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Committing first version of the XMCTSGuard python package

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Luca Orlandi
2026-04-16 15:52:12 +02:00
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This package can be used to detect and correct faults in SXR diagnostics
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into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.

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# XMCTS data cleaning
This repository contains a project to resolve the faults in the data acquisition of the XMCTS diagnostic of W7-X.
## Getting started
First of all you should create a python environment to install all of the needed dependencies.
The recommended way is to use venv, one can create a venv by executing the following command:
`python -m venv path\to\venv\env-name`
Once the virtual enviroment has been created, one can install the package by moving inside the project main folder and executing the following command:
`python -m build`
follwing this, a folder called dist is created and one can then install the package by runnning:
`python -m pip install dist/xmctsguard-vnum.tar.gz`
where vnum is the current version of the package, starting from 0.1.0.
Once this operation is completed all of the required libraries should have been installed and the package can be used.
For instance the gui can be called by executing the command XMCTSGuard-gui.
## Structure
The project is structured in the following way:
XMCTSGuard/
├── src/
│ └── XMCTSGuard/
│ ├── __init__.py # Makes this a package
│ ├── main.py # Entry point to launch the GUI
│ │
│ ├── gui/ # All Things Visual
│ │ ├── __init__.py
│ │ ├── main_window.py # Your Main Class
│ │ ├── widgets.py # Custom buttons, sliders, etc.
│ │ └── helpers.py # GUI-only helper functions
│ │
│ ├── engine/ # The "Brain" (Neural Network)
│ │ ├── __init__.py
│ │ ├── model.py # The NN class
│ │ ├── trainer.py # Training logic
│ │ ├── database.py # Data loading
│ │ └── callbacks.py # Training monitors
│ │
│ └── analysis/ # The Bridge
│ ├── __init__.py
│ └── processors.py # Functions that use the NN to analyze data
│
├── data/ # Local storage for datasets (git-ignored)
├── tests/ # Your test files
├── pyproject.toml # Build config
└── README.md
## Usage
The project leverages a neural network (NN) based on a AutoEncoder (AE) architecture, contained in engine/model.py to give an ansatz of what the correct brightness "profile" should look like. Subsequently, the correlation between the ansatz and the measured profile is computed, this gives a metric to understand the distance of the measured profile from the usual distribution of profiles in W7-X. Moreover, the ansatz for the profile is also used to compute the distance of each diode from the reconstructed brightness. Via a threshold on the residuals between these two curves, it is possible to highlight the outliers in the measured profile and correct their values knowing the gain ratio between the old pulse and the new pulse.
Once the analysis is completed one can save (cache) the "new" data, using the same format of the qxtdataaccess gui, so that it is usable for other purposes.
One can install the package on
## Neural Network
The NN used in this project, previously introduced, is developed using the `lightning` python library ([lightning docs](https://lightning.ai/docs/overview/getting-started)), which is based on `pytorch`.
All of the necessary code for the NN is contained in the src/app/engine folder. The model.py file contains the base file with the network structure, trainer.py contains the functions used for the neural network training and database.py has the data loader and dataset classes for file reading and manipulation to make them NN compliant.
### Training
The training of the NN is done via running the script train.py in the enigine folder. There is a config dictionary with the various parameters it is possible to tweak for each running. Before training a database over which doing the training procedure should be created, this can be done by running the function `consolidate_pulses` contained in src/engine/pulse_dataset.py script The training procedure can be run by calling the `train_autoencoder` function in src/engine/train.py. Refer to this function documentation for more information.
#### Attention
It may be that the trainig routine, if run locally on IPPs PCs, tries to select the available GPUs, even if it may not be posible to use them.
If this is the case, before running the training procedure, one should run the following command:
`export CUDA_VISIBLE_DEVICES=''`
in order to deselect the possibility of using said GPUs.
### Jupyter Notebooks
This is a useful tool for exploring the code and see hands-on examples on how the various steps work together, however it can be messy inside a .git repository, in order to avoid embedding in the version control a great amount of useless data and plot, the use of the nbstripout package is strongly recommended. This package, a possible implementation can be found [here](https://pypi.org/project/nbstripout/) under the 'Using as a Git filter' section.
### Notice
For any problems, doubts or error with the code, contact luca.orlandi@igi.cnr.it

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pyproject.toml Normal file
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[build-system]
requires = ["hatchling >= 1.26"]
build-backend = "hatchling.build"
[project]
name = "XMCTSGuard"
version = "0.1.0"
description = "Anomaly detection and cleaning for XMCTS SXR data"
authors = [
{name = "Luca Orlandi", email = "luca.orlandi@igi.cnr.it"}
]
readme = "README.md"
requires-python = ">=3.11"
license = {text = "GPL v3"}
license-files = ["LICEN[CS]E*"]
# List your library dependencies here (what pip install will grab)
dependencies = [
"numpy",
"scipy",
"matplotlib",
"seaborn",
"torch",
"lightning",
"PyQt6",
"h5py",
"umap-learn"
]
[project.optional-dependencies]
# Dependencies for development and research notebooks
dev = [
"pytest",
"jupyterlab",
"ipywidgets",
]
[project.scripts]
# This creates a terminal command to launch your GUI!
# Format: command_name = "package.module:function"
XMCTSGuard-gui = "XMCTSGuard.main:start_app"
# Project urls
[project.urls]
Homepage = "https://github.com/pypa/sampleproject"
Issues = "https://github.com/pypa/sampleproject/issues"
[tool.setuptools.packages.find]
# Tells setuptools where to look for your code
where = ["src"]

174
requirements.txt Normal file
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@@ -0,0 +1,174 @@
absl-py==2.4.0
aiohappyeyeballs==2.6.1
aiohttp==3.13.1
aiosignal==1.4.0
anyio==4.12.1
argon2-cffi==25.1.0
argon2-cffi-bindings==25.1.0
arrow==1.4.0
asttokens==3.0.1
async-lru==2.1.0
attrs==25.4.0
babel==2.18.0
beautifulsoup4==4.14.3
bleach==6.3.0
cbor==1.0.0
certifi==2026.1.4
cffi==2.0.0
charset-normalizer==3.4.4
comm==0.2.3
contourpy==1.3.3
cycler==0.12.1
debugpy==1.8.20
decorator==5.2.1
defusedxml==0.7.1
executing==2.2.1
fastjsonschema==2.21.2
filelock==3.20.0
fonttools==4.60.1
fqdn==1.5.1
frozenlist==1.8.0
fsspec==2025.9.0
gevent==25.9.1
greenlet==3.3.2
grequests==0.7.0
grpcio==1.80.0
h11==0.16.0
h5py==3.15.1
httpcore==1.0.9
httpx==0.28.1
idna==3.11
ImageIO==2.37.3
ipykernel==7.2.0
ipympl==0.10.0
ipython==9.10.0
ipython_pygments_lexers==1.1.1
ipywidgets==8.1.8
isoduration==20.11.0
jedi==0.19.2
Jinja2==3.1.6
joblib==1.5.2
json5==0.13.0
jsonpointer==3.0.0
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter==1.1.1
jupyter-console==6.6.3
jupyter-events==0.12.0
jupyter-lsp==2.3.0
jupyter_client==8.8.0
jupyter_core==5.9.1
jupyter_server==2.17.0
jupyter_server_terminals==0.5.4
jupyterlab==4.5.4
jupyterlab_pygments==0.3.0
jupyterlab_server==2.28.0
jupyterlab_widgets==3.0.16
kiwisolver==1.4.9
lark==1.3.1
lazy-loader==0.5
lightning==2.5.5
lightning-utilities==0.15.2
Markdown==3.10.2
MarkupSafe==3.0.3
matplotlib==3.10.7
matplotlib-inline==0.2.1
mistune==3.2.0
mpmath==1.3.0
multidict==6.7.0
nbclient==0.10.4
nbconvert==7.17.0
nbformat==5.10.4
nest-asyncio==1.6.0
networkx==3.5
notebook==7.5.3
notebook_shim==0.2.4
numpy==2.3.4
nvidia-cublas-cu12==12.8.4.1
nvidia-cuda-cupti-cu12==12.8.90
nvidia-cuda-nvrtc-cu12==12.8.93
nvidia-cuda-runtime-cu12==12.8.90
nvidia-cudnn-cu12==9.10.2.21
nvidia-cufft-cu12==11.3.3.83
nvidia-cufile-cu12==1.13.1.3
nvidia-curand-cu12==10.3.9.90
nvidia-cusolver-cu12==11.7.3.90
nvidia-cusparse-cu12==12.5.8.93
nvidia-cusparselt-cu12==0.7.1
nvidia-nccl-cu12==2.27.5
nvidia-nvjitlink-cu12==12.8.93
nvidia-nvshmem-cu12==3.3.20
nvidia-nvtx-cu12==12.8.90
osa==0.2.3
overrides==7.7.0
packaging==25.0
pandas==2.3.3
pandocfilters==1.5.1
parso==0.8.6
pexpect==4.9.0
pillow==12.0.0
platformdirs==4.9.2
prometheus_client==0.24.1
prompt_toolkit==3.0.52
propcache==0.4.1
protobuf==6.33.5
psutil==7.2.2
ptyprocess==0.7.0
pure_eval==0.2.3
pycparser==3.0
Pygments==2.19.2
pyparsing==3.2.5
PyQt5==5.15.11
PyQt5-Qt5==5.15.18
PyQt5_sip==12.18.0
PyQt6==6.10.2
PyQt6-Qt6==6.10.2
PyQt6_sip==13.11.0
python-dateutil==2.9.0.post0
python-json-logger==4.0.0
pytorch-lightning==2.5.5
pytz==2025.2
PyYAML==6.0.3
pyzmq==27.1.0
referencing==0.37.0
requests==2.32.5
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rfc3987-syntax==1.1.0
rpds-py==0.30.0
scikit-image==0.26.0
scikit-learn==1.7.2
scipy==1.16.2
seaborn==0.13.2
Send2Trash==2.1.0
six==1.17.0
soupsieve==2.8.3
stack-data==0.6.3
sympy==1.14.0
tensorboard==2.20.0
tensorboard-data-server==0.7.2
tensorboardX==2.6.4
terminado==0.18.1
threadpoolctl==3.6.0
tifffile==2026.3.3
tinycss2==1.4.0
torch==2.9.0
torchmetrics==1.8.2
tornado==6.5.4
tqdm==4.67.1
traitlets==5.14.3
triton==3.5.0
typing_extensions==4.15.0
tzdata==2025.2
umap-learn==0.5.12
uri-template==1.3.0
urllib3==2.6.3
wcwidth==0.6.0
webcolors==25.10.0
webencodings==0.5.1
websocket-client==1.9.0
Werkzeug==3.1.8
widgetsnbextension==4.0.15
yarl==1.22.0
zope.event==6.1
zope.interface==8.2

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src/XMCTSGuard/__init__.py Normal file
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# Hoisting the main entry points
from .main import start_app
from .engine.model import AutoEncoder
# Metadata
__version__ = "0.1.0"
__author__ = "Luca Orlandi"
# Defining what 'from my_app import *' does
__all__ = ["start_app", "AutoEncoder"]

0
src/XMCTSGuard/analysis/__init__.py Normal file
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src/XMCTSGuard/analysis/processors.py Normal file
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import numpy as np
import torch
import gc
from ..engine.utils import reconstruct, pearson, detect_outliers_residual, fill_outlier_gaps, compute_correlation_series, apply_gain, reconstruct_only_batched
from ..engine.model import AutoEncoder
# ─────────────────────────────────────────────────────────────────────────────
# Core analysis (orchestration only — heavy lifting delegated to helpers)
# ─────────────────────────────────────────────────────────────────────────────
def run_analysis(pid, model_path, min_data, max_data, gain_ratio,
z_thresh=0.2,
progress_cb=None):
import gc
import torch
def _p(msg):
if progress_cb:
progress_cb(msg)
# ── imports ───────────────────────────────────────────────────────────────
try:
from ..utils import load_sxr_data
except ImportError as e:
raise ImportError(
f"Could not import project modules: {e}\n"
"Make sure 'model.py' and 'read_h5f.py' are on sys.path."
) from e
# ── model ─────────────────────────────────────────────────────────────────
_p("Loading model …")
model = AutoEncoder.load_from_checkpoint(model_path)
model.eval()
# ── data ──────────────────────────────────────────────────────────────────
_p(f"Loading SXR data for PID {pid} …")
try:
data, fpath, data_dict = load_sxr_data(pid)
except Exception:
_p(f"Data for PID {pid} not found — launching data access GUI …")
try:
import gui_qxt_loader as gqxt
import subprocess
subprocess.run(["python", os.path.abspath(gqxt.__file__)])
data, fpath, data_dict = load_sxr_data(pid)
except ImportError as e:
raise ImportError(
f"Could not import data access modules: {e}\n"
"Make sure 'gui_qxt_loader.py' is on sys.path."
) from e
timedata = data[0, :].astype(np.float32)
signals = data[1:, :].astype(np.float32)
diode_ref = data[152,:].astype(np.float32)
del data
gc.collect()
diode_keys = [k for k in data_dict.keys() if k != "timedata"]
del data_dict
gc.collect()
# ── reference time instant (peak emission) ────────────────────────────────
max_emission = int(np.argmax(diode_ref))
time_instant = float(timedata[max_emission])
profile = signals[:, max_emission].copy()
n_diodes = len(profile)
x = np.arange(n_diodes)
# ── original reconstruction (single profile snapshot) ────────────────────
_p("Running model on original profile …")
rec_original = reconstruct(model, profile, min_data, max_data)
corr_original = pearson(profile, rec_original)
# ── model-residuals outlier detection ─────────────────────────────────────
_p(f"Running model-residuals detection (z={z_thresh}) …")
outlier_mask = detect_outliers_residual(profile, rec_original, z_thresh)
outlier_mask = fill_outlier_gaps(outlier_mask, profile, tol=0.1)
mask = ~outlier_mask
_p(f"[Residuals] {int(outlier_mask.sum())} diodes flagged.")
# ── gain-corrected single profile and its reconstruction ──────────────────
adjusted = apply_gain(profile, outlier_mask, gain_ratio)
rec_adjusted = reconstruct(model, adjusted, min_data, max_data)
corr_adjusted = pearson(adjusted, rec_adjusted)
gc.collect()
# ── downsampled time-series indices (for corr plot + spectral) ────────────
step = max(10, signals.shape[1] // 500)
indices = np.arange(0, signals.shape[1], step)
# ── correlation time-series (on original signals) ─────────────────────────
_p("Computing correlation time-series …")
correlations = compute_correlation_series(
model, signals, indices, min_data, max_data
)
gc.collect()
# ── snapshot for spectral analysis ────────────────────────────────────────
signals_sampled = signals[:, indices].copy()
# ── gain-corrected full time series (no model pass) ───────────────────────
_p("Applying gain correction to full time-series …")
adjusted_full = signals.copy()
if gain_ratio > 1:
adjusted_full[~outlier_mask, :] *= gain_ratio
else:
adjusted_full[outlier_mask, :] *= gain_ratio
# ── model reconstruction of the gain-corrected full time series ───────────
_p("Running model on adjusted full time-series …")
rec_adjusted_full = reconstruct_only_batched(
model, adjusted_full, min_data, max_data
)
gc.collect()
_p("Done.")
return dict(
pid = pid,
time_instant = time_instant,
x = x,
timedata = timedata[indices],
timedata_full = timedata,
profile = profile,
rec_original = rec_original,
outlier_mask = outlier_mask,
gain_ratio = gain_ratio,
mask = mask,
adjusted = adjusted,
rec_adjusted = rec_adjusted,
adjusted_full = adjusted_full,
rec_adjusted_full = rec_adjusted_full,
corr_original = corr_original,
corr_adjusted = corr_adjusted,
correlations = correlations,
diode_signal = diode_ref[indices],
file_path = fpath,
diode_keys = diode_keys,
method = "residuals",
signals_sampled = signals_sampled,
z_thresh = z_thresh,
)

7
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from .model import AutoEncoder, VariationalAutoEncoder
from .dataset import AEDataset, AEDataModule
from .pulse_dataset import PulseProfileDataset, PulseDataModule
from .train import train_autoencoder
__all__ = ["AutoEncoder", "VariationalAutoEncoder", "AEDataset", "AEDataModule",
"PulseProfileDataset", "PulseDataModule", "train_autoencoder"]

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src/XMCTSGuard/engine/callbacks.py Normal file
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import torch
from lightning import Callback, LightningModule, Trainer
from lightning.pytorch.loggers import TensorBoardLogger
from lightning.pytorch.callbacks import EarlyStopping, LearningRateMonitor
class SaveBest(Callback):
def __init__(self, monitor: str, logger: TensorBoardLogger) -> None:
super().__init__()
self.monitor = monitor
self.logger = logger
self.best_loss = float('inf')
def on_validation_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
loss = trainer.callback_metrics[self.monitor]
if loss < self.best_loss:
self.best_loss = loss
trainer.save_checkpoint(f"{self.logger.log_dir}/best_model_.ckpt")
return super().on_validation_end(trainer, pl_module)
def on_train_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
print(f"Best {self.monitor} loss: {self.best_loss}")
return super().on_train_end(trainer, pl_module)
class SaveEveryNEpochs(Callback):
def __init__(self, n: int, logger: TensorBoardLogger) -> None:
super().__init__()
self.n = n
self.logger = logger
def on_train_epoch_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
if (trainer.current_epoch + 1) % self.n == 0:
trainer.save_checkpoint(f"{self.logger.log_dir}/epoch_{trainer.current_epoch + 1}.ckpt")
return super().on_train_epoch_end(trainer, pl_module)
class PrintLearningRate(Callback):
def on_train_epoch_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
optimizer = trainer.optimizers[0]
lr = optimizer.param_groups[0]['lr']
print(f"Epoch {trainer.current_epoch + 1}: Learning Rate = {lr}")
return super().on_train_epoch_end(trainer, pl_module)
class BetaWarmUp(Callback):
def __init__(self, start_epoch: int, initial_beta: float, final_beta: float, warmup_epochs: int) -> None:
super().__init__()
self.start_epoch = start_epoch
self.initial_beta = initial_beta
self.final_beta = final_beta
self.warmup_epochs = warmup_epochs
def on_train_epoch_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
if self.start_epoch <= trainer.current_epoch < self.start_epoch + self.warmup_epochs:
epoch_in_warmup = trainer.current_epoch - self.start_epoch
beta = self.initial_beta + (self.final_beta - self.initial_beta) * (epoch_in_warmup / self.warmup_epochs)
elif trainer.current_epoch >= self.start_epoch + self.warmup_epochs:
beta = self.final_beta
else:
beta = self.initial_beta
if hasattr(pl_module, "set_beta"):
pl_module.set_beta(beta)
print(f"Epoch {trainer.current_epoch + 1}: Beta = {beta}")
else:
print("Warning: Model does not have a set_beta method.")
return super().on_train_epoch_start(trainer, pl_module)

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import torch
import lightning as L
import numpy as np
from torch.utils.data import random_split, Dataset, DataLoader
import os
class AEDataset(Dataset):
def __init__(self, profiles: torch.Tensor, pids: np.ndarray, times: np.ndarray,
cameras: np.ndarray, mag_confs: np.ndarray, clippings: np.ndarray):
self.profiles = profiles
self.pids = pids
self.times = times
self.cameras = cameras
self.mag_confs = mag_confs
self.clippings = clippings
def __len__(self):
return len(self.profiles)
def __getitem__(self, idx: int):
result = {'profile': self.profiles[idx]}
if self.pids is not None:
result['pid'] = self.pids[idx]
if self.times is not None:
result['time'] = self.times[idx]
if self.cameras is not None:
result['camera'] = self.cameras[idx]
if self.mag_confs is not None:
result['mag_conf'] = self.mag_confs[idx]
if self.clippings is not None:
result['clipping'] = self.clippings[idx]
return result
class AEDataModule(L.LightningDataModule):
def __init__(self, data_dir: str = '.', file_name: str = 'compressed_profiles.npz', batch_size: int = 32, normalization_strategy: str = 'minmax', nprofiles: int = None):
super().__init__()
self.data_dir = data_dir
self.file_name = file_name
self.batch_size = batch_size
self.normalization_strategy = normalization_strategy
self.nprofiles = nprofiles
self.data = None
def prepare_data(self):
# Check if the file in data_dir exists otherwise create it calling the create_db method
path = os.path.join(self.data_dir, self.file_name)
if not os.path.exists(path):
print(f"The file {self.file_name} does not exist in path {self.data_dir}")
print("It can be created by calling the right functions in create_db.py.")
print("See the documentation there for more details")
# from create_db import create_database_time_instants, divide_by_camera
# divide_by_camera(data_dir=r"\\share\mp\E5-Praktikanten\Orlandi_Luca\_data\npz_files")
# create_database_time_instants(os.path.join(self.data_dir, self.file_name))
def setup(self, stage : str = None):
# Initialize a seed for all the randomic operations
generator = torch.Generator().manual_seed(42) # Seed for reproducibility
if self.data is None:
# Load the dataset from the specified directory and file
self.data = np.load(f"{self.data_dir}/{self.file_name}")
if self.nprofiles is None:
# Here the data is loaded in the correct format from the specified file
profiles = torch.tensor(self.data['profiles'], dtype=torch.float32)
pids = self.data['pids']
times = self.data['times']
cameras = self.data['cameras'] if 'cameras' in self.data else None
mag_confs = self.data['mag_confs'] if 'mag_confs' in self.data else None
clippings = self.data['clippings'] if 'clippings' in self.data else None
else:
indices = torch.randperm(len(self.data['profiles']), generator=generator)[:self.nprofiles]
profiles = torch.tensor(self.data['profiles'][indices], dtype=torch.float32)
pids = self.data['pids'][indices]
times = self.data['times'][indices]
cameras = self.data['cameras'][indices] if 'cameras' in self.data else None
mag_confs = self.data['mag_confs'][indices] if 'mag_confs' in self.data else None
clippings = self.data['clippings'][indices] if 'clippings' in self.data else None
# Normalize the data
profiles = self.normalize(profiles)
# Initialize the Dataset
dataset = AEDataset(profiles, pids, times, cameras, mag_confs, clippings)
# Create the dataset train, validation, and test splits
self.train_data, self.val_data, self.test_data = random_split(
dataset, [0.8, 0.1, 0.1], generator=generator,)
def train_dataloader(self):
return DataLoader(self.train_data, batch_size=self.batch_size, drop_last=True)
def val_dataloader(self):
return DataLoader(self.val_data, batch_size=self.batch_size, drop_last=True)
def test_dataloader(self):
return DataLoader(self.test_data, batch_size=self.batch_size, drop_last=True)
def normalize(self, data: torch.Tensor) -> torch.Tensor:
self.normalization = True
if self.normalization_strategy == 'minmax':
self.min_val = data.min()
self.max_val = data.max()
return (data - self.min_val) / (self.max_val - self.min_val)
elif self.normalization_strategy == 'zscore':
self.mean = data.mean()
self.std = data.std()
return (data - self.mean) / self.std
elif self.normalization_strategy == 'robust':
self.median = data.median()
self.iqr = data.quantile(0.75) - data.quantile(0.25)
return (data - self.median) / self.iqr
elif self.normalization_strategy == 'none':
return data
else:
raise ValueError(f"Unknown normalization strategy: {self.normalization_strategy}, accepted strategies are: minmax, zscore, robust, none")
def denormalize(self, data: torch.Tensor) -> torch.Tensor:
if not self.normalization:
print("Data was not normalized, returning original data")
return data
if self.normalization_strategy == 'minmax':
return data * (self.max_val - self.min_val) + self.min_val
elif self.normalization_strategy == 'zscore':
return data * self.std + self.mean
elif self.normalization_strategy == 'robust':
return data * self.iqr + self.median
elif self.normalization_strategy == 'none':
return data
else:
raise ValueError(f"Unknown normalization strategy: {self.normalization_strategy}, accepted strategies are: minmax, zscore, robust, none")
def divide_by_camera(self, camera_list: list = None) -> dict:
"""
Divides the input data by the camera.
Args:
camera_list (list, optional): The list of cameras to divide the data in.
Returns:
(dict): The modified data dictionary.
"""
# Load the raw data from the directory and filename
data = np.load(f"{self.data_dir}/{self.file_name}")
# Create the lists to hold the divided data
profiles = []
pids = []
times = []
camera_ids = []
# Define camera ranges
camera_ranges = {
'1A': (0, 17), '1B': (18, 35), '1C': (36, 53), '1D': (54, 71), '1E': (72, 89),
'2A': (90, 107), '2B': (108, 125), '2C': (126, 143), '2D': (144, 161), '2E': (162, 179),
'3A': (180, 197), '3B': (198, 215), '3C': (216, 233), '3D': (234, 251), '3E': (252, 269),
'4A': (270, 287), '4B': (288, 305), '4C': (306, 323), '4D': (324, 341), '4E': (342, 359)
}
#Redefine the profiles arrangements
if camera_list is None:
camera_list = list(camera_ranges.keys())
for profile in data["profiles"]:
for camera in camera_list:
if camera not in camera_ranges: # Check if camera is in the defined ranges and skip if not
continue
start, end = camera_ranges[camera]
profiles.append(profile[start:end]) # Append the sliced data for the current camera
pids.append(data["pids"])
times.append(data["times"])
camera_ids.append(camera)
# Combine the lists into a dictionary
self.data = {
'profiles': np.array(profiles),
'pids': np.array(pids),
'times': np.array(times),
'camera_ids': np.array(camera_ids)
}
# save the file to an npz
print("Saving divided data by camera to npz file...")
np.savez_compressed(f"{self.data_dir}/db_by_camera.npz", **self.data)
print("Data saved successfully.")

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import torch
import torch.nn as nn
import lightning as L
class AutoEncoder(L.LightningModule):
def __init__(self, input_dim, geometry, learning_rate=1e-3, activation=torch.nn.ReLU()):
super().__init__()
self.input_dim = input_dim
self.geometry = geometry
self.learning_rate = learning_rate
self.activation = activation
self.save_hyperparameters(ignore=['activation'])
assert len(geometry) >= 2, "Geometry must have at least two elements: hidden and latent sizes"
# --- Encoder ---
self.encoder = nn.Sequential()
in_dim = input_dim
for i, out_dim in enumerate(geometry[:-1]):
self.encoder.add_module(f"encoder_layer_{i}", nn.Linear(in_dim, out_dim))
# self.encoder.add_module(f"encoder_batchnorm_{i}", nn.BatchNorm1d(out_dim)) # serve per l'ampiezza (batch)
# self.encoder.add_module(f"encoder_regularization_{i}", nn.L1Loss())
# self.encoder.add_module(f"encoder_dropout_{i}", nn.Dropout(0.2))
self.encoder.add_module(f"encoder_activation_{i}", activation)
in_dim = out_dim
self.encoder.add_module("encoder_output_layer", nn.Linear(in_dim, geometry[-1]))
# --- Decoder (mirror of encoder) ---
self.decoder = nn.Sequential()
reversed_geometry = list(reversed(geometry))
in_dim = reversed_geometry[0]
for i, out_dim in enumerate(reversed_geometry[1:]):
self.decoder.add_module(f"decoder_layer_{i}", nn.Linear(in_dim, out_dim))
self.decoder.add_module(f"decoder_activation_{i}", activation)
in_dim = out_dim
self.decoder.add_module("decoder_output_layer", nn.Linear(in_dim, input_dim))
def encode(self, x):
return self.encoder(x)
def decode(self, z):
return self.decoder(z)
def forward(self, x):
return self.decode(self.encode(x))
def reconstruct(self, x):
return self.forward(x)
def _common_step(self, batch, step="train"):
x = batch # use batch['profile'] if the datamodule is AEDataModule
reconstructed = self.forward(x)
loss = self.loss_function(reconstructed, x)
self.log_dict({f"{step}/loss": loss,}, batch_size=x.size(0))
return loss
def training_step(self, batch, batch_idx):
return self._common_step(batch, step="train")
def validation_step(self, batch, batch_idx):
return self._common_step(batch, step="val")
def test_step(self, batch, batch_idx):
return self._common_step(batch, step="test")
def loss_function(self, reconstructed, original):
return torch.nn.functional.mse_loss(reconstructed, original)
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=self.learning_rate)
class VariationalAutoEncoder(L.LightningModule):
def __init__(self, input_dim, geometry, beta=4, learning_rate=1e-3, activation=torch.nn.ReLU()):
"""
Args:
geometry (list[int]): List of layer sizes, e.g. [784, 256, 64, 16]
beta (float): Weighting factor for the KL divergence loss
learning_rate (float): Learning rate for the optimizer
activation (nn.Module): Activation function to use between layers
"""
super().__init__()
self.input_dim = input_dim
self.geometry = geometry
self.set_beta(beta)
self.learning_rate = learning_rate
self.activation = activation
self.save_hyperparameters()
assert len(self.geometry) >= 2, "Geometry must have at least input and latent size"
# Build encoder
self.encoder = nn.Sequential()
self.encoder.add_module("encoder_input_layer", nn.Linear(self.input_dim, self.geometry[0]))
self.encoder.add_module("encoder_input_activation", self.activation)
for i, (in_dim, out_dim) in enumerate(zip(self.geometry[:-1], self.geometry[1:])):
self.encoder.add_module(f"encoder_layer_{i}", nn.Linear(in_dim, out_dim))
self.encoder.add_module(f"encoder_activation_{i}", self.activation)
self.fc_mu = nn.Linear(self.geometry[-1], self.geometry[-1]//2)
self.fc_logvar = nn.Linear(self.geometry[-1], self.geometry[-1]//2)
# Build decoder (mirror of encoder)
self.decoder_input = nn.Linear(self.geometry[-1]//2, self.geometry[-1])
self.decoder = nn.Sequential()
for i, (in_dim, out_dim) in enumerate(zip(reversed(self.geometry[1:]), reversed(self.geometry[:-1]))):
self.decoder.add_module(f"decoder_layer_{len(self.geometry) - i - 1}", nn.Linear(in_dim, out_dim))
self.decoder.add_module(f"decoder_activation_{len(self.geometry) - i - 1}", self.activation)
self.decoder.add_module("decoder_output_layer", nn.Linear(self.geometry[0], self.input_dim))
def encode(self, x):
h = self.encoder(x)
mu = self.fc_mu(h)
logvar = self.fc_logvar(h)
return mu, logvar
def reparametrize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def decode(self, z):
h = self.decoder_input(z)
return self.decoder(h)
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparametrize(mu, logvar)
y = self.decode(z)
return y, mu, logvar
def set_beta(self, beta):
self.beta = beta
def _common_step(self, batch, step="train"):
x = batch['profile']
reconstructed, mu, logvar = self.forward(x)
loss, mse, kl = self.loss_function(reconstructed, x, mu, logvar)
self.log_dict({f"{step}/loss": loss, f"{step}/mse": mse, f"{step}/kl": kl})
return loss
def training_step(self, batch, batch_idx):
return self._common_step(batch, step="train")
def validation_step(self, batch, batch_idx):
return self._common_step(batch, step="val")
def test_step(self, batch, batch_idx):
return self._common_step(batch, step="test")
def loss_function(self, reconstructed, original, mu, logvar):
# Reconstruction loss (MSE)
mse = torch.nn.functional.mse_loss(reconstructed, original)
# KL divergence loss
kl = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
return mse + self.beta * kl, mse, kl
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=self.learning_rate)

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import optuna
import lightning as L
import torch
# ------------- Seed everything -------------
L.seed_everything(42) # Lightning helper for deterministic runs
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
from model import AutoEncoder
from dataset import AEDataModule
from lightning.pytorch.loggers import TensorBoardLogger
from lightning.pytorch.callbacks import ModelCheckpoint, EarlyStopping
from lightning.pytorch import Trainer
class Objective:
def __init__(self, data_module: AEDataModule, input_dim: int):
self.data_module = data_module
self.input_dim = input_dim
def __call__(self, trial):
# Suggest hyperparameters
learning_rate = trial.suggest_float('learning_rate', 1e-5, 1e-2)
hidden_size_0 = trial.suggest_categorical('hidden_size_0', [64, 128, 256, 512, 1024])
hidden_size_1 = trial.suggest_categorical('hidden_size_1', [64, 128, 256, 512, 1024])
hidden_size_2 = trial.suggest_categorical('hidden_size_2', [64, 128, 256, 512, 1024])
hidden_size_3 = trial.suggest_categorical('hidden_size_3', [64, 128, 256, 512, 1024])
latent_size = trial.suggest_categorical('latent_size', [4, 8, 16, 32, 64, 128, 256])
geometry = [hidden_size_0, hidden_size_1, hidden_size_2, latent_size]
# Initialize model
model = AutoEncoder(input_dim=self.input_dim, geometry=geometry, learning_rate=learning_rate)
# Logger
logger = TensorBoardLogger("OOptuna", name=f"4layers/trial_{trial.number}")
# Callbacks
checkpoint_callback = ModelCheckpoint(
monitor='val/loss',
dirpath='checkpoints',
filename=f'4layers/trial_{trial.number}' + '-{epoch:02d}-{val/loss:.4f}',
save_top_k=1,
mode='min',
)
early_stopping_callback = EarlyStopping(
monitor='val/loss',
patience=5,
mode='min'
)
# Trainer
trainer = Trainer(
max_epochs=50,
logger=logger,
callbacks=[checkpoint_callback, early_stopping_callback],
accelerator='auto',
devices='auto'
)
# Train the model
trainer.fit(model, self.data_module)
# Return the best validation loss
return checkpoint_callback.best_model_score.item()
if __name__ == "__main__":
# Data module
data_module = AEDataModule(data_dir='data', file_name='profs_w_magclip.npz',
batch_size=64, normalization_strategy='minmax', nprofiles=100_000)
data_module.prepare_data()
data_module.setup()
# Input dimension
sample_batch = next(iter(data_module.train_dataloader()))
input_dim = sample_batch['profile'].shape[1]
# Optuna study
study = optuna.create_study(direction='minimize')
study.optimize(Objective(data_module, input_dim), n_trials=200)
print("Best trial:")
trial = study.best_trial
print(f" Value: {trial.value}")
print(" Params: ")
for key, value in trial.params.items():
print(f" {key}: {value}")

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"""
PulseDataModule — per-frame [360] profile sampling for PyTorch Lightning.
WORKFLOW
--------
1. Run consolidate_pulses() ONCE to merge each pulse's 360 .h5f files into
a single memory-mapped [T x 360] float32 .npz file per pulse.
2. Use PulseDataModule normally in your Lightning Trainer.
Each sample returned is a 1-D tensor of shape [360] — one angular profile
at one timestep.
WHY CONSOLIDATION?
------------------
With T > 100 000 timesteps, opening 360 files per __getitem__ call would cost
~360 ms per sample — completely impractical. Memory-mapped .npz files let the
OS page-cache serve random timestep reads at RAM speed after the first epoch
warm-up, with no per-sample file-open overhead.
DIRECTORY LAYOUT (input)
------------------------
root/
├── pulse001/
│ ├── 000.h5f (degree 0)
│ ├── 001.h5f (degree 1)
│ └── ... (up to 359.h5f)
├── pulse002/
│ └── ...
└── pulseN/
DIRECTORY LAYOUT (after consolidation)
---------------------------------------
consolidated/
├── pulse001.npz contains "data" array of shape [T, 361], float32
├── pulse002.npz
└── pulseN.npz
"""
from __future__ import annotations
import random
import time
from pathlib import Path
from typing import Callable, Optional
import re
import h5py
import numpy as np
import torch
from torch.utils.data import DataLoader, Dataset
import lightning as L
# ---------------------------------------------------------------------------
# Step 1 — one-time consolidation
# ---------------------------------------------------------------------------
def consolidate_pulses(
root: str | Path,
out_dir: str | Path,
data_key: str = "XMCTSdata",
dtype: np.dtype = np.float32,
overwrite: bool = False,
decimation_factor: int = 1, # <--- Added parameter
) -> None:
"""
For each pulse directory, stack its 360 .h5f files into a single
memory-mappable [T x 361] .npz file.
Files within a pulse are sorted lexicographically, so name them so that
sort order == degree order (e.g. 000.h5f ... 359.h5f).
Args:
root: root data directory containing pulse sub-directories.
out_dir: where to write the consolidated .npz files.
data_key: HDF5 dataset key inside each file.
dtype: output dtype (float32 recommended; halves storage vs float64).
overwrite: if False, skips pulses whose .npz already exists.
decimation_factor: Keep only every N-th timestep (default 1 means keep all).
"""
root = Path(root)
out_dir = Path(out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
pulse_dirs = sorted(p for p in root.iterdir() if p.is_dir())
print(f"Consolidating {len(pulse_dirs)} pulses -> {out_dir} with decimation={decimation_factor}")
pattern = re.compile(r"t(000|090|180|270)$")
for i, pulse_dir in enumerate(pulse_dirs):
out_path = out_dir / f"{pulse_dir.name}.npz"
print(out_path)
if out_path.exists() and not overwrite:
print(f" [{i+1}/{len(pulse_dirs)}] {pulse_dir.name} -- skipped (already exists)")
continue
times = sorted([
f for f in pulse_dir.glob("*.h5f")
if pattern.search(f.stem)
])
h5_files = sorted([
f for f in pulse_dir.glob("*.h5f")
if not pattern.search(f.stem)
])
if not h5_files:
print(f" [{i+1}/{len(pulse_dirs)}] {pulse_dir.name} -- skipped (no .h5f files)")
continue
t0 = time.perf_counter()
# Read the first time files to get the original length T
length = np.inf
for h5_path in times:
with h5py.File(h5_path, "r") as f:
# We can load the whole time array to check its length first
tmp_time = np.asarray(f[data_key][str(h5_path)[-7:-4]], dtype=dtype)
if length > tmp_time.shape[0]:
# Apply decimation right away to the stored time array
time_arr = tmp_time[::decimation_factor]
length = tmp_time.shape[0]
T_original = length
T_decimated = time_arr.shape[0]
# Allocate output with the new decimated T dimension
matrix = np.empty((T_decimated, 361), dtype=dtype)
matrix[:, 0] = time_arr
for col, h5_path in enumerate(h5_files):
with h5py.File(h5_path, "r") as f:
# Slice the dataset directly inside HDF5: [:T_original:decimation_factor]
# This only reads the needed values from disk into RAM.
dataset = f[data_key][str(h5_path)[-7:-4]]
decimated_col = np.asarray(dataset[:T_original:decimation_factor], dtype=dtype)
matrix[:, col + 1] = decimated_col
np.savez_compressed(out_path, data=matrix)
elapsed = time.perf_counter() - t0
size_mb = matrix.nbytes / 1024**2
print(
f" [{i+1}/{len(pulse_dirs)}] {pulse_dir.name}"
f" -> {out_path.name} shape={matrix.shape}"
f" {size_mb:.0f} MB {elapsed:.1f}s"
)
# ---------------------------------------------------------------------------
# Step 2 — Dataset
# ---------------------------------------------------------------------------
class MinMaxNormalize:
"""
Scales tensor values to the range [0, 1].
If global_min and global_max are provided, it performs Global Normalization.
If left as None, it performs Per-Sample Normalization (min and max are
calculated dynamically for every single 360-profile).
"""
def __init__(
self,
global_min: Optional[float] = None,
global_max: Optional[float] = None
):
self.global_min = global_min
self.global_max = global_max
def __call__(self, x: torch.Tensor) -> torch.Tensor:
# Use provided globals, otherwise compute local min/max
x_min = self.global_min if self.global_min is not None else x.min()
x_max = self.global_max if self.global_max is not None else x.max()
denom = x_max - x_min
# Prevent division by zero if the profile is completely flat
if denom < 1e-8:
return torch.zeros_like(x)
return (x - x_min) / denom
# Module-level mmap cache — each worker process gets its own copy,
# so there is no cross-process sharing or locking needed.
_MMAP_CACHE: dict[str, np.ndarray] = {}
def _get_mmap(path: str) -> np.ndarray:
"""Return (and cache) a read-only memory-mapped array for *path*."""
if path not in _MMAP_CACHE:
container = np.load(path, mmap_mode="r")
_MMAP_CACHE[path] = container["data"]
return _MMAP_CACHE[path]
class PulseProfileDataset(Dataset):
"""
Flat dataset where each sample is a [360]-length angular profile
at one timestep from one pulse.
The index is a flat list of (npz_path, t) pairs.
Memory-mapped arrays are opened lazily and cached per worker process,
so the OS page-cache handles repeated access at RAM speed.
Args:
index: list of (npz_path: str, t: int) tuples.
transform: optional callable applied to the tensor.
"""
def __init__(
self,
index: list[tuple[str, int]],
transform: Optional[Callable[[torch.Tensor], torch.Tensor]] = None,
):
self.index = index
self.transform = transform
def __len__(self) -> int:
return len(self.index)
def __getitem__(self, idx: int) -> torch.Tensor:
path, t = self.index[idx]
arr = _get_mmap(path) # [T x 361], read-only mmap
profile = arr[t] # shape [361], one page read
x = torch.from_numpy(profile[1:].copy()) # .copy() detaches from mmap buffer
if self.transform is not None:
x = self.transform(x)
return x
# ---------------------------------------------------------------------------
# Step 3 — Lightning DataModule
# ---------------------------------------------------------------------------
class PulseDataModule(L.LightningDataModule):
"""
Lightning DataModule for per-frame profile samples.
Requires that consolidate_pulses() has already been run.
Splitting is done at the PULSE level to prevent data leakage
(consecutive timesteps within a pulse are strongly correlated).
Args:
consolidated_dir: directory containing the per-pulse .npz files.
val_frac: fraction of pulses held out for validation.
test_frac: fraction of pulses held out for testing.
batch_size: profiles per batch.
num_workers: DataLoader workers (4-8 is usually optimal).
pin_memory: pin CPU tensors for faster GPU transfer.
seed: RNG seed for reproducible pulse-level splits.
transform: optional per-sample transform.
t_start: first timestep index to include (default 0).
t_end: last timestep index (exclusive); None = all.
"""
def __init__(
self,
consolidated_dir: str | Path,
val_frac: float = 0.1,
test_frac: float = 0.1,
batch_size: int = 512,
num_workers: int = 4,
pin_memory: bool = False,
seed: int = 42,
transform: Optional[Callable] = None,
t_start: int = 0,
t_end: Optional[int] = None,
):
super().__init__()
self.consolidated_dir = Path(consolidated_dir)
self.val_frac = val_frac
self.test_frac = test_frac
self.batch_size = batch_size
self.num_workers = num_workers
self.pin_memory = pin_memory
self.seed = seed
self.transform = transform
self.t_start = t_start
self.t_end = t_end
self._train_index: list[tuple[str, int]] = []
self._val_index: list[tuple[str, int]] = []
self._test_index: list[tuple[str, int]] = []
# ------------------------------------------------------------------
def setup(self, stage: Optional[str] = None) -> None:
npz_files = sorted(self.consolidated_dir.glob("*.npz"))
if not npz_files:
raise FileNotFoundError(
f"No .npz files found in {self.consolidated_dir}. "
"Run consolidate_pulses() first."
)
rng = random.Random(self.seed)
rng.shuffle(npz_files)
n = len(npz_files)
n_test = max(1, round(n * self.test_frac))
n_val = max(1, round(n * self.val_frac))
n_train = n - n_val - n_test
splits = {
"train": npz_files[:n_train],
"val": npz_files[n_train: n_train + n_val],
"test": npz_files[n_train + n_val:],
}
for split, files in splits.items():
index = self._build_flat_index(files)
setattr(self, f"_{split}_index", index)
self._print_summary(splits)
def _build_flat_index(self, npz_files: list[Path]) -> list[tuple[str, int]]:
"""Return a flat list of (path, t) pairs for all timesteps in all pulses."""
index = []
for path in npz_files:
with np.load(str(path), mmap_mode="r") as container:
T = container["data"].shape[0]
t_end = min(self.t_end, T) if self.t_end is not None else T
index.extend((str(path), t) for t in range(self.t_start, t_end))
return index
def _print_summary(self, splits: dict) -> None:
for split, files in splits.items():
idx = getattr(self, f"_{split}_index")
print(
f"[PulseDataModule] {split:5s}: "
f"{len(files):3d} pulses | {len(idx):>10,} timestep samples"
)
# ------------------------------------------------------------------
def _make_loader(self, index: list[tuple[str, int]], shuffle: bool) -> DataLoader:
if shuffle:
index = index.copy()
random.shuffle(index)
return DataLoader(
PulseProfileDataset(index, transform=self.transform),
batch_size=self.batch_size,
shuffle=False, # already shuffled above
num_workers=self.num_workers,
pin_memory=self.pin_memory,
persistent_workers=self.num_workers > 0,
)
def train_dataloader(self) -> DataLoader:
return self._make_loader(self._train_index, shuffle=True)
def val_dataloader(self) -> DataLoader:
return self._make_loader(self._val_index, shuffle=False)
def test_dataloader(self) -> DataLoader:
return self._make_loader(self._test_index, shuffle=False)
# ------------------------------------------------------------------
@property
def n_angles(self) -> int:
with np.load(str(self._train_index[0][0]), mmap_mode="r") as container:
return container["data"].shape[1]
@property
def n_train_samples(self) -> int:
return len(self._train_index)
# ---------------------------------------------------------------------------
# Usage
# ---------------------------------------------------------------------------
if __name__ == "__main__":
# ---- Run ONCE to consolidate ----------------------------------------
root_dir = input(f"Select folder where .h5 files are cached")
out_dir = input(f"Select folder to write the database files, should be ./data")
consolidate_pulses(
root=root_dir,
out_dir="/home/IPP-HGW/orluca/devel/XMCTSGuard/data/consolidated/",
data_key="XMCTSdata", # adjust to your HDF5 key
dtype=np.float32,
overwrite=False, # set True to re-consolidate
decimation_factor=1_000,
)
# ---- Then use normally in Lightning ---------------------------------
scaler = MinMaxNormalize(global_min=0.0, global_max=10.0)
dm = PulseDataModule(
consolidated_dir="data/consolidated/",
val_frac=0.1,
test_frac=0.1,
batch_size=512,
num_workers=4,
seed=42,
transform=scaler
# t_start=1000, # optional: trim transient at start of pulse
# t_end=90_000, # optional: trim tail
)
dm.setup()
for batch in dm.train_dataloader():
print(f"Batch shape : {batch.shape}") # Will be [512, 361] because of the time array in col 0
print(f"Dtype : {batch.dtype}")
break

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import torch
import lightning as L
from lightning.pytorch.callbacks import EarlyStopping
from lightning.pytorch.loggers import TensorBoardLogger
from .pulse_dataset import PulseDataModule, MinMaxNormalize
from .dataset import AEDataModule
from .model import AutoEncoder, VariationalAutoEncoder
from .callbacks import SaveBest, SaveEveryNEpochs, BetaWarmUp
from ..visualization.plots import GAIN_PIDS as gain_list
def train_autoencoder(data_dir, file_name=None, input_dim=360, geometry=[64, 32, 16, 8],
beta=1, gamma=1, batch_size=32, max_epochs=100,
normalization_strategy='minmax', learning_rate=3e-4,
activation=torch.nn.ReLU(), nprofiles=None, model_kind='AE',
sel_device=None):
"""
Trains an Autoencoder (AE) or Variational Autoencoder (VAE) on pulse profile data
from the Wendelstein 7-X (W7-X) stellarator experiment, using PyTorch Lightning
for training orchestration and TensorBoard for logging.
The function handles the full training pipeline end-to-end:
1. Data loading and normalization via a PulseDataModule.
2. Model instantiation (AE or VAE) with the specified architecture.
3. Training with automatic hardware acceleration and configurable callbacks.
4. Validation and testing after training.
Parameters
----------
data_dir : str or Path
Path to the consolidated directory containing the pulse profile dataset.
This is passed directly to PulseDataModule as `consolidated_dir`.
file_name : str or None, optional
Name of a specific data file to load from `data_dir`. If None, the data
module is expected to discover files automatically. Currently unused in
the active code path (legacy parameter). Default is None.
input_dim : int, optional
Dimensionality of each input sample (i.e., the number of features per
pulse profile). Must match the actual feature size of the loaded data.
Default is 360.
geometry : list of int, optional
Defines the encoder's layer sizes from input to the bottleneck. Each
integer specifies the number of neurons in that hidden layer. The decoder
is constructed as the mirror image of the encoder.
Example: [64, 32, 16, 8] produces the architecture:
Encoder: input_dim → 64 → 32 → 16 → 8 (bottleneck)
Decoder: 8 → 16 → 32 → 64 → input_dim
Default is [64, 32, 16, 8].
beta : float or int, optional
Weight applied to the KL-divergence term in the VAE loss function.
Only relevant when model_kind='VAE'. A value of 1 corresponds to a
standard VAE; values > 1 produce a β-VAE, which encourages greater
disentanglement of latent representations. Ignored for AE. Default is 1.
gamma : float or int, optional
Additional loss scaling factor for the VAE objective. Only relevant when
model_kind='VAE'. Interpretation depends on the VariationalAutoEncoder
implementation (e.g., may weight reconstruction loss separately).
Ignored for AE. Default is 1.
batch_size : int, optional
Number of samples per training batch. Note: this parameter is accepted
for interface compatibility but the active code path hardcodes
batch_size=64 inside PulseDataModule. Default is 32.
max_epochs : int, optional
Maximum number of training epochs. Training may stop earlier if an
EarlyStopping callback is enabled (currently commented out).
Default is 100.
normalization_strategy : str, optional
Normalization strategy label. Currently unused in the active code path
(legacy parameter); normalization is handled by MinMaxNormalize with
fixed global_min=0.0 and global_max=10.0. Default is 'minmax'.
learning_rate : float, optional
Initial learning rate passed to the model's optimizer (Adam or similar,
depending on the model implementation). Default is 3e-4.
activation : torch.nn.Module, optional
Activation function applied between hidden layers in both encoder and
decoder. Any torch.nn activation module is valid (e.g., torch.nn.ReLU(),
torch.nn.Tanh(), torch.nn.LeakyReLU()). Default is torch.nn.ReLU().
nprofiles : int or None, optional
Maximum number of profiles to load from the dataset. If None, all
available profiles are used. Currently unused in the active code path
(legacy parameter). Default is None.
model_kind : {'AE', 'VAE'}, optional
Selects the model architecture to train:
- 'AE' : Standard deterministic AutoEncoder (AutoEncoder class).
- 'VAE' : Variational AutoEncoder (VariationalAutoEncoder class),
which learns a probabilistic latent space using the
reparameterization trick and a KL-divergence regularization term.
Default is 'AE'.
sel_device : str or None, optional
Target compute device (e.g., 'cpu', 'cuda', 'mps'). Currently unused;
device selection is handled automatically by PyTorch Lightning via
accelerator='auto'. Default is None.
Returns
-------
model : AutoEncoder or VariationalAutoEncoder
The trained model instance after fitting. The model's weights correspond
to the final training epoch (not necessarily the best validation checkpoint,
unless SaveBest restores them — check the SaveBest callback implementation).
Side Effects
------------
- Writes TensorBoard logs to: ./W7-X_QXT/<logger_name>/
where logger_name is 'AE{input_dim}' or 'VAE{input_dim}'.
- Saves model checkpoints via SaveBest (monitors val/loss) and
SaveEveryNEpochs (every 100 epochs) callbacks. Checkpoint paths are
determined by the logger configuration.
- Runs trainer.validate() and trainer.test() after training, logging
results to TensorBoard.
Raises
------
ValueError
Raised implicitly if model_kind is not 'AE' or 'VAE', as the model
variable will be undefined when passed to the trainer.
Notes
-----
**Hardcoded data module settings:**
The PulseDataModule is initialized with val_frac=0.0, test_frac=0.0,
num_workers=4, and seed=42, regardless of the values passed to
normalization_strategy, or nprofiles. These parameters are currently kept
in the signature for backward compatibility but have no effect on behavior.
**Normalization:**
MinMaxNormalize is applied with global_min=0.0 and global_max=10.0.
These bounds should be verified against the actual data range to avoid
clipping or poor normalization.
**Validation and test splits:**
Since val_frac=0.0 and test_frac=0.0, trainer.validate() and
trainer.test() will run on the training set. Consider adjusting
val_frac and test_frac for proper generalization evaluation.
**Callbacks (active):**
- SaveBest: Saves the checkpoint with the lowest val/loss.
- SaveEveryNEpochs(100): Saves a checkpoint every 100 epochs.
**Callbacks (disabled):**
- EarlyStopping: Would halt training if val/loss does not improve for
10 consecutive epochs.
- BetaWarmUp: Would gradually increase the β term from 0 to 0.1 over
50 epochs starting at epoch 10 (useful for VAE training stability).
Examples
--------
Train a standard AE on pulse data:
>>> model = train_autoencoder(
... data_dir='/data/w7x/pulses',
... input_dim=360,
... geometry=[128, 64, 32, 16],
... model_kind='AE',
... max_epochs=200,
... learning_rate=1e-3,
... )
Train a β-VAE with a stronger KL penalty:
>>> model = train_autoencoder(
... data_dir='/data/w7x/pulses',
... input_dim=360,
... geometry=[64, 32, 16],
... model_kind='VAE',
... beta=1e-5,
... gamma=1,
... max_epochs=300,
... activation=torch.nn.LeakyReLU(),
... )
See Also
--------
AutoEncoder : Standard deterministic autoencoder model class.
VariationalAutoEncoder : VAE model class with KL-divergence regularization.
PulseDataModule : Lightning DataModule for loading and batching pulse profiles.
MinMaxNormalize : Transform class for min-max feature scaling.
SaveBest : Callback that saves the best model checkpoint by monitored metric.
SaveEveryNEpochs : Callback that saves checkpoints at fixed epoch intervals.
"""
# TODO: implement better device selection practice
# # Initialize the data module
# data_module = AEDataModule(data_dir, file_name, batch_size, normalization_strategy, nprofiles=nprofiles)
# data_module.prepare_data()
# data_module.setup()
# data_module.exclude_pids(gain_list)
scaler = MinMaxNormalize(global_min=0.0, global_max=10.0)
data_module = PulseDataModule(consolidated_dir=data_dir,
val_frac=0.1, test_frac=0.1, batch_size=batch_size, num_workers=4, seed=42,
transform=scaler,
)
data_module.setup()
# Initialize the model
if model_kind == 'AE':
model = AutoEncoder(input_dim=input_dim, geometry=geometry, learning_rate=learning_rate, activation=activation)
logger_name = f"AE{input_dim}"
elif model_kind == 'VAE':
model = VariationalAutoEncoder(input_dim=input_dim, geometry=geometry, beta=beta, gamma=gamma, learning_rate=learning_rate, activation=activation)
logger_name = f"VAE{input_dim}"
# Initialize a logger
logger = TensorBoardLogger("W7-X_QXT", name=logger_name)
# Initialize the trainer
trainer = L.Trainer(
logger=logger,
max_epochs=max_epochs,
accelerator='auto',
callbacks=[
SaveBest(monitor="val/loss", logger=logger),
SaveEveryNEpochs(100, logger=logger),
# EarlyStopping(monitor="val/loss", patience=10, mode="min"),
# BetaWarmUp(start_epoch=10, initial_beta=0, final_beta=0.1, warmup_epochs=50),
],
devices='auto',)
# Train the model
trainer.fit(model, data_module)
trainer.validate(model, data_module)
trainer.test(model, data_module)
# # Evaluate the model on the validation set
# if data_module.val_data is not None:
# trainer.validate(model, datamodule=data_module)
# # Evaluate the model on the test set
# if data_module.test_data is not None:
# trainer.test(model, datamodule=data_module)
return model
if __name__ == "__main__":
data_dir = "../data"
file_name = "20251028_all_data.npz"
input_dim = 360
geometry = [64, 32, 32]
beta = 1e-7
gamma = 1e-4
batch_size = 32
max_epochs = 3000
normalization_strategy = 'minmax' # Options: 'minmax', 'zscore', 'robust', 'none'
learning_rate = 5e-5
nprofiles = 10000
activation = torch.nn.ReLU()
model_kind = 'AE'
# Train the autoencoder
train_autoencoder(data_dir, file_name, input_dim, geometry, beta, gamma, batch_size,
max_epochs, normalization_strategy, learning_rate, activation,
nprofiles, model_kind=model_kind)

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# ─────────────────────────────────────────────────────────────────────────────
# Pure computation helpers (stateless, importable, testable)
# ─────────────────────────────────────────────────────────────────────────────
import numpy as np
def normalize(arr: np.ndarray, min_data: float, max_data: float) -> np.ndarray:
"""Min-max normalise to [0, 1], returned as float32."""
return ((arr - min_data) / (max_data - min_data)).astype(np.float32)
def denormalize(arr: np.ndarray, min_data: float, max_data: float) -> np.ndarray:
"""Invert min-max normalisation."""
return arr * (max_data - min_data) + min_data
def model_forward_single(model, profile_norm: np.ndarray) -> np.ndarray:
"""Run the autoencoder on one normalised 1-D profile (float32 numpy array)."""
import torch
with torch.no_grad():
return model(torch.from_numpy(profile_norm).unsqueeze(0)).squeeze(0).numpy()
def model_forward_batch(model, batch_norm: np.ndarray) -> np.ndarray:
"""
Run the autoencoder on a batch shaped (n_samples, n_diodes).
Returns (n_samples, n_diodes) float32.
"""
import torch
with torch.no_grad():
return model(torch.from_numpy(batch_norm)).numpy()
def reconstruct(model, profile: np.ndarray,
min_data: float, max_data: float) -> np.ndarray:
"""Normalise → model → denormalise for a single 1-D profile."""
rec_norm = model_forward_single(model, normalize(profile, min_data, max_data))
return denormalize(rec_norm, min_data, max_data)
def pearson(a: np.ndarray, b: np.ndarray) -> float:
"""Pearson correlation coefficient between two 1-D arrays."""
return float(np.corrcoef(a, b)[0, 1])
def apply_gain(profile: np.ndarray,
outlier_mask: np.ndarray,
gain_ratio: float) -> np.ndarray:
"""
Return a gain-corrected copy of *profile*.
gain_ratio = old_gain / new_gain.
When gain_ratio > 1 the majority of channels recorded with the higher
old gain and look inflated — they are the ones to scale; the spatial
outliers are the channels that did NOT change gain.
When gain_ratio < 1 the detected outlier channels are the ones that
changed gain and need to be rescaled.
"""
adjusted = profile.copy()
if gain_ratio > 1:
adjusted[~outlier_mask] *= gain_ratio # majority-changed channels
else:
adjusted[outlier_mask] *= gain_ratio # outlier channels changed gain
return adjusted
def fill_outlier_gaps(outlier_mask: np.ndarray,
profile: np.ndarray,
tol: float = 0.10) -> np.ndarray:
"""
For each pair of consecutive flagged diodes (i, j) that are at most
8 positions apart, check whether every diode m between them is within
*tol* (relative) of the average of profile[i] and profile[j]. If so,
flag m as well — this closes small intra-camera gaps in the outlier region.
"""
filled = outlier_mask.copy()
outlier_indices = np.where(outlier_mask)[0]
for k in range(len(outlier_indices) - 1):
i = outlier_indices[k]
j = outlier_indices[k + 1]
if j - i > 8: # only close gaps within the same camera
continue
ref = (profile[i] + profile[j]) / 2.0
for m in range(i + 1, j):
if abs(profile[m] - ref) / (abs(ref) + 1e-10) <= tol:
filled[m] = True
return filled
# ── Single-pass model-residuals outlier detection ────────────────────────────
def detect_outliers_residual(profile: np.ndarray,
reconstruction: np.ndarray,
z_thresh: float) -> np.ndarray:
"""
Flag diodes where (profile − reconstruction) > mean + z_thresh · std.
Only positive residuals are caught: diodes whose signal sits well above
what the autoencoder expects given the rest of the spatial profile.
"""
residuals = profile - reconstruction
threshold = np.mean(residuals) + z_thresh * np.std(residuals)
return residuals > threshold
# ── Batched helpers ───────────────────────────────────────────────────────────
def compute_correlation_series(model, signals: np.ndarray,
indices: np.ndarray,
min_data: float, max_data: float,
batch_size: int = 256) -> np.ndarray:
"""
Compute per-time-step Pearson ρ(signal, AE(signal)) for the given column
indices of *signals* (shape: n_diodes × n_time), in batches.
Returns a float32 array of length len(indices).
"""
correlations = np.empty(len(indices), dtype=np.float32)
col = 0
for start in range(0, len(indices), batch_size):
idx_batch = indices[start : start + batch_size]
batch_sig = signals[:, idx_batch] # (n_diodes, b)
batch_norm = normalize(batch_sig.T, min_data, max_data) # (b, n_diodes)
rec_norm = model_forward_batch(model, batch_norm) # (b, n_diodes)
rec_dn = denormalize(rec_norm, min_data, max_data)
for j in range(len(idx_batch)):
correlations[col] = pearson(batch_sig[:, j], rec_dn[j, :])
col += 1
del batch_sig, batch_norm, rec_norm, rec_dn
return correlations
def reconstruct_only_batched(model, signals: np.ndarray,
min_data: float, max_data: float,
batch_size: int = 256) -> np.ndarray:
"""
Pass every time step of *signals* (n_diodes × n_time) through the
autoencoder in batches. No gain correction is applied here — the caller
is responsible for providing already-corrected data.
Returns rec shaped (n_diodes, n_time). *signals* is NOT mutated.
"""
n_time = signals.shape[1]
rec = np.empty_like(signals)
for start in range(0, n_time, batch_size):
end = min(start + batch_size, n_time)
batch = normalize(signals[:, start:end].T,
min_data, max_data) # (b, n_diodes)
out = model_forward_batch(model, batch) # (b, n_diodes)
rec[:, start:end] = denormalize(out.T, min_data, max_data)
del batch, out
return rec

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from .main_window import WorkflowGUI
__all__ = ["WorkflowGUI"]

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src/XMCTSGuard/gui/helpers.py Normal file
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# ─────────────────────────────────────────────────────────────────────────────
# Small UI helpers
# ─────────────────────────────────────────────────────────────────────────────
from PyQt6.QtWidgets import QLabel, QLineEdit, QFrame
def lbl_h(text, obj_name="dim"):
w = QLabel(text)
w.setObjectName(obj_name)
return w
def divider_h():
f = QFrame()
f.setObjectName("divider")
f.setFrameShape(QFrame.Shape.HLine)
return f
def entry_h(default=""):
return QLineEdit(default)

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src/XMCTSGuard/gui/main_window.py Normal file
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# ─────────────────────────────────────────────────────────────────────────────
# Main window
# ─────────────────────────────────────────────────────────────────────────────
from PyQt6.QtWidgets import (
QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, QLabel, QPushButton,
QFileDialog, QMessageBox, QFrame, QSizePolicy, QGridLayout,
)
from matplotlib.figure import Figure
import matplotlib.gridspec as gridspec
from matplotlib.backends.backend_qtagg import FigureCanvasQTAgg, NavigationToolbar2QT
from PyQt6.QtCore import Qt, QThread
import os
import numpy as np
from .helpers import *
from .styles import *
from .workers import AnalysisWorker
from .widgets import SpectralAnalysisDialog
from ..engine.utils import *
from ..utils import *
class WorkflowGUI(QMainWindow):
def __init__(self):
super().__init__()
self.setWindowTitle("SXR Profile Anomaly Detector")
self.resize(1480, 860)
self.setMinimumSize(1100, 700)
self.setStyleSheet(STYLESHEET)
self._thread = None
self._worker = None
self._results = None
self._build_ui()
# ── layout ────────────────────────────────────────────────────────────────
def _build_ui(self):
root = QWidget()
self.setCentralWidget(root)
root_v = QVBoxLayout(root)
root_v.setContentsMargins(0, 0, 0, 0)
root_v.setSpacing(0)
topbar = QFrame()
topbar.setObjectName("topbar")
topbar.setFixedHeight(52)
tb_h = QHBoxLayout(topbar)
tb_h.setContentsMargins(20, 0, 20, 0)
tb_h.addWidget(lbl_h("⚡ SXR Anomaly Detector", "title"))
tb_h.addStretch()
self._status_lbl = lbl_h("Ready", "status")
tb_h.addWidget(self._status_lbl)
root_v.addWidget(topbar)
body = QWidget()
body_h = QHBoxLayout(body)
body_h.setContentsMargins(12, 12, 12, 12)
body_h.setSpacing(10)
root_v.addWidget(body, stretch=1)
sidebar = QFrame()
sidebar.setObjectName("sidebar")
sidebar.setFixedWidth(264)
sidebar.setSizePolicy(QSizePolicy.Policy.Fixed,
QSizePolicy.Policy.Expanding)
self._build_sidebar(sidebar)
body_h.addWidget(sidebar)
plot_w = QWidget()
self._build_plots(plot_w)
body_h.addWidget(plot_w, stretch=1)
def _build_sidebar(self, parent):
lay = QVBoxLayout(parent)
lay.setContentsMargins(14, 10, 14, 14)
lay.setSpacing(4)
def section(text):
lay.addSpacing(10)
lay.addWidget(lbl_h(text, "section"))
lay.addWidget(divider_h())
def field(label, default):
lay.addWidget(lbl_h(label))
e = entry_h(default)
lay.addWidget(e)
return e
section("SHOT")
self._pid_edit = field("PID", "20250401.49")
section("MODEL")
lay.addWidget(lbl_h("Checkpoint path"))
path_row = QWidget()
path_h = QHBoxLayout(path_row)
path_h.setContentsMargins(0, 0, 0, 0)
path_h.setSpacing(4)
self._model_edit = entry_h(
"path/to/ckpt/file/best_model_.ckpt"
)
browse_btn = QPushButton("…")
browse_btn.setObjectName("browse")
browse_btn.setFixedWidth(28)
browse_btn.clicked.connect(self._browse_model)
path_h.addWidget(self._model_edit)
path_h.addWidget(browse_btn)
lay.addWidget(path_row)
section("NORMALISATION")
self._min_edit = field("min_data", "-0.2294265627861023")
self._max_edit = field("max_data", "8.772955894470215")
section("GAIN RATIO")
gain_row = QWidget()
gain_h = QHBoxLayout(gain_row)
gain_h.setContentsMargins(0, 0, 0, 0)
gain_h.setSpacing(6)
for label, val, attr in [("Old gain", "5", "_old_gain"),
("New gain", "2", "_new_gain")]:
gain_h.addWidget(lbl_h(label))
e = entry_h(val)
e.setFixedWidth(46)
setattr(self, attr, e)
gain_h.addWidget(e)
lay.addWidget(gain_row)
section("OUTLIER DETECTION")
# ── single greyed-out method indicator ────────────────────────────────
self._method_btn = QPushButton("Model Residuals")
self._method_btn.setObjectName("method_btn")
self._method_btn.setCheckable(True)
self._method_btn.setChecked(True)
self._method_btn.setEnabled(False) # purely decorative
lay.addWidget(self._method_btn)
# ── z-threshold ───────────────────────────────────────────────────────
lay.addSpacing(4)
lay.addWidget(lbl_h("Z-threshold"))
self._z_edit = entry_h("0.2")
lay.addWidget(self._z_edit)
# ── action buttons ────────────────────────────────────────────────────
lay.addStretch()
self._run_btn = QPushButton("▶ RUN ANALYSIS")
self._run_btn.setObjectName("run")
self._run_btn.clicked.connect(self._run)
lay.addWidget(self._run_btn)
self._save_btn = QPushButton("💾 SAVE ADJUSTED DATA")
self._save_btn.setObjectName("run")
self._save_btn.setEnabled(False)
self._save_btn.clicked.connect(self._save_adjusted_data)
lay.addWidget(self._save_btn)
self._save_rec_btn = QPushButton("💾 SAVE MODEL RECONSTRUCTION")
self._save_rec_btn.setObjectName("run")
self._save_rec_btn.setEnabled(False)
self._save_rec_btn.clicked.connect(self._save_reconstruction_data)
lay.addWidget(self._save_rec_btn)
self._spectral_btn = QPushButton("📊 SPECTRAL ANALYSIS")
self._spectral_btn.setObjectName("spectral")
self._spectral_btn.setEnabled(False)
self._spectral_btn.setToolTip(
"Per-diode PSD heatmap, total power, and spectral entropy")
self._spectral_btn.clicked.connect(self._open_spectral_analysis)
lay.addWidget(self._spectral_btn)
# ── correlation readout ───────────────────────────────────────────────
lay.addSpacing(6)
self._corr_widget = QWidget()
self._corr_layout = QGridLayout(self._corr_widget)
self._corr_layout.setContentsMargins(0, 0, 0, 0)
self._corr_layout.setSpacing(3)
lay.addWidget(self._corr_widget)
def _build_plots(self, parent):
lay = QVBoxLayout(parent)
lay.setContentsMargins(0, 0, 0, 0)
lay.setSpacing(0)
self._fig = Figure(figsize=(10, 7), dpi=100, facecolor=BG)
gs = gridspec.GridSpec(2, 2, figure=self._fig,
hspace=0.42, wspace=0.20,
left=0.07, right=0.93,
top=0.93, bottom=0.08)
self._ax_orig = self._fig.add_subplot(gs[0, 0])
self._ax_adj = self._fig.add_subplot(gs[0, 1])
self._ax_rec = self._fig.add_subplot(gs[1, 0])
self._ax_corr = self._fig.add_subplot(gs[1, 1])
self.twin_ax_corr = self._ax_corr.twinx()
for ax, title in [
(self._ax_orig, "Original profile vs Reconstruction"),
(self._ax_adj, "Adjusted profile (outliers in red)"),
(self._ax_rec, "Adjusted profile vs AE reconstruction"),
(self._ax_corr, "Correlation over time"),
]:
ax.grid(True)
ax.set_facecolor(PANEL)
ax.set_title(title, color=FG, pad=8)
canvas = FigureCanvasQTAgg(self._fig)
canvas.setSizePolicy(QSizePolicy.Policy.Expanding,
QSizePolicy.Policy.Expanding)
lay.addWidget(canvas, stretch=1)
tb_container = QWidget()
tb_container.setStyleSheet(f"background:{PANEL};")
tb_h = QHBoxLayout(tb_container)
tb_h.setContentsMargins(4, 0, 4, 0)
toolbar = NavigationToolbar2QT(canvas, tb_container)
toolbar.setStyleSheet(f"background:{PANEL}; color:{FG};")
tb_h.addWidget(toolbar)
lay.addWidget(tb_container)
self._canvas = canvas
# ── handlers ──────────────────────────────────────────────────────────────
def _browse_model(self):
path, _ = QFileDialog.getOpenFileName(
self, "Select model checkpoint", "",
"Checkpoint (*.ckpt);;All files (*.*)"
)
if path:
self._model_edit.setText(path)
def _run(self):
try:
pid = self._pid_edit.text().strip()
model_path = self._model_edit.text().strip()
min_data = float(self._min_edit.text())
max_data = float(self._max_edit.text())
gain_ratio = float(self._old_gain.text()) / float(self._new_gain.text())
except ValueError as e:
QMessageBox.critical(self, "Parameter error", str(e))
return
try:
z_thresh = float(self._z_edit.text())
except ValueError:
z_thresh = 0.2
import gc
self._results = None
self._save_btn.setEnabled(False)
self._save_rec_btn.setEnabled(False)
self._spectral_btn.setEnabled(False)
gc.collect()
self._run_btn.setEnabled(False)
self._run_btn.setText("⏳ Running …")
self._status_lbl.setText("Starting …")
self._thread = QThread()
self._worker = AnalysisWorker(
pid, model_path, min_data, max_data, gain_ratio,
z_thresh = z_thresh,
)
self._worker.moveToThread(self._thread)
self._thread.started.connect(self._worker.run)
self._worker.progress.connect(self._status_lbl.setText)
self._worker.finished.connect(self._on_results)
self._worker.error.connect(self._on_error)
self._worker.finished.connect(self._thread.quit)
self._worker.error.connect(self._thread.quit)
self._thread.start()
def _on_error(self, msg):
self._run_btn.setEnabled(True)
self._run_btn.setText("▶ RUN ANALYSIS")
self._status_lbl.setText("Error — see dialog")
QMessageBox.critical(self, "Analysis failed", msg)
def _on_results(self, r):
self._results = r
self._run_btn.setEnabled(True)
self._run_btn.setText("▶ RUN ANALYSIS")
self._save_btn.setEnabled(True)
self._save_rec_btn.setEnabled(True)
self._spectral_btn.setEnabled(True)
self._status_lbl.setText(
f"PID {r['pid']} | t = {r['time_instant']:.2f} ms"
f" | ρ_orig = {r['corr_original']:.4f}"
f" | ρ_adj = {r['corr_adjusted']:.4f}"
)
self._plot_results(r)
self._update_corr_readout(r)
# ── spectral analysis ─────────────────────────────────────────────────────
def _open_spectral_analysis(self):
if self._results is None:
return
dlg = SpectralAnalysisDialog(self._results, parent=self)
dlg.show()
# ── plotting ──────────────────────────────────────────────────────────────
def _plot_results(self, r):
for ax in (self._ax_orig, self._ax_adj,
self._ax_rec, self._ax_corr):
ax.cla()
ax.grid(True)
ax.set_facecolor(PANEL)
self.twin_ax_corr.cla()
self.twin_ax_corr.yaxis.set_label_position("right")
self.twin_ax_corr.yaxis.tick_right()
x, ti, pid = r["x"], r["time_instant"], r["pid"]
# ── panel 1: original vs AE ───────────────────────────────────────────
ax = self._ax_orig
ax.plot(x, r["profile"], color=ACCENT, lw=1.8, label="Original")
ax.plot(x, r["rec_original"], color=ACCENT2, lw=1.8, ls="--",
label=f"AE recon (ρ = {r['corr_original']:.4f})")
ax.set_title("Original vs AE Reconstruction", color=FG, pad=8)
ax.set_xlabel("Diode index")
ax.set_ylabel("Signal [V]")
ax.legend(fontsize=8)
# ── panel 2: adjusted profile with outliers ───────────────────────────
ax = self._ax_adj
out = r["outlier_mask"]
ax.plot(x, r["profile"], color=FG_DIM, lw=1.2, ls="--",
alpha=0.6, label="Original")
ax.plot(x, r["adjusted"], color=ACCENT3, lw=1.8, label="Adjusted")
ax.plot(x, r["rec_original"], color=ACCENT2, lw=1.2, ls=":",
alpha=0.7, label="AE reconstruction")
if out.any():
ax.scatter(x[out], r["profile"][out],
color=ACCENT4, marker="x", s=80, lw=1.8,
label=f"Outliers ({out.sum()})", zorder=5)
ax.set_title(
f"Adjusted Profile [Model Residuals z={r.get('z_thresh', '?')}]",
color=FG, pad=8)
ax.set_xlabel("Diode index")
ax.set_ylabel("Signal [V]")
ax.legend(fontsize=8)
# ── panel 3: adjusted + AE reconstruction ────────────────────────────
ax = self._ax_rec
ax.plot(x, r["profile"], color=FG_DIM, lw=1.2, ls="--",
alpha=0.6, label="Original")
ax.plot(x, r["adjusted"], color=ACCENT3, lw=1.8, label="Adjusted")
ax.plot(x, r["rec_adjusted"], color=ACCENT2, lw=1.8, ls="--",
label=f"AE recon (adj) (ρ = {r['corr_adjusted']:.4f})")
ax.set_title("Adjusted vs AE Reconstruction", color=FG, pad=8)
ax.set_xlabel("Diode index")
ax.set_ylabel("Signal [V]")
ax.legend(fontsize=8)
# ── panel 4: correlation time-series ──────────────────────────────────
ax = self._ax_corr
t = r["timedata"]
cor = r["correlations"]
c = np.where(cor < 0.9, ACCENT4, ACCENT)
ax.scatter(t, cor, c=c, s=6, zorder=3)
ax.plot(t, cor, color=ACCENT, lw=1.0, alpha=0.4, zorder=2)
ax.axhline(0.9, color=WARNING, lw=1.2, ls="--", label="Threshold 0.9")
ax.axvline(ti, color=ACCENT2, lw=1.2, ls=":",
label=f"t = {ti:.1f} ms")
self.twin_ax_corr.plot(t, r["diode_signal"], color=ACCENT2,
lw=1.0, alpha=0.45, label="Diode #151")
self.twin_ax_corr.set_ylabel("Diode #151 [V]", color=ACCENT2, fontsize=8)
self.twin_ax_corr.tick_params(axis="y", labelcolor=ACCENT2, labelsize=7)
self.twin_ax_corr.set_facecolor(PANEL)
ax.set_title("Correlation over Time", color=FG, pad=8)
ax.set_xlabel("Time [ms]")
ax.set_ylabel("Pearson ρ", color=ACCENT)
ax.tick_params(axis="y", labelcolor=ACCENT)
ax.set_ylim(-0.1, 1.05)
l1, lb1 = ax.get_legend_handles_labels()
l2, lb2 = self.twin_ax_corr.get_legend_handles_labels()
ax.legend(l1 + l2, lb1 + lb2, fontsize=7, loc="lower left")
self._fig.suptitle(
f"PID: {pid} | t_max = {ti:.2f} ms",
color=FG, fontsize=11, y=0.99,
)
self._canvas.draw_idle()
def _update_corr_readout(self, r):
# clear previous widgets
while self._corr_layout.count():
item = self._corr_layout.takeAt(0)
if item.widget():
item.widget().deleteLater()
row = 0
# ── correlation summary ───────────────────────────────────────────────
for label, value in [("ρ original :", r["corr_original"]),
("ρ adjusted :", r["corr_adjusted"])]:
name = "corr_ok" if value >= 0.9 else "corr_bad"
lbl = lbl_h(label)
val = QLabel(f"{value:.4f}")
val.setObjectName(name)
val.setAlignment(Qt.AlignmentFlag.AlignRight)
self._corr_layout.addWidget(lbl, row, 0)
self._corr_layout.addWidget(val, row, 1)
row += 1
verdict = "▸ NORMAL" if r["corr_original"] >= 0.9 else "▸ ANOMALY"
obj = "verdict_ok" if r["corr_original"] >= 0.9 else "verdict_bad"
v_lbl = QLabel(verdict)
v_lbl.setObjectName(obj)
v_lbl.setAlignment(Qt.AlignmentFlag.AlignRight)
self._corr_layout.addWidget(v_lbl, row, 0, 1, 2)
row += 1
# ── residuals detection summary ───────────────────────────────────────
row += 1
self._corr_layout.addWidget(divider_h(), row, 0, 1, 2)
row += 1
hdr = QLabel("── Residuals detection ──")
hdr.setObjectName("diode_corr_header")
hdr.setAlignment(Qt.AlignmentFlag.AlignCenter)
self._corr_layout.addWidget(hdr, row, 0, 1, 2)
row += 1
n_out = int(r["outlier_mask"].sum())
for lbl_txt, val_txt in [
("z-threshold", f"{r.get('z_thresh', '?')}"),
("n flagged", f"{n_out}"),
]:
lbl_w = QLabel(lbl_txt + " :")
lbl_w.setObjectName("diode_corr_row")
val_w = QLabel(val_txt)
val_w.setObjectName("diode_corr_row")
val_w.setAlignment(Qt.AlignmentFlag.AlignRight)
self._corr_layout.addWidget(lbl_w, row, 0)
self._corr_layout.addWidget(val_w, row, 1)
row += 1
# ── save ──────────────────────────────────────────────────────────────────
def _save_adjusted_data(self):
if self._results is None:
QMessageBox.warning(self, "No data", "Run the analysis first.")
return
r = self._results
h5py_path = r["file_path"]
pid = r["pid"]
adjusted_full = r["adjusted_full"] # gain-corrected, no model pass
timedata = r["timedata_full"]
diode_keys = r["diode_keys"]
save_dir = os.path.join(os.path.dirname(h5py_path))
os.makedirs(save_dir, exist_ok=True)
try:
# Save the t000, t090, t180 and t270 files with adjusted keyword
save_tfiles(h5py_path)
# Save the x000, ..., x360 files with the adjusted data
save_xfiles(h5py_path, adjusted_full)
except Exception as e:
QMessageBox.critical(self, "Save failed", str(e))
def _save_reconstruction_data(self):
if self._results is None:
QMessageBox.warning(self, "No data", "Run the analysis first.")
return
import h5py
r = self._results
h5py_path = r["file_path"]
pid = r["pid"]
rec_adjusted_full = r["rec_adjusted_full"] # model reconstruction of gain-corrected data
timedata = r["timedata_full"]
diode_keys = r["diode_keys"]
save_dir = os.path.join(os.path.dirname(h5py_path))
os.makedirs(save_dir, exist_ok=True)
try:
for i, key in enumerate(diode_keys):
dst_path = os.path.join(
save_dir,
f"xmcts{format_pid(pid)}reconstructed_x{key}.h5f"
)
with h5py.File(dst_path, "w") as f:
grp = f.create_group("XMCTSdata")
grp.create_dataset("timedata", data=timedata)
grp.create_dataset(key, data=rec_adjusted_full[i, :])
self._status_lbl.setText(
f"Saved {len(diode_keys)} reconstruction files → {save_dir}")
except Exception as e:
QMessageBox.critical(self, "Save failed", str(e))

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import matplotlib.pyplot as plt
# ── colour palette ────────────────────────────────────────────────────────────
BG = "#0f1117"
PANEL = "#1a1d27"
ACCENT = "#4f8ef7"
ACCENT2 = "#f7a24f"
ACCENT3 = "#4ff7a2"
ACCENT4 = "#f74f4f"
FG = "#e8eaf0"
FG_DIM = "#6b7280"
BORDER = "#2a2d3a"
SUCCESS = "#22c55e"
WARNING = "#f59e0b"
DANGER = "#ef4444"
# ── matplotlib style ──────────────────────────────────────────────────────────
plt.rcParams.update({
"figure.facecolor": BG,
"axes.facecolor": PANEL,
"axes.edgecolor": BORDER,
"axes.labelcolor": FG,
"axes.titlecolor": FG,
"xtick.color": FG_DIM,
"ytick.color": FG_DIM,
"grid.color": BORDER,
"grid.linestyle": "--",
"grid.alpha": 0.5,
"legend.facecolor": PANEL,
"legend.edgecolor": BORDER,
"legend.labelcolor": FG,
"text.color": FG,
"font.family": "monospace",
"font.size": 9,
})
# ── global stylesheet ─────────────────────────────────────────────────────────
STYLESHEET = f"""
QWidget {{
background-color: {BG};
color: {FG};
font-family: "Courier New", monospace;
font-size: 10px;
}}
QFrame#sidebar {{
background-color: {PANEL};
border-right: 1px solid {BORDER};
}}
QFrame#topbar {{
background-color: {PANEL};
border-bottom: 1px solid {BORDER};
}}
QLabel#title {{
color: {ACCENT};
font-size: 14px;
font-weight: bold;
}}
QLabel#section {{
color: {ACCENT};
font-size: 9px;
font-weight: bold;
}}
QLabel#dim {{
color: {FG_DIM};
font-size: 9px;
}}
QLineEdit {{
background-color: #252836;
color: {FG};
border: 1px solid {BORDER};
border-radius: 3px;
padding: 4px 6px;
font-size: 9px;
}}
QLineEdit:focus {{
border: 1px solid {ACCENT};
}}
QLineEdit:disabled {{
background-color: #1a1d27;
color: {FG_DIM};
border: 1px solid {BORDER};
}}
QPushButton#run {{
background-color: {ACCENT};
color: #0f1117;
font-weight: bold;
font-size: 11px;
border: none;
border-radius: 4px;
padding: 9px;
}}
QPushButton#run:hover {{
background-color: #7aabff;
}}
QPushButton#run:disabled {{
background-color: #2a3550;
color: {FG_DIM};
}}
QPushButton#browse {{
background-color: {BORDER};
color: {FG};
border: none;
border-radius: 3px;
padding: 4px 8px;
font-size: 10px;
}}
QPushButton#browse:hover {{
background-color: #3a3d4a;
}}
QFrame#divider {{
background-color: {BORDER};
max-height: 1px;
}}
QLabel#corr_ok {{ color: {SUCCESS}; font-weight: bold; }}
QLabel#corr_bad {{ color: {DANGER}; font-weight: bold; }}
QLabel#verdict_ok {{ color: {SUCCESS}; font-size: 12px; font-weight: bold; }}
QLabel#verdict_bad {{ color: {DANGER}; font-size: 12px; font-weight: bold; }}
QLabel#status {{ color: {FG_DIM}; font-size: 9px; }}
QPushButton#method_btn {{
background-color: {BORDER};
color: {FG_DIM};
border: 1px solid {BORDER};
border-radius: 3px;
padding: 4px 6px;
font-size: 9px;
}}
QPushButton#method_btn:checked {{
background-color: #1e3a5f;
color: {ACCENT};
border: 1px solid {ACCENT};
}}
QPushButton#method_btn:disabled {{
background-color: #1e3a5f;
color: {ACCENT};
border: 1px solid {ACCENT};
opacity: 0.7;
}}
QPushButton#spectral {{
background-color: #1a2e1a;
color: {ACCENT3};
font-weight: bold;
font-size: 11px;
border: 1px solid {ACCENT3};
border-radius: 4px;
padding: 9px;
}}
QPushButton#spectral:hover {{
background-color: #253525;
}}
QPushButton#spectral:disabled {{
background-color: {PANEL};
color: {FG_DIM};
border: 1px solid {BORDER};
}}
QComboBox {{
background-color: #252836;
color: {FG};
border: 1px solid {BORDER};
border-radius: 3px;
padding: 2px 4px;
}}
QComboBox:disabled {{
background-color: #1a1d27;
color: {FG_DIM};
}}
QComboBox QAbstractItemView {{
background-color: #252836;
color: {FG};
selection-background-color: {ACCENT};
}}
QLabel#diode_corr_header {{
color: {ACCENT3};
font-size: 9px;
font-weight: bold;
}}
QLabel#diode_corr_row {{
color: {FG_DIM};
font-size: 8px;
font-family: monospace;
}}
"""

229
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from PyQt6 import QtWidgets
from PyQt6.QtWidgets import (
QWidget, QVBoxLayout, QHBoxLayout, QSizePolicy, QDialog, QComboBox,
)
from matplotlib.backends.backend_qtagg import FigureCanvasQTAgg, NavigationToolbar2QT
from matplotlib.figure import Figure
import matplotlib.gridspec as gridspec
import numpy as np
from scipy.signal import welch
from scipy.stats import entropy as scipy_entropy
from .styles import *
from .helpers import *
# ─────────────────────────────────────────────────────────────────────────────
# Spectral Analysis Dialog
# ─────────────────────────────────────────────────────────────────────────────
class SpectralAnalysisDialog(QDialog):
"""
Stand-alone spectral analysis window.
Three panels (all sharing the same diode-index x-axis):
• Top — 2-D PSD heatmap (diode × frequency, log₁₀ scale)
• Middle — total spectral power per diode
• Bottom — spectral entropy per diode
Outlier diodes from the parent analysis are highlighted with red bands
in all three panels.
"""
def __init__(self, results: dict, parent=None):
super().__init__(parent)
self.setWindowTitle(f"Spectral Analysis — PID {results['pid']}")
self.resize(1240, 860)
self.setMinimumSize(900, 600)
self.setStyleSheet(STYLESHEET)
self._r = results
self._build_ui()
self._compute_and_plot()
def _build_ui(self):
root = QVBoxLayout(self)
root.setContentsMargins(10, 10, 10, 10)
root.setSpacing(6)
ctrl = QWidget()
ctrl.setStyleSheet(f"background:{PANEL}; border-radius:4px;"
f" border:1px solid {BORDER}; padding:2px;")
ctrl_h = QHBoxLayout(ctrl)
ctrl_h.setContentsMargins(12, 6, 12, 6)
ctrl_h.setSpacing(14)
def _combo(items, width=90, default_idx=0):
c = QComboBox()
c.addItems(items)
c.setCurrentIndex(default_idx)
c.setFixedWidth(width)
c.currentIndexChanged.connect(self._replot)
return c
ctrl_h.addWidget(lbl_h("Window:", "dim"))
self._win_combo = _combo(["hann", "hamming", "blackman", "boxcar"])
ctrl_h.addWidget(self._win_combo)
ctrl_h.addWidget(lbl_h("nperseg:", "dim"))
self._nperseg_combo = _combo(["64", "128", "256", "512"], 70, 2)
ctrl_h.addWidget(self._nperseg_combo)
ctrl_h.addWidget(lbl_h("f-max (Hz):", "dim"))
self._fmax_combo = _combo(
["All", "50", "100", "200", "500", "1000", "2000", "5000"], 80)
ctrl_h.addWidget(self._fmax_combo)
ctrl_h.addWidget(lbl_h("Colormap:", "dim"))
self._cmap_combo = _combo(
["inferno", "magma", "plasma", "viridis", "cividis", "hot"], 80)
ctrl_h.addWidget(self._cmap_combo)
ctrl_h.addStretch()
self._info_lbl = lbl_h("", "dim")
ctrl_h.addWidget(self._info_lbl)
root.addWidget(ctrl)
self._fig = Figure(figsize=(11, 8), dpi=100, facecolor=BG)
canvas = FigureCanvasQTAgg(self._fig)
canvas.setSizePolicy(QSizePolicy.Policy.Expanding,
QSizePolicy.Policy.Expanding)
root.addWidget(canvas, stretch=1)
tb_w = QWidget()
tb_w.setStyleSheet(f"background:{PANEL};")
tb_h = QHBoxLayout(tb_w)
tb_h.setContentsMargins(4, 0, 4, 0)
toolbar = NavigationToolbar2QT(canvas, tb_w)
toolbar.setStyleSheet(f"background:{PANEL}; color:{FG};")
tb_h.addWidget(toolbar)
root.addWidget(tb_w)
self._canvas = canvas
def _compute_psd_matrix(self, nperseg: int, window: str):
sigs = self._r["signals_sampled"]
timedata = self._r["timedata"]
dt_ms = float(np.median(np.diff(timedata)))
fs_est = 1.0 / (dt_ms * 1e-3)
n_diodes = sigs.shape[0]
freqs, _ = welch(sigs[0], fs=fs_est, nperseg=nperseg, window=window)
psd_mat = np.empty((n_diodes, len(freqs)), dtype=np.float32)
for i in range(n_diodes):
_, p = welch(sigs[i].astype(np.float64),
fs=fs_est, nperseg=nperseg, window=window)
psd_mat[i] = p.astype(np.float32)
return freqs, psd_mat, fs_est
def _compute_and_plot(self):
nperseg = int(self._nperseg_combo.currentText())
window = self._win_combo.currentText()
self._freqs, self._psd, self._fs = self._compute_psd_matrix(nperseg, window)
self._draw()
def _replot(self):
self._compute_and_plot()
def _draw(self):
r = self._r
freqs = self._freqs
psd = self._psd
outlier_mask = r["outlier_mask"]
n_diodes = psd.shape[0]
x = np.arange(n_diodes)
cmap = self._cmap_combo.currentText()
fmax_txt = self._fmax_combo.currentText().replace(" ", "")
freq_mask = np.ones(len(freqs), dtype=bool) if fmax_txt == "All" \
else freqs <= float(fmax_txt)
freqs_plt = freqs[freq_mask]
psd_plt = psd[:, freq_mask]
total_power = psd_plt.sum(axis=1)
spec_entropy = np.array([
float(scipy_entropy(psd_plt[i] / (psd_plt[i].sum() + 1e-30)))
for i in range(n_diodes)
], dtype=np.float32)
dom_freq = freqs_plt[np.argmax(psd_plt, axis=1)]
self._fig.clf()
gs = gridspec.GridSpec(3, 2, figure=self._fig,
height_ratios=[3, 1, 1], width_ratios=[30, 1],
hspace=0.52, wspace=0.03,
left=0.07, right=0.95, top=0.93, bottom=0.07)
ax_heat = self._fig.add_subplot(gs[0, 0])
ax_cbar = self._fig.add_subplot(gs[0, 1])
ax_power = self._fig.add_subplot(gs[1, 0])
ax_entr = self._fig.add_subplot(gs[2, 0])
for ax in (ax_heat, ax_power, ax_entr):
ax.set_facecolor(PANEL)
ax.grid(True, alpha=0.35)
self._fig.suptitle(
f"Spectral Analysis — PID: {r['pid']} | "
f"window={self._win_combo.currentText()} "
f"nperseg={self._nperseg_combo.currentText()} "
f"fs≈{self._fs:.0f} Hz",
color=FG, fontsize=10, y=0.985,
)
im = ax_heat.imshow(
np.log10(psd_plt.T + 1e-30), aspect="auto", origin="lower",
extent=[-0.5, n_diodes - 0.5, freqs_plt[0], freqs_plt[-1]],
cmap=cmap, interpolation="nearest",
)
ax_heat.plot(x, dom_freq, color=ACCENT3, lw=1.2, ls="--",
alpha=0.8, label="Dominant freq / diode")
ax_heat.set_ylabel("Frequency (Hz)", color=FG)
ax_heat.set_title(
"Power Spectral Density [log₁₀(V²/Hz)] — each column is one diode",
color=FG, pad=6, fontsize=9)
ax_heat.tick_params(labelbottom=False)
ax_heat.legend(fontsize=7, loc="upper right")
cb = self._fig.colorbar(im, cax=ax_cbar)
cb.set_label("log₁₀ PSD", color=FG, fontsize=8)
cb.ax.yaxis.set_tick_params(color=FG_DIM, labelsize=7)
plt.setp(cb.ax.yaxis.get_ticklabels(), color=FG_DIM)
ax_power.fill_between(x, total_power, alpha=0.20, color=ACCENT)
ax_power.plot(x, total_power, color=ACCENT, lw=1.5,
label="Total spectral power")
ax_power.set_ylabel("Total power\n(V²/Hz)", color=FG, fontsize=8)
ax_power.tick_params(labelbottom=False)
ax_entr.fill_between(x, spec_entropy, alpha=0.20, color=ACCENT2)
ax_entr.plot(x, spec_entropy, color=ACCENT2, lw=1.5,
label="Spectral entropy")
ax_entr.set_ylabel("Entropy\n(nats)", color=FG, fontsize=8)
ax_entr.set_xlabel("Diode index", color=FG)
if outlier_mask.any():
out_idx = np.where(outlier_mask)[0]
for ax in (ax_heat, ax_power, ax_entr):
for idx in out_idx:
ax.axvspan(idx - 0.5, idx + 0.5,
color=ACCENT4, alpha=0.22, linewidth=0, zorder=0)
ax_power.scatter(out_idx, total_power[out_idx],
color=ACCENT4, s=50, zorder=6,
label=f"Outliers ({len(out_idx)})")
ax_entr.scatter(out_idx, spec_entropy[out_idx],
color=ACCENT4, s=50, zorder=6,
label=f"Outliers ({len(out_idx)})")
for ax in (ax_power, ax_entr):
ax.legend(fontsize=7, loc="upper right")
for ax in (ax_heat, ax_power, ax_entr):
ax.set_xlim(-0.5, n_diodes - 0.5)
n_out = int(outlier_mask.sum())
self._info_lbl.setText(
f"{n_diodes} diodes | {n_out} flagged | "
f"{len(freqs_plt)} freq bins | fs ≈ {self._fs:.0f} Hz"
)
self._canvas.draw_idle()

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# ─────────────────────────────────────────────────────────────────────────────
# Background worker
# ─────────────────────────────────────────────────────────────────────────────
from PyQt6.QtCore import QObject, pyqtSignal
from ..analysis.processors import run_analysis
class AnalysisWorker(QObject):
progress = pyqtSignal(str)
finished = pyqtSignal(dict)
error = pyqtSignal(str)
def __init__(self, pid, model_path, min_data, max_data, gain_ratio,
z_thresh=0.2):
super().__init__()
self.pid = pid
self.model_path = model_path
self.min_data = min_data
self.max_data = max_data
self.gain_ratio = gain_ratio
self.z_thresh = z_thresh
def run(self):
try:
results = run_analysis(
self.pid, self.model_path,
self.min_data, self.max_data, self.gain_ratio,
z_thresh = self.z_thresh,
progress_cb = self.progress.emit,
)
self.finished.emit(results)
except Exception:
import traceback
self.error.emit(traceback.format_exc())

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# src/my_app/main.py
import sys
from .gui.main_window import WorkflowGUI
from .engine.model import AutoEncoder # Optional: if you want to pre-load
def start_app():
"""
The entry point function that initializes everything.
"""
# 1. Initialize the GUI Application object
# (Example using PyQt/PySide logic)
from PyQt6.QtWidgets import QApplication
app = QApplication(sys.argv)
# # 2. Optional: Initialize the Engine or Analysis logic
# # You can pass these into the GUI class so the GUI "owns" the engine.
# model = AutoEncoder()
# 3. Create the Main Window
# We pass the model to the window so it can call inference later
window = WorkflowGUI()
window.show()
# 4. Start the event loop
sys.exit(app.exec())
if __name__ == "__main__":
start_app()

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import os
import h5py
import numpy as np
def format_pid(pid: str) -> str:
"""
Format a program ID (e.g. '20250305.50') into a zero-padded directory name
(e.g. '20250305_050').
"""
head, sep, tail = pid.strip().rpartition('.')
if sep:
frac = tail
if frac.isdigit() and len(frac) < 3:
frac = frac.zfill(3)
return f"{head}_{frac}"
return pid.replace('.', '_')
def read_h5f_directory(h5py_path: str, decimate: int = 100) -> dict:
"""
Read all .h5f files in the given directory and return a dictionary of datasets.
- Files ending in _t000, _t090, _t180, _t270 are treated as reference files:
only the time base (dataset ending in '0') is extracted from them.
- All other .h5f files have their full XMCTSdata content loaded (excluding 'timedata').
Parameters
----------
h5py_path : str
Path to the directory containing the .h5f files.
decimate : int
Takes data every n = decimate entries
Returns
-------
dict
Dictionary mapping dataset names to numpy arrays.
Includes a 'timedata' key from the reference files.
"""
REFERENCE_SUFFIXES = ("_t000.h5f", "_t090.h5f", "_t180.h5f", "_t270.h5f")
data_dict = {}
length = np.inf
# Check if the data has consistent length, otherwise cut it to the smallest length
for f in os.listdir(h5py_path):
# if there is adjusted in the name skip it
if f.__contains__('adjusted'): continue
h5f_file = os.path.join(h5py_path, f)
if f.endswith(".h5f"):
with h5py.File(h5f_file, "r") as h5f:
if "XMCTSdata" in h5f:
for dataset in h5f["XMCTSdata"].keys():
if dataset != "timedata":
length_tmp = h5f["XMCTSdata"][dataset][:].shape[0]
if length_tmp < length:
length = length_tmp
for f in sorted(os.listdir(h5py_path)):
h5f_file = os.path.join(h5py_path, f)
# if there is adjusted in the name skip it
if f.__contains__('adjusted'): continue
# Reference files: extract time base only
if any(f.endswith(s) for s in REFERENCE_SUFFIXES):
with h5py.File(h5f_file, "r") as h5f:
if "XMCTSdata" in h5f:
for dataset in h5f["XMCTSdata"].keys():
if dataset.endswith("000"):
data_dict["timedata"] = h5f["XMCTSdata"][dataset][:length:decimate]
continue
# Data files: extract all datasets except 'timedata'
if f.endswith(".h5f"):
with h5py.File(h5f_file, "r") as h5f:
if "XMCTSdata" in h5f:
for dataset in h5f["XMCTSdata"].keys():
if dataset != "timedata":
data_dict[dataset] = h5f["XMCTSdata"][dataset][:length:decimate]
return data_dict
def save_tfiles(h5py_path: str, keyword: str = 'adjusted'):
"""
Duplicate specific HDF5 files in a directory with a modified naming convention.
Iterates through a directory to find files ending in specific suffixes
(0, 90, 180, 270). For each match, it creates a copy where the middle
section of the filename is replaced by the provided keyword.
Parameters
----------
h5py_path : str
The system path to the directory containing the .h5f files.
keyword : str, optional
The string to insert into the new filename (default is 'adjusted').
This replaces the variable middle segment of the original filename.
Returns
-------
None
Copies are created directly on the filesystem.
"""
import shutil
import os
REFERENCE_SUFFIXES = ("_t000.h5f", "_t090.h5f", "_t180.h5f", "_t270.h5f")
for f in sorted(os.listdir(h5py_path)):
if f.endswith(REFERENCE_SUFFIXES):
h5f_file = os.path.join(h5py_path, f)
# Split and rebuild filename logic
parts = f.split('_')
exp_num = parts[1][:3]
new_f = f"{parts[0]}_{exp_num}{keyword}_{parts[-1]}"
new_h5f_file = os.path.join(h5py_path, new_f)
shutil.copy2(h5f_file, new_h5f_file)
def save_xfiles(h5py_path: str, adjusted_data: np.ndarray, keyword: str = 'adjusted'):
"""
Duplicate HDF5 files and update specific internal datasets with adjusted data.
This function iterates through a directory, skipping specific reference
orientation files. For all other files, it creates a copy with a modified
filename containing a keyword. Inside each copy, it replaces the dataset
corresponding to a specific diode index (000-360) within the 'XMCTSdata'
group using the provided array.
Parameters
----------
h5py_path : str
The system path to the directory containing the .h5f files.
adjusted_data : np.ndarray
The numerical array to be written into the 'XMCTSdata' dataset.
keyword : str, optional
The string to insert into the new filename (default is 'adjusted').
This is appended to the 3-digit experiment ID extracted from the
original filename.
Returns
-------
None
New files are created and modified on the filesystem.
"""
import shutil
REFERENCE_SUFFIXES = ("_t000.h5f", "_t090.h5f", "_t180.h5f", "_t270.h5f")
diodes = [f"{i:03}" for i in range(361)]
count = 0
for f in sorted(os.listdir(h5py_path)):
# Process only files that are NOT the reference suffixes
if not f.endswith(REFERENCE_SUFFIXES):
print(f)
h5f_file = os.path.join(h5py_path, f)
# Filename logic:
parts = f.split('_')
exp_num = parts[1][:3]
new_f = f"{parts[0]}_{exp_num}{keyword}_{parts[-1]}"
new_h5f_file = os.path.join(h5py_path, new_f)
shutil.copy2(h5f_file, new_h5f_file)
with h5py.File(new_h5f_file, 'r+') as nf:
# Replaces data in the dataset corresponding to the current count
# Example: nf['XMCTSdata']['000'] = adjusted_data
nf['XMCTSdata'][diodes[count]][...] = adjusted_data[count, :]
count += 1
def build_data_array(data_dict: dict) -> np.ndarray:
"""
Stack all arrays in the data dictionary into a 2D numpy array.
The resulting shape is (n_channels, n_timepoints), where the first row
corresponds to 'timedata' and the remaining rows are the diode channels.
Parameters
----------
data_dict : dict
Dictionary as returned by read_h5f_directory().
Returns
-------
np.ndarray
2D array of shape (n_channels, n_timepoints).
"""
return np.array(list(data_dict.values()))
def sort_sxr_data(data: np.ndarray) -> np.ndarray:
"""
Organize the stacked arrays so that the time base is sorted.
Parameters
----------
data : np.ndarray
array returned by build_data_array()
Returns
-------
np.ndarray
Sorted 2D array of shape (n_channels, n_timepoints).
"""
sorted_indices = np.argsort(data[:, 0])
return data[sorted_indices, :]
def get_sxr_data():
raise NotImplementedError
def load_sxr_data(pid: str, base_path: str = "/home/IPP-HGW/orluca/devel/data/HDF/_data/OP2",
decimate: int = 1) -> np.ndarray:
"""
Full pipeline: given a program ID, load all SXR diode data and return
a 2D numpy array of shape (n_channels, n_timepoints).
Parameters
----------
pid : str
Program ID, e.g. '20250305.50'.
base_path : str
Root directory where the HDF data folders are stored.
Returns
-------
np.ndarray
2D array of shape (n_channels, n_timepoints).
"""
pid_formatted = format_pid(pid)
h5py_path = os.path.join(base_path, pid_formatted, "")
data_dict = read_h5f_directory(h5py_path, decimate)
data = build_data_array(data_dict)
return data, h5py_path, data_dict
if __name__ == "__main__":
pid = "20221019.25"
data, h5py_path, data_dict = load_sxr_data(pid)
print(f"Loaded data array with shape: {data.shape}")
print(f"H5F path: {h5py_path}")
print("First time instant across all channels:")
for d in data[:, 0]:
print(d)

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src/XMCTSGuard/visualization/__init__.py Normal file
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src/XMCTSGuard/visualization/gui_plots.py Normal file
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src/XMCTSGuard/visualization/themes.py Normal file
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import seaborn as sns
import matplotlib.pyplot as plt
def apply_style():
"""
Sets a global aesthetic for the entire library
"""
sns.set_theme(
context="notebook",
style="whitegrid",
palette="muted",
font_scale=1.2,
)

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tests/__init__.py Normal file
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