| """Graptoloidea Specimens dataset.""" | |
| import os | |
| import random | |
| from typing import List | |
| import datasets | |
| import pandas as pd | |
| import numpy as np | |
| _CITATION = """\ | |
| """ | |
| _DESCRIPTION = """\ | |
| Dataset Description: | |
| The Graptoloidea Specimens Imaging dataset is a curated collection of over 1,300 image-text pairs, focusing on Graptoloidea specimens. It encompasses detailed attributes such as species classification, geological stages, and specific locality information (with coordinates), complemented by high-quality images of each specimen. This dataset serves as a valuable resource for paleontological research, offering insights into the morphological diversity and geological distribution of Graptoloidea. | |
| Highlights: | |
| - Comprehensive Collection: Over 1,300 unique specimens, each with a corresponding high-quality image and descriptive text. | |
| - Diverse Geological Coverage: Specimens span different geological stages, offering a timeline of the Graptoloidea evolution. | |
| - Rich Annotations: Apart from visual data, the dataset includes detailed taxonomic classification, geological context, and precise locality information. | |
| - Research-Ready: Ideal for tasks like paleontological classification, morphological analysis, age estimation, and geographical distribution studies. | |
| - Educational Value: Serves as an invaluable resource for educational and outreach programs, providing tangible insights into paleontology. | |
| """ | |
| _HOMEPAGE = "https://zenodo.org/records/6194943" | |
| _license = "" | |
| _URLS = { | |
| "part1": "https://zenodo.org/records/6194943/files/graptolite%20specimens%20with%20scale.zip.001?download=1", | |
| "part2": "https://zenodo.org/records/6194943/files/graptolite%20specimens%20with%20scale.zip.002?download=1", | |
| } | |
| class GraptoloideaSpecimensDataset(datasets.GeneratorBasedBuilder): | |
| """Imaging for graptoloidea specimens with extra information""" | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "Suborder": datasets.Value("string"), | |
| "Infraorder": datasets.Value("string"), | |
| "Family (Subfamily)": datasets.Value("string"), | |
| "Genus": datasets.Value("string"), | |
| "Tagged Species Name": datasets.Value("string"), | |
| "Image": datasets.Value("string"), | |
| "Stage": datasets.Value("string"), | |
| "Mean Age Value": datasets.Value("float64"), | |
| "Locality (Longitude, Latitude, Horizon)": datasets.Value("string"), | |
| "Reference (Specimens Firstly Published)": datasets.Value("string"), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: | |
| downloaded_files = dl_manager.download(_URLS) | |
| combined_zip_path = os.path.join(dl_manager.manual_dir, 'combined.zip') | |
| with open(combined_zip_path, 'wb') as f_out: | |
| for part in ['part1', 'part2']: | |
| with open(downloaded_files[part], 'rb') as f_in: | |
| f_out.write(f_in.read()) | |
| with zipfile.ZipFile(combined_zip_path, 'r') as zip_ref: | |
| zip_ref.extractall(dl_manager.manual_dir) | |
| all_files = [os.path.join(dl_manager.manual_dir, f) for f in os.listdir(dl_manager.manual_dir) | |
| if os.path.isfile(os.path.join(dl_manager.manual_dir, f))] | |
| random.shuffle(all_files) | |
| split_index = int(0.8 * len(all_files)) | |
| train_files = all_files[:split_index] | |
| validation_files = all_files[split_index:] | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={"filepath": train_files}), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={"filepath": validation_files}), | |
| ] | |