diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index eeb4483..967c4bd 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,7 +3,7 @@ default_language_version: repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.6.0 + rev: v5.0.0 hooks: - id: check-yaml stages: [commit] @@ -29,7 +29,7 @@ repos: - id: cargo-check - repo: https://github.com/charliermarsh/ruff-pre-commit - rev: "v0.4.2" + rev: "v0.6.9" hooks: - id: ruff args: [--fix, --exit-non-zero-on-fix, --unsafe-fixes] @@ -81,7 +81,7 @@ repos: # jupyter notebook linting - repo: https://github.com/nbQA-dev/nbQA - rev: 1.8.5 + rev: 1.8.7 hooks: - id: nbqa-black args: ["--line-length=99"] diff --git a/Cargo.toml b/Cargo.toml index 3e5b723..dabf2be 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -2,12 +2,23 @@ name = "deepchopper" version = "0.1.0" edition = "2021" +authors = ["Yangyang Li "] +description = "A Genomic Language Model for Chimera Artifact Detection in Nanopore Direct RNA Sequencing" +documentation = "https://docs.rs/bitflags" +homepage = "https://serde.rs" +repository = "https://github.com/rust-lang/cargo" +license = "Apache-2.0" +readme = "README.md" [lib] # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html name = "deepchopper" crate-type = ["cdylib", "rlib"] +[[bin]] +path = "src/bin/predict.rs" +name = "deepchopper-chop" + [profile.dev] debug = 1 diff --git a/README.md b/README.md index 97d3403..1dccf45 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,21 @@ -# logo **DeepChopper** [![social](https://img.shields.io/github/stars/ylab-hi/DeepChopper?style=social)](https://github.com/ylab-hi/DeepChopper/stargazers) +# logo **DeepChopper** [![social](https://img.shields.io/github/stars/ylab-hi/DeepChopper?style=social)](https://github.com/ylab-hi/DeepChopper/stargazers) -- [Feature](#feature) -- [🤜 Contribution](#%F0%9F%A4%9C-contribution) +- [ **DeepChopper** ](#-deepchopper-) + - [Install](#install) + - [Usage](#usage) + - [Command-Line Interface](#command-line-interface) + - [Library](#library) + - [Cite](#cite) + - [🤜 Contribution](#-contribution) + - [Build Environment](#build-environment) + - [Install Dependencies](#install-dependencies) -Language models identify chimeric artificial reads in NanoPore direct-RNA sequencing data. -DeepChopper leverages language model to accurately detect and chop these aritificial sequences which may cause chimeric reads, ensuring higher quality and more reliable sequencing results. -By integrating seamlessly with existing workflows, DeepChopper provides a robust solution for researchers and bioinformaticians working with NanoPorea direct-RNA sequencing data. +DeepChopper leverages language model to accurately detect and chop artificial sequences which may cause chimeric reads, ensuring higher quality and more reliable sequencing results. +By integrating seamlessly with existing workflows, DeepChopper provides a robust solution for researchers and bioinformatics working with NanoPore direct-RNA sequencing data. ## Install @@ -33,37 +39,71 @@ DeepChopper can be installed using pip, the Python package installer. Follow the 4. Verify the installation: ```bash - deepchopper --version + deepchopper --help ``` +5. DeepChopper include a Rust command line tool for faster performance. + +```bash +cargo install deepchopper +``` + For GPU support, ensure you have CUDA installed on your system, then install the GPU version: ```bash pip install deepchopper[gpu] ``` -Note: If you encounter any issues, -please check our GitHub repository for troubleshooting guides or to report a problem. +Note: If you encounter any issues, please check our GitHub repository for troubleshooting guides or to report a problem. ## Usage +We provide a [complete guide](./documentation/tutorial.md) on how to use DeepChopper for NanoPore direct-RNA sequencing data. +Below is a brief overview of the command-line interface and library usage. + +### Command-Line Interface + +DeepChopper provides a command-line interface (CLI) for easy access to its features. In total, there are three commands: `encode`, `predict`, and `chop`. +DeepChopper can be used to encode, predict, and chop chimeric reads in direct-RNA sequencing data. + +Firstly, we need to encode the input data using the `encode` command, which will generate a `.parquet` file. + +```bash +deepchopper endcode +``` + +Next, we can use the `predict` command to predict chimeric reads in the encoded data. + ```bash -deepchopper endcode --input +deepchopper predict --ouput-path predictions ``` +Finally, we can use the `chop` command to chop the chimeric reads in the input data. + ```bash -deepchopper predict --input --output +deepchopper chop raw.fq ``` +Besides, DeepChopper provides a web-based user interface for users to interact with the tool. +However, the web-based application can only take one FASTQ record at a time. + ```bash -deepchopper chop --input --output +deepchopper web +``` + +### Library + +```python +import deepchopper + +model = deepchopper.DeepChopper.from_pretrained("yangliz5/deepchopper") ``` ## Cite ## 🤜 Contribution -**Build Environment** +### Build Environment ```bash git clone https://github.com/ylab-hi/DeepChopper.git @@ -72,7 +112,7 @@ conda env create -n environment.yaml conda activate deepchopper ``` -**Install Dependencies** +### Install Dependencies ```bash pip install pipx diff --git a/deepchopper/__init__.py b/deepchopper/__init__.py index 59d5b81..2fdf7d3 100644 --- a/deepchopper/__init__.py +++ b/deepchopper/__init__.py @@ -1,6 +1,7 @@ """DeepChopper package.""" -from . import data, eval, models, train, utils +from . import data, eval, models, train, ui, utils from .deepchopper import * # noqa: F403 +from .models import DeepChopper -__all__ = ["models", "utils", "data", "train", "eval"] +__all__ = ["models", "utils", "data", "train", "eval", "DeepChopper", "ui"] diff --git a/deepchopper/cli.py b/deepchopper/cli.py index 2376132..d0bc6be 100644 --- a/deepchopper/cli.py +++ b/deepchopper/cli.py @@ -1,32 +1,27 @@ import logging -import multiprocessing -from functools import partial from pathlib import Path -from typing import Annotated +from typing import TYPE_CHECKING, Annotated +import lightning +import torch import typer -from datasets import load_dataset from rich import print from rich.logging import RichHandler -from transformers import AutoTokenizer, Trainer, TrainingArguments + +import deepchopper from .deepchopper import ( - convert_multiple_fqs_to_one_fq, default, encode_fq_path_to_parquet, encode_fq_path_to_parquet_chunk, ) -from .models.llm import ( - DataCollatorForTokenClassificationWithQual, - TokenClassification, - compute_metrics, - tokenize_and_align_labels_and_quals, -) from .utils import ( highlight_target, - summary_predict, ) +if TYPE_CHECKING: + from lightning.pytorch import LightningDataModule + FORMAT = "%(message)s" logging.basicConfig( level=logging.INFO, @@ -34,15 +29,13 @@ handlers=[RichHandler()], ) -TMPOUTPUT = Path.cwd() / "deepchopper_predict" - def random_show_seq(dataset, sample: int = 3): """Randomly selects 'sample' number of sequences from the given dataset and prints their IDs and targets. Parameters: - dataset : A list of dictionaries where each dictionary represents a sequence with keys 'id', 'seq', and 'target'. - sample (int): The number of sequences to randomly select from the dataset. Default is 3. + dataset : A list of dictionaries where each dictionary represents a sequence with keys 'id', 'seq', and 'target'. + sample (int): The number of sequences to randomly select from the dataset. Default is 3. """ total = len(dataset) import secrets @@ -53,174 +46,11 @@ def random_show_seq(dataset, sample: int = 3): highlight_target(dataset[highlight_id]["seq"], *dataset[highlight_id]["target"]) -def load_model_from_checkpoint(check_point: Path): - """Load the model from the given path.""" - if isinstance(check_point, str): - check_point = Path(check_point) - resume_tokenizer = AutoTokenizer.from_pretrained(check_point, trust_remote_code=True) - resume_model = TokenClassification.from_pretrained(check_point) - return resume_tokenizer, resume_model - - -def load_dataset_from_checkpont(check_point: Path, data_path: Path, resume_tokenizer, max_sample: int | None = None): - """Load the dataset and model from the given paths.""" - if isinstance(check_point, str): - check_point = Path(check_point) - - if isinstance(data_path, str): - data_path = Path(data_path) - - if data_path.suffix in (".fq", ".fastq"): - msg = f"Please Encoded {data_path.with_suffix('.fq')} first using `deepchopper encode`" - raise ValueError(msg) - - if data_path.suffix != ".parquet": - msg = f"Unsupported file format: {data_path.suffix}" - raise ValueError(msg) - - if max_sample is None: - max_sample: str = "100%" - - eval_dataset = load_dataset( - "parquet", - data_files={"predict": data_path.as_posix()}, - num_proc=multiprocessing.cpu_count(), - split=f"predict[:{max_sample}]", - ).with_format("torch") - - tokenized_eval_dataset = eval_dataset.map( - partial( - tokenize_and_align_labels_and_quals, - tokenizer=resume_tokenizer, - max_length=resume_tokenizer.max_len_single_sentence, - ), - num_proc=multiprocessing.cpu_count(), # type: ignore - ).remove_columns(["id", "seq", "qual", "target"]) - - return eval_dataset, tokenized_eval_dataset - - -def load_trainer( - resume_tokenizer, - resume_model, - batch_size: int = 24, - *, - show_metrics: bool = False, - use_cpu=False, -): - """Load the trainer with the given model and tokenizer.""" - data_collator = DataCollatorForTokenClassificationWithQual(resume_tokenizer) - training_args = TrainingArguments( - output_dir=TMPOUTPUT.as_posix(), - learning_rate=2e-5, - per_device_train_batch_size=batch_size, - per_device_eval_batch_size=batch_size, - num_train_epochs=1, - weight_decay=0.01, - evaluation_strategy="epoch", - save_strategy="epoch", - load_best_model_at_end=True, - push_to_hub=False, - torch_compile=False, - report_to="wandb", # type: ignore - run_name="eval", - use_cpu=use_cpu, - ) - - compute_metrics_func = compute_metrics if show_metrics else None - - # Initialize our Trainer - return Trainer( - model=resume_model, - args=training_args, - tokenizer=resume_tokenizer, - data_collator=data_collator, - compute_metrics=compute_metrics_func, - ) - - app = typer.Typer( context_settings={"help_option_names": ["-h", "--help"]}, ) -@app.command( - help="DeepChopper is All You Need", -) -def predict( - check_point: Path, - data_path: Path, - output_path: Path | None = None, - batch_size: int = 12, - max_sample: int | None = None, - min_region_length_for_smooth: int = 1, - max_distance_for_smooth: Annotated[int, typer.Option()] = 1, - *, - show_metrics: Annotated[bool, typer.Option(help="if show metrics")] = False, - show_sample: Annotated[bool, typer.Option(help="if show sample")] = False, - save_predict: Annotated[bool, typer.Option(help="if save predict")] = False, -): - """Predict the given dataset using the given model and tokenizer.""" - resume_tokenizer, resume_model = load_model_from_checkpoint(check_point) - ( - eval_dataset, - tokenized_eval_dataset, - ) = load_dataset_from_checkpont(check_point, data_path, resume_tokenizer, max_sample) - - if show_sample: - random_show_seq(eval_dataset, sample=3) - - trainer = load_trainer(resume_tokenizer, resume_model, batch_size=batch_size, show_metrics=show_metrics) - predicts = trainer.predict(tokenized_eval_dataset) # type: ignore - - true_prediction, true_label = summary_predict(predictions=predicts[0], labels=predicts[1]) - - # if show_sample: - # alignment_predict(true_prediction[0], true_label[0]) - # for idx, preds in enumerate(true_prediction): - # record_id = eval_dataset[idx]["id"] - # seq = eval_dataset[idx]["seq"] - # smooth_predict_targets = smooth_label_region( - # preds, - # min_region_length_for_smooth, - # max_distance_for_smooth, - # min_interval_length_for_discard=0, - # ) - - # targets = eval_dataset[idx]["target"] - - # if isinstance(targets, Tensor): - # targets = targets.tolist() - # # zip two consecutive elements - # targets = [(targets[i], targets[i + 1]) for i in range(0, len(targets), 2)] - # print(f"{record_id=}") - # hightlight_predicts(seq, targets, smooth_predict_targets) - - # _selected_seqs, _selected_intervals = remove_intervals_and_keep_left(seq, smooth_predict_targets) - - # print(predicts[2]) - # elif save_predict: - # del eval_dataset - # del tokenized_eval_dataset - # if output_path is None: - # outout_path = data_path.with_suffix(".chopped.fq.gz") - # else: - # if not output_path.exists(): - # output_path.mkdir(parents=True, exist_ok=True) - # outout_path = output_path / data_path.with_suffix(".chopped.fq.gz").name - - # write_predicts( - # data_path, - # outout_path, - # true_prediction, - # min_region_length_for_smooth, - # max_distance_for_smooth, - # ) - - # if TMPOUTPUT.exists(): - # TMPOUTPUT.rmdir() - - @app.command( help="DeepChopper is All You Need: encode the given fastq", ) @@ -256,23 +86,79 @@ def encode(data_folder: Path, *, chunk: bool = False, chunk_size: int = 1000000, @app.command( - help="DeepChopper is All You Need: collect the given fastq", + help="DeepChopper is All You Need", ) -def collect(data_folder: Path, output: Path): - """Collect multiple fastq files to one fastq file.""" - if not data_folder.exists(): - msg = f"Folder {data_folder} does not exist." - raise ValueError(msg) +def predict( + # check_point: Path, + data_path: Path, + output_path: Path | None = None, + batch_size: int = 12, + num_workers: int = 0, + max_sample: int | None = None, + *, + limit_predict_batches: int | None = None, +): + """Predict the given dataset using the given model and tokenizer.""" + tokenizer = deepchopper.models.llm.load_tokenizer_from_hyena_model(model_name="hyenadna-small-32k-seqlen") + + datamodule: LightningDataModule = deepchopper.data.fq_datamodule.FqDataModule( + train_data_path="dummy.parquet", + tokenizer=tokenizer, + predict_data_path=data_path, + batch_size=batch_size, + num_workers=num_workers, + max_predict_samples=max_sample, + ) - fq_files = ( - [data_folder] - if data_folder.is_file() - else list(data_folder.glob("*.fq")) - + list(data_folder.glob("*.fastq")) - + list(data_folder.glob("*.fq.gz")) - + list(data_folder.glob("*.fastq.gz")) + model = deepchopper.DeepChopper.from_pretrained("yangliz5/deepchopper") + + output_path = Path(output_path or "predictions") + + callbacks = [deepchopper.models.callbacks.CustomWriter(output_dir=output_path, write_interval="batch")] + + accelerator = "cpu" if torch.cuda.is_available() else "gpu" + trainer = lightning.pytorch.trainer.Trainer( + accelerator=accelerator, + devices=-1, + callbacks=callbacks, + deterministic=False, + logger=False, + limit_predict_batches=limit_predict_batches, ) - convert_multiple_fqs_to_one_fq(fq_files, output) + + trainer.predict(model=model, dataloaders=datamodule, return_predictions=False) + + +@app.command( + help="DeepChopper is All You Need: chop your reads!", +) +def chop( + predicts: list[Path], + fq: Path, + smooth_window_size: int = 21, + min_interval_size: int = 13, + approved_interval_number: int = 20, + max_process_intervals: int = 4, + min_read_length_after_chop: int = 20, + output_chopped_seqs: Annotated[bool, typer.Option(help="if outupt chopped seqs")] = False, + chop_type: str = "all", + threads: int = 2, + output_prefix: str | None = None, + max_batch_size: int | None = None, +): + from shutil import which + + if which("deepchopper-chop") is None: + print("deepchopper-chop is not installed. Please use `cargo install deepchopper-chop` to install it.") + raise SystemExit + + +@app.command( + help="DeepChopper is All You Need: ui!", +) +def web(): + """Run the web interface.""" + deepchopper.ui.main() if __name__ == "__main__": diff --git a/deepchopper/data/__init__.py b/deepchopper/data/__init__.py index 3b7863e..f39a145 100644 --- a/deepchopper/data/__init__.py +++ b/deepchopper/data/__init__.py @@ -1,6 +1,7 @@ """Data.""" +from . import fq_datamodule from .encode_fq import encode_fq_files_in_folder_to_parquet, encode_one_fq_file from .hg_data import load_and_split_dataset -__all__ = ["encode_one_fq_file", "encode_fq_files_in_folder_to_parquet", "load_and_split_dataset"] +__all__ = ["encode_one_fq_file", "encode_fq_files_in_folder_to_parquet", "load_and_split_dataset", "fq_datamodule"] diff --git a/deepchopper/data/fq_datamodule.py b/deepchopper/data/fq_datamodule.py index 826b6f9..b16414b 100644 --- a/deepchopper/data/fq_datamodule.py +++ b/deepchopper/data/fq_datamodule.py @@ -10,10 +10,6 @@ from lightning import LightningDataModule from torch.utils.data import DataLoader, Dataset -from deepchopper.deepchopper import ( - default, - encode_fq_path_to_parquet, -) from deepchopper.models.llm import ( DataCollatorForTokenClassificationWithQual, tokenize_and_align_labels_and_quals, @@ -127,15 +123,15 @@ def prepare_data(self) -> None: data_paths.append(self.hparams.predict_data_path) for data_path in data_paths: - if data_path in (".fq", ".fastq"): - encode_fq_path_to_parquet( - data_path, - default.KMER_SIZE, - bases=default.BASES, - qual_offset=default.QUAL_OFFSET, - vectorized_target=default.VECTORIZED_TARGET, - ) - elif Path(data_path).suffix == ".parquet": + # # if data_path in (".fq", ".fastq"): + # encode_fq_path_to_parquet( + # data_path, + # default.KMER_SIZE, + # bases=default.BASES, + # qual_offset=default.QUAL_OFFSET, + # vectorized_target=default.VECTORIZED_TARGET, + # ) + if Path(data_path).suffix == ".parquet": pass else: msg = f"Data file {data_path} is not in FastQ or Parquet format." diff --git a/deepchopper/models/__init__.py b/deepchopper/models/__init__.py index 4e685c7..2489235 100644 --- a/deepchopper/models/__init__.py +++ b/deepchopper/models/__init__.py @@ -1 +1,4 @@ """models.""" + +from . import basic_module, callbacks +from .dc_hg import DeepChopper diff --git a/deepchopper/models/basic_module.py b/deepchopper/models/basic_module.py index c86c2b7..1b61cf0 100644 --- a/deepchopper/models/basic_module.py +++ b/deepchopper/models/basic_module.py @@ -1,6 +1,7 @@ from typing import Any import torch +from huggingface_hub import PyTorchModelHubMixin from lightning import LightningModule from torch import nn from torchmetrics import MaxMetric, MeanMetric @@ -30,7 +31,7 @@ def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: return loss + penalty -class TokenClassificationLit(LightningModule): +class TokenClassificationLit(LightningModule, PyTorchModelHubMixin): """A PyTorch Lightning module for training a token classification model.""" def __init__( diff --git a/deepchopper/models/callbacks.py b/deepchopper/models/callbacks.py index c0dabfd..d0f5bf9 100644 --- a/deepchopper/models/callbacks.py +++ b/deepchopper/models/callbacks.py @@ -25,6 +25,7 @@ def write_on_batch_end(self, trainer, pl_module, prediction, batch_indices, batc torch.save(save_prediction, folder / f"{trainer.global_rank}_{batch_idx}.pt") def write_on_epoch_end(self, trainer, pl_module, predictions, batch_indices): + # WARN: This is a simple implementation that saves all predictions in a single file if not self.output_dir.exists(): self.output_dir.mkdir(parents=False, exist_ok=True) diff --git a/deepchopper/models/dc_hg.py b/deepchopper/models/dc_hg.py new file mode 100644 index 0000000..7405ec2 --- /dev/null +++ b/deepchopper/models/dc_hg.py @@ -0,0 +1,36 @@ +from functools import partial + +import torch + +from . import basic_module, llm +from .basic_module import TokenClassificationLit + + +class DeepChopper: + @classmethod + def from_pretrained(cls, model_name: str, **kwargs) -> "DeepChopper": + """Load a pretrained model.""" + return TokenClassificationLit.from_pretrained( + "yangliz5/deepchopper", + net=llm.hyena.TokenClassificationModule( + number_of_classes=2, + backbone_name="hyenadna-small-32k-seqlen", + freeze_backbone=False, + head=llm.TokenClassificationHead( + input_size=256, + lin1_size=1024, + lin2_size=1024, + num_class=2, + use_identity_layer_for_qual=True, + use_qual=True, + ), + ), + optimizer=partial( + torch.optim.Adam, + lr=0.0002, + weight_decay=0, + ), + scheduler=partial(torch.optim.lr_scheduler.ReduceLROnPlateau, mode="min", factor=0.1, patience=10), + criterion=basic_module.ContinuousIntervalLoss(lambda_penalty=0), + compile=False, + ) diff --git a/deepchopper/models/kan.py b/deepchopper/models/kan.py index 017db8e..3092fd5 100644 --- a/deepchopper/models/kan.py +++ b/deepchopper/models/kan.py @@ -1,5 +1,3 @@ -import torch -import torch.nn.functional as F # noqa: N812 from torch import nn diff --git a/deepchopper/models/llm/tokenizer.py b/deepchopper/models/llm/tokenizer.py index 71493bc..1c6da88 100644 --- a/deepchopper/models/llm/tokenizer.py +++ b/deepchopper/models/llm/tokenizer.py @@ -110,7 +110,7 @@ def load_tokenizer_from_hyena_model(model_name): # bfloat16 for better speed and reduced memory usage model_name = f"LongSafari/{model_name}-hf" return AutoTokenizer.from_pretrained( - model_name, max_length=max_length, truncation=True, padding=True, trust_remote_code=True + model_name, max_length=max_length, truncation=True, padding=True, trust_remote_code=True, force_download=False ) diff --git a/deepchopper/ui/__init__.py b/deepchopper/ui/__init__.py index e69de29..c28a133 100644 --- a/deepchopper/ui/__init__.py +++ b/deepchopper/ui/__init__.py @@ -0,0 +1 @@ +from .main import main diff --git a/deepchopper/ui/main.py b/deepchopper/ui/main.py index 895cee1..f3906f6 100644 --- a/deepchopper/ui/main.py +++ b/deepchopper/ui/main.py @@ -3,35 +3,29 @@ from pathlib import Path import gradio as gr +import lightning +import torch from datasets import Dataset +from torch.utils.data import DataLoader -from deepchopper.deepchopper import ( - default, - encode_qual, - remove_intervals_and_keep_left, - smooth_label_region, -) +import deepchopper +from deepchopper.deepchopper import default, encode_qual, remove_intervals_and_keep_left, smooth_label_region from deepchopper.models.llm import ( tokenize_and_align_labels_and_quals, ) -from deepchopper.predict import ( - load_model_from_checkpoint, - load_trainer, -) from deepchopper.utils import ( summary_predict, ) -check_point = Path("/Users/ylk4626/ClionProjects/DeepChopper/tests/checkpoint/checkpoint-20007") -tokenizer, model = load_model_from_checkpoint(check_point) -trainer = load_trainer(tokenizer, model, show_metrics=False, use_cpu=True) - def parse_fq_record(text: str): + """Parse a single FASTQ record into a dictionary.""" lines = text.strip().split("\n") for i in range(0, len(lines), 4): content = lines[i : i + 4] record_id, seq, _, qual = content + assert len(seq) == len(qual) # noqa: S101 + yield { "id": record_id, "seq": seq, @@ -41,6 +35,7 @@ def parse_fq_record(text: str): def load_dataset(text: str, tokenizer): + """Load dataset from text.""" dataset = Dataset.from_generator(parse_fq_record, gen_kwargs={"text": text}).with_format("torch") tokenized_dataset = dataset.map( partial( @@ -53,50 +48,119 @@ def load_dataset(text: str, tokenizer): return dataset, tokenized_dataset -def predict(text: str): - min_region_length_for_smooth = 1 - max_distance_for_smooth = 1 +def predict( + text: str, + smooth_window_size: int = 21, + min_interval_size: int = 13, + approved_interval_number: int = 20, + max_process_intervals: int = 8, # default is 4 + batch_size: int = 1, + num_workers: int = 1, +): + tokenizer = deepchopper.models.llm.load_tokenizer_from_hyena_model(model_name="hyenadna-small-32k-seqlen") dataset, tokenized_dataset = load_dataset(text, tokenizer) - predicts = trainer.predict(tokenized_dataset) # type: ignore - true_prediction, _true_label = summary_predict(predictions=predicts[0], labels=predicts[1]) - highted_text = [] - total_intervals = [] + dataloader = DataLoader(tokenized_dataset, batch_size=batch_size, num_workers=num_workers) + model = deepchopper.DeepChopper.from_pretrained("yangliz5/deepchopper") + + accelerator = "cpu" if torch.cuda.is_available() else "gpu" + trainer = lightning.pytorch.trainer.Trainer( + accelerator=accelerator, + devices=-1, + deterministic=False, + logger=False, + ) + + predicts = trainer.predict(model=model, dataloaders=dataloader, return_predictions=True) + + assert len(predicts) == 1 # noqa: S101 + + smooth_interval_json = [] + highlighted_text = [] + + for idx, preds in enumerate(predicts): + true_prediction, _true_label = summary_predict(predictions=preds[0], labels=preds[1]) - for idx, preds in enumerate(true_prediction): _id = dataset[idx]["id"] seq = dataset[idx]["seq"] - smooth_predict_targets = smooth_label_region(preds, min_region_length_for_smooth, max_distance_for_smooth) + + smooth_predict_targets = smooth_label_region( + true_prediction[0], smooth_window_size, min_interval_size, approved_interval_number + ) + + if not smooth_predict_targets or len(smooth_predict_targets) > max_process_intervals: + continue + # zip two consecutive elements - _selected_seqs, _selected_intervals = remove_intervals_and_keep_left(seq, smooth_predict_targets) + _selected_seqs, selected_intervals = remove_intervals_and_keep_left(seq, smooth_predict_targets) + total_intervals = sorted(selected_intervals + smooth_predict_targets) + + smooth_interval_json.extend({"start": i[0], "end": i[1]} for i in smooth_predict_targets) + + highlighted_text.extend( + (seq[interval[0] : interval[1]], "ada" if interval in smooth_predict_targets else None) + for interval in total_intervals + ) + return smooth_interval_json, highlighted_text + + +def process_input(text: str | None, file: str | None): + """Process the input and return the prediction.""" + if not text and not file: + gr.Warning("Both text and file are empty") + + if file: + MAX_LINES = 4 + file_content = [] + with Path(file).open() as f: + for idx, line in enumerate(f): + if idx >= MAX_LINES: + break + file_content.append(line) + file_content = "".join(file_content) + return predict(text=file_content) + + return predict(text=text) + + +def create_gradio_app(): + """Create a Gradio app for DeepChopper.""" + example = ( + "@1065:1135|393d635c-64f0-41ed-8531-12174d8efb28+f6a60069-1fcf-4049-8e7c-37523b4e273f\n" + "GCAGCTATGAATGCAAGGCCACAAGGTGGATGGAAGAGTTGTGGAACCAAAGAGCTGTCTTCCAGAGAAGATTTCGAGATAAGTCGCCCATCAGTGAACAAGATATTGTTGGTGGCATTTGATGAGAACGTTCCAAGATTATTGACAGATTAGTGAAAAGTAAGATTGAAATCATGACTGACCGTAAGTGGCAAGAAAGGGCTTTTGCCTTTGTAACCTTTGACGACCATGACTCCGTGGATAAGATTGTCATTCAGAATACCATACTGTGAATGGCCACATCTTTATTGTGAAGTTAGAAAAGCCCTGTCAAAGCAAGAGATGAATCAGTGCTTCTCCAGCCAAAGAGGTCGAAGTGGTTCTGGAAACTTTGGTGGTGGTCGTGGAGGTGGTTTCGGTGGGAATGACAACTCGGTCGTGGAGGAAACTTCAGTGGTCGTGGTGGCTTTGGTGGCAGCCGTGGTGGTGGTGGATATGGTGGCAGTGGGGATGGCTATAATGGATTTGGTAATGATGGAAGCAATTTGGAGGTGGTGGAAGCTACAATGATTTTGGGAATTACAACAATCAGTCTTCAAATTTTGGACCCCTAGGAGGAAATTTTGGTAGAAGCTCTGGCCCCATGGCGGTGGAGGCCAAATACTTTTGCAAACCACGAAACCAAGGTGGCTATGGCGGTCCAGCAGCAGCAGTAGCTATGGCAGTGGCAGAAGATTTTAATTAGGAAACAAAGCTTAGCAGGAGAGGAGAGCCAGAGAAGTGACAGGGAAGTACAGGTTACAACAGATTTGTGAACTCAGCCCAAGCACAGTGGTGGCAGGGCCTAGCTGCTACAAAGAAGACATGTTTTAGACAAATACTCATGTGTATGGGCAAAACTTGAGGACTGTATTTGTGACTAACTGTATAACAGGTTATTTTAGTTTCTGTTTGTGGAAAGTGTAAAGCATTCCAACAAAGGTTTTTAATGTAGATTTTTTTTTTTGCACCCCATGCTGTTGATTTGCTAAATGTAACAGTCTGATCGTGACGCTGAATAAATGTCTTTTTTAAAAAAAAAAAAAAGCTCCCTCCCATCCCCTGCTGCTAACTGATCCCATTATATCTAACCTGCCCCCCCATATCACCTGCTCCCGAGCTACCTAAGAACAGCTAAAAGAGCACACCCGCATGTAGCAAAATAGTGGGAAGATTATAGGTAGAGGCGACAAACCTACCGAGCCTGGTGATAGCTGGTTGTCCTAGATAGAATCTTAGTTCAACTTTAAATTTGCCCACAGAACCCTCTAAATCCCCTTGTAAATTTAACTGTTAGTCCAAAGAGGAACAGCTCTTTGGACACTAGGAAAAAACCTTGTAGAGAGTAAAAAATCAACACCCA\n" + "+\n" + ".0==?SSSSSSSSSSSH2216<868;SSSSSSSSSQQSRSIIHEDDESSSSSSJIKMGEKISSJJICCBDQ?;;8:;,**(&$'+501)\"#$()+%&&0<5+*/('%'))))'''$##\"\"\"\"%&--$\"\"\"('%)1L3*'')'#\"#&+*$&\"\"#*(&'''+,,<;9=ANOSJDCADHGMOQSSD=7>BRRSPIEEEOQSSQ4->LIC7EE045///03IIJQSSSNGE6('.5??@A@=,,EGRSPKJ<==<556GFLLQRANSSSSSSSSG...*%%%(***(%'3@LOOSSSSM...7BCMMSSSSSSSSSSSSSSSDFIPSSSGGGGPOQLIHIL4103HMSILLNOSSSSSSSSSS22CBCGSHHHHSSSSSSSSD??@<<<:DDDSSSSSSSSSSA@6688OSSSSSROJJKLSNNNMSSSSQPOOSOOQSSSSSRRHIHISSRSSSSSSSSSSSJFF=??@SSQRK:424<444FFG///1S@@@ASNNNNPN:4JMDDLPSSSSSSBA?B?@@+'&'BD**8EDEFQPIMLE$$&',79CSJJPSGA+***DN;3-('&(;>6(()/-,,)%')1FRNNJ-:=>GC;&;CHNFFDCEEKJLFA22/27A.....HSQLHL))8<=?JSSSFGSKIHDDCCEFDAA@CFJKLNL>:9/1>>?OSLK@+HPSA;>>>K;;;;SSSSOQLPPMORSSSSSQSSSSSSS=:9**?D889SSRFFEDKJJJEEDKSSSNNOSSS.---,&*++SSSSQRSSSSQPGED<<89<@GJ999:SSKBBBAJHK=SSSJJKNMGHKKHQA<<>OPKFEAACDHJKMORB/)'((6**)15DA99;JSQSSS2())+J))EGMQOMMKJF>?<@4,3;B>87FSSSSSSSSSSSSSSS<<::5658@AHMMSSRECC448/=<<>SSCB:5546;=>BJHINJFDPPPPPPPPPPPPPP%'*%$%+-%'(-22&&%('''&&&#\"\"%&'+0,,0;:1&\"\"%'(+++8'**(\"$$#&$'**//.3497$\"3CFHLOSSSSR:887:;;FSSRPRSSS4433$#$%&$$-056>@:;>=@?AHEFEC;*EKMSSRSRRDB>=AFRSSSSBSOOPSMDAABHH976951-9DHPQO/---?@ELSSQSRJHKKBKKLSSLINSOSSQSRIMSSSSSS>?MKIINSSGSSSSSSSQQMK544MJKKNKHGGLFFGBDB?EHIKGD?@DHPPIIF555)&(+,ADSSSSRQSSSQSS=9/0JJMSQSOSSO/97=B@=:>" + ) - total_intervals.extend(_selected_intervals) - total_intervals.extend(smooth_predict_targets) + with gr.Blocks(theme=gr.themes.Soft()) as demo: + gr.Markdown("# DeepChopper: DNA Sequence Analysis") - if total_intervals: - total_intervals.sort() - for interval in total_intervals: - if interval in smooth_predict_targets: - highted_text.append((seq[interval[0] : interval[1]], "ada")) - else: - highted_text.append((seq[interval[0] : interval[1]], None)) + with gr.Row(): + with gr.Column(scale=1): + text_input = gr.Textbox( + label="Input DNA Sequence", placeholder="Paste your DNA sequence here...", lines=10 + ) + file_input = gr.File(label="Or upload a FASTQ file") + submit_btn = gr.Button("Analyze", variant="primary") - metrics = predicts[2] - metrics["intervals"] = smooth_predict_targets - return metrics, highted_text + with gr.Column(scale=1): + json_output = gr.JSON(label="Detected Artificial Regions") + highlighted_text = gr.HighlightedText(label="Highlighted Sequence") + submit_btn.click(fn=process_input, inputs=[text_input, file_input], outputs=[json_output, highlighted_text]) -def main(): - demo = gr.Interface( - fn=predict, - inputs=[ - "text", - ], - outputs=["json", gr.HighlightedText()], - examples=[[]], - ) + gr.Examples( + examples=[[example]], + inputs=[text_input], + ) + + return demo - demo.launch() + +def main(): + """Launch the Gradio app.""" + app = create_gradio_app() + app.launch() if __name__ == "__main__": diff --git a/deepchopper/utils/print.py b/deepchopper/utils/print.py index 1fe59b2..ee72e1e 100644 --- a/deepchopper/utils/print.py +++ b/deepchopper/utils/print.py @@ -1,6 +1,7 @@ import typing import numpy as np +import torch from rich.console import Console from rich.highlighter import RegexHighlighter from rich.text import Text @@ -14,8 +15,8 @@ def hightlight_predicts( seq: str, targets: list[tuple[int, int]], predicts: list[tuple[int, int]], - style="bold magenta", - width=80, + style: str = "bold magenta", + width: int = 80, ): """Highlight the predicted and labeled sequences.""" target_seq = Text(seq) @@ -67,6 +68,13 @@ def summary_predict(predictions, labels): """Summarize the predictions and labels.""" predictions = np.argmax(predictions, axis=2) # Initialize lists to hold the filtered predictions and labels + + if isinstance(predictions, torch.Tensor): + predictions = predictions.cpu().numpy() + + if isinstance(labels, torch.Tensor): + labels = labels.cpu().numpy() + true_predictions, true_labels = rust_summary_predict(predictions, labels, IGNORE_INDEX) return true_predictions, true_labels diff --git a/documentation/.nojekyll b/documentation/.nojekyll new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/documentation/.nojekyll @@ -0,0 +1 @@ + diff --git a/documentation/index.html b/documentation/index.html new file mode 100644 index 0000000..a67bfb3 --- /dev/null +++ b/documentation/index.html @@ -0,0 +1,318 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + A Genomic Language Model for Chimera Artifact Detection in Nanopore Direct RNA Sequencing + + + + + + + + + + + + + + + + + + + + +
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A Genomic Language Model for Chimera Artifact Detection in Nanopore Direct RNA Sequencing

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+ + + Yangyang Li*, + + Tingyou Wang*, + + Rendong Yang + +
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Abstract

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+ Nanopore Direct-RNA sequencing has revolutionized transcriptomics but is challenged by artificial chimeric reads that compromise data integrity. + We present DeepChopper, a novel large language model tailored for biological sequences, which accurately identifies and removes artificial sequences in NanoPore Direct-RNA sequencing data without relying on alignment information. + DeepChopper's hybrid architecture, combining HyenaDNA for long-range dependency modeling with quality-aware processing, achieves both broad context understanding and single nucleotide resolution. + Across multiple cell lines and sequencing platforms, DeepChopper reduced chimeric reads by 62-84% and improved supporting rates from 8-19% to 43-55% compared to existing methods. + In particular, in gene fusion detection, DeepChopper reduced false positives by 89% while increasing the proportion of supported fusions from 2% to 17%. + By improving data quality, DeepChopper significantly improves the reliability of downstream analyses, particularly in cancer genomics and transcriptomics. + This work demonstrates the powerful potential of large language models in analyzing complex biological data, paving the way for advancements in genomics and biotechnology. +

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expose the modules object (__webpack_modules__) +/******/ __webpack_require__.m = modules; +/******/ +/******/ // expose the module cache +/******/ __webpack_require__.c = installedModules; +/******/ +/******/ // define getter function for harmony exports +/******/ __webpack_require__.d = function(exports, name, getter) { +/******/ if(!__webpack_require__.o(exports, name)) { +/******/ Object.defineProperty(exports, name, { +/******/ configurable: false, +/******/ enumerable: true, +/******/ get: getter +/******/ }); +/******/ } +/******/ }; +/******/ +/******/ // getDefaultExport function for compatibility with non-harmony modules +/******/ __webpack_require__.n = function(module) { +/******/ var getter = module && module.__esModule ? +/******/ function getDefault() { return module['default']; } : +/******/ function getModuleExports() { return module; }; +/******/ __webpack_require__.d(getter, 'a', getter); +/******/ return getter; +/******/ }; +/******/ +/******/ // Object.prototype.hasOwnProperty.call +/******/ __webpack_require__.o = function(object, property) { return Object.prototype.hasOwnProperty.call(object, property); }; +/******/ +/******/ // __webpack_public_path__ +/******/ __webpack_require__.p = ""; +/******/ +/******/ // Load entry module and return exports +/******/ return __webpack_require__(__webpack_require__.s = 5); +/******/ }) +/************************************************************************/ +/******/ ([ +/* 0 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* unused harmony export addClasses */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "d", function() { return removeClasses; }); +/* unused harmony export show */ +/* unused harmony export hide */ +/* unused harmony export offset */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "e", function() { return width; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return height; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return outerHeight; }); +/* unused harmony export outerWidth */ +/* unused harmony export position */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return css; }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__type__ = __webpack_require__(2); + + +var addClasses = function addClasses(element, classes) { + classes = Array.isArray(classes) ? classes : classes.split(' '); + classes.forEach(function (cls) { + element.classList.add(cls); + }); +}; + +var removeClasses = function removeClasses(element, classes) { + classes = Array.isArray(classes) ? classes : classes.split(' '); + classes.forEach(function (cls) { + element.classList.remove(cls); + }); +}; + +var show = function show(elements) { + elements = Array.isArray(elements) ? elements : [elements]; + elements.forEach(function (element) { + element.style.display = ''; + }); +}; + +var hide = function hide(elements) { + elements = Array.isArray(elements) ? elements : [elements]; + elements.forEach(function (element) { + element.style.display = 'none'; + }); +}; + +var offset = function offset(element) { + var rect = element.getBoundingClientRect(); + return { + top: rect.top + document.body.scrollTop, + left: rect.left + document.body.scrollLeft + }; +}; + +// returns an element's width +var width = function width(element) { + return element.getBoundingClientRect().width || element.offsetWidth; +}; +// returns an element's height +var height = function height(element) { + return element.getBoundingClientRect().height || element.offsetHeight; +}; + +var outerHeight = function outerHeight(element) { + var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + var height = element.offsetHeight; + if (withMargin) { + var style = window.getComputedStyle(element); + height += parseInt(style.marginTop) + parseInt(style.marginBottom); + } + return height; +}; + +var outerWidth = function outerWidth(element) { + var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + var width = element.offsetWidth; + if (withMargin) { + var style = window.getComputedStyle(element); + width += parseInt(style.marginLeft) + parseInt(style.marginRight); + } + return width; +}; + +var position = function position(element) { + return { + left: element.offsetLeft, + top: element.offsetTop + }; +}; + +var css = function css(element, obj) { + if (!obj) { + return window.getComputedStyle(element); + } + if (Object(__WEBPACK_IMPORTED_MODULE_0__type__["b" /* isObject */])(obj)) { + var style = ''; + Object.keys(obj).forEach(function (key) { + style += key + ': ' + obj[key] + ';'; + }); + + element.style.cssText += style; + } +}; + +/***/ }), +/* 1 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (immutable) */ __webpack_exports__["a"] = detectSupportsPassive; +function detectSupportsPassive() { + var supportsPassive = false; + + try { + var opts = Object.defineProperty({}, 'passive', { + get: function get() { + supportsPassive = true; + } + }); + + window.addEventListener('testPassive', null, opts); + window.removeEventListener('testPassive', null, opts); + } catch (e) {} + + return supportsPassive; +} + +/***/ }), +/* 2 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isFunction; }); +/* unused harmony export isNumber */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return isString; }); +/* unused harmony export isDate */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return isObject; }); +/* unused harmony export isEmptyObject */ +/* unused harmony export isNode */ +/* unused harmony export isVideo */ +/* unused harmony export isHTML5 */ +/* unused harmony export isIFrame */ +/* unused harmony export isYoutube */ +/* unused harmony export isVimeo */ +var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; + +var isFunction = function isFunction(unknown) { + return typeof unknown === 'function'; +}; +var isNumber = function isNumber(unknown) { + return typeof unknown === "number"; +}; +var isString = function isString(unknown) { + return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]'; +}; +var isDate = function isDate(unknown) { + return (Object.prototype.toString.call(unknown) === '[object Date]' || unknown instanceof Date) && !isNaN(unknown.valueOf()); +}; +var isObject = function isObject(unknown) { + return (typeof unknown === 'function' || (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && !!unknown) && !Array.isArray(unknown); +}; +var isEmptyObject = function isEmptyObject(unknown) { + for (var name in unknown) { + if (unknown.hasOwnProperty(name)) { + return false; + } + } + return true; +}; + +var isNode = function isNode(unknown) { + return !!(unknown && unknown.nodeType === HTMLElement | SVGElement); +}; +var isVideo = function isVideo(unknown) { + return isYoutube(unknown) || isVimeo(unknown) || isHTML5(unknown); +}; +var isHTML5 = function isHTML5(unknown) { + return isNode(unknown) && unknown.tagName === 'VIDEO'; +}; +var isIFrame = function isIFrame(unknown) { + return isNode(unknown) && unknown.tagName === 'IFRAME'; +}; +var isYoutube = function isYoutube(unknown) { + return isIFrame(unknown) && !!unknown.src.match(/\/\/.*?youtube(-nocookie)?\.[a-z]+\/(watch\?v=[^&\s]+|embed)|youtu\.be\/.*/); +}; +var isVimeo = function isVimeo(unknown) { + return isIFrame(unknown) && !!unknown.src.match(/vimeo\.com\/video\/.*/); +}; + +/***/ }), +/* 3 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var EventEmitter = function () { + function EventEmitter() { + var events = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : []; + + _classCallCheck(this, EventEmitter); + + this.events = new Map(events); + } + + _createClass(EventEmitter, [{ + key: "on", + value: function on(name, cb) { + var _this = this; + + this.events.set(name, [].concat(_toConsumableArray(this.events.has(name) ? this.events.get(name) : []), [cb])); + + return function () { + return _this.events.set(name, _this.events.get(name).filter(function (fn) { + return fn !== cb; + })); + }; + } + }, { + key: "emit", + value: function emit(name) { + for (var _len = arguments.length, args = Array(_len > 1 ? _len - 1 : 0), _key = 1; _key < _len; _key++) { + args[_key - 1] = arguments[_key]; + } + + return this.events.has(name) && this.events.get(name).map(function (fn) { + return fn.apply(undefined, args); + }); + } + }]); + + return EventEmitter; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (EventEmitter); + +/***/ }), +/* 4 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var Coordinate = function () { + function Coordinate() { + var x = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + var y = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 0; + + _classCallCheck(this, Coordinate); + + this._x = x; + this._y = y; + } + + _createClass(Coordinate, [{ + key: 'add', + value: function add(coord) { + return new Coordinate(this._x + coord._x, this._y + coord._y); + } + }, { + key: 'sub', + value: function sub(coord) { + return new Coordinate(this._x - coord._x, this._y - coord._y); + } + }, { + key: 'distance', + value: function distance(coord) { + var deltaX = this._x - coord._x; + var deltaY = this._y - coord._y; + + return Math.sqrt(Math.pow(deltaX, 2) + Math.pow(deltaY, 2)); + } + }, { + key: 'max', + value: function max(coord) { + var x = Math.max(this._x, coord._x); + var y = Math.max(this._y, coord._y); + + return new Coordinate(x, y); + } + }, { + key: 'equals', + value: function equals(coord) { + if (this == coord) { + return true; + } + if (!coord || coord == null) { + return false; + } + return this._x == coord._x && this._y == coord._y; + } + }, { + key: 'inside', + value: function inside(northwest, southeast) { + if (this._x >= northwest._x && this._x <= southeast._x && this._y >= northwest._y && this._y <= southeast._y) { + + return true; + } + return false; + } + }, { + key: 'constrain', + value: function constrain(min, max) { + if (min._x > max._x || min._y > max._y) { + return this; + } + + var x = this._x, + y = this._y; + + if (min._x !== null) { + x = Math.max(x, min._x); + } + if (max._x !== null) { + x = Math.min(x, max._x); + } + if (min._y !== null) { + y = Math.max(y, min._y); + } + if (max._y !== null) { + y = Math.min(y, max._y); + } + + return new Coordinate(x, y); + } + }, { + key: 'reposition', + value: function reposition(element) { + element.style['top'] = this._y + 'px'; + element.style['left'] = this._x + 'px'; + } + }, { + key: 'toString', + value: function toString() { + return '(' + this._x + ',' + this._y + ')'; + } + }, { + key: 'x', + get: function get() { + return this._x; + }, + set: function set() { + var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + this._x = value; + return this; + } + }, { + key: 'y', + get: function get() { + return this._y; + }, + set: function set() { + var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + this._y = value; + return this; + } + }]); + + return Coordinate; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Coordinate); + +/***/ }), +/* 5 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +Object.defineProperty(__webpack_exports__, "__esModule", { value: true }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_index__ = __webpack_require__(6); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_type__ = __webpack_require__(2); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__ = __webpack_require__(3); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_4__components_autoplay__ = __webpack_require__(7); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__ = __webpack_require__(9); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_6__components_infinite__ = __webpack_require__(10); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_7__components_loop__ = __webpack_require__(11); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_8__components_navigation__ = __webpack_require__(13); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_9__components_pagination__ = __webpack_require__(15); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_10__components_swipe__ = __webpack_require__(18); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_11__components_transitioner__ = __webpack_require__(19); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_12__defaultOptions__ = __webpack_require__(22); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_13__templates__ = __webpack_require__(23); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_14__templates_item__ = __webpack_require__(24); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _defineProperty(obj, key, value) { if (key in obj) { Object.defineProperty(obj, key, { value: value, enumerable: true, configurable: true, writable: true }); } else { obj[key] = value; } return obj; } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + + + + + + + + + + + + + + + + + +var bulmaCarousel = function (_EventEmitter) { + _inherits(bulmaCarousel, _EventEmitter); + + function bulmaCarousel(selector) { + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + _classCallCheck(this, bulmaCarousel); + + var _this = _possibleConstructorReturn(this, (bulmaCarousel.__proto__ || Object.getPrototypeOf(bulmaCarousel)).call(this)); + + _this.element = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelector(selector) : selector; + // An invalid selector or non-DOM node has been provided. + if (!_this.element) { + throw new Error('An invalid selector or non-DOM node has been provided.'); + } + _this._clickEvents = ['click', 'touch']; + + // Use Element dataset values to override options + var elementConfig = _this.element.dataset ? Object.keys(_this.element.dataset).filter(function (key) { + return Object.keys(__WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */]).includes(key); + }).reduce(function (obj, key) { + return _extends({}, obj, _defineProperty({}, key, _this.element.dataset[key])); + }, {}) : {}; + // Set default options - dataset attributes are master + _this.options = _extends({}, __WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */], options, elementConfig); + + _this._id = Object(__WEBPACK_IMPORTED_MODULE_0__utils_index__["a" /* uuid */])('slider'); + + _this.onShow = _this.onShow.bind(_this); + + // Initiate plugin + _this._init(); + return _this; + } + + /** + * Initiate all DOM element containing datePicker class + * @method + * @return {Array} Array of all datePicker instances + */ + + + _createClass(bulmaCarousel, [{ + key: '_init', + + + /**************************************************** + * * + * PRIVATE FUNCTIONS * + * * + ****************************************************/ + /** + * Initiate plugin instance + * @method _init + * @return {Slider} Current plugin instance + */ + value: function _init() { + this._items = Array.from(this.element.children); + + // Load plugins + this._breakpoint = new __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__["a" /* default */](this); + this._autoplay = new __WEBPACK_IMPORTED_MODULE_4__components_autoplay__["a" /* default */](this); + this._navigation = new __WEBPACK_IMPORTED_MODULE_8__components_navigation__["a" /* default */](this); + this._pagination = new __WEBPACK_IMPORTED_MODULE_9__components_pagination__["a" /* default */](this); + this._infinite = new __WEBPACK_IMPORTED_MODULE_6__components_infinite__["a" /* default */](this); + this._loop = new __WEBPACK_IMPORTED_MODULE_7__components_loop__["a" /* default */](this); + this._swipe = new __WEBPACK_IMPORTED_MODULE_10__components_swipe__["a" /* default */](this); + + this._build(); + + if (Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["a" /* isFunction */])(this.options.onReady)) { + this.options.onReady(this); + } + + return this; + } + + /** + * Build Slider HTML component and append it to the DOM + * @method _build + */ + + }, { + key: '_build', + value: function _build() { + var _this2 = this; + + // Generate HTML Fragment of template + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_13__templates__["a" /* default */])(this.id)); + // Save pointers to template parts + this._ui = { + wrapper: this.node.firstChild, + container: this.node.querySelector('.slider-container') + + // Add slider to DOM + };this.element.appendChild(this.node); + this._ui.wrapper.classList.add('is-loading'); + this._ui.container.style.opacity = 0; + + this._transitioner = new __WEBPACK_IMPORTED_MODULE_11__components_transitioner__["a" /* default */](this); + + // Wrap all items by slide element + this._slides = this._items.map(function (item, index) { + return _this2._createSlide(item, index); + }); + + this.reset(); + + this._bindEvents(); + + this._ui.container.style.opacity = 1; + this._ui.wrapper.classList.remove('is-loading'); + } + + /** + * Bind all events + * @method _bindEvents + * @return {void} + */ + + }, { + key: '_bindEvents', + value: function _bindEvents() { + this.on('show', this.onShow); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + this.off('show', this.onShow); + } + }, { + key: '_createSlide', + value: function _createSlide(item, index) { + var slide = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_14__templates_item__["a" /* default */])()).firstChild; + slide.dataset.sliderIndex = index; + slide.appendChild(item); + return slide; + } + + /** + * Calculate slider dimensions + */ + + }, { + key: '_setDimensions', + value: function _setDimensions() { + var _this3 = this; + + if (!this.options.vertical) { + if (this.options.centerMode) { + this._ui.wrapper.style.padding = '0px ' + this.options.centerPadding; + } + } else { + this._ui.wrapper.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this.slidesToShow; + if (this.options.centerMode) { + this._ui.wrapper.style.padding = this.options.centerPadding + ' 0px'; + } + } + + this._wrapperWidth = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this._ui.wrapper); + this._wrapperHeight = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._ui.wrapper); + + if (!this.options.vertical) { + this._slideWidth = Math.ceil(this._wrapperWidth / this.slidesToShow); + this._containerWidth = Math.ceil(this._slideWidth * this._slides.length); + this._ui.container.style.width = this._containerWidth + 'px'; + } else { + this._slideWidth = Math.ceil(this._wrapperWidth); + this._containerHeight = Math.ceil(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this._slides.length); + this._ui.container.style.height = this._containerHeight + 'px'; + } + + this._slides.forEach(function (slide) { + slide.style.width = _this3._slideWidth + 'px'; + }); + } + }, { + key: '_setHeight', + value: function _setHeight() { + if (this.options.effect !== 'translate') { + this._ui.container.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[this.state.index]) + 'px'; + } + } + + // Update slides classes + + }, { + key: '_setClasses', + value: function _setClasses() { + var _this4 = this; + + this._slides.forEach(function (slide) { + Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["d" /* removeClasses */])(slide, 'is-active is-current is-slide-previous is-slide-next'); + if (Math.abs((_this4.state.index - 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-slide-previous'); + } + if (Math.abs(_this4.state.index % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-current'); + } + if (Math.abs((_this4.state.index + 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-slide-next'); + } + }); + } + + /**************************************************** + * * + * GETTERS and SETTERS * + * * + ****************************************************/ + + /** + * Get id of current datePicker + */ + + }, { + key: 'onShow', + + + /**************************************************** + * * + * EVENTS FUNCTIONS * + * * + ****************************************************/ + value: function onShow(e) { + this._navigation.refresh(); + this._pagination.refresh(); + this._setClasses(); + } + + /**************************************************** + * * + * PUBLIC FUNCTIONS * + * * + ****************************************************/ + + }, { + key: 'next', + value: function next() { + if (!this.options.loop && !this.options.infinite && this.state.index + this.slidesToScroll > this.state.length - this.slidesToShow && !this.options.centerMode) { + this.state.next = this.state.index; + } else { + this.state.next = this.state.index + this.slidesToScroll; + } + this.show(); + } + }, { + key: 'previous', + value: function previous() { + if (!this.options.loop && !this.options.infinite && this.state.index === 0) { + this.state.next = this.state.index; + } else { + this.state.next = this.state.index - this.slidesToScroll; + } + this.show(); + } + }, { + key: 'start', + value: function start() { + this._autoplay.start(); + } + }, { + key: 'pause', + value: function pause() { + this._autoplay.pause(); + } + }, { + key: 'stop', + value: function stop() { + this._autoplay.stop(); + } + }, { + key: 'show', + value: function show(index) { + var force = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + // If all slides are already visible then return + if (!this.state.length || this.state.length <= this.slidesToShow) { + return; + } + + if (typeof index === 'Number') { + this.state.next = index; + } + + if (this.options.loop) { + this._loop.apply(); + } + if (this.options.infinite) { + this._infinite.apply(); + } + + // If new slide is already the current one then return + if (this.state.index === this.state.next) { + return; + } + + this.emit('before:show', this.state); + this._transitioner.apply(force, this._setHeight.bind(this)); + this.emit('after:show', this.state); + + this.emit('show', this); + } + }, { + key: 'reset', + value: function reset() { + var _this5 = this; + + this.state = { + length: this._items.length, + index: Math.abs(this.options.initialSlide), + next: Math.abs(this.options.initialSlide), + prev: undefined + }; + + // Fix options + if (this.options.loop && this.options.infinite) { + this.options.loop = false; + } + if (this.options.slidesToScroll > this.options.slidesToShow) { + this.options.slidesToScroll = this.slidesToShow; + } + this._breakpoint.init(); + + if (this.state.index >= this.state.length && this.state.index !== 0) { + this.state.index = this.state.index - this.slidesToScroll; + } + if (this.state.length <= this.slidesToShow) { + this.state.index = 0; + } + + this._ui.wrapper.appendChild(this._navigation.init().render()); + this._ui.wrapper.appendChild(this._pagination.init().render()); + + if (this.options.navigationSwipe) { + this._swipe.bindEvents(); + } else { + this._swipe._bindEvents(); + } + + this._breakpoint.apply(); + // Move all created slides into slider + this._slides.forEach(function (slide) { + return _this5._ui.container.appendChild(slide); + }); + this._transitioner.init().apply(true, this._setHeight.bind(this)); + + if (this.options.autoplay) { + this._autoplay.init().start(); + } + } + + /** + * Destroy Slider + * @method destroy + */ + + }, { + key: 'destroy', + value: function destroy() { + var _this6 = this; + + this._unbindEvents(); + this._items.forEach(function (item) { + _this6.element.appendChild(item); + }); + this.node.remove(); + } + }, { + key: 'id', + get: function get() { + return this._id; + } + }, { + key: 'index', + set: function set(index) { + this._index = index; + }, + get: function get() { + return this._index; + } + }, { + key: 'length', + set: function set(length) { + this._length = length; + }, + get: function get() { + return this._length; + } + }, { + key: 'slides', + get: function get() { + return this._slides; + }, + set: function set(slides) { + this._slides = slides; + } + }, { + key: 'slidesToScroll', + get: function get() { + return this.options.effect === 'translate' ? this._breakpoint.getSlidesToScroll() : 1; + } + }, { + key: 'slidesToShow', + get: function get() { + return this.options.effect === 'translate' ? this._breakpoint.getSlidesToShow() : 1; + } + }, { + key: 'direction', + get: function get() { + return this.element.dir.toLowerCase() === 'rtl' || this.element.style.direction === 'rtl' ? 'rtl' : 'ltr'; + } + }, { + key: 'wrapper', + get: function get() { + return this._ui.wrapper; + } + }, { + key: 'wrapperWidth', + get: function get() { + return this._wrapperWidth || 0; + } + }, { + key: 'container', + get: function get() { + return this._ui.container; + } + }, { + key: 'containerWidth', + get: function get() { + return this._containerWidth || 0; + } + }, { + key: 'slideWidth', + get: function get() { + return this._slideWidth || 0; + } + }, { + key: 'transitioner', + get: function get() { + return this._transitioner; + } + }], [{ + key: 'attach', + value: function attach() { + var _this7 = this; + + var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : '.slider'; + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + var instances = new Array(); + + var elements = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector]; + [].forEach.call(elements, function (element) { + if (typeof element[_this7.constructor.name] === 'undefined') { + var instance = new bulmaCarousel(element, options); + element[_this7.constructor.name] = instance; + instances.push(instance); + } else { + instances.push(element[_this7.constructor.name]); + } + }); + + return instances; + } + }]); + + return bulmaCarousel; +}(__WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["default"] = (bulmaCarousel); + +/***/ }), +/* 6 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return uuid; }); +/* unused harmony export isRtl */ +/* unused harmony export defer */ +/* unused harmony export getNodeIndex */ +/* unused harmony export camelize */ +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +var uuid = function uuid() { + var prefix = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ''; + return prefix + ([1e7] + -1e3 + -4e3 + -8e3 + -1e11).replace(/[018]/g, function (c) { + return (c ^ crypto.getRandomValues(new Uint8Array(1))[0] & 15 >> c / 4).toString(16); + }); +}; +var isRtl = function isRtl() { + return document.documentElement.getAttribute('dir') === 'rtl'; +}; + +var defer = function defer() { + this.promise = new Promise(function (resolve, reject) { + this.resolve = resolve; + this.reject = reject; + }.bind(this)); + + this.then = this.promise.then.bind(this.promise); + this.catch = this.promise.catch.bind(this.promise); +}; + +var getNodeIndex = function getNodeIndex(node) { + return [].concat(_toConsumableArray(node.parentNode.children)).indexOf(node); +}; +var camelize = function camelize(str) { + return str.replace(/-(\w)/g, toUpper); +}; + +/***/ }), +/* 7 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__ = __webpack_require__(3); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_device__ = __webpack_require__(8); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + + +var onVisibilityChange = Symbol('onVisibilityChange'); +var onMouseEnter = Symbol('onMouseEnter'); +var onMouseLeave = Symbol('onMouseLeave'); + +var defaultOptions = { + autoplay: false, + autoplaySpeed: 3000 +}; + +var Autoplay = function (_EventEmitter) { + _inherits(Autoplay, _EventEmitter); + + function Autoplay(slider) { + _classCallCheck(this, Autoplay); + + var _this = _possibleConstructorReturn(this, (Autoplay.__proto__ || Object.getPrototypeOf(Autoplay)).call(this)); + + _this.slider = slider; + + _this.onVisibilityChange = _this.onVisibilityChange.bind(_this); + _this.onMouseEnter = _this.onMouseEnter.bind(_this); + _this.onMouseLeave = _this.onMouseLeave.bind(_this); + return _this; + } + + _createClass(Autoplay, [{ + key: 'init', + value: function init() { + this._bindEvents(); + return this; + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + document.addEventListener('visibilitychange', this.onVisibilityChange); + if (this.slider.options.pauseOnHover) { + this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter); + this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave); + } + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + document.removeEventListener('visibilitychange', this.onVisibilityChange); + this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter); + this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave); + } + }, { + key: 'start', + value: function start() { + var _this2 = this; + + this.stop(); + if (this.slider.options.autoplay) { + this.emit('start', this); + this._interval = setInterval(function () { + if (!(_this2._hovering && _this2.slider.options.pauseOnHover)) { + if (!_this2.slider.options.centerMode && _this2.slider.state.next >= _this2.slider.state.length - _this2.slider.slidesToShow && !_this2.slider.options.loop && !_this2.slider.options.infinite) { + _this2.stop(); + } else { + _this2.slider.next(); + } + } + }, this.slider.options.autoplaySpeed); + } + } + }, { + key: 'stop', + value: function stop() { + this._interval = clearInterval(this._interval); + this.emit('stop', this); + } + }, { + key: 'pause', + value: function pause() { + var _this3 = this; + + var speed = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + if (this.paused) { + return; + } + if (this.timer) { + this.stop(); + } + this.paused = true; + if (speed === 0) { + this.paused = false; + this.start(); + } else { + this.slider.on('transition:end', function () { + if (!_this3) { + return; + } + _this3.paused = false; + if (!_this3.run) { + _this3.stop(); + } else { + _this3.start(); + } + }); + } + } + }, { + key: 'onVisibilityChange', + value: function onVisibilityChange(e) { + if (document.hidden) { + this.stop(); + } else { + this.start(); + } + } + }, { + key: 'onMouseEnter', + value: function onMouseEnter(e) { + this._hovering = true; + if (this.slider.options.pauseOnHover) { + this.pause(); + } + } + }, { + key: 'onMouseLeave', + value: function onMouseLeave(e) { + this._hovering = false; + if (this.slider.options.pauseOnHover) { + this.pause(); + } + } + }]); + + return Autoplay; +}(__WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["a"] = (Autoplay); + +/***/ }), +/* 8 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* unused harmony export isIE */ +/* unused harmony export isIETouch */ +/* unused harmony export isAndroid */ +/* unused harmony export isiPad */ +/* unused harmony export isiPod */ +/* unused harmony export isiPhone */ +/* unused harmony export isSafari */ +/* unused harmony export isUiWebView */ +/* unused harmony export supportsTouchEvents */ +/* unused harmony export supportsPointerEvents */ +/* unused harmony export supportsTouch */ +/* unused harmony export pointerDown */ +/* unused harmony export pointerMove */ +/* unused harmony export pointerUp */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return pointerEnter; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return pointerLeave; }); +var isIE = window.navigator.pointerEnabled || window.navigator.msPointerEnabled; +var isIETouch = window.navigator.msPointerEnabled && window.navigator.msMaxTouchPoints > 1 || window.navigator.pointerEnabled && window.navigator.maxTouchPoints > 1; +var isAndroid = navigator.userAgent.match(/(Android);?[\s\/]+([\d.]+)?/); +var isiPad = navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/); +var isiPod = navigator.userAgent.match(/(iPod)(.*OS\s([\d_]+))?/); +var isiPhone = !navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/) && navigator.userAgent.match(/(iPhone\sOS)\s([\d_]+)/); +var isSafari = navigator.userAgent.toLowerCase().indexOf('safari') >= 0 && navigator.userAgent.toLowerCase().indexOf('chrome') < 0 && navigator.userAgent.toLowerCase().indexOf('android') < 0; +var isUiWebView = /(iPhone|iPod|iPad).*AppleWebKit(?!.*Safari)/i.test(navigator.userAgent); + +var supportsTouchEvents = !!('ontouchstart' in window); +var supportsPointerEvents = !!('PointerEvent' in window); +var supportsTouch = supportsTouchEvents || window.DocumentTouch && document instanceof DocumentTouch || navigator.maxTouchPoints; // IE >=11 +var pointerDown = !supportsTouch ? 'mousedown' : 'mousedown ' + (supportsTouchEvents ? 'touchstart' : 'pointerdown'); +var pointerMove = !supportsTouch ? 'mousemove' : 'mousemove ' + (supportsTouchEvents ? 'touchmove' : 'pointermove'); +var pointerUp = !supportsTouch ? 'mouseup' : 'mouseup ' + (supportsTouchEvents ? 'touchend' : 'pointerup'); +var pointerEnter = supportsTouch && supportsPointerEvents ? 'pointerenter' : 'mouseenter'; +var pointerLeave = supportsTouch && supportsPointerEvents ? 'pointerleave' : 'mouseleave'; + +/***/ }), +/* 9 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var onResize = Symbol('onResize'); + +var Breakpoints = function () { + function Breakpoints(slider) { + _classCallCheck(this, Breakpoints); + + this.slider = slider; + this.options = slider.options; + + this[onResize] = this[onResize].bind(this); + + this._bindEvents(); + } + + _createClass(Breakpoints, [{ + key: 'init', + value: function init() { + this._defaultBreakpoint = { + slidesToShow: this.options.slidesToShow, + slidesToScroll: this.options.slidesToScroll + }; + this.options.breakpoints.sort(function (a, b) { + return parseInt(a.changePoint, 10) > parseInt(b.changePoint, 10); + }); + this._currentBreakpoint = this._getActiveBreakpoint(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + window.addEventListener('resize', this[onResize]); + window.addEventListener('orientationchange', this[onResize]); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + window.removeEventListener('resize', this[onResize]); + window.removeEventListener('orientationchange', this[onResize]); + } + }, { + key: '_getActiveBreakpoint', + value: function _getActiveBreakpoint() { + //Get breakpoint for window width + var _iteratorNormalCompletion = true; + var _didIteratorError = false; + var _iteratorError = undefined; + + try { + for (var _iterator = this.options.breakpoints[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) { + var point = _step.value; + + if (point.changePoint >= window.innerWidth) { + return point; + } + } + } catch (err) { + _didIteratorError = true; + _iteratorError = err; + } finally { + try { + if (!_iteratorNormalCompletion && _iterator.return) { + _iterator.return(); + } + } finally { + if (_didIteratorError) { + throw _iteratorError; + } + } + } + + return this._defaultBreakpoint; + } + }, { + key: 'getSlidesToShow', + value: function getSlidesToShow() { + return this._currentBreakpoint ? this._currentBreakpoint.slidesToShow : this._defaultBreakpoint.slidesToShow; + } + }, { + key: 'getSlidesToScroll', + value: function getSlidesToScroll() { + return this._currentBreakpoint ? this._currentBreakpoint.slidesToScroll : this._defaultBreakpoint.slidesToScroll; + } + }, { + key: 'apply', + value: function apply() { + if (this.slider.state.index >= this.slider.state.length && this.slider.state.index !== 0) { + this.slider.state.index = this.slider.state.index - this._currentBreakpoint.slidesToScroll; + } + if (this.slider.state.length <= this._currentBreakpoint.slidesToShow) { + this.slider.state.index = 0; + } + + if (this.options.loop) { + this.slider._loop.init().apply(); + } + + if (this.options.infinite) { + this.slider._infinite.init().apply(); + } + + this.slider._setDimensions(); + this.slider._transitioner.init().apply(true, this.slider._setHeight.bind(this.slider)); + this.slider._setClasses(); + + this.slider._navigation.refresh(); + this.slider._pagination.refresh(); + } + }, { + key: onResize, + value: function value(e) { + var newBreakPoint = this._getActiveBreakpoint(); + if (newBreakPoint.slidesToShow !== this._currentBreakpoint.slidesToShow) { + this._currentBreakpoint = newBreakPoint; + this.apply(); + } + } + }]); + + return Breakpoints; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Breakpoints); + +/***/ }), +/* 10 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var Infinite = function () { + function Infinite(slider) { + _classCallCheck(this, Infinite); + + this.slider = slider; + } + + _createClass(Infinite, [{ + key: 'init', + value: function init() { + if (this.slider.options.infinite && this.slider.options.effect === 'translate') { + if (this.slider.options.centerMode) { + this._infiniteCount = Math.ceil(this.slider.slidesToShow + this.slider.slidesToShow / 2); + } else { + this._infiniteCount = this.slider.slidesToShow; + } + + var frontClones = []; + var slideIndex = 0; + for (var i = this.slider.state.length; i > this.slider.state.length - 1 - this._infiniteCount; i -= 1) { + slideIndex = i - 1; + frontClones.unshift(this._cloneSlide(this.slider.slides[slideIndex], slideIndex - this.slider.state.length)); + } + + var backClones = []; + for (var _i = 0; _i < this._infiniteCount + this.slider.state.length; _i += 1) { + backClones.push(this._cloneSlide(this.slider.slides[_i % this.slider.state.length], _i + this.slider.state.length)); + } + + this.slider.slides = [].concat(frontClones, _toConsumableArray(this.slider.slides), backClones); + } + return this; + } + }, { + key: 'apply', + value: function apply() {} + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.slider.options.infinite) { + if (this.slider.state.next >= this.slider.state.length) { + this.slider.state.index = this.slider.state.next = this.slider.state.next - this.slider.state.length; + this.slider.transitioner.apply(true); + } else if (this.slider.state.next < 0) { + this.slider.state.index = this.slider.state.next = this.slider.state.length + this.slider.state.next; + this.slider.transitioner.apply(true); + } + } + } + }, { + key: '_cloneSlide', + value: function _cloneSlide(slide, index) { + var newSlide = slide.cloneNode(true); + newSlide.dataset.sliderIndex = index; + newSlide.dataset.cloned = true; + var ids = newSlide.querySelectorAll('[id]') || []; + ids.forEach(function (id) { + id.setAttribute('id', ''); + }); + return newSlide; + } + }]); + + return Infinite; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Infinite); + +/***/ }), +/* 11 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_dom__ = __webpack_require__(12); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + +var Loop = function () { + function Loop(slider) { + _classCallCheck(this, Loop); + + this.slider = slider; + } + + _createClass(Loop, [{ + key: "init", + value: function init() { + return this; + } + }, { + key: "apply", + value: function apply() { + if (this.slider.options.loop) { + if (this.slider.state.next > 0) { + if (this.slider.state.next < this.slider.state.length) { + if (this.slider.state.next > this.slider.state.length - this.slider.slidesToShow && Object(__WEBPACK_IMPORTED_MODULE_0__utils_dom__["a" /* isInViewport */])(this.slider._slides[this.slider.state.length - 1], this.slider.wrapper)) { + this.slider.state.next = 0; + } else { + this.slider.state.next = Math.min(Math.max(this.slider.state.next, 0), this.slider.state.length - this.slider.slidesToShow); + } + } else { + this.slider.state.next = 0; + } + } else { + if (this.slider.state.next <= 0 - this.slider.slidesToScroll) { + this.slider.state.next = this.slider.state.length - this.slider.slidesToShow; + } else { + this.slider.state.next = 0; + } + } + } + } + }]); + + return Loop; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Loop); + +/***/ }), +/* 12 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isInViewport; }); +var isInViewport = function isInViewport(element, html) { + var rect = element.getBoundingClientRect(); + html = html || document.documentElement; + return rect.top >= 0 && rect.left >= 0 && rect.bottom <= (window.innerHeight || html.clientHeight) && rect.right <= (window.innerWidth || html.clientWidth); +}; + +/***/ }), +/* 13 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_navigation__ = __webpack_require__(14); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Navigation = function () { + function Navigation(slider) { + _classCallCheck(this, Navigation); + + this.slider = slider; + + this._clickEvents = ['click', 'touch']; + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])(); + + this.onPreviousClick = this.onPreviousClick.bind(this); + this.onNextClick = this.onNextClick.bind(this); + this.onKeyUp = this.onKeyUp.bind(this); + } + + _createClass(Navigation, [{ + key: 'init', + value: function init() { + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_navigation__["a" /* default */])(this.slider.options.icons)); + this._ui = { + previous: this.node.querySelector('.slider-navigation-previous'), + next: this.node.querySelector('.slider-navigation-next') + }; + + this._unbindEvents(); + this._bindEvents(); + + this.refresh(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + var _this = this; + + this.slider.wrapper.addEventListener('keyup', this.onKeyUp); + this._clickEvents.forEach(function (clickEvent) { + _this._ui.previous.addEventListener(clickEvent, _this.onPreviousClick); + _this._ui.next.addEventListener(clickEvent, _this.onNextClick); + }); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + var _this2 = this; + + this.slider.wrapper.removeEventListener('keyup', this.onKeyUp); + this._clickEvents.forEach(function (clickEvent) { + _this2._ui.previous.removeEventListener(clickEvent, _this2.onPreviousClick); + _this2._ui.next.removeEventListener(clickEvent, _this2.onNextClick); + }); + } + }, { + key: 'onNextClick', + value: function onNextClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + if (this.slider.options.navigation) { + this.slider.next(); + } + } + }, { + key: 'onPreviousClick', + value: function onPreviousClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + if (this.slider.options.navigation) { + this.slider.previous(); + } + } + }, { + key: 'onKeyUp', + value: function onKeyUp(e) { + if (this.slider.options.keyNavigation) { + if (e.key === 'ArrowRight' || e.key === 'Right') { + this.slider.next(); + } else if (e.key === 'ArrowLeft' || e.key === 'Left') { + this.slider.previous(); + } + } + } + }, { + key: 'refresh', + value: function refresh() { + // let centerOffset = Math.floor(this.options.slidesToShow / 2); + if (!this.slider.options.loop && !this.slider.options.infinite) { + if (this.slider.options.navigation && this.slider.state.length > this.slider.slidesToShow) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.remove('is-hidden'); + if (this.slider.state.next === 0) { + this._ui.previous.classList.add('is-hidden'); + this._ui.next.classList.remove('is-hidden'); + } else if (this.slider.state.next >= this.slider.state.length - this.slider.slidesToShow && !this.slider.options.centerMode) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } else if (this.slider.state.next >= this.slider.state.length - 1 && this.slider.options.centerMode) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } + } else { + this._ui.previous.classList.add('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } + } + } + }, { + key: 'render', + value: function render() { + return this.node; + } + }]); + + return Navigation; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Navigation); + +/***/ }), +/* 14 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function (icons) { + return "
" + icons.previous + "
\n
" + icons.next + "
"; +}); + +/***/ }), +/* 15 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_pagination__ = __webpack_require__(16); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__ = __webpack_require__(17); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + + +var Pagination = function () { + function Pagination(slider) { + _classCallCheck(this, Pagination); + + this.slider = slider; + + this._clickEvents = ['click', 'touch']; + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__["a" /* default */])(); + + this.onPageClick = this.onPageClick.bind(this); + this.onResize = this.onResize.bind(this); + } + + _createClass(Pagination, [{ + key: 'init', + value: function init() { + this._pages = []; + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_pagination__["a" /* default */])()); + this._ui = { + container: this.node.firstChild + }; + + this._count = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll); + + this._draw(); + this.refresh(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + var _this = this; + + window.addEventListener('resize', this.onResize); + window.addEventListener('orientationchange', this.onResize); + + this._clickEvents.forEach(function (clickEvent) { + _this._pages.forEach(function (page) { + return page.addEventListener(clickEvent, _this.onPageClick); + }); + }); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + var _this2 = this; + + window.removeEventListener('resize', this.onResize); + window.removeEventListener('orientationchange', this.onResize); + + this._clickEvents.forEach(function (clickEvent) { + _this2._pages.forEach(function (page) { + return page.removeEventListener(clickEvent, _this2.onPageClick); + }); + }); + } + }, { + key: '_draw', + value: function _draw() { + this._ui.container.innerHTML = ''; + if (this.slider.options.pagination && this.slider.state.length > this.slider.slidesToShow) { + for (var i = 0; i <= this._count; i++) { + var newPageNode = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__["a" /* default */])()).firstChild; + newPageNode.dataset.index = i * this.slider.slidesToScroll; + this._pages.push(newPageNode); + this._ui.container.appendChild(newPageNode); + } + this._bindEvents(); + } + } + }, { + key: 'onPageClick', + value: function onPageClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + this.slider.state.next = e.currentTarget.dataset.index; + this.slider.show(); + } + }, { + key: 'onResize', + value: function onResize() { + this._draw(); + } + }, { + key: 'refresh', + value: function refresh() { + var _this3 = this; + + var newCount = void 0; + + if (this.slider.options.infinite) { + newCount = Math.ceil(this.slider.state.length - 1 / this.slider.slidesToScroll); + } else { + newCount = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll); + } + if (newCount !== this._count) { + this._count = newCount; + this._draw(); + } + + this._pages.forEach(function (page) { + page.classList.remove('is-active'); + if (parseInt(page.dataset.index, 10) === _this3.slider.state.next % _this3.slider.state.length) { + page.classList.add('is-active'); + } + }); + } + }, { + key: 'render', + value: function render() { + return this.node; + } + }]); + + return Pagination; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Pagination); + +/***/ }), +/* 16 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }), +/* 17 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }), +/* 18 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Swipe = function () { + function Swipe(slider) { + _classCallCheck(this, Swipe); + + this.slider = slider; + + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])(); + + this.onStartDrag = this.onStartDrag.bind(this); + this.onMoveDrag = this.onMoveDrag.bind(this); + this.onStopDrag = this.onStopDrag.bind(this); + + this._init(); + } + + _createClass(Swipe, [{ + key: '_init', + value: function _init() {} + }, { + key: 'bindEvents', + value: function bindEvents() { + var _this = this; + + this.slider.container.addEventListener('dragstart', function (e) { + if (!_this._supportsPassive) { + e.preventDefault(); + } + }); + this.slider.container.addEventListener('mousedown', this.onStartDrag); + this.slider.container.addEventListener('touchstart', this.onStartDrag); + + window.addEventListener('mousemove', this.onMoveDrag); + window.addEventListener('touchmove', this.onMoveDrag); + + window.addEventListener('mouseup', this.onStopDrag); + window.addEventListener('touchend', this.onStopDrag); + window.addEventListener('touchcancel', this.onStopDrag); + } + }, { + key: 'unbindEvents', + value: function unbindEvents() { + var _this2 = this; + + this.slider.container.removeEventListener('dragstart', function (e) { + if (!_this2._supportsPassive) { + e.preventDefault(); + } + }); + this.slider.container.removeEventListener('mousedown', this.onStartDrag); + this.slider.container.removeEventListener('touchstart', this.onStartDrag); + + window.removeEventListener('mousemove', this.onMoveDrag); + window.removeEventListener('touchmove', this.onMoveDrag); + + window.removeEventListener('mouseup', this.onStopDrag); + window.removeEventListener('mouseup', this.onStopDrag); + window.removeEventListener('touchcancel', this.onStopDrag); + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onStartDrag', + value: function onStartDrag(e) { + if (e.touches) { + if (e.touches.length > 1) { + return; + } else { + e = e.touches[0]; + } + } + + this._origin = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](e.screenX, e.screenY); + this.width = this.slider.wrapperWidth; + this.slider.transitioner.disable(); + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onMoveDrag', + value: function onMoveDrag(e) { + if (this._origin) { + var point = e.touches ? e.touches[0] : e; + this._lastTranslate = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](point.screenX - this._origin.x, point.screenY - this._origin.y); + if (e.touches) { + if (Math.abs(this._lastTranslate.x) > Math.abs(this._lastTranslate.y)) { + if (!this._supportsPassive) { + e.preventDefault(); + } + e.stopPropagation(); + } + } + } + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onStopDrag', + value: function onStopDrag(e) { + if (this._origin && this._lastTranslate) { + if (Math.abs(this._lastTranslate.x) > 0.2 * this.width) { + if (this._lastTranslate.x < 0) { + this.slider.next(); + } else { + this.slider.previous(); + } + } else { + this.slider.show(true); + } + } + this._origin = null; + this._lastTranslate = null; + } + }]); + + return Swipe; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Swipe); + +/***/ }), +/* 19 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__transitions_fade__ = __webpack_require__(20); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__transitions_translate__ = __webpack_require__(21); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Transitioner = function () { + function Transitioner(slider) { + _classCallCheck(this, Transitioner); + + this.slider = slider; + this.options = slider.options; + + this._animating = false; + this._animation = undefined; + + this._translate = new __WEBPACK_IMPORTED_MODULE_1__transitions_translate__["a" /* default */](this, slider, slider.options); + this._fade = new __WEBPACK_IMPORTED_MODULE_0__transitions_fade__["a" /* default */](this, slider, slider.options); + } + + _createClass(Transitioner, [{ + key: 'init', + value: function init() { + this._fade.init(); + this._translate.init(); + return this; + } + }, { + key: 'isAnimating', + value: function isAnimating() { + return this._animating; + } + }, { + key: 'enable', + value: function enable() { + this._animation && this._animation.enable(); + } + }, { + key: 'disable', + value: function disable() { + this._animation && this._animation.disable(); + } + }, { + key: 'apply', + value: function apply(force, callback) { + // If we don't force refresh and animation in progress then return + if (this._animating && !force) { + return; + } + + switch (this.options.effect) { + case 'fade': + this._animation = this._fade; + break; + case 'translate': + default: + this._animation = this._translate; + break; + } + + this._animationCallback = callback; + + if (force) { + this._animation && this._animation.disable(); + } else { + this._animation && this._animation.enable(); + this._animating = true; + } + + this._animation && this._animation.apply(); + + if (force) { + this.end(); + } + } + }, { + key: 'end', + value: function end() { + this._animating = false; + this._animation = undefined; + this.slider.state.index = this.slider.state.next; + if (this._animationCallback) { + this._animationCallback(); + } + } + }]); + + return Transitioner; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Transitioner); + +/***/ }), +/* 20 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_css__ = __webpack_require__(0); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + +var Fade = function () { + function Fade(transitioner, slider) { + var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {}; + + _classCallCheck(this, Fade); + + this.transitioner = transitioner; + this.slider = slider; + this.options = _extends({}, options); + } + + _createClass(Fade, [{ + key: 'init', + value: function init() { + var _this = this; + + if (this.options.effect === 'fade') { + this.slider.slides.forEach(function (slide, index) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(slide, { + position: 'absolute', + left: 0, + top: 0, + bottom: 0, + 'z-index': slide.dataset.sliderIndex == _this.slider.state.index ? 0 : -2, + opacity: slide.dataset.sliderIndex == _this.slider.state.index ? 1 : 0 + }); + }); + } + return this; + } + }, { + key: 'enable', + value: function enable() { + var _this2 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this2.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this2.slider.state.next; + })[0]; + if (this._newSlide) { + this._newSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._newSlide.style.transition = this.options.duration + 'ms ' + this.options.timing; + if (this._oldSlide) { + this._oldSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._oldSlide.style.transition = this.options.duration + 'ms ' + this.options.timing; + } + } + } + }, { + key: 'disable', + value: function disable() { + var _this3 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this3.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this3.slider.state.next; + })[0]; + if (this._newSlide) { + this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._newSlide.style.transition = 'none'; + if (this._oldSlide) { + this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._oldSlide.style.transition = 'none'; + } + } + } + }, { + key: 'apply', + value: function apply(force) { + var _this4 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this4.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this4.slider.state.next; + })[0]; + + if (this._oldSlide && this._newSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, { + opacity: 0 + }); + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, { + opacity: 1, + 'z-index': force ? 0 : -1 + }); + } + } + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.options.effect === 'fade') { + if (this.transitioner.isAnimating() && e.target == this._newSlide) { + if (this._newSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, { + 'z-index': 0 + }); + this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + } + if (this._oldSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, { + 'z-index': -2 + }); + this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + } + } + this.transitioner.end(); + } + } + }]); + + return Fade; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Fade); + +/***/ }), +/* 21 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Translate = function () { + function Translate(transitioner, slider) { + var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {}; + + _classCallCheck(this, Translate); + + this.transitioner = transitioner; + this.slider = slider; + this.options = _extends({}, options); + + this.onTransitionEnd = this.onTransitionEnd.bind(this); + } + + _createClass(Translate, [{ + key: 'init', + value: function init() { + this._position = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](this.slider.container.offsetLeft, this.slider.container.offsetTop); + this._bindEvents(); + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + this.slider.container.addEventListener('transitionend', this.onTransitionEnd); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + this.slider.container.removeEventListener('transitionend', this.onTransitionEnd); + } + }, { + key: 'enable', + value: function enable() { + this.slider.container.style.transition = this.options.duration + 'ms ' + this.options.timing; + } + }, { + key: 'disable', + value: function disable() { + this.slider.container.style.transition = 'none'; + } + }, { + key: 'apply', + value: function apply() { + var _this = this; + + var maxOffset = void 0; + if (this.options.effect === 'translate') { + var slide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this.slider.state.next; + })[0]; + var slideOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](slide.offsetLeft, slide.offsetTop); + if (this.options.centerMode) { + maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container))); + } else { + maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.wrapper)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.wrapper))); + } + var nextOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.min(Math.max(slideOffset.x * -1, maxOffset.x * -1), 0), Math.min(Math.max(slideOffset.y * -1, maxOffset.y * -1), 0)); + if (this.options.loop) { + if (!this.options.vertical && Math.abs(this._position.x) > maxOffset.x) { + nextOffset.x = 0; + this.slider.state.next = 0; + } else if (this.options.vertical && Math.abs(this._position.y) > maxOffset.y) { + nextOffset.y = 0; + this.slider.state.next = 0; + } + } + + this._position.x = nextOffset.x; + this._position.y = nextOffset.y; + if (this.options.centerMode) { + this._position.x = this._position.x + this.slider.wrapperWidth / 2 - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(slide) / 2; + } + + if (this.slider.direction === 'rtl') { + this._position.x = -this._position.x; + this._position.y = -this._position.y; + } + this.slider.container.style.transform = 'translate3d(' + this._position.x + 'px, ' + this._position.y + 'px, 0)'; + + /** + * update the index with the nextIndex only if + * the offset of the nextIndex is in the range of the maxOffset + */ + if (slideOffset.x > maxOffset.x) { + this.slider.transitioner.end(); + } + } + } + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.options.effect === 'translate') { + + if (this.transitioner.isAnimating() && e.target == this.slider.container) { + if (this.options.infinite) { + this.slider._infinite.onTransitionEnd(e); + } + } + this.transitioner.end(); + } + } + }]); + + return Translate; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Translate); + +/***/ }), +/* 22 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var defaultOptions = { + initialSlide: 0, + slidesToScroll: 1, + slidesToShow: 1, + + navigation: true, + navigationKeys: true, + navigationSwipe: true, + + pagination: true, + + loop: false, + infinite: false, + + effect: 'translate', + duration: 300, + timing: 'ease', + + autoplay: false, + autoplaySpeed: 3000, + pauseOnHover: true, + breakpoints: [{ + changePoint: 480, + slidesToShow: 1, + slidesToScroll: 1 + }, { + changePoint: 640, + slidesToShow: 2, + slidesToScroll: 2 + }, { + changePoint: 768, + slidesToShow: 3, + slidesToScroll: 3 + }], + + onReady: null, + icons: { + 'previous': '\n \n ', + 'next': '\n \n ' + } +}; + +/* harmony default export */ __webpack_exports__["a"] = (defaultOptions); + +/***/ }), +/* 23 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function (id) { + return "
\n
\n
"; +}); + +/***/ }), +/* 24 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
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"symbol" : typeof obj; }; + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + +var isString = function isString(unknown) { + return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]'; +}; + +var bulmaSlider = function (_EventEmitter) { + _inherits(bulmaSlider, _EventEmitter); + + function bulmaSlider(selector) { + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + _classCallCheck(this, bulmaSlider); + + var _this = _possibleConstructorReturn(this, (bulmaSlider.__proto__ || Object.getPrototypeOf(bulmaSlider)).call(this)); + + _this.element = typeof selector === 'string' ? document.querySelector(selector) : selector; + // An invalid selector or non-DOM node has been provided. + if (!_this.element) { + throw new Error('An invalid selector or non-DOM node has been provided.'); + } + + _this._clickEvents = ['click']; + /// Set default options and merge with instance defined + _this.options = _extends({}, options); + + _this.onSliderInput = _this.onSliderInput.bind(_this); + + _this.init(); + return _this; + } + + /** + * Initiate all DOM element containing selector + * @method + * @return {Array} Array of all slider instances + */ + + + _createClass(bulmaSlider, [{ + key: 'init', + + + /** + * Initiate plugin + * @method init + * @return {void} + */ + value: function init() { + this._id = 'bulmaSlider' + new Date().getTime() + Math.floor(Math.random() * Math.floor(9999)); + this.output = this._findOutputForSlider(); + + this._bindEvents(); + + if (this.output) { + if (this.element.classList.contains('has-output-tooltip')) { + // Get new output position + var newPosition = this._getSliderOutputPosition(); + + // Set output position + this.output.style['left'] = newPosition.position; + } + } + + this.emit('bulmaslider:ready', this.element.value); + } + }, { + key: '_findOutputForSlider', + value: function _findOutputForSlider() { + var _this2 = this; + + var result = null; + var outputs = document.getElementsByTagName('output') || []; + + Array.from(outputs).forEach(function (output) { + if (output.htmlFor == _this2.element.getAttribute('id')) { + result = output; + return true; + } + }); + return result; + } + }, { + key: '_getSliderOutputPosition', + value: function _getSliderOutputPosition() { + // Update output position + var newPlace, minValue; + + var style = window.getComputedStyle(this.element, null); + // Measure width of range input + var sliderWidth = parseInt(style.getPropertyValue('width'), 10); + + // Figure out placement percentage between left and right of input + if (!this.element.getAttribute('min')) { + minValue = 0; + } else { + minValue = this.element.getAttribute('min'); + } + var newPoint = (this.element.value - minValue) / (this.element.getAttribute('max') - minValue); + + // Prevent bubble from going beyond left or right (unsupported browsers) + if (newPoint < 0) { + newPlace = 0; + } else if (newPoint > 1) { + newPlace = sliderWidth; + } else { + newPlace = sliderWidth * newPoint; + } + + return { + 'position': newPlace + 'px' + }; + } + + /** + * Bind all events + * @method _bindEvents + * @return {void} + */ + + }, { + key: '_bindEvents', + value: function _bindEvents() { + if (this.output) { + // Add event listener to update output when slider value change + this.element.addEventListener('input', this.onSliderInput, false); + } + } + }, { + key: 'onSliderInput', + value: function onSliderInput(e) { + e.preventDefault(); + + if (this.element.classList.contains('has-output-tooltip')) { + // Get new output position + var newPosition = this._getSliderOutputPosition(); + + // Set output position + this.output.style['left'] = newPosition.position; + } + + // Check for prefix and postfix + var prefix = this.output.hasAttribute('data-prefix') ? this.output.getAttribute('data-prefix') : ''; + var postfix = this.output.hasAttribute('data-postfix') ? this.output.getAttribute('data-postfix') : ''; + + // Update output with slider value + this.output.value = prefix + this.element.value + postfix; + + this.emit('bulmaslider:ready', this.element.value); + } + }], [{ + key: 'attach', + value: function attach() { + var _this3 = this; + + var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 'input[type="range"].slider'; + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + var instances = new Array(); + + var elements = isString(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector]; + elements.forEach(function (element) { + if (typeof element[_this3.constructor.name] === 'undefined') { + var instance = new bulmaSlider(element, options); + element[_this3.constructor.name] = instance; + instances.push(instance); + } else { + instances.push(element[_this3.constructor.name]); + } + }); + + return instances; + } + }]); + + return bulmaSlider; +}(__WEBPACK_IMPORTED_MODULE_0__events__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["default"] = (bulmaSlider); + +/***/ }), +/* 1 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var EventEmitter = function () { + function EventEmitter() { + var listeners = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : []; + + _classCallCheck(this, EventEmitter); + + this._listeners = new Map(listeners); + this._middlewares = new Map(); + } + + _createClass(EventEmitter, [{ + key: "listenerCount", + value: function listenerCount(eventName) { + if (!this._listeners.has(eventName)) { + return 0; + } + + var eventListeners = this._listeners.get(eventName); + return eventListeners.length; + } + }, { + key: "removeListeners", + value: function removeListeners() { + var _this = this; + + var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + var middleware = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + if (eventName !== null) { + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this.removeListeners(e, middleware); + }); + } else { + this._listeners.delete(eventName); + + if (middleware) { + this.removeMiddleware(eventName); + } + } + } else { + this._listeners = new Map(); + } + } + }, { + key: "middleware", + value: function middleware(eventName, fn) { + var _this2 = this; + + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this2.middleware(e, fn); + }); + } else { + if (!Array.isArray(this._middlewares.get(eventName))) { + this._middlewares.set(eventName, []); + } + + this._middlewares.get(eventName).push(fn); + } + } + }, { + key: "removeMiddleware", + value: function removeMiddleware() { + var _this3 = this; + + var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + + if (eventName !== null) { + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this3.removeMiddleware(e); + }); + } else { + this._middlewares.delete(eventName); + } + } else { + this._middlewares = new Map(); + } + } + }, { + key: "on", + value: function on(name, callback) { + var _this4 = this; + + var once = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; + + if (Array.isArray(name)) { + name.forEach(function (e) { + return _this4.on(e, callback); + }); + } else { + name = name.toString(); + var split = name.split(/,|, | /); + + if (split.length > 1) { + split.forEach(function (e) { + return _this4.on(e, callback); + }); + } else { + if (!Array.isArray(this._listeners.get(name))) { + this._listeners.set(name, []); + } + + this._listeners.get(name).push({ once: once, callback: callback }); + } + } + } + }, { + key: "once", + value: function once(name, callback) { + this.on(name, callback, true); + } + }, { + key: "emit", + value: function emit(name, data) { + var _this5 = this; + + var silent = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; + + name = name.toString(); + var listeners = this._listeners.get(name); + var middlewares = null; + var doneCount = 0; + var execute = silent; + + if (Array.isArray(listeners)) { + listeners.forEach(function (listener, index) { + // Start Middleware checks unless we're doing a silent emit + if (!silent) { + middlewares = _this5._middlewares.get(name); + // Check and execute Middleware + if (Array.isArray(middlewares)) { + middlewares.forEach(function (middleware) { + middleware(data, function () { + var newData = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + + if (newData !== null) { + data = newData; + } + doneCount++; + }, name); + }); + + if (doneCount >= middlewares.length) { + execute = true; + } + } else { + execute = true; + } + } + + // If Middleware checks have been passed, execute + if (execute) { + if (listener.once) { + listeners[index] = null; + } + listener.callback(data); + } + }); + + // Dirty way of removing used Events + while (listeners.indexOf(null) !== -1) { + listeners.splice(listeners.indexOf(null), 1); + } + } + } + }]); + + return EventEmitter; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (EventEmitter); + +/***/ }) +/******/ ])["default"]; +}); \ No newline at end of file diff --git a/documentation/static/js/bulma-slider.min.js b/documentation/static/js/bulma-slider.min.js new file mode 100644 index 0000000..7e62685 --- /dev/null +++ b/documentation/static/js/bulma-slider.min.js @@ -0,0 +1 @@ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.bulmaSlider=e():t.bulmaSlider=e()}("undefined"!=typeof self?self:this,function(){return function(n){var r={};function i(t){if(r[t])return r[t].exports;var e=r[t]={i:t,l:!1,exports:{}};return n[t].call(e.exports,e,e.exports,i),e.l=!0,e.exports}return i.m=n,i.c=r,i.d=function(t,e,n){i.o(t,e)||Object.defineProperty(t,e,{configurable:!1,enumerable:!0,get:n})},i.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return i.d(e,"a",e),e},i.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},i.p="",i(i.s=0)}([function(t,e,n){"use strict";Object.defineProperty(e,"__esModule",{value:!0}),n.d(e,"isString",function(){return l});var r=n(1),i=Object.assign||function(t){for(var e=1;e=l.length&&(s=!0)):s=!0),s&&(t.once&&(u[e]=null),t.callback(r))});-1!==u.indexOf(null);)u.splice(u.indexOf(null),1)}}]),e}();e.a=i}]).default}); 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DeepChopper Encode + +Now that we have our basecalled sequences, we'll use DeepChopper to encode the data. +This step prepares the data for the prediction model. + +```bash +# Encode the FASTQ file +deepchopper encode raw.fastq +``` + +If you have a large dataset, you can use `--chunk` to encode dataset by chunk, which avoid memory issues: + +```bash +deepchopper encode raw.fastq --chunk --chunk-size 100000 +``` + +This command will generate a Parquet file (`encoded_data.parquet`) or multiple Parquets files (if encoding by chunk) containing the encoded sequences. + +## 4. DeepChopper Predict + +With our encoded data, we can now use DeepChopper to predict chimeric reads. + +```bash +# Predict chimeric reads +deepchopper predict raw.parquet --ouput-path predictions + +# if encoded by chunk +# deepchopper predict raw_chunk1.parquet --ouput-path predictions_chunk1 +# deepchopper predict raw_chunk2.parquet --ouput-path predictions_chunk2 +``` + +This step will analyze the encoded data and produce a CSV file (`predictions.csv`) containing predictions for each read, indicating whether it's likely to be chimeric or not. + +## 5. DeepChopper Chop + +Finally, we'll use DeepChopper to chop the identified chimeric reads, removing artificial sequences and preserving the genuine RNA sequences. + +```bash +# Chop chimeric reads +deepchopper chop predictions/0 raw.fastq + +# if encoded by chunk +# deepchopper chop predictions_chunk1/0 prediction_chunk2/0 raw.fastq +``` + +This command takes the original FASTQ file (`raw.fastq`) and the predictions (`predictions`), and produces a new FASTQ file (with suffix `.chop.fq.bgz`) with the chimeric reads chopped. + +## Conclusion + +You've now successfully processed your Nanopore direct-RNA sequencing data using DeepChopper! +The file with suffix `.chop.fq.bgz` contains your sequencing data with chimeric artificial reads identified and removed, providing you with higher quality data for your downstream analyses. + +Remember to adjust file paths and names according to your specific setup and data. +For more advanced usage and options, refer to the DeepChopper documentation or use the `--help` flag with each command. + +Happy sequencing! diff --git a/notebooks/smooth2.ipynb b/notebooks/smooth2.ipynb index 647bab8..5dbf839 100644 --- a/notebooks/smooth2.ipynb +++ b/notebooks/smooth2.ipynb @@ -14956,7 +14956,9 @@ "cell_type": "code", "execution_count": 12, "id": "36c9d2ed-52a1-4f56-beb0-29697f208039", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -15282,7 +15284,9 @@ "cell_type": "code", "execution_count": 43, "id": "f0fc7385-e6e0-4031-87d4-959deac7e2ae", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -190987,9 +190991,9 @@ ], "metadata": { "kernelspec": { - "display_name": "dc", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "dc" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/poetry.lock b/poetry.lock index d229a22..6cf5341 100644 --- a/poetry.lock +++ b/poetry.lock @@ -42,100 +42,127 @@ files = [ ] [[package]] -name = 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"sys_platform == 'linux'" }, { version = "^2.1.0", source = "PyPI", markers = "sys_platform == 'darwin'" }, ] + lightning = "^2.1.2" torchmetrics = "^1.2.0" rich = "^13.7.0" -omegaconf = "^2.3.0" -hydra-core = "^1.3.2" -hydra-colorlog = "^1.2.0" -hydra-optuna-sweeper = "^1.2.0" scipy = "1.11.3" transformers = "^4.37.2" safetensors = "^0.4.2" -pyarrow = "^15.0.0" pandas = "^2.2.0" datasets = "^2.17.1" seqeval = "^1.2.2" -tokenizers = "^0.15.2" einops = "^0.7.0" -accelerate = "^0.27.2" +tokenizers = "^0.15.2" torchinfo = "^1.8.0" evaluate = "^0.4.1" -deepspeed = "^0.13.4" typer = "^0.9.0" -nvidia-cuda-cupti-cu11 = { version = "^11.8.87", markers = "sys_platform == 'linux'" } -nvidia-cuda-nvrtc-cu11 = { version = "^11.8.89", markers = "sys_platform == 'linux'" } -nvidia-cuda-runtime-cu11 = { version = "^11.8.89", markers = "sys_platform == 'linux'" } -nvidia-cudnn-cu11 = { version = "^8.9.6.50", markers = "sys_platform == 'linux'" } -nvidia-cufft-cu11 = { version = "^10.9.0.58", markers = "sys_platform == 'linux'" } -nvidia-curand-cu11 = { version = "^10.3.0.86", markers = "sys_platform == 'linux'" } -nvidia-cusolver-cu11 = { version = "^11.4.1.48", markers = "sys_platform == 'linux'" } -nvidia-cusparse-cu11 = { version = "^11.7.5.86", markers = "sys_platform == 'linux'" } -nvidia-nccl-cu11 = { version = "^2.19.3", markers = "sys_platform == 'linux'" } -nvidia-nvtx-cu11 = { version = "^11.8.86", markers = "sys_platform == 'linux'" } -nvidia-cublas-cu11 = { version = "^11.11.3.6", markers = "sys_platform == 'linux'" } -gradio = "^4.21.0" -mamba-ssm = "^1.2.0.post1" -causal-conv1d = "^1.2.0.post2" +gradio = "4.26.0" +fastapi = "0.112.2" [[tool.poetry.source]] name = "pytorch" @@ -99,8 +81,8 @@ priority = "explicit" name = "PyPI" priority = "primary" - [tool.poetry.group.dev.dependencies] +pyarrow = "^15.0.0" pytest-sugar = "^0.9.7" pytest = "^7.4.3" ipdb = "^0.13.13" @@ -121,12 +103,30 @@ fire = "^0.5.0" wandb = "^0.17.0" ruff = "^0.4.7" hydra-submitit-launcher = "^1.2.0" -hydra-optuna-sweeper = "^1.2.0" onnx = "^1.16.0" onnxscript = "^0.1.0.dev20240407" plotly = "^5.20.0" gget = "^0.28.4" joblib = "^1.4.0" +hydra-core = "^1.3.2" +hydra-colorlog = "^1.2.0" +hydra-optuna-sweeper = "^1.2.0" +omegaconf = "^2.3.0" +accelerate = "^0.27.2" +deepspeed = "^0.13.4" +nvidia-cuda-cupti-cu11 = { version = "^11.8.87", markers = "sys_platform == 'linux'" } +nvidia-cuda-nvrtc-cu11 = { version = "^11.8.89", markers = "sys_platform == 'linux'" } +nvidia-cuda-runtime-cu11 = { version = "^11.8.89", markers = "sys_platform == 'linux'" } +nvidia-cudnn-cu11 = { version = "^8.9.6.50", markers = "sys_platform == 'linux'" } +nvidia-cufft-cu11 = { version = "^10.9.0.58", markers = "sys_platform == 'linux'" } +nvidia-curand-cu11 = { version = "^10.3.0.86", markers = "sys_platform == 'linux'" } +nvidia-cusolver-cu11 = { version = "^11.4.1.48", markers = "sys_platform == 'linux'" } +nvidia-cusparse-cu11 = { version = "^11.7.5.86", markers = "sys_platform == 'linux'" } +nvidia-nccl-cu11 = { version = "^2.19.3", markers = "sys_platform == 'linux'" } +nvidia-nvtx-cu11 = { version = "^11.8.86", markers = "sys_platform == 'linux'" } +nvidia-cublas-cu11 = { version = "^11.11.3.6", markers = "sys_platform == 'linux'" } +mamba-ssm = { version = "^1.2.0.post1", markers = "sys_platform == 'linux'" } +causal-conv1d = { version = "^1.2.0.post2", markers = "sys_platform == 'linux'" } [project.urls] diff --git a/scripts/compute_distance.py b/scripts/compute_distance.py index 4a5d601..9330549 100644 --- a/scripts/compute_distance.py +++ b/scripts/compute_distance.py @@ -1,4 +1,4 @@ -import pandas as pd +import pandas as pd import sys import logging from rich.logging import RichHandler @@ -17,25 +17,25 @@ @dataclass -class Segment: - chrom: str +class Segment: + chrom: str start: int - end: int + end: int def overlap(self, other): return self.chrom == other.chrom and self.start <= other.end and self.end >= other.start - + def load_read_data(filter_full_df): from collections import defaultdict data = defaultdict(list) for r, a in zip(filter_full_df.read_name, filter_full_df.alignments): - a1, a2 = a.split(",") + a1, a2 = a.split(",") a1_chrom, a1_start, a1_end,_ = a1.split(":") a2_chrom, a2_start, a2_end,_ = a2.split(":") - a1_seg = Segment(a1_chrom, int(a1_start), int(a1_end)) + a1_seg = Segment(a1_chrom, int(a1_start), int(a1_end)) a2_seg = Segment(a2_chrom, int(a2_start), int(a2_end)) if a1_seg.chrom > a2_seg.chrom: @@ -48,13 +48,13 @@ def load_read_data(filter_full_df): def get_similarity(r1, r2, data): if r1 == r2: return True - + r1_s1, r1_s2 = data[r1] - r2_s1, r2_s2 = data[r2] + r2_s1, r2_s2 = data[r2] - flag1 = r1_s1.overlap(r2_s1) + flag1 = r1_s1.overlap(r2_s1) flag2 = r1_s2.overlap(r2_s2) - + return flag1 and flag2 @@ -106,6 +106,6 @@ def main(full_df_path: Path, sp_df_path: Path, export_with_sp: bool = False): logger.info(f"Distance matrix shape: {distance_matrix.shape}") np.savez(f"{full_df_path}_distance.npz", distance_matrix=distance_matrix, id_to_read=id_to_read, read_to_id=read_to_id) - + if __name__ == "__main__": fire.Fire(main) diff --git a/src/cli.rs b/src/cli.rs new file mode 100644 index 0000000..15d8d08 --- /dev/null +++ b/src/cli.rs @@ -0,0 +1,165 @@ +use ahash::HashMap; +use anyhow::Result; +use bstr::BStr; +use derive_builder::Builder; +use rayon::prelude::*; +use std::path::PathBuf; + +use crate::default; +use crate::output; +use crate::output::ChopType; +use crate::smooth::load_predicts_from_batch_pts; + +#[derive(Debug, Builder)] +pub struct PredictOptions { + pub predicts: Vec, + + pub fq: PathBuf, + + #[builder(default = "Some(2)")] + pub threads: Option, + + /// max predict batch size + pub max_batch_size: Option, + + /// smooth_window_size + #[builder(default = "21")] + pub smooth_window_size: usize, + + /// min_interval_size + #[builder(default = "13")] + pub min_interval_size: usize, + + /// approved_interval_number + #[builder(default = "20")] + pub approved_interval_number: usize, + + /// max process intervals + #[builder(default = "4")] + pub max_process_intervals: usize, + + /// min read length after chop + #[builder(default = "20")] + pub min_read_length_after_chop: usize, + + /// output chopped fq file only + #[builder(default = "false")] + pub output_chopped_seqs: bool, + + /// selected chopped type + #[builder(default = "ChopType::All")] + pub chop_type: ChopType, + + /// prefix for output files + pub output_prefix: Option, +} + +pub fn predict_cli(cli: &PredictOptions) -> Result<()> { + log::info!("export {:?} chopped type ", cli.chop_type); + + let all_predicts = cli + .predicts + .par_iter() // NOTE: par_iter will hang in python + .map(|path| { + load_predicts_from_batch_pts( + path.to_path_buf(), + default::IGNORE_LABEL, + cli.max_batch_size, + ) + }) + .collect::>>()? + .into_par_iter() + .flatten() + .collect::>(); + + let all_predicts_number = all_predicts.len(); + log::info!("Collect {} predicts", all_predicts_number); + + // load fq file + let mut _records = output::read_noodel_records_from_fq_or_zip_fq(&cli.fq)?; + let fq_records = _records + .into_par_iter() + .map(|record| { + let id = String::from_utf8(record.definition().name().to_vec()).unwrap(); + (id, record) + }) + .collect::>(); + + log::info!( + "Load {} fq records from {}", + fq_records.len(), + cli.fq.display() + ); + + let res = all_predicts + .into_par_iter() + .map(|(id, predict)| { + let fq_record = fq_records.get(&id).expect("id not found"); + + if predict.seq.len() < default::MIN_READ_LEN { + return Ok(vec![fq_record.clone()]); + } + + let smooth_intervals = predict.smooth_and_select_intervals( + cli.smooth_window_size, + cli.min_interval_size, + cli.approved_interval_number, + ); + + if smooth_intervals.len() > cli.max_process_intervals || smooth_intervals.is_empty() { + return Ok(vec![fq_record.clone()]); + } + + if predict.seq.len() != fq_record.quality_scores().len() { + // truncate seq prediction, do not process + return Ok(vec![fq_record.clone()]); + }; + + if cli.output_chopped_seqs { + output::split_noodle_records_by_intervals( + BStr::new(&predict.seq), + id.as_bytes().into(), + fq_record.quality_scores(), + &smooth_intervals, + ) + } else { + output::split_noodle_records_by_remove_intervals( + BStr::new(&predict.seq), + id.as_bytes().into(), + fq_record.quality_scores(), + &smooth_intervals, + cli.min_read_length_after_chop, + true, // NOTE: add annotation for terminal or internal chop + &cli.chop_type, + ) + } + }) + .collect::>>()? + .into_par_iter() + .flatten() + .collect::>(); + + let output_file = if let Some(prefix) = &cli.output_prefix { + format!( + "{}.{}pd.{}record.chop.fq.bgz", + prefix, + all_predicts_number, + res.len() + ) + } else { + let file_stem = cli.fq.file_stem().unwrap().to_str().unwrap(); + format!( + "{}.{}pd.{}record.chop.fq.bgz", + file_stem, + all_predicts_number, + res.len() + ) + }; + + if cli.output_chopped_seqs { + log::info!("Output chopped adapters"); + } + + output::write_fq_parallel_for_noodle_record(&res, output_file.clone().into(), cli.threads)?; + Ok(()) +} diff --git a/src/lib.rs b/src/lib.rs index f21b102..8d4f0f0 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -5,6 +5,7 @@ pub mod kmer; pub mod output; pub mod python; // pub mod tokenizer; +pub mod cli; pub mod smooth; pub mod stat; pub mod types; diff --git a/src/python.rs b/src/python.rs index 22802ef..3cecbd5 100644 --- a/src/python.rs +++ b/src/python.rs @@ -1,9 +1,11 @@ use crate::{ + cli::{self, predict_cli}, default::{BASES, KMER_SIZE, MIN_CHOPED_SEQ_LEN, QUAL_OFFSET, VECTORIZED_TARGET}, fq_encode::{self, Encoder}, kmer::{self, vertorize_target}, output::{self, write_json, write_parquet}, - smooth, stat, + smooth::{self}, + stat, types::{Element, Id2KmerTable, Kmer2IdTable}, utils, }; @@ -14,10 +16,10 @@ use numpy::{IntoPyArray, PyArray2, PyArray3}; use parquet::arrow::arrow_reader::ParquetRecordBatchReaderBuilder; use pyo3::prelude::*; use rayon::prelude::*; +use std::str::FromStr; use std::{ops::Range, path::PathBuf}; use ahash::HashMap; - use log::{debug, error, info, warn}; #[pyfunction] @@ -554,15 +556,15 @@ fn get_label_region(labels: Vec) -> Vec<(usize, usize)> { #[pyfunction] fn smooth_label_region( labels: Vec, - length_between_intervals_for_merge: usize, - min_interval_length_threshold: usize, - min_interval_length_for_discard: usize, + smooth_window_size: usize, + min_interval_size: usize, + approved_interval_number: usize, ) -> Vec<(usize, usize)> { utils::smooth_label_region( &labels, - length_between_intervals_for_merge, - min_interval_length_threshold, - min_interval_length_for_discard, + smooth_window_size, + min_interval_size, + approved_interval_number, ) .par_iter() .map(|r| (r.start, r.end)) @@ -702,6 +704,57 @@ fn parse_psl_by_qname(file_path: PathBuf) -> Result, + fq: PathBuf, + smooth_window_size: usize, + min_interval_size: usize, + approved_interval_number: usize, + max_process_intervals: usize, + min_read_length_after_chop: usize, + output_chopped_seqs: bool, + chop_type: &str, + threads: Option, + output_prefix: Option, + max_batch_size: Option, +) -> Result<()> { + let chop_type = output::ChopType::from_str(chop_type).unwrap(); + + let options = cli::PredictOptions { + predicts, + fq, + threads, + max_batch_size, + smooth_window_size, + min_interval_size, + approved_interval_number, + max_process_intervals, + min_read_length_after_chop, + output_chopped_seqs, + chop_type, + output_prefix, + }; + + predict_cli(&options)?; + Ok(()) +} + /// A Python module implemented in Rust. #[pymodule] fn deepchopper(m: &Bound<'_, PyModule>) -> PyResult<()> { @@ -762,6 +815,9 @@ fn deepchopper(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_function(wrap_pyfunction!(smooth::load_predicts_from_batch_pts, m)?)?; m.add_function(wrap_pyfunction!(parse_psl_by_qname, m)?)?; + // add clis + m.add_function(wrap_pyfunction!(py_predict_cli, m)?)?; + m.add_class::()?; m.add_class::()?; m.add_class::()?; diff --git a/src/smooth/blat.rs b/src/smooth/blat.rs index 88f027c..ad22a8c 100644 --- a/src/smooth/blat.rs +++ b/src/smooth/blat.rs @@ -1,6 +1,6 @@ use std::fs::File; -use std::io::BufReader; use std::io::prelude::*; +use std::io::BufReader; use std::io::Write; use std::path::Path; use std::path::PathBuf; @@ -204,4 +204,4 @@ mod tests { let alignments = blat(seq, blat_cli, hg38_2bit, Some(file)).unwrap(); println!("{:?}", alignments); } -} \ No newline at end of file +} diff --git a/src/utils.rs b/src/utils.rs index 38b8fd1..3a7b4a7 100644 --- a/src/utils.rs +++ b/src/utils.rs @@ -3,6 +3,8 @@ use noodles::fastq; use rayon::prelude::*; use std::{fs::File, io::BufReader, ops::Range, path::Path}; +use crate::smooth::majority_voting; + pub fn summary_predict_generic( predictions: &[Vec], labels: &[Vec], @@ -395,67 +397,41 @@ pub fn get_label_region(labels: &[i8]) -> Vec> { /// e.g. 00110011100 -> 0011111100 pub fn smooth_label_region( labels: &[i8], - length_between_intervals_for_merge: usize, - min_interval_length_threshold: usize, - min_interval_length_for_discard: usize, + smooth_window_size: usize, + min_interval_size: usize, + approved_interval_size: usize, ) -> Vec> { - let labels_region = get_label_region(labels); - let mut smoothed_regions = vec![]; - - for region in labels_region { - if region.len() < min_interval_length_threshold { - continue; - } - - if smoothed_regions.is_empty() { - smoothed_regions.push(region); - continue; - } + let labels_region = get_label_region(&majority_voting(labels, smooth_window_size)); + let results = labels_region + .par_iter() + .filter_map(|interval| { + if interval.end - interval.start >= min_interval_size { + return Some(interval.clone()); + } + None + }) + .collect::>(); - let last_region = smoothed_regions.last_mut().unwrap(); - if region.start - last_region.end <= length_between_intervals_for_merge { - last_region.end = region.end; - } else { - smoothed_regions.push(region); - } + if results.len() > approved_interval_size { + return vec![]; } - // remove regions that are too short - smoothed_regions.retain(|region| region.len() >= min_interval_length_for_discard); - smoothed_regions + results } + pub fn smooth_label_region_u8( labels: &[u8], - length_between_intervals_for_merge: usize, - min_interval_length_threshold: usize, - min_interval_length_for_discard: usize, + smooth_window_size: usize, + min_interval_size: usize, + approved_interval_size: usize, ) -> Vec> { let labels = labels.par_iter().map(|x| *x as i8).collect::>(); - let labels_region = get_label_region(&labels); - - let mut smoothed_regions = vec![]; - - for region in labels_region { - if region.len() < min_interval_length_threshold { - continue; - } - - if smoothed_regions.is_empty() { - smoothed_regions.push(region); - continue; - } - - let last_region = smoothed_regions.last_mut().unwrap(); - if region.start - last_region.end <= length_between_intervals_for_merge { - last_region.end = region.end; - } else { - smoothed_regions.push(region); - } - } - - // remove regions that are too short - smoothed_regions.retain(|region| region.len() >= min_interval_length_for_discard); - smoothed_regions + smooth_label_region( + &labels, + smooth_window_size, + min_interval_size, + approved_interval_size, + ) } #[cfg(test)] @@ -524,27 +500,4 @@ mod tests { let regions = get_label_region(&labels); assert_eq!(regions, vec![1..3, 4..6]); } - - #[test] - fn test_smooth_label_region() { - // Test case 1: no regions to merge - let labels1 = [0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0]; // 2,4 and 6,9 - let merge_threshold1 = 2; - let distance_threshold1 = 2; - let expected_result1 = vec![2..9]; - assert_eq!( - smooth_label_region(&labels1, merge_threshold1, distance_threshold1, 0), - expected_result1 - ); - - // Test case 2: merge 1s regions that are close to each other - let labels2 = [0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0]; // 2,4 and 6,9 - let merge_threshold2 = 1; - let distance_threshold2 = 2; - let expected_result2 = vec![2..4, 6..9]; - assert_eq!( - smooth_label_region(&labels2, merge_threshold2, distance_threshold2, 0), - expected_result2 - ); - } }