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Chunk transfer functions #463

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merged 3 commits into from
Dec 12, 2023
Merged

Chunk transfer functions #463

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150c5a5
test transfer functions > 2147483647 bytes
talonchandler Dec 11, 2023
02af45b
YX chunks for transfer functions
talonchandler Dec 11, 2023
1c6f81e
Revert "test transfer functions > 2147483647 bytes"
talonchandler Dec 11, 2023
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56 changes: 34 additions & 22 deletions recOrder/cli/compute_transfer_function.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@

import click
import numpy as np
from iohub import open_ome_zarr
from iohub.ngff import open_ome_zarr, Position
from waveorder.models import (
inplane_oriented_thick_pol3d,
isotropic_fluorescent_thick_3d,
Expand Down Expand Up @@ -45,13 +45,15 @@ def generate_and_save_birefringence_transfer_function(settings, dataset):
] = intensity_to_stokes_matrix.cpu().numpy()[None, None, None, ...]


def generate_and_save_phase_transfer_function(settings, dataset, zyx_shape):
def generate_and_save_phase_transfer_function(
settings: ReconstructionSettings, dataset: Position, zyx_shape: tuple
):
"""Generates and saves the phase transfer function to the dataset, based on the settings.

Parameters
----------
settings: ReconstructionSettings
dataset: NGFF Node
dataset: Position
The dataset that will be updated.
zyx_shape : tuple
A tuple of integers specifying the input data's shape in (Z, Y, X) order
Expand Down Expand Up @@ -81,12 +83,16 @@ def generate_and_save_phase_transfer_function(settings, dataset, zyx_shape):
)

# Save
dataset[
"absorption_transfer_function"
] = absorption_transfer_function.cpu().numpy()[None, None, ...]
dataset[
"phase_transfer_function"
] = phase_transfer_function.cpu().numpy()[None, None, ...]
dataset.create_image(
"absorption_transfer_function",
absorption_transfer_function.cpu().numpy()[None, None, ...],
chunks=(1, 1, 1, zyx_shape[1], zyx_shape[2]),
)
dataset.create_image(
"phase_transfer_function",
phase_transfer_function.cpu().numpy()[None, None, ...],
chunks=(1, 1, 1, zyx_shape[1], zyx_shape[2]),
)

elif settings.reconstruction_dimension == 3:
# Calculate transfer functions
Expand All @@ -98,25 +104,29 @@ def generate_and_save_phase_transfer_function(settings, dataset, zyx_shape):
**settings.phase.transfer_function.dict(),
)
# Save
dataset[
"real_potential_transfer_function"
] = real_potential_transfer_function.cpu().numpy()[None, None, ...]
dataset[
"imaginary_potential_transfer_function"
] = imaginary_potential_transfer_function.cpu().numpy()[
None, None, ...
]
dataset.create_image(
"real_potential_transfer_function",
real_potential_transfer_function.cpu().numpy()[None, None, ...],
chunks=(1, 1, 1, zyx_shape[1], zyx_shape[2]),
)
dataset.create_image(
"imaginary_potential_transfer_function",
imaginary_potential_transfer_function.cpu().numpy()[
None, None, ...
],
chunks=(1, 1, 1, zyx_shape[1], zyx_shape[2]),
)


def generate_and_save_fluorescence_transfer_function(
settings, dataset, zyx_shape
settings: ReconstructionSettings, dataset: Position, zyx_shape: tuple
):
"""Generates and saves the fluorescence transfer function to the dataset, based on the settings.

Parameters
----------
settings: ReconstructionSettings
dataset: NGFF Node
dataset: Position
The dataset that will be updated.
zyx_shape : tuple
A tuple of integers specifying the input data's shape in (Z, Y, X) order
Expand All @@ -135,9 +145,11 @@ def generate_and_save_fluorescence_transfer_function(
)
)
# Save
dataset[
"optical_transfer_function"
] = optical_transfer_function.cpu().numpy()[None, None, ...]
dataset.create_image(
"optical_transfer_function",
optical_transfer_function.cpu().numpy()[None, None, ...],
chunks=(1, 1, 1, zyx_shape[1], zyx_shape[2]),
)


def compute_transfer_function_cli(
Expand Down