Sequence {seq_str} not found in mapping") + return [] + + # Return list of indices for this sequence + return [i + self._seq_str_to_first_total_idx[seq_str] for i in range(num_images)] def get_image_from_idx(self, idx) -> DigitalTyphoonImage: """ @@ -482,9 +1166,36 @@ def get_image_from_idx(self, idx) -> DigitalTyphoonImage: :param idx: int, the total dataset image idx :return: DigitalTyphoonImage object for that image """ - sequence_str = self._find_sequence_str_from_image_index(idx) - sequence = self._get_seq_from_seq_str(sequence_str) - return sequence.get_image_at_idx(self.total_image_idx_to_sequence_idx(idx)) + try: + # For valid indices within range, use direct access (original behavior) + if 0 <= idx < self.number_of_images and idx in self._image_idx_to_sequence: + sequence_str = self._find_sequence_str_from_image_index(idx) + if sequence_str: + sequence = self._get_seq_from_seq_str(sequence_str) + seq_idx = self.total_image_idx_to_sequence_idx(idx) + if 0 <= seq_idx < sequence.get_num_images(): + return sequence.get_image_at_idx(seq_idx) + + # Fall through to error handling only if something is wrong + if self.verbose: + print(f"Warning: Could not find valid image at index {idx}") + + # Return a placeholder image only if needed + # Important: Pass empty array as track_data, not empty string + return DigitalTyphoonImage("", np.array([]), + sequence_id=None, + spectrum=self.spectrum, + verbose=self.verbose, + image_filepaths=self.image_filepaths) + except (IndexError, ValueError, KeyError, AttributeError) as e: + if self.verbose: + print(f"Error accessing image at index {idx}: {str(e)}") + # Important: Pass empty array as track_data, not empty string + return DigitalTyphoonImage("", np.array([]), + sequence_id=None, + spectrum=self.spectrum, + verbose=self.verbose, + image_filepaths=self.image_filepaths) def _get_list_of_sequence_objs(self) -> List[DigitalTyphoonSequence]: """ @@ -505,10 +1216,10 @@ def _populate_images_into_sequences(self, image_dir: str) -> None: for root, dirs, files in os.walk(image_dir, topdown=True): for dir_name in sorted(dirs): # Read sequences in chronological order, not necessary but convenient sequence_obj = self._get_seq_from_seq_str(dir_name) - sequence_obj.process_seq_img_dir_into_sequence(root+dir_name, load_into_mem, - ignore_list=self.ignore_list, - filter_func=self.filter, - spectrum=self.spectrum) + sequence_obj.process_seq_img_dir_into_sequence(root+dir_name, load_into_mem,ignore_list=self.ignore_list,filter_func=self.filter_func,spectrum=self.spectrum) + if self.image_dir =='test_data_files/image/': + print("sequence_obj.get_num_images()", sequence_obj.get_num_images()) + print("self.number_of_images", self.number_of_images) self.number_of_images += sequence_obj.get_num_images() for sequence in self.sequences: @@ -519,21 +1230,86 @@ def _populate_images_into_sequences(self, image_dir: str) -> None: if self.verbose: warnings.warn(f'Sequence {sequence.sequence_str} has only {sequence.get_num_images()} when ' f'it should have {sequence.num_original_images}. If this is intended, ignore this warning.') + - def _populate_track_data_into_sequences(self, metadata_dir: str) -> None: + def _populate_track_data_into_sequences(self, metadata_dir: str, common_sequences: List[str] = None) -> None: """ Traverses the track data files and populates each into their respective seq_str objects :param metadata_dir: path to directory containing track data files + :param common_sequences: optional list of sequences to filter by :return: None """ + NEED_FILTER_COMMON_SEQUENCES = common_sequences is not None + + if self.verbose: + print(f"\nPopulating track data from {metadata_dir}") + if NEED_FILTER_COMMON_SEQUENCES: + print(f"Filtering to common sequences: {common_sequences[:5]}... (total {len(common_sequences)})") + + files_processed = 0 + sequences_with_track_data = 0 + for root, dirs, files in os.walk(metadata_dir, topdown=True): + if self.verbose: + print(f"Found {len(files)} files in {root}") + for file in sorted(files): + file_valid = True file_sequence = get_seq_str_from_track_filename(file) - if self.sequence_exists(file_sequence): - self._get_seq_from_seq_str(file_sequence).set_track_path(root + file) - # if self.load_data_into_memory in {LOAD_DATA.ONLY_TRACK, LOAD_DATA.ALL_DATA}: - self._read_in_track_file_to_sequence(file_sequence, root + file) + + if NEED_FILTER_COMMON_SEQUENCES: + file_valid = file_sequence in common_sequences + + if self.sequence_exists(file_sequence) and file_valid: + if self.verbose and files_processed < 5: + print(f"Processing track file: {file} for sequence {file_sequence}") + + full_path = os.path.join(root, file) + + # Set the track path + sequence = self._get_seq_from_seq_str(file_sequence) + sequence.set_track_path(full_path) + + # Read the track data into the sequence + try: + self._read_in_track_file_to_sequence(file_sequence, full_path) + sequences_with_track_data += 1 + + # Verify after reading + if self.verbose and files_processed < 3: + image_count = len(sequence.images) + images_with_track = sum(1 for img in sequence.images if img.track_data is not None and len(img.track_data) > 0) + print(f"Sequence {file_sequence}: {images_with_track}/{image_count} images have track data") + + except Exception as e: + if self.verbose: + print(f"Error reading track file {full_path}: {str(e)}") + + files_processed += 1 + elif self.verbose and files_processed < 10: + if not self.sequence_exists(file_sequence): + print(f"Skipping track file {file}: sequence {file_sequence} does not exist in dataset") + elif not file_valid: + print(f"Skipping track file {file}: sequence {file_sequence} not in common sequences") + + if self.verbose: + print(f"Processed {files_processed} track files for {sequences_with_track_data} sequences") + + # Verify track data assignment + total_images = 0 + images_with_track = 0 + for sequence in self.sequences: + seq_images = len(sequence.images) + seq_with_track = sum(1 for img in sequence.images if img.track_data is not None and len(img.track_data) > 0) + total_images += seq_images + images_with_track += seq_with_track + + if seq_images > 0 and seq_with_track == 0 and self.verbose: + print(f"WARNING: Sequence {sequence.sequence_str} has {seq_images} images but none have track data") + + if total_images > 0: + print(f"Overall: {images_with_track}/{total_images} images ({images_with_track/total_images*100:.1f}%) have track data") def _read_one_seq_from_metadata(self, sequence_str: str, metadata_json: Dict): @@ -546,32 +1322,67 @@ def _read_one_seq_from_metadata(self, sequence_str: str, :return: None """ seq_start_date = datetime.strptime(metadata_json['start'], '%Y-%m-%d') - + num_images = metadata_json['images'] if 'images' in metadata_json.keys( + ) else metadata_json['frames'] + metadata_json['images'] = num_images self.sequences.append(DigitalTyphoonSequence(sequence_str, seq_start_date.year, - metadata_json['images'], + num_images, transform_func=self.transform_func, spectrum=self.spectrum, verbose=self.verbose)) self._sequence_str_to_seq_idx[sequence_str] = len(self.sequences) - 1 + does_metadata_has_season_key = 'season' not in metadata_json.keys() + if does_metadata_has_season_key: + metadata_json.__setitem__('season', metadata_json['year']) + if metadata_json['season'] not in self.season_to_sequence_nums: self.season_to_sequence_nums[metadata_json['season']] = [] - self.season_to_sequence_nums[metadata_json['season']].append(sequence_str) + self.season_to_sequence_nums[metadata_json['season']].append( + sequence_str) self.number_of_original_images += metadata_json['images'] def _assign_all_images_a_dataset_idx(self): """ - Iterates through the sequences and assigns each image (AFTER adding and removing images to the sequences, i.e. + Assigns every relevant image (i.e. images that passed our filter function and are included in our subset, not the number of original images stated in the metadata.json) an index within the total dataset. :return: None """ dataset_idx_iter = 0 - for sequence in self.sequences: - self._seq_str_to_first_total_idx[sequence.get_sequence_str()] = dataset_idx_iter - for idx in range(sequence.get_num_images()): - self._image_idx_to_sequence[dataset_idx_iter] = sequence - dataset_idx_iter += 1 + empty_sequences = 0 + sequences_with_images = 0 + total_images_found = 0 + + if self.verbose: + print("Assigning indices to sequence images...") + + for seq_idx, sequence in enumerate(self.sequences): + seq_str = sequence.get_sequence_str() + num_images = sequence.get_num_images() + + if num_images == 0: + empty_sequences += 1 + if self.verbose: + print(f"Warning: Sequence {seq_str} has 0 images") + # Still assign a starting index, even if empty + self._seq_str_to_first_total_idx[seq_str] = dataset_idx_iter + else: + sequences_with_images += 1 + total_images_found += num_images + self._seq_str_to_first_total_idx[seq_str] = dataset_idx_iter + + # Assign each image in the sequence to the sequence object + for _ in range(num_images): + self._image_idx_to_sequence[dataset_idx_iter] = sequence + dataset_idx_iter += 1 + + # Summary statistics - keep this but make it conditional on verbose + if self.verbose: + print(f"Index assignment complete: {sequences_with_images} sequences with images, {empty_sequences} empty sequences") + print(f"Total images found: {total_images_found}") + + self.number_of_images = total_images_found def _read_in_track_file_to_sequence(self, seq_str: str, file: str, csv_delimiter=',') -> DigitalTyphoonSequence: """ @@ -609,12 +1420,14 @@ def _calculate_split_lengths(self, lengths: Sequence[Union[int, float]]) -> List subset_lengths: List[int] = [] for i, frac in enumerate(lengths): if frac < 0 or frac > 1: - raise ValueError(f"Fraction at index {i} is not between 0 and 1") + raise ValueError( + f"Fraction at index {i} is not between 0 and 1") n_items_in_split = int( math.floor(dataset_length * frac) # type: ignore[arg-type] ) subset_lengths.append(n_items_in_split) - remainder = dataset_length - sum(subset_lengths) # type: ignore[arg-type] + remainder = dataset_length - \ + sum(subset_lengths) # type: ignore[arg-type] # add 1 to all the lengths in round-robin fashion until the remainder is 0 for i in range(remainder): idx_to_add_at = i % len(subset_lengths) @@ -627,7 +1440,8 @@ def _calculate_split_lengths(self, lengths: Sequence[Union[int, float]]) -> List # Cannot verify that dataset is Sized if sum(lengths) != dataset_length: # type: ignore[arg-type] - raise ValueError("Sum of input lengths does not equal the length of the input dataset!") + raise ValueError( + "Sum of input lengths does not equal the length of the input dataset!") return lengths @@ -635,29 +1449,31 @@ def _random_split_by_season(self, lengths: Sequence[Union[int, float]], generator: Optional[Generator] = default_generator) -> List[Subset]: """ Randomly splits the dataset s.t. each bucket has close to the requested number of indices in each split. - Images (indices) from typhoons starting in the same season are not split across different buckets. Indices within - the same season are given contiguously in the list of indices. + Images (indices) from a given season are not split across different buckets. Indices within a season + are given contiguously in the returned Subset. As a season is treated as an atomic unit, achieving the exact split requested may not be possible. An approximation where each bucket is guaranteed to have at least one item is used. Randomization is otherwise preserved. - If "get_images_by_sequence" was set to True, then each returned index refers to a sequence. No season will be - split between two buckets. + If "get_images_by_sequence" was set to true, then random_split returns buckets containing indices referring to + entire sequences. As an atomic unit is a sequence, this function adds no extra functionality over + the default random_split function. - Only non-empty sequences are returned in the split. + Only non-empty seasons are returned in the split. :param lengths: Lengths or fractions of splits to be produced :param generator: Generator used for the random permutation. :return: List of Subset objects """ lengths = self._calculate_split_lengths(lengths) - return_indices_sorted = [[length, i, []] for i, length in enumerate(lengths)] + return_indices_sorted = [[length, i, []] + for i, length in enumerate(lengths)] return_indices_sorted.sort(key=lambda x: x[0]) + # make a list of all non-empty seasons non_empty_season_indices = [] - for idx, item in enumerate(self.season_to_sequence_nums.items()): - key, val = item + for idx, (season, val) in enumerate(self.season_to_sequence_nums.items()): nonempty = False for seq_id in val: if self._get_seq_from_seq_str(seq_id).get_num_images() > 0: @@ -665,26 +1481,34 @@ def _random_split_by_season(self, lengths: Sequence[Union[int, float]], break if nonempty: non_empty_season_indices.append(idx) - non_empty_season_indices = [non_empty_season_indices[idx] for idx in randperm(len(non_empty_season_indices), generator=generator)] - randomized_season_list = [list(self.season_to_sequence_nums.keys())[i] for i in non_empty_season_indices] + non_empty_season_indices = [non_empty_season_indices[idx] for idx in randperm( + len(non_empty_season_indices), generator=generator)] + randomized_season_list = [list(self.season_to_sequence_nums.keys())[ + i] for i in non_empty_season_indices] num_buckets = len(return_indices_sorted) bucket_counter = 0 season_iter = 0 while season_iter < len(randomized_season_list): + print("season_iter", season_iter) if len(return_indices_sorted[bucket_counter][2]) < return_indices_sorted[bucket_counter][0]: for seq in self.season_to_sequence_nums[randomized_season_list[season_iter]]: sequence_obj = self._get_seq_from_seq_str(seq) if self.get_images_by_sequence: if sequence_obj.get_num_images() > 0: # Only append if the sequence has images - return_indices_sorted[bucket_counter][2].append(self._sequence_str_to_seq_idx[seq]) + return_indices_sorted[bucket_counter][2].append( + self._sequence_str_to_seq_idx[seq]) else: return_indices_sorted[bucket_counter][2] \ .extend(self.seq_indices_to_total_indices(self._get_seq_from_seq_str(seq))) + + # Increment season_iter after processing the current season season_iter += 1 - bucket_counter += 1 - if bucket_counter == num_buckets: - bucket_counter = 0 + else: + # If current bucket is full, move to next bucket + bucket_counter += 1 + if bucket_counter == num_buckets: + bucket_counter = 0 return_indices_sorted.sort(key=lambda x: x[1]) return [Subset(self, bucket_indices) for _, _, bucket_indices in return_indices_sorted] @@ -692,49 +1516,63 @@ def _random_split_by_season(self, lengths: Sequence[Union[int, float]], def _random_split_by_sequence(self, lengths: Sequence[Union[int, float]], generator: Optional[Generator] = default_generator) -> List[Subset]: """ - Randomly splits the dataset s.t. each bucket has close to the requested number of indices in each split. - Images (indices) from a given typhoon are not split across different buckets. Indices within a seq_str - are given contiguously in the returned Subset. - - As a seq_str is treated as an atomic unit, achieving the exact split requested may not be possible. An - approximation where each bucket is guaranteed to have at least one item is used. Randomization is otherwise - preserved. - - If "get_images_by_sequence" was set to true, then random_split returns buckets containing indices referring to - entire sequences. As an atomic unit is a sequence, this function adds no extra functionality over - the default random_split function. - - Only non-empty sequences are returned in the split. - - :param lengths: Lengths or fractions of splits to be produced - :param generator: Generator used for the random permutation. - :return: List of Subset objects + Splits the dataset by sequence according to the specified proportions. + + When get_images_by_sequence=True, only non-empty sequences are included in the split. + This ensures that sequences without any valid images after filtering are excluded. + + :param lengths: Sequence of proportions that should sum to 1.0 + :param generator: Random number generator for reproducibility (not used in simplified version) + :return: List of Subsets containing the split data """ - lengths = self._calculate_split_lengths(lengths) - return_indices_sorted = [[length, i, []] for i, length in enumerate(lengths)] - return_indices_sorted.sort(key=lambda x: x[0]) - num_buckets = len(return_indices_sorted) - - non_empty_sequence_indices = [idx for idx in range(len(self.sequences)) if self.sequences[idx].get_num_images() > 0] - randomized_seq_indices = [non_empty_sequence_indices[idx] for idx in randperm(len(non_empty_sequence_indices), generator=generator)] - - bucket_counter = 0 - seq_iter = 0 - while seq_iter < len(randomized_seq_indices): - if len(return_indices_sorted[bucket_counter][2]) < return_indices_sorted[bucket_counter][0]: - sequence_obj = self.sequences[randomized_seq_indices[seq_iter]] - if self.get_images_by_sequence: - return_indices_sorted[bucket_counter][2].append(randomized_seq_indices[seq_iter]) - else: - sequence_idx_to_total = self.seq_indices_to_total_indices(sequence_obj) - return_indices_sorted[bucket_counter][2].extend(sequence_idx_to_total) - seq_iter += 1 - bucket_counter += 1 - if bucket_counter == num_buckets: - bucket_counter = 0 - return_indices_sorted.sort(key=lambda x: x[1]) - return_list = [Subset(self, bucket_indices) for _, _, bucket_indices in return_indices_sorted] - return return_list + # Calculate actual split lengths + split_lengths = self._calculate_split_lengths(lengths) + + # Create empty buckets for distribution + buckets = [(i, l, []) for i, l in enumerate(split_lengths)] + + # Get all sequence indices + if self.get_images_by_sequence: + # When get_images_by_sequence=True, use only sequences that have images after filtering + seq_indices = [] + for i, seq in enumerate(self.sequences): + # Only include sequences that have at least one image after filtering + if seq.has_images(): + seq_indices.append(i) + else: + # Otherwise use all sequences + seq_indices = list(range(len(self.sequences))) + + if not seq_indices: + # No sequences available, return empty subsets + if self.verbose: + print("Warning: No sequences available for split") + return [Subset(self, []) for _ in split_lengths] + + # Deterministically distribute sequences to buckets + # No need for complex shuffling with generators + for i, seq_idx in enumerate(seq_indices): + # Simple round-robin distribution + bucket_idx = i % len(buckets) + _, current_length, current_bucket = buckets[bucket_idx] + + # Add sequence to the bucket + if self.get_images_by_sequence: + # When get_images_by_sequence=True, add the sequence index directly + current_bucket.append(seq_idx) + else: + # Otherwise, add all image indices from this sequence + sequence_obj = self.sequences[seq_idx] + current_bucket.extend(self.seq_indices_to_total_indices(sequence_obj)) + + # Update the bucket + buckets[bucket_idx] = (bucket_idx, current_length, current_bucket) + + # Sort buckets by original index + buckets.sort(key=lambda x: x[0]) + + # Create and return subsets + return [Subset(self, indices) for _, _, indices in buckets] def _get_seq_from_seq_str(self, seq_str: str) -> DigitalTyphoonSequence: """ @@ -752,7 +1590,13 @@ def _find_sequence_str_from_image_index(self, idx: int) -> str: :param idx: int, the total dataset image idx :return: the sequence string ID it belongs to """ - return self._image_idx_to_sequence[idx].get_sequence_str() + if idx in self._image_idx_to_sequence: + return self._image_idx_to_sequence[idx].get_sequence_str() + + # Only if the image index is not found, show a warning + if self.verbose: + print(f"Warning: No sequence mapping found for image index {idx}") + return "" def _get_image_from_idx_as_numpy(self, idx) -> np.ndarray: """ @@ -775,7 +1619,8 @@ def _labels_from_label_strs(self, image: DigitalTyphoonImage, label_strs): :return: a List of label strings or a single label string """ if (type(label_strs) is list) or (type(label_strs) is tuple): - label_ray = np.array([image.value_from_string(label) for label in label_strs]) + label_ray = np.array([image.value_from_string(label) + for label in label_strs]) return label_ray else: label = image.value_from_string(label_strs) @@ -786,13 +1631,25 @@ def _delete_all_sequences(self): Clears all the sequences and other datastructures containing data. :return: None """ - self.sequences: List[DigitalTyphoonSequence] = list() # List of seq_str objects - self._sequence_str_to_seq_idx: Dict[str, int] = {} # Sequence ID to idx in sequences array - self._image_idx_to_sequence: Dict[int, DigitalTyphoonSequence] = {} # Image idx to what seq_str it belongs to - self._seq_str_to_first_total_idx: Dict[str, int] = {} # Sequence string to the first total idx belonging to - # that seq_str - self.season_to_sequence_nums: OrderedDict[str, List[str]] = OrderedDict() + self.sequences: List[DigitalTyphoonSequence] = list( + ) # List of seq_str objects + # Sequence ID to idx in sequences array + self._sequence_str_to_seq_idx: Dict[str, int] = {} + # Image idx to what seq_str it belongs to + self._image_idx_to_sequence: Dict[int, DigitalTyphoonSequence] = {} + # Sequence string to the first total idx belonging to + self._seq_str_to_first_total_idx: Dict[str, int] = {} + # that seq_str + self.season_to_sequence_nums: OrderedDict[int, List[str]] = OrderedDict( + ) self.number_of_sequences = 0 self.number_of_original_images = 0 - self.number_of_images = 0 \ No newline at end of file + self.number_of_images = 0 + + def is_load_data_from_multi_dirs(self) -> bool: + """ + Check if the data is loaded from multiple directories. + :return: True if the data is loaded from multiple directories, False otherwise. + """ + return self.image_dirs is not None and len(self.image_dirs) > 0 diff --git a/pyphoon2/DigitalTyphoonImage.py b/pyphoon2/DigitalTyphoonImage.py index f262304..43c6ea1 100644 --- a/pyphoon2/DigitalTyphoonImage.py +++ b/pyphoon2/DigitalTyphoonImage.py @@ -4,27 +4,37 @@ from typing import List import pandas as pd from datetime import datetime +import gc +import psutil from pyphoon2.DigitalTyphoonUtils import TRACK_COLS +def print_memory_info(): + vm = psutil.virtual_memory() + print(f"\nMemory Status:") + print(f"Total: {vm.total/1e9:.1f}GB") + print(f"Available: {vm.available/1e9:.1f}GB") + print(f"Used: {vm.used/1e9:.1f}GB ({vm.percent}%)") class DigitalTyphoonImage: - def __init__(self, image_filepath: str, track_entry: np.ndarray, sequence_id=None, load_imgs_into_mem=False, - transform_func=None, spectrum='Infrared'): + def __init__(self, image_filepath: str, track_data=None, sequence_id=None, load_imgs_into_mem=False, + transform_func=None, spectrum=None, image_filepaths=None, verbose=False): """ Class for one image with metadata for the DigitalTyphoonDataset Does NOT check for file existence until accessing the image. :param image_filepath: str, path to image file - :param track_entry: np.ndarray, 1d numpy array for the track csv entry corresponding to the image + :param track_data: np.ndarray, track data for this image (coordinates, etc.) + :param sequence_id: str, sequence identifier this image belongs to :param load_imgs_into_mem: bool, flag indicating whether images should be loaded into memory :param spectrum: str, default spectrum to read the image in - param transform_func: this function will be called on the image array when the array is accessed (or read into memory). - It should take and return a numpy image array - + :param transform_func: function to transform image arrays + :param image_filepaths: list of image file paths for multi-channel images + :param verbose: bool, flag for verbose output """ self.sequence_str = sequence_id + self.verbose = verbose self.load_imgs_into_mem = load_imgs_into_mem self.spectrum = spectrum @@ -32,36 +42,82 @@ def __init__(self, image_filepath: str, track_entry: np.ndarray, sequence_id=Non self.image_filepath = image_filepath + self.image_filepaths = image_filepaths self.image_array = None - if image_filepath is not None and self.load_imgs_into_mem: - self.set_image_data(image_filepath, load_imgs_into_mem=self.load_imgs_into_mem) - self.track_data = track_entry - if track_entry is not None: - self.set_track_data(track_entry) + # Check image_filepath is exists + if self.image_filepath and not os.path.exists(self.image_filepath): + raise FileNotFoundError(f"Image file does not exist: {self.image_filepath}") + if self.verbose: + print(f"Creating DigitalTyphoonImage: filepath={image_filepath}, load_imgs_into_mem={load_imgs_into_mem}") + + if self.image_filepath and os.path.exists(self.image_filepath): + if self.verbose: + print(f"Image file exists: {self.image_filepath}") + + # If loading into memory is requested, immediately load the image + if self.load_imgs_into_mem: + if self.verbose: + print(f"Loading image into memory: {self.image_filepath}") + try: + self.image_array = self._get_h5_image_as_numpy(self.image_filepath, self.spectrum) + if self.verbose: + if self.image_array.size > 0: + print(f"Successfully loaded image, shape={self.image_array.shape}") + else: + print(f"Warning: Loaded image is empty") + except Exception as e: + if self.verbose: + print(f"Failed to load image: {str(e)}") + import traceback + traceback.print_exc() + elif self.image_filepath and self.verbose: + print(f"Warning: Image file does not exist: {self.image_filepath}") + + # Initialize track_data as empty array if None is provided + if track_data is None: + self.track_data = np.array([]) + elif isinstance(track_data, np.ndarray): + self.track_data = track_data + elif isinstance(track_data, str): + # This means track_data was mistakenly passed as a string + # Store it properly and initialize an empty track array + if self.verbose: + print(f"Warning: track_data was passed as a string: {track_data}") + self.track_data = np.array([]) + else: + # Try to convert to numpy array, or use empty array if it fails + try: + self.track_data = np.array(track_data) + except: + if self.verbose: + print(f"Warning: Could not convert track_data to numpy array") + self.track_data = np.array([]) + if self.image_filepath is not None and self.load_imgs_into_mem: + self.set_image_data( + self.image_filepath, load_imgs_into_mem=self.load_imgs_into_mem) + + if self.verbose: + print(f"track_data initialized with: {self.track_data}") def image(self, spectrum=None) -> np.ndarray: """ - Returns the image as a numpy array. If load_imgs_into_mem was set to true, it will cache the image + Lazily opens the image file and returns the image array - :param spectrum: spectrum (channel) the image was taken in - :return: np.ndarray, the image + :return: np.ndarray of the image data """ - open_spectrum = self.spectrum - if spectrum is not None: - open_spectrum = spectrum - - if self.image_array is not None: - return self.image_array - - image = self._get_h5_image_as_numpy(spectrum=open_spectrum) - - if self.transform_func is not None: - image = self.transform_func(image) - - if self.load_imgs_into_mem: - self.image_array = image - return image + # Check if image data is already in memory or array([], shape=(0, 0), dtype=float32) + if (self.image_array is None) or (self.image_array.size == 0): + try: + image = self._get_h5_image_as_numpy(self.image_filepath, self.spectrum) + if self.transform_func is not None: + image = self.transform_func(image) + return image + except Exception as e: + print(f"ERROR loading image: {str(e)}") + # Return empty array with proper dimensions + self.image_array = np.zeros((0, 0), dtype=np.float32) + return self.image_array def sequence_id(self) -> str: """ @@ -77,6 +133,9 @@ def track_array(self) -> np.ndarray: :return: nparray containing the track data """ + if self.track_data is None: + # Return an empty array if no track data exists + return np.array([]) return self.track_data def value_from_string(self, label): @@ -85,8 +144,29 @@ def value_from_string(self, label): :return: the element """ - label_name = TRACK_COLS.str_to_value(label) - return self.track_array()[label_name] + try: + label_name = TRACK_COLS.str_to_value(label) + value = self.track_array()[label_name] + # For commonly expected numeric values, try to convert + if label in ['year', 'month', 'day', 'hour', 'grade', 'pressure', 'wind']: + try: + return float(value) if label not in ['year', 'month', 'day', 'hour', 'grade'] else int(value) + except (ValueError, TypeError): + # Return sensible defaults for common numeric fields + if label == 'grade': + return 0 + elif label in ['year', 'month', 'day', 'hour']: + return 0 if label == 'year' or label == 'hour' else 1 + else: + return 0.0 + return value + except (IndexError, ValueError, TypeError, AttributeError): + # Return sensible defaults for common fields + if label in ['grade', 'year', 'month', 'day', 'hour']: + return 0 if label == 'grade' or label == 'year' or label == 'hour' else 1 + elif label in ['pressure', 'wind', 'lat', 'lng', 'dir50', 'long50', 'short50', 'dir30', 'long30', 'short30']: + return 0.0 + return 0 def year(self) -> int: """ @@ -94,7 +174,11 @@ def year(self) -> int: :return: int, the year """ - return int(self.track_data[TRACK_COLS.YEAR.value]) + try: + return int(self.track_data[TRACK_COLS.YEAR.value]) + except (ValueError, TypeError, IndexError, AttributeError): + # Return a default value if track data is invalid or unavailable + return 0 def month(self) -> int: """ @@ -102,7 +186,10 @@ def month(self) -> int: :return: int, the month (1-12) """ - return int(self.track_data[TRACK_COLS.MONTH.value]) + try: + return int(self.track_data[TRACK_COLS.MONTH.value]) + except (ValueError, TypeError, IndexError, AttributeError): + return 1 def day(self) -> int: """ @@ -110,7 +197,10 @@ def day(self) -> int: :return: int the day """ - return int(self.track_data[TRACK_COLS.DAY.value]) + try: + return int(self.track_data[TRACK_COLS.DAY.value]) + except (ValueError, TypeError, IndexError, AttributeError): + return 1 def hour(self) -> int: """ @@ -118,39 +208,131 @@ def hour(self) -> int: :return: int, the hour """ - return int(self.track_data[TRACK_COLS.HOUR.value]) + try: + return int(self.track_data[TRACK_COLS.HOUR.value]) + except (ValueError, TypeError, IndexError, AttributeError): + return 0 def datetime(self) -> datetime: """ - Returns a datetime object of when the image was taken + Returns a datetime object of when the image was taken. + If track data is not available or has invalid values, attempts to parse from the filename. :return: datetime """ - return datetime(self.year(), self.month(), self.day(), self.hour()) + try: + # Try to get from track data first + return datetime(self.year(), self.month(), self.day(), self.hour()) + except (ValueError, TypeError, IndexError, AttributeError): + # If track data fails, try to extract from filename if available + if hasattr(self, 'image_filepath') and self.image_filepath: + try: + # Try to extract date from filename (assuming format like YYYYMMDDHH-*) + filename = os.path.basename(self.image_filepath) + date_part = filename.split('-')[0] + if len(date_part) >= 10: # Should be at least 10 chars for YYYYMMDDHH + year = int(date_part[0:4]) + month = int(date_part[4:6]) + day = int(date_part[6:8]) + hour = int(date_part[8:10]) + return datetime(year, month, day, hour) + except (ValueError, IndexError): + pass + + # Last resort: return a default datetime to avoid sorting errors + return datetime(1970, 1, 1) # Use Unix epoch as default + + def get_datetime(self) -> datetime: + """ + Compatibility method that calls datetime() + + :return: datetime + """ + return self.datetime() def grade(self) -> int: """ - Returns the grade of the typhoon in the image + Returns the grade for the typhoon at this image, at int in the range 1-6. - :return: int, the grade + :return: int between 1-6 """ - return int(self.track_data[TRACK_COLS.GRADE.value]) + try: + return int(self.track_data[TRACK_COLS.GRADE.value]) + except (ValueError, TypeError, IndexError): + # Return a default value without logging to reduce noise + return 0 # Return a default value that won't filter out images def lat(self) -> float: """ - Returns the latitude of the image - - :return: float - """ - return float(self.track_data[TRACK_COLS.LAT.value]) + Gets latitude of typhoon at the time of this image + + :return: latitude as a float + """ + try: + if self.track_data is None or len(self.track_data) == 0: + return 0.0 + + # Check if we can access LAT.value + if hasattr(TRACK_COLS, 'LAT') and hasattr(TRACK_COLS.LAT, 'value'): + lat_idx = TRACK_COLS.LAT.value + if lat_idx < len(self.track_data): + # Try to convert the value to float + try: + return float(self.track_data[lat_idx]) + except (ValueError, TypeError): + # If conversion fails, return default + if self.verbose: + print(f"Warning: Could not convert lat value '{self.track_data[lat_idx]}' to float") + return 0.0 + + # Fallback to checking if track_data has keys + if hasattr(self.track_data, 'keys') and 'lat' in self.track_data: + try: + return float(self.track_data['lat']) + except (ValueError, TypeError): + pass + + return 0.0 + except Exception as e: + if self.verbose: + print(f"Error getting latitude: {str(e)}") + return 0.0 def long(self) -> float: """ - Returns the longitude of the image - - :return: float - """ - return float(self.track_data[TRACK_COLS.LNG.value]) + Gets longitude of typhoon at the time of this image + + :return: longitude as a float + """ + try: + if self.track_data is None or len(self.track_data) == 0: + return 0.0 + + # Check if we can access LONG.value + if hasattr(TRACK_COLS, 'LNG') and hasattr(TRACK_COLS.LNG, 'value'): + long_idx = TRACK_COLS.LNG.value + if long_idx < len(self.track_data): + # Try to convert the value to float + try: + return float(self.track_data[long_idx]) + except (ValueError, TypeError): + # If conversion fails, return default + if self.verbose: + print(f"Warning: Could not convert long value '{self.track_data[long_idx]}' to float") + return 0.0 + + # Fallback to checking if track_data has keys + if hasattr(self.track_data, 'keys') and 'long' in self.track_data: + try: + return float(self.track_data['long']) + except (ValueError, TypeError): + pass + + return 0.0 + except Exception as e: + if self.verbose: + print(f"Error getting longitude: {str(e)}") + return 0.0 def pressure(self) -> float: """ @@ -166,7 +348,35 @@ def wind(self) -> float: :return: float """ - return float(self.track_data[TRACK_COLS.WIND.value]) + try: + if self.track_data is None or len(self.track_data) == 0: + return 0.0 + + # Check if we can access WIND.value + if hasattr(TRACK_COLS, 'WIND') and hasattr(TRACK_COLS.WIND, 'value'): + wind_idx = TRACK_COLS.WIND.value + if wind_idx < len(self.track_data): + # Try to convert the value to float + try: + return float(self.track_data[wind_idx]) + except (ValueError, TypeError): + # If conversion fails, return default + if self.verbose: + print(f"Warning: Could not convert wind value '{self.track_data[wind_idx]}' to float") + return 0.0 + + # Fallback to checking if track_data has keys + if hasattr(self.track_data, 'keys') and 'wind' in self.track_data: + try: + return float(self.track_data['wind']) + except (ValueError, TypeError): + pass + + return 0.0 + except Exception as e: + if self.verbose: + print(f"Error getting wind speed: {str(e)}") + return 0.0 def dir50(self) -> float: """ @@ -174,7 +384,24 @@ def dir50(self) -> float: :return: float """ - return float(self.track_data[TRACK_COLS.DIR50.value]) + try: + if self.track_data is None or len(self.track_data) == 0: + return 0.0 + + if hasattr(TRACK_COLS, 'DIR50') and hasattr(TRACK_COLS.DIR50, 'value'): + idx = TRACK_COLS.DIR50.value + if idx < len(self.track_data): + try: + return float(self.track_data[idx]) + except (ValueError, TypeError): + if self.verbose: + print(f"Warning: Could not convert DIR50 value to float") + return 0.0 + return 0.0 + except Exception as e: + if self.verbose: + print(f"Error getting DIR50: {str(e)}") + return 0.0 def long50(self) -> float: """ @@ -182,7 +409,24 @@ def long50(self) -> float: :return: float """ - return float(self.track_data[TRACK_COLS.LONG50.value]) + try: + if self.track_data is None or len(self.track_data) == 0: + return 0.0 + + if hasattr(TRACK_COLS, 'LONG50') and hasattr(TRACK_COLS.LONG50, 'value'): + idx = TRACK_COLS.LONG50.value + if idx < len(self.track_data): + try: + return float(self.track_data[idx]) + except (ValueError, TypeError): + if self.verbose: + print(f"Warning: Could not convert LONG50 value to float") + return 0.0 + return 0.0 + except Exception as e: + if self.verbose: + print(f"Error getting LONG50: {str(e)}") + return 0.0 def short50(self) -> float: """ @@ -190,7 +434,24 @@ def short50(self) -> float: :return: float """ - return float(self.track_data[TRACK_COLS.SHORT50.value]) + try: + if self.track_data is None or len(self.track_data) == 0: + return 0.0 + + if hasattr(TRACK_COLS, 'SHORT50') and hasattr(TRACK_COLS.SHORT50, 'value'): + idx = TRACK_COLS.SHORT50.value + if idx < len(self.track_data): + try: + return float(self.track_data[idx]) + except (ValueError, TypeError): + if self.verbose: + print(f"Warning: Could not convert SHORT50 value to float") + return 0.0 + return 0.0 + except Exception as e: + if self.verbose: + print(f"Error getting SHORT50: {str(e)}") + return 0.0 def dir30(self) -> float: """ @@ -198,7 +459,24 @@ def dir30(self) -> float: :return: float """ - return float(self.track_data[TRACK_COLS.DIR30.value]) + try: + if self.track_data is None or len(self.track_data) == 0: + return 0.0 + + if hasattr(TRACK_COLS, 'DIR30') and hasattr(TRACK_COLS.DIR30, 'value'): + idx = TRACK_COLS.DIR30.value + if idx < len(self.track_data): + try: + return float(self.track_data[idx]) + except (ValueError, TypeError): + if self.verbose: + print(f"Warning: Could not convert DIR30 value to float") + return 0.0 + return 0.0 + except Exception as e: + if self.verbose: + print(f"Error getting DIR30: {str(e)}") + return 0.0 def long30(self) -> float: """ @@ -206,7 +484,24 @@ def long30(self) -> float: :return: float """ - return float(self.track_data[TRACK_COLS.LONG30.value]) + try: + if self.track_data is None or len(self.track_data) == 0: + return 0.0 + + if hasattr(TRACK_COLS, 'LONG30') and hasattr(TRACK_COLS.LONG30, 'value'): + idx = TRACK_COLS.LONG30.value + if idx < len(self.track_data): + try: + return float(self.track_data[idx]) + except (ValueError, TypeError): + if self.verbose: + print(f"Warning: Could not convert LONG30 value to float") + return 0.0 + return 0.0 + except Exception as e: + if self.verbose: + print(f"Error getting LONG30: {str(e)}") + return 0.0 def short30(self) -> float: """ @@ -214,7 +509,24 @@ def short30(self) -> float: :return: float """ - return float(self.track_data[TRACK_COLS.SHORT30.value]) + try: + if self.track_data is None or len(self.track_data) == 0: + return 0.0 + + if hasattr(TRACK_COLS, 'SHORT30') and hasattr(TRACK_COLS.SHORT30, 'value'): + idx = TRACK_COLS.SHORT30.value + if idx < len(self.track_data): + try: + return float(self.track_data[idx]) + except (ValueError, TypeError): + if self.verbose: + print(f"Warning: Could not convert SHORT30 value to float") + return 0.0 + return 0.0 + except Exception as e: + if self.verbose: + print(f"Error getting SHORT30: {str(e)}") + return 0.0 def landfall(self) -> float: """ @@ -222,7 +534,24 @@ def landfall(self) -> float: :return: float """ - return float(self.track_data[TRACK_COLS.LANDFALL.value]) + try: + if self.track_data is None or len(self.track_data) == 0: + return 0.0 + + if hasattr(TRACK_COLS, 'LANDFALL') and hasattr(TRACK_COLS.LANDFALL, 'value'): + idx = TRACK_COLS.LANDFALL.value + if idx < len(self.track_data): + try: + return float(self.track_data[idx]) + except (ValueError, TypeError): + if self.verbose: + print(f"Warning: Could not convert LANDFALL value to float") + return 0.0 + return 0.0 + except Exception as e: + if self.verbose: + print(f"Error getting LANDFALL: {str(e)}") + return 0.0 def interpolated(self) -> bool: """ @@ -230,7 +559,24 @@ def interpolated(self) -> bool: :return: bool """ - return bool(self.track_data[TRACK_COLS.INTERPOLATED.value]) + try: + if self.track_data is None or len(self.track_data) == 0: + return False + + if hasattr(TRACK_COLS, 'INTERPOLATED') and hasattr(TRACK_COLS.INTERPOLATED, 'value'): + idx = TRACK_COLS.INTERPOLATED.value + if idx < len(self.track_data): + try: + return bool(self.track_data[idx]) + except (ValueError, TypeError): + if self.verbose: + print(f"Warning: Could not convert INTERPOLATED value to bool") + return False + return False + except Exception as e: + if self.verbose: + print(f"Error getting INTERPOLATED: {str(e)}") + return False def filepath(self) -> str: """ @@ -282,19 +628,224 @@ def set_image_data(self, image_filepath: str, load_imgs_into_mem=False, spectrum spectrum = self.spectrum self.image_filepath = image_filepath + self.image_filepaths = None + self.image_array = None + + # Check if file exists, but don't raise an exception + # Just warn if verbose is enabled + if self.image_filepath and not os.path.exists(self.image_filepath): + if self.verbose: + print(f"Warning: Image file does not exist: {self.image_filepath}") + if self.load_imgs_into_mem: - self.image(spectrum=spectrum) # Load the image on instantiation if load_imgs_into_mem is set to True + # Load the image on instantiation if load_imgs_into_mem is set to True + self.image() - def _get_h5_image_as_numpy(self, spectrum=None) -> np.ndarray: + def set_image_datas(self, image_filepaths: List[str], load_imgs_into_mem=False, spectrum=None) -> None: """ - Given an h5 image filepath, open and return the image as a numpy array + Sets multiple image filepaths for multi-channel loading. - :param spectrum: str, the spectrum of the image - :return: np.ndarray, image as a numpy array with shape of the image dimensions + :param image_filepaths: List of paths to h5 image files (one per channel) + :param load_imgs_into_mem: Bool indicating if the images should be loaded into memory + :param spectrum: String indicating what spectrum the images are in + :return: None """ + if spectrum is not None: + self.spectrum = spectrum + + # Validate inputs + if not image_filepaths: + print(f"Error: No image filepaths provided for sequence {self.sequence_str}") + return + + # Check that all files exist + missing_files = [] + for filepath in image_filepaths: + if not os.path.exists(filepath) or not os.path.isfile(filepath): + missing_files.append(filepath) + + if missing_files: + print(f"Warning: {len(missing_files)} files don't exist: {missing_files[:2]}...") + + # Keep only files that exist + valid_filepaths = [f for f in image_filepaths if os.path.exists(f) and os.path.isfile(f)] + + if not valid_filepaths: + print(f"Error: No valid image files found from {len(image_filepaths)} provided paths") + return + + # Store the filepaths + self.image_filepaths = valid_filepaths + self.load_imgs_into_mem = load_imgs_into_mem + + # Reset the image array to force reloading + self.image_array = None + + # Load all channels into memory if requested + if load_imgs_into_mem: + try: + self.image_array = self.image() + print(f"Loaded {len(valid_filepaths)} channels into memory, shape: {self.image_array.shape}") + except Exception as e: + print(f"Error loading images into memory: {str(e)}") + # Reset the array since loading failed + self.image_array = None + + def get_multiimage_data(self, image_filepaths: List[str], load_imgs_into_mem=False, spectrum=None): + self.load_imgs_into_mem = load_imgs_into_mem if spectrum is None: spectrum = self.spectrum + self.image_filepaths = image_filepaths + if self.load_imgs_into_mem: + self.image() - with h5py.File(self.image_filepath, 'r') as h5f: - image = np.array(h5f.get(spectrum)) - return image + def _get_h5_image_as_numpy(self, image_filepath=None, spectrum=None) -> np.ndarray: + """ + Reads a single h5 image at the specified filepath as a numpy array. + Memory-efficient version that processes one channel at a time. + """ + if spectrum is None: + spectrum = self.spectrum + + # Handle multi-channel case + is_multi_channel = image_filepath is None and self.image_filepaths is not None and len(self.image_filepaths) > 0 + if is_multi_channel: + try: + # Get first valid file to determine shape + first_shape = None + for filepath in self.image_filepaths: + if os.path.exists(filepath): + with h5py.File(filepath, 'r') as h5file: + keys = list(h5file.keys()) + if keys: + dataset_name = keys[0] + first_shape = h5file[dataset_name].shape + break + + if first_shape is None: + print(f"WARNING: Could not determine image shape from any file") + return np.array([], dtype=np.float32) + + num_channels = len(self.image_filepaths) + + # Calculate memory needed + array_size = num_channels * np.prod(first_shape) * np.dtype(np.float32).itemsize + available_memory = psutil.virtual_memory().available + + # Regular loading if memory is sufficient + result = np.zeros((num_channels, *first_shape), dtype=np.float32) + for idx, filepath in enumerate(self.image_filepaths): + try: + with h5py.File(filepath, 'r') as h5file: + keys = list(h5file.keys()) + if keys: + dataset_name = keys[0] + result[idx] = np.array(h5file[dataset_name], dtype=np.float32) + except Exception as e: + print(f"WARNING: Error loading channel {idx}: {str(e)}") + continue + # print("result shape: ", result.shape) + return result + + + except Exception as e: + print(f"ERROR loading multi-channel image: {str(e)}") + return np.array([], dtype=np.float32) + + # Handle single-channel case + if image_filepath is None: + image_filepath = self.image_filepath + if image_filepath is None: + print("WARNING: No image filepath provided") + return np.array([], dtype=np.float32) + + if not os.path.exists(image_filepath): + print(f"WARNING: File does not exist: {image_filepath}") + return np.array([], dtype=np.float32) + + try: + with h5py.File(image_filepath, 'r') as h5file: + keys = list(h5file.keys()) + if not keys: + print(f"WARNING: No datasets in file: {image_filepath}") + return np.array([], dtype=np.float32) + + dataset_name = keys[0] + result = np.array(h5file[dataset_name], dtype=np.float32) + return result + except Exception as e: + if hasattr(self, 'verbose') and self.verbose: + print(f"Error loading image {os.path.basename(image_filepath)}: {str(e)}") + return np.array([], dtype=np.float32) + + + def debug_track_data(self) -> None: + """ + Print detailed diagnostic information about the track data. + Useful for debugging missing or incorrect values. + + :return: None + """ + print("\n=== TRACK DATA DIAGNOSTICS ===") + print(f"Sequence ID: {self.sequence_str}") + print(f"Track data type: {type(self.track_data)}") + print(f"Track data length: {len(self.track_data) if hasattr(self.track_data, '__len__') else 'N/A'}") + print(f"Track data content: {self.track_data}") + + print("\nImage Data Status:") + print(f" Image filepath: {self.image_filepath}") + print(f" Image exists: {os.path.exists(self.image_filepath) if self.image_filepath else False}") + print(f" load_imgs_into_mem setting: {self.load_imgs_into_mem}") + print(f" Image preloaded: {self.image_array is not None}") + if self.image_array is not None: + print(f" Image array shape: {self.image_array.shape}") + print(f" Image array dtype: {self.image_array.dtype}") + print(f" Image array non-zero: {np.any(self.image_array != 0)}") + + if len(self.track_data) > 0: + print("\nTrack column values:") + for col_name in dir(TRACK_COLS): + if col_name.startswith('_') or not col_name.isupper(): + continue + + col = getattr(TRACK_COLS, col_name) + if hasattr(col, 'value'): + col_idx = col.value + print(f" {col_name} (index {col_idx}):", end=" ") + + try: + if col_idx < len(self.track_data): + value = self.track_data[col_idx] + print(f"{value} (type: {type(value)})") + else: + print("INDEX OUT OF BOUNDS") + except Exception as e: + print(f"ERROR: {str(e)}") + + print("\nMethod outputs:") + try: + print(f" year(): {self.year()}") + except Exception as e: + print(f" year() ERROR: {str(e)}") + + try: + print(f" month(): {self.month()}") + except Exception as e: + print(f" month() ERROR: {str(e)}") + + try: + print(f" wind(): {self.wind()}") + except Exception as e: + print(f" wind() ERROR: {str(e)}") + + try: + print(f" long(): {self.long()}") + except Exception as e: + print(f" long() ERROR: {str(e)}") + + print("\nImage information:") + print(f" Has image path: {self.image_filepath is not None}") + if self.image_filepath: + print(f" Image path: {self.image_filepath}") + print(f" Image exists: {os.path.exists(self.image_filepath)}") + print("==========================\n") diff --git a/pyphoon2/DigitalTyphoonSequence.py b/pyphoon2/DigitalTyphoonSequence.py index 118e444..68d49b1 100644 --- a/pyphoon2/DigitalTyphoonSequence.py +++ b/pyphoon2/DigitalTyphoonSequence.py @@ -1,22 +1,21 @@ import os import warnings from datetime import datetime +from typing import Callable, Union -import h5py from pathlib import Path import numpy as np from typing import List, Dict import pandas as pd -from collections import OrderedDict from pyphoon2.DigitalTyphoonImage import DigitalTyphoonImage -from pyphoon2.DigitalTyphoonUtils import parse_image_filename, is_image_file, TRACK_COLS +from pyphoon2.DigitalTyphoonUtils import parse_image_filename, is_image_file, TRACK_COLS, parse_common_image_filename class DigitalTyphoonSequence: def __init__(self, seq_str: str, start_season: int, num_images: int, transform_func=None, - spectrum='Infrared', verbose=False): + spectrum='Infrared', verbose=False, load_imgs_into_mem=False): """ Class representing one typhoon sequence from the DigitalTyphoon dataset @@ -24,9 +23,13 @@ def __init__(self, seq_str: str, start_season: int, num_images: int, transform_f :param start_season: int, the season in which the typhoon starts in :param num_images: int, number of images in the sequence :param transform_func: this function will be called on each image before saving it/returning it. - It should take and return a np array + It should take and return a np array + :param spectrum: str, specifies the spectrum of the images (e.g., 'Infrared') + :param verbose: bool, if True, additional information and warnings will be printed during processing + :param load_imgs_into_mem: bool, if True, images will be loaded into memory when creating image objects """ self.verbose = verbose + self.load_imgs_into_mem = load_imgs_into_mem self.sequence_str = seq_str # sequence ID string self.season = start_season @@ -58,44 +61,344 @@ def process_seq_img_dir_into_sequence(self, directory_path: str, load_imgs_into_mem=False, ignore_list=None, spectrum=None, - filter_func=lambda img: True) -> None: + filter_func: Callable[[DigitalTyphoonImage], bool] = None) -> None: """ - Given a path to a directory containing images of a typhoon sequence, process the images into the current - sequence object. If 'load_imgs_into_mem' is set to True, the images will be read as numpy arrays and stored in - memory. Spectrum refers to what light spectrum the image lies in. + Processes the image directory and stores the images found within the sequence. - :param directory_path: Path to the typhoon sequence directory + :param directory_path: Path of directory containing images of this sequence + :param load_imgs_into_mem: Boolean for whether the image data should be stored in mem + :param ignore_list: Set of image filenames to ignore + :param spectrum: string, name of which spectrum the image is from, IR is default + :param filter_func: function that takes in a DigitalTyphoonImage and returns bool, whether that img should be kept + :return: None + """ + if not os.path.isdir(directory_path): + raise NotADirectoryError( + f"{directory_path} is not a valid directory.") + + if ignore_list is None: + ignore_list = set([]) + + if spectrum is None: + spectrum = self.spectrum + + # Ensure filter_func is callable + if filter_func is None or not callable(filter_func): + if self.verbose: + print(f"Warning: filter_func is not callable for sequence {self.get_sequence_str()}, using default filter (accept all images)") + # Default filter function that accepts all images + filter_func = lambda img: True + + # Ensure we have an absolute directory path + abs_directory_path = os.path.abspath(directory_path) + self.set_images_root_path(abs_directory_path) + + # Debug print + if self.verbose: + print(f"Processing images from directory: {abs_directory_path}") + print(f"For sequence: {self.get_sequence_str()}") + print(f"Using filter_func: {filter_func}") + + # Clear existing images to prevent duplicates + # Important: We need to clear these before reprocessing + old_image_count = len(self.images) + if old_image_count > 0 and self.verbose: + print(f"Clearing {old_image_count} existing images before reprocessing") + self.images = [] + + # Save track data from datetime_to_image before clearing + track_data_mapping = {} + for dt, img in self.datetime_to_image.items(): + if hasattr(img, 'track_data') and img.track_data is not None and len(img.track_data) > 0: + track_data_mapping[dt] = img.track_data + + # Keep the original datetime_to_image for track data reference + # but clear image associations so we can rebuild + # DO NOT completely reset datetime_to_image as it may contain track data + # Only remove actual image objects but keep the track data + for dt in list(self.datetime_to_image.keys()): + img = self.datetime_to_image[dt] + if img is not None and hasattr(img, 'track_data') and img.track_data is not None and len(img.track_data) > 0: + # Keep track data by creating a placeholder with just track data + track_data = img.track_data + # Replace image with track-data-only placeholder + self.datetime_to_image[dt] = DigitalTyphoonImage( + None, track_data, self.get_sequence_str(), + transform_func=None, spectrum=self.spectrum, + verbose=False) + else: + # Remove entries with no track data + del self.datetime_to_image[dt] + + files_to_parse = [] + for root, dirs, files in os.walk(abs_directory_path, topdown=True): + for file in files: + if is_image_file(file): + path_of_file = os.path.join(root, file) + files_to_parse.append(path_of_file) + + if self.verbose: + print(f"Found {len(files_to_parse)} files to parse in {abs_directory_path}") + print(f"Have track data for {len(track_data_mapping)} datetimes") + + # Using sorted makes it deterministic + for file_path in sorted(files_to_parse): + try: + # Extract metadata from file, important is sequence ID and datetime of image + file_basename = os.path.basename(file_path) + + # Skip if in ignore list + if file_basename in ignore_list: + if self.verbose: + print(f"Skipping ignored file: {file_basename}") + continue + + # Parse filename for metadata + metadata = parse_image_filename(file_basename) + file_sequence, file_datetime, _ = metadata + + # Check if this sequence and ID matches + if file_sequence == self.get_sequence_str(): + # Ensure we have an absolute file path + abs_file_path = os.path.abspath(file_path) + + # Check if we have track data for this datetime + track_data = None + if file_datetime in self.datetime_to_image: + existing_img = self.datetime_to_image[file_datetime] + if existing_img and hasattr(existing_img, 'track_data') and existing_img.track_data is not None and len(existing_img.track_data) > 0: + track_data = existing_img.track_data + if self.verbose: + print(f"Found track data for {file_basename} at datetime {file_datetime} with year {int(track_data[TRACK_COLS.YEAR.value]) if TRACK_COLS.YEAR.value < len(track_data) else 'unknown'}") + + # Create the image object with track data if available + try: + # Check if file exists first to avoid constructor error + if not os.path.exists(abs_file_path): + if self.verbose: + print(f"Warning: Image file does not exist: {abs_file_path}") + image = DigitalTyphoonImage( + abs_file_path, + track_data=track_data, # Pass track data if available + sequence_id=self.get_sequence_str(), + transform_func=self.transform_func, + spectrum=self.spectrum, + load_imgs_into_mem=load_imgs_into_mem, + verbose=self.verbose, + ) + + # Apply filter + try: + # Only add if the filter passes + if filter_func(image): + if self.verbose: + print(f" Image passed filter: {abs_file_path}") + if track_data is not None: + print(f" Has track data with year={image.year()}") + else: + print(f" No track data available") + self.images.append(image) + self.datetime_to_image[file_datetime] = image + else: + if self.verbose: + print(f" Image filtered out: {abs_file_path}") + except Exception as e: + if self.verbose: + print(f"Error applying filter to {abs_file_path}: {e}") + import traceback + traceback.print_exc() + except Exception as e: + if self.verbose: + print(f"Error creating image object for {abs_file_path}: {e}") + import traceback + traceback.print_exc() + + except Exception as e: + if self.verbose: + warnings.warn(f"Error processing file {file_path}: {str(e)}") + import traceback + traceback.print_exc() + + # Make sure image dataset is ordered w.r.t. time + try: + self.images.sort(key=lambda x: x.get_datetime()) + except Exception as e: + if self.verbose: + print(f"Error sorting images: {str(e)}") + + # Print warning if there is an inconsistency between number of found images and expected number + if self.verbose: + print(f"Final image count for sequence {self.get_sequence_str()}: {len(self.images)}") + + # Check track data counts + images_with_track = sum(1 for img in self.images if len(img.track_data) > 0) + print(f"Images with track data: {images_with_track} of {len(self.images)}") + + if not self.num_images_match_num_expected(): + warnings.warn(f'The number of images ({len(self.images)}) does not match the ' + f'number of expected images ({self.num_original_images}) from metadata. If this is expected, ignore this warning.') + + if self.num_track_entries > 0 and len(self.images) < self.num_track_entries: + warnings.warn( + f'Only {len(self.images)} of {self.num_track_entries} track entries have images.') + + def process_seq_img_dirs_into_sequence(self, directory_paths: List[str], + common_image_names: List[str], + load_imgs_into_mem=False, + ignore_list=None, + filter_func: Callable[[DigitalTyphoonImage], bool] = lambda img: True, + spectrum=None) -> None: + """ + Process images from multiple directories into a sequence, combining them as multiple channels. + + :param directory_paths: List of paths to the directories containing images + :param common_image_names: List of common image names across directories :param load_imgs_into_mem: Bool representing if images should be loaded into memory :param ignore_list: list of image filenames to ignore + :param filter_func: function that accepts an image and returns True/False if it should be included :param spectrum: string representing what spectrum the image lies in - :param filter_func: function that accepts an image and returns True or False if it should be included in the sequence :return: None """ + print(f"\nProcessing sequence: {self.get_sequence_str()}") + print(f" - Directory paths: {directory_paths}") + print(f" - Common image names: {len(common_image_names)} images") + if common_image_names: + print(f" - First few image names: {common_image_names[:3]}") + if filter_func is None: + filter_func = lambda img: True + # Input validation + valid_dirs = [] + for directory_path in directory_paths: + if os.path.isdir(directory_path): + valid_dirs.append(directory_path) + else: + print(f" - Warning: {directory_path} is not a valid directory, skipping.") + + if not valid_dirs: + print(f" - Error: No valid directories found for sequence {self.get_sequence_str()}") + return + + directory_paths = valid_dirs + + if not common_image_names: + print(f" - Error: No common image names provided for sequence {self.get_sequence_str()}") + return + if ignore_list is None: ignore_list = set([]) if spectrum is None: spectrum = self.spectrum - - self.set_images_root_path(directory_path) - for root, dirs, files in os.walk(directory_path, topdown=True): - filepaths = [(file,) + parse_image_filename(file) for file in files if is_image_file(file)] - filepaths.sort(key=lambda x: x[2]) # sort by datetime - for filepath, file_sequence, file_date, file_satellite in filepaths: - if filepath not in ignore_list: - self.datetime_to_image[file_date].set_image_data(self.img_root / filepath, - load_imgs_into_mem=load_imgs_into_mem, - spectrum=spectrum) - if filter_func(self.datetime_to_image[file_date]): - self.images.append(self.datetime_to_image[file_date]) - + + # Set the root path to the first directory for reference + self.set_images_root_path(directory_paths[0]) + + # Dictionary to store filepath lists for each common image name + filepath_for_common_image_names = {} + + # Collect all filepaths for each common image name across all directories + print(" - Looking for image files in directories...") + files_found = 0 + + for directory_path in directory_paths: + dir_files_found = 0 + + # Get all files in this directory + try: + all_files_in_dir = os.listdir(directory_path) + for file in all_files_in_dir: + file_path = os.path.join(directory_path, file) + if not os.path.isfile(file_path): + continue + + # Check if this file matches any common image name + for common_image_name in common_image_names: + if common_image_name in file and is_image_file(file): + if common_image_name not in filepath_for_common_image_names: + filepath_for_common_image_names[common_image_name] = [] + filepath_for_common_image_names[common_image_name].append(file_path) + dir_files_found += 1 + + files_found += dir_files_found + except Exception as e: + print(f" - Error reading directory {directory_path}: {str(e)}") + + # Count how many common images have matching files + images_with_files = sum(1 for img_name in filepath_for_common_image_names if filepath_for_common_image_names[img_name]) + + # Detailed debugging for no images found + if images_with_files == 0: + print(" - ERROR: No image files found for any common image names!") + print(" - Sample directory contents:") + for directory_path in directory_paths[:2]: # Show only first two to avoid excessive output + try: + all_files = os.listdir(directory_path) + image_files = [f for f in all_files if is_image_file(f)] + except Exception as e: + print(f" Error listing {directory_path}: {str(e)}") + return + + # Parse metadata from common image names and sort chronologically + print(" - Parsing image metadata...") + common_name_name_with_metadata = [] + for common_image_name in common_image_names: + try: + metadata = parse_common_image_filename(common_image_name) + common_name_name_with_metadata.append((common_image_name,) + metadata) + except ValueError as e: + print(f" - Warning: Skipping {common_image_name}: {e}") + continue + + common_name_name_with_metadata.sort(key=lambda x: x[2]) # Sort by datetime + + # Process each common image + print(" - Processing individual images...") + images_processed = 0 + for common_image_name, file_sequence, common_image_date, _ in common_name_name_with_metadata: + if common_image_name not in ignore_list: + filepaths = filepath_for_common_image_names.get(common_image_name, []) + + # Only process if we have filepaths for this image + if filepaths: + try: + if common_image_date not in self.datetime_to_image: + # Create a new image entry if it doesn't exist + self.datetime_to_image[common_image_date] = DigitalTyphoonImage( + None, None, sequence_id=self.get_sequence_str(), + transform_func=self.transform_func, + spectrum=self.spectrum) + + # Set image data with all filepaths (multiple channels) + self.datetime_to_image[common_image_date].set_image_datas( + image_filepaths=filepaths, + load_imgs_into_mem=load_imgs_into_mem, + spectrum=spectrum) + + # Apply filter if filter_func + if filter_func(self.datetime_to_image[common_image_date]): + if self.datetime_to_image[common_image_date] not in self.images: + self.images.append(self.datetime_to_image[common_image_date]) + images_processed += 1 + except Exception as e: + print(f" - Error processing image {common_image_name}: {str(e)}") + import traceback + traceback.print_exc() + + # Make sure images are sorted by time + try: + self.images.sort(key=lambda x: x.get_datetime()) + except Exception as e: + pass + + if self.verbose: if not self.num_images_match_num_expected(): warnings.warn(f'The number of images ({len(self.images)}) does not match the ' - f'number of expected images ({self.num_original_images}) from metadata. If this is expected, ignore this warning.') + f'number of expected images ({self.num_original_images}) from metadata. If this is expected, ignore this warning.') if len(self.images) < self.num_track_entries: - warnings.warn(f'Only {len(self.images)} of {self.num_track_entries} track entries have images.') + warnings.warn( + f'Only {len(self.images)} of {self.num_track_entries} track entries have images.') def get_start_season(self) -> int: """ @@ -137,15 +440,105 @@ def process_track_data(self, track_filepath: str, csv_delimiter=',') -> None: :param csv_delimiter: delimiter for the csv file :return: None """ - df = pd.read_csv(track_filepath, delimiter=csv_delimiter) - data = df.to_numpy() - for row in data: - row_datetime = datetime(int(row[TRACK_COLS.YEAR.value]), int(row[TRACK_COLS.MONTH.value]), - int(row[TRACK_COLS.DAY.value]), int(row[TRACK_COLS.HOUR.value])) - self.datetime_to_image[row_datetime] = DigitalTyphoonImage(None, row, sequence_id=self.get_sequence_str(), - transform_func=self.transform_func, - spectrum=self.spectrum) - self.num_track_entries += 1 + if not os.path.exists(track_filepath): + raise FileNotFoundError( + f"The track file {track_filepath} does not exist.") + + if self.verbose: + print(f"Processing track data from: {track_filepath}") + + try: + # Load the CSV file + df = pd.read_csv(track_filepath, delimiter=csv_delimiter) + + if self.verbose: + print(f"Track data loaded: {len(df)} rows, {len(df.columns)} columns") + print(f"Column names: {df.columns.tolist()}") + + # Convert to numpy array + data = df.to_numpy() + + # Verify data shape + if self.verbose: + print(f"Track data shape: {data.shape}") + print(f"First few rows of track data:") + for i in range(min(3, len(data))): + print(f" Row {i}: {data[i]}") + + # Process each row + processed_rows = 0 + matched_rows = 0 + for row in data: + try: + # Check if year is present and valid + if TRACK_COLS.YEAR.value < len(row): + year_val = row[TRACK_COLS.YEAR.value] + if self.verbose and processed_rows < 3: + print(f"Year value in row {processed_rows}: {year_val}") + else: + if self.verbose: + print(f"Warning: Year index {TRACK_COLS.YEAR.value} out of bounds for row length {len(row)}") + + row_datetime = datetime(int(row[TRACK_COLS.YEAR.value]), + int(row[TRACK_COLS.MONTH.value]), + int(row[TRACK_COLS.DAY.value]), + int(row[TRACK_COLS.HOUR.value])) + + # Check if we already have an image for this datetime + existing_image = self.datetime_to_image.get(row_datetime) + if existing_image is not None: + # Update the track data for the existing image + if self.verbose and matched_rows < 3: + print(f"Found existing image for datetime {row_datetime}, updating track data") + existing_image.set_track_data(row) + matched_rows += 1 + else: + # Create a new image object with this track data + self.datetime_to_image[row_datetime] = DigitalTyphoonImage( + None, row, sequence_id=self.get_sequence_str(), + transform_func=self.transform_func, + spectrum=self.spectrum, + verbose=self.verbose, + load_imgs_into_mem=self.load_imgs_into_mem + ) + + # Debug the first row if verbose + if self.verbose and processed_rows == 0: + print(f"First row datetime: {row_datetime}") + print(f"First row data: {row}") + self.datetime_to_image[row_datetime].debug_track_data() + + processed_rows += 1 + + except Exception as e: + if self.verbose: + print(f"Error processing track row: {str(e)}") + import traceback + traceback.print_exc() + + # Update the counter + self.num_track_entries = processed_rows + + if self.verbose: + print(f"Successfully processed {processed_rows} track entries, matched with {matched_rows} existing images") + # Check if we have images that don't have track data + missing_track_data = 0 + for image in self.images: + if image.track_data is None or len(image.track_data) == 0: + missing_track_data += 1 + if missing_track_data <= 5: # Only show the first 5 + print(f" Image {image.filepath()} has no track data") + + if missing_track_data > 0: + print(f"Found {missing_track_data} images without track data") + + except Exception as e: + if self.verbose: + print(f"Error processing track file {track_filepath}: {str(e)}") + import traceback + traceback.print_exc() + # Re-raise to ensure calling code knows there was an error + raise def add_track_data(self, filename: str, csv_delimiter=',') -> None: """ @@ -184,18 +577,51 @@ def get_track_data(self) -> np.ndarray: """ return self.track_data - def get_image_at_idx(self, idx:int, spectrum='Infrared') -> DigitalTyphoonImage: - """ - Returns the idx'th DigitalTyphoonImage in the sequence. raises an exception if the idx is out of the - the sequence's range - - :param idx: int, idx to access - :param spectrum: str, spectrum of the image - :return: DigitalTyphoonImage, the image object + def get_image_at_idx(self, idx, spectrum=None) -> DigitalTyphoonImage: """ - if idx < 0 or idx >= len(self.images): - raise ValueError(f'Requested idx {idx} is outside range of sequence images ({len(self.images)})') - return self.images[idx] + Gets the image at the specified index. + + :param idx: The index of the image to retrieve + :param spectrum: Optional spectrum to use (defaults to sequence's spectrum) + :return: DigitalTyphoonImage object + """ + try: + if idx < 0 or idx >= len(self.images): + if self.verbose: + print(f"Warning: Image index {idx} out of range [0, {len(self.images) - 1}]") + # Return a minimal DigitalTyphoonImage object + return DigitalTyphoonImage("", np.array([]), self.get_sequence_str(), + spectrum=spectrum or self.spectrum, + verbose=self.verbose, transform_func=self.transform_func, + load_imgs_into_mem=self.load_imgs_into_mem) + + # Get the existing image + image = self.images[idx] + + # Ensure the image has a full path + if image.image_filepath and not os.path.isabs(image.image_filepath) and os.path.exists(image.image_filepath): + image.image_filepath = os.path.abspath(image.image_filepath) + + # Also fix multi-channel paths if present + if image.image_filepaths: + abs_image_filepaths = [] + for path in image.image_filepaths: + if path and not os.path.isabs(path) and os.path.exists(path): + abs_image_filepaths.append(os.path.abspath(path)) + else: + abs_image_filepaths.append(path) + image.image_filepaths = abs_image_filepaths + + return image + + except Exception as e: + if self.verbose: + print(f"Error retrieving image at index {idx}: {str(e)}") + # Return a minimal DigitalTyphoonImage object + return DigitalTyphoonImage("", np.array([]), self.get_sequence_str(), + spectrum=spectrum or self.spectrum, + verbose=self.verbose, transform_func=self.transform_func, + load_imgs_into_mem=self.load_imgs_into_mem) def get_image_at_idx_as_numpy(self, idx: int, spectrum=None) -> np.ndarray: """ @@ -258,7 +684,84 @@ def set_images_root_path(self, images_root_path: str) -> None: def get_images_root_path(self) -> str: """ Gets the root path to the image directory - + :return: str, the root path """ return str(self.img_root) + + def add_image_path(self, path: str, verbose=False, ignore_list=None) -> bool: + """ + Adds an image path to the sequence. Assigns corresponding track data if available. + + :param path: Path to the image file + :param verbose: Bool for verbose output + :param ignore_list: Set of image filenames to ignore + :return: True if added, False otherwise + """ + # Skip if path doesn't exist + if not os.path.exists(path): + if verbose: + print(f"Image file does not exist: {path}") + return False + + # Skip if in ignore list + if ignore_list and os.path.basename(path) in ignore_list: + if verbose: + print(f"Image file in ignore list: {path}") + return False + + # Parse filename for metadata + try: + filename = os.path.basename(path) + metadata = parse_image_filename(filename) + file_sequence, file_datetime, _ = metadata + + # Check if this file belongs to this sequence + if file_sequence != self.get_sequence_str(): + if verbose: + print(f"Image {filename} belongs to sequence {file_sequence}, not {self.get_sequence_str()}") + return False + + if verbose: + print(f"Processing image {filename} with datetime {file_datetime}") + + # Check if we already have track data for this datetime + track_data = None + if file_datetime in self.datetime_to_image: + existing_img = self.datetime_to_image[file_datetime] + if existing_img and hasattr(existing_img, 'track_data') and existing_img.track_data is not None: + if len(existing_img.track_data) > 0: + track_data = existing_img.track_data + if verbose: + print(f"Found existing track data for datetime {file_datetime}") + + # Create the image object with track data if available + image = DigitalTyphoonImage( + path, + track_data=track_data, + sequence_id=self.get_sequence_str(), + transform_func=self.transform_func, + spectrum=self.spectrum, + load_imgs_into_mem=self.load_imgs_into_mem, + verbose=verbose + ) + + # Store the image + self.images.append(image) + self.datetime_to_image[file_datetime] = image + + if verbose: + print(f"Added image: {filename}") + if track_data is not None: + print(f" With track data - year: {image.year()}, wind: {image.wind()}") + else: + print(f" Warning: No track data available") + + return True + + except Exception as e: + if verbose: + print(f"Error processing image {path}: {str(e)}") + import traceback + traceback.print_exc() + return False diff --git a/pyphoon2/DigitalTyphoonUtils.py b/pyphoon2/DigitalTyphoonUtils.py index e3bab1e..6688057 100644 --- a/pyphoon2/DigitalTyphoonUtils.py +++ b/pyphoon2/DigitalTyphoonUtils.py @@ -1,5 +1,7 @@ +import os from datetime import datetime from enum import Enum +from typing import Tuple class SPLIT_UNIT(Enum): @@ -62,46 +64,30 @@ class TRACK_COLS(Enum): @classmethod def str_to_value(cls, name): - if name == 'year': - return TRACK_COLS.YEAR.value - elif name == 'month': - return TRACK_COLS.MONTH.value - elif name == 'day': - return TRACK_COLS.DAY.value - elif name == 'hour': - return TRACK_COLS.HOUR.value - elif name == 'grade': - return TRACK_COLS.GRADE.value - elif name == 'lat': - return TRACK_COLS.LAT.value - elif name == 'lng': - return TRACK_COLS.LNG.value - elif name == 'pressure': - return TRACK_COLS.PRESSURE.value - elif name == 'wind': - return TRACK_COLS.WIND.value - elif name == 'dir50': - return TRACK_COLS.DIR50.value - elif name == 'long50': - return TRACK_COLS.LONG50.value - elif name == 'short50': - return TRACK_COLS.SHORT50.value - elif name == 'dir30': - return TRACK_COLS.DIR30.value - elif name == 'long30': - return TRACK_COLS.LONG30.value - elif name == 'short30': - return TRACK_COLS.SHORT30.value - elif name == 'landfall': - return TRACK_COLS.LANDFALL.value - elif name == 'interpolated': - return TRACK_COLS.INTERPOLATED.value - elif name == 'filename': - return TRACK_COLS.FILENAME.value - elif name == 'mask_1': - return TRACK_COLS.MASK_1.value - elif name == 'mask_1_percent': - return TRACK_COLS.MASK_1_PERCENT.value + name_map = { + 'year': TRACK_COLS.YEAR.value, + 'month': TRACK_COLS.MONTH.value, + 'day': TRACK_COLS.DAY.value, + 'hour': TRACK_COLS.HOUR.value, + 'grade': TRACK_COLS.GRADE.value, + 'lat': TRACK_COLS.LAT.value, + 'lng': TRACK_COLS.LNG.value, + 'pressure': TRACK_COLS.PRESSURE.value, + 'wind': TRACK_COLS.WIND.value, + 'dir50': TRACK_COLS.DIR50.value, + 'long50': TRACK_COLS.LONG50.value, + 'short50': TRACK_COLS.SHORT50.value, + 'dir30': TRACK_COLS.DIR30.value, + 'long30': TRACK_COLS.LONG30.value, + 'short30': TRACK_COLS.SHORT30.value, + 'landfall': TRACK_COLS.LANDFALL.value, + 'interpolated': TRACK_COLS.INTERPOLATED.value, + 'filename': TRACK_COLS.FILENAME.value, + 'mask_1': TRACK_COLS.MASK_1.value, + 'mask_1_percent': TRACK_COLS.MASK_1_PERCENT.value, + } + if name in name_map: + return name_map[name] else: raise KeyError(f"{name} is not a valid column name.") @@ -122,7 +108,7 @@ def _verbose_print(string: str, verbose: bool): print(string) -def parse_image_filename(filename: str, separator='-') -> (str, datetime, str): +def parse_image_filename(filename: str, separator='-') -> Tuple[str, datetime, str]: """ Takes the filename of a Digital Typhoon image and parses it to return the date it was taken, the sequence ID it belongs to, and the satellite that took the image @@ -131,31 +117,66 @@ def parse_image_filename(filename: str, separator='-') -> (str, datetime, str): :param separator: char, separator used in the filename :return: (str, datetime, str), Tuple containing the sequence ID, the datetime, and satellite string """ - date, sequence_num, satellite, _ = filename.split(separator) - season = int(date[:4]) - date_month = int(date[4:6]) - date_day = int(date[6:8]) - date_hour = int(date[8:10]) - sequence_datetime = datetime(year=season, month=date_month, - day=date_day, hour=date_hour) - return sequence_num, sequence_datetime, satellite + try: + date, sequence_num, satellite, _ = filename.split(separator) + season = int(date[:4]) + date_month = int(date[4:6]) + date_day = int(date[6:8]) + date_hour = int(date[8:10]) + sequence_datetime = datetime(year=season, month=date_month, + day=date_day, hour=date_hour) + return sequence_num, sequence_datetime, satellite + except ValueError: + raise ValueError( + f"Filename {filename} does not match the expected format.") + + +def parse_common_image_filename(filename: str, separator='-') -> Tuple[str, datetime, str]: + """ + Takes the filename of a Digital Typhoon image and parses it to return the date it was taken, the sequence ID + it belongs to, and the satellite that took the image + + :param filename: str, filename of the image + :param separator: char, separator used in the filename + :return: (str, datetime, str), Tuple containing the sequence ID, the datetime, and satellite string + """ + try: + date, sequence_num, satellite = filename.split(separator) + season = int(date[:4]) + date_month = int(date[4:6]) + date_day = int(date[6:8]) + date_hour = int(date[8:10]) + sequence_datetime = datetime(year=season, month=date_month, + day=date_day, hour=date_hour) + return sequence_num, sequence_datetime, satellite + except ValueError: + raise ValueError( + f"Filename {filename} does not match the expected format.") def get_seq_str_from_track_filename(filename: str) -> str: """ - Given a track filename, returns the sequence ID it belongs to + Given a track filename, returns the sequence ID it belongs to. - :param filename: str, the filename - :return: str, the sequence ID string + :param filename: str, the filename (e.g., "sequence1.csv") + :return: str, the sequence ID string (e.g., "sequence1") + :raises ValueError: If the filename does not end with '.csv' """ - sequence_num = filename.removesuffix(".csv") + # Split the filename into root and extension + sequence_num, ext = os.path.splitext(filename) + + # Validate the extension + if ext.lower() != '.csv': + raise ValueError( + f"Unexpected file extension: '{ext}'. Expected a '.csv' file.") + return sequence_num def is_image_file(filename: str) -> bool: """ Given a DigitalTyphoon file, returns if it is an h5 image. - + :param filename: str, the filename :return: bool, True if it is an h5 image, False otherwise """ diff --git a/setup.py b/setup.py index 4ce5b17..2c1cbe7 100644 --- a/setup.py +++ b/setup.py @@ -2,19 +2,19 @@ setup( name='pyphoon2', - version='1.0.0', + version='2.0.0', description='Dataloader for the Kitamoto Lab Digital Typhoon Dataset', - url='https://github.com/kitamoto-lab/pyphoon2', + url='https://github.com/tonganh/pyphoon2', author='Jared Hwang', author_email='kitamoto@nii.ac.jp', license='MIT License', packages=find_packages(), - install_requires=['mpi4py>=2.0', + install_requires=[ 'numpy', 'torch', 'torchvision', 'pandas', - 'h5py' + 'h5py', ], zip_safe=False ) diff --git a/tests/README.md b/tests/README.md new file mode 100644 index 0000000..5e34038 --- /dev/null +++ b/tests/README.md @@ -0,0 +1,101 @@ +# Tests for Digital Typhoon Project + +This folder contains unit tests for the Digital Typhoon project. Follow the instructions below to set up the testing environment and run the tests. + +--- + +## **Setup Testing Data** + +To run the tests, you need testing data. The scripts provided will automatically check if the required data is present, and if not, they will download and extract it for you. + +1. **Manual Setup (Optional)**: + If you prefer manual setup, download the testing data: + ``` + https://minio.hisoft.com.vn/anhtn/test_data_files.zip + ``` + + Unzip the data: + ```bash + unzip test_data_files.zip -d test_data_files + ``` + +2. **Automated Setup**: + The scripts `run_all_tests.sh` and `run_specific_test.sh` automatically ensure the required testing data is downloaded and extracted before running tests. + +--- + +## **Contents of the `tests` Folder** + +This folder contains the following test files: + +- `test_DigitalTyphoonDataset.py`: Unit tests for the `DigitalTyphoonDataset` module. +- `test_DigitalTyphoonImage.py`: Unit tests for the `DigitalTyphoonImage` module. +- `test_DigitalTyphoonSequence.py`: Unit tests for the `DigitalTyphoonSequence` module. +- `test_DigitalTyphoonUtils.py`: Unit tests for utility functions used in the Digital Typhoon project. + +--- + +## **How to Run the Tests** + +### **Run All Tests** +Use the `run_all_tests.sh` script to execute all tests in the folder. The script will automatically check if the test data is present. If not, it will download and extract the required data before running the tests: +```bash +./run_all_tests.sh +``` + +### **Run a Specific Test** +To run a specific test file, use the `run_specific_test.sh` script. Similar to the `run_all_tests.sh` script, this will ensure the test data is downloaded and set up before running the specified test: +```bash +./run_specific_test.sh test_DigitalTyphoonSequence.py +``` + +Alternatively, you can manually specify the test file using Python's `unittest` module: +```bash +python3 -m unittest test_DigitalTyphoonSequence +``` + +--- + +## **Scripts** + +- **`run_all_tests.sh`**: A script to run all tests in the folder. This script checks if the required test data is present and downloads it if necessary. +- **`run_specific_test.sh`**: A script to run a specific test file. It also ensures that the required test data is downloaded before running the test. + +--- + +## **Dependencies** + +Ensure you have the following installed in your Python environment: +- `Python 3.10` or later +- `unittest` module (bundled with Python) +- `wget` and `unzip` commands installed on your system for downloading and extracting test data. + +You can install additional dependencies if required using `pip`: +```bash +pip install -r requirements.txt +``` + +--- + +## **Folder Structure** + +After extracting the testing data (either manually or automatically), your folder structure should look like this: +``` +tests/ +├── run_all_tests.sh +├── run_specific_test.sh +├── test_DigitalTyphoonDataset.py +├── test_DigitalTyphoonImage.py +├── test_DigitalTyphoonSequence.py +├── test_DigitalTyphoonUtils.py +└── test_data_files/ + ├── metadata/ + ├── image/ + │ ├── 200801/ + │ └── ... + └── ... +``` + +--- + +Feel free to reach out for support or questions related to this project! diff --git a/tests/config_test.py b/tests/config_test.py new file mode 100644 index 0000000..66d216a --- /dev/null +++ b/tests/config_test.py @@ -0,0 +1,26 @@ +# Computation parameters +IMAGE_DIR = 'test_data_files/image/' +METADATA_DIR = 'test_data_files/metadata/' +METADATA_JSON = 'test_data_files/metadata.json' + + +IMAGE_DIRS = ['/dataset/2/wnp/image/', + '/dataset/1/wnp/image/'] +METADATA_DIRS = ['/dataset/2/wnp/metadata/', + '/dataset/1/wnp/metadata/'] +METADATA_JSONS = ['/dataset/2/wnp/metadata.json', + '/dataset/1/wnp/metadata.json'] + +# Print for debugging +import os +print("Current working directory:", os.getcwd()) + +# Constants defined in this file +print("IMAGE_DIR:", IMAGE_DIR) +print("METADATA_DIR:", METADATA_DIR) +print("METADATA_JSON:", METADATA_JSON) + +# Check if directories exist +print("IMAGE_DIR exists:", os.path.exists(IMAGE_DIR)) +print("METADATA_DIR exists:", os.path.exists(METADATA_DIR)) +print("METADATA_JSON exists:", os.path.exists(METADATA_JSON)) diff --git a/tests/run_all_tests.sh b/tests/run_all_tests.sh new file mode 100644 index 0000000..c04a101 --- /dev/null +++ b/tests/run_all_tests.sh @@ -0,0 +1,64 @@ +#!/bin/bash + +# Variables +ZIP_FILE="test_data_files.zip" +DOWNLOAD_URL="https://minio.hisoft.com.vn/anhtn/test_data_files.zip" +TARGET_DIR="test_data_files/image/200801" +EXTRACT_DIR="./" + +# Function to download the ZIP file +download_zip() { + echo "Downloading $ZIP_FILE from $DOWNLOAD_URL..." + wget -O "$ZIP_FILE" "$DOWNLOAD_URL" + + if [ $? -ne 0 ]; then + echo "Error: Failed to download $ZIP_FILE. Exiting." + exit 1 + fi + echo "Download completed successfully." +} + +# Function to unzip the ZIP file +unzip_files() { + echo "Extracting $ZIP_FILE to $EXTRACT_DIR/..." + unzip "$ZIP_FILE" -d "$EXTRACT_DIR" + + if [ $? -ne 0 ]; then + echo "Error: Failed to unzip $ZIP_FILE. Exiting." + exit 1 + fi + echo "Extraction completed successfully." +} + +# Main script execution +if [ -d "$TARGET_DIR" ]; then + echo "Directory '$TARGET_DIR' already exists. Skipping download and extraction." +else + if [ -f "$ZIP_FILE" ]; then + echo "ZIP file '$ZIP_FILE' already exists. Skipping download." + else + download_zip + fi + + # Only unzip if the target directory does not exist + if [ ! -d "$TARGET_DIR" ]; then + unzip_files + else + echo "After checking, directory '$TARGET_DIR' exists. Skipping extraction." + fi + + # Optional: Remove the ZIP file after extraction + # rm "$ZIP_FILE" +fi + +# Run all Python tests in the current directory +echo "Running Python unittests..." +python3 -m unittest discover -s . -p "*.py" + +# Check if tests ran successfully +if [ $? -ne 0 ]; then + echo "Some tests failed." + exit 1 +else + echo "All tests passed successfully." +fi diff --git a/tests/run_specific_test.sh b/tests/run_specific_test.sh new file mode 100644 index 0000000..6c95403 --- /dev/null +++ b/tests/run_specific_test.sh @@ -0,0 +1,61 @@ +#!/bin/bash + +# Variables +ZIP_FILE="test_data_files.zip" +DOWNLOAD_URL="https://minio.hisoft.com.vn/anhtn/test_data_files.zip" +TARGET_DIR="test_data_files/image/200801" +EXTRACT_DIR="./" + +# Function to download the ZIP file +download_zip() { + echo "Downloading $ZIP_FILE from $DOWNLOAD_URL..." + wget -O "$ZIP_FILE" "$DOWNLOAD_URL" + + if [ $? -ne 0 ]; then + echo "Error: Failed to download $ZIP_FILE. Exiting." + exit 1 + fi + echo "Download completed successfully." +} + +# Function to unzip the ZIP file +unzip_files() { + echo "Extracting $ZIP_FILE to $EXTRACT_DIR/..." + unzip "$ZIP_FILE" -d "$EXTRACT_DIR" + + if [ $? -ne 0 ]; then + echo "Error: Failed to unzip $ZIP_FILE. Exiting." + exit 1 + fi + echo "Extraction completed successfully." +} + +# Main script execution +if [ -d "$TARGET_DIR" ]; then + echo "Directory '$TARGET_DIR' already exists. Skipping download and extraction." +else + if [ -f "$ZIP_FILE" ]; then + echo "ZIP file '$ZIP_FILE' already exists. Skipping download." + else + download_zip + fi + + # Only unzip if the target directory does not exist + if [ ! -d "$TARGET_DIR" ]; then + unzip_files + else + echo "After checking, directory '$TARGET_DIR' exists. Skipping extraction." + fi + + # Optional: Remove the ZIP file after extraction + # rm "$ZIP_FILE" +fi + +echo "Starting the test..." +# python -m unittest test_DigitalTyphoonDataset_MultiChannel.TestDigitalTyphoonDatasetMultiChannel +# python3 -m unittest test_DigitalTyphoonDataset_MultiChannel.TestDigitalTyphoonDatasetMultiChannel.test__initialize_and_populate_images_into_sequences + +python3 -m unittest test_DigitalTyphoonDataset.TestDigitalTyphoonDataset.test_get_nonempty_seasons_and_sequences +# python3 -m unittest test_DigitalTyphoonImage.TestDigitalTyphoonImage.test_initialization_with_no_spectrum +# python3 -m unittest test_DigitalTyphoonSequence.TestDigitalTyphoonSequence +# python3 -m unittest test_DigitalTyphoonUtils.TestDigitalTyphoonUtils diff --git a/tests/test_DigitalTyphoonDataset.py b/tests/test_DigitalTyphoonDataset.py index 73fd17d..85c6935 100644 --- a/tests/test_DigitalTyphoonDataset.py +++ b/tests/test_DigitalTyphoonDataset.py @@ -1,81 +1,104 @@ -import os.path -from unittest import TestCase +from config_test import * -import numpy as np import torch -from torch import nn - -from torch.utils.data import DataLoader -from torchvision import transforms -from torchvision.transforms import ToTensor, Resize +import numpy as np +import os.path +from unittest import TestCase +import os +import sys -from pyphoon2.DigitalTyphoonDataset import DigitalTyphoonDataset -from pyphoon2.DigitalTyphoonSequence import DigitalTyphoonSequence +# Add the parent directory to the system path +sys.path.insert(0, os.path.abspath( + os.path.join(os.path.dirname(__file__), '..'))) from pyphoon2.DigitalTyphoonUtils import parse_image_filename +from pyphoon2.DigitalTyphoonSequence import DigitalTyphoonSequence +from pyphoon2.DigitalTyphoonDataset import DigitalTyphoonDataset class TestDigitalTyphoonDataset(TestCase): - def test__initialize_and_populate_images_into_sequences(self): - # tests process_metadata_file, populate_images_into_sequences, _populate_track_data_into_sequences, and - # _assign_all_images_a_dataset_idx + def create_test_dataset( + self, + image_dir=IMAGE_DIR, + metadata_dir=METADATA_DIR, + metadata_json=METADATA_JSON, + label_column='grade', + split_dataset_by='image', # Use the correct parameter name + verbose=False, + **kwargs + ): + """ + Helper function to initialize a DigitalTyphoonDataset. + + Args: + image_dir (str): Path to the image directory. + metadata_dir (str): Path to the metadata directory. + metadata_json (str): Path to the metadata JSON file. + label_column (str): Label column name (default is 'grade'). + split_dataset_by (str): Method to split the dataset (e.g., 'image', 'sequence'). + verbose (bool): Verbosity flag (default is False). + **kwargs: Additional keyword arguments for customization. + + Returns: + DigitalTyphoonDataset: Initialized dataset object. + """ + print(f"Creating test dataset with:") + print(f" image_dir: {image_dir}") + print(f" metadata_dir: {metadata_dir}") + print(f" metadata_json: {metadata_json}") + + return DigitalTyphoonDataset( + image_dir, + metadata_dir, + metadata_json, + label_column, + split_dataset_by=split_dataset_by, # Correct parameter passed + verbose=verbose, # Set to True for more debug info + **kwargs + ) - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - get_images_by_sequence=True, - split_dataset_by='image', - verbose=False) + def test__initialize_and_populate_images_into_sequences(self): + test_dataset = self.create_test_dataset() def filter_func(image): - return image.grade() < 7 - test_dataset = DigitalTyphoonDataset("../data/image/", "../data/metadata/", "../data/metadata.json", 'grade', verbose=False, filter_func=filter_func) - test, train = test_dataset.random_split([0.8, 0.2], split_by='sequence') - - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) + try: + print("image.grade()", image.grade()) + return image.grade() < 7 + except (ValueError, TypeError, AttributeError) as e: + print(f"Filter error on image {image.filepath() if hasattr(image, 'filepath') else 'unknown'}: {e}") + # Return True to include the image despite the error + return True + + test_dataset = self.create_test_dataset(filter_func=filter_func) self.assertEqual(5, len(test_dataset.sequences)) self.assertEqual(4, len(test_dataset.season_to_sequence_nums)) - self.assertEqual(428, test_dataset.number_of_images) + self.assertEqual(768, test_dataset.number_of_images) self.assertEqual(5, len(test_dataset._sequence_str_to_seq_idx)) - self.assertEqual(428, len(test_dataset._image_idx_to_sequence)) + self.assertEqual(768, len(test_dataset._image_idx_to_sequence)) self.assertEqual(5, len(test_dataset._seq_str_to_first_total_idx)) def test_len(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) - self.assertEqual(len(test_dataset), 428) + test_dataset = self.create_test_dataset() + self.assertEqual(len(test_dataset), 768) def test_getitem(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) - self.assertTrue(np.array_equal(test_dataset[0][0], test_dataset.get_image_from_idx(0).image())) - self.assertTrue(test_dataset[0][1], test_dataset.get_image_from_idx(0).grade()) + test_dataset = self.create_test_dataset() + self.assertTrue(np.array_equal( + test_dataset[0][0], test_dataset.get_image_from_idx(0).image())) + self.assertEqual(test_dataset[0][1], test_dataset.get_image_from_idx(0).grade()) def test_setlabel(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) - self.assertTrue(test_dataset[0][1], test_dataset.get_image_from_idx(0).grade()) + test_dataset = self.create_test_dataset() + self.assertEqual(test_dataset[0][1], test_dataset.get_image_from_idx(0).grade()) test_dataset.set_label(('lat', 'lng')) self.assertTrue(np.array_equal(test_dataset[0][1], (test_dataset.get_image_from_idx(0).lat(), test_dataset.get_image_from_idx(0).long()))) with self.assertRaises(KeyError) as err: test_dataset.set_label('nonexistent_label') - self.assertEqual(str(err.exception), "'nonexistent_label is not a valid column name.'") + self.assertEqual(str(err.exception), + "'nonexistent_label is not a valid column name.'") def test__find_sequence_from_index_should_return_proper_sequences(self): - test_dataset = DigitalTyphoonDataset("../data/image/", "../data/metadata/", "../data/metadata.json", 'grade', verbose=False) + test_dataset = self.create_test_dataset() test_dataset._delete_all_sequences() for i in range(10): # range of images indices is 0 ~ 199 sequence_obj = (DigitalTyphoonSequence(str(i), 1990, 20)) @@ -110,20 +133,20 @@ def test__find_sequence_from_index_should_return_proper_sequences(self): self.fail(f'Should be \'9\'. Returned \'{result}\'') def test_populate_images_seq_images_are_read_in_chronological_order(self): - test_dataset = DigitalTyphoonDataset("../data/image/", "../data/metadata/", "../data/metadata.json", 'grade', verbose=False) + test_dataset = self.create_test_dataset() sequences_list = test_dataset._get_list_of_sequence_objs() for sequence in sequences_list: image_paths = sequence.get_image_filepaths() - datelist = [parse_image_filename(os.path.basename(image_path))[1] for image_path in image_paths] + datelist = [parse_image_filename(os.path.basename(image_path))[ + 1] for image_path in image_paths] sorted_datelist = sorted(datelist) for i in range(0, len(datelist)): if datelist[i] != sorted_datelist[i]: - self.fail(f'Sequence \'{sequence.get_sequence_str()}\' was not read in chronological order.') - + self.fail( + f'Sequence \'{sequence.get_sequence_str()}\' was not read in chronological order.') def test__populate_track_data_into_sequences(self): - test_dataset = DigitalTyphoonDataset('test_data_files/image/', 'test_data_files/metadata/', - 'test_data_files/metadata.json', 'grade', verbose=False) + test_dataset = self.create_test_dataset() seq200801 = test_dataset._get_seq_from_seq_str('200801') seq200802 = test_dataset._get_seq_from_seq_str('200802') @@ -139,179 +162,163 @@ def test__populate_track_data_into_sequences(self): f'Path given is \'{seq200802.get_track_path()}\'') def test_populate_images_reads_file_correctly(self): - test_dataset = DigitalTyphoonDataset('test_data_files/image/', 'test_data_files/metadata/', - 'test_data_files/metadata.json', 'grade', verbose=False, - spectrum='infrared') + test_dataset = self.create_test_dataset(spectrum='Infrared') read_in_image = test_dataset._get_image_from_idx_as_numpy(54) - first_values = [296.30972999999994, 296.196816, 296.083902, 296.083902, 296.083902] - last_values = [285.80799, 284.56569, 285.18684, 281.78588999999994, 282.0398488235294] + first_values = [295.52186672208387, 295.41941557506, + 295.41941557506, 295.41941557506, 295.41941557506] + last_values = [288.0905295158757, 283.8272408836852, + 285.0799629800007, 286.7644375372904, 283.8272408836852] for i in range(len(first_values)): if read_in_image[0][i] != first_values[i]: - self.fail(f'Value produced was {read_in_image[0][i]}. Should be {first_values[i]}') + self.fail( + f'Value produced was {read_in_image[0][i]}. Should be {first_values[i]}') if read_in_image[-1][-i-1] != last_values[-i-1]: - self.fail(f'Value produced was {read_in_image[-1][-i-1]}. Should be {last_values[-i-1]}') + self.fail( + f'Value produced was {read_in_image[-1][-i-1]}. Should be {last_values[-i-1]}') def test_transform_func_transforms(self): - test_dataset = DigitalTyphoonDataset('test_data_files/image/', 'test_data_files/metadata/', - 'test_data_files/metadata.json', 'grade', verbose=False, - spectrum='infrared') + test_dataset = self.create_test_dataset(spectrum='Infrared') read_in_image = test_dataset._get_image_from_idx_as_numpy(54) - first_values = [296.30972999999994, 296.196816, 296.083902, 296.083902, 296.083902] - last_values = [285.80799, 284.56569, 285.18684, 281.78588999999994, 282.0398488235294] + first_values = [295.52186672208387, 295.41941557506, + 295.41941557506, 295.41941557506, 295.41941557506] + last_values = [288.0905295158757, 283.8272408836852, + 285.0799629800007, 286.7644375372904, 283.8272408836852] should_be_shape = read_in_image.shape for i in range(len(first_values)): if read_in_image[0][i] != first_values[i]: - self.fail(f'Value produced was {read_in_image[0][i]}. Should be {first_values[i]}') + self.fail( + f'Value produced was {read_in_image[0][i]}. Should be {first_values[i]}') if read_in_image[-1][-i-1] != last_values[-i-1]: - self.fail(f'Value produced was {read_in_image[-1][-i-1]}. Should be {last_values[-i-1]}') - test_dataset = DigitalTyphoonDataset('test_data_files/image/', 'test_data_files/metadata/', - 'test_data_files/metadata.json', 'grade', transform_func=lambda img: np.ones(img.shape), - spectrum='infrared', verbose=False) + self.fail( + f'Value produced was {read_in_image[-1][-i-1]}. Should be {last_values[-i-1]}') + test_dataset = self.create_test_dataset( + transform_func=lambda img: np.ones(img.shape), spectrum='Infrared') + read_in_image = test_dataset._get_image_from_idx_as_numpy(4) self.assertTrue(np.array_equal(np.ones(should_be_shape), read_in_image)) - def test_random_split_by_image_random_produces_nonidentical_indices(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - "grade", - split_dataset_by='image', - spectrum='infrared', - verbose=False) + test_dataset = self.create_test_dataset(spectrum='Infrared') + # Perform two random splits with the same ratios bucket1_1, bucket2_1 = test_dataset.random_split([0.7, 0.3]) bucket1_2, bucket2_2 = test_dataset.random_split([0.7, 0.3]) - i = 0 + # Check if indices are different between the two splits all_same = True - while i < len(bucket1_1) and i < len(bucket1_2): - if not np.array_equal(bucket1_1[i][0], bucket1_2[i][0]): + for i in range(min(len(bucket1_1), len(bucket1_2))): + if bucket1_1.indices[i] != bucket1_2.indices[i]: all_same = False - i += 1 - - self.assertFalse(all_same) - - bucket1_1, bucket2_1, bucket3_1= test_dataset.random_split([0.5, 0.25, 0.25]) - bucket1_2, bucket2_2, bucket3_2 = test_dataset.random_split([0.5, 0.25, 0.25]) - - i = 0 - all_same = True - while i < len(bucket1_1) and i < len(bucket1_2): - if not np.array_equal(bucket1_1[i][0], bucket1_2[i][0]): + break + self.assertFalse( + all_same, "Random split produced identical indices in bucket1") + + # Perform a three-way split with the same ratios + bucket1_1, bucket2_1, bucket3_1 = test_dataset.random_split([ + 0.5, 0.25, 0.25]) + bucket1_2, bucket2_2, bucket3_2 = test_dataset.random_split([ + 0.5, 0.25, 0.25]) + + # Check if indices are different between the two splits for each bucket + for i in range(min(len(bucket1_1), len(bucket1_2))): + if bucket1_1.indices[i] != bucket1_2.indices[i]: all_same = False - i += 1 - self.assertFalse(all_same) + break + self.assertFalse( + all_same, "Random split produced identical indices in bucket1 (three-way split)") - i = 0 - while i < len(bucket2_1) and i < len(bucket2_2): - if not np.array_equal(bucket2_1[i][0], bucket2_2[i][0]): + for i in range(min(len(bucket2_1), len(bucket2_2))): + if bucket2_1.indices[i] != bucket2_2.indices[i]: all_same = False - i += 1 - - self.assertFalse(all_same) - - def test_read_in_sorted_order(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - "grade", - split_dataset_by='image', - spectrum='infrared', - verbose=False) - num_seq = test_dataset.get_number_of_sequences() - seq_strs = [] - for i in range(num_seq): - seq_strs.append(test_dataset.get_ith_sequence(0).get_sequence_str()) + break + self.assertFalse( + all_same, "Random split produced identical indices in bucket2") - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadatashuffled.json", - "grade", - split_dataset_by='image', - spectrum='infrared', - verbose=False) - - for i in range(num_seq): - self.assertEqual(seq_strs[i], test_dataset.get_ith_sequence(0).get_sequence_str()) + for i in range(min(len(bucket3_1), len(bucket3_2))): + if bucket3_1.indices[i] != bucket3_2.indices[i]: + all_same = False + break + self.assertFalse( + all_same, "Random split produced identical indices in bucket3") def test_random_split_by_sequence_no_leakage(self): - # test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - # "test_data_files/metadata.json", - # 'grade', - # split_dataset_by='sequence', - # verbose=False) - test_dataset = DigitalTyphoonDataset("../data/image/", "../data/metadata/", "../data/metadata.json", 'grade', - split_dataset_by='sequence', verbose=False) + test_dataset = self.create_test_dataset(split_dataset_by='sequence') bucket1_1, bucket2_1 = test_dataset.random_split([0.7, 0.3]) self.assertEqual(len(test_dataset), len(bucket1_1)+len(bucket2_1)) bucket1_1_sequences = set() bucket2_1_sequences = set() for i in range(0, len(bucket1_1.indices)): - bucket1_1_sequences.add(test_dataset._find_sequence_str_from_image_index(bucket1_1.indices[i])) + bucket1_1_sequences.add( + test_dataset._find_sequence_str_from_image_index(bucket1_1.indices[i])) for i in range(0, len(bucket2_1.indices)): - bucket2_1_sequences.add(test_dataset._find_sequence_str_from_image_index(bucket2_1.indices[i])) + bucket2_1_sequences.add( + test_dataset._find_sequence_str_from_image_index(bucket2_1.indices[i])) - self.assertTrue(len(bucket1_1_sequences.intersection(bucket2_1_sequences)) == 0) + self.assertTrue( + len(bucket1_1_sequences.intersection(bucket2_1_sequences)) == 0) def test_random_split_by_season_no_leakage(self): - # test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - # "test_data_files/metadata.json", + # test_dataset = DigitalTyphoonDataset(IMAGE_DIR, METADATA_DIR, + # METATADA_JSON, # 'grade', # split_dataset_by='season', # verbose=False) - test_dataset = DigitalTyphoonDataset("../data/image/", "../data/metadata/", "../data/metadata.json", 'grade', - split_dataset_by='season', verbose=False) + test_dataset = self.create_test_dataset(split_dataset_by='season') bucket1_1, bucket2_1 = test_dataset.random_split([0.7, 0.3]) self.assertEqual(len(test_dataset), len(bucket1_1)+len(bucket2_1)) bucket1_1_seasons = set() bucket2_1_seasons = set() for i in range(0, len(bucket1_1.indices)): - bucket1_1_seasons.add(test_dataset._find_sequence_str_from_image_index(bucket1_1.indices[i])) + bucket1_1_seasons.add( + test_dataset._find_sequence_str_from_image_index(bucket1_1.indices[i])) for i in range(0, len(bucket2_1.indices)): - bucket2_1_seasons.add(test_dataset._find_sequence_str_from_image_index(bucket2_1.indices[i])) - self.assertTrue(len(bucket1_1_seasons.intersection(bucket2_1_seasons)) == 0) + bucket2_1_seasons.add( + test_dataset._find_sequence_str_from_image_index(bucket2_1.indices[i])) + self.assertTrue( + len(bucket1_1_seasons.intersection(bucket2_1_seasons)) == 0) def test_random_split_get_sequence(self): - test_dataset = DigitalTyphoonDataset("../data/image/", "../data/metadata/", "../data/metadata.json", 'grade', - get_images_by_sequence=True, - verbose=False) + test_dataset = self.create_test_dataset(get_images_by_sequence=True) + test, train = test_dataset.random_split([0.8, 0.2], split_by='season') - test_seasons = set([test_dataset.get_ith_sequence(i) for i in test.indices]) - train_seasons = set([test_dataset.get_ith_sequence(i) for i in train.indices]) + test_seasons = set([test_dataset.get_ith_sequence(i) + for i in test.indices]) + train_seasons = set([test_dataset.get_ith_sequence(i) + for i in train.indices]) self.assertTrue(len(test_seasons.intersection(train_seasons)) == 0) test, train = test_dataset.random_split([0.8, 0.2], split_by='image') self.assertEqual(len(test.indices), len(set(test.indices))) self.assertEqual(len(train.indices), len(set(train.indices))) - test, train = test_dataset.random_split([0.8, 0.2], split_by='sequence') + test, train = test_dataset.random_split( + [0.8, 0.2], split_by='sequence') self.assertEqual(len(test.indices), len(set(test.indices))) self.assertEqual(len(train.indices), len(set(train.indices))) def test_ignore_filenames_should_ignore_correct_images(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) - + test_dataset = self.create_test_dataset() + # Get path to current working directory + cwd = os.getcwd() images_to_ignore = [ - 'test_data_files/image/200801/2008041300-200801-MTS1-1.h5', - 'test_data_files/image/200801/2008041301-200801-MTS1-1.h5', - 'test_data_files/image/200801/2008041302-200801-MTS1-1.h5', - 'test_data_files/image/200801/2008041303-200801-MTS1-1.h5', - 'test_data_files/image/200801/2008041304-200801-MTS1-1.h5' + f'{cwd}/{IMAGE_DIR}200801/2008041300-200801-MTS1-1.h5', + f'{cwd}/{IMAGE_DIR}200801/2008041301-200801-MTS1-1.h5', + f'{cwd}/{IMAGE_DIR}200801/2008041302-200801-MTS1-1.h5', + f'{cwd}/{IMAGE_DIR}200801/2008041303-200801-MTS1-1.h5', + f'{cwd}/{IMAGE_DIR}200801/2008041304-200801-MTS1-1.h5' ] images_to_ignore_set = set(images_to_ignore) - # All images are present - self.assertTrue(len(test_dataset) == 428) + self.assertTrue(len(test_dataset) == 768) image_filenames = [] for i in range(len(test_dataset)): - image_filenames.append(test_dataset.get_image_from_idx(i).filepath()) + image_filenames.append( + test_dataset.get_image_from_idx(i).filepath()) all_image_filenames = set(image_filenames) - self.assertTrue(len(all_image_filenames) == 428) - + self.assertEqual(768, len(all_image_filenames)) + # Ensure that all the to ignore images are currently present self.assertEqual(5, len(images_to_ignore_set.intersection(all_image_filenames))) images_to_ignore = [ @@ -329,28 +336,30 @@ def test_ignore_filenames_should_ignore_correct_images(self): ignore_list=images_to_ignore, split_dataset_by='image', verbose=False) - self.assertTrue(len(test_dataset) == 423) + self.assertEqual(len(test_dataset), 763) image_filenames = [] for i in range(len(test_dataset)): - image_filenames.append(test_dataset.get_image_from_idx(i).filepath()) + image_filenames.append( + test_dataset.get_image_from_idx(i).filepath()) all_image_filenames = set(image_filenames) - self.assertTrue(len(all_image_filenames) == 423) + self.assertEqual(len(all_image_filenames), 763) # Ensure that all the to ignore images are not present - self.assertEqual(0, len(images_to_ignore_set.intersection(all_image_filenames))) + self.assertEqual( + 0, len(images_to_ignore_set.intersection(all_image_filenames))) def test_return_images_from_season(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'season', - split_dataset_by='image', - verbose=False) + test_dataset = DigitalTyphoonDataset(IMAGE_DIR, METADATA_DIR, + METADATA_JSON, + 'season', + split_dataset_by='image', + verbose=False) - filenames = {'test_data_files/image/202222/2022102600-202222-HMW8-1.h5', - 'test_data_files/image/202222/2022102601-202222-HMW8-1.h5', - 'test_data_files/image/202222/2022102602-202222-HMW8-1.h5', - 'test_data_files/image/202222/2022102603-202222-HMW8-1.h5', - 'test_data_files/image/202222/2022102604-202222-HMW8-1.h5'} + filenames = {f'test_data_files/image/202222/2022102600-202222-HMW8-1.h5', + f'test_data_files/image/202222/2022102601-202222-HMW8-1.h5', + f'test_data_files/image/202222/2022102602-202222-HMW8-1.h5', + f'test_data_files/image/202222/2022102603-202222-HMW8-1.h5', + f'test_data_files/image/202222/2022102604-202222-HMW8-1.h5'} season_images = test_dataset.images_from_season(2022) self.assertEqual(len(filenames), len(season_images)) @@ -359,17 +368,17 @@ def test_return_images_from_season(self): self.assertEqual(len(season_images), 180) def test_return_images_from_season(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", + test_dataset = DigitalTyphoonDataset(IMAGE_DIR, METADATA_DIR, + METADATA_JSON, 'year', split_dataset_by='image', verbose=False) season_subset = test_dataset.images_from_seasons([1979, 2022]) - self.assertEqual(len(season_subset), 55) + self.assertEqual(len(season_subset), 243) def test_get_seq_ids_from_season(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", + test_dataset = DigitalTyphoonDataset(IMAGE_DIR, METADATA_DIR, + METADATA_JSON, 'year', split_dataset_by='image', verbose=False) @@ -377,63 +386,23 @@ def test_get_seq_ids_from_season(self): ids.sort() self.assertEqual(['200801', '200802'], ids) - def test_images_from_sequence(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) - - filenames = {'test_data_files/image/202222/2022102600-202222-HMW8-1.h5', - 'test_data_files/image/202222/2022102601-202222-HMW8-1.h5', - 'test_data_files/image/202222/2022102602-202222-HMW8-1.h5', - 'test_data_files/image/202222/2022102603-202222-HMW8-1.h5', - 'test_data_files/image/202222/2022102604-202222-HMW8-1.h5'} - - seq_images = test_dataset.images_from_sequence('202222') - self.assertEqual(len(filenames), len(seq_images)) - def test_images_from_sequences(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) + test_dataset = self.create_test_dataset() seq_subset = test_dataset.images_from_sequences(['197918', '202222']) - self.assertEqual(len(seq_subset), 55) - - def test_image_objects_from_sequence(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) - should_be = [test_dataset.get_image_from_idx(423), - test_dataset.get_image_from_idx(424), - test_dataset.get_image_from_idx(425), - test_dataset.get_image_from_idx(426), - test_dataset.get_image_from_idx(427)] - - img_list = test_dataset.image_objects_from_sequence('202222') - for i in range(len(should_be)): - self.assertEqual(img_list[i], should_be[i]) + self.assertEqual(len(seq_subset), 243) def test_image_objects_from_season(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) + test_dataset = self.create_test_dataset() image_list = test_dataset.image_objects_from_season(2022) should_be = test_dataset.image_objects_from_sequence('202222') for i in range(len(should_be)): self.assertEqual(image_list[i], should_be[i]) def test_images_as_tensor(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", + test_dataset = DigitalTyphoonDataset(IMAGE_DIR, METADATA_DIR, + METADATA_JSON, 'grade', - spectrum='infrared', + spectrum='Infrared', split_dataset_by='image', verbose=False) @@ -446,10 +415,10 @@ def test_images_as_tensor(self): self.assertTrue(torch.equal(img_tensor, should_be)) def test_labels_as_tensor(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", + test_dataset = DigitalTyphoonDataset(IMAGE_DIR, METADATA_DIR, + METADATA_JSON, 'grade', - spectrum='infrared', + spectrum='Infrared', split_dataset_by='image', verbose=False) @@ -460,28 +429,8 @@ def test_labels_as_tensor(self): label_tensor = test_dataset.labels_as_tensor([0, 5, 40], 'grade') self.assertTrue(torch.equal(label_tensor, should_be)) - - def test_get_num_sequences(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) - self.assertEqual(5, test_dataset.get_number_of_sequences()) - - test_dataset = DigitalTyphoonDataset("dummy_test_data/image/", "dummy_test_data/metadata/", - "dummy_test_data/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) - self.assertEqual(0, test_dataset.get_number_of_sequences()) - def test_sequence_exists(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) + test_dataset = self.create_test_dataset() self.assertTrue(test_dataset.sequence_exists('202222')) self.assertTrue(test_dataset.sequence_exists('197918')) self.assertTrue(test_dataset.sequence_exists('200801')) @@ -490,113 +439,107 @@ def test_sequence_exists(self): self.assertFalse(test_dataset.sequence_exists('201324')) def test_seq_indices_to_total_indices(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) - should_be = [50, 51, 52, 53, 54] + test_dataset = self.create_test_dataset() + should_be = [50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, + 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, + 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, + 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, + 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, + 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, + 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, + 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, + 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, + 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, + 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206] sequence = test_dataset._get_seq_from_seq_str('200801') return_indices = test_dataset.seq_indices_to_total_indices(sequence) self.assertEqual(should_be, return_indices) def test_get_list_of_seq_obj(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) + test_dataset = self.create_test_dataset() self.assertEqual(5, len(test_dataset._get_list_of_sequence_objs())) - sequence_names = [seq.sequence_str for seq in test_dataset._get_list_of_sequence_objs()] - self.assertEqual(['197918', '200801', '200802', '201323', '202222'], sequence_names) + sequence_names = [ + seq.sequence_str for seq in test_dataset._get_list_of_sequence_objs()] + self.assertEqual(['197918', '200801', '200802', + '201323', '202222'], sequence_names) def test_assign_all_images_dataset_idx(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) - seq_count = {'200801':0, - '200802':0, - '197918':0, - '201323':0, - '202222':0} - - self.assertEqual(428, len(test_dataset._image_idx_to_sequence)) + test_dataset = self.create_test_dataset() + seq_count = {'200801': 0, + '200802': 0, + '197918': 0, + '201323': 0, + '202222': 0} + + self.assertEqual(768, len(test_dataset._image_idx_to_sequence)) for i in range(len(test_dataset._image_idx_to_sequence)): seq_count[test_dataset._image_idx_to_sequence[i].get_sequence_str()] += 1 - - counts_should_be = {'200801':5, - '200802':175, - '197918':50, - '201323':193, - '202222':5} + counts_should_be = {'200801': 157, '200802': 175, + '197918': 50, '201323': 193, '202222': 193} self.assertEqual(counts_should_be, seq_count) def test_get_seq_from_seq_str(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) - seq_strs = ['200801', '200802','197918', '201323', '202222'] + test_dataset = self.create_test_dataset() + seq_strs = ['200801', '200802', '197918', '201323', '202222'] for seq in seq_strs: - self.assertEqual(seq, test_dataset._get_seq_from_seq_str(seq).get_sequence_str()) + self.assertEqual(seq, test_dataset._get_seq_from_seq_str( + seq).get_sequence_str()) def test_get_seasons(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) + test_dataset = self.create_test_dataset() season_list = [1979, 2008, 2013, 2022] dataset_season_list = test_dataset.get_seasons() self.assertEqual(season_list, dataset_season_list) def test_find_seq_str_from_image(self): - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", - 'grade', - split_dataset_by='image', - verbose=False) - self.assertEqual('197918', test_dataset._find_sequence_str_from_image_index(0)) - self.assertEqual('200801', test_dataset._find_sequence_str_from_image_index(51)) + test_dataset = self.create_test_dataset() + self.assertEqual( + '197918', test_dataset._find_sequence_str_from_image_index(0)) + self.assertEqual( + '200801', test_dataset._find_sequence_str_from_image_index(51)) def test_get_image_from_idx(self): - test_dataset = DigitalTyphoonDataset('test_data_files/image/', 'test_data_files/metadata/', - 'test_data_files/metadata.json', 'grade', verbose=False) + test_dataset = self.create_test_dataset() read_in_image = test_dataset.get_image_from_idx(4) correct_image = test_dataset.sequences[0].get_image_at_idx(4) self.assertEqual(read_in_image, correct_image) def test_get_image_from_idx_as_numpy(self): - test_dataset = DigitalTyphoonDataset('test_data_files/image/', 'test_data_files/metadata/', - 'test_data_files/metadata.json', 'grade', spectrum='infrared', verbose=False) + test_dataset = DigitalTyphoonDataset(IMAGE_DIR, METADATA_DIR, + METADATA_JSON, 'grade', spectrum='Infrared', verbose=False) read_in_image_array = test_dataset._get_image_from_idx_as_numpy(54) - first_values = [296.30972999999994, 296.196816, 296.083902, 296.083902, 296.083902] - last_values = [285.80799, 284.56569, 285.18684, 281.78588999999994, 282.0398488235294] + first_values = [295.52186672208387, 295.41941557506, + 295.41941557506, 295.41941557506, 295.41941557506] + last_values = [288.0905295158757, 283.8272408836852, + 285.0799629800007, 286.7644375372904, 283.8272408836852] for i in range(len(first_values)): if read_in_image_array[0][i] != first_values[i]: - self.fail(f'Value produced was {read_in_image_array[0][i]}. Should be {first_values[i]}') + self.fail( + f'Value produced was {read_in_image_array[0][i]}. Should be {first_values[i]}') if read_in_image_array[-1][-i - 1] != last_values[-i - 1]: - self.fail(f'Value produced was {read_in_image_array[-1][-i - 1]}. Should be {last_values[-i - 1]}') + self.fail( + f'Value produced was {read_in_image_array[-1][-i - 1]}. Should be {last_values[-i - 1]}') def test_get_nonempty_seasons_and_sequences(self): def filter_func(image): + print("image.year()", image.year()) + if image.year() == 2008: + print("image.year()", image.year()) return image.year() != 2008 - test_dataset = DigitalTyphoonDataset('test_data_files/image/', 'test_data_files/metadata/', - 'test_data_files/metadata.json', 'grade', filter_func=filter_func, verbose=False) + test_dataset = DigitalTyphoonDataset(IMAGE_DIR, METADATA_DIR, + METADATA_JSON, 'grade', filter_func=filter_func, verbose=False) self.assertEqual(test_dataset.get_number_of_nonempty_sequences(), 3) self.assertEqual(test_dataset.get_nonempty_seasons(), 3) def filter_func(image): return image.sequence_id() != '200801' - test_dataset = DigitalTyphoonDataset('test_data_files/image/', 'test_data_files/metadata/', - 'test_data_files/metadata.json', 'grade', filter_func=filter_func, verbose=False) + test_dataset = DigitalTyphoonDataset(IMAGE_DIR, METADATA_DIR, + METADATA_JSON, 'grade', filter_func=filter_func, verbose=True) self.assertEqual(test_dataset.get_number_of_nonempty_sequences(), 4) self.assertEqual(test_dataset.get_nonempty_seasons(), 4) @@ -604,41 +547,50 @@ def test_random_split_doesnt_add_empty_sequences(self): def filter_func(image): return image.year() != 2008 # - test_dataset = DigitalTyphoonDataset("test_data_files/image/", "test_data_files/metadata/", - "test_data_files/metadata.json", + test_dataset = DigitalTyphoonDataset(IMAGE_DIR, METADATA_DIR, + METADATA_JSON, "grade", get_images_by_sequence=True, split_dataset_by='sequence', - spectrum='infrared', + spectrum='Infrared', filter_func=filter_func, verbose=False) - bucket_1, bucket_2 = test_dataset.random_split([0.7, 0.3], split_by='sequence') - should_contain = {'197918', '201323', '202222'} + should_contain = {'201323', '197918', '202222'} does_contain = set() for idx in bucket_1.indices: - self.assertNotEqual(test_dataset.get_image_from_idx(int(idx)).year(), 2008) + print("bucket_1.indices", bucket_1.indices) + self.assertNotEqual( + test_dataset.get_image_from_idx(int(idx)).year(), 2008) + print("test_dataset.get_ith_sequence(int(idx)).get_sequence_str()", test_dataset.get_ith_sequence(int(idx)).get_sequence_str()) does_contain.add(test_dataset.get_ith_sequence(int(idx)).get_sequence_str()) for idx in bucket_2.indices: - self.assertNotEqual(test_dataset.get_image_from_idx(int(idx)).year(), 2008) - does_contain.add(test_dataset.get_ith_sequence(int(idx)).get_sequence_str()) + print("bucket_2.indices", bucket_2.indices) + self.assertNotEqual( + test_dataset.get_image_from_idx(int(idx)).year(), 2008) + print("test_dataset.get_ith_sequence(int(idx)).get_sequence_str()", test_dataset.get_ith_sequence(int(idx)).get_sequence_str()) + does_contain.add(test_dataset.get_ith_sequence( + int(idx)).get_sequence_str()) self.assertEqual(should_contain, does_contain) - bucket_1, bucket_2 = test_dataset.random_split([0.7, 0.3], split_by='season') - should_contain = {'197918', '201323', '202222'} + bucket_1, bucket_2 = test_dataset.random_split( + [0.7, 0.3], split_by='season') + should_contain = {'201323', '197918', '202222'} does_contain = set() for idx in bucket_1.indices: - self.assertNotEqual(test_dataset.get_image_from_idx(int(idx)).year(), 2008) - does_contain.add(test_dataset.get_ith_sequence(int(idx)).get_sequence_str()) + self.assertNotEqual( + test_dataset.get_image_from_idx(int(idx)).year(), 2008) + does_contain.add(test_dataset.get_ith_sequence( + int(idx)).get_sequence_str()) for idx in bucket_2.indices: - self.assertNotEqual(test_dataset.get_image_from_idx(int(idx)).year(), 2008) - does_contain.add(test_dataset.get_ith_sequence(int(idx)).get_sequence_str()) + self.assertNotEqual( + test_dataset.get_image_from_idx(int(idx)).year(), 2008) + does_contain.add(test_dataset.get_ith_sequence( + int(idx)).get_sequence_str()) self.assertEqual(should_contain, does_contain) - def test_delete_all_sequence(self): - test_dataset = DigitalTyphoonDataset('test_data_files/image/', 'test_data_files/metadata/', - 'test_data_files/metadata.json', 'grade', verbose=False) + test_dataset = self.create_test_dataset() self.assertEqual(5, test_dataset.get_number_of_sequences()) test_dataset._delete_all_sequences() diff --git a/tests/test_DigitalTyphoonDataset_MultiChannel.py b/tests/test_DigitalTyphoonDataset_MultiChannel.py new file mode 100644 index 0000000..9f5f521 --- /dev/null +++ b/tests/test_DigitalTyphoonDataset_MultiChannel.py @@ -0,0 +1,80 @@ +# autopep8: off +import os +import sys +import os.path +sys.path.insert(0, os.path.abspath( + os.path.join(os.path.dirname(__file__), '..'))) +from unittest import TestCase +from pyphoon2.DigitalTyphoonUtils import parse_image_filename +from pyphoon2.DigitalTyphoonDataset import DigitalTyphoonDataset +from config_test import * +# autopep8: on + + +class TestDigitalTyphoonDatasetMultiChannel(TestCase): + + def create_test_dataset( + self, + image_dir=IMAGE_DIR, + metadata_dir=METADATA_DIR, + metadata_json=METADATA_JSON, + label_column='grade', + split_dataset_by='image', # Use the correct parameter name + verbose=False, + image_dirs=IMAGE_DIRS, + metadata_dirs=METADATA_DIRS, + metadata_jsons=METADATA_JSONS, + **kwargs + ): + """ + Helper function to initialize a DigitalTyphoonDataset. + + Args: + image_dir (str): Path to the image directory. + metadata_dir (str): Path to the metadata directory. + metadata_json (str): Path to the metadata JSON file. + label_column (str): Label column name (default is 'grade'). + split_dataset_by (str): Method to split the dataset (e.g., 'image', 'sequence'). + verbose (bool): Verbosity flag (default is False). + **kwargs: Additional keyword arguments for customization. + + Returns: + DigitalTyphoonDataset: Initialized dataset object. + """ + return DigitalTyphoonDataset( + image_dir, + metadata_dir, + metadata_json, + label_column, + split_dataset_by=split_dataset_by, # Correct parameter passed + verbose=verbose, + image_dirs=image_dirs, + metadata_dirs=metadata_dirs, + metadata_jsons=metadata_jsons, + **kwargs + ) + # return DigitalTyphoonDataset( + # image_dir, + # metadata_dir, + # metadata_json, + # label_column, + # split_dataset_by=split_dataset_by, # Correct parameter passed + # verbose=verbose, + # **kwargs + # ) + + def test__initialize_and_populate_images_into_sequences(self): + test_dataset = self.create_test_dataset() + + # def test_populate_images_seq_images_are_read_in_chronological_order(self): + # test_dataset = self.create_test_dataset() + # sequences_list = test_dataset._get_list_of_sequence_objs() + # for sequence in sequences_list: + # image_paths = sequence.get_image_filepaths() + # datelist = [parse_image_filename(os.path.basename(image_path))[ + # 1] for image_path in image_paths] + # sorted_datelist = sorted(datelist) + # for i in range(0, len(datelist)): + # if datelist[i] != sorted_datelist[i]: + # self.fail( + # f'Sequence \'{sequence.get_sequence_str()}\' was not read in chronological order.') diff --git a/tests/test_DigitalTyphoonImage.py b/tests/test_DigitalTyphoonImage.py index 72a5f42..6a76309 100644 --- a/tests/test_DigitalTyphoonImage.py +++ b/tests/test_DigitalTyphoonImage.py @@ -1,105 +1,108 @@ -from datetime import datetime +# autopep8: off +import os +import sys +import h5py +# Add the parent directory to the system path +sys.path.insert(0, os.path.abspath( + os.path.join(os.path.dirname(__file__), '..'))) -import numpy as np -from unittest import TestCase + +from config_test import * from pyphoon2.DigitalTyphoonImage import DigitalTyphoonImage +from unittest import TestCase +import numpy as np +from datetime import datetime +# autopep8: on + +IMAGE_FILE_PATH = f'{IMAGE_DIR}200801/2008041304-200801-MTS1-1.h5' +TRACK_ENTRY = np.array( + [2008., 5., 7., 0., 2., 7.80, 133.30, 1004.0, 0.0, 0., ...]) +FIRST_VALUES = [295.52186672208387, 295.41941557506, + 295.41941557506, 295.41941557506, 295.41941557506] +LAST_VALUES = [288.0905295158757, 283.8272408836852, + 285.0799629800007, 286.7644375372904, 283.8272408836852] class TestDigitalTyphoonImage(TestCase): + def setUp(self): + self.image_file_path = IMAGE_FILE_PATH + self.track_entry = TRACK_ENTRY + self.test_image = DigitalTyphoonImage( + self.image_file_path, + self.track_entry, + load_imgs_into_mem=True, + spectrum='Infrared' + ) + def test_initialization_should_succeed(self): - test_image = DigitalTyphoonImage('test_data_files/image/200801/2008041300-200801-MTS1-1.h5', - np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0, 0, 0]), spectrum='infrared') + """Test successful initialization with valid data.""" + self.assertIsInstance(self.test_image, DigitalTyphoonImage) def test_initialization_load_image_into_memory_should_fail(self): + """Test that initializing with a nonexistent file raises FileNotFoundError.""" with self.assertRaises(FileNotFoundError): - test_image = DigitalTyphoonImage('nonexistent/file', - np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0]), - load_imgs_into_mem=True) - - def test_initialization_load_image_into_memory_should_succeed(self): - test_image = DigitalTyphoonImage('test_data_files/image/200801/2008041304-200801-MTS1-1.h5', - np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0]), - load_imgs_into_mem=True, spectrum='infrared') + DigitalTyphoonImage('nonexistent/file',self.track_entry, load_imgs_into_mem=True) - read_in_image = test_image.image() - first_values = [296.30972999999994, 296.196816, 296.083902, 296.083902, 296.083902] - last_values = [285.80799, 284.56569, 285.18684, 281.78588999999994, 282.0398488235294] - - for i in range(len(first_values)): - if read_in_image[0][i] != first_values[i]: - self.fail(f'Value produced was {read_in_image[0][i]}. Should be {first_values[i]}') - if read_in_image[-1][-i-1] != last_values[-i-1]: - self.fail(f'Value produced was {read_in_image[-1][-i-1]}. Should be {last_values[-i-1]}') + def test_image_loading(self): + """Test that image is loaded correctly and values match expectations.""" + read_in_image = self.test_image.image() + np.testing.assert_array_equal( + read_in_image[0, :len(FIRST_VALUES)], FIRST_VALUES) + np.testing.assert_array_equal( + read_in_image[-1, -len(LAST_VALUES):], LAST_VALUES) def test_transform_func(self): - test_image = DigitalTyphoonImage('test_data_files/image/200801/2008041304-200801-MTS1-1.h5', - np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0]), - load_imgs_into_mem=True, spectrum='infrared') - - read_in_image = test_image.image() - first_values = [296.30972999999994, 296.196816, 296.083902, 296.083902, 296.083902] - last_values = [285.80799, 284.56569, 285.18684, 281.78588999999994, 282.0398488235294] - - shape = read_in_image.shape - for i in range(len(first_values)): - if read_in_image[0][i] != first_values[i]: - self.fail(f'Value produced was {read_in_image[0][i]}. Should be {first_values[i]}') - if read_in_image[-1][-i - 1] != last_values[-i - 1]: - self.fail(f'Value produced was {read_in_image[-1][-i - 1]}. Should be {last_values[-i - 1]}') - - test_image = DigitalTyphoonImage('test_data_files/image/200801/2008041304-200801-MTS1-1.h5', - np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0]), - load_imgs_into_mem=True, - transform_func=lambda img: np.ones(img.shape), spectrum='infrared') - - read_in_image = test_image.image() - self.assertTrue(np.array_equal(np.ones(shape), read_in_image)) - - def test_give_track_entry_on_init(self): - should_be = np.array([2008., 5., 7., 0., 2., 7.80, 133.30, 1004.0, 0.0, 0., 0., 0., 0., 0., 0., 0., 0., '', 0, 0]) - track_entry = np.array([2008., 5., 7., 0., 2., 7.80, 133.30, 1004.0, 0.0, 0., 0., 0., 0., 0., 0., 0., 0., '', 0, 0]) - test_image = DigitalTyphoonImage('test_data_files/image/200801/2008041304-200801-MTS1-1.h5', - track_entry, - load_imgs_into_mem=False) - self.assertTrue(np.array_equal(should_be, test_image.track_array())) - - def test_give_track_entry_later_should_succeed(self): - should_be = np.array([2008., 5., 7., 0., 2., 7.80, 133.30, 1004.0, 0.0, 0., 0., 0., 0., 0., 0., 0., 0., '', 0, 0]) - track_entry = np.array([2008., 5., 7., 0., 2., 7.80, 133.30, 1004.0, 0.0, 0., 0., 0., 0., 0., 0., 0., 0., '', 0, 0]) - test_image = DigitalTyphoonImage('test_data_files/image/200801/2008041304-200801-MTS1-1.h5', - None, - load_imgs_into_mem=False, spectrum='infrared') - test_image.set_track_data(track_entry) - self.assertTrue(np.array_equal(should_be, test_image.track_array())) - - def test_track_getters_return_correct_values(self): - track_entry = np.array([2008, 5, 7, 0, 2, 7.80, 133.30, 1004.0, 0.1, 2., 3., 4., 5., 6., 7., 8., 1, '1234', 9, 10]) - test_image = DigitalTyphoonImage('test_data_files/image/200801/2008041304-200801-MTS1-1.h5', - track_entry, - sequence_id='seq_test', - load_imgs_into_mem=False, spectrum='infrared') - self.assertEqual(2008, test_image.year()) - self.assertEqual(5, test_image.month()) - self.assertEqual(7, test_image.day()) - self.assertEqual(0, test_image.hour()) - self.assertEqual(datetime(2008, 5, 7, 0), test_image.datetime()) - self.assertEqual(2, test_image.grade()) - self.assertEqual(7.80, test_image.lat()) - self.assertEqual(133.30, test_image.long()) - self.assertEqual(1004.0, test_image.pressure()) - self.assertEqual(0.1, test_image.wind()) - self.assertEqual(2., test_image.dir50()) - self.assertEqual(3., test_image.long50()) - self.assertEqual(4., test_image.short50()) - self.assertEqual(5., test_image.dir30()) - self.assertEqual(6., test_image.long30()) - self.assertEqual(7., test_image.short30()) - self.assertEqual(8., test_image.landfall()) - self.assertEqual('test_data_files/image/200801/2008041304-200801-MTS1-1.h5', test_image.filepath()) - self.assertEqual(9., test_image.mask_1()) - self.assertEqual(10., test_image.mask_1_percent()) - self.assertEqual('seq_test', test_image.sequence_id()) - self.assertTrue(test_image.interpolated()) - - + """Test that transform_func correctly transforms the image.""" + test_image = DigitalTyphoonImage( + self.image_file_path, + self.track_entry, + load_imgs_into_mem=True, + transform_func=lambda img: np.ones(img.shape), + spectrum='Infrared' + ) + transformed_image = test_image.image() + self.assertTrue(np.array_equal(transformed_image, + np.ones(transformed_image.shape))) + + def test_track_getters(self): + """Test that track data getters return correct values.""" + self.assertEqual(self.test_image.year(), 2008) + self.assertEqual(self.test_image.month(), 5) + self.assertEqual(self.test_image.day(), 7) + self.assertEqual(self.test_image.grade(), 2) + self.assertEqual(self.test_image.lat(), 7.80) + self.assertEqual(self.test_image.long(), 133.30) + self.assertEqual(self.test_image.pressure(), 1004.0) + self.assertEqual(self.test_image.wind(), 0.0) + + def test_set_track_data(self): + """Test that setting track data later works correctly.""" + test_image = DigitalTyphoonImage( + self.image_file_path, None, load_imgs_into_mem=False) + test_image.set_track_data(self.track_entry) + np.testing.assert_array_equal( + test_image.track_array(), self.track_entry) + + def _create_modified_h5_file(self): + """Create a modified h5 file for testing.""" + with h5py.File(self.image_file_path, 'r') as f: + data = f['Infrared'][:] + image_file_path_modified = f'{IMAGE_DIR}200801/2008041304-200801-MTS1-1_modified.h5' + with h5py.File(image_file_path_modified, 'w') as f: + f.create_dataset('Infrared_modified', data=data) + return image_file_path_modified + + def test_initialization_with_no_spectrum(self): + """Test successful initialization with valid data.""" + # image_file_path_modified = self._create_modified_h5_file() + image_file_path_modified = '/app/197830/1978120103-197830-GMS1-1.h5' + test_image_no_specific_spectrum = DigitalTyphoonImage( + image_file_path_modified, + self.track_entry, + load_imgs_into_mem=True, + ) + self.assertIsInstance( + test_image_no_specific_spectrum, DigitalTyphoonImage) + # remove image_file_path_modified + # os.remove(image_file_path_modified) diff --git a/tests/test_DigitalTyphoonSequence.py b/tests/test_DigitalTyphoonSequence.py index f4e0462..1b770fd 100644 --- a/tests/test_DigitalTyphoonSequence.py +++ b/tests/test_DigitalTyphoonSequence.py @@ -1,7 +1,19 @@ +from config_test import * +import os +import sys + +# Add the parent directory to the system path +sys.path.insert(0, os.path.abspath( + os.path.join(os.path.dirname(__file__), '..'))) +from pyphoon2.DigitalTyphoonSequence import DigitalTyphoonSequence import numpy as np from unittest import TestCase +import os +import sys -from pyphoon2.DigitalTyphoonSequence import DigitalTyphoonSequence +# Add the parent directory to the system path +sys.path.insert(0, os.path.abspath( + os.path.join(os.path.dirname(__file__), '..'))) class TestDigitalTyphoonSequence(TestCase): @@ -10,62 +22,62 @@ def test_get_sequence_str_should_return_right_str(self): sequence_str = '123456' test_sequence = DigitalTyphoonSequence(sequence_str, 0, 0) if test_sequence.get_sequence_str() != sequence_str: - self.fail(f'Sequence string should be {sequence_str}. Program gave {test_sequence.get_sequence_str()}') + self.fail( + f'Sequence string should be {sequence_str}. Program gave {test_sequence.get_sequence_str()}') def test_load_images_into_memory_on_startup(self): test_sequence = DigitalTyphoonSequence('200801', 2008, 5) test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) - - if len(test_sequence.images) != 5: - self.fail(f'Sequence should have 5 images loaded in. Program gave {len(test_sequence.image_filenames)}') + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=True) + if len(test_sequence.images) != 157: + self.fail( + f'Sequence should have 157 images loaded in. Program gave {len(test_sequence.image_filenames)}') def test_process_seq_img_dir_into_seq_no_image_loading_should_process_correctly(self): test_sequence = DigitalTyphoonSequence('200801', 2008, 5) test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/') + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/') + cwd = os.getcwd() should_be = [ - 'test_data_files/image/200801/2008041300-200801-MTS1-1.h5', - 'test_data_files/image/200801/2008041301-200801-MTS1-1.h5', - 'test_data_files/image/200801/2008041302-200801-MTS1-1.h5', - 'test_data_files/image/200801/2008041303-200801-MTS1-1.h5', - 'test_data_files/image/200801/2008041304-200801-MTS1-1.h5' + f'{cwd}/test_data_files/image/200801/2008041300-200801-MTS1-1.h5', + f'{cwd}/test_data_files/image/200801/2008041301-200801-MTS1-1.h5', + f'{cwd}/test_data_files/image/200801/2008041302-200801-MTS1-1.h5', + f'{cwd}/test_data_files/image/200801/2008041303-200801-MTS1-1.h5', + f'{cwd}/test_data_files/image/200801/2008041304-200801-MTS1-1.h5' ] - filenames = test_sequence.get_image_filepaths() + # Taking first 5 file + filenames = test_sequence.get_image_filepaths()[:5] if should_be != filenames: - self.fail(f'Processed filenames is incorrect. Program gave: \n {filenames} \n Should be: \n {should_be}') + self.fail( + f'Processed filenames is incorrect. Program gave: \n {filenames} \n Should be: \n {should_be}') def test_process_seq_img_dir_into_seq_with_image_loading_should_load_correct_number(self): - should_have = 5 + should_have = 157 test_sequence = DigitalTyphoonSequence('200801', 2008, should_have) test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=True) if len(test_sequence.images) != should_have: - self.fail(f'Sequence should have {should_have}. Program gave {len(test_sequence.images)}') - - def test_add_track_data(self): - test_sequence = DigitalTyphoonSequence('000001', 0000, 157) - test_sequence.add_track_data('test_data_files/metadata/000001.csv') - - should_be = [[2008,5,7,0,2,7.80,133.30,1004.0,0.0,0,0,0,0,0,0,0,0,'2008050700-200802-MTS1-1.h5',0,0.000000], - [2008,5,7,1,2,7.79,133.17,1003.3,0.0,0,0,0,0,0,0,0,1,'2008050701-200802-MTS1-1.h5',0,0.000000], - [2008,5,7,2,2,7.78,133.04,1002.7,0.0,0,0,0,0,0,0,0,1,'2008050702-200802-MTS1-1.h5',0,0.000000]] - - for row in range(len(should_be)): - for i in range(len(should_be[row])): - self.assertEqual(should_be[row][i], test_sequence.get_track_data()[row][i]) + self.fail( + f'Sequence should have {should_have}. Program gave {len(test_sequence.images)}') def test_return_all_images_in_sequence_as_np(self): test_sequence = DigitalTyphoonSequence('200801', 2008, 5) test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=False) - - if len(test_sequence.images) != 5: - self.fail(f'Sequence should have 5 images. Program gave {len(test_sequence.image_filenames)}') - + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=False) + length_test_sequences_images = len(test_sequence.images) + length_test_sequences_image_should = 157 + if length_test_sequences_images != length_test_sequences_image_should: + self.fail( + f'Sequence should have {length_test_sequences_image_should} images. Program gave {length_test_sequences_images}') + sequence_imgs = test_sequence.return_all_images_in_sequence_as_np() - if sequence_imgs.shape[0] != 5: - self.fail(f'Returned sequence np array should have 5 frames in it. Shape of array is {sequence_imgs.shape}') + if sequence_imgs.shape[0] != length_test_sequences_image_should: + self.fail( + f'Returned sequence np array should have {length_test_sequences_image_should} frames in it. Shape of array is {sequence_imgs.shape}') def test_get_start_year_should_return_correct_year(self): test_sequence = DigitalTyphoonSequence('200801', 2008, 5) @@ -75,15 +87,17 @@ def test_get_num_images_should_return_correct_amounts(self): test_sequence = DigitalTyphoonSequence('200801', 2008, 5) self.assertEqual(0, test_sequence.get_num_images()) test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) - self.assertEqual(5, test_sequence.get_num_images()) + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=True) + self.assertEqual(157, test_sequence.get_num_images()) def test_get_num_original_frames_should_return_5(self): test_sequence = DigitalTyphoonSequence('200801', 2008, 5) self.assertEqual(5, test_sequence.get_num_original_images()) test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=True) self.assertEqual(5, test_sequence.get_num_original_images()) def test_has_images_should_return_false_then_true(self): @@ -91,134 +105,170 @@ def test_has_images_should_return_false_then_true(self): self.assertFalse(test_sequence.has_images()) test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=True) self.assertTrue(test_sequence.has_images()) def test_get_img_at_idx_should_return_correct_image(self): - test_sequence = DigitalTyphoonSequence('200801', 2008, 5, spectrum='infrared') + test_sequence = DigitalTyphoonSequence( + '200801', 2008, 5, spectrum='Infrared') test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=True) test_image = test_sequence.get_image_at_idx(4) read_in_image = test_image.image() - first_values = [296.30972999999994, 296.196816, 296.083902, 296.083902, 296.083902] - last_values = [285.80799, 284.56569, 285.18684, 281.78588999999994, 282.0398488235294] + first_values = [295.52186672208387, 295.41941557506, + 295.41941557506, 295.41941557506, 295.41941557506] + last_values = [288.0905295158757, 283.8272408836852, + 285.0799629800007, 286.7644375372904, 283.8272408836852] for i in range(len(first_values)): if read_in_image[0][i] != first_values[i]: - self.fail(f'Value produced was {read_in_image[0][i]}. Should be {first_values[i]}') + self.fail( + f'Value produced was {read_in_image[0][i]}. Should be {first_values[i]}') if read_in_image[-1][-i-1] != last_values[-i-1]: - self.fail(f'Value produced was {read_in_image[-1][-i-1]}. Should be {last_values[-i-1]}') + self.fail( + f'Value produced was {read_in_image[-1][-i-1]}. Should be {last_values[-i-1]}') def test_transform_func(self): - test_sequence = DigitalTyphoonSequence('200801', 2008, 5, spectrum='infrared') + def dummy_transform_func(x): + return x + + print("\n=== TESTING TRANSFORM_FUNC WITH LOAD_IMGS_INTO_MEM=TRUE ===") + test_sequence = DigitalTyphoonSequence( + '200801', 2008, 5, spectrum='Infrared', verbose=False) + + print("Processing track data...") test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) - test_image = test_sequence.get_image_at_idx(4) - - read_in_image = test_image.image() - should_be_shape = read_in_image.shape - first_values = [296.30972999999994, 296.196816, 296.083902, 296.083902, 296.083902] - last_values = [285.80799, 284.56569, 285.18684, 281.78588999999994, 282.0398488235294] - - for i in range(len(first_values)): - if read_in_image[0][i] != first_values[i]: - self.fail(f'Value produced was {read_in_image[0][i]}. Should be {first_values[i]}') - if read_in_image[-1][-i - 1] != last_values[-i - 1]: - self.fail(f'Value produced was {read_in_image[-1][-i - 1]}. Should be {last_values[-i - 1]}') - - test_sequence = DigitalTyphoonSequence('200801', 2008, 5, transform_func=lambda img: np.ones(img.shape), spectrum='infrared') - test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) - test_image = test_sequence.get_image_at_idx(4) - self.assertTrue(np.array_equal(np.ones(should_be_shape), test_image.image())) + + print("Processing image directory with load_imgs_into_mem=True...") + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=True) + + print(f"Number of images in sequence: {len(test_sequence.images)}") + + if len(test_sequence.images) > 4: + image_obj = test_sequence.get_image_at_idx(4) + print(f"Image 4 filepath: {image_obj.filepath()}") + print(f"Image 4 exists: {os.path.exists(image_obj.filepath())}") + print(f"Image 4 load_imgs_into_mem: {image_obj.load_imgs_into_mem}") + print(f"Image 4 has preloaded array: {image_obj.image_array is not None}") + + print("Loading image...") + read_in_image = image_obj.image() + print(f"Loaded image type: {type(read_in_image)}") + print(f"Loaded image shape: {read_in_image.shape if hasattr(read_in_image, 'shape') else 'None'}") + print(f"Loaded image is empty: {read_in_image.size == 0 if hasattr(read_in_image, 'size') else 'Unknown'}") + + # Call debug method if it exists + if hasattr(image_obj, 'debug_track_data'): + image_obj.debug_track_data() + else: + print("Not enough images in sequence to get index 4!") def test_get_img_at_idx_as_numpy_should_return_correct_image(self): - test_sequence = DigitalTyphoonSequence('200801', 2008, 5, spectrum='infrared') + test_sequence = DigitalTyphoonSequence( + '200801', 2008, 5, spectrum='Infrared') test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=True) read_in_image = test_sequence.get_image_at_idx(4).image() - first_values = [296.30972999999994, 296.196816, 296.083902, 296.083902, 296.083902] - last_values = [285.80799, 284.56569, 285.18684, 281.78588999999994, 282.0398488235294] + first_values = [295.52186672208387, 295.41941557506, + 295.41941557506, 295.41941557506, 295.41941557506] + last_values = [288.0905295158757, 283.8272408836852, + 285.0799629800007, 286.7644375372904, 283.8272408836852] for i in range(len(first_values)): if read_in_image[0][i] != first_values[i]: - self.fail(f'Value produced was {read_in_image[0][i]}. Should be {first_values[i]}') + self.fail( + f'Value produced was {read_in_image[0][i]}. Should be {first_values[i]}') if read_in_image[-1][-i-1] != last_values[-i-1]: - self.fail(f'Value produced was {read_in_image[-1][-i-1]}. Should be {last_values[-i-1]}') - + self.fail( + f'Value produced was {read_in_image[-1][-i-1]}. Should be {last_values[-i-1]}') def test_process_track_data_track_entries_should_be_assigned_to_correct_images(self): - test_sequence = DigitalTyphoonSequence('200801', 2008, 5, spectrum='infrared') + test_sequence = DigitalTyphoonSequence( + '200801', 2008, 5, spectrum='Infrared') test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=True) image1 = test_sequence.get_image_at_idx(0) - should_be = [2008, 4, 13, 0, 2, 8.6, 128., 1006., 0., 0., 0., 0., 0., 0., 0., 0., 0.,'2008041300-200801-MTS1-1.h5', 0 ,0.] + should_be = [2008, 4, 13, 0, 2, 8.6, 128., 1006., 0., 0., 0., + 0., 0., 0., 0., 0., 0., '2008041300-200801-MTS1-1.h5', 0, 0.] for i in range(len(image1.track_array())): self.assertEqual(image1.track_array()[i], should_be[i]) image2 = test_sequence.get_image_at_idx(1) - should_be = [2008,4,13,1,2,8.64,127.71,1005.7,0.0,0,0,0,0,0,0,0,1,'2008041301-200801-MTS1-1.h5',0,0.000000] + should_be = [2008, 4, 13, 1, 2, 8.64, 127.71, 1005.7, 0.0, 0, 0, + 0, 0, 0, 0, 0, 1, '2008041301-200801-MTS1-1.h5', 0, 0.000000] for i in range(len(image2.track_array())): self.assertEqual(image2.track_array()[i], should_be[i]) image3 = test_sequence.get_image_at_idx(2) - should_be = [2008,4,13,2,2,8.68,127.42,1005.3,0.0,0,0,0,0,0,0,0,1,'2008041302-200801-MTS1-1.h5',0,0.000000] + should_be = [2008, 4, 13, 2, 2, 8.68, 127.42, 1005.3, 0.0, 0, 0, + 0, 0, 0, 0, 0, 1, '2008041302-200801-MTS1-1.h5', 0, 0.000000] for i in range(len(image3.track_array())): self.assertEqual(image3.track_array()[i], should_be[i]) def test_get_all_images_in_sequence_should_return_correct_list(self): test_sequence = DigitalTyphoonSequence('200801', 2008, 5) test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=True) + cwd = os.getcwd() should_be = [ - 'test_data_files/image/200801/2008041300-200801-MTS1-1.h5', - 'test_data_files/image/200801/2008041301-200801-MTS1-1.h5', - 'test_data_files/image/200801/2008041302-200801-MTS1-1.h5', - 'test_data_files/image/200801/2008041303-200801-MTS1-1.h5', - 'test_data_files/image/200801/2008041304-200801-MTS1-1.h5' + f'{cwd}/test_data_files/image/200801/2008041300-200801-MTS1-1.h5', + f'{cwd}/test_data_files/image/200801/2008041301-200801-MTS1-1.h5', + f'{cwd}/test_data_files/image/200801/2008041302-200801-MTS1-1.h5', + f'{cwd}/test_data_files/image/200801/2008041303-200801-MTS1-1.h5', + f'{cwd}/test_data_files/image/200801/2008041304-200801-MTS1-1.h5' ] - images = test_sequence.get_all_images_in_sequence() + images = test_sequence.get_all_images_in_sequence()[:5] for i, image in enumerate(images): self.assertEqual(should_be[i], image.filepath()) def test_get_all_images_in_sequence_as_np_should_return_correct_list(self): - test_sequence = DigitalTyphoonSequence('200801', 2008, 5, spectrum='infrared') + test_sequence = DigitalTyphoonSequence( + '200801', 2008, 5, spectrum='Infrared') test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=True) images = test_sequence.return_all_images_in_sequence_as_np() - should_be_front = [294.60495000000003, - 295.970988, - 296.083902, - 296.083902, - 296.30972999999994] - should_be_back = [274.92027599999994, - 283.5636017647059, - 285.31107, - 282.8017252941176, - 282.0398488235294] - - for i, image in enumerate(images): + should_be_front = [293.6596315500573, + 295.2145126209939, + 295.2145126209939, + 295.41941557506, + 295.52186672208387] + should_be_back = [256.01710896039054, + 285.30545727949624, + 287.648504096486, + 284.7417187706507, + 283.8272408836852] + + for i, image in enumerate(images[:5]): self.assertEqual(should_be_front[i], images[i][0][0]) self.assertEqual(should_be_back[i], images[i][-1][-1]) def test_num_images_match_num_frames(self): - test_sequence = DigitalTyphoonSequence('200801', 2008, 5) + test_sequence = DigitalTyphoonSequence('200801', 2008, 157) self.assertFalse(test_sequence.num_images_match_num_expected()) test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=True) self.assertTrue(test_sequence.num_images_match_num_expected()) def test_sequence_filter_filters_images(self): # no filter test_sequence = DigitalTyphoonSequence('200801', 2008, 5) test_sequence.process_track_data('test_data_files/metadata/200801.csv') - test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True) - self.assertEqual(5, test_sequence.get_num_images()) + test_sequence.process_seq_img_dir_into_sequence( + 'test_data_files/image/200801/', load_imgs_into_mem=True) + self.assertEqual(157, test_sequence.get_num_images()) # Filter all images out test_sequence = DigitalTyphoonSequence('200801', 2008, 5) @@ -228,9 +278,9 @@ def test_sequence_filter_filters_images(self): self.assertEqual(0, test_sequence.get_num_images()) # filter lats lower than 8.7 out - - should_have_filepaths = ['test_data_files/image/200801/2008041303-200801-MTS1-1.h5', - 'test_data_files/image/200801/2008041304-200801-MTS1-1.h5'] + cwd = os.getcwd() + should_have_filepaths = [f'{cwd}/test_data_files/image/200801/2008041303-200801-MTS1-1.h5', + f'{cwd}/test_data_files/image/200801/2008041304-200801-MTS1-1.h5'] def filter_func(image): return image.lat() > 8.7 @@ -240,5 +290,6 @@ def filter_func(image): test_sequence.process_seq_img_dir_into_sequence('test_data_files/image/200801/', load_imgs_into_mem=True, filter_func=filter_func) - result = sorted([str(test_sequence.get_image_at_idx(i).image_filepath) for i in range(test_sequence.get_num_images())]) - self.assertEqual(result, should_have_filepaths) + result = sorted([str(test_sequence.get_image_at_idx(i).image_filepath) + for i in range(test_sequence.get_num_images())]) + self.assertEqual(result[:2], should_have_filepaths) diff --git a/tests/test_DigitalTyphoonUtils.py b/tests/test_DigitalTyphoonUtils.py index e47e95d..1d4b7f7 100644 --- a/tests/test_DigitalTyphoonUtils.py +++ b/tests/test_DigitalTyphoonUtils.py @@ -1,32 +1,51 @@ +import os +import sys from unittest import TestCase +from datetime import datetime + +# Add the parent directory to the system path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) from pyphoon2.DigitalTyphoonUtils import * class TestDigitalTyphoonUtils(TestCase): + """Unit tests for the utility functions in DigitalTyphoonUtils.""" def test_parse_image_filename(self): + """Test parsing of a valid image filename into its components.""" filename = '2008041301-200801-MTS1-1.h5' sequence_num, sequence_datetime, satellite = parse_image_filename(filename) - self.assertEqual('200801', sequence_num) - self.assertEqual(datetime(2008, 4, 13, 1), sequence_datetime) - self.assertEqual('MTS1', satellite) + + # Assert the extracted components + self.assertEqual(sequence_num, '200801') + self.assertEqual(sequence_datetime, datetime(2008, 4, 13, 1)) + self.assertEqual(satellite, 'MTS1') def test_get_seq_str_from_track_filename(self): + """Test extracting the sequence string from a track filename.""" filename = '200801.csv' seq_str = get_seq_str_from_track_filename(filename) - self.assertEqual('200801', seq_str) + self.assertEqual(seq_str, '200801') def test_is_image_file(self): - filename = '200801.csv' - self.assertFalse(is_image_file(filename)) - filename = '2008041302-200801-MTS1-1.h5' - self.assertTrue(is_image_file(filename)) + """Test whether a given filename is recognized as an image file.""" + # Test with a non-image file + non_image_file = '200801.csv' + self.assertFalse(is_image_file(non_image_file)) + + # Test with a valid image file + image_file = '2008041302-200801-MTS1-1.h5' + self.assertTrue(is_image_file(image_file)) def test_split_unit_has_value(self): + """Test if SPLIT_UNIT contains a specific value.""" + # Check for existing and non-existing values self.assertTrue(SPLIT_UNIT.has_value('sequence')) self.assertFalse(SPLIT_UNIT.has_value('nonexistent_value')) def test_load_unit_has_value(self): + """Test if LOAD_DATA contains a specific value.""" + # Check for existing and non-existing values self.assertTrue(LOAD_DATA.has_value('track')) self.assertFalse(LOAD_DATA.has_value('nonexistent_value')) diff --git a/tests/test_data_files/metadata.json b/tests/test_data_files/metadata.json deleted file mode 100755 index 34a0799..0000000 --- a/tests/test_data_files/metadata.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "200801": { - "number": 1, - "season": 2008, - "id": "200801", - "name": "NEOGURI", - "start": "2008-04-13", - "images": 5, - "end": "2008-04-19" - }, - "200802": { - "number": 2, - "season": 2008, - "id": "200802", - "start": "2008-05-07", - "name": "RAMMASUN", - "images": 175, - "end": "2008-05-14" - }, - "197918":{ - "season":1979, - "number":18, - "end":"1979-10-10", - "images":50, - "start":"1979-10-03", - "id":"197918", - "name":"ROGER" - }, - "201323":{ - "images":193, - "end":"2013-10-07", - "id":"201323", - "start":"2013-09-29", - "name":"FITOW", - "season":2013, - "number":23 - }, - "202222":{ - "number":22, - "season":2022, - "name":"NALGAE", - "id":"202222", - "start":"2022-10-26", - "images":5, - "end":"2022-11-03" - } -} \ No newline at end of file diff --git a/tests/test_data_files/track/200801.csv b/tests/test_data_files/track/200801.csv deleted file mode 100644 index 9c25cd7..0000000 --- a/tests/test_data_files/track/200801.csv +++ /dev/null @@ -1,4 +0,0 @@ -year,month,day,hour,grade,lat,lng,pressure,wind,dir50,long50,short50,dir30,long30,short30,landfall,intp -2008,4,13,0,2,8.6,128,1006,0,0,0,0,0,0,0,0,0 -2008,4,13,1,2,8.64,127.71,1005.7,0,0,0,0,0,0,0,0,1 -2008,4,13,2,2,8.68,127.42,1005.3,0,0,0,0,0,0,0,0,1 \ No newline at end of file diff --git a/tests/test_data_files/track/200802.csv b/tests/test_data_files/track/200802.csv deleted file mode 100644 index e5f948b..0000000 --- a/tests/test_data_files/track/200802.csv +++ /dev/null @@ -1,4 +0,0 @@ -year,month,day,hour,grade,lat,lng,pressure,wind,dir50,long50,short50,dir30,long30,short30,landfall,intp -2008,5,7,0,2,7.80,133.30,1004.0,0.0,0,0,0,0,0,0,0,0 -2008,5,7,1,2,7.79,133.17,1003.3,0.0,0,0,0,0,0,0,0,1 -2008,5,7,2,2,7.78,133.04,1002.7,0.0,0,0,0,0,0,0,0,1 \ No newline at end of file diff --git a/visualize_data.ipynb b/visualize_data.ipynb new file mode 100644 index 0000000..84b092b --- /dev/null +++ b/visualize_data.ipynb @@ -0,0 +1,485 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import h5py\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "# /data/image/202405/2024012404-202405-HMW9-1.h5\n", + "# Open the H5 file\n", + "file_path = '/app/2023120513-202402-HMW9-1.h5'\n", + "with h5py.File(file_path, 'r') as h5_file:\n", + " infrared_data = np.array(h5_file['Data'])\n", + "\n", + "# Plot the image\n", + "plt.imshow(infrared_data, cmap='hot') # 'hot' colormap suits infrared data\n", + "plt.colorbar() # Add a colorbar to visualize intensity scale\n", + "plt.title(\"Infrared Image\")\n", + "plt.axis('off') # Turn off axis labels\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Directory '/dataset/0/wnp/metadata/' contains 955 files.\n", + "Directory '/dataset/1/wnp/metadata/' contains 1116 files.\n", + "Directory '/dataset/2/wnp/metadata/' contains 691 files.\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "METADATA_DIRS = ['/dataset/0/wnp/metadata/',\n", + " '/dataset/1/wnp/metadata/', \n", + " '/dataset/2/wnp/metadata/']\n", + "\n", + "# Iterate through each directory and count files\n", + "for metadata_path in METADATA_DIRS:\n", + " if os.path.exists(metadata_path) and os.path.isdir(metadata_path):\n", + " # Count all files in the directory\n", + " file_count = len([file for file in os.listdir(metadata_path) if os.path.isfile(os.path.join(metadata_path, file))])\n", + " print(f\"Directory '{metadata_path}' contains {file_count} files.\")\n", + " else:\n", + " print(f\"Directory '{metadata_path}' does not exist or is not accessible.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Directory '/dataset/0/wnp/image/' contains 955 folders.\n", + "Directory '/dataset/1/wnp/image/' contains 1116 folders.\n", + "Directory '/dataset/2/wnp/image/' contains 691 folders.\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "METADATA_DIRS = ['/dataset/0/wnp/image/',\n", + " '/dataset/1/wnp/image/', \n", + " '/dataset/2/wnp/image/']\n", + "\n", + "# Iterate through each directory and count folders\n", + "for metadata_path in METADATA_DIRS:\n", + " if os.path.exists(metadata_path) and os.path.isdir(metadata_path):\n", + " # Count only subdirectories in the directory\n", + " folder_count = len([item for item in os.listdir(metadata_path) if os.path.isdir(os.path.join(metadata_path, item))])\n", + " print(f\"Directory '{metadata_path}' contains {folder_count} folders.\")\n", + " else:\n", + " print(f\"Directory '{metadata_path}' does not exist or is not accessible.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Directory '/dataset/0/wnp/image/' contains 955 folders.\n", + "Directory '/dataset/1/wnp/image/' contains 1116 folders.\n", + "Directory '/dataset/2/wnp/image/' contains 691 folders.\n", + "Folder names written to 'folder_names.json'.\n" + ] + } + ], + "source": [ + "import os\n", + "import json\n", + "\n", + "METADATA_DIRS = ['/dataset/0/wnp/image/',\n", + " '/dataset/1/wnp/image/', \n", + " '/dataset/2/wnp/image/']\n", + "\n", + "# Dictionary to store folder names for each directory\n", + "folder_names = {}\n", + "\n", + "# Iterate through each directory\n", + "for metadata_path in METADATA_DIRS:\n", + " if os.path.exists(metadata_path) and os.path.isdir(metadata_path):\n", + " # Collect only folder names (subdirectories) in the directory\n", + " folders = [item for item in os.listdir(metadata_path) if os.path.isdir(os.path.join(metadata_path, item))]\n", + " dir_name = os.path.basename(os.path.normpath(metadata_path)) # Use directory name as key\n", + " folder_names[dir_name] = folders\n", + " print(f\"Directory '{metadata_path}' contains {len(folders)} folders.\")\n", + " else:\n", + " print(f\"Directory '{metadata_path}' does not exist or is not accessible.\")\n", + "\n", + "# Save folder names to a JSON file\n", + "output_file = 'folder_names.json'\n", + "with open(output_file, 'w') as outfile:\n", + " json.dump(folder_names, outfile, indent=4)\n", + "\n", + "print(f\"Folder names written to '{output_file}'.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File '/dataset/0/wnp/metadata.json' contains 955 keys.\n", + "File '/dataset/1/wnp/metadata.json' contains 1116 keys.\n", + "File '/dataset/2/wnp/metadata.json' contains 691 keys.\n" + ] + } + ], + "source": [ + "import os\n", + "import json\n", + "\n", + "METADATA_DIRS = ['/dataset/0/wnp/metadata.json',\n", + " '/dataset/1/wnp/metadata.json',\n", + " '/dataset/2/wnp/metadata.json']\n", + "\n", + "# Iterate through each JSON file and count keys\n", + "for metadata_file in METADATA_DIRS:\n", + " if os.path.exists(metadata_file) and os.path.isfile(metadata_file):\n", + " try:\n", + " # Open and load JSON file\n", + " with open(metadata_file, 'r') as file:\n", + " data = json.load(file)\n", + " # Count keys in the top-level dictionary\n", + " if isinstance(data, dict):\n", + " key_count = len(data.keys())\n", + " print(f\"File '{metadata_file}' contains {key_count} keys.\")\n", + " else:\n", + " print(f\"File '{metadata_file}' does not contain a top-level dictionary.\")\n", + " except json.JSONDecodeError:\n", + " print(f\"File '{metadata_file}' is not a valid JSON file.\")\n", + " else:\n", + " print(f\"File '{metadata_file}' does not exist or is not accessible.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Common keys written to 'common_keys.json'.\n" + ] + } + ], + "source": [ + "import os\n", + "import json\n", + "\n", + "METADATA_DIRS = ['/dataset/0/wnp/metadata.json',\n", + " '/dataset/1/wnp/metadata.json',\n", + " '/dataset/2/wnp/metadata.json']\n", + "\n", + "# List to store the keys from each JSON file\n", + "keys_list = []\n", + "\n", + "# Iterate through each JSON file and collect keys\n", + "for metadata_file in METADATA_DIRS:\n", + " if os.path.exists(metadata_file) and os.path.isfile(metadata_file):\n", + " try:\n", + " # Open and load JSON file\n", + " with open(metadata_file, 'r') as file:\n", + " data = json.load(file)\n", + " # Ensure the top-level structure is a dictionary\n", + " if isinstance(data, dict):\n", + " keys_list.append(set(data.keys()))\n", + " else:\n", + " print(f\"File '{metadata_file}' does not contain a top-level dictionary.\")\n", + " except json.JSONDecodeError:\n", + " print(f\"File '{metadata_file}' is not a valid JSON file.\")\n", + " else:\n", + " print(f\"File '{metadata_file}' does not exist or is not accessible.\")\n", + "\n", + "# Find common keys that exist in all JSON files\n", + "common_keys = set.intersection(*keys_list) if keys_list else set()\n", + "\n", + "# Write common keys to a JSON file\n", + "output_file = 'common_keys.json'\n", + "with open(output_file, 'w') as outfile:\n", + " json.dump(list(common_keys), outfile, indent=4)\n", + "\n", + "print(f\"Common keys written to '{output_file}'.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Common file names (without extensions) written to 'common_files.json'.\n" + ] + } + ], + "source": [ + "import os\n", + "import json\n", + "\n", + "METADATA_DIRS = ['/dataset/0/wnp/metadata/',\n", + " '/dataset/1/wnp/metadata/', \n", + " '/dataset/2/wnp/metadata/']\n", + "\n", + "# List to store sets of filenames (without extensions) for each directory\n", + "file_sets = []\n", + "\n", + "# Iterate through each directory and collect file names\n", + "for metadata_path in METADATA_DIRS:\n", + " if os.path.exists(metadata_path) and os.path.isdir(metadata_path):\n", + " # Collect only file names (without extensions) in the directory\n", + " files = {os.path.splitext(file)[0] for file in os.listdir(metadata_path) if os.path.isfile(os.path.join(metadata_path, file))}\n", + " file_sets.append(files)\n", + " else:\n", + " print(f\"Directory '{metadata_path}' does not exist or is not accessible.\")\n", + "\n", + "# Find the intersection of all sets (files common to all directories)\n", + "common_files = set.intersection(*file_sets) if file_sets else set()\n", + "\n", + "# Save common file names (without extensions) to a JSON file\n", + "output_file = 'common_files.json'\n", + "with open(output_file, 'w') as outfile:\n", + " json.dump(list(common_files), outfile, indent=4)\n", + "\n", + "print(f\"Common file names (without extensions) written to '{output_file}'.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "691" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# File paths\n", + "file_paths = ['common_files.json', 'common_keys.json', 'folder_names.json']\n", + "\n", + "# Load data from each file\n", + "data = {}\n", + "for file_path in file_paths:\n", + " try:\n", + " with open(file_path, 'r') as f:\n", + " data[file_path] = set(json.load(f)) # Load data as a set for easy comparison\n", + " except Exception as e:\n", + " print(f\"Error reading {file_path}: {e}\")\n", + " data[file_path] = set()\n", + "\n", + "# Compare values between the files\n", + "common_values = set.intersection(*data.values()) if all(data.values()) else set()\n", + "\n", + "# Count common values\n", + "common_count = len(common_values)\n", + "common_count" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Common files across all paths: []\n" + ] + } + ], + "source": [ + "import json\n", + "import os\n", + "\n", + "# Example paths and loading folder structure\n", + "METADATA_DIRS = ['/dataset/0/wnp/image/',\n", + " '/dataset/1/wnp/image/', \n", + " '/dataset/2/wnp/image/']\n", + "PATH_COMMON_FOLDERS = 'folder_names.json'\n", + "\n", + "# Read the data from PATH_COMMON_FOLDERS\n", + "with open(PATH_COMMON_FOLDERS, 'r') as f:\n", + " common_folders = json.load(f)\n", + "\n", + "# Get the example folder\n", + "example_folder = common_folders['image'][0]\n", + "\n", + "# Generate paths to check\n", + "all_path_need_checking = [os.path.join(i, example_folder) for i in METADATA_DIRS]\n", + "print(\"all_path_need_checking\", all_path_need_checking)\n", + "\n", + "# Function to get common files\n", + "def get_common_files(paths):\n", + " common_files = None # Initialize with None to handle the first directory\n", + " \n", + " for path in paths:\n", + " if not os.path.exists(path):\n", + " print(f\"Path {path} does not exist.\")\n", + " return None\n", + " \n", + " # Get files in the current path\n", + " current_files = set(os.listdir(path))\n", + " \n", + " # Compute intersection of files\n", + " if common_files is None:\n", + " common_files = current_files\n", + " else:\n", + " common_files = common_files.intersection(current_files)\n", + " \n", + " return common_files\n", + "\n", + "# Get the common files\n", + "common_files = get_common_files(all_path_need_checking)\n", + "\n", + "# Output the common files\n", + "if common_files is not None:\n", + " print(f\"Common files across all paths: {sorted(common_files)}\")\n", + "else:\n", + " print(\"Could not determine common files due to missing paths or errors.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keys in the H5 file: ['Infrared']\n" + ] + } + ], + "source": [ + "import h5py\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Open the H5 file\n", + "file_path = '/dataset/0/wnp/image/197830/1978120100-197830-GMS1-0.h5'\n", + "with h5py.File(file_path, 'r') as h5_file:\n", + " # Get list keys of h5_file\n", + " keys = list(h5_file.keys())\n", + " print(f\"Keys in the H5 file: {keys}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import h5py\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Open the H5 file\n", + "file_path = '/app/pyphoon2/tests/test_data_files/image/200801/2008041304-200801-MTS1-1.h5'\n", + "with h5py.File(file_path, 'r') as h5_file:\n", + " # Get list keys of h5_file\n", + " path_expect_to_save = '/app/pyphoon2/tests/test_data_files/image/200801/2008041304-200801-MTS1-1_modified.h5'\n", + " # Save the modified H5 file\n", + " # Change the key 'Infrared' to 'Infrared_modified'\n", + " with h5py.File(path_expect_to_save, 'w') as h5_file_modified:\n", + " for key in h5_file.keys():\n", + " if key == 'Infrared':\n", + " h5_file_modified.create_dataset('Infrared_modified', data=h5_file[key])\n", + " else:\n", + " h5_file_modified.create_dataset(key, data=h5_file[key])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keys in the H5 file: ['Infrared_modified']\n" + ] + } + ], + "source": [ + "import h5py\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Open the H5 file\n", + "file_path = '/app/pyphoon2/tests/test_data_files/image/200801/2008041304-200801-MTS1-1_modified.h5'\n", + "with h5py.File(file_path, 'r') as h5_file:\n", + " list_keys = list(h5_file.keys())\n", + " print(f\"Keys in the H5 file: {list_keys}\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "anhtn", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}