Tolerancing - Monte Carlo Simulations

[1]:

from optiland.samples.objectives import CookeTriplet
from optiland.tolerancing.core import Tolerancing
from optiland.tolerancing.monte_carlo import MonteCarlo
from optiland.tolerancing.perturbation import DistributionSampler

[2]:

optic = CookeTriplet()
optic.draw()

[2]:

(<Figure size 1000x400 with 1 Axes>, <Axes: xlabel='Z [mm]', ylabel='Y [mm]'>)

../../_images/gallery_tolerancing_monte_carlo_2_1.png

[3]:

tolerancing = Tolerancing(optic)

[4]:

# loop through all surfaces and add perturbations
for k in range(1, 7):
    # X-tilt
    sampler = DistributionSampler("normal", loc=0, scale=0.01)
    tolerancing.add_perturbation("tilt", sampler, surface_number=k, axis="x")

    # Y-tilt
    sampler = DistributionSampler("normal", loc=0, scale=0.01)
    tolerancing.add_perturbation("tilt", sampler, surface_number=k, axis="y")

    # X-decenter
    sampler = DistributionSampler("normal", loc=0, scale=0.1)
    tolerancing.add_perturbation("decenter", sampler, surface_number=k, axis="x")

    # Y-decenter
    sampler = DistributionSampler("normal", loc=0, scale=0.1)
    tolerancing.add_perturbation("decenter", sampler, surface_number=k, axis="y")

Define operands:

[5]:

input_data = {
    "optic": optic,
    "surface_number": -1,
    "Hx": 0,
    "Hy": 1,
    "wavelength": 0.55,
    "num_rays": 5,
}
tolerancing.add_operand("rms_spot_size", input_data, target=0)

input_data = {"optic": optic, "Hx": 0, "Hy": 1, "wavelength": 0.55, "num_rays": 5}
tolerancing.add_operand("OPD_difference", input_data)

input_data = {
    "optic": optic,
    "surface_number": -1,
    "Hx": 0,
    "Hy": 1,
    "Px": 0,
    "Py": 0,
    "wavelength": 0.55,
}
tolerancing.add_operand("real_y_intercept", input_data)

[6]:

monte_carlo = MonteCarlo(tolerancing)

[7]:

monte_carlo.run(num_iterations=1000)

[8]:

monte_carlo.view_histogram(kde=False)

[8]:

(<Figure size 1200x400 with 3 Axes>,
 array([<Axes: xlabel='0: rms spot size', ylabel='Count'>,
        <Axes: xlabel='1: OPD difference', ylabel='Count'>,
        <Axes: xlabel='2: real y intercept', ylabel='Count'>], dtype=object))

../../_images/gallery_tolerancing_monte_carlo_9_1.png

[9]:

monte_carlo.view_cdf()

[9]:

(<Figure size 1200x400 with 3 Axes>,
 array([<Axes: title={'center': '0: rms spot size'}, xlabel='0: rms spot size'>,
        <Axes: title={'center': '1: OPD difference'}, xlabel='1: OPD difference'>,
        <Axes: title={'center': '2: real y intercept'}, xlabel='2: real y intercept'>],
       dtype=object))

../../_images/gallery_tolerancing_monte_carlo_10_1.png

[10]:

monte_carlo.view_heatmap(vmin=-0.2, vmax=0.2, figsize=(10, 10))

[10]:

(<Figure size 1000x1000 with 2 Axes>, <Axes: >)

../../_images/gallery_tolerancing_monte_carlo_11_1.png

Get pandas dataframe:

[11]:

df = monte_carlo.get_results()

[12]:

df.head()

[12]:

	Tilt X, Surface 1	Tilt Y, Surface 1	Decenter X, Surface 1	Decenter Y, Surface 1	Tilt X, Surface 2	Tilt Y, Surface 2	Decenter X, Surface 2	Decenter Y, Surface 2	Tilt X, Surface 3	Tilt Y, Surface 3	...	Tilt Y, Surface 5	Decenter X, Surface 5	Decenter Y, Surface 5	Tilt X, Surface 6	Tilt Y, Surface 6	Decenter X, Surface 6	Decenter Y, Surface 6	0: rms spot size	1: OPD difference	2: real y intercept
0	0.017101	0.004347	0.062023	-0.024010	0.009357	-0.009618	0.177260	0.002227	0.000371	-0.002458	...	0.007942	-0.013222	0.039562	0.004838	0.025309	0.244792	0.066258	0.106817	0.526503	17.950359
1	0.008688	0.007426	-0.065470	-0.059611	0.002687	0.001223	0.032123	0.026973	-0.012476	0.000293	...	-0.002570	-0.138804	0.083596	-0.017014	0.008544	0.079301	-0.048180	0.019086	0.100060	17.579841
2	-0.016516	0.006235	0.011653	0.221212	0.002248	0.011009	0.053946	0.226214	0.011375	-0.011319	...	-0.012501	-0.100817	-0.156901	0.012034	-0.011094	0.054170	-0.021322	0.038273	0.268324	18.949907
3	-0.003050	0.008699	0.001844	0.073871	-0.007099	-0.001430	-0.170168	-0.022760	-0.015863	-0.013193	...	-0.005998	0.137115	-0.079970	0.011045	0.009429	-0.035159	0.147585	0.109370	0.940473	19.710776
4	0.009338	0.009694	-0.079002	-0.250629	0.003639	-0.015144	-0.109551	0.091593	-0.001599	0.005951	...	-0.004353	-0.021168	-0.098273	0.002178	-0.014339	-0.013662	0.050109	0.096594	0.597469	17.468444

5 rows × 27 columns