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/* | |
Chapel's Library for `Tom's Obvious, Minimal Language (TOML) <https://github.com/toml-lang/toml>`_. | |
This module provides support for parsing and writing toml files. | |
Will list a few example use-cases once interface is finalized. | |
Node is a placeholder name for the data structure that holds the parsed toml. | |
Could be named something else more related to toml? e.g. -> tomlTable | |
*/ |
module SomeAnimals { | |
class Animal { | |
var animalType: string; | |
} | |
class Bird: Animal { | |
proc init(aType: string) { | |
this.animalType = aType; | |
} |
! This file was written by the model and records the non-default parameters used at run-time. | |
! === module MOM === | |
ENABLE_THERMODYNAMICS = False ! [Boolean] default = True | |
! If true, Temperature and salinity are used as state | |
! variables. | |
ADIABATIC = True ! [Boolean] default = False | |
! There are no diapycnal mass fluxes if ADIABATIC is | |
! true. This assumes that KD = KDML = 0.0 and that | |
! there is no buoyancy forcing, but makes the model |
"MOM Experiment" | |
1 1 1 0 0 0 | |
"ocean_snap_5day", 5,"days", 1,"days", "time" | |
"ocean_mean_5day", 5,"days", 1,"days", "time" | |
"ocean_mean_month", 1,"months", 1,"days", "time" | |
"ocean_mean_annual", 12,"months", 1,"days", "time" | |
#"prog_%4yr_%3dy", 5,"days",1,"days","Time",365,"days" | |
#"ave_prog_%4yr_%3dy", 5,"days",1,"days","Time",365,"days" |
netcdf ocean_mean_annual { | |
dimensions: | |
xh = 44 ; | |
yh = 40 ; | |
time = UNLIMITED ; // (0 currently) | |
nv = 2 ; | |
zl = 2 ; | |
xq = 44 ; | |
yq = 40 ; | |
variables: |
NOTE from PE 0: MPP_DOMAINS_SET_STACK_SIZE: stack size set to 32768. | |
&MPP_IO_NML | |
HEADER_BUFFER_VAL= 16384, | |
GLOBAL_FIELD_ON_ROOT_PE=T, | |
IO_CLOCKS_ON=F, | |
SHUFFLE= 0, | |
DEFLATE_LEVEL= -1, | |
CF_COMPLIANCE=F, | |
/ | |
NOTE from PE 0: MPP_IO_SET_STACK_SIZE: stack size set to 131072. |
netcdf ocean_mean_month { | |
dimensions: | |
xh = 44 ; | |
yh = 40 ; | |
time = UNLIMITED ; // (0 currently) | |
nv = 2 ; | |
zl = 2 ; | |
xq = 44 ; | |
yq = 40 ; | |
variables: |
from sentence_transformers import SentenceTransformer | |
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') | |
sentences = [ | |
"That is a very happy Person", | |
"That is a Happy Dog", | |
"Today is a sunny day" | |
] | |
embeddings = model.encode(sentences) |