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from warnings import warn
import matplotlib as mpl
from transivent import configure_logging, get_waveform_params, process_file
# --- User configuration dictionary ---
CONFIG = {
"SMOOTH_WIN_T": 10e-3, # smoothing window in seconds (set to None to use frequency)
"SMOOTH_WIN_F": None, # smoothing window in Hz (set to None to use time)
"DETECTION_SNR": 3, # point-by-point detection threshold, <MIN_EVENT_KEEP_SNR
"MIN_EVENT_KEEP_SNR": 5, # min event (max-)amplitude in multiples of global noise
"MIN_EVENT_T": 0.75e-6, # minimum event duration (seconds)
"WIDEN_FRAC": 10, # fraction of event length to widen detected events
"SIGNAL_POLARITY": 1, # Signal polarity: -1 for negative events (below background), +1 for positive events (above background)
"LOG_LEVEL": "INFO", # logging level: DEBUG, INFO, WARNING, ERROR, CRITICAL
"MAX_PLOT_POINTS": 10000, # Downsample threshold for plotting
"ENVELOPE_MODE_LIMIT": 10e-3, # Use envelope when time span >10ms, show thresholds when <10ms
"YSCALE_MODE": "snr", # y-scale mode for event plotter: 'snr', 'percent' or 'raw'
"FILTER_TYPE": "median", # Filter type: "savgol", "gaussian", "moving_average", "median"
"FILTER_ORDER": 3, # Order of the savgol filter for smoothing
"CHUNK_SIZE": None, # Set to None to disable chunking
# ---
"DATA_PATH": "../hycav/data/2025-07-17_bsa/",
"MEASUREMENTS": [
{
"data": "RefCurve_2025-07-17_0_065114.Wfm.bin",
},
{
"data": "RefCurve_2025-07-17_1_065214.Wfm.bin",
},
{
"data": "RefCurve_2025-07-17_2_065510.Wfm.bin",
},
{
"data": "RefCurve_2025-07-17_3_065814.Wfm.bin",
},
{
"data": "RefCurve_2025-07-17_4_065850.Wfm.bin",
},
{
"data": "RefCurve_2025-07-17_5_070003.Wfm.bin",
},
{
"data": "RefCurve_2025-07-17_6_070045.Wfm.bin",
},
{
"data": "RefCurve_2025-07-17_7_070339.Wfm.bin",
},
],
}
def main() -> None:
"""
Main function to process all measurements.
"""
# Configure logging
configure_logging(CONFIG.get("LOG_LEVEL", "INFO"))
for measurement in CONFIG["MEASUREMENTS"]:
# Merge global config with measurement-specific overrides
merged_config = CONFIG.copy()
merged_config.update(measurement)
# Extract parameters for process_file
name = merged_config["data"]
sidecar = merged_config.get("sidecar")
params = get_waveform_params(
name, data_path=merged_config["DATA_PATH"], sidecar=sidecar
)
sampling_interval = params["sampling_interval"]
# Call with explicit parameters
process_file(
name=name,
sampling_interval=sampling_interval,
data_path=merged_config["DATA_PATH"],
smooth_win_t=merged_config.get("SMOOTH_WIN_T"),
smooth_win_f=merged_config.get("SMOOTH_WIN_F"),
detection_snr=merged_config.get("DETECTION_SNR", 3.0),
min_event_keep_snr=merged_config.get("MIN_EVENT_KEEP_SNR", 6.0),
min_event_t=merged_config.get("MIN_EVENT_T", 0.75e-6),
widen_frac=merged_config.get("WIDEN_FRAC", 10.0),
signal_polarity=merged_config.get("SIGNAL_POLARITY", -1),
max_plot_points=merged_config.get("MAX_PLOT_POINTS", 10000),
envelope_mode_limit=merged_config.get("ENVELOPE_MODE_LIMIT", 10e-3),
sidecar=sidecar,
crop=merged_config.get("crop"),
yscale_mode=merged_config.get("YSCALE_MODE", "snr"),
show_plots=True,
filter_type=merged_config.get("FILTER_TYPE", "gaussian"),
filter_order=merged_config.get("FILTER_ORDER", 2),
chunk_size=merged_config.get("CHUNK_SIZE"),
)
if __name__ == "__main__":
# Set Matplotlib rcParams directly here
for optn, val in {
"backend": "QtAgg",
"figure.constrained_layout.use": True,
"figure.dpi": 90,
# "figure.figsize": (8.5 / 2.55, 6 / 2.55),
"font.family": ("sans-serif",),
"font.size": 11,
"legend.fontsize": "x-small",
"legend.handlelength": 1.5,
"legend.handletextpad": 0.6,
"lines.markersize": 4.0,
"lines.markeredgewidth": 1.6,
"lines.linewidth": 1.8,
"xtick.labelsize": 10,
"xtick.major.size": 3,
"xtick.direction": "in",
"ytick.labelsize": 10,
"ytick.direction": "in",
"ytick.major.size": 3,
"axes.formatter.useoffset": False,
"axes.formatter.use_mathtext": True,
"errorbar.capsize": 3.0,
"axes.linewidth": 1.4,
"xtick.major.width": 1.4,
"xtick.minor.width": 1.1,
"ytick.major.width": 1.4,
"ytick.minor.width": 1.1,
"axes.labelsize": 11,
}.items():
if isinstance(val, (list, tuple)):
val = tuple(val)
try:
mpl.rcParams[optn] = val
except KeyError:
warn(f"mpl rcparams key '{optn}' not recognised as a valid rc parameter.")
main()
|
