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