Dataset5dstem - stack reduction, slicing, Nion Swift, match Dataset4dstem API convention

[bobleesj]

What does this PR do?

Closes #149

Adds Dataset5dstem class for 5D-STEM data (time series, tilt series, dose fractionation, etc.).

# Construction
Dataset5dstem.from_file(file_path, file_type=None, **kwargs)
Dataset5dstem.from_array(array, name=None, origin=None, sampling=None, units=None,
                         signal_units="arb. units", stack_type="generic", stack_values=None)
Dataset5dstem.from_4dstem(datasets, stack_type="generic", stack_values=None, name=None)

# Properties
data.shape              # (frames, scan_row, scan_col, k_row, k_col)
data.array              # underlying numpy array
data.stack_type         # "time", "tilt", "energy", "dose", "focus", "generic"
data.stack_values       # explicit values for stack axis (or None)
data.virtual_images     # dict[str, Dataset3d] of cached virtual images
data.virtual_detectors  # dict[str, dict] of detector configs
data.origin             # origin for each dimension
data.sampling           # sampling for each dimension
data.units              # units for each dimension
len(data)               # number of frames

# Diffraction pattern properties -> Dataset3d (frames, k_row, k_col)
data.dp_mean            # mean DP per frame
data.dp_max             # max DP per frame
data.dp_median          # median DP per frame

# Indexing - shape (4, 6, 7, 3, 5) example
data[2]              # -> Dataset4dstem (6, 7, 3, 5) single frame
data[1:3]            # -> Dataset5dstem (2, 6, 7, 3, 5) substack
data[:, 4, 1]        # -> Dataset3d (4, 3, 5) single scan position
data[:, 1:5, 2:6]    # -> Dataset5dstem (4, 4, 4, 3, 5) scan crop
data[..., 0:2, 1:4]  # -> Dataset5dstem (4, 6, 7, 2, 3) k-space crop

# Iteration
for frame in data:   # yields Dataset4dstem

# Stack reductions -> Dataset4dstem
data.stack_mean()
data.stack_sum()
data.stack_max()
data.stack_min()
data.stack_std()

# Virtual imaging -> Dataset3d (frames, scan_row, scan_col)
data.get_virtual_image(mask=None, mode=None, geometry=None, name="virtual_image", attach=True)
# mode="circle":  geometry=((cy, cx), radius) or (None, radius) for auto center
# mode="annular": geometry=((cy, cx), (r_inner, r_outer)) or (None, (r_inner, r_outer))
# Full auto:      geometry=None  # auto-detects both center and radius
# Per-frame:      geometry=[((cy0, cx0), r0), ((cy1, cx1), r1), ...]

# Virtual image management
data.show_virtual_images(figsize=None, **kwargs)
data.regenerate_virtual_images()
data.update_virtual_detector(name, mask=None, mode=None, geometry=None)
data.clear_virtual_images()
data.clear_all_virtual_data()

# Visualization
data.show()  # raises NotImplementedError - use data[i].dp_mean.show() instead

# Binning
data.bin(2)                   # bins all dims including stack
data.bin(2, axes=(1,2,3,4))   # preserves stack

# Copy
data.copy()

# String representations
repr(data)  # Dataset5dstem(shape=..., dtype=..., stack_type='...')
str(data)   # multi-line formatted output

What should the PR reviewer do?

This PR is built atop of #150.

Demo:

data = read_5dstem("/Users/macbook/data/toma/data_PULENC5R0VFJP4SDI1SX6EVZC.h5")
print(data)

Prints

Dataset5dstem '5D-STEM dataset'
  shape: (10, 512, 512, 12, 12) (10 frames)
  stack_type: 'time'
  scan sampling: [0.00390625 0.00390625] ['nm', 'nm']
  k sampling: [0.00612091 0.00612091] ['rad', 'rad']

I will ask for review @gvarnavi after #150 merged and share a notebook.