[GOAL] Demo viewing of large multi-chan timeseries data with multi-time-resolution generator and dynamic accessor #87
Comments
droumis
changed the title
droumis
changed the title
Just to point out, |
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To kick this off I suggest let's break this into three tasks:
I would not use the miniscope data for anything here as that actually does not need xarray-datatree approach at all, each 512x512 image fits in memory just fine, so as long as they can be loaded independently and lazily we already have no problem handling this data. If we do want some data we can't currently handle we'd need to collect some huge EM imagery where each individual frame has to be downsampled ahead of time to be workable. For now I'd suggest @ahuang11 start with task 1 and then let's revisit at the next CZI meeting. |
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Oh I suppose the part I missed about the microscopy imagery dataset is that you indeed might want a time cross-section so it might still be useful to resample it along the time dimension, but let's start with the ephys dataset anyway. |
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💯 Thanks @philippjfr! I updated the issue description in accordance with your suggested task priority list. @ahuang11, sound good? |
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Note if you don't want to use the |
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That sounds good to me. The only thing is that I can't guarantee I'll have time to get to this by next Tuesday (depends on how successful / straightforward the other task I have is); I'll try though! |
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No worries it's not a super high priority. |
I work with that kind of data! Here's an example: (neuroglancer link) I have a lot of data like this, and I would love to be able to browse it from a jupyter notebook. In fact, there is (to my knowledge) no python solution for browsing this data in an acceptable way. I'd be super excited to try anything you make on some of our datasets. |
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Okay I got around to kicking this off, starting with task 1 First off, the following code is heavily adapted from https://github.com/carbonplan/ndpyramid/blob/main/ndpyramid/core.py All I did was make it accept a different function (instead of ds.coarsen). Reading the Zarr conventions for multiscale pyramids https://forum.image.sc/t/multiscale-arrays-v0-1/37930, CarbonPlan did a great job with The only thing I think that could be invalid is: "The paths to the arrays in dataset series MUST be ordered from largest (i.e. highest resolution) to smallest." In Anyhow, I think this accomplishes task 1 (for 1D data). I will work on task 2 tomorrow; let me know if you have any concerns before I proceed! import h5py
import xarray as xr
import datatree as dt
from tsdownsample import MinMaxLTTBDownsampler
def downsample(ds, n_out):
time_index = MinMaxLTTBDownsampler().downsample(ds["time"], ds["data"], n_out=n_out)
return ds.isel(time=time_index)
# adapted from https://github.com/carbonplan/ndpyramid/blob/main/ndpyramid/core.py
def multiscales_template(
*,
datasets: list = None,
type: str = "",
method: str = "",
version: str = "",
args: list = None,
kwargs: dict = None,
):
if datasets is None:
datasets = []
if args is None:
args = []
if kwargs is None:
kwargs = {}
# https://forum.image.sc/t/multiscale-arrays-v0-1/37930
return [
{
"datasets": datasets,
"type": type,
"metadata": {
"method": method,
"version": version,
"args": args,
"kwargs": kwargs,
},
}
]
def pyramid_downsample(
ds: xr.Dataset, *, factors: list[int], **kwargs
) -> dt.DataTree:
"""Create a multiscale pyramid via coarsening of a dataset by given factors
Parameters
----------
ds : xarray.Dataset
The dataset to coarsen.
factors : list[int]
The factors to coarsen by.
kwargs : dict
Additional keyword arguments to pass to xarray.Dataset.coarsen.
"""
factors = sorted(factors)
# multiscales spec
save_kwargs = locals()
del save_kwargs["ds"]
attrs = {
"multiscales": multiscales_template(
datasets=[{"path": str(i)} for i in range(len(factors))],
type="pick",
method="pyramid_downsample",
version="0.1",
kwargs=save_kwargs,
)
}
# set up pyramid
plevels = {}
# pyramid data
for key, factor in enumerate(factors):
factor += 1
result = downsample(ds, len(ds["data"]) // factor)
plevels[str(key)] = result
plevels["/"] = xr.Dataset(attrs=attrs)
return dt.DataTree.from_dict(plevels)
h5_f = h5py.File("allensdk_cache/session_715093703/session_715093703.nwb", "r")
times = h5_f["acquisition"]["raw_running_wheel_rotation"]["timestamps"]
data = h5_f["acquisition"]["raw_running_wheel_rotation"]["data"]
ts_ds = xr.DataArray(data, coords={"time": times}, dims=["time"], name="data").to_dataset()
ts_dt = pyramid_downsample(ts_ds, factors=[0, 1, 2, 4, 8])
ts_dt.to_zarr("timeseries.zarr", mode="w")
dt.open_datatree("timeseries.zarr", engine="zarr")
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Hey @ahuang11 , looking great so far!! Can you test it on the electrophysiology data? I don't imagine it would be any different, but the It would be so great to see it working for both this real allensdk LFP data as well as the simulated data mentioned in this issue's description (e.g. datasets.holoviz.org/ephys_sim/v1/ephys_sim_neuropixels_200s_384ch.h5). |
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Is it running wheel signal voltage?
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Sorry, the probe LFP file is actually linked from within that 'session' file. Ian's nb shows how to grab the probe LFP file data. probe_id = session.probes.index.values[0]
lfp = session.get_lfp(probe_id) # This will download 2 GB of LFP dataThe NWB file that we want is now stored locally. We need its filename to read it directly so that we don't have to use the AllenSDK any more. lfp_nwb_filename = os.path.join(local_cache_dir, f"session_{session_id}", f"probe_{probe_id}_lfp.nwb")
f = h5py.File(lfp_nwb_filename, "r")
lfp_data = f[f"acquisition/probe_{probe_id}_lfp/probe_{probe_id}_lfp_data/data"]
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Thanks! I realized the line that downloads that was commented out that's why I couldn't find it Anyhow, I was able to use the LFP data and generate pyramids for it. I was wondering though, should 
Lastly, a couple issues I couldn't resolve today:
MRVE questions: https://discourse.pangeo.io/t/return-a-3d-object-alongside-1d-object-in-apply-ufunc/4008 Codeimport h5py
import xarray as xr
import dask.array as da
import datatree as dt
from tsdownsample import MinMaxLTTBDownsampler
def _help_downsample(data, time, n_out):
indices = MinMaxLTTBDownsampler().downsample(time, data, n_out=n_out)
return data[indices], indices
def apply_downsample(ts_ds, n_out):
ts_ds = ts_ds.load()
ts_ds_downsampled, indices = xr.apply_ufunc(
_help_downsample,
ts_ds["data"],
ts_ds["time"],
kwargs=dict(n_out=n_out),
input_core_dims=[["time"], ["time"]],
output_core_dims=[["time"], ["indices"]],
exclude_dims=set(("time",)),
vectorize=True,
dask="parallelized",
output_sizes={"indices": n_out},
)
ts_ds_downsampled["time"] = ts_ds["time"].isel(time=indices.values[0])
return ts_ds_downsampled.rename("data")
def build_dataset(f, data_key, dims):
coords = {f[dim] for dim in dims.values()}
data = f[data_key]
ds = xr.DataArray(
da.from_array(data, name="data", chunks=(data.shape[0], 1)),
dims=dims,
coords=coords,
).to_dataset()
return ds
# adapted from https://github.com/carbonplan/ndpyramid/blob/main/ndpyramid/core.py
def multiscales_template(
*,
datasets: list = None,
type: str = "",
method: str = "",
version: str = "",
args: list = None,
kwargs: dict = None,
):
if datasets is None:
datasets = []
if args is None:
args = []
if kwargs is None:
kwargs = {}
# https://forum.image.sc/t/multiscale-arrays-v0-1/37930
return [
{
"datasets": datasets,
"type": type,
"metadata": {
"method": method,
"version": version,
"args": args,
"kwargs": kwargs,
},
}
]
def pyramid_downsample(ds: xr.Dataset, *, factors: list[int], **kwargs) -> dt.DataTree:
"""Create a multiscale pyramid via coarsening of a dataset by given factors
Parameters
----------
ds : xarray.Dataset
The dataset to coarsen.
factors : list[int]
The factors to coarsen by.
kwargs : dict
Additional keyword arguments to pass to xarray.Dataset.coarsen.
"""
# multiscales spec
save_kwargs = locals()
del save_kwargs["ds"]
attrs = {
"multiscales": multiscales_template(
datasets=[{"path": str(i)} for i in range(len(factors))],
type="pick",
method="pyramid_downsample",
version="0.1",
kwargs=save_kwargs,
)
}
# set up pyramid
plevels = {}
# pyramid data
for key, factor in enumerate(factors):
factor += 1
result = apply_downsample(ds, len(ds["data"]) // factor)
plevels[str(key)] = result
plevels["/"] = xr.Dataset(attrs=attrs)
return dt.DataTree.from_dict(plevels)
f = h5py.File("allensdk_cache/session_715093703/probe_810755797_lfp.nwb", "r")
ts_ds = build_dataset(
f,
"acquisition/probe_810755797_lfp_data/data",
{
"time": "acquisition/probe_810755797_lfp_data/timestamps",
"channel": "acquisition/probe_810755797_lfp_data/electrodes",
},
)
ts_dt = pyramid_downsample(ts_ds, factors=[0, 1, 2, 4, 8])
f = h5py.File("allensdk_cache/session_715093703/probe_810755797_lfp.nwb", "r")
ts_ds = build_dataset(
f,
"acquisition/probe_810755797_lfp_data/data",
{
"time": "acquisition/probe_810755797_lfp_data/timestamps",
"channel": "acquisition/probe_810755797_lfp_data/electrodes",
},
)
ts_dt = pyramid_downsample(ts_ds, factors=[0, 1, 2, 4, 8])
ts_dt.to_zarr("timeseries.zarr", mode="w")
dt.open_datatree("timeseries.zarr", engine="zarr") |
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For task 2, load based on zoom level: Screen.Recording.2024-02-01.at.10.45.02.PM.movimport numpy as np
import holoviews as hv
from holoviews.operation.datashader import datashade
hv.extension("bokeh")
def rescale(x_range):
nlevels = len(ts_dt)
sub_ds = ts_dt[str(nlevels - 1)].isel(channel=0).ds
if x_range:
x_slice = slice(*x_range)
subset_length = ts_dt["0"].sel(time=x_slice)["time"].size
level = str(nlevels - np.argmin(np.abs(lengths - subset_length)) - 1)
sub_ds = ts_dt[level].sel(channel=0, time=x_slice).ds
print(f"Using {level} for {subset_length} samples")
return hv.Curve(sub_ds, ["time"], ["data"])
range_stream = hv.streams.RangeX()
lengths = np.array([ts_dt[f"{i}"]["time"].size for i in range(len(ts_dt))])
dmap = hv.DynamicMap(rescale, streams=[range_stream])
dmap |
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The latest code, although it has been brought to my attention that apply_ufunc might not be needed here. So, I'm wondering whether each channel should be downsampled individually (looped) or together (stacked)? import h5py
import xarray as xr
import dask.array as da
import datatree as dt
from tsdownsample import MinMaxLTTBDownsampler
def _help_downsample(data, time, n_out):
indices = MinMaxLTTBDownsampler().downsample(time, data, n_out=n_out)
return data[indices], indices
def apply_downsample(ts_ds, n_out):
ts_ds_downsampled, indices = xr.apply_ufunc(
_help_downsample,
ts_ds["data"],
ts_ds["time"],
kwargs=dict(n_out=n_out),
input_core_dims=[["time"], ["time"]],
output_core_dims=[["time"], ["indices"]],
exclude_dims=set(("time",)),
vectorize=True,
dask="parallelized",
dask_gufunc_kwargs=dict(output_sizes={"time": n_out, "indices": n_out}),
)
print(indices)
print(indices[0])
ts_ds_downsampled["time"] = ts_ds["time"].isel(time=indices.values[0])
return ts_ds_downsampled.rename("data")
def build_dataset(f, data_key, dims):
coords = {f[dim] for dim in dims.values()}
data = f[data_key]
ds = xr.DataArray(
da.from_array(data, name="data", chunks=(data.shape[0], 1)),
dims=dims,
coords=coords,
).to_dataset()
return ds
# adapted from https://github.com/carbonplan/ndpyramid/blob/main/ndpyramid/core.py
def multiscales_template(
*,
datasets: list = None,
type: str = "",
method: str = "",
version: str = "",
args: list = None,
kwargs: dict = None,
):
if datasets is None:
datasets = []
if args is None:
args = []
if kwargs is None:
kwargs = {}
# https://forum.image.sc/t/multiscale-arrays-v0-1/37930
return [
{
"datasets": datasets,
"type": type,
"metadata": {
"method": method,
"version": version,
"args": args,
"kwargs": kwargs,
},
}
]
def pyramid_downsample(ds: xr.Dataset, *, factors: list[int], **kwargs) -> dt.DataTree:
"""Create a multiscale pyramid via coarsening of a dataset by given factors
Parameters
----------
ds : xarray.Dataset
The dataset to coarsen.
factors : list[int]
The factors to coarsen by.
kwargs : dict
Additional keyword arguments to pass to xarray.Dataset.coarsen.
"""
# multiscales spec
save_kwargs = locals()
del save_kwargs["ds"]
attrs = {
"multiscales": multiscales_template(
datasets=[{"path": str(i)} for i in range(len(factors))],
type="pick",
method="pyramid_downsample",
version="0.1",
kwargs=save_kwargs,
)
}
# set up pyramid
plevels = {}
# pyramid data
for key, factor in enumerate(factors):
factor += 1
result = apply_downsample(ds, len(ds["data"]) // factor)
plevels[str(key)] = result
plevels["/"] = xr.Dataset(attrs=attrs)
return dt.DataTree.from_dict(plevels)
f = h5py.File("allensdk_cache/session_715093703/probe_810755797_lfp.nwb", "r")
ts_ds = build_dataset(
f,
"acquisition/probe_810755797_lfp_data/data",
{
"time": "acquisition/probe_810755797_lfp_data/timestamps",
"channel": "acquisition/probe_810755797_lfp_data/electrodes",
},
).isel(channel=[0, 1, 2, 3, 4])
ts_dt = pyramid_downsample(ts_ds, factors=[0, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512])
ts_dt.to_zarr("timeseries.zarr", mode="w")
ts_dt = dt.open_datatree("timeseries.zarr", engine="zarr")
import numpy as np
import holoviews as hv
from holoviews.operation.datashader import datashade
hv.extension("bokeh")
def rescale(x_range):
nlevels = len(ts_dt)
sub_ds = ts_dt[str(nlevels - 1)].isel(channel=0).ds
if x_range:
x_slice = slice(*x_range)
subset_length = ts_dt["0"].sel(time=x_slice)["time"].size
level = str(nlevels - np.argmin(np.abs(lengths - subset_length)) - 1)
sub_ds = ts_dt[level].sel(channel=0, time=x_slice).ds
print(f"Using {level} {lengths[level]} for {subset_length} samples")
return hv.Curve(sub_ds, ["time"], ["data"])
range_stream = hv.streams.RangeX()
lengths = np.array([ts_dt[f"{i}"]["time"].size for i in range(len(ts_dt))])
dmap = hv.DynamicMap(rescale, streams=[range_stream])
dmap |
Timeseries should be downsampled individually (although it can of course happen in parallel) since the LTTB algorithm attempts to preserve the structure of each trace separately. But as you point out that means that their coordinates are no longer aligned making it much more difficult and expensive to store them. So I'm not 100% sure what the best approach is here. We may have to do something simple like averaging across channels and then computing the downsampling indices on the averaged data. |
hmmm... that's tricky. In the short term, I think we just need to make a hard tradeoff here. Using LTTB is ideal for a single timeseries, but trying to use it on a full dataset and still maintain alignment through some approach like averaging across channels feels like a whole research project. For this first pass, I suggest we focus on prioritizing speed and just apply decimation to keep the timeseries aligned. I know decimation is a bit of a dirty word around here but I think it's a necessary first step. In the future we can revisit, and perhaps if a user zooms in [enough] then it becomes more reasonable to apply LTTB per timeseries for the data in the viewport. @philippjfr, thoughts? |
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Not sure if decimation or downsampling is the better approach here. Certainly we could simply apply |
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Ensure the number of pixels in height matches the number of channels. So preserve the channels dimension but resample the time dimension. |
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Thanks to the advice from Deepak Cherian and Sam Levang (pydata/xarray#8695 (comment)), I was able to make a mini breakthrough of keeping all the downsampled times, while keeping it blazingly fast (if I'm not mistaken... :P). 10 channels can be downsampled and exported to zarr in 1 minute 20 seconds and 1.48 GBs of disk space (potentially can save more if I use different dtypes and reduce the number of zoom levels) It's a combination of apply_ufunc + recreating the data array with the unique coordinates as a multi-dimensional coordinate. Codeimport h5py
import xarray as xr
import dask.array as da
import datatree as dt
from tsdownsample import MinMaxLTTBDownsampler
def _help_downsample(data, time, n_out):
indices = MinMaxLTTBDownsampler().downsample(time, data, n_out=n_out)
return data[indices], indices
def apply_downsample(ts_ds, n_out):
print(n_out)
times = ts_ds["time"]
ts_ds_downsampled, time_indices = xr.apply_ufunc(
_help_downsample,
ts_ds,
times,
kwargs=dict(n_out=n_out),
input_core_dims=[["time"], ["time"]],
output_core_dims=[["time"], ["indices"]],
exclude_dims=set(("time",)),
vectorize=False,
dask="parallelized",
dask_gufunc_kwargs=dict(output_sizes={"time": n_out, "indices": n_out}),
)
ts_da_downsampled = ts_ds_downsampled.to_dataarray("channel")
times_subset = times.values[time_indices.to_array().values]
return xr.DataArray(
ts_da_downsampled.data,
dims=["channel", "time"],
coords={"multi_time": (("channel", "time"), times_subset)},
name="data",
)
def build_dataset(f, data_key, dims):
coords = {f[dim] for dim in dims.values()}
data = f[data_key]
ds = xr.DataArray(
da.from_array(data, name="data", chunks=(data.shape[0], 1)),
dims=dims,
coords=coords,
).to_dataset()
return ds
# adapted from https://github.com/carbonplan/ndpyramid/blob/main/ndpyramid/core.py
def multiscales_template(
*,
datasets: list = None,
type: str = "",
method: str = "",
version: str = "",
args: list = None,
kwargs: dict = None,
):
if datasets is None:
datasets = []
if args is None:
args = []
if kwargs is None:
kwargs = {}
# https://forum.image.sc/t/multiscale-arrays-v0-1/37930
return [
{
"datasets": datasets,
"type": type,
"metadata": {
"method": method,
"version": version,
"args": args,
"kwargs": kwargs,
},
}
]
def pyramid_downsample(ds: xr.Dataset, *, factors: list[int], **kwargs) -> dt.DataTree:
"""Create a multiscale pyramid via coarsening of a dataset by given factors
Parameters
----------
ds : xarray.Dataset
The dataset to coarsen.
factors : list[int]
The factors to coarsen by.
kwargs : dict
Additional keyword arguments to pass to xarray.Dataset.coarsen.
"""
ds = ds["data"].to_dataset("channel")
# multiscales spec
save_kwargs = locals()
del save_kwargs["ds"]
attrs = {
"multiscales": multiscales_template(
datasets=[{"path": str(i)} for i in range(len(factors))],
type="pick",
method="pyramid_downsample",
version="0.1",
kwargs=save_kwargs,
)
}
# set up pyramid
plevels = {}
# pyramid data
for key, factor in enumerate(factors):
factor += 1
result = apply_downsample(ds, len(ds["time"]) // factor)
plevels[str(key)] = result
plevels["/"] = xr.Dataset(attrs=attrs)
return dt.DataTree.from_dict(plevels)
f = h5py.File("allensdk_cache/session_715093703/probe_810755797_lfp.nwb", "r")
ts_ds = build_dataset(
f,
"acquisition/probe_810755797_lfp_data/data",
{
"time": "acquisition/probe_810755797_lfp_data/timestamps",
"channel": "acquisition/probe_810755797_lfp_data/electrodes",
},
).isel(channel=slice(0, 10))In regards to the plot, I implemented part of it. Codeimport numpy as np
import holoviews as hv
from holoviews.operation.datashader import datashade
hv.extension("bokeh")
def _extract_ds(ts_dt, level, channel):
ds = (
ts_dt[str(level)]
.isel(channel=channel)
.ds.swap_dims({"time": "multi_time"})
.rename({"multi_time": "time"})
)
return ds
def rescale(x_range):
if x_range is None:
x_range = time_da.min().item(), time_da.max().item()
x_slice = slice(*[float(x) for x in x_range])
sub_length = time_da.sel(time=x_slice).size
min_index = np.argmin(np.abs(lengths - sub_length))
zoom_level = max_index - min_index - 1
curves = hv.Overlay(kdims="Channel")
for channel in channels:
sub_ds = _extract_ds(ts_dt, zoom_level, channel).sel(time=x_slice)
curves *= hv.Curve(sub_ds, ["time"], ["data"], label=f"channel {channel}").opts(
color="black",
line_width=1,
subcoordinate_y=True,
subcoordinate_scale=3,
)
return curves.opts(
xlabel="Time (s)",
ylabel="Channel",
show_legend=False,
padding=0,
aspect=1.5,
responsive=True,
shared_axes=False,
framewise=False,
)
time_da = _extract_ds(ts_dt, 0, 0)["time"]
channels = ts_dt["0"].ds["channel"].values
lengths = np.array(
[ts_dt[f"{i}"].isel(channel=0).ds["time"].size for i in range(len(ts_dt))]
)
max_index = len(lengths)
range_stream = hv.streams.RangeX()
dmap = hv.DynamicMap(rescale, streams=[range_stream])
dmapThe issue is that the range stream doesn't trigger, maybe because it's an overlay?? 
I've tried to work around this, by creating an overlay of each individual dynamicmap: Codefrom functools import partial
import numpy as np
import holoviews as hv
hv.extension("bokeh")
def _extract_ds(ts_dt, level, ch):
ds = (
ts_dt[str(level)]
.isel(channel=ch)
.ds.swap_dims({"time": "multi_time"})
.rename({"multi_time": "time"})
)
return ds
def rescale(ch, x_range):
if x_range is None:
x_range = time_da[0].item(), time_da[-1].item()
x_slice = slice(*[float(x) for x in x_range])
sub_length = time_da.sel(time=x_slice).size
min_index = np.argmin(np.abs(lengths - sub_length))
zoom_level = max_index - min_index - 1
sub_ds = _extract_ds(ts_dt, zoom_level, ch).sel(time=x_slice)
curve = hv.Curve(sub_ds, ["time"], ["data"], label=f"ch{ch}").opts(
color="black",
line_width=1,
subcoordinate_y=True,
subcoordinate_scale=3,
)
print(zoom_level)
return curve
time_da = _extract_ds(ts_dt, 0, 0)["time"]
channels = ts_dt["0"].ds["channel"].values
lengths = np.array(
[ts_dt[f"{i}"].isel(channel=0).ds["time"].size for i in range(len(ts_dt))]
)
max_index = len(lengths)
curves = hv.Overlay(kdims=["Channel"])
range_stream = hv.streams.RangeX()
for channel in channels:
curves *= hv.DynamicMap(
partial(rescale, ch=channel), streams=[range_stream]
)
curves.opts(
xlabel="Time (s)",
ylabel="Channel",
show_legend=False,
)However, still doesn't work. Screen.Recording.2024-02-15.at.4.08.47.PM.mov |
|
Trying it on all channels: Took ~10 mins and outputs 10 GBs |
|
The bug is subcoordinates break overlaid dynamicmaps |
|
oooo, multi-dimensional coordinates, nice! Great work so far @ahuang11 Could you spend a bit of time filing MRE HoloViews issues for the subcoordinates breaking overlaid DMap and for the range stream not triggering? These both seem pretty important to address. I'm not sure about the performance implications of creating a DMap for every channel vs using a single DMap with an overlay of curves. @philippjfr, any intuition about this? |
|
MRVE here: |
|
Simon suggested I pull latest main, and it fixed it! from functools import partial
import numpy as np
import holoviews as hv
hv.extension("bokeh")
def _extract_ds(ts_dt, level, ch):
ds = (
ts_dt[str(level)]
.isel(channel=ch)
.ds.swap_dims({"time": "multi_time"})
.rename({"multi_time": "time"})
)
return ds
def rescale(ch, x_range):
if x_range is None:
x_range = time_da[0].item(), time_da[-1].item()
x_slice = slice(*[float(x) for x in x_range])
sub_length = time_da.sel(time=x_slice).size
min_index = np.argmin(np.abs(lengths - sub_length))
zoom_level = max_index - min_index - 1
sub_ds = _extract_ds(ts_dt, zoom_level, ch).sel(time=x_slice)
curve = hv.Curve(sub_ds, ["time"], ["data"], label=f"ch{ch}").opts(
color="black",
line_width=1,
subcoordinate_y=True,
subcoordinate_scale=3,
default_tools=["pan", "xwheel_zoom"],
)
return curve
time_da = _extract_ds(ts_dt, 0, 0)["time"]
channels = ts_dt["0"].ds["channel"].values
lengths = np.array(
[ts_dt[f"{i}"].isel(channel=0).ds["time"].size for i in range(len(ts_dt))]
)
max_index = len(lengths)
curves = hv.Overlay()
range_stream = hv.streams.RangeX()
for channel in channels:
curve = hv.DynamicMap(partial(rescale, ch=channel), streams=[range_stream])
curves *= curve
curves.opts(
xlabel="Time (s)",
ylabel="Channel",
show_legend=False,
padding=0,
aspect=1.5,
responsive=True,
shared_axes=False,
framewise=False,
)Screen.Recording.2024-02-19.at.2.27.13.PM.mov |
|
In this iteration I added:
Screen.Recording.2024-02-26.at.2.35.10.PM.movimport numpy as np
import panel as pn
import datatree as dt
import holoviews as hv
from scipy.stats import zscore
from holoviews.plotting.links import RangeToolLink
from holoviews.operation.datashader import rasterize
from bokeh.models import HoverTool
hv.extension("bokeh")
CLIM_MUL = 0.5
MAX_CHANNELS = 40
ZOOM_MUL = 10 # inflate the zoomed in time slice by this factor
X_PADDING = 0.2 # padding for the x_range
def _extract_ds(ts_dt, level, channel):
ds = (
ts_dt[str(level)]
.sel(channel=channel)
.ds.swap_dims({"time": "multi_time"})
.rename({"multi_time": "time"})
)
return ds
def rescale(x_range):
if x_range is None:
x_range = time_da.min().item(), time_da.max().item()
x_padding = (x_range[1] - x_range[0]) * X_PADDING
x_padded = (x_range[0] - x_padding, x_range[1] + x_padding)
time_slice = slice(*[float(x) for x in x_padded])
sub_length = time_da.sel(time=time_slice).size
min_index = np.argmin(np.abs(lengths - (sub_length * ZOOM_MUL)))
zoom_level = max_index - min_index - 1
curves = hv.Overlay(kdims="Channel")
for channel in channels:
hover = HoverTool(
tooltips=[
("Channel", str(channel)),
("Time", "$x s"),
("Amplitude", "$y µV"),
]
)
sub_ds = _extract_ds(ts_dt, zoom_level, channel).sel(time=time_slice).load()
curve = hv.Curve(sub_ds, ["time"], ["data"], label=f"ch{channel}").opts(
color="black",
line_width=1,
subcoordinate_y=True,
subcoordinate_scale=3,
default_tools=["xwheel_zoom", "xpan", "reset", hover],
)
curves *= curve
return curves.opts(
xlabel="Time (s)",
ylabel="Channel",
title=f"level {zoom_level} for x=({x_range[0]:.1f}, {x_range[1]:.1f})",
show_legend=False,
padding=0,
aspect=1.5,
responsive=True,
framewise=True,
axiswise=True,
)
ts_dt = dt.open_datatree("pyramid.zarr", engine="zarr").sel(
channel=slice(0, MAX_CHANNELS)
)
time_da = _extract_ds(ts_dt, 0, 0)["time"]
channels = ts_dt["0"].ds["channel"].values
data = ts_dt["0"].ds["data"].values.T
lengths = np.array(
[ts_dt[f"{i}"].isel(channel=0).ds["time"].size for i in range(len(ts_dt))]
)
max_index = len(lengths)
range_stream = hv.streams.RangeX()
dmap = hv.DynamicMap(rescale, streams=[range_stream])
y_positions = range(len(channels))
yticks = [(i, ich) for i, ich in enumerate(channels)]
z_data = zscore(data.T, axis=1)
minimap = rasterize(
hv.Image((time_da, y_positions, z_data), ["Time (s)", "Channel"], "Amplitude (uV)")
)
minimap = minimap.opts(
cmap="RdBu_r",
xlabel="",
yticks=[yticks[0], yticks[-1]],
toolbar="disable",
height=120,
responsive=True,
clim=(-z_data.std() * CLIM_MUL, z_data.std() * CLIM_MUL),
)
tool_link = RangeToolLink(
minimap,
dmap,
axes=["x"],
boundsx=(0, time_da.max().item() // 2),
)
pn.template.FastListTemplate(main=[(dmap + minimap).cols(1)]).show() |
Those steps sound good. I'd also like to see some profiling to see where we spend most of the time currently. |
|
Is there a tool native to profiling DynamicMaps or do I just wrap |
|
I'd use |
|
For the deep image stack use-case, I didn't need to use a pyramid to get it running efficiently. I think I only needed to persist the right/bottom datasets because I think upon VLine/HLine update, it triggered unnecessary re-computation when the data was static. I also refactored hvPlot into HoloViews, but not sure that was necessary or not. Screen.Recording.2024-02-26.at.6.03.21.PM.movimport xarray as xr
import panel as pn
pn.extension(throttled=True)
import holoviews as hv
from holoviews.operation.datashader import rasterize
hv.extension("bokeh")
DATA_ARRAY = "10000frames"
DATA_PATH = f"miniscope/miniscope_sim_{DATA_ARRAY}.zarr"
ldataset = xr.open_zarr(DATA_PATH, chunks="auto")
data = ldataset[DATA_ARRAY]
FRAMES_PER_SECOND = 30
FRAMES = data.coords["frame"].values
def plot_image(value):
return hv.Image(data.sel(frame=value), kdims=["width", "height"]).opts(
cmap="Viridis",
frame_height=400,
frame_width=400,
colorbar=False,
)
# Create a video player widget
video_player = pn.widgets.Player(
length=len(data.coords["frame"]),
interval=1000 // FRAMES_PER_SECOND, # ms
value=int(FRAMES.min()),
max_width=400,
max_height=90,
loop_policy="loop",
sizing_mode="stretch_width",
)
# Create the main plot
main_plot = hv.DynamicMap(
plot_image, kdims=["value"], streams=[video_player.param.value]
)
# frame indicator lines on side plots
line_opts = dict(color="red", alpha=0.6, line_width=3)
dmap_hline = hv.DynamicMap(pn.bind(lambda value: hv.HLine(value), video_player)).opts(
**line_opts
)
dmap_vline = hv.DynamicMap(pn.bind(lambda value: hv.VLine(value), video_player)).opts(
**line_opts
)
# height side view
right_data = data.mean(["width"]).persist()
right_plot = rasterize(
hv.Image(right_data, kdims=["frame", "height"]).opts(
cmap="Viridis",
frame_height=400,
frame_width=200,
colorbar=False,
title="_",
)
)
# width side view
bottom_data = data.mean(["height"]).persist()
bottom_plot = rasterize(
hv.Image(bottom_data, kdims=["width", "frame"]).opts(
cmap="Viridis",
frame_height=200,
frame_width=400,
colorbar=False,
)
)
video_player.margin = (20, 20, 20, 70) # center widget over main
sim_app = pn.Column(
video_player,
pn.Row(main_plot, right_plot * dmap_vline),
bottom_plot * dmap_hline,
)
sim_appIt seems that since the data is only about 400 MBs after taking the average, I can load it for even better performance. 
Takes about 10 seconds to initialize the plots on an M2 Pro. Screen.Recording.2024-02-26.at.6.10.33.PM.mov |
|
I tested the miniscope with rasterize's streams only set to RangeXY and without load/persist. The Screen.Recording.2024-02-27.at.12.12.15.PM.movimport xarray as xr
import panel as pn
pn.extension(throttled=True)
import holoviews as hv
from holoviews.operation.datashader import rasterize
hv.extension("bokeh")
DATA_ARRAY = "10000frames"
DATA_PATH = f"miniscope/miniscope_sim_{DATA_ARRAY}.zarr"
ldataset = xr.open_zarr(DATA_PATH, chunks="auto")
data = ldataset[DATA_ARRAY]
FRAMES_PER_SECOND = 30
FRAMES = data.coords["frame"].values
def plot_image(value):
return hv.Image(data.sel(frame=value), kdims=["width", "height"]).opts(
cmap="Viridis",
frame_height=400,
frame_width=400,
colorbar=False,
)
# Create a video player widget
video_player = pn.widgets.Player(
length=len(data.coords["frame"]),
interval=1000 // FRAMES_PER_SECOND, # ms
value=int(FRAMES.min()),
max_width=400,
max_height=90,
loop_policy="loop",
sizing_mode="stretch_width",
)
# Create the main plot
main_plot = hv.DynamicMap(
plot_image, kdims=["value"], streams=[video_player.param.value]
)
# frame indicator lines on side plots
line_opts = dict(color="red", alpha=0.6, line_width=3)
dmap_hline = hv.DynamicMap(pn.bind(lambda value: hv.HLine(value), video_player)).opts(
**line_opts
)
dmap_vline = hv.DynamicMap(pn.bind(lambda value: hv.VLine(value), video_player)).opts(
**line_opts
)
from holoviews.streams import RangeXY
# height side view
right_data = data.mean(["width"])
right_plot = rasterize(
hv.Image(right_data, kdims=["frame", "height"]).opts(
cmap="Viridis",
frame_height=400,
frame_width=200,
colorbar=False,
title="_",
),
streams=[RangeXY()],
)
# width side view
bottom_data = data.mean(["height"])
bottom_plot = rasterize(
hv.Image(bottom_data, kdims=["width", "frame"]).opts(
cmap="Viridis",
frame_height=200,
frame_width=400,
colorbar=False,
),
streams=[RangeXY()],
)
video_player.margin = (20, 20, 20, 70) # center widget over main
sim_app = pn.Column(
video_player,
pn.Row(main_plot, right_plot * dmap_vline),
bottom_plot * dmap_hline,
)
sim_app |
Please try to make a minimum reproducible example. |
|
Made it depend on plot_size instead, where it finds the width of the plot and subtracts it with the sliced zoom level's size, and finds the closest one. However, I think it still needs a zoom multiplier due to the number of channels (takes a while to load). Screen.Recording.2024-02-27.at.4.20.56.PM.movimport numpy as np
import panel as pn
import datatree as dt
import holoviews as hv
from scipy.stats import zscore
from holoviews.plotting.links import RangeToolLink
from holoviews.operation.datashader import rasterize
from bokeh.models.tools import WheelZoomTool, HoverTool
hv.extension("bokeh")
CLIM_MUL = 0.5
MAX_CHANNELS = 40
X_PADDING = 0.2 # padding for the x_range
def _extract_ds(ts_dt, level, channel):
ds = (
ts_dt[str(level)]
.sel(channel=channel)
.ds.swap_dims({"time": "multi_time"})
.rename({"multi_time": "time"})
)
return ds
def rescale(x_range, y_range, width, scale, height):
if x_range is None:
x_range = time_da.min().item(), time_da.max().item()
if y_range is None:
y_range = 0, num_channels
x_padding = (x_range[1] - x_range[0]) * X_PADDING
time_slice = slice(x_range[0] - x_padding, x_range[1] + x_padding)
if width is None or height is None:
zoom_level = num_levels - 1
else:
sizes = [
_extract_ds(ts_dt, zoom_level, 0)["time"].sel(time=time_slice).size
for zoom_level in range(num_levels)
]
zoom_level = np.argmin(np.abs(np.array(sizes) - width))
curves = hv.Overlay(kdims="Channel")
for channel in channels:
hover = HoverTool(
tooltips=[
("Channel", str(channel)),
("Time", "$x s"),
("Amplitude", "$y µV"),
]
)
sub_ds = _extract_ds(ts_dt, zoom_level, channel).sel(time=time_slice).load()
curve = hv.Curve(sub_ds, ["time"], ["data"], label=f"ch{channel}").opts(
color="black",
line_width=1,
subcoordinate_y=True,
subcoordinate_scale=3,
default_tools=["pan", "reset", WheelZoomTool(), hover],
)
curves *= curve
return curves.opts(
xlabel="Time (s)",
ylabel="Channel",
title=f"level {zoom_level} ({x_range[0]:.2f}s - {x_range[1]:.2f}s)",
show_legend=False,
padding=0,
aspect=1.5,
responsive=True,
framewise=True,
axiswise=True,
)
ts_dt = dt.open_datatree("pyramid.zarr", engine="zarr").sel(
channel=slice(0, MAX_CHANNELS)
)
num_levels = len(ts_dt)
time_da = _extract_ds(ts_dt, 0, 0)["time"]
channels = ts_dt["0"].ds["channel"].values
num_channels = len(channels)
data = ts_dt["0"].ds["data"].values.T
range_stream = hv.streams.RangeXY()
size_stream = hv.streams.PlotSize()
dmap = hv.DynamicMap(rescale, streams=[size_stream, range_stream])
y_positions = range(num_channels)
yticks = [(i, ich) for i, ich in enumerate(channels)]
z_data = zscore(data.T, axis=1)
minimap = rasterize(
hv.Image((time_da, y_positions, z_data), ["Time (s)", "Channel"], "Amplitude (uV)")
)
minimap = minimap.opts(
cmap="RdBu_r",
xlabel="",
yticks=[yticks[0], yticks[-1]],
toolbar="disable",
height=120,
responsive=True,
clim=(-z_data.std() * CLIM_MUL, z_data.std() * CLIM_MUL),
)
tool_link = RangeToolLink(
minimap,
dmap,
axes=["x", "y"],
boundsx=(0, time_da.max().item() // 2),
boundsy=(0, len(channels)),
)
pn.template.FastListTemplate(main=[(dmap + minimap).cols(1)]).show()I also used Simon's suggestion of instantiating a zoom tool manually thru bokeh, but still can't get the Y-range to work properly in subcoordinate and the box is unlinked. MRE below: |
droumis
changed the title
droumis
changed the title
Regarding that, since zoom levels scale in powers (https://wiki.openstreetmap.org/wiki/Zoom_levels, e.g. z=0, 1 tile, z=1, 4 tiles, z=2, 16 tiles), I came up with a formula to determine the downscale factors to cover the entire range. It depends on the length of the data, the typical screen width that the users will view this in, and desired number of zoom levels (num_factors). |
Codeimport h5py
import numpy as np
import xarray as xr
import dask.array as da
import datatree as dt
from tsdownsample import MinMaxLTTBDownsampler
def _help_downsample(data, time, n_out):
print(f"{len(time)} -> {n_out} samples")
indices = MinMaxLTTBDownsampler().downsample(time, data, n_out=n_out)
return data[indices], indices
def apply_downsample(ts_da, n_out):
times = ts_da["time"]
if n_out != len(ts_da["time"]):
print("Downsampling")
ts_ds_downsampled, time_indices = xr.apply_ufunc(
_help_downsample,
ts_da,
times,
kwargs=dict(n_out=n_out),
input_core_dims=[["time"], ["time"]],
output_core_dims=[["time"], ["indices"]],
exclude_dims=set(("time",)),
vectorize=False,
dask="parallelized",
dask_gufunc_kwargs=dict(output_sizes={"time": n_out, "indices": n_out}),
)
ts_da_downsampled = ts_ds_downsampled.to_dataarray("channel")
times_subset = times.values[time_indices.to_array().values]
else:
print("No downsampling")
ts_da_downsampled = ts_da.to_dataarray("channel")
times_subset = np.vstack(
[times.values for _ in range(ts_da_downsampled["channel"].size)]
)
return xr.DataArray(
ts_da_downsampled.values,
dims=["channel", "time"],
coords={"multi_time": (("channel", "time"), times_subset)},
name="data",
)
def build_dataset(f, data_key, dims, use_dask=True):
coords = {key: f[value] for key, value in dims.items()}
data = f[data_key]
if len(dims) != data.ndim:
dims["channel"] = None
if use_dask:
array = da.from_array(data, name="data", chunks=(data.shape[0], 5))
else:
array = data
ds = xr.DataArray(
array,
dims=dims,
coords=coords,
name="data"
).to_dataset()
return ds
# adapted from https://github.com/carbonplan/ndpyramid/blob/main/ndpyramid/core.py
def multiscales_template(
*,
datasets: list = None,
type: str = "",
method: str = "",
version: str = "",
args: list = None,
kwargs: dict = None,
):
if datasets is None:
datasets = []
if args is None:
args = []
if kwargs is None:
kwargs = {}
# https://forum.image.sc/t/multiscale-arrays-v0-1/37930
return [
{
"datasets": datasets,
"type": type,
"metadata": {
"method": method,
"version": version,
"args": args,
"kwargs": kwargs,
},
}
]
def pyramid_downsample(ds: xr.Dataset, *, factors: list[int], **kwargs) -> dt.DataTree:
"""Create a multiscale pyramid via coarsening of a dataset by given factors
Parameters
----------
ds : xarray.Dataset
The dataset to coarsen.
factors : list[int]
The factors to coarsen by.
kwargs : dict
Additional keyword arguments to pass to xarray.Dataset.coarsen.
"""
ds = ds["data"].to_dataset("channel")
factors = np.array(factors).astype(int).tolist()
# multiscales spec
save_kwargs = locals()
del save_kwargs["ds"]
attrs = {
"multiscales": multiscales_template(
datasets=[{"path": str(i)} for i in range(len(factors))],
type="pick",
method="pyramid_downsample",
version="0.1",
kwargs=save_kwargs,
)
}
# set up pyramid
plevels = {}
# pyramid data
for key, factor in enumerate(factors):
factor += 1
result = apply_downsample(ds, len(ds["time"]) // factor)
plevels[str(key)] = result
plevels["/"] = xr.Dataset(attrs=attrs)
return dt.DataTree.from_dict(plevels)Fixed a couple bugs with loading and I did some timings too. For preprocessing, the bottleneck is downsampling:
|
Codeimport numpy as np
import panel as pn
import datatree as dt
import holoviews as hv
from scipy.stats import zscore
from holoviews.plotting.links import RangeToolLink
from holoviews.operation.datashader import rasterize
from bokeh.models.tools import WheelZoomTool, HoverTool
hv.extension("bokeh")
CLIM_MUL = 0.5
MAX_CHANNELS = 100
X_PADDING = 0.2 # padding for the x_range
def _extract_ds(ts_dt, level, channel):
ds = (
ts_dt[str(level)]
.sel(channel=channel)
.ds.swap_dims({"time": "multi_time"})
.rename({"multi_time": "time"})
)
return ds
def rescale(x_range, y_range, width, scale, height):
import time
s = time.perf_counter()
print(f"- Update triggered! {width=} {x_range=}")
if x_range is None:
x_range = time_da.min().item(), time_da.max().item()
if y_range is None:
y_range = 0, num_channels
x_padding = (x_range[1] - x_range[0]) * X_PADDING
time_slice = slice(x_range[0] - x_padding, x_range[1] + x_padding)
if width is None or height is None:
zoom_level = num_levels - 1
size = data.size
else:
sizes = [
_extract_ds(ts_dt, zoom_level, 0)["time"].sel(time=time_slice).size
for zoom_level in range(num_levels)
]
zoom_level = np.argmin(np.abs(np.array(sizes) - width))
size = sizes[zoom_level]
e = time.perf_counter()
print(f"Zoom level computation took {e-s:.2f}s")
title = (
f"level {zoom_level} ({x_range[0]:.2f}s - {x_range[1]:.2f}s) "
f"(WxH: {width}x{height}) (length: {size})"
)
# if zoom_level == pn.state.cache.get("current_zoom_level") and pn.state.cache.get(
# "curves"
# ):
# cached_x_range = pn.state.cache["x_range"]
# if x_range[0] >= cached_x_range[0] and x_range[1] <= cached_x_range[1]:
# print(f"Using cached curves! {zoom_level=}")
# if x_range != cached_x_range:
# print(f"Different x_range: {x_range} {cached_x_range}")
# return pn.state.cache["curves"].opts(title=title)
curves = hv.Overlay(kdims="Channel")
for channel in channels:
hover = HoverTool(
tooltips=[
("Channel", str(channel)),
("Time", "$x s"),
("Amplitude", "$y µV"),
]
)
sub_ds = _extract_ds(ts_dt, zoom_level, channel).sel(time=time_slice).load()
curve = hv.Curve(sub_ds, ["time"], ["data"], label=f"ch{channel}").opts(
color="black",
line_width=1,
subcoordinate_y=True,
subcoordinate_scale=3,
default_tools=["pan", "reset", WheelZoomTool(), hover],
)
curves *= curve
print(f"Overlaying curves took {time.perf_counter()-e:.2f}s")
curves = curves.opts(
xlabel="Time (s)",
ylabel="Channel",
title=title,
show_legend=False,
padding=0,
aspect=1.5,
responsive=True,
framewise=True,
axiswise=True,
)
pn.state.cache["current_zoom_level"] = zoom_level
pn.state.cache["x_range"] = x_range
pn.state.cache["curves"] = curves
print(f"Using updated curves! {x_range} {zoom_level}\n\n")
return curves
ts_dt = dt.open_datatree("pyramid_4_evenly_factored.zarr", engine="zarr").sel(
channel=slice(0, MAX_CHANNELS)
)
num_levels = len(ts_dt)
time_da = _extract_ds(ts_dt, 0, 0)["time"]
channels = ts_dt["0"].ds["channel"].values
num_channels = len(channels)
data = ts_dt["0"].ds["data"].values.T
range_stream = hv.streams.RangeXY()
size_stream = hv.streams.PlotSize()
dmap = hv.DynamicMap(rescale, streams=[size_stream, range_stream])
y_positions = range(num_channels)
yticks = [(i, ich) for i, ich in enumerate(channels)]
z_data = zscore(data.T, axis=1)
minimap = rasterize(
hv.Image((time_da, y_positions, z_data), ["Time (s)", "Channel"], "Amplitude (uV)")
)
minimap = minimap.opts(
cmap="RdBu_r",
xlabel="",
yticks=[yticks[0], yticks[-1]],
toolbar="disable",
height=120,
responsive=True,
clim=(-z_data.std() * CLIM_MUL, z_data.std() * CLIM_MUL),
)
tool_link = RangeToolLink(
minimap,
dmap,
axes=["x", "y"],
boundsx=(0, time_da.max().item() // 2),
boundsy=(0, len(channels)),
)
pn.template.FastListTemplate(main=[(dmap + minimap).cols(1)]).show()Note that the run times of computing the slices is short. So I think it's a matter of rendering that is slow; and that's in part due to it has to render 90-384 curves (channels) even though it's zoomed on only a couple channels(?). If Also, on init, it executes the DynamicMap three times before settling. I think this is because on:
I tried caching the curves if zoom_level == pn.state.cache.get("current_zoom_level") and pn.state.cache.get(
"curves"
):
cached_x_range = pn.state.cache["x_range"]
if x_range[0] >= cached_x_range[0] and x_range[1] <= cached_x_range[1]:
print(f"Using cached curves! {zoom_level=}")
if x_range != cached_x_range:
print(f"Different x_range: {x_range} {cached_x_range}")
return pn.state.cache["curves"].opts(title=title)However, it doesn't help much because it's all on the rendering side I think.. |
Nice work, Andrew! I think the above line is probably supposed to be something like
Is this to avoid division by zero and/or another reason?
@philippjfr, is there any way to combine the init execution of DynamicMap to combine updates from multiple streams? |
|
For a limited number of channels, this is pretty good! (Note, there's a range issue with the first channel, which I thought had been resolved already, but hopefully it will be addressed with the work that Simon and Maxime are doing soon for subcoords). Screen.Recording.2024-03-12.at.2.46.53.PM.movHowever, it looks like once the number of channels exceeds ~20 for this particular dataset (I tested 10, 20, 30, 40, 50), then the zoom level does not update quickly enough when zooming in (at least within a couple of minutes). For larger channel counts, I'm also seeing the DynamicMap get executed several times for each minimap range adjustment, which is definitely harming the performance (may need to try throttling the callback?). In fact, sometimes, it doesn't ever stop executing and seems to get stuck in a loop. Here's the code, bringing together what Andrew has done above: Code# First, download a dataset, e.g.:
# (3GB) datasets.holoviz.org/ephys_sim/v1/ephys_sim_neuropixels_10s_384ch.h5
from pathlib import Path
import h5py
import numpy as np
import xarray as xr
import dask.array as da
import datatree as dt
from tsdownsample import MinMaxLTTBDownsampler
def _help_downsample(data, time, n_out):
print(f"{len(time)} -> {n_out} samples")
indices = MinMaxLTTBDownsampler().downsample(time, data, n_out=n_out)
return data[indices], indices
def apply_downsample(ts_da, n_out):
times = ts_da["time"]
if n_out != len(ts_da["time"]):
print("Downsampling")
ts_ds_downsampled, time_indices = xr.apply_ufunc(
_help_downsample,
ts_da,
times,
kwargs=dict(n_out=n_out),
input_core_dims=[["time"], ["time"]],
output_core_dims=[["time"], ["indices"]],
exclude_dims=set(("time",)),
vectorize=False,
dask="parallelized",
dask_gufunc_kwargs=dict(output_sizes={"time": n_out, "indices": n_out}),
)
ts_da_downsampled = ts_ds_downsampled.to_dataarray("channel")
times_subset = times.values[time_indices.to_array().values]
else:
print("No downsampling")
ts_da_downsampled = ts_da.to_dataarray("channel")
times_subset = np.vstack(
[times.values for _ in range(ts_da_downsampled["channel"].size)]
)
return xr.DataArray(
ts_da_downsampled.values,
dims=["channel", "time"],
coords={"multi_time": (("channel", "time"), times_subset)},
name="data",
)
def build_dataset(f, data_key, dims, use_dask=True):
if data_key not in f:
raise KeyError(f"{data_key} not found in file.")
for key, value in dims.items():
if value not in f:
raise KeyError(f"Dimension '{value}' for key '{key}' not found in file.")
coords = {key: f[value] for key, value in dims.items()}
data = f[data_key]
if "channel" not in dims:
dims["channel"] = np.arange(data.shape[1])
if use_dask:
array = da.from_array(data, name="data", chunks=(data.shape[0], 5))
else:
array = data
ds = xr.DataArray(
array,
dims=dims,
coords=coords,
name="data"
).to_dataset()
return ds
# adapted from https://github.com/carbonplan/ndpyramid/blob/main/ndpyramid/core.py
def multiscales_template(
*,
datasets: list = None,
type: str = "",
method: str = "",
version: str = "",
args: list = None,
kwargs: dict = None,
):
if datasets is None:
datasets = []
if args is None:
args = []
if kwargs is None:
kwargs = {}
# https://forum.image.sc/t/multiscale-arrays-v0-1/37930
return [
{
"datasets": datasets,
"type": type,
"metadata": {
"method": method,
"version": version,
"args": args,
"kwargs": kwargs,
},
}
]
def pyramid_downsample(ds: xr.Dataset, *, factors: list[int], **kwargs) -> dt.DataTree:
"""Create a multiscale pyramid via coarsening of a dataset by given factors
Parameters
----------
ds : xarray.Dataset
The dataset to coarsen.
factors : list[int]
The factors to coarsen by.
kwargs : dict
Additional keyword arguments to pass to xarray.Dataset.coarsen.
"""
ds = ds["data"].to_dataset("channel")
factors = np.array(factors).astype(int).tolist()
# multiscales spec
save_kwargs = locals()
del save_kwargs["ds"]
attrs = {
"multiscales": multiscales_template(
datasets=[{"path": str(i)} for i in range(len(factors))],
type="pick",
method="pyramid_downsample",
version="0.1",
kwargs=save_kwargs,
)
}
# set up pyramid
plevels = {}
# pyramid data
for key, factor in enumerate(factors):
factor += 1
result = apply_downsample(ds, len(ds["time"]) // factor)
plevels[str(key)] = result
plevels["/"] = xr.Dataset(attrs=attrs)
return dt.DataTree.from_dict(plevels)
# %% Create time pyramid dataset
PYRAMID_FILE = 'pyramid_neuropix_10s.zarr'
CREATE_PYRAMID = True
if CREATE_PYRAMID:
# f = h5py.File(Path.home() / Path("allensdk_cache/session_715093703/probe_810755797_lfp.nwb"), "r")
# ts_ds = build_dataset(
# f,
# "acquisition/probe_810755797_lfp_data/data",
# {
# "time": "acquisition/probe_810755797_lfp_data/timestamps",
# "channel": "acquisition/probe_810755797_lfp_data/electrodes",
# },
# ).isel(channel=list(np.arange(0,4)))
f = h5py.File(Path.home() / Path("data/ephys_sim_neuropixels/ephys_sim_neuropixels_10s_384ch.h5"), "r")
ts_ds = build_dataset(
f,
"/recordings",
{
"time": "timestamps",
# channels don't have a name in this dataset, but they have other metadata like location on the probe
# Just exclude the channel dim and have incrementing dummy labels be used instead
},
)
data_length = ts_ds["time"].size
screen_width = 1500 #(in px)
num_factors = 4
target_factor = data_length / screen_width
max_zoom = int(np.log2(target_factor))
all_factors = 2 ** np.arange(max_zoom + 1)
sub_factors = all_factors[::max_zoom // (num_factors - 1)]
if sub_factors[-1] != all_factors[-1]:
sub_factors = np.append(sub_factors, all_factors[-1])
ts_dt = pyramid_downsample(ts_ds, factors=sub_factors)
ts_dt.to_zarr(PYRAMID_FILE, mode="w")
ts_dt = dt.open_datatree(PYRAMID_FILE, engine="zarr")
# %% Plot
import numpy as np
import panel as pn
import datatree as dt
import holoviews as hv
from scipy.stats import zscore
from holoviews.plotting.links import RangeToolLink
from holoviews.operation.datashader import rasterize
from bokeh.models.tools import WheelZoomTool, HoverTool
hv.extension("bokeh")
CLIM_MUL = 0.5
MAX_CHANNELS = 20
X_PADDING = 0.2 # padding for the x_range
def _extract_ds(ts_dt, level, channel):
ds = (
ts_dt[str(level)]
.sel(channel=channel)
.ds.swap_dims({"time": "multi_time"})
.rename({"multi_time": "time"})
)
return ds
def rescale(x_range, y_range, width, scale, height):
import time
s = time.perf_counter()
print(f"- Update triggered! {width=} {x_range=}")
if x_range is None:
x_range = time_da.min().item(), time_da.max().item()
if y_range is None:
y_range = 0, num_channels
x_padding = (x_range[1] - x_range[0]) * X_PADDING
time_slice = slice(x_range[0] - x_padding, x_range[1] + x_padding)
if width is None or height is None:
zoom_level = num_levels - 1
size = data.size
else:
sizes = [
_extract_ds(ts_dt, zoom_level, 0)["time"].sel(time=time_slice).size
for zoom_level in range(num_levels)
]
zoom_level = np.argmin(np.abs(np.array(sizes) - width))
size = sizes[zoom_level]
e = time.perf_counter()
print(f"Zoom level computation took {e-s:.2f}s")
title = (
f"level {zoom_level} ({x_range[0]:.2f}s - {x_range[1]:.2f}s) "
f"(WxH: {width}x{height}) (length: {size})"
)
# if zoom_level == pn.state.cache.get("current_zoom_level") and pn.state.cache.get(
# "curves"
# ):
# cached_x_range = pn.state.cache["x_range"]
# if x_range[0] >= cached_x_range[0] and x_range[1] <= cached_x_range[1]:
# print(f"Using cached curves! {zoom_level=}")
# if x_range != cached_x_range:
# print(f"Different x_range: {x_range} {cached_x_range}")
# return pn.state.cache["curves"].opts(title=title)
curves = hv.Overlay(kdims="Channel")
for channel in channels:
hover = HoverTool(
tooltips=[
("Channel", str(channel)),
("Time", "$x s"),
("Amplitude", "$y µV"),
]
)
sub_ds = _extract_ds(ts_dt, zoom_level, channel).sel(time=time_slice).load()
curve = hv.Curve(sub_ds, ["time"], ["data"], label=f"ch{channel}").opts(
color="black",
line_width=1,
subcoordinate_y=True,
subcoordinate_scale=1,
default_tools=["pan", "reset", WheelZoomTool(), hover],
)
curves *= curve
print(f"Overlaying curves took {time.perf_counter()-e:.2f}s")
curves = curves.opts(
xlabel="Time (s)",
ylabel="Channel",
title=title,
show_legend=False,
padding=0,
aspect=1.5,
responsive=True,
framewise=True,
axiswise=True,
)
pn.state.cache["current_zoom_level"] = zoom_level
pn.state.cache["x_range"] = x_range
pn.state.cache["curves"] = curves
print(f"Using updated curves! {x_range} {zoom_level}\n\n")
return curves
ts_dt = dt.open_datatree(PYRAMID_FILE, engine="zarr").sel(
channel=slice(0, MAX_CHANNELS)
)
num_levels = len(ts_dt)
time_da = _extract_ds(ts_dt, 0, 0)["time"]
channels = ts_dt["0"].ds["channel"].values
num_channels = len(channels)
data = ts_dt["0"].ds["data"].values.T
range_stream = hv.streams.RangeXY()
size_stream = hv.streams.PlotSize()
dmap = hv.DynamicMap(rescale, streams=[size_stream, range_stream])
y_positions = range(num_channels)
yticks = [(i, ich) for i, ich in enumerate(channels)]
z_data = zscore(data.T, axis=1)
minimap = rasterize(
hv.Image((time_da, y_positions, z_data), ["Time (s)", "Channel"], "Amplitude (uV)")
)
minimap = minimap.opts(
cmap="RdBu_r",
xlabel="",
yticks=[yticks[0], yticks[-1]],
toolbar="disable",
height=120,
responsive=True,
clim=(-z_data.std() * CLIM_MUL, z_data.std() * CLIM_MUL),
)
tool_link = RangeToolLink(
minimap,
dmap,
axes=["x", "y"],
boundsx=(0, time_da.max().item() // 2),
boundsy=(0, len(channels)),
)
pn.template.FastListTemplate(main=[(dmap + minimap).cols(1)]).servable() |
Yes! Thanks for spotting that.
I think I initially conflated factor == zoom_level, e.g. on tile maps, zoom level 0 is the least coarse zoom; I think could refactor to remove that += 1 and add -1 to datasets
Performance can be significantly improved if we are able to utilize Another idea is to bias the streams to use coarser zoom levels so that only a max number of points are shown at a given time, depending on the number of channels, e.g. if initial zoom_level was 4 for 10 channels, use 6 instead for 15 channels, and 8 for 20 channels.
I don't think it's a matter of throttling. I think it's multiple streams re-triggering; related: "the init execution of DynamicMap to combine updates from multiple streams" |
I think this is worth trying. As you increase the number of channels, you generally likely shrink the vertical real estate of each of the individual channels, so downsampling to a courser zoom level seems ok theory, beyond what the x-range stream alone indicates. I think it will require some manual tweaking to find the sweet spot. |
|
Waiting on: |
|
Added a PR in ndpyramids here carbonplan/ndpyramid#94 to abstract out some of the functionality so that it's not duplicated and can be simplified to just: import h5py
import xarray as xr
import dask.array as da
import datatree as dt
from ndpyramid import pyramid_create
from tsdownsample import MinMaxLTTBDownsampler
def _help_downsample(data, time, n_out):
indices = MinMaxLTTBDownsampler().downsample(time, data, n_out=n_out)
return data[indices], indices
def apply_downsample(ts_ds, factor, dims):
dim = dims[0]
n_out = len(ts_ds["data"]) // factor
ts_ds_downsampled, indices = xr.apply_ufunc(
_help_downsample,
ts_ds["data"],
ts_ds[dim],
kwargs=dict(n_out=n_out),
input_core_dims=[[dim], [dim]],
output_core_dims=[[dim], ["indices"]],
exclude_dims=set((dim,)),
vectorize=True,
dask="parallelized",
dask_gufunc_kwargs=dict(output_sizes={dim: n_out, "indices": n_out}),
)
ts_ds_downsampled[dim] = ts_ds[dim].isel(time=indices.values[0])
return ts_ds_downsampled.rename("data")
def build_dataset(f, data_key, dims):
coords = {f[dim] for dim in dims.values()}
data = f[data_key]
ds = xr.DataArray(
da.from_array(data, name="data", chunks=(data.shape[0], 1)),
dims=dims,
coords=coords,
).to_dataset()
return ds
f = h5py.File("allensdk_cache/session_715093703/probe_810755797_lfp.nwb", "r")
ts_ds = build_dataset(
f,
"acquisition/probe_810755797_lfp_data/data",
{
"time": "acquisition/probe_810755797_lfp_data/timestamps",
"channel": "acquisition/probe_810755797_lfp_data/electrodes",
},
).isel(channel=[0, 1, 2, 3, 4])
ts_dt = pyramid_create(
ts_ds,
factors=[1, 2],
dims=["time"],
func=apply_downsample,
type_label="pick",
method_label="pyramid_downsample",
)
ts_dt |
droumis
changed the title
droumis
changed the title
|
The PR carbonplan/ndpyramid#120 is now merged; awaiting next release. |
|
Closing, as this demo is captured in the notebook here via PR 96. Great work @ahuang11, @philippjfr and team |
Problem:
On their own, our current methods like Datashader and downsampling are insufficient for data that cannot be fully loaded into memory.
Description/Solution/Goals:
This project aims to enable effective processing and visualization of biological datasets that exceed available memory limits. The task is to develop a proof of concept for an xarray-datatree-based multi-resolution generator and dynamic accessor. This involves generating and storing incrementally downsampled versions of a large dataset, and then accessing the appropriate resolution copy based on viewport and screen parameters. We want to leverage existing work and standards as much as possible, aligning with the geo and bio communities.
Potential Methods and Tools to Leverage:
Tasks:
Implement a data interface in HoloViews that wraps xarray-datatree and loads the appropriate subset of data automatically given a configurable max data size.Repeat the relevant steps above for the microscopy data use case and dataset.Use-Cases, Starter Viz Code, and Datasets:
Stacked timeseries:
Summary
Code
Data
Note... I recommend working through this notebook on accessing ephys HDF5 Datasets into xarray via Kerchunk and Zarr that Ian created. I can imagine a situation in which the approach to a multiresolution access just utilizes kerchunk references instead of downsampled data copies; although I'm not sure how that would work with xarray-datatree - maybe it would have to be either kerchunk or xarray-datatree, but not both. Maybe we could consult Martin.
Miniscope Image Stack:UPDATE: solved without needing multi-res handlingSummary
Code
Data
Additional Notes and Resources:
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