modelweights

.gitignore

@@ -0,0 +1,15 @@
+.log/
+.vscode/
+__pycache__/
+assets_local/
+*/__pycache__/
+.venv/
+_t*.py 
+_*.yaml
+_develop_*.py
+outputs/
+jupyter/*
+*.csv 
+third_party/*
+dev_*.py
+.venv

ecg_scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5c5e04979442ad03982b01b68c922fa7501bdbbacc6a4ad2b6ce3f85143fde5a
+size 903

echoingecg.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31c9e1077dd2bea9a2372787beea9971269cf50ba7367f026e4d76e40d33db32
+size 943726418

preprocessor.py

@@ -0,0 +1,86 @@
+import torch
+from math import gcd
+from typing import Optional, Union
+import joblib
+
+import numpy as np
+from scipy import signal
+
+def load_scaler_joblib(path: str) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Load ecg_scaler.pkl and return center and scale as torch tensors.
+    Args:
+        path: Path to the joblib file.
+    Returns:
+        center: torch.Tensor
+        scale: torch.Tensor
+    """
+    sc = joblib.load(path)
+    center = torch.from_numpy(sc.mean_.astype(np.float32))
+    scale = torch.from_numpy(sc.scale_.astype(np.float32)).clamp_min(1e-8)
+    return center, scale
+
+class ECGTransform:
+    """
+    Unified ECG preprocessing: downsampling and scaling.
+    Usage:
+        transform = ECGTransform(center, scale, src_fs=512, target_fs=100)
+        ecg_out = transform(ecg_in)
+    """
+    def __init__(
+        self,
+        center: Union[np.ndarray, torch.Tensor],
+        scale: Union[np.ndarray, torch.Tensor],
+        src_fs: int = 100, #we assume the input ECG is already at 100Hz
+        target_fs: int = 100,
+        band: Optional[tuple[float, float]] = (0.5, 40.0),
+        bp_order: int = 4,
+        axis: int = -1,
+    ) -> None:
+        self.center = torch.as_tensor(center, dtype=torch.float32)
+        self.scale = torch.as_tensor(scale, dtype=torch.float32).clamp_min(1e-8)
+        self.src_fs = src_fs
+        self.target_fs = target_fs
+        self.band = band
+        self.bp_order = bp_order
+        self.axis = axis
+
+    def downsample(self, x: np.ndarray) -> np.ndarray:
+        x = np.asarray(x)
+        if self.band is not None:
+            lowcut, highcut = self.band
+            max_high = 0.45 * self.target_fs
+            highcut = min(highcut, max_high)
+            nyq = self.src_fs / 2.0
+            if lowcut <= 0:
+                wn = highcut / nyq
+                sos = signal.butter(self.bp_order, wn, btype="low", output="sos")
+            else:
+                wn = (lowcut / nyq, highcut / nyq)
+                sos = signal.butter(self.bp_order, wn, btype="band", output="sos")
+            x = signal.sosfiltfilt(sos, x, axis=self.axis)
+        g = gcd(self.src_fs, self.target_fs)
+        up = self.target_fs // g
+        down = self.src_fs // g
+        y = signal.resample_poly(x, up, down, axis=self.axis, window=("kaiser", 5.0), padtype="median")
+        return y
+
+    def scale(self, ecg: torch.Tensor) -> torch.Tensor:
+        ecg = ecg.to(torch.float32)
+        ecg = (ecg - self.center[:, None]) / self.scale[:, None]
+        return ecg
+
+    def __call__(self, x: np.ndarray) -> torch.Tensor:
+        """
+        Downsample and scale ECG data.
+        Args:
+            x: np.ndarray, shape (leads, time) 
+        Returns:
+            torch.Tensor, shape (leads, time)
+        """
+        if self.src_fs != self.target_fs:
+            x = self.downsample(x)
+        if not isinstance(x, torch.Tensor):
+            x = torch.from_numpy(x)
+        x = self.scale(x)
+        return x

requirements.txt

@@ -0,0 +1,182 @@
+accelerate==1.10.0
+aiohappyeyeballs==2.6.1
+aiohttp==3.12.15
+aiosignal==1.4.0
+annotated-types==0.7.0
+antlr4-python3-runtime==4.9.3
+anyio==4.10.0
+appnope==0.1.4
+argon2-cffi==25.1.0
+argon2-cffi-bindings==25.1.0
+arrow==1.3.0
+asttokens==3.0.0
+async-lru==2.0.5
+attrs==25.3.0
+babel==2.17.0
+beartype==0.21.0
+beautifulsoup4==4.13.4
+bleach==6.2.0
+braceexpand==0.1.7
+certifi==2025.8.3
+cffi==1.17.1
+charset-normalizer==3.4.3
+click==8.2.1
+comm==0.2.3
+contourpy==1.3.3
+cycler==0.12.1
+debugpy==1.8.16
+decorator==5.2.1
+defusedxml==0.7.1
+einops==0.8.1
+executing==2.2.0
+fastjsonschema==2.21.1
+filelock==3.18.0
+fire==0.7.0
+fonttools==4.59.0
+fqdn==1.5.1
+frozenlist==1.7.0
+fsspec==2025.7.0
+ftfy==6.3.1
+gitdb==4.0.12
+gitpython==3.1.45
+h11==0.16.0
+h5py==3.14.0
+hf-xet==1.1.7
+httpcore==1.0.9
+httpx==0.28.1
+huggingface-hub==0.34.4
+hydra-core==1.3.2
+idna==3.10
+imageio==2.37.0
+iopath==0.1.10
+ipykernel==6.30.1
+ipython==9.4.0
+ipython-pygments-lexers==1.1.1
+ipywidgets==8.1.7
+isoduration==20.11.0
+isort==6.0.1
+jedi==0.19.2
+jinja2==3.1.6
+joblib==1.5.1
+json5==0.12.0
+jsonpointer==3.0.0
+jsonschema==4.25.0
+jsonschema-specifications==2025.4.1
+jupyter==1.1.1
+jupyter-client==8.6.3
+jupyter-console==6.6.3
+jupyter-core==5.8.1
+jupyter-events==0.12.0
+jupyter-lsp==2.2.6
+jupyter-server==2.16.0
+jupyter-server-terminals==0.5.3
+jupyterlab==4.4.5
+jupyterlab-pygments==0.3.0
+jupyterlab-server==2.27.3
+jupyterlab-widgets==3.0.15
+kernels==0.9.0
+kiwisolver==1.4.8
+lark==1.2.2
+lazy-loader==0.4
+lightning-utilities==0.15.2
+markupsafe==3.0.2
+matplotlib==3.10.5
+matplotlib-inline==0.1.7
+mistune==3.1.3
+mpmath==1.3.0
+multidict==6.6.3
+mypy==1.17.1
+mypy-extensions==1.1.0
+nbclient==0.10.2
+nbconvert==7.16.6
+nbformat==5.10.4
+nest-asyncio==1.6.0
+networkx==3.5
+notebook==7.4.5
+notebook-shim==0.2.4
+numpy==2.2.6
+omegaconf==2.3.0
+openai-harmony==0.0.4
+opencv-python==4.12.0.88
+overrides==7.7.0
+packaging==25.0
+pandas==2.3.1
+pandocfilters==1.5.1
+parso==0.8.4
+pathspec==0.12.1
+peft==0.17.0
+pexpect==4.9.0
+pillow==11.3.0
+platformdirs==4.3.8
+portalocker==3.2.0
+prometheus-client==0.22.1
+prompt-toolkit==3.0.51
+propcache==0.3.2
+protobuf==6.31.1
+psutil==7.0.0
+ptyprocess==0.7.0
+pure-eval==0.2.3
+pycparser==2.22
+pydantic==2.11.7
+pydantic-core==2.33.2
+pydicom==3.0.1
+pygments==2.19.2
+pyparsing==3.2.3
+python-dateutil==2.9.0.post0
+python-json-logger==3.3.0
+pytorch-lightning==2.5.2
+pytz==2025.2
+pyyaml==6.0.2
+pyzmq==27.0.1
+referencing==0.36.2
+regex==2025.7.34
+requests==2.32.4
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rfc3987-syntax==1.1.0
+rpds-py==0.27.0
+ruff==0.12.8
+safetensors==0.6.2
+scikit-image==0.25.2
+scikit-learn==1.7.1
+scipy==1.16.1
+seaborn==0.13.2
+send2trash==1.8.3
+sentry-sdk==2.34.1
+setuptools==80.9.0
+six==1.17.0
+smmap==5.0.2
+sniffio==1.3.1
+soundfile==0.13.1
+soupsieve==2.7
+stack-data==0.6.3
+sympy==1.14.0
+termcolor==3.1.0
+terminado==0.18.1
+threadpoolctl==3.6.0
+tifffile==2025.6.11
+timm==1.0.19
+tinycss2==1.4.0
+tokenizers==0.21.4
+torch==2.8.0
+torchaudio==2.8.0
+torchmetrics==1.8.1
+torchvision==0.23.0
+tornado==6.5.2
+tqdm==4.67.1
+traitlets==5.14.3
+transformers==4.55.0
+types-python-dateutil==2.9.0.20250809
+typing-extensions==4.14.1
+typing-inspection==0.4.1
+tzdata==2025.2
+uri-template==1.3.0
+urllib3==2.5.0
+wandb==0.21.1
+wcwidth==0.2.13
+webcolors==24.11.1
+webencodings==0.5.1
+websocket-client==1.8.0
+wfdb==4.3.0
+widgetsnbextension==4.0.14
+yarl==1.20.1