--- dataset_info: - config_name: 30V_Jan24 features: - name: config dtype: string - name: traj_id dtype: string - name: shape list: int64 - name: data list: list: list: uint8 - name: left_right list: list: int64 - name: barycenter list: list: float64 splits: - name: train num_bytes: 1011799274 num_examples: 220 download_size: 32061722 dataset_size: 1011799274 - config_name: 60V_Dec24 features: - name: config dtype: string - name: traj_id dtype: string - name: shape list: int64 - name: data list: list: list: uint8 - name: left_right list: list: int64 - name: barycenter list: list: float64 splits: - name: train num_bytes: 1107240702 num_examples: 605 download_size: 34858112 dataset_size: 1107240702 - config_name: default features: - name: config dtype: string - name: traj_id dtype: string - name: shape list: int64 - name: data list: list: list: uint8 - name: left_right list: list: int64 - name: barycenter list: list: float64 splits: - name: train num_bytes: 2119039976 num_examples: 825 download_size: 66925984 dataset_size: 2119039976 configs: - config_name: 30V_Jan24 data_files: - split: train path: 30V_Jan24/train-* - config_name: 60V_Dec24 data_files: - split: train path: 60V_Dec24/train-* - config_name: default data_files: - split: train path: data/train-* --- # Descriptions ## Converting script ```py import pickle from pathlib import Path import numpy as np from datasets import Dataset DATA_DIR = Path("/path/to/cached/hugging_face/datasets/for/MLDS-NUS/Experimental_Images") # should end with something like "snapshots/fd299418e9435f8fd98956a3f0a7344d208cc142" def calc_left_right(data: np.ndarray): left_rights = [] for im in data: nonzero_columns = (im != 0).any(axis=-2) left = nonzero_columns.argmax() if nonzero_columns.any() else -1 # Find the rightmost non-zero column right = len(nonzero_columns) - 1 - nonzero_columns[::-1].argmax() if nonzero_columns.any() else -1 left_right = np.array([left, right]) left_rights.append(left_right) left_rights = np.stack(left_rights, axis=0) # shape: (seq_len, 2) return left_rights def calc_barycenter(data: np.ndarray) -> np.ndarray: """ Calculate the barycenter of the polymer from the snapshot. Assumes snapshot shape is (100, 500). """ xx = np.arange(data.shape[-2]).reshape(-1, 1) bary_x = (data * xx).sum(axis=(-2, -1)) / data.sum(axis=(-2, -1)) yy = np.arange(data.shape[-1]).reshape(1, -1) bary_y = (data * yy).sum(axis=(-2, -1)) / data.sum(axis=(-2, -1)) barycenter = np.stack([bary_x, bary_y], axis=-1) # (seq_len, 2) return barycenter def gen(): for folder in ["30V_Jan24", "60V_Dec24"]: with open(DATA_DIR / f"{folder}.pkl", "rb") as f: data = pickle.load(f) for k, v in data.items(): frame = np.clip(v, 0, 255).astype(np.uint8) # save memory left_rights = calc_left_right(255 - frame) barycenters = calc_barycenter(255 - frame) yield { "config": folder, "traj_id": k, "shape": list(frame.shape), "data": frame, "left_right": left_rights, "barycenter": barycenters, } ds = Dataset.from_generator(gen) ds = ds.with_format("numpy") ds.push_to_hub("MLDS-NUS/polymer-dynamics_experimental-data") # upload by configs def gen(folder: str): with open(DATA_DIR / f"{folder}.pkl", "rb") as f: data = pickle.load(f) for k, v in data.items(): frame = np.clip(v, 0, 255).astype(np.uint8) left_rights = calc_left_right(255 - frame) barycenters = calc_barycenter(255 - frame) yield { "config": folder, "traj_id": k, "shape": list(frame.shape), "data": frame, "left_right": left_rights, "barycenter": barycenters, } for config_name in ["30V_Jan24", "60V_Dec24"]: ds = Dataset.from_generator(lambda cn=config_name: gen(cn)) ds = ds.with_format("numpy") ds.push_to_hub( "MLDS-NUS/polymer-dynamics_experimental-data", config_name=config_name, data_dir=f"{config_name}", ) ``` ## How to use Directly loading by [datasets](https://huggingface.co/docs/datasets/installation) is supported now! ```py from datasets import load_dataset import numpy as np hf_dataset_30V = load_dataset("MLDS-NUS/polymer-dynamics_experimental-data", config_name="30V_Jan24") hf_dataset_60V = load_dataset("MLDS-NUS/polymer-dynamics_experimental-data", config_name="60V_Jan24") hf_dataset_30V = hf_dataset_30V.with_format("numpy")["train"] hf_dataset_60V = hf_dataset_60V.with_format("numpy")["train"] for sample in hf_dataset_30V: for k, v in sample.items(): if isinstance(v, np.ndarray): print(f"{k}: {type(v)}, shape={v.shape}, dtype={v.dtype}") else: print(f"{k}: {v}") ``` output: ```txt config: 30V_Jan24 traj_id: 30V_Tra_0 shape: , shape=(3,), dtype=int64 data: , shape=(160, 100, 350), dtype=int64 left_right: , shape=(160, 2), dtype=int64 barycenter: , shape=(160, 2), dtype=float32 ``` ## How to contribute ```py import numpy as np from datasets import Dataset def gen(config_name: str): for data in your_database_retriever(config_name): frame = ... traj_id = ... shape = ... left_rights = ... barycenters = ... yield { "config": config_name, "traj_id": traj_id, "shape": shape, "data": data, # a np.ndarray object of shape `shape` "left_right": left_rights, "barycenter": barycenters, } config_name = ... ds = Dataset.from_generator(lambda cn=config_name: gen(cn)) ds = ds.with_format("numpy") ds.push_to_hub( "MLDS-NUS/polymer-dynamics_experimental-data", config_name=config_name, data_dir=f"{config_name}", ) ```