imaris-ims-file-reader 0.1.6

Class for reading Bitplane imaris files (*.ims)

imaris-ims-file-reader

Imaris file format reader - *.ims

pip install imaris-ims-file-reader

from imaris_ims_file_reader.ims import ims

a = ims(myFile.ims)

# Slice a like a numpy array always with 5 axes to access the highest resolution - level 0 - (t,c,z,y,x)
a[0,0,5,:,:] # Time point 0, Channel 0, z-layer 5

# Slice in 6 axes to designate the desired resolution level to work with - 0 is default and the highest resolution
a[3,0,0,5,:,:] # Resolution Level 3, Time point 0, Channel 0, z-layer 5

print(a.ResolutionLevelLock)
print(a.ResolutionLevels)
print(a.TimePoints)
print(a.Channels)
print(a.shape)
print(a.chunks)
print(a.dtype)
print(a.ndim)

# A 'resolution lock' can be set when making the class which allows for 5 axis slicing that always extracts from that resoltion level
a = ims(myFile.ims,ResolutionLevelLock=3)

# Change ResolutionLevelLock after the class is open
a.change_resolution_lock(2)
print(a.ResolutionLevelLock)

# The 'squeeze_output' option returns arrays in their reduced form similar to a numpy array.  This is True by default to maintain behavior similar to numpy; however, some applications may benefit from predictably returning a 5 axis array.  For example, napari prefers to have outputs with the same number of axes as the input.
a = ims(myFile.ims)
print(a[0,0,0].shape)
#(1024,1024)

a = ims(myFile.ims, squeeze_output=False)
print(a[0,0,0].shape)
#(1,1,1,1024,1024)

Change Log:

v0.1.3:

Class name has been changed to all lowercase ('ims') to be compatible with many other dependent applications.

v0.1.4:

Bug Fix: Issue #4, get_Volume_At_Specific_Resolution does not extract the desired time point and color

v0.1.5:

-Compatibility changes for Napari.

-Default behaviour changed to always return a 5-dim array. squeeze_output=True can be specified to remove all single dims by automatically calling np.squeeze on outputs.

v0.1.6:

-Return default behaviour back to squeeze_output=True so that the reader performance more like a normal numpy array.