def _str_to_quantity(v, values, prop_name):
if isinstance(v, str):
quantity = string_to_quantity(v)
values["property_units"] = {prop_name: quantity.unit}
return quantity.value
if isinstance(v, float):
return v
def __init__(self, kernel_dimension, inverse_dimension):
minimum = inverse_dimension["minimum"]
maximum = inverse_dimension["maximum"]
count = inverse_dimension.get("count", 32)
scale = inverse_dimension.get("scale", "linear")
label = inverse_dimension.get("label", None)
unit = kernel_dimension.coordinates[0].unit
x_min = string_to_quantity(minimum).to(unit).value
x_max = string_to_quantity(maximum).to(unit).value
check_log = scale == "log"
if check_log:
x_min, x_max = np.log10(x_min), np.log10(x_max)
coords = (np.arange(count) / (count - 1)) * (x_max - x_min) + x_min
if check_log:
coords = 10**(coords)
lbl_ = f"log({self.__class__.__name__} / {unit})" if check_log else None
label = label if label is not None else lbl_
inverse_dimension = cp.as_dimension(array=coords,
unit=str(unit),
label=label)
meta = {
"log":
check_log,
"label":
f"log({self.__class__.__name__} / {unit})" if check_log else None,
}
inverse_dimension.application = {
"com.github.deepanshs.mrinversion": meta
}
super().__init__(kernel_dimension, inverse_dimension, 1, 1)
def enforce_units(value: str,
required_type: str,
default_unit: str,
throw_error=True):
"""Enforces a required type and default unit on the value."""
try:
value = string_to_quantity(value)
data_type = value.unit.physical_type
if required_type != data_type:
raise Exception(
f"A {required_type} value is required but got a {data_type} instead"
)
return value.to(default_unit).value
except Exception as e:
if throw_error:
raise e
return None
"type",
"function",
"config",
"decompose_spectrum",
"integration_density",
"integration_volume",
"number_of_sidebands",
"name",
"description",
"transition_query",
"ch1",
"P",
"channels",
]
CONST = string_to_quantity("1")
class Parseable(BaseModel):
"""Base class for all objects that can be parsed easily from JSON with units
Don't directly use this. Rather inherit from it and implement a data model
and property units and defaults
"""
name: str = None
description: str = None
label: str = None
property_unit_types: ClassVar[Dict] = {}
property_default_units: ClassVar[Dict] = {}
property_units: Dict = {}
# -*- coding: utf-8 -*-
import numpy as np
from csdmpy.units import string_to_quantity
__author__ = "Deepansh Srivastava"
__email__ = "*****@*****.**"
const = string_to_quantity("1")
def _get_broadcast_shape(array, dim, ndim):
"""Return the broadcast shape of a vector `array` at dimension `dim` for `ndim`
total dimensions."""
none = [np.newaxis for _ in range(ndim + 1)]
if isinstance(dim, int):
dim = [dim]
for dim_ in dim:
none[-dim_ - 1] = slice(None, None, None)
return array[tuple(none)]
def _str_to_quantity(v, values, prop_name):
if isinstance(v, str):
quantity = string_to_quantity(v)
values["property_units"] = {prop_name: quantity.unit}
return quantity.value
if isinstance(v, float):
return v
