Leone, Mark A [LGS]
68989ed8e4
Rename plugin funcs to make implemenation more intuitive Add ID for snap. Remove redundant left/right id func. Show err if snap file not found Track per-file snapshot error
199 lines
No EOL
5.4 KiB
Python
199 lines
No EOL
5.4 KiB
Python
import numpy as np
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import re
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import os
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import sys
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import importlib
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lightning_bolt_bot = np.array( [[0,0,0,0,0,0,0], # 0
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[0,0,0,0,0,1,0], # 1
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[0,0,0,0,1,1,0], # 2
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[0,0,0,1,1,0,0], # 3
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[0,0,1,1,1,0,0], # 4
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[0,1,1,1,0,0,0], # 5
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[0,1,1,1,1,1,0], # 6
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[0,0,0,1,1,1,0], # 7
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[0,0,1,1,1,0,0], # 8
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[0,0,1,1,0,0,0], # 9
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[0,1,1,0,0,0,0], #10
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[0,1,0,0,0,0,0], #11
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[0,0,0,0,0,0,0]],#12
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dtype=bool).T
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# Cut out bottom row because top segment is one row shorter
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lightning_bolt_top = np.array( [[0,0,0,0,0,0,0], # 0
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[0,0,0,0,0,1,0], # 1
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[0,0,0,0,1,1,0], # 2
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[0,0,0,1,1,0,0], # 3
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[0,0,1,1,1,0,0], # 4
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[0,1,1,1,0,0,0], # 5
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[0,1,1,1,1,1,0], # 6
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[0,0,0,1,1,1,0], # 7
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[0,0,1,1,1,0,0], # 8
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[0,0,1,1,0,0,0], # 9
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[0,1,1,0,0,0,0], #10
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[0,1,0,0,0,0,0]],#11
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dtype=bool).T
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# This table represents the 3x3 grid of LEDs to be drawn for each fill ratio
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lookup_table = np.array(
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[
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[
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[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0]
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],
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[
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[0, 0, 0],
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[0, 1, 0],
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[0, 0, 0]
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],
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[
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[0, 1, 0],
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[0, 1, 0],
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[0, 0, 0]
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],
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[
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[0, 1, 1],
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[0, 1, 0],
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[0, 0, 0]
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],
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[
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[0, 1, 1],
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[0, 1, 1],
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[0, 0, 0]
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],
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[
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[0, 1, 1],
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[0, 1, 1],
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[0, 0, 1]
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],
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[
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[0, 1, 1],
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[0, 1, 1],
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[0, 1, 1]
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],
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[
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[0, 1, 1],
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[0, 1, 1],
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[1, 1, 1]
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],
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[
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[0, 1, 1],
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[1, 1, 1],
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[1, 1, 1]
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],
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[
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[1, 1, 1],
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[1, 1, 1],
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[1, 1, 1]
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]
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]
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)
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id_patterns = {
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"cpu": np.array([
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[0, 0, 1, 1, 1, 0, 0],
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[0, 0, 1, 0, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 0],
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[0, 0, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 0, 0, 0],
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[0, 0, 1, 0, 1, 0, 0],
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[0, 0, 1, 1, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 0, 1, 0, 0],
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[0, 0, 1, 0, 1, 0, 0],
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[0, 0, 1, 0, 1, 0, 0],
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[0, 0, 1, 1, 1, 0, 0]
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]).T,
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"mem-bat": np.array([
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[0, 1, 1, 0, 1, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 0, 0, 0],
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[0, 0, 1, 0, 1, 0, 0],
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[0, 0, 1, 1, 0, 0, 0],
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[0, 0, 1, 0, 1, 0, 0],
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[0, 0, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 1, 0, 0],
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[0, 0, 0, 1, 0, 0, 0],
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[0, 0, 0, 1, 0, 0, 0],
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[0, 0, 0, 1, 0, 0, 0],
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]).T,
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"disk": np.array([
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[0, 0, 1, 1, 0, 0, 0],
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[0, 0, 1, 0, 1, 0, 0],
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[0, 0, 1, 0, 1, 0, 0],
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[0, 0, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 1, 0, 0],
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[0, 0, 1, 0, 0, 0, 0],
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[0, 0, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 1, 0, 0],
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[0, 0, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 0],
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[0, 0, 1, 0, 1, 0, 0],
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[0, 0, 1, 1, 0, 0, 0],
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[0, 0, 1, 0, 1, 0, 0],
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]).T,
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"net": np.array([
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[0, 0, 1, 0, 0, 1, 0],
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[0, 0, 1, 1, 0, 1, 0],
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[0, 0, 1, 0, 1, 1, 0],
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[0, 0, 1, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 1, 0, 0],
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[0, 0, 1, 0, 0, 0, 0],
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[0, 0, 1, 1, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 0],
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[0, 0, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 1, 0, 0],
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[0, 0, 0, 1, 0, 0, 0],
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[0, 0, 0, 1, 0, 0, 0],
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[0, 0, 0, 1, 0, 0, 0],
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]).T,
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"snap": np.array([
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[0,0,0,0,0,0,0,0,0],
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[0,0,0,1,1,1,1,0,0],
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[0,0,1,0,0,0,0,0,0],
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[0,0,1,0,0,0,0,0,0],
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[0,0,0,1,1,1,0,0,0],
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[0,0,0,0,0,0,1,0,0],
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[0,0,0,0,0,0,1,0,0],
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[0,0,1,1,1,1,0,0,0],
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[0,0,0,0,0,0,0,0,0],
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[0,0,1,1,0,0,1,0,0],
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[0,0,1,1,0,0,1,0,0],
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[0,0,1,0,1,0,1,0,0],
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[0,0,1,0,1,0,1,0,0],
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[0,0,1,0,0,1,1,0,0],
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[0,0,1,0,0,1,1,0,0],
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[0,0,1,0,0,0,1,0,0],
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[0,0,0,0,0,0,0,0,0],
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[0,0,0,1,1,1,0,0,0],
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[0,0,1,0,0,0,1,0,0],
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[0,0,1,0,0,0,1,0,0],
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[0,0,1,1,1,1,1,0,0],
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[0,0,1,0,0,0,1,0,0],
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[0,0,1,0,0,0,1,0,0],
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[0,0,1,0,0,0,1,0,0],
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[0,0,0,0,0,0,0,0,0],
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[0,0,1,1,1,1,0,0,0],
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[0,0,1,0,0,0,1,0,0],
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[0,0,1,0,0,0,1,0,0],
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[0,0,1,1,1,1,0,0,0],
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[0,0,1,0,0,0,0,0,0],
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[0,0,1,0,0,0,0,0,0],
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[0,0,1,0,0,0,0,0,0],
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[0,0,0,0,0,0,0,0,0],
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[0,0,0,0,0,0,0,0,0]
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]).T
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} |