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[DOC] fixes #48

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balbasty merged 2 commits into from
Jul 9, 2026
Merged

[DOC] fixes #48

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366d8ba
[DOC] fix (cc.map -> cc.ctx.map) + improve readability of some examples
balbasty Jul 9, 2026
66916be
[DOC] fix a couple of wrong references in api index
balbasty Jul 9, 2026
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6 changes: 3 additions & 3 deletions docs/api/index.md
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Original file line number Diff line number Diff line change
Expand Up @@ -88,12 +88,12 @@ We define aliases for these meta-transforms under `cc.ctx`:
options:
heading_level: 3

??? quote "<code>[cc.ctx.exclude](cts/#cornucopia.ctx.exclude) is [cc.RandomizedTransform](special/#cornucopia.special.RandomizedTransform)</code>"
??? quote "<code>[cc.ctx.exclude](cts/#cornucopia.ctx.exclude) is [cc.ExcludeKeysTransform](special/#cornucopia.special.RandomizedTransform)</code>"
::: cornucopia.ctx.exclude
options:
heading_level: 3

??? quote "<code>[cc.ctx.consume](cts/#cornucopia.ctx.consume) is [cc.ExcludeKeysTransform](special/#cornucopia.special.ExcludeKeysTransform)</code>"
??? quote "<code>[cc.ctx.consume](cts/#cornucopia.ctx.consume) is [cc.ConsumeKeysTransform](special/#cornucopia.special.ExcludeKeysTransform)</code>"
::: cornucopia.ctx.consume
options:
heading_level: 3
Expand Down Expand Up @@ -641,6 +641,6 @@ We define aliases for these meta-transforms under `cc.ctx`:
heading_level: 3

??? quote "<code>[cc.SynthFromLabelTransform](intensity/#cornucopia.synth.SynthFromLabelTransform)(...)</code> <br/>Synthesize an MRI from an existing label map."
::: cornucopia.synth.IntensityTransform
::: cornucopia.synth.SynthFromLabelTransform
options:
heading_level: 3
24 changes: 14 additions & 10 deletions docs/start.md
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Expand Up @@ -240,25 +240,28 @@ This is done using the `cc.MappedTransform` meta-transform (or the
`cc.ctx.map` utility function):

```python
# using positionals
geom = cc.RandomElasticTransform()
noise = cc.GaussianNoiseTransform()
trf = cc.SequentialTranform([geom, cc.map(noise, None)])

# using positionals
trf = cc.SequentialTranform([
geom, # apply `geom` to all tensors
cc.ctx.map(noise, None) # apply `noise` to the first tensor only
])
img, lab = trf(img, lab)

# using keywords
geom = cc.RandomElasticTransform()
noise = cc.GaussianNoiseTransform()
trf = cc.SequentialTranform([geom, cc.map(image=noise)])
trf = cc.SequentialTranform([
geom, # apply `geom` to all tensors
cc.ctx.map(image=noise) # apply `noise` to the `image` key.
])
img, lab = trf(image=img, label=lab)

# using dictionaries
dat = [dict(image=img1, label=lab1), dict(image=img2, label=lab2)]
geom = cc.RandomElasticTransform()
noise = cc.GaussianNoiseTransform()
trf = cc.SequentialTranform([
geom,
cc.map(image=noise, nested=True) # !! must be `nested`
cc.ctx.map(image=noise, nested=True) # !! must be `nested`
])
dat = trf(dat)
```
Expand All @@ -269,11 +272,12 @@ Alternatively, a transform can be applied selectively to a set of keys
```python
geom = cc.RandomElasticTransform()
noise = cc.GaussianNoiseTransform()

# Only apply `noise` to the `image` key
trf = cc.SequentialTranform([geom, cc.ctx.include(noise, "image")])
img, lab = trf(image=img, label=lab)

geom = cc.RandomElasticTransform()
noise = cc.GaussianNoiseTransform()
# Apply `noise` to all tensors _except_ the `label` key.
trf = cc.SequentialTranform([geom, cc.ctx.exclude(noise, "label")])
img, lab = trf(image=img, label=lab)
```
Expand Down
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