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config.py
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from common.fft import MaskRange
"""General"""
# Options:
# 'plotly': Fast, interactive, has unique volume plot, but not support large datasets
# 'matplotlib': Supports large datasets, general option.
GRAPHIC_BACKEND = 'matplotlib'
# Set to False to disable output.
# Or a name for the output file common index.
PLOTLY_OUTPUT = False
# Set Matplotlib animation output
MPL_ANI_OUTPUT = True
# Matplotlib animation output speed (0 ~ 1)
MPL_ANI_OUTPUT_SPEED = 0.1
# Options:
# 'simulation': Use wave generator.
# 'real': Use real data from data folder.
# 'fem': Use fem data from data folder.
DATA_SOURCE = 'fem'
# Set fft filter shape
# modify detail filter logic in prepare/(real.py or sim.py)
FILTER_WHITELIST: dict[str, MaskRange] = {
'f_range': (0, 4999999),
'kx_range': (0, 1269),
'ky_range': (-20, 20),
}
FILTER_BLACKLIST: dict[str, MaskRange] = {
'f_range': None,
'kx_range': (500, 1300),
'ky_range': (-200, 200),
}
"""Simulation"""
# 'pulse' or 'wave' if DATA_SOURCE == 'simulation'
# There is two default examples
# Create new models in 'models/' folder and process them in prepare_sim.py
SIMULATION_TYPE = 'wave'
"""Real/FEM Data"""
"""
LOADING NODES ID
middle upper 4 layer: 99050, 99049
middle upper 3.5 layer: 105053, 105052
middle upper 3 layer: 85043, 85042
middle upper 2.5 layer: 91046, 91045
middle upper 2 layer: 71036, 71035
middle upper 1.5 layer: 77039, 77038
middle upper 1 layer: 57029, 57028
middle upper 0.5 layer: 63032, 63031
middle center layer: 43022, 43021
middle lower 0.5 layer: 49025, 49024
middle lower 1 layer: 29015, 29014
middle lower 1.5 layer: 35018, 35017
middle lower 2 layer: 15008, 15007
middle lower 2.5 layer: 21011, 21010
middle lower 3 layer: 1004, 1003
middle lower 3.5 layer: 7004, 7003
middle lower 4 layer: 13007, 13006
left upper 4 layer: 100050
left upper 3.5 layer: 106053
left upper 3 layer: 86043
left upper 2.5 layer: 92046
left upper 2 layer: 72036
left upper 1.5 layer: 78039
left upper 1 layer: 58029
left upper 0.5 layer: 64032
left center layer: 44022
left lower 0.5 layer: 50025
left lower 1 layer: 30015
left lower 1.5 layer: 36018
left lower 2 layer: 16008
left lower 2.5 layer: 22011
left lower 3 layer: 2001
left lower 3.5 layer: 8004
left lower 4 layer: 14007
in [0/0/90/90]s
load posision should be -2 ~ 2
in [90/90/0/0]s
load posision should be -4 ~ -2 and 2 ~ 4
"""
# Path to data folder
# disp_verify
# DATA_BASE_DIR = 'data/li/disp_verify/90.90.0.0.s'
# DATA_BASE_DIR = 'data/li/disp_verify/0.0.90.90.s'
# DATA_BASE_DIR = 'data/li/disp_verify/90.0.90.0.s'
# DATA_BASE_DIR = 'data/li/disp_verify/0.90.0.90.s'
# exp
# DATA_BASE_DIR = 'data/li/2023-06-28-AE504S/0.0.90.90.s-90'
# DATA_BASE_DIR = 'data/li/2023-06-28-AE504S/0.0.90.90.s-0'
# DATA_BASE_DIR = 'data/li/2023-06-28-AE504S/0.90.0.90.s-90'
# DATA_BASE_DIR = 'data/li/2023-06-28-AE504S/0.90.0.90.s-0'
# fem
DATA_BASE_DIR = 'data/li/2023-12-10-FEM/90.90.0.0.s'
# DATA_BASE_DIR = 'data/li/2023-12-10-FEM/90.0.90.0.s'
# DATA_BASE_DIR = 'data/li/2023-12-10-FEM/0.0.90.90.s'
# DATA_BASE_DIR = 'data/li/2023-12-10-FEM/0.90.0.90.s'
# FEM output file name
# FEM_DATA_FILENAME = 'data-left-upper-1.5.csv' # for [0-0-90-90]s or [90-0-90-0]s
# FEM_DATA_FILENAME = 'data-left-lower-2.5.csv' # for [0-90-0-90]s
# FEM_DATA_FILENAME = 'data-left-lower-3.csv' # for [90-90-0-0]s
# FEM_DATA_FILENAME = 'data-left-lower-1.csv' # for [0-0-90-90]s
# FEM_DATA_FILENAME = 'data-middle-lower-1.csv' # for [0-0-90-90]s
FEM_DATA_FILENAME = 'data-middle-lower-3.csv' # for [90-90-0-0]s
# Down sampling
# Matplotlib 3d mask plot might need this
DOWN_SAMPLING = False
# Downsampling ratio (0 ~ 1)
DOWN_SAMPLING_RATIO = 0.45