Grid / pack

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Grid / pack#

Grid#

The gf.grid() function can take a list (or 2D array) of objects and arrange them along a grid. This is often useful for making parameter sweeps. If the separation argument is true, grid is arranged such that the elements are guaranteed not to touch, with a spacing distance between them. If separation is false, elements are spaced evenly along a grid. The align_x/align_y arguments specify intra-row/intra-column alignment. The edge_x/edge_y arguments specify inter-row/inter-column alignment (unused if separation = True).

import gdsfactory as gf

components_list = []
for width1 in [1, 6, 9]:
    for width2 in [1, 2, 4, 8]:
        D = gf.components.taper(length=10, width1=width1, width2=width2, layer=(1, 0))
        components_list.append(D)

c = gf.grid(
    tuple(components_list),
    spacing=(5, 1),
    shape=(3, 4),
    align_x="x",
    align_y="y",
)
c.plot()
../_images/5d4183e778d0cb6841c2dc7efecd0c94884d077aca3356b85a61ce208f73819e.png

Pack#

The gf.pack() function packs geometries together into rectangular bins. If a max_size is specified, the function will create as many bins as is necessary to pack all the geometries and then return a list of the filled-bin Components.

Here we generate several random shapes then pack them together automatically. We allow the bin to be as large as needed to fit all the Components by specifying max_size = (None, None). By setting aspect_ratio = (2,1), we specify the rectangular bin it tries to pack them into should be twice as wide as it is tall:

import numpy as np

import gdsfactory as gf

np.random.seed(5)
D_list = [gf.components.rectangle(size=(i, i)) for i in range(1, 10)]

D_packed_list = gf.pack(
    D_list,  # Must be a list or tuple of Components
    spacing=1.25,  # Minimum distance between adjacent shapes
    aspect_ratio=(2, 1),  # (width, height) ratio of the rectangular bin
    max_size=(None, None),  # Limits the size into which the shapes will be packed
    density=1.05,  # Values closer to 1 pack tighter but require more computation
    sort_by_area=True,  # Pre-sorts the shapes by area
)
D = D_packed_list[0]  # Only one bin was created, so we plot that
D.plot()
../_images/3cc3156280324c0f73291bdaeee9c11e30f72fe2d63b45cae1c084b65cb902a6.png

Say we need to pack many shapes into multiple 500x500 unit die. If we set max_size = (500,500) the shapes will be packed into as many 500x500 unit die as required to fit them all:

np.random.seed(1)
D_list = [
    gf.components.ellipse(radii=tuple(np.random.rand(2) * n + 2)) for n in range(120)
]
D_packed_list = gf.pack(
    D_list,  # Must be a list or tuple of Components
    spacing=4,  # Minimum distance between adjacent shapes
    aspect_ratio=(1, 1),  # Shape of the box
    max_size=(500, 500),  # Limits the size into which the shapes will be packed
    density=1.05,  # Values closer to 1 pack tighter but require more computation
    sort_by_area=True,  # Pre-sorts the shapes by area
)

print(len(D_packed_list))

4

/home/runner/work/gdsfactory/gdsfactory/gdsfactory/pack.py:240: UserWarning: unable to pack in one component, creating 4 components
  warnings.warn(f"unable to pack in one component, creating {groups} components")

c = gf.grid(D_packed_list)
c
../_images/277166b5afd4032d24a13b80b157bcb2902c21ac3bfbf0f21aee427b1d8eab48.png

Note that the packing problem is an NP-complete problem, so gf.pack() may be slow if there are more than a few hundred Components to pack (in that case, try pre-packing a few dozen at a time then packing the resulting bins).

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