iblatlas.flatmaps
Techniques to project the brain volume onto 2D images for visualisation purposes.
Functions
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FIXME Document! Which publication to reference? Are these specifically for flat maps? |
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Vectorized version of the swanson bitmap file. |
Classes
The Allen Atlas flatmap. |
- class FlatMap(flatmap='dorsal_cortex', res_um=25)[source]
Bases:
AllenAtlas
The Allen Atlas flatmap.
- FIXME Document! How are these flatmaps determined? Are they related to the Swansan atlas or is
that something else?
- plot_flatmap(depth=0, volume='annotation', mapping='Allen', region_values=None, ax=None, **kwargs)[source]
Displays the 2D image corresponding to the flatmap.
If there are several depths, by default it will display the first one.
- Parameters:
depth (int) – Index of the depth to display in the flatmap volume (the last dimension).
volume ({'image', 'annotation', 'boundary', 'value'}) –
‘image’ - Allen image volume.
’annotation’ - Allen annotation volume.
’boundary’ - outline of boundaries between all regions.
- ’volume’ - custom volume, must pass in volume of shape BrainAtlas.image.shape as
regions_value argument.
mapping (str, default='Allen') – The brain region mapping to use.
region_values (numpy.array) – An array the shape of the brain atlas image containing custom region values. Used when volume value is ‘volume’.
ax (matplotlib.pyplot.Axes, optional) – A set of axes to plot to.
**kwargs – See matplotlib.pyplot.imshow.
- Returns:
The plotted image axes.
- Return type:
matplotlib.pyplot.Axes
- circles(N=5, atlas=None, display='flat')[source]
- Parameters:
N – number of circles
atlas – brain atlas at 25 m
display – “flat” or “pyramid”
- Returns:
2D map of indices, ap_coordinate, ml_coordinate
- swanson(filename='swanson2allen.npz')[source]
- FIXME Document! Which publication to reference? Are these specifically for flat maps?
Shouldn’t this be made into an Atlas class with a mapping or scaling applied?
- Parameters:
filename –
- swanson_json(filename='swansonpaths.json', remap=True)[source]
Vectorized version of the swanson bitmap file. The vectorized version was generated from swanson() using matlab contour to find the paths for each region. The paths for each region were then simplified using the Ramer Douglas Peucker algorithm https://rdp.readthedocs.io/en/latest/
- Parameters:
filename –
remap –