101 lines
3.2 KiB
Python
101 lines
3.2 KiB
Python
from collections import deque
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from logging import getLogger
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from scipy.signal import savgol_filter
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import numpy as np
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_logger = getLogger(__name__)
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initialized = False
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def initialize(params):
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global initialized, buffer, device, step_length, edge_type, refinement, dark_event, fel_on_event, use_dark, calib, use_filter, filter_window
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device = params["device"]
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step_length = params["step_length"]
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edge_type = params["edge_type"]
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refinement = params["refinement"]
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dark_event = params["dark_event"]
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fel_on_event = params["fel_on_event"]
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buffer = deque(maxlen=params["buffer_length"])
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use_dark = params["use_dark"]
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calib = params["calib"]
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filter_window = params["filter_window"]
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use_filter = params['filter']
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initialized = True
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def _interpolate_row(y_known, x_known, x_interp):
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y_interp = np.interp(x_interp, x_known, y_known)
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return y_interp
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def find_edge(data, step_length=50, edge_type="falling", refinement=1):
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# refine data
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data_length = data.shape[1]
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refined_data = np.apply_along_axis(
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_interpolate_row,
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axis=1,
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arr=data,
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x_known=np.arange(data_length),
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x_interp=np.arange(0, data_length - 1, refinement),
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)
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# prepare a step function and refine it
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step_waveform = np.ones(shape=(step_length,))
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if edge_type == "rising":
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step_waveform[: int(step_length / 2)] = -1
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elif edge_type == "falling":
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step_waveform[int(step_length / 2) :] = -1
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step_waveform = np.interp(
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x=np.arange(0, step_length - 1, refinement), xp=np.arange(step_length), fp=step_waveform
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)
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# find edges
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xcorr = np.apply_along_axis(np.correlate, 1, refined_data, v=step_waveform, mode="valid")
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edge_position = np.argmax(xcorr, axis=1).astype(float) * refinement
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xcorr_amplitude = np.amax(xcorr, axis=1)
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# correct edge_position for step_length
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edge_position += np.floor(step_length / 2)
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return {"edge_pos": edge_position, "xcorr": xcorr, "xcorr_ampl": xcorr_amplitude, "signal":data}
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def process(data, pulse_id, timestamp, params):
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if not initialized:
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initialize(params)
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# Read stream inputs
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prof_sig = data[params["prof_sig"]]
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if use_filter:
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prof_sig = savgol_filter(prof_sig,filter_window,3)
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events = data[params["events"]]
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if prof_sig.ndim == 1:
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prof_sig = prof_sig[np.newaxis, :]
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if events[dark_event] and use_dark:
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buffer.append(prof_sig)
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edge_results = {"edge_pos": np.nan, "xcorr": np.nan, "xcorr_ampl": np.nan, "signal":np.nan}
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else:
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if events[fel_on_event] and buffer:
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prof_sig = prof_sig / np.mean(buffer, axis=0)
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edge_results = find_edge(prof_sig, step_length, edge_type, refinement)
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elif events[fel_on_event] and not use_dark:
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edge_results = find_edge(prof_sig, step_length, edge_type, refinement)
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else:
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edge_results = {"edge_pos": np.nan, "xcorr": np.nan, "xcorr_ampl": np.nan, "signal":np.nan}
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# calib edge
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edge_results["arrival_time"] = edge_results["edge_pos"] * calib
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# Set bs outputs
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output = {}
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for key, value in edge_results.items():
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output[f"{device}:{key}"] = value
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return output
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