Ffs simple
from rgrow import Tile, TileSet
import matplotlib.pyplot as plt
import numpy as np
tiles = {f't_{i}_{j}':
Tile(
edges = [f'v_{i}_{j}', f'h_{i}_{j+1}', f'v_{i+1}_{j}', f'h_{i}_{j}'],
name = f't_{i}_{j}',
stoic = 1.0
)
for i in range(0, 20)
for j in range(0, 20)}
for (i, j) in []:
tiles[f't_{i}_{j}'].stoic = 5
tileset = TileSet(
list(tiles.values()),
gse=5.5,
gmc=10.0,
model="kTAM",
fission="keep-weighted",
)
sys = tileset.create_system()
res = tileset.run_ffs(keep_configs=True, target_size=50, max_configs=200)
res
FFSResult(5.4283e-10 M/s, [0.029120559114735003, 0.04484304932735426, 0.10845986984815618, 0.2403846153846154, 0.23201856148491878, 0.390625, 0.23809523809523808, 0.47619047619047616, 0.5586592178770949, 0.4219409282700422, 0.6369426751592356, 0.6410256410256411, 0.5434782608695652, 0.6134969325153374, 0.7518796992481203, 0.6711409395973155, 0.6944444444444444, 0.7575757575757576, 0.7633587786259542, 0.8403361344537815, 0.78125, 0.8, 0.819672131147541, 0.8849557522123894, 0.8771929824561403, 0.8620689655172413, 0.9523809523809523, 0.9803921568627451, 0.8620689655172413, 0.9615384615384616, 0.9615384615384616, 0.9174311926605505, 0.9523809523809523, 0.9803921568627451, 1.0, 0.9803921568627451, 0.9900990099009901, 0.9523809523809523, 0.9803921568627451, 0.9803921568627451, 0.9803921568627451, 1.0, 1.0, 1.0, 0.9615384615384616, 0.9900990099009901, 1.0, 1.0])
plt.plot(2+np.arange(len(res.forward_vec)), res.forward_vec)
plt.xlabel('size')
plt.ylabel('forward probability')
Text(0, 0.5, 'forward probability')
a = np.array(res.forward_vec)
full_prob = a[::-1].cumprod()[::-1]
plt.plot(2+np.arange(len(res.forward_vec)), full_prob)
plt.xlabel('size')
plt.ylabel('finish vs melt probability')
Text(0, 0.5, 'finish vs melt probability')
surfaces = res.surfaces_dataframe()
configs = res.configs_dataframe()
res
FFSResult(5.4283e-10 M/s, [0.029120559114735003, 0.04484304932735426, 0.10845986984815618, 0.2403846153846154, 0.23201856148491878, 0.390625, 0.23809523809523808, 0.47619047619047616, 0.5586592178770949, 0.4219409282700422, 0.6369426751592356, 0.6410256410256411, 0.5434782608695652, 0.6134969325153374, 0.7518796992481203, 0.6711409395973155, 0.6944444444444444, 0.7575757575757576, 0.7633587786259542, 0.8403361344537815, 0.78125, 0.8, 0.819672131147541, 0.8849557522123894, 0.8771929824561403, 0.8620689655172413, 0.9523809523809523, 0.9803921568627451, 0.8620689655172413, 0.9615384615384616, 0.9615384615384616, 0.9174311926605505, 0.9523809523809523, 0.9803921568627451, 1.0, 0.9803921568627451, 0.9900990099009901, 0.9523809523809523, 0.9803921568627451, 0.9803921568627451, 0.9803921568627451, 1.0, 1.0, 1.0, 0.9615384615384616, 0.9900990099009901, 1.0, 1.0])
configs
shape: (4_900, 10)
| surface_index | config_index | size | time | previous_config | canvas | min_i | min_j | shape_i | shape_j |
|---|---|---|---|---|---|---|---|---|---|
| u64 | u64 | u32 | f64 | u64 | list[u32] | u64 | u64 | u64 | u64 |
| 0 | 0 | 2 | 0.0 | 257 | [257, 258] | 32 | 32 | 1 | 2 |
| 0 | 1 | 2 | 0.0 | 206 | [206, 226] | 32 | 32 | 2 | 1 |
| 0 | 2 | 2 | 0.0 | 234 | [214, 234] | 32 | 32 | 2 | 1 |
| 0 | 3 | 2 | 0.0 | 314 | [314, 315] | 32 | 32 | 1 | 2 |
| 0 | 4 | 2 | 0.0 | 72 | [72, 92] | 32 | 32 | 2 | 1 |
| … | … | … | … | … | … | … | … | … | … |
| 48 | 95 | 50 | 1.164999 | 76 | [0, 129, … 0] | 27 | 28 | 8 | 8 |
| 48 | 96 | 50 | 0.687998 | 69 | [0, 209, … 335] | 31 | 28 | 7 | 8 |
| 48 | 97 | 50 | 1.323112 | 57 | [0, 22, … 0] | 29 | 27 | 10 | 7 |
| 48 | 98 | 50 | 1.137091 | 7 | [0, 0, … 0] | 27 | 27 | 8 | 9 |
| 48 | 99 | 50 | 0.649366 | 69 | [209, 210, … 0] | 31 | 29 | 8 | 7 |
surfaces = res.surfaces
surfaces
[FFSLevelRef(n_configs=100, n_trials=100, target_size=2, p_r=1, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=3434, target_size=3, p_r=0.029120559114735003, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=2230, target_size=4, p_r=0.04484304932735426, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=922, target_size=5, p_r=0.10845986984815618, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=416, target_size=6, p_r=0.2403846153846154, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=431, target_size=7, p_r=0.23201856148491878, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=256, target_size=8, p_r=0.390625, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=420, target_size=9, p_r=0.23809523809523808, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=210, target_size=10, p_r=0.47619047619047616, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=179, target_size=11, p_r=0.5586592178770949, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=237, target_size=12, p_r=0.4219409282700422, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=157, target_size=13, p_r=0.6369426751592356, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=156, target_size=14, p_r=0.6410256410256411, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=184, target_size=15, p_r=0.5434782608695652, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=163, target_size=16, p_r=0.6134969325153374, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=133, target_size=17, p_r=0.7518796992481203, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=149, target_size=18, p_r=0.6711409395973155, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=144, target_size=19, p_r=0.6944444444444444, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=132, target_size=20, p_r=0.7575757575757576, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=131, target_size=21, p_r=0.7633587786259542, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=119, target_size=22, p_r=0.8403361344537815, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=128, target_size=23, p_r=0.78125, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=125, target_size=24, p_r=0.8, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=122, target_size=25, p_r=0.819672131147541, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=113, target_size=26, p_r=0.8849557522123894, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=114, target_size=27, p_r=0.8771929824561403, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=116, target_size=28, p_r=0.8620689655172413, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=105, target_size=29, p_r=0.9523809523809523, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=102, target_size=30, p_r=0.9803921568627451, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=116, target_size=31, p_r=0.8620689655172413, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=104, target_size=32, p_r=0.9615384615384616, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=104, target_size=33, p_r=0.9615384615384616, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=109, target_size=34, p_r=0.9174311926605505, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=105, target_size=35, p_r=0.9523809523809523, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=102, target_size=36, p_r=0.9803921568627451, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=100, target_size=37, p_r=1, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=102, target_size=38, p_r=0.9803921568627451, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=101, target_size=39, p_r=0.9900990099009901, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=105, target_size=40, p_r=0.9523809523809523, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=102, target_size=41, p_r=0.9803921568627451, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=102, target_size=42, p_r=0.9803921568627451, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=102, target_size=43, p_r=0.9803921568627451, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=100, target_size=44, p_r=1, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=100, target_size=45, p_r=1, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=100, target_size=46, p_r=1, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=104, target_size=47, p_r=0.9615384615384616, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=101, target_size=48, p_r=0.9900990099009901, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=100, target_size=49, p_r=1, has_stored_states=true),
FFSLevelRef(n_configs=100, n_trials=100, target_size=50, p_r=1, has_stored_states=true)]
surfaces[10].configs
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array([[0, 0, 0, ..., 0, 0, 0],
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array([[0, 0, 0, ..., 0, 0, 0],
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[0, 0, 0, ..., 0, 0, 0]], dtype=uint32)]
state = surfaces[10].states[10]
state
FFSStateRef(n_tiles=12, time=0.048159772254851636 s, events=122, size=(64, 64), total_rate=4851.620447968194)
plt.imshow(state.canvas_view)
<matplotlib.image.AxesImage at 0x7fe574b25af0>
