148 lines
4.1 KiB
Plaintext
148 lines
4.1 KiB
Plaintext
---
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title: Delays anaylsis
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format:
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html:
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toc: true
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toc-title: Contents
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code-block-bg: true
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code-block-border-left: "#31BAE9"
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code-line-numbers: true
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highlight-style: atom-one
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link-external-icon: true
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link-external-newwindow: true
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eqn-number: true
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---
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### 1. Delays
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We noticed a delay in the recodings if you were to plot a comparision between
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the base relacs recording and the new generated open-ephys.
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```{python}
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# | echo: False
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import pathlib
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import numpy as np
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import pandas as pd
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import plotly.express as px
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import plotly.graph_objects as go
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import rlxnix as rlx
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import scipy.signal as signal
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from plotly.subplots import make_subplots
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from rich.progress import track
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from rich.table import Table
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from util import calc_lag, plot_line_comparision, trial_plot
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dataset_path = pathlib.Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2-recording.nix")
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relacs_path = pathlib.Path(
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"../oephys2nix/test/Test1/2025-10-08-aa-invivo-2_relacs/2025-10-08-aa-invivo-2.nix"
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)
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dataset = rlx.Dataset(str(dataset_path))
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relacs = rlx.Dataset(str(relacs_path))
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# INFO: Select the first stimulus of the calibration repro
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repro_d = dataset.repro_runs("FileStimulus_1")[0].stimuli[2]
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repro_r = relacs.repro_runs("FileStimulus_1")[0].stimuli[2]
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sinus, t = repro_d.trace_data("sinus")
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sinus_r, t_r = repro_r.trace_data("V-1")
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stimulus_oe, t = repro_d.trace_data("stimulus")
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stimulus_re, t_r = repro_r.trace_data("GlobalEFieldStimulus")
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local_eod_oe, t = repro_d.trace_data("local-eod")
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local_eod_re, t_r = repro_r.trace_data("LocalEOD-1")
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global_eod_oe, t = repro_d.trace_data("global-eod")
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global_eod_re, t_r = repro_r.trace_data("EOD")
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ttl, t = repro_d.trace_data("ttl-line")
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fig = plot_line_comparision(
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t_r,
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t,
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stimulus_re,
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stimulus_oe - np.mean(stimulus_oe),
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["stimulus-relacs", "stimulus-open-ephys"],
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)
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fig.show()
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```
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### 2. Look at differnt RePros
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Currently implemented repros are:
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- [x] [Baseline](baseline.qmd)
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- [x] [Calibration](calibration.qmd)
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- [x] [FI Curve](fi_curve.qmd)
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- [x] [File Stimulus](filestimulus.qmd)
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- [ ] Sams
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- [ ] Chrips
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- [ ] Beats
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### 3. General Delay in detail
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```{python}
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rich_tabel = Table("Repro Run", "Signal", "Lag (samples)")
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names_lanes = ["sinus", "local-eod", "global-eod", "stimulus"]
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dataframe = []
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for repro_idx, (repro_d, repro_r) in enumerate(
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zip(dataset.repro_runs(), relacs.repro_runs(), strict=True)
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):
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if not repro_d.stimuli:
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lags = calc_lag(repro_d, repro_r)
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for lag, names_lane in zip(lags, names_lanes, strict=True):
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rich_tabel.add_row(f"{repro_d.name}", f"{names_lane}", f"{lag}")
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dataframe.append(
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{"ReproName": repro_d.name, "Line": names_lane, "Lag": lag, "Trial": 0}
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)
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else:
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lags_lanes = {f"{key}": [] for key in names_lanes}
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for trial, (stim_oe, stim_re) in enumerate(
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zip(repro_d.stimuli, repro_r.stimuli, strict=True)
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):
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lags = calc_lag(stim_oe, stim_re)
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for lag, names_lane in zip(lags, names_lanes):
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lags_lanes[names_lane].append(lag)
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dataframe.append(
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{"ReproName": repro_d.name, "Line": names_lane, "Lag": lag, "Trial": trial}
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)
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for lane in lags_lanes:
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mean_lag = np.mean(lags_lanes[lane])
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std_lag = np.std(lags_lanes[lane])
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rich_tabel.add_row(f"{repro_d.name}", f"{lane}", f"{mean_lag:.2f}\u00b1{std_lag:.2f}")
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rich_tabel
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```
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```{python}
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repros = dataset.repro_runs("Baseline")
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print(repros)
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exclude = []
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for rep in repros:
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exclude.append(rep.name)
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df = pd.DataFrame(dataframe)
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df = df[~df["ReproName"].isin(exclude)]
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fig = px.box(
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df,
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x="Line",
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y="Lag",
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color="Line",
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title="Lag Distribution Across Different Signals",
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labels={"Line": "Signal", "Lag": "Lag (samples)"},
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)
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fig.update_layout(template="plotly_dark")
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fig.show()
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fig = px.box(
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df,
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x="Line",
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y="Lag",
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color="ReproName",
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title="Lag Distribution by Signal and Repro Run",
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labels={"Line": "Signal", "Lag": "Lag (samples)"},
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)
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fig.update_layout(template="plotly_dark")
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fig.show()
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```
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