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(457.83810271602636), np.float64(48.50287781525401))

[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
0 457.838103 257.010232 289.378226
1 457.295253 259.072942 289.627120
2 457.760178 260.182073 290.268666
3 453.995201 257.189940 285.613583 
[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

/tmp/ipykernel_2866/2414770940.py:2: FutureWarning: `RegionProperties.equivalent_diameter` is deprecated starting in version 0.26 and will be removed in version 2.0. Use `RegionProperties.equivalent_diameter_area` instead.
  pr[1].equivalent_diameter

[8]:

np.float64(195.49311541527658)

[9]:

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

/tmp/ipykernel_2866/3781991249.py:1: FutureWarning: `RegionProperties.equivalent_diameter` is deprecated starting in version 0.26 and will be removed in version 2.0. Use `RegionProperties.equivalent_diameter_area` instead.
  max_diameter = pr[0].equivalent_diameter

[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 0x7a971c51afd0>)
../_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 0x7a971779c7d0>
../_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_2866/533071019.py:2: RuntimeWarning: Mean of empty slice
  g = img[t, 0][labels[t] == roi].mean() - bgs["G"][t]
/home/docs/checkouts/readthedocs.org/user_builds/nima/envs/latest/lib/python3.13/site-packages/numpy/_core/_methods.py:142: 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 0x7a971c254cd0>]
../_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/docs/checkouts/readthedocs.org/user_builds/nima/envs/latest/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 0x7a9714fd4410>
../_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 0x7a9714d77390>
../_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 0x7a9714df9450>)
../_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 0x7a9714d407d0>)
../_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_2866/2721790190.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")

~/checkouts/readthedocs.org/user_builds/nima/envs/latest/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, superres, out, out_inplace, _multifile, _useframes, **kwargs)
   1168         ):
   1169             files = files[0]
   1170
   1171         if isinstance(files, str) or not isinstance(files, Sequence):
-> 1172             with TiffFile(
   1173                 files,
   1174                 mode=mode,
   1175                 name=name,

~/checkouts/readthedocs.org/user_builds/nima/envs/latest/lib/python3.13/site-packages/tifffile/tifffile.py in ?(self, file, mode, name, offset, size, omexml, superres, _multifile, _useframes, _parent, **is_flags)
   4276                 raise ValueError(msg)
   4277             self._omexml = omexml
   4278             self.is_ome = True
   4279
-> 4280         fh = FileHandle(file, mode=mode, name=name, offset=offset, size=size)
   4281         self._fh = fh
   4282         self._multifile = True if _multifile is None else bool(_multifile)
   4283         self._files = {fh.name: self}

~/checkouts/readthedocs.org/user_builds/nima/envs/latest/lib/python3.13/site-packages/tifffile/tifffile.py in ?(self, file, mode, name, offset, size)
  13530         self._offset = -1 if offset is None else offset
  13531         self._size = -1 if size is None else size
  13532         self._close = True
  13533         self._lock = NullContext()
> 13534         self.open()
  13535         assert self._fh is not None

~/checkouts/readthedocs.org/user_builds/nima/envs/latest/lib/python3.13/site-packages/tifffile/tifffile.py in ?(self)
  13550                 msg = f'invalid mode {self._mode}'
  13551                 raise ValueError(msg)
  13552             self._file = os.path.realpath(self._file)
  13553             self._dir, self._name = os.path.split(self._file)
> 13554             self._fh = open(  # noqa: SIM115
  13555                 self._file, self._mode, encoding=None
  13556             )
  13557             self._close = True

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_2866/1736577656.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")

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[58], line 1
----> 1 hv.save(chans, "a.png")

File ~/checkouts/readthedocs.org/user_builds/nima/envs/latest/lib/python3.13/site-packages/holoviews/util/__init__.py:852, in save(obj, filename, fmt, backend, resources, toolbar, title, **kwargs)
    850     from ..plotting.bokeh.util import silence_warnings
    851     with silence_warnings(FIXED_SIZING_MODE):
--> 852         return renderer_obj.save(obj, filename, fmt=fmt, resources=resources, title=title)
    853 return renderer_obj.save(obj, filename, fmt=fmt, resources=resources, title=title)

File ~/checkouts/readthedocs.org/user_builds/nima/envs/latest/lib/python3.13/site-packages/holoviews/plotting/renderer.py:599, in Renderer.save(self_or_cls, obj, basename, fmt, key, info, options, resources, title, **kwargs)
    596     plot.layout.save(basename, embed=True, resources=resources, title=title)
    597     return
--> 599 rendered = self_or_cls(plot, fmt)
    600 if rendered is None: return
    601 (_data, info) = rendered

File ~/checkouts/readthedocs.org/user_builds/nima/envs/latest/lib/python3.13/site-packages/holoviews/plotting/renderer.py:198, in Renderer.__call__(self, obj, fmt, **kwargs)
    196     return self.static_html(plot), info
    197 else:
--> 198     data = self._figure_data(plot, fmt, **kwargs)
    199     data = self._apply_post_render_hooks(data, obj, fmt)
    200     return data, info

File ~/checkouts/readthedocs.org/user_builds/nima/envs/latest/lib/python3.13/site-packages/holoviews/plotting/bokeh/renderer.py:127, in BokehRenderer._figure_data(self, plot, fmt, doc, as_script, **kwargs)
    125 elif fmt == 'png':
    126     from bokeh.io.export import get_screenshot_as_png
--> 127     img = get_screenshot_as_png(plot.state, driver=state.webdriver)
    128     imgByteArr = BytesIO()
    129     img.save(imgByteArr, format='PNG')

File ~/checkouts/readthedocs.org/user_builds/nima/envs/latest/lib/python3.13/site-packages/bokeh/io/export.py:275, in get_screenshot_as_png(obj, driver, timeout, resources, width, height, scale_factor, state)
    272         raise ValueError(f'Expected the web driver to have a device pixel ratio greater than {scale_factor}. '
    273                          f'Was given a web driver with a device pixel ratio of {device_pixel_ratio}.')
    274 else:
--> 275     web_driver = webdriver_control.get(scale_factor=scale_factor)
    276 web_driver.maximize_window()
    277 web_driver.get(f"file://{tmp.path}")

File ~/checkouts/readthedocs.org/user_builds/nima/envs/latest/lib/python3.13/site-packages/bokeh/io/webdriver.py:176, in _WebdriverState.get(self, scale_factor)
    173 if not self.reuse or self.current is None or not scale_factor_less_than_web_driver_device_pixel_ratio(
    174         scale_factor, self.current):
    175     self.reset()
--> 176     self.current = self.create(scale_factor=scale_factor)
    177 return self.current

File ~/checkouts/readthedocs.org/user_builds/nima/envs/latest/lib/python3.13/site-packages/bokeh/io/webdriver.py:180, in _WebdriverState.create(self, kind, scale_factor)
    179 def create(self, kind: DriverKind | None = None, scale_factor: float = 1) -> WebDriver:
--> 180     driver = self._create(kind, scale_factor=scale_factor)
    181     self._drivers.add(driver)
    182     return driver

File ~/checkouts/readthedocs.org/user_builds/nima/envs/latest/lib/python3.13/site-packages/bokeh/io/webdriver.py:198, in _WebdriverState._create(self, kind, scale_factor)
    195         self.kind = "firefox"
    196         return driver
--> 198     raise RuntimeError("Neither firefox and geckodriver nor a variant of chromium browser and " \
    199                        "chromedriver are available on system PATH. You can install the former " \
    200                        "with 'conda install -c conda-forge firefox geckodriver'.")
    201 elif driver_kind == "chromium":
    202     return create_chromium_webdriver(scale_factor=scale_factor)

RuntimeError: Neither firefox and geckodriver nor a variant of chromium browser and chromedriver are available on system PATH. You can install the former with 'conda install -c conda-forge firefox geckodriver'.

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_2866/1764924339.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_2866/3523771648.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_2866/3640934118.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_2866/1261574846.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_2866/2279777199.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_2866/3322840880.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_2866/4117421567.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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