7618398226

Committed 22 Jan 2024 10:53PM UTC coverage: 98.47%. Remained the same

Build # 7618398226

Build Type

push

github

Committed by

web-flow

Commit Message

Bump pillow from 10.1.0 to 10.2.0 in /tests (#718)

Bumps [pillow](https://github.com/python-pillow/Pillow) from 10.1.0 to 10.2.0.
- [Release notes](https://github.com/python-pillow/Pillow/releases)
- [Changelog](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst)
- [Commits](https://github.com/python-pillow/Pillow/compare/10.1.0...10.2.0)

---
updated-dependencies:
- dependency-name: pillow
  dependency-type: direct:production
...

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Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

Run Details

3475 of 3529 relevant lines covered (98.47%)

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100.0

/foolbox/plot.py

from typing import Tuple, Any, Optional
import numpy as np
import eagerpy as ep


def images(
    images: Any,
    *,
    n: Optional[int] = None,
    data_format: Optional[str] = None,
    bounds: Tuple[float, float] = (0, 1),
    ncols: Optional[int] = None,
    nrows: Optional[int] = None,
    figsize: Optional[Tuple[float, float]] = None,
    scale: float = 1,
    **kwargs: Any,
) -> None:
    import matplotlib.pyplot as plt

    x: ep.Tensor = ep.astensor(images)
    if x.ndim != 4:
        raise ValueError(
            "expected images to have four dimensions: (N, C, H, W) or (N, H, W, C)"
        )
    if n is not None:
        x = x[:n]
    if data_format is None:
        channels_first = x.shape[1] == 1 or x.shape[1] == 3
        channels_last = x.shape[-1] == 1 or x.shape[-1] == 3
        if channels_first == channels_last:
            raise ValueError("data_format ambigous, please specify it explicitly")
    else:
        channels_first = data_format == "channels_first"
        channels_last = data_format == "channels_last"
        if not channels_first and not channels_last:
            raise ValueError(
                "expected data_format to be 'channels_first' or 'channels_last'"
            )
    assert channels_first != channels_last
    x_np = x.numpy()
    if channels_first:
        x_np = np.transpose(x_np, axes=(0, 2, 3, 1))
    min_, max_ = bounds
    x_np = (x_np - min_) / (max_ - min_)

    if nrows is None and ncols is None:
        nrows = 1
    if ncols is None:
        assert nrows is not None
        ncols = (len(x_np) + nrows - 1) // nrows
    elif nrows is None:
        nrows = (len(x_np) + ncols - 1) // ncols
    if figsize is None:
        figsize = (ncols * scale, nrows * scale)
    fig, axes = plt.subplots(
        ncols=ncols,
        nrows=nrows,
        figsize=figsize,
        squeeze=False,
        constrained_layout=True,
        **kwargs,
    )

    for row in range(nrows):
        for col in range(ncols):
            ax = axes[row][col]
            ax.set_xticks([])
            ax.set_yticks([])
            ax.axis("off")
            i = row * ncols + col
            if i < len(x):
                if x_np.shape[-1] == 1:
                    ax.imshow(x_np[i][:, :, 0])
                else:
                    ax.imshow(x_np[i])

1	from typing import Tuple, Any, Optional	10✔
2	import numpy as np	10✔
3	import eagerpy as ep	10✔
4
5
6	def images(	10✔
7	images: Any,
8	*,
9	n: Optional[int] = None,
10	data_format: Optional[str] = None,
11	bounds: Tuple[float, float] = (0, 1),
12	ncols: Optional[int] = None,
13	nrows: Optional[int] = None,
14	figsize: Optional[Tuple[float, float]] = None,
15	scale: float = 1,
16	**kwargs: Any,
17	) -> None:
18	import matplotlib.pyplot as plt	8✔
19
20	x: ep.Tensor = ep.astensor(images)	8✔
21	if x.ndim != 4:	8✔
22	raise ValueError(	8✔
23	"expected images to have four dimensions: (N, C, H, W) or (N, H, W, C)"
24	)
25	if n is not None:	8✔
26	x = x[:n]	8✔
27	if data_format is None:	8✔
28	channels_first = x.shape[1] == 1 or x.shape[1] == 3	8✔
29	channels_last = x.shape[-1] == 1 or x.shape[-1] == 3	8✔
30	if channels_first == channels_last:	8✔
31	raise ValueError("data_format ambigous, please specify it explicitly")	8✔
32	else:
33	channels_first = data_format == "channels_first"	8✔
34	channels_last = data_format == "channels_last"	8✔
35	if not channels_first and not channels_last:	8✔
36	raise ValueError(	8✔
37	"expected data_format to be 'channels_first' or 'channels_last'"
38	)
39	assert channels_first != channels_last	8✔
40	x_np = x.numpy()	8✔
41	if channels_first:	8✔
42	x_np = np.transpose(x_np, axes=(0, 2, 3, 1))	8✔
43	min_, max_ = bounds	8✔
44	x_np = (x_np - min_) / (max_ - min_)	8✔
45
46	if nrows is None and ncols is None:	8✔
47	nrows = 1	8✔
48	if ncols is None:	8✔
49	assert nrows is not None	8✔
50	ncols = (len(x_np) + nrows - 1) // nrows	8✔
51	elif nrows is None:	8✔
52	nrows = (len(x_np) + ncols - 1) // ncols	8✔
53	if figsize is None:	8✔
54	figsize = (ncols * scale, nrows * scale)	8✔
55	fig, axes = plt.subplots(	8✔
56	ncols=ncols,
57	nrows=nrows,
58	figsize=figsize,
59	squeeze=False,
60	constrained_layout=True,
61	**kwargs,
62	)
63
64	for row in range(nrows):	8✔
65	for col in range(ncols):	8✔
66	ax = axes[row][col]	8✔
67	ax.set_xticks([])	8✔
68	ax.set_yticks([])	8✔
69	ax.axis("off")	8✔
70	i = row * ncols + col	8✔
71	if i < len(x):	8✔
72	if x_np.shape[-1] == 1:	8✔
73	ax.imshow(x_np[i][:, :, 0])	8✔
74	else:
75	ax.imshow(x_np[i])	8✔

bethgelab / foolbox / 7618398226

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