File size: 3,825 Bytes
b293748 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | from __future__ import annotations
import torch
# Dataset-level statistics provided by user.
CELSIUS_TO_KELVIN_OFFSET = 273.15
Y_MEAN = 289.74267177946783
Y_STD = 10.933397487585731
SALINITY_MEAN = 34.54260282159372
SALINITY_STD = 1.158266487751096
PLOT_STD_MULTIPLIER = 2.5
PLOT_TEMP_MIN = -10.740821939496481
PLOT_TEMP_MAX = 43.92616549843217
PLOT_SALINITY_MIN = 30.0
PLOT_SALINITY_MAX = 40.0
PLOT_CMAP = "turbo"
PLOT_SALINITY_CMAP = "winter"
def temperature_normalize(mode: str, tensor: torch.Tensor) -> torch.Tensor:
"""Compute temperature normalize and return the result.
Args:
mode (str): Input value.
tensor (torch.Tensor): Tensor input for the computation.
Returns:
torch.Tensor: Tensor output produced by this call.
"""
if mode not in {"norm", "denorm"}:
raise ValueError("mode must be 'norm' or 'denorm'")
mean = torch.as_tensor(Y_MEAN, dtype=tensor.dtype, device=tensor.device)
std = torch.as_tensor(Y_STD, dtype=tensor.dtype, device=tensor.device)
kelvin_offset = torch.as_tensor(
CELSIUS_TO_KELVIN_OFFSET, dtype=tensor.dtype, device=tensor.device
)
if mode == "norm":
tensor_kelvin = tensor + kelvin_offset
return (tensor_kelvin - mean) / std
denorm_kelvin = tensor * std + mean
# Convert back to Celsius so callers keep receiving physical temperatures in C.
return denorm_kelvin - kelvin_offset
def salinity_normalize(mode: str, tensor: torch.Tensor) -> torch.Tensor:
"""Compute salinity normalization and return the result.
Args:
mode (str): Input value.
tensor (torch.Tensor): Tensor input for the computation.
Returns:
torch.Tensor: Tensor output produced by this call.
"""
if mode not in {"norm", "denorm"}:
raise ValueError("mode must be 'norm' or 'denorm'")
mean = torch.as_tensor(SALINITY_MEAN, dtype=tensor.dtype, device=tensor.device)
std = torch.as_tensor(SALINITY_STD, dtype=tensor.dtype, device=tensor.device)
if mode == "norm":
return (tensor - mean) / std
return tensor * std + mean
def salinity_to_plot_unit(
tensor: torch.Tensor,
*,
tensor_is_normalized: bool = True,
) -> torch.Tensor:
"""Compute salinity plot unit and return the result.
Args:
tensor (torch.Tensor): Tensor input for the computation.
tensor_is_normalized (bool): Boolean flag controlling behavior.
Returns:
torch.Tensor: Tensor output produced by this call.
"""
salinity = (
salinity_normalize(mode="denorm", tensor=tensor)
if tensor_is_normalized
else tensor
)
s_min = torch.as_tensor(
PLOT_SALINITY_MIN, dtype=salinity.dtype, device=salinity.device
)
s_max = torch.as_tensor(
PLOT_SALINITY_MAX, dtype=salinity.dtype, device=salinity.device
)
denom = torch.clamp(s_max - s_min, min=torch.finfo(salinity.dtype).eps)
return ((salinity - s_min) / denom).clamp(0.0, 1.0)
def temperature_to_plot_unit(
tensor: torch.Tensor,
*,
tensor_is_normalized: bool = True,
) -> torch.Tensor:
"""Compute temperature to plot unit and return the result.
Args:
tensor (torch.Tensor): Tensor input for the computation.
tensor_is_normalized (bool): Boolean flag controlling behavior.
Returns:
torch.Tensor: Tensor output produced by this call.
"""
temp = (
temperature_normalize(mode="denorm", tensor=tensor)
if tensor_is_normalized
else tensor
)
t_min = torch.as_tensor(PLOT_TEMP_MIN, dtype=temp.dtype, device=temp.device)
t_max = torch.as_tensor(PLOT_TEMP_MAX, dtype=temp.dtype, device=temp.device)
denom = torch.clamp(t_max - t_min, min=torch.finfo(temp.dtype).eps)
return ((temp - t_min) / denom).clamp(0.0, 1.0)
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