4. dev with dask#

[1]:

from collections import defaultdict

import dask.array as da
import holoviews as hv
import hvplot
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import skimage
import tifffile as tff
from scipy import ndimage

from nima import nima, utils

%load_ext autoreload
%autoreload 2

fp = "../../tests/data/1b_c16_15.tif"

[2]:

daimg = da.from_zarr(tff.imread(fp, aszarr=True))
daimg

[2]:
Array Chunk
Bytes 6.00 MiB 512.00 kiB
Shape (4, 3, 512, 512) (1, 1, 512, 512)
Dask graph 12 chunks in 2 graph layers
Data type uint16 numpy.ndarray
4 1 512 512 3 
[3]:

utils.bg(daimg[0, 0].compute())

[3]:

(np.float64(457.8380994040984), np.float64(48.50287340564226))

[4]:

def dabg(daimg):
    r = defaultdict(list)
    n_t, n_c = daimg.shape[:2]
    for t in range(n_t):
        for c in range(n_c):
            r[c].append(utils.bg(daimg[t, c].compute())[0])
    return pd.DataFrame(r)


dabg(daimg)

[4]:
0 1 2
0 457.838099 257.010244 289.378226
1 457.295254 259.072941 289.627118
2 457.760167 260.182049 290.268666
3 453.995203 257.189940 285.613624 
[5]:

def dabg_fg(daimg, erf_pvalue=1e-100, size=10):
    n_t, n_c = daimg.shape[:2]
    bgs = defaultdict(list)
    fgs = defaultdict(list)
    for t in range(n_t):
        p = np.ones(daimg.shape[-2:])
        multichannel = daimg[t].compute()
        for c in range(n_c):
            av, sd = utils.bg(multichannel[c])
            p = p * utils.prob(multichannel[c], av, sd)
            bgs[c].append(av)
        mask = ndimage.median_filter((p) ** (1 / n_c), size=size) < erf_pvalue
        for c in range(n_c):
            fgs[c].append(np.ma.mean(np.ma.masked_array(multichannel[c], mask=~mask)))
    return pd.DataFrame(bgs), pd.DataFrame(fgs)


dfb, dff = dabg_fg(daimg)

[6]:

plt.subplot(121)
((dff - dfb)[0] / (dff - dfb)[2]).plot(marker="s")
plt.grid()
plt.subplot(122)
((dff - dfb)[2] / (dff - dfb)[1]).plot(marker="o")
plt.grid()
../_images/tutorials_dev_xr_hvplot_holoviews_6_0.png

NEXT:

  • make utils.bg and utils.prob work with dask arrays

[7]:

def dmask(daim, erf_pvalue=1e-100, size=10):
    n_c = daim.shape[0]
    im = daim[0].compute()
    p = utils.prob(im, *utils.bg(im))
    for c in range(1, n_c):
        im = daim[c].compute()
        p = p * utils.prob(im, *utils.bg(im))
    p = ndimage.median_filter((p) ** (1 / n_c), size=size)
    mask = p < erf_pvalue
    return skimage.morphology.remove_small_objects(mask)
    # mask = skimage.morphology.remove_small_holes(mask)
    # return np.ma.masked_array(plane, mask=~mask), np.ma.masked_array(plane, mask=mask)


mask = dmask(daimg[2])

lab, nlab = ndimage.label(mask)
lab, nlab

[7]:

(array([[0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        ...,
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0]], shape=(512, 512), dtype=int32),
 2)

[8]:

pr = skimage.measure.regionprops(lab, intensity_image=daimg[0][0])
pr[1].equivalent_diameter

[8]:

np.float64(195.49311541527658)

[9]:

max_diameter = pr[0].equivalent_diameter
size = int(max_diameter * 0.3)
size

[9]:

47

[10]:

t = 0
mask = dmask(daimg[t])
# skimage.io.imshow(mask)
lab, nlab = ndimage.label(mask)

distance = ndimage.distance_transform_edt(mask)
# distance = skimage.filters.gaussian(distance, sigma=0)   min_distance=size,
coords = skimage.feature.peak_local_max(
    distance, footprint=np.ones((size, size)), labels=lab
)
mm = np.zeros(distance.shape, dtype=bool)
mm[tuple(coords.T)] = True
# markers, _ = ndimage.label(mm)
markers = skimage.measure.label(mm)

labels = skimage.segmentation.watershed(-distance, markers, mask=mask)

_, (ax0, ax1, ax2) = plt.subplots(1, 3)
ax0.imshow(distance)
ax1.imshow(labels)
ax2.imshow(labels == 3)
coords

[10]:

array([[122, 329],
       [122, 510],
       [475, 125],
       [341, 116],
       [421,   1]])
../_images/tutorials_dev_xr_hvplot_holoviews_11_1.png 
[11]:

masks = [dmask(daimg[t]) for t in range(4)]

[12]:

masks = np.stack(masks)
masks.shape

[12]:

(4, 512, 512)

[13]:

tff.imshow(masks)

[13]:

(<Figure size 988.8x604.8 with 3 Axes>,
 <Axes: >,
 <matplotlib.image.AxesImage at 0x7f8ecc9aa0d0>)
../_images/tutorials_dev_xr_hvplot_holoviews_14_1.png 
[14]:

distance = ndimage.distance_transform_edt(masks)

distance = skimage.filters.gaussian(distance, sigma=5)

[15]:

import impy

impy.array(distance).imshow()
../_images/tutorials_dev_xr_hvplot_holoviews_16_0.png 
[15]:
nameNo name
shape4(t), 512(y), 512(x)
label shapeNo label
dtypefloat64
sourceNone
scaleScaleView(t=1.0000, y=1.0000, x=1.0000)
[16]:

for t in range(4):
    coords = skimage.feature.peak_local_max(distance[t], footprint=np.ones((130, 130)))
    print(coords)

[[114 346]
 [473 128]
 [344 110]]
[[114 346]
 [473 128]
 [344 110]]
[[114 346]
 [473 128]
 [344 110]]
[[114 346]
 [473 128]
 [344 110]]

[17]:

co = np.stack([coords, coords, coords, coords])

[18]:

coords.T

[18]:

array([[114, 473, 344],
       [346, 128, 110]])

[19]:

mm = np.zeros(masks[0].shape, dtype=bool)
mm[tuple(co.T)] = True
# markers, _ = ndimage.label(mm)
markers = skimage.measure.label(np.stack([mm, mm, mm, mm]))

labels = skimage.segmentation.watershed(-distance, markers, mask=masks)

_, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(labels[3])
ax2.imshow(labels[3] == 4)

[19]:

<matplotlib.image.AxesImage at 0x7f8ecc2c3c50>
../_images/tutorials_dev_xr_hvplot_holoviews_20_1.png 
[20]:

img = tff.imread(fp)

[21]:

dim, _, _ = nima.read_tiff(fp, channels=["R", "G", "C"])

(4, 512, 512)

[22]:

res = nima.d_bg(dim)
bgs = res[1]

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[22], line 1
----> 1 res = nima.d_bg(dim)
      2 bgs = res[1]

TypeError: d_bg() missing 1 required positional argument: 'bg_params'

[23]:

def ratio(t, roi):
    g = img[t, 0][labels[t] == roi].mean() - bgs["G"][t]
    r = img[t, 1][labels[t] == roi].mean() - bgs["R"][t]
    c = img[t, 2][labels[t] == roi].mean() - bgs["C"][t]
    return g / c, c / r


ratio(1, 4)

/tmp/ipykernel_2189/533071019.py:2: RuntimeWarning: Mean of empty slice.
  g = img[t, 0][labels[t] == roi].mean() - bgs["G"][t]
/home/runner/work/nima/nima/.venv/lib/python3.13/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[23], line 8
      4     c = img[t, 2][labels[t] == roi].mean() - bgs["C"][t]
      5     return g / c, c / r
----> 8 ratio(1, 4)

Cell In[23], line 2, in ratio(t, roi)
      1 def ratio(t, roi):
----> 2     g = img[t, 0][labels[t] == roi].mean() - bgs["G"][t]
      3     r = img[t, 1][labels[t] == roi].mean() - bgs["R"][t]
      4     c = img[t, 2][labels[t] == roi].mean() - bgs["C"][t]

NameError: name 'bgs' is not defined

[24]:

rph = defaultdict(list)
rcl = defaultdict(list)
for roi in range(1, 5):
    for t in range(4):
        ph, cl = ratio(t, roi)
        rph[roi].append(ph)
        rcl[roi].append(cl)

plt.plot(rph[1])
plt.plot(rph[2])
plt.plot(rph[3])
plt.plot(rph[4])

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[24], line 5
      3 for roi in range(1, 5):
      4     for t in range(4):
----> 5         ph, cl = ratio(t, roi)
      6         rph[roi].append(ph)
      7         rcl[roi].append(cl)

Cell In[23], line 2, in ratio(t, roi)
      1 def ratio(t, roi):
----> 2     g = img[t, 0][labels[t] == roi].mean() - bgs["G"][t]
      3     r = img[t, 1][labels[t] == roi].mean() - bgs["R"][t]
      4     c = img[t, 2][labels[t] == roi].mean() - bgs["C"][t]

NameError: name 'bgs' is not defined

[25]:

plt.plot(rcl[1])
plt.plot(rcl[2])
plt.plot(rcl[3])
plt.plot(rcl[4])

[25]:

[<matplotlib.lines.Line2D at 0x7f8ebfb01810>]
../_images/tutorials_dev_xr_hvplot_holoviews_26_1.png 
[26]:

t = 2
mask = dmask(daimg[t])
# skimage.io.imshow(mask)
lab, nlab = ndimage.label(mask)
lab[~mask] = -1
# lab[lab==1] = -1
labels_ws = skimage.segmentation.random_walker(
    daimg[t, 1].compute(), lab, beta=1e10, mode="bf"
)
# labels_ws = skimage.segmentation.random_walker(-distance, lab, beta=10000, mode="bf")

_, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(labels_ws)
ax2.imshow(labels_ws == 2)

/home/runner/work/nima/nima/.venv/lib/python3.13/site-packages/skimage/segmentation/random_walker_segmentation.py:545: UserWarning: Random walker only segments unlabeled areas, where labels == 0. No zero valued areas in labels were found. Returning provided labels.
  (labels, nlabels, mask, inds_isolated_seeds, isolated_values) = _preprocess(labels)

[26]:

<matplotlib.image.AxesImage at 0x7f8ebfba1950>
../_images/tutorials_dev_xr_hvplot_holoviews_27_2.png 
[27]:

imar = impy.imread(fp)

imar.label_threshold()

[27]:
name1b_c16_15.tif
shape4(t), 3(c), 512(y), 512(x)
dtypeuint16
source../../tests/data/1b_c16_15.tif
scaleScaleView(t=1.0000, c=1.0000, y=0.2000, x=0.2000)
[28]:

imar[:, 2].imshow(label=1)
../_images/tutorials_dev_xr_hvplot_holoviews_29_0.png 
[28]:
name1b_c16_15.tif
shape4(t), 512(y), 512(x)
label shape4(t), 512(y), 512(x)
dtypeuint16
source../../tests/data/1b_c16_15.tif
scaleScaleView(t=1.0000, y=0.2000, x=0.2000)
[29]:

def dmask0(im, erf_pvalue=1e-100, size=10):
    p = utils.prob(im[0], *utils.bg(im[0]))
    for img in im[1:]:
        p = p * utils.prob(img, *utils.bg(img))
    p = ndimage.median_filter((p) ** (1 / len(im)), size=size)
    mask = p < erf_pvalue
    return skimage.morphology.remove_small_objects(mask)

[30]:

dmask0(imar[1])

[30]:

array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]], shape=(512, 512))

[31]:

plt.imshow(skimage.measure.label(mask))

[31]:

<matplotlib.image.AxesImage at 0x7f8ebe8c4190>
../_images/tutorials_dev_xr_hvplot_holoviews_32_1.png 
[32]:

distance = skimage.filters.gaussian(distance, sigma=30)
tff.imshow(distance)

[32]:

(<Figure size 988.8x604.8 with 1 Axes>,
 <Axes: >,
 <matplotlib.image.AxesImage at 0x7f8ebe922210>)
../_images/tutorials_dev_xr_hvplot_holoviews_33_1.png 
[33]:

np.transpose(np.nonzero(skimage.morphology.local_maxima(distance)))

[33]:

array([[  3, 110, 353],
       [  3, 346, 107],
       [  3, 456,  18],
       [  3, 486, 128]])

[34]:

tff.imshow(ndimage.label(mask)[0])

[34]:

(<Figure size 988.8x604.8 with 2 Axes>,
 <Axes: >,
 <matplotlib.image.AxesImage at 0x7f8ebc7a4190>)
../_images/tutorials_dev_xr_hvplot_holoviews_35_1.png 
[35]:

res[1]

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[35], line 1
----> 1 res[1]

NameError: name 'res' is not defined

[36]:

res[2]["G"][2][0]

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[36], line 1
----> 1 res[2]["G"][2][0]

NameError: name 'res' is not defined

[37]:

res[1].plot()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[37], line 1
----> 1 res[1].plot()

NameError: name 'res' is not defined

[38]:

import hvplot.pandas

[39]:

res[1].hvplot()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[39], line 1
----> 1 res[1].hvplot()

NameError: name 'res' is not defined

[40]:

import xarray as xr

[41]:

xim = xr.DataArray(
    data=[dim["G"], dim["R"], dim["C"]],
    dims=["channel", "time", "y", "x"],
    coords={
        "channel": ["Green", "Red", "Cyan"],
        "time": [0, 1, 2, 3],
        "y": range(512),
        "x": range(512),
    },
)

[42]:

import hvplot.xarray

[43]:

xim.sel(time=0, channel="Green").hvplot(width=400, height=300)

[43]:

[44]:

xim.sel(time=0).hvplot(
    width=300,
    subplots=True,
    by="channel",
)

[44]:

[45]:

hvplot.extension(
    "bokeh",
    "matplotlib",
)

[46]:

img = xim.sel(time=0).sel(channel="Red")

[47]:

hvimg = hv.Image(img)
#%%opts Image style(cmap='viridis') #%%opts Image [aspect=1388/1038] #%%output size=300 f = xim.hvplot(frame_width=170, subplots=True, row="channel", col="time", yaxis=False, colorbar=False, xaxis=False) fhv.save(f, "b.png", dpi=200)
[48]:

# %%opts Image [aspect=1388/1038]

f = xim.sel(channel="Red").hvplot(
    frame_width=300,
    frame_height=200,
    subplots=True,
    col="time",
    yaxis=False,
    colorbar=False,
    xaxis=False,
    cmap="Reds",
) + xim.sel(channel="Cyan").hvplot(
    subplots=True, col="time", yaxis=False, colorbar=False, xaxis=False, cmap="Greens"
)
f

[48]:

[49]:

import aicsimageio

aicsimageio.__version__

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[49], line 1
----> 1 import aicsimageio
      3 aicsimageio.__version__

ModuleNotFoundError: No module named 'aicsimageio'

[50]:

reader = aicsimageio.readers.tiff_reader.TiffReader
aim1 = aicsimageio.AICSImage(
    "/home/dati/dt-evolv/data/2022-06-17/images/Vero-Hek/2022-06-14/13080/TimePoint_1/6w-20Xph1-SpikeTest3_A02_s570_w14510D534-71A3-4EB5-B48F-F4331FE96517.tif",
    reader=reader,
)
aim2 = aicsimageio.AICSImage(
    "/home/dati/dt-evolv/data/2022-06-17/images/Vero-Hek/2022-06-14/13080/TimePoint_1/6w-20Xph1-SpikeTest3_A02_s570_w25049D5AC-5888-492F-891D-8BECC1AB67DF.tif",
    reader=reader,
)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[50], line 1
----> 1 reader = aicsimageio.readers.tiff_reader.TiffReader
      2 aim1 = aicsimageio.AICSImage(
      3     "/home/dati/dt-evolv/data/2022-06-17/images/Vero-Hek/2022-06-14/13080/TimePoint_1/6w-20Xph1-SpikeTest3_A02_s570_w14510D534-71A3-4EB5-B48F-F4331FE96517.tif",
      4     reader=reader,
      5 )
      6 aim2 = aicsimageio.AICSImage(
      7     "/home/dati/dt-evolv/data/2022-06-17/images/Vero-Hek/2022-06-14/13080/TimePoint_1/6w-20Xph1-SpikeTest3_A02_s570_w25049D5AC-5888-492F-891D-8BECC1AB67DF.tif",
      8     reader=reader,
      9 )

NameError: name 'aicsimageio' is not defined

[51]:

x1 = aim1.xarray_data
x2 = aim2.xarray_data

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[51], line 1
----> 1 x1 = aim1.xarray_data
      2 x2 = aim2.xarray_data

NameError: name 'aim1' is not defined

[52]:

# Create a new Dataset with new coordinates
ds = xr.Dataset({"c1": x1, "c2": x2})

# Assuming ds is your Dataset
new_coords = {"Frame": [1, 2], "excitation_wavelength": [400, 500]}

# Use assign_coords to set new coordinates
ds_assigned = ds.assign_coords(**new_coords)

ds_assigned

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[52], line 2
      1 # Create a new Dataset with new coordinates
----> 2 ds = xr.Dataset({"c1": x1, "c2": x2})
      4 # Assuming ds is your Dataset
      5 new_coords = {"Frame": [1, 2], "excitation_wavelength": [400, 500]}

NameError: name 'x1' is not defined

[53]:

aim2.metadata[220:230] == aim1.metadata[220:230]

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[53], line 1
----> 1 aim2.metadata[220:230] == aim1.metadata[220:230]

NameError: name 'aim2' is not defined

[54]:

im = x1.to_numpy()[0, 0, 0]

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[54], line 1
----> 1 im = x1.to_numpy()[0, 0, 0]

NameError: name 'x1' is not defined

[55]:

im1 = tff.imread("/home/dati/dt-evolv/data/2022-06-17/flat_w1.tif")
im2 = tff.imread("/home/dati/dt-evolv/data/2022-06-17/flat_w2.tif")

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
/tmp/ipykernel_2189/2721790190.py in ?()
----> 1 im1 = tff.imread("/home/dati/dt-evolv/data/2022-06-17/flat_w1.tif")
      2 im2 = tff.imread("/home/dati/dt-evolv/data/2022-06-17/flat_w2.tif")

~/work/nima/nima/.venv/lib/python3.13/site-packages/tifffile/tifffile.py in ?(files, selection, aszarr, key, series, level, squeeze, maxworkers, buffersize, mode, name, offset, size, pattern, axesorder, categories, imread, imreadargs, sort, container, chunkshape, chunkdtype, axestiled, ioworkers, chunkmode, fillvalue, zattrs, multiscales, omexml, out, out_inplace, _multifile, _useframes, **kwargs)
   1158         ):
   1159             files = files[0]
   1160
   1161         if isinstance(files, str) or not isinstance(files, Sequence):
-> 1162             with TiffFile(
   1163                 files,
   1164                 mode=mode,
   1165                 name=name,

~/work/nima/nima/.venv/lib/python3.13/site-packages/tifffile/tifffile.py in ?(self, file, mode, name, offset, size, omexml, _multifile, _useframes, _parent, **is_flags)
   4240                 raise ValueError('invalid OME-XML')
   4241             self._omexml = omexml
   4242             self.is_ome = True
   4243
-> 4244         fh = FileHandle(file, mode=mode, name=name, offset=offset, size=size)
   4245         self._fh = fh
   4246         self._multifile = True if _multifile is None else bool(_multifile)
   4247         self._files = {fh.name: self}

~/work/nima/nima/.venv/lib/python3.13/site-packages/tifffile/tifffile.py in ?(self, file, mode, name, offset, size)
  13350         self._offset = -1 if offset is None else offset
  13351         self._size = -1 if size is None else size
  13352         self._close = True
  13353         self._lock = NullContext()
> 13354         self.open()
  13355         assert self._fh is not None

~/work/nima/nima/.venv/lib/python3.13/site-packages/tifffile/tifffile.py in ?(self)
  13369             if self._mode not in {'rb', 'r+b', 'wb', 'xb'}:
  13370                 raise ValueError(f'invalid mode {self._mode}')
  13371             self._file = os.path.realpath(self._file)
  13372             self._dir, self._name = os.path.split(self._file)
> 13373             self._fh = open(self._file, self._mode, encoding=None)
  13374             self._close = True
  13375             self._offset = max(0, self._offset)
  13376         elif isinstance(self._file, FileHandle):

FileNotFoundError: [Errno 2] No such file or directory: '/home/dati/dt-evolv/data/2022-06-17/flat_w1.tif'

[56]:

%%opts Image [aspect=1388/1038]

%%opts Image.Cyan style(cmap=plt.cm.Blues)
%%opts Image.Green style(cmap=plt.cm.Greens)
%%opts Image.Red style(cmap=plt.cm.Reds)

/tmp/ipykernel_2189/1736577656.py:1: HoloviewsDeprecationWarning: IPython magic is deprecated and will be removed in version 1.23.0.
  get_ipython().run_cell_magic('opts', 'Image [aspect=1388/1038]', '\n%%opts Image.Cyan style(cmap=plt.cm.Blues)\n%%opts Image.Green style(cmap=plt.cm.Greens)\n%%opts Image.Red style(cmap=plt.cm.Reds)\n')

[57]:

chans = (
    hv.Image(dim["C"][0], group="cyan")
    + hv.Image(dim["G"][2], group="green")
    + hv.Image(dim["R"][1], group="red")
)

chans

[57]:

[58]:

hv.save(chans, "a.png")

5. Holoviews#

[59]:

hv.notebook_extension()
cm = plt.cm.inferno_r
channels = ["G", "R", "C"]
dim, n_ch, times = nima.read_tiff(fp, channels)

dimm = nima.d_median(dim)
f = nima.d_show(dimm, cmap=cm)

/tmp/ipykernel_2189/1764924339.py:1: HoloviewsDeprecationWarning: Calling 'hv.extension()' without arguments is deprecated and will be removed in version 1.23.0, use 'hv.extension("matplotlib")' instead.
  hv.notebook_extension()
ⓘ 
(4, 512, 512)

[60]:

%%opts Image [aspect=512/512]

%%opts Image.Cyan style(cmap=plt.cm.Blues)
%%opts Image.Green style(cmap=plt.cm.Greens)
%%opts Image.Red style(cmap=plt.cm.Reds)

chans = hv.Image(dim['C'][0], group='cyan') \
    + hv.Image(dim['G'][0], group='green') \
    + hv.Image(dim['R'][0], group='red')

chans

/tmp/ipykernel_2189/3523771648.py:1: HoloviewsDeprecationWarning: IPython magic is deprecated and will be removed in version 1.23.0.
  get_ipython().run_cell_magic('opts', 'Image [aspect=512/512]', "\n%%opts Image.Cyan style(cmap=plt.cm.Blues)\n%%opts Image.Green style(cmap=plt.cm.Greens)\n%%opts Image.Red style(cmap=plt.cm.Reds)\n\nchans = hv.Image(dim['C'][0], group='cyan') \\\n    + hv.Image(dim['G'][0], group='green') \\\n    + hv.Image(dim['R'][0], group='red')\n\nchans\n")

[60]:

[61]:

c = [(i, hv.Image(im)) for i, im in enumerate(dim["C"])]
c = hv.HoloMap(c, kdims=["Frame"])
g = [(i, hv.Image(im)) for i, im in enumerate(dim["G"])]
g = hv.HoloMap(g, kdims=["Frame"])
r = [(i, hv.Image(im)) for i, im in enumerate(dim["R"])]
r = hv.HoloMap(r, kdims=["Frame"])

[62]:

%%output holomap='auto'
%%opts Image style(cmap='viridis')
(c + g).select(Frame={0,5,6,7,10,30}).cols(2)

/tmp/ipykernel_2189/3640934118.py:1: HoloviewsDeprecationWarning: IPython magic is deprecated and will be removed in version 1.23.0.
  get_ipython().run_cell_magic('output', "holomap='auto'", "%%opts Image style(cmap='viridis')\n(c + g).select(Frame={0,5,6,7,10,30}).cols(2)\n")
/tmp/ipykernel_2189/1261574846.py:1: HoloviewsDeprecationWarning: IPython magic is deprecated and will be removed in version 1.23.0.
  get_ipython().run_cell_magic('opts', "Image style(cmap='viridis')", '(c + g).select(Frame={0,5,6,7,10,30}).cols(2)\n')

[62]:

[63]:

c[::20].overlay("Frame")

[63]:

[64]:

wl = hv.Dimension("excitation wavelength", unit="nm")
c = c.add_dimension(wl, 1, 458)
g = g.add_dimension(wl, 1, 488)
r = r.add_dimension(wl, 1, 561)

channels = c.clone()
channels.update(g)
channels.update(r)

[65]:

%%opts Image style(cmap='viridis')
%%output size=300
channels[::5].grid(['Frame', 'excitation wavelength'])

/tmp/ipykernel_2189/2279777199.py:1: HoloviewsDeprecationWarning: IPython magic is deprecated and will be removed in version 1.23.0.
  get_ipython().run_cell_magic('opts', "Image style(cmap='viridis')", "%%output size=300\nchannels[::5].grid(['Frame', 'excitation wavelength'])\n")
/tmp/ipykernel_2189/3322840880.py:1: HoloviewsDeprecationWarning: IPython magic is deprecated and will be removed in version 1.23.0.
  get_ipython().run_cell_magic('output', 'size=300', "channels[::5].grid(['Frame', 'excitation wavelength'])\n")

[65]:

[66]:

t = [(i, hv.Image(im)) for i, im in enumerate(dim["C"])]

[67]:

hv.HoloMap([(i, hv.Image(im)) for i, im in enumerate(dim["C"])], kdims=["frame"])

[67]:

[68]:

hv.NdLayout(
    {
        k: hv.HoloMap(
            [(i, hv.Image(im)) for i, im in enumerate(dim[k])], kdims=["frame"]
        )
        for k in dim
    },
    kdims=["channels"],
)[::4]

[68]:

[69]:

%%opts Image (cmap='viridis')
%%opts Image.A [aspect=2]
im = hv.Image(dim["G"][1], bounds=(0, 0, 512, 512))
im2 = hv.Image(dim['C'][1], bounds=(0, 0, 512, 512))
im3 = hv.Image(dimm['C'][1], bounds=(0, 0, 512, 512))
((im * hv.HLine(y=350)) + im.sample(y=350) + (im2 * hv.HLine(y=150)) + im2.sample(y=150) * im3.sample(y=150)).cols(3)

/tmp/ipykernel_2189/4117421567.py:1: HoloviewsDeprecationWarning: IPython magic is deprecated and will be removed in version 1.23.0.
  get_ipython().run_cell_magic('opts', "Image (cmap='viridis')", '%%opts Image.A [aspect=2]\nim = hv.Image(dim["G"][1], bounds=(0, 0, 512, 512))\nim2 = hv.Image(dim[\'C\'][1], bounds=(0, 0, 512, 512))\nim3 = hv.Image(dimm[\'C\'][1], bounds=(0, 0, 512, 512))\n((im * hv.HLine(y=350)) + im.sample(y=350) + (im2 * hv.HLine(y=150)) + im2.sample(y=150) * im3.sample(y=150)).cols(3)\n')

[69]:
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