From d6f89ddbd3272c6e23f4d9327a1b0016cb442d34 Mon Sep 17 00:00:00 2001
From: s184058 <s184058@student.dtu.dk>
Date: Mon, 19 Feb 2024 15:28:28 +0100
Subject: [PATCH] Slice viz overhaul
---
README.md | 8 +-
docs/index.md | 8 +-
docs/releases.md | 2 +-
docs/viz.md | 2 +-
qim3d/tests/viz/test_img.py | 99 +++++++++---
qim3d/viz/__init__.py | 2 +-
qim3d/viz/img.py | 289 +++++++++++++++++++++++-------------
7 files changed, 278 insertions(+), 132 deletions(-)
diff --git a/README.md b/README.md
index b2d8dd6b..899596a1 100644
--- a/README.md
+++ b/README.md
@@ -32,7 +32,7 @@ vol = qim3d.io.load("path/to/file.tif", virtual_stack=True)
```
## Visualize data
-You can easily check slices from your volume using `slice_viz`
+You can easily check slices from your volume using `slices`
```python
import qim3d
@@ -40,13 +40,13 @@ import qim3d
img = qim3d.examples.fly_150x256x256
# By default shows the middle slice
-qim3d.viz.slice_viz(img)
+qim3d.viz.slices(img)
# Or we can specifly positions
-qim3d.viz.slice_viz(img, position=[0,32,128])
+qim3d.viz.slices(img, position=[0,32,128])
# Parameters for size and colormap are also possible
-qim3d.viz.slice_viz(img, img_width=6, img_height=6, cmap="inferno")
+qim3d.viz.slices(img, img_width=6, img_height=6, cmap="inferno")
```
diff --git a/docs/index.md b/docs/index.md
index b2d702e3..ced94f6c 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -31,7 +31,7 @@ vol = qim3d.io.load("path/to/file.tif", virtual_stack=True)
```
### Visualize data
-You can easily check slices from your volume using `slice_viz`
+You can easily check slices from your volume using `slices`
```python
import qim3d
@@ -39,13 +39,13 @@ import qim3d
img = qim3d.examples.fly_150x256x256
# By default shows the middle slice
-qim3d.viz.slice_viz(img)
+qim3d.viz.slices(img)
# Or we can specifly positions
-qim3d.viz.slice_viz(img, position=[0,32,128])
+qim3d.viz.slices(img, position=[0,32,128])
# Parameters for size and colormap are also possible
-qim3d.viz.slice_viz(img, img_width=6, img_height=6, cmap="inferno")
+qim3d.viz.slices(img, img_width=6, img_height=6, cmap="inferno")
```
diff --git a/docs/releases.md b/docs/releases.md
index 5a72d14c..f50c0240 100644
--- a/docs/releases.md
+++ b/docs/releases.md
@@ -31,7 +31,7 @@ Includes new develoments toward the usability of the library, as well as its int
- For the local thicknes GUI, now it is possible to pass and receive numpy arrays instead of using the upload functionality.
- Improved data loader
- Now the extensions `tif`, `h5` and `txm` are supported.
-- Added `qim3d.viz.slice_viz` for easy slice visualization.
+- Added `qim3d.viz.slices` for easy slice visualization.
- U-net model creation
- Model availabe from `qim3d.models.UNet`
- Data augmentation class at `qim3d.utils.Augmentation`
diff --git a/docs/viz.md b/docs/viz.md
index 5a92ef56..095e9e52 100644
--- a/docs/viz.md
+++ b/docs/viz.md
@@ -12,7 +12,7 @@ import qim3d
members:
- grid_overview
- grid_pred
- - slice_viz
+ - slices
diff --git a/qim3d/tests/viz/test_img.py b/qim3d/tests/viz/test_img.py
index 06fec0d0..17cc3072 100644
--- a/qim3d/tests/viz/test_img.py
+++ b/qim3d/tests/viz/test_img.py
@@ -1,13 +1,12 @@
+import torch
+import numpy as np
import qim3d
-import matplotlib.pyplot as plt
import pytest
-
-from torch import ones
from qim3d.utils.internal_tools import temp_data
# unit tests for grid overview
def test_grid_overview():
- random_tuple = (ones(1,256,256),ones(256,256))
+ random_tuple = (torch.ones(1,256,256),torch.ones(256,256))
n_images = 10
train_set = [random_tuple for t in range(n_images)]
@@ -16,7 +15,7 @@ def test_grid_overview():
def test_grid_overview_tuple():
- random_tuple = (ones(256,256),ones(256,256))
+ random_tuple = (torch.ones(256,256),torch.ones(256,256))
with pytest.raises(ValueError,match="Data elements must be tuples"):
qim3d.viz.grid_overview(random_tuple,num_images=1)
@@ -42,22 +41,88 @@ def test_grid_pred():
# unit tests for slice visualization
-def test_slice_viz():
- example_volume = ones(10,10,10)
+def test_slices_numpy_array_input():
+ example_volume = np.ones((10, 10, 10))
+ img_width = 3
+ fig = qim3d.viz.slices(example_volume, n_slices=1, img_width=img_width)
+ assert fig.get_figwidth() == img_width
+
+def test_slices_torch_tensor_input():
+ example_volume = torch.ones((10,10,10))
img_width = 3
- fig = qim3d.viz.slice_viz(example_volume,n_slices = 1, img_width = img_width)
+ fig = qim3d.viz.slices(example_volume,n_slices = 1, img_width = img_width)
assert fig.get_figwidth() == img_width
+def test_slices_wrong_input_format():
+ input = 'not_a_volume'
+ with pytest.raises(ValueError, match = 'Input must be a numpy.ndarray or torch.Tensor'):
+ qim3d.viz.slices(input)
+
+def test_slices_not_volume():
+ example_volume = np.ones((10,10))
+ with pytest.raises(ValueError, match = 'The provided object is not a volume as it has less than 3 dimensions.'):
+ qim3d.viz.slices(example_volume)
+
+def test_slices_wrong_position_format1():
+ example_volume = np.ones((10,10,10))
+ with pytest.raises(ValueError, match = 'Position not recognized. Choose an integer, list of integers or one of the following strings: "start", "mid" or "end".'):
+ qim3d.viz.slices(example_volume, position = 'invalid_slice')
+
+def test_slices_wrong_position_format2():
+ example_volume = np.ones((10,10,10))
+ with pytest.raises(ValueError, match = 'Position not recognized. Choose an integer, list of integers or one of the following strings: "start", "mid" or "end".'):
+ qim3d.viz.slices(example_volume, position = 1.5)
+
+def test_slices_wrong_position_format3():
+ example_volume = np.ones((10,10,10))
+ with pytest.raises(ValueError, match = 'Position not recognized. Choose an integer, list of integers or one of the following strings: "start", "mid" or "end".'):
+ qim3d.viz.slices(example_volume, position = [1, 2, 3.5])
+
+def test_slices_invalid_axis_value():
+ example_volume = np.ones((10,10,10))
+ with pytest.raises(ValueError, match = "Invalid value for 'axis'. It should be an integer between 0 and 2"):
+ qim3d.viz.slices(example_volume, axis = 3)
+
+def test_slices_show_title_option():
+ example_volume = np.ones((10, 10, 10))
+ img_width = 3
+ fig = qim3d.viz.slices(example_volume, n_slices=1, img_width=img_width, show_title=False)
+ # Assert that titles are not shown
+ assert all(ax.get_title() == '' for ax in fig.get_axes())
-def test_slice_viz_not_volume():
- example_volume = ones(10,10)
- dim = example_volume.ndim
- with pytest.raises(ValueError, match = f"Given array is not a volume! Current dimension: {dim}"):
- qim3d.viz.slice_viz(example_volume)
+def test_slices_interpolation_option():
+ example_volume = torch.ones((10, 10, 10))
+ img_width = 3
+ interpolation_method = 'bilinear'
+ fig = qim3d.viz.slices(example_volume, n_slices=1, img_width=img_width, interpolation=interpolation_method)
+
+ for ax in fig.get_axes():
+ # Access the interpolation method used for each Axes object
+ actual_interpolation = ax.images[0].get_interpolation()
+
+ # Assert that the actual interpolation method matches the expected method
+ assert actual_interpolation == interpolation_method
+
+def test_slices_multiple_slices():
+ example_volume = np.ones((10, 10, 10))
+ img_width = 3
+ n_slices = 3
+ fig = qim3d.viz.slices(example_volume, n_slices=n_slices, img_width=img_width)
+ # Add assertions for the expected number of subplots in the figure
+ assert len(fig.get_axes()) == n_slices
+
+def test_slices_axis_argument():
+ # Non-symmetric input
+ example_volume = np.arange(1000).reshape((10, 10, 10))
+ img_width = 3
+ # Call the function with different values of the axis
+ fig_axis_0 = qim3d.viz.slices(example_volume, n_slices=1, img_width=img_width, axis=0)
+ fig_axis_1 = qim3d.viz.slices(example_volume, n_slices=1, img_width=img_width, axis=1)
+ fig_axis_2 = qim3d.viz.slices(example_volume, n_slices=1, img_width=img_width, axis=2)
-def test_slice_viz_wrong_slice():
- example_volume = ones(10,10,10)
- with pytest.raises(ValueError, match = 'Position not recognized. Choose an integer, list, array or "start","mid","end".'):
- qim3d.viz.slice_viz(example_volume, position = 'invalid_slice')
+ # Ensure that different axes result in different plots
+ assert not np.allclose(fig_axis_0.get_axes()[0].images[0].get_array(), fig_axis_1.get_axes()[0].images[0].get_array())
+ assert not np.allclose(fig_axis_1.get_axes()[0].images[0].get_array(), fig_axis_2.get_axes()[0].images[0].get_array())
+ assert not np.allclose(fig_axis_2.get_axes()[0].images[0].get_array(), fig_axis_0.get_axes()[0].images[0].get_array())
\ No newline at end of file
diff --git a/qim3d/viz/__init__.py b/qim3d/viz/__init__.py
index 1c36c157..98132cca 100644
--- a/qim3d/viz/__init__.py
+++ b/qim3d/viz/__init__.py
@@ -1,2 +1,2 @@
from .visualizations import plot_metrics
-from .img import grid_pred, grid_overview, slice_viz
+from .img import grid_pred, grid_overview, slices
diff --git a/qim3d/viz/img.py b/qim3d/viz/img.py
index 804cdd28..f228800c 100644
--- a/qim3d/viz/img.py
+++ b/qim3d/viz/img.py
@@ -1,15 +1,22 @@
"""
Provides a collection of visualization functions.
"""
+
+from typing import List, Optional, Union
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from matplotlib import colormaps
import torch
import numpy as np
from qim3d.io.logger import log
+import math
import qim3d.io
+import os
+
-def grid_overview(data, num_images=7, cmap_im="gray", cmap_segm="viridis", alpha=0.5, show = False):
+def grid_overview(
+ data, num_images=7, cmap_im="gray", cmap_segm="viridis", alpha=0.5, show=False
+):
"""Displays an overview grid of images, labels, and masks (if they exist).
Labels are the annotated target segmentations
@@ -22,14 +29,14 @@ def grid_overview(data, num_images=7, cmap_im="gray", cmap_segm="viridis", alpha
cmap_im (str, optional): The colormap to be used for displaying input images. Defaults to 'gray'.
cmap_segm (str, optional): The colormap to be used for displaying labels. Defaults to 'viridis'.
alpha (float, optional): The transparency level of the label and mask overlays. Defaults to 0.5.
- show (bool, optional): If True, displays the plot. Defaults to False.
+ show (bool, optional): If True, displays the plot (i.e. calls plt.show()). Defaults to False.
Raises:
ValueError: If the data elements are not tuples.
Returns:
- fig (matplotlib.figure.Figure): The figure with an overview of the images and their labels.
+ fig (matplotlib.figure.Figure): The figure with an overview of the images and their labels.
Example:
```python
@@ -75,7 +82,9 @@ def grid_overview(data, num_images=7, cmap_im="gray", cmap_segm="viridis", alpha
# Make new list such that possible augmentations remain identical for all three rows
plot_data = [data[idx] for idx in range(num_images)]
- fig = plt.figure(figsize=(2 * num_images, 9 if has_mask else 6), constrained_layout=True)
+ fig = plt.figure(
+ figsize=(2 * num_images, 9 if has_mask else 6), constrained_layout=True
+ )
# create 2 (3) x 1 subfigs
subfigs = fig.subfigures(nrows=3 if has_mask else 2, ncols=1)
@@ -92,15 +101,22 @@ def grid_overview(data, num_images=7, cmap_im="gray", cmap_segm="viridis", alpha
else:
ax.imshow(plot_data[col][row].squeeze(), cmap=cmap_im)
ax.axis("off")
-
+
if show:
plt.show()
plt.close()
-
+
return fig
-def grid_pred(in_targ_preds, num_images=7, cmap_im="gray", cmap_segm="viridis", alpha=0.5,show = False):
+def grid_pred(
+ in_targ_preds,
+ num_images=7,
+ cmap_im="gray",
+ cmap_segm="viridis",
+ alpha=0.5,
+ show=False,
+):
"""Displays a grid of input images, predicted segmentations, ground truth segmentations, and their comparison.
Displays a grid of subplots representing different aspects of the input images and segmentations.
@@ -119,7 +135,7 @@ def grid_pred(in_targ_preds, num_images=7, cmap_im="gray", cmap_segm="viridis",
cmap_im (str, optional): Color map for input images. Defaults to "gray".
cmap_segm (str, optional): Color map for segmentations. Defaults to "viridis".
alpha (float, optional): Alpha value for transparency. Defaults to 0.5.
- show (bool, optional): If True, displays the plot. Defaults to False.
+ show (bool, optional): If True, displays the plot (i.e. calls plt.show()). Defaults to False.
Returns:
fig (matplotlib.figure.Figure): The figure with images, labels and the label prediction from the trained models.
@@ -131,9 +147,9 @@ def grid_pred(in_targ_preds, num_images=7, cmap_im="gray", cmap_segm="viridis",
dataset = MySegmentationDataset()
model = MySegmentationModel()
in_targ_preds = qim3d.utils.models.inference(dataset,model)
- grid_pred(in_targ_preds, cmap_im='viridis', alpha=0.5)
+ grid_pred(in_targ_preds, cmap_im='viridis', alpha=0.5)
"""
-
+
# Check if dataset have at least specified number of images
if len(in_targ_preds[0]) < num_images:
log.warning(
@@ -144,21 +160,21 @@ def grid_pred(in_targ_preds, num_images=7, cmap_im="gray", cmap_segm="viridis",
num_images = len(in_targ_preds[0])
# Take only the number of images from in_targ_preds
- inputs,targets,preds = [items[:num_images] for items in in_targ_preds]
-
+ inputs, targets, preds = [items[:num_images] for items in in_targ_preds]
+
# Adapt segmentation cmap so that background is transparent
colors_segm = colormaps.get_cmap(cmap_segm)(np.linspace(0, 1, 256))
colors_segm[:128, 3] = 0
custom_cmap = LinearSegmentedColormap.from_list("CustomCmap", colors_segm)
-
+
N = num_images
H = inputs[0].shape[-2]
W = inputs[0].shape[-1]
- comp_rgb = torch.zeros((N,4,H,W))
- comp_rgb[:,1,:,:] = targets.logical_and(preds)
- comp_rgb[:,0,:,:] = targets.logical_xor(preds)
- comp_rgb[:,3,:,:] = targets.logical_or(preds)
+ comp_rgb = torch.zeros((N, 4, H, W))
+ comp_rgb[:, 1, :, :] = targets.logical_and(preds)
+ comp_rgb[:, 0, :, :] = targets.logical_xor(preds)
+ comp_rgb[:, 3, :, :] = targets.logical_or(preds)
row_titles = [
"Input images",
@@ -187,9 +203,7 @@ def grid_pred(in_targ_preds, num_images=7, cmap_im="gray", cmap_segm="viridis",
ax.axis("off")
elif row == 2: # Ground truth segmentation
ax.imshow(inputs[col], cmap=cmap_im)
- ax.imshow(
- targets[col], cmap=custom_cmap, alpha=alpha
- )
+ ax.imshow(targets[col], cmap=custom_cmap, alpha=alpha)
ax.axis("off")
else:
ax.imshow(inputs[col], cmap=cmap_im)
@@ -202,105 +216,172 @@ def grid_pred(in_targ_preds, num_images=7, cmap_im="gray", cmap_segm="viridis",
return fig
-def slice_viz(input, position = None, n_slices = 5, cmap = "viridis", axis = False, img_height = 4, img_width = 4, show = False):
- """ Displays one or several slices from a 3d array.
- By default if `position` is None, slice_viz plots an overview of the entire stack.
- If `position` is given as a string or integer, slice_viz will plot an overview with `n_slices` figures around that position.
- If `position` is given as a list or array, `n_slices` will be ignored and the idxs from `position` will be plotted.
-
+def slices(
+ vol: Union[np.ndarray, torch.Tensor],
+ axis: int = 0,
+ position: Optional[Union[str, int, List[int]]] = None,
+ n_slices: int = 5,
+ max_cols: int = 5,
+ cmap: str = "viridis",
+ img_height: int = 2,
+ img_width: int = 2,
+ show: bool = False,
+ show_position: bool = True,
+ interpolation: Optional[str] = None,
+) -> plt.Figure:
+ """Displays one or several slices from a 3d volume.
+
+ By default if `position` is None, slices plots `n_slices` linearly spaced slices.
+ If `position` is given as a string or integer, slices will plot an overview with `n_slices` figures around that position.
+ If `position` is given as a list, `n_slices` will be ignored and the slices from `position` will be plotted.
+
Args:
- input (str, numpy.ndarray): Path to the file or 3-dimensional array.
- position (str, int, list, array, optional): One or several slicing levels.
- n_slices (int, optional): Defines how many slices the user wants.
- cmap (str, optional): Specifies the color map for the image.
- axis (bool, optional): Specifies whether the axes should be included.
+ vol (np.ndarray or torch.Tensor): The 3D volume to be sliced.
+ axis (int, optional): Specifies the axis, or dimension, along which to slice. Defaults to 0.
+ position (str, int, list, optional): One or several slicing levels. If None, linearly spaced slices will be displayed. Defaults to None.
+ n_slices (int, optional): Defines how many slices the user wants to be displayed. Defaults to 5.
+ max_cols (int, optional): The maximum number of columns to be plotted. Defaults to 5.
+ cmap (str, optional): Specifies the color map for the image. Defaults to "viridis".
img_height(int, optional): Height of the figure.
img_width(int, optional): Width of the figure.
- show (bool, optional): If True, displays the plot. Defaults to False.
+ show (bool, optional): If True, displays the plot (i.e. calls plt.show()). Defaults to False.
+ show_position (bool, optional): If True, displays the position of the slices. Defaults to True.
+ interpolation (str, optional): Specifies the interpolation method for the image. Defaults to None.
Returns:
fig (matplotlib.figure.Figure): The figure with the slices from the 3d array.
Raises:
- ValueError: If the file or array is not a 3D volume.
- ValueError: If provided string for 'position' argument is not valid (not upper, middle or bottom).
-
+ ValueError: If the input is not a numpy.ndarray or torch.Tensor.
+ ValueError: If the axis to slice along is not a valid choice, i.e. not an integer between 0 and the number of dimensions of the volume minus 1.
+ ValueError: If the file or array is not a volume with at least 3 dimensions.
+ ValueError: If the `position` keyword argument is not a integer, list of integers or one of the following strings: "start", "mid" or "end".
+
Example:
- image_path = '/my_image_path/my_image.tif'
- slice_viz(image_path)
+ vol_path = '/my_vol_path/my_vol.tif'
+ vol = qim3d.io.load(vol_path)
+ slices(vol, axis = 1, position = 'mid', n_slices = 3, cmap = 'viridis', img_height = 4, img_width = 4, show = True, show_position = True, interpolation = None)
"""
-
- # Filepath input
- if isinstance(input,str):
- vol = qim3d.io.load(input) # Function has its own ValueErrors
- dim = vol.ndim
-
- # Numpy array input
- elif isinstance(input,(np.ndarray,torch.Tensor)):
- vol = input
- dim = input.ndim
-
- if dim != 3:
- raise ValueError(f"Given array is not a volume! Current dimension: {dim}")
+ # Numpy array or Torch tensor input
+ if not isinstance(vol, (np.ndarray, torch.Tensor)):
+ raise ValueError("Input must be a numpy.ndarray or torch.Tensor")
+
+ if vol.ndim < 3:
+ raise ValueError(
+ "The provided object is not a volume as it has less than 3 dimensions."
+ )
+
+ # Ensure axis is a valid choice
+ if not (0 <= axis < vol.ndim):
+ raise ValueError(
+ f"Invalid value for 'axis'. It should be an integer between 0 and {vol.ndim - 1}."
+ )
+
+ # Get total number of slices in the specified dimension
+ n_total = vol.shape[axis]
+
+ # Position is not provided - will use linearly spaced slices
if position is None:
- height = np.linspace(0,vol.shape[0]-1,n_slices).astype(int)
-
+ slice_idxs = np.linspace(0, n_total - 1, n_slices, dtype=int)
# Position is a string
- elif isinstance(position,str):
-
- if position.lower() in ['mid','middle']:
- expansion_start = int(vol.shape[0]/2)
- height = np.linspace(expansion_start - n_slices / 2,expansion_start + n_slices / 2,n_slices).astype(int)
-
- elif position.lower() in ['top','upper', 'start']:
- expansion_start = 0
- height = np.linspace(expansion_start,n_slices-1,n_slices).astype(int)
-
- elif position.lower() in ['bot','bottom', 'end']:
- expansion_start = vol.shape[0]-1
- height = np.linspace(expansion_start - n_slices,expansion_start,n_slices).astype(int)
-
- else:
- raise ValueError('Position not recognized. Choose an integer, list, array or "start","mid","end".')
-
-
- # Position is an integer
- elif isinstance(position,int):
- expansion_start = position
- n_stacks = vol.shape[0]-1
-
- # if linspace would extend beyond n_stacks
- if expansion_start + n_slices > n_stacks:
- height = np.linspace(n_stacks - n_slices,n_stacks,n_slices).astype(int)
-
- # if linspace would extend below 0
- elif expansion_start - n_slices < 0:
- height = np.linspace(0,n_slices-1,n_slices).astype(int)
-
- else:
- height = np.linspace(expansion_start - n_slices / 2,expansion_start + n_slices / 2,n_slices).astype(int)
-
-
- # Position is a list or array of integers
- elif isinstance(position,(list,np.ndarray)):
- height = position
-
- num_images = len(height)
-
-
- fig = plt.figure(figsize=(img_width * num_images, img_height), constrained_layout = True)
- axs = fig.subplots(nrows = 1, ncols = num_images)
-
- for col, ax in enumerate(np.atleast_1d(axs)):
- ax.imshow(vol[height[col],:,:],cmap = cmap)
- ax.set_title(f'Slice {height[col]}', fontsize=6*img_height)
- if not axis:
- ax.axis('off')
-
+ elif isinstance(position, str) and position.lower() in ["start", "mid", "end"]:
+ if position.lower() == "start":
+ slice_idxs = _get_slice_range(0, n_slices, n_total)
+ elif position.lower() == "mid":
+ slice_idxs = _get_slice_range(n_total // 2, n_slices, n_total)
+ elif position.lower() == "end":
+ slice_idxs = _get_slice_range(n_total - 1, n_slices, n_total)
+ # Position is an integer
+ elif isinstance(position, int):
+ slice_idxs = _get_slice_range(position, n_slices, n_total)
+ # Position is a list of integers
+ elif isinstance(position, list) and all(isinstance(idx, int) for idx in position):
+ slice_idxs = position
+ else:
+ raise ValueError(
+ 'Position not recognized. Choose an integer, list of integers or one of the following strings: "start", "mid" or "end".'
+ )
+
+
+ # Make grid
+ nrows = math.ceil(n_slices / max_cols)
+ ncols = min(n_slices, max_cols)
+
+ # Generate figure
+ fig, axs = plt.subplots(
+ nrows=nrows,
+ ncols=ncols,
+ figsize=(ncols * img_height, nrows * img_width),
+ constrained_layout=True,
+ )
+ if nrows == 1:
+ axs = [axs] # Convert to a list for uniformity
+
+ # Convert Torch tensor to NumPy array in order to use the numpy.take method
+ if isinstance(vol, torch.Tensor):
+ vol = vol.numpy()
+
+ # Run through each ax of the grid
+ for i, ax_row in enumerate(axs):
+ for j, ax in enumerate(np.atleast_1d(ax_row)):
+ slice_idx = i * max_cols + j
+ try:
+ slice_img = vol.take(slice_idxs[slice_idx], axis=axis)
+ ax.imshow(slice_img, cmap=cmap, interpolation=interpolation)
+
+ if show_position:
+ ax.text(
+ 0.0,
+ 1.0,
+ f"slice {slice_idxs[slice_idx]} ",
+ transform=ax.transAxes,
+ color="white",
+ fontsize=8,
+ va="top",
+ ha="left",
+ bbox=dict(facecolor="#303030", linewidth=0, pad=0),
+ )
+
+ ax.text(
+ 1.0,
+ 0.0,
+ f"axis {axis} ",
+ transform=ax.transAxes,
+ color="white",
+ fontsize=8,
+ va="bottom",
+ ha="right",
+ bbox=dict(facecolor="#303030", linewidth=0, pad=0),
+ )
+
+ except IndexError:
+ # Not a problem, because we simply do not have a slice to show
+ pass
+
+ # Hide the axis, so that we have a nice grid
+ ax.axis("off")
+
if show:
plt.show()
+
plt.close()
- return fig
\ No newline at end of file
+ return fig
+
+
+def _get_slice_range(position: int, n_slices: int, n_total):
+ """Helper function for `slices`. Returns the range of slices to be displayed around the given position."""
+ start_idx = position - n_slices // 2
+ end_idx = (
+ position + n_slices // 2 if n_slices % 2 == 0 else position + n_slices // 2 + 1
+ )
+ slice_idxs = np.arange(start_idx, end_idx)
+
+ if slice_idxs[0] < 0:
+ slice_idxs = np.arange(0, n_slices)
+ elif slice_idxs[-1] > n_total:
+ slice_idxs = np.arange(n_total - n_slices, n_total)
+
+ return slice_idxs
--
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