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wendtalexander 2025-10-21 15:06:19 +02:00
parent 307709834b
commit d5499eb3e8
5 changed files with 93 additions and 81 deletions
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43
doc/baseline.qmd
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@ -19,23 +19,22 @@ Lets look at the calibration and the first trial of the recording.
```{python} ```{python}
import pathlib import pathlib
import rlxnix as rlx
import plotly.graph_objects as go
import numpy as np import numpy as np
import plotly.graph_objects as go
import rlxnix as rlx
import scipy.signal as signal import scipy.signal as signal
from plotly.subplots import make_subplots from plotly.subplots import make_subplots
from util import plot_line_comparision, trial_plot
from util import trial_plot, plot_line_comparision
dataset_path = pathlib.Path("../oephys2nix/test/AllStimuli/2025-10-20-aa-invivo-2-recording.nix") dataset_path = pathlib.Path("../oephys2nix/test/AllStimuli/2025-10-20-aa-invivo-2-recording.nix")
relacs_path = pathlib.Path("../oephys2nix/test/AllStimuli/2025-10-20-aa-invivo-2_relacs/2025-10-20-aa-invivo-2_relacs.nix") relacs_path = pathlib.Path(
"../oephys2nix/test/AllStimuli/2025-10-20-aa-invivo-2_relacs/2025-10-20-aa-invivo-2_relacs.nix"
)
dataset = rlx.Dataset(str(dataset_path)) dataset = rlx.Dataset(str(dataset_path))
relacs = rlx.Dataset(str(relacs_path)) relacs = rlx.Dataset(str(relacs_path))
#INFO: Select the first stimulus of the calibration repro # INFO: Select the first stimulus of the calibration repro
repro_d = dataset.repro_runs("BaselineActivity")[0] repro_d = dataset.repro_runs("BaselineActivity")[0]
repro_r = relacs.repro_runs("BaselineActivity")[0] repro_r = relacs.repro_runs("BaselineActivity")[0]
@ -57,7 +56,7 @@ ttl, t = repro_d.trace_data("ttl-line")
If you zoom in you can see a little delay between the different recording systems. It seems that open-ephys is before the relacs recording. If you zoom in you can see a little delay between the different recording systems. It seems that open-ephys is before the relacs recording.
```{python} ```{python}
#| echo: False # | echo: False
# 2. Add traces to the FIRST subplot (row=1, col=1) # 2. Add traces to the FIRST subplot (row=1, col=1)
# Note: Plotly rows and columns are 1-indexed # Note: Plotly rows and columns are 1-indexed
fig = trial_plot(repro_d, repro_r, 1.0) fig = trial_plot(repro_d, repro_r, 1.0)
@ -70,12 +69,13 @@ print(f"Duration of the dataset {repro_d.duration}")
print(f"Duration of the relacs {repro_r.duration}") print(f"Duration of the relacs {repro_r.duration}")
# Resample the open-ephys data # Resample the open-ephys data
sinus_resampled = signal.resample(sinus, len(sinus_r)) sinus_resampled = signal.resample(sinus, len(sinus_r))
``` ```
```{python} ```{python}
#| echo: False # | echo: False
fig= plot_line_comparision(t_r, t_r, sinus_r, sinus_resampled, ["sinus-relacs", "sinus-resampled-open-ephys"]) fig = plot_line_comparision(
t_r, t_r, sinus_r, sinus_resampled, ["sinus-relacs", "sinus-resampled-open-ephys"]
)
fig.show() fig.show()
``` ```
We need to scale the two signals We need to scale the two signals
@ -85,13 +85,10 @@ oephys_lanes = [sinus, local_eod_oe, global_eod_oe, stimulus_oe]
relacs_lanes = [sinus_r, local_eod_re, global_eod_re, stimulus_re] relacs_lanes = [sinus_r, local_eod_re, global_eod_re, stimulus_re]
names_lanes = ["sinus", "local-eod", "global-eod", "stimulus"] names_lanes = ["sinus", "local-eod", "global-eod", "stimulus"]
samples_20kHz = t[-1] * 20_000 samples_20kHz = t[-1] * 20_000
print(samples_20kHz)
print(f"Total duration {t[-1]}")
print(repro_d.duration)
lags_lanes = [] lags_lanes = []
for oephys_lane, relacs_lane, names_lane in zip(oephys_lanes, relacs_lanes, names_lanes, strict=True): for oephys_lane, relacs_lane, names_lane in zip(
print(oephys_lane.shape) oephys_lanes, relacs_lanes, names_lanes, strict=True
print(relacs_lane.shape) ):
oephys_lane_resampled = signal.resample(oephys_lane, int(samples_20kHz)) oephys_lane_resampled = signal.resample(oephys_lane, int(samples_20kHz))
correlation = signal.correlate(oephys_lane_resampled, relacs_lane, mode="full") correlation = signal.correlate(oephys_lane_resampled, relacs_lane, mode="full")
lags = signal.correlation_lags(oephys_lane_resampled.size, relacs_lane.size, mode="full") lags = signal.correlation_lags(oephys_lane_resampled.size, relacs_lane.size, mode="full")
@ -101,7 +98,13 @@ for oephys_lane, relacs_lane, names_lane in zip(oephys_lanes, relacs_lanes, name
``` ```
```{python} ```{python}
#| echo: False # | echo: False
fig = plot_line_comparision(t_r, t_r, np.roll(sinus_r, lags_lanes[0]), sinus_resampled, ["rolled sinus-relacs", "sinus-resampled-open-ephys"]) fig = plot_line_comparision(
t_r,
t_r,
np.roll(sinus_r, lags_lanes[0]),
sinus_resampled,
["rolled sinus-relacs", "sinus-resampled-open-ephys"],
)
fig.show() fig.show()
``` ```

4
doc/calibration.qmd
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@ -58,7 +58,7 @@ If you zoom in you can see a little delay between the different recording system
```{python} ```{python}
# | echo: False # | echo: False
fig = trial_plot(repro_d, repro_r) fig = trial_plot(repro_d, repro_r, 0.41)
fig.show() fig.show()
``` ```
### Correlation between the Signals ### Correlation between the Signals
@ -85,8 +85,6 @@ lags_lanes = []
for oephys_lane, relacs_lane, names_lane in zip( for oephys_lane, relacs_lane, names_lane in zip(
oephys_lanes, relacs_lanes, names_lanes, strict=True oephys_lanes, relacs_lanes, names_lanes, strict=True
): ):
print(oephys_lane.shape)
print(relacs_lane.shape)
oephys_lane_resampled = signal.resample(oephys_lane, len(relacs_lane)) oephys_lane_resampled = signal.resample(oephys_lane, len(relacs_lane))
correlation = signal.correlate(oephys_lane_resampled, relacs_lane, mode="full") correlation = signal.correlate(oephys_lane_resampled, relacs_lane, mode="full")
lags = signal.correlation_lags(oephys_lane_resampled.size, relacs_lane.size, mode="full") lags = signal.correlation_lags(oephys_lane_resampled.size, relacs_lane.size, mode="full")

44
doc/fi_curve.qmd
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@ -19,23 +19,22 @@ Lets look at the calibration and the first trial of the recording.
```{python} ```{python}
import pathlib import pathlib
import rlxnix as rlx
import plotly.graph_objects as go
import numpy as np import numpy as np
import plotly.graph_objects as go
import rlxnix as rlx
import scipy.signal as signal import scipy.signal as signal
from plotly.subplots import make_subplots from plotly.subplots import make_subplots
from util import plot_line_comparision, trial_plot
from util import trial_plot, plot_line_comparision
dataset_path = pathlib.Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2-recording.nix") dataset_path = pathlib.Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2-recording.nix")
relacs_path = pathlib.Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2_relacs/2025-10-08-aa-invivo-2.nix") relacs_path = pathlib.Path(
"../oephys2nix/test/Test1/2025-10-08-aa-invivo-2_relacs/2025-10-08-aa-invivo-2.nix"
)
dataset = rlx.Dataset(str(dataset_path)) dataset = rlx.Dataset(str(dataset_path))
relacs = rlx.Dataset(str(relacs_path)) relacs = rlx.Dataset(str(relacs_path))
#INFO: Select the first stimulus of the calibration repro # INFO: Select the first stimulus of the calibration repro
repro_d = dataset.repro_runs("FICurve_1")[0].stimuli[2] repro_d = dataset.repro_runs("FICurve_1")[0].stimuli[2]
repro_r = relacs.repro_runs("FICurve_1")[0].stimuli[2] repro_r = relacs.repro_runs("FICurve_1")[0].stimuli[2]
@ -57,10 +56,8 @@ ttl, t = repro_d.trace_data("ttl-line")
If you zoom in you can see a little delay between the different recording systems. It seems that open-ephys is before the relacs recording. If you zoom in you can see a little delay between the different recording systems. It seems that open-ephys is before the relacs recording.
```{python} ```{python}
#| echo: False # | echo: False
# 2. Add traces to the FIRST subplot (row=1, col=1) fig = trial_plot(repro_d, repro_r, 0.41)
# Note: Plotly rows and columns are 1-indexed
fig = trial_plot(repro_d, repro_r)
fig.show() fig.show()
``` ```
### Correlation between the Signals ### Correlation between the Signals
@ -70,12 +67,13 @@ print(f"Duration of the dataset {repro_d.duration}")
print(f"Duration of the relacs {repro_r.duration}") print(f"Duration of the relacs {repro_r.duration}")
# Resample the open-ephys data # Resample the open-ephys data
sinus_resampled = signal.resample(sinus, len(sinus_r)) sinus_resampled = signal.resample(sinus, len(sinus_r))
``` ```
```{python} ```{python}
#| echo: False # | echo: False
fig= plot_line_comparision(t_r, t_r, sinus_r, sinus_resampled, ["sinus-relacs", "sinus-resampled-open-ephys"]) fig = plot_line_comparision(
t_r, t_r, sinus_r, sinus_resampled, ["sinus-relacs", "sinus-resampled-open-ephys"]
)
fig.show() fig.show()
``` ```
We need to scale the two signals We need to scale the two signals
@ -85,9 +83,9 @@ oephys_lanes = [sinus, local_eod_oe, global_eod_oe, stimulus_oe]
relacs_lanes = [sinus_r, local_eod_re, global_eod_re, stimulus_re] relacs_lanes = [sinus_r, local_eod_re, global_eod_re, stimulus_re]
names_lanes = ["sinus", "local-eod", "global-eod", "stimulus"] names_lanes = ["sinus", "local-eod", "global-eod", "stimulus"]
lags_lanes = [] lags_lanes = []
for oephys_lane, relacs_lane, names_lane in zip(oephys_lanes, relacs_lanes, names_lanes, strict=True): for oephys_lane, relacs_lane, names_lane in zip(
print(oephys_lane.shape) oephys_lanes, relacs_lanes, names_lanes, strict=True
print(relacs_lane.shape) ):
oephys_lane_resampled = signal.resample(oephys_lane, len(relacs_lane)) oephys_lane_resampled = signal.resample(oephys_lane, len(relacs_lane))
correlation = signal.correlate(oephys_lane_resampled, relacs_lane, mode="full") correlation = signal.correlate(oephys_lane_resampled, relacs_lane, mode="full")
lags = signal.correlation_lags(oephys_lane_resampled.size, relacs_lane.size, mode="full") lags = signal.correlation_lags(oephys_lane_resampled.size, relacs_lane.size, mode="full")
@ -97,7 +95,13 @@ for oephys_lane, relacs_lane, names_lane in zip(oephys_lanes, relacs_lanes, name
``` ```
```{python} ```{python}
#| echo: False # | echo: False
fig = plot_line_comparision(t_r, t_r, np.roll(sinus_r, lags_lanes[0]), sinus_resampled, ["rolled sinus-relacs", "sinus-resampled-open-ephys"]) fig = plot_line_comparision(
t_r,
t_r,
np.roll(sinus_r, lags_lanes[0]),
sinus_resampled,
["rolled sinus-relacs", "sinus-resampled-open-ephys"],
)
fig.show() fig.show()
``` ```

41
doc/filestimulus.qmd
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@ -18,23 +18,22 @@ format:
```{python} ```{python}
import pathlib import pathlib
import rlxnix as rlx
import plotly.graph_objects as go
import numpy as np import numpy as np
import plotly.graph_objects as go
import rlxnix as rlx
import scipy.signal as signal import scipy.signal as signal
from plotly.subplots import make_subplots from plotly.subplots import make_subplots
from util import plot_line_comparision, trial_plot
from util import trial_plot, plot_line_comparision
dataset_path = pathlib.Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2-recording.nix") dataset_path = pathlib.Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2-recording.nix")
relacs_path = pathlib.Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2_relacs/2025-10-08-aa-invivo-2.nix") relacs_path = pathlib.Path(
"../oephys2nix/test/Test1/2025-10-08-aa-invivo-2_relacs/2025-10-08-aa-invivo-2.nix"
)
dataset = rlx.Dataset(str(dataset_path)) dataset = rlx.Dataset(str(dataset_path))
relacs = rlx.Dataset(str(relacs_path)) relacs = rlx.Dataset(str(relacs_path))
#INFO: Select the first stimulus of the calibration repro # INFO: Select the first stimulus of the calibration repro
repro_d = dataset.repro_runs("FileStimulus_1")[0].stimuli[2] repro_d = dataset.repro_runs("FileStimulus_1")[0].stimuli[2]
repro_r = relacs.repro_runs("FileStimulus_1")[0].stimuli[2] repro_r = relacs.repro_runs("FileStimulus_1")[0].stimuli[2]
@ -56,10 +55,10 @@ ttl, t = repro_d.trace_data("ttl-line")
If you zoom in you can see a little delay between the different recording systems. It seems that open-ephys is before the relacs recording. If you zoom in you can see a little delay between the different recording systems. It seems that open-ephys is before the relacs recording.
```{python} ```{python}
#| echo: False # | echo: False
# 2. Add traces to the FIRST subplot (row=1, col=1) # 2. Add traces to the FIRST subplot (row=1, col=1)
# Note: Plotly rows and columns are 1-indexed # Note: Plotly rows and columns are 1-indexed
fig = trial_plot(repro_d, repro_r) fig = trial_plot(repro_d, repro_r,1.01)
fig.show() fig.show()
``` ```
@ -73,8 +72,10 @@ sinus_resampled = signal.resample(sinus, len(sinus_r))
``` ```
```{python} ```{python}
#| echo: False # | echo: False
fig= plot_line_comparision(t_r, t_r, sinus_r, sinus_resampled, ["sinus-relacs", "sinus-resampled-open-ephys"]) fig = plot_line_comparision(
t_r, t_r, sinus_r, sinus_resampled, ["sinus-relacs", "sinus-resampled-open-ephys"]
)
fig.show() fig.show()
``` ```
We need to scale the two signals We need to scale the two signals
@ -84,7 +85,9 @@ oephys_lanes = [sinus, local_eod_oe, global_eod_oe, stimulus_oe]
relacs_lanes = [sinus_r, local_eod_re, global_eod_re, stimulus_re] relacs_lanes = [sinus_r, local_eod_re, global_eod_re, stimulus_re]
names_lanes = ["sinus", "local-eod", "global-eod", "stimulus"] names_lanes = ["sinus", "local-eod", "global-eod", "stimulus"]
lags_lanes = [] lags_lanes = []
for oephys_lane, relacs_lane, names_lane in zip(oephys_lanes, relacs_lanes, names_lanes, strict=True): for oephys_lane, relacs_lane, names_lane in zip(
oephys_lanes, relacs_lanes, names_lanes, strict=True
):
print(oephys_lane.shape) print(oephys_lane.shape)
print(relacs_lane.shape) print(relacs_lane.shape)
oephys_lane_resampled = signal.resample(oephys_lane, len(relacs_lane)) oephys_lane_resampled = signal.resample(oephys_lane, len(relacs_lane))
@ -96,9 +99,15 @@ for oephys_lane, relacs_lane, names_lane in zip(oephys_lanes, relacs_lanes, name
``` ```
```{python} ```{python}
#| echo: False # | echo: False
fig = plot_line_comparision(t_r, t, np.roll(stimulus_re, lags_lanes[-1]), stimulus_oe-np.mean(stimulus_oe), ["rolled sinus-relacs", "sinus-resampled-open-ephys"]) fig = plot_line_comparision(
t_r,
t,
np.roll(stimulus_re, lags_lanes[-1]),
stimulus_oe - np.mean(stimulus_oe),
["rolled sinus-relacs", "sinus-resampled-open-ephys"],
)
fig.show() fig.show()
print(f"The lag of the whitenoise is {lags_lanes[-1] * (1/20_000) * 1000} milli seconds") print(f"The lag of the whitenoise is {lags_lanes[-1] * (1 / 20_000) * 1000} milli seconds")
``` ```

2
doc/sam.qmd
View File

@ -86,8 +86,6 @@ lags_lanes = []
for oephys_lane, relacs_lane, names_lane in zip( for oephys_lane, relacs_lane, names_lane in zip(
oephys_lanes, relacs_lanes, names_lanes, strict=True oephys_lanes, relacs_lanes, names_lanes, strict=True
): ):
print(oephys_lane.shape)
print(relacs_lane.shape)
oephys_lane_resampled = signal.resample(oephys_lane, len(relacs_lane)) oephys_lane_resampled = signal.resample(oephys_lane, len(relacs_lane))
correlation = signal.correlate(oephys_lane_resampled, relacs_lane, mode="full") correlation = signal.correlate(oephys_lane_resampled, relacs_lane, mode="full")
lags = signal.correlation_lags(oephys_lane_resampled.size, relacs_lane.size, mode="full") lags = signal.correlation_lags(oephys_lane_resampled.size, relacs_lane.size, mode="full")