"""
Units Handling Submodule
========================
`xclim`'s `pint`-based unit registry is an extension of the registry defined in `cf-xarray`.
This module defines most unit handling methods.
"""
from __future__ import annotations
import logging
import warnings
from collections.abc import Callable
from copy import deepcopy
from importlib.resources import files
from inspect import signature
from typing import Any, Literal, cast
import cf_xarray.units
import numpy as np
import pandas as pd
import pint
import xarray as xr
from boltons.funcutils import wraps
from yaml import safe_load
from xclim.core._exceptions import ValidationError
from xclim.core._types import Quantified
from xclim.core.calendar import get_calendar, parse_offset
from xclim.core.options import datacheck
from xclim.core.utils import InputKind, infer_kind_from_parameter
try:
from xarray import DataTree
except ImportError:
DataTree = False
logging.getLogger("pint").setLevel(logging.ERROR)
__all__ = [
"amount2lwethickness",
"amount2rate",
"cf_conversion",
"check_units",
"convert_units_to",
"declare_relative_units",
"declare_units",
"ensure_absolute_temperature",
"ensure_cf_units",
"ensure_delta",
"flux2rate",
"infer_context",
"infer_sampling_units",
"lwethickness2amount",
"pint2cfattrs",
"pint2cfunits",
"pint_multiply",
"rate2amount",
"rate2flux",
"str2pint",
"to_agg_units",
"units",
"units2pint",
]
# shamelessly adapted from `cf-xarray` (which adopted it from MetPy and xclim itself)
units = deepcopy(cf_xarray.units.units)
# Changing the default string format for units/quantities.
# CF is implemented by cf-xarray, g is the most versatile float format.
units.formatter.default_format = "gcf"
# CF-xarray forces numpy arrays even for scalar values, not sure why.
# We don't want that in xclim, the magnitude of a scalar is a scalar (float).
units.force_ndarray_like = False
# Define dimensionalities for convenience with the `declare_units` decorator
units.define("[precipitation] = [mass] / [length] ** 2 / [time]")
units.define("[discharge] = [length] ** 3 / [time]")
units.define("[radiation] = [power] / [length]**2")
# Default context. This is essentially a convenience, so that we can pass a context systemtically to pint's methods.
null = pint.Context("none")
units.add_context(null)
# Convenience context for common transformation involving liquid water
hydro = pint.Context("hydro")
hydro.add_transformation(
"[mass] / [length]**2",
"[length]",
lambda ureg, x: x / (1000 * ureg.kg / ureg.m**3),
)
hydro.add_transformation(
"[length]",
"[mass] / [length]**2",
lambda ureg, x: x * (1000 * ureg.kg / ureg.m**3),
)
hydro.add_transformation(
"[mass] / [length]**2 / [time]",
"[length] / [time]",
lambda ureg, x: x / (1000 * ureg.kg / ureg.m**3),
)
hydro.add_transformation(
"[length] / [time]",
"[mass] / [length]**2 / [time]",
lambda ureg, x: x * (1000 * ureg.kg / ureg.m**3),
)
units.add_context(hydro)
# Set as application registry
pint.set_application_registry(units)
with (files("xclim.data") / "variables.yml").open() as variables:
CF_CONVERSIONS = safe_load(variables)["conversions"]
_CONVERSIONS = {}
# FIXME: This needs to be properly annotated for mypy compliance.
# See: https://mypy.readthedocs.io/en/stable/generics.html#declaring-decorators
def _register_conversion(conversion, direction):
"""
Register a conversion function to be automatically picked up in `convert_units_to`.
The function must correspond to a name in `CF_CONVERSIONS`, so to a section in
`xclim/data/variables.yml::conversions`.
"""
if conversion not in CF_CONVERSIONS:
raise NotImplementedError(
"Automatic conversion functions must have a corresponding section in xclim/data/variables.yml"
)
def _func_register(func: Callable) -> Callable:
_CONVERSIONS[(conversion, direction)] = func
return func
return _func_register
[docs]
def units2pint(
value: xr.DataArray | units.Unit | units.Quantity | dict | str,
) -> pint.Unit:
"""
Return the pint Unit for the DataArray units.
Parameters
----------
value : xr.DataArray or pint.Unit or pint.Quantity or dict or str
Input data array or string representing a unit (with no magnitude).
Returns
-------
pint.Unit
Units of the data array.
Notes
-----
To avoid ambiguity related to differences in temperature vs absolute temperatures, set the `units_metadata`
attribute to `"temperature: difference"` or `"temperature: on_scale"` on the DataArray.
"""
# Value is already a pint unit or a pint quantity
if isinstance(value, units.Unit):
return value
if isinstance(value, units.Quantity):
# This is a pint.PlainUnit, which is not the same as a pint.Unit
return cast(pint.Unit, value.units)
# We only need the attributes
if isinstance(value, xr.DataArray):
value = value.attrs
if isinstance(value, str):
unit = value
metadata = None
elif isinstance(value, dict):
unit = value["units"]
metadata = value.get("units_metadata", None)
else:
raise NotImplementedError(f"Value of type `{type(value)}` not supported.")
# Catch user errors undetected by Pint
degree_ex = ["deg", "degree", "degrees"]
unit_ex = [
"C",
"K",
"F",
"Celsius",
"Kelvin",
"Fahrenheit",
"celsius",
"kelvin",
"fahrenheit",
]
possibilities = [f"{d} {u}" for d in degree_ex for u in unit_ex]
if unit.strip() in possibilities:
raise ValidationError("Remove white space from temperature units, e.g. use `degC`.")
pu = units.parse_units(unit)
if metadata == "temperature: difference":
return (1 * pu - 1 * pu).units
return pu
[docs]
def pint2cfunits(value: units.Quantity | units.Unit) -> str:
"""
Return a CF-compliant unit string from a `pint` unit.
Parameters
----------
value : pint.Unit
Input unit.
Returns
-------
str
Units following CF-Convention, using symbols.
"""
if isinstance(value, pint.Quantity | units.Quantity):
value = value.units
# Force "1" if the formatted string is "" (pint < 0.24)
return f"{value:~cf}" or "1"
[docs]
def pint2cfattrs(value: units.Quantity | units.Unit, is_difference=None) -> dict:
"""
Return CF-compliant units attributes from a `pint` unit.
Parameters
----------
value : pint.Unit
Input unit.
is_difference : bool
Whether the value represent a difference in temperature, which is ambiguous in the case of absolute
temperature scales like Kelvin or Rankine. It will automatically be set to True if units are "delta_*"
units.
Returns
-------
dict
Units following CF-Convention, using symbols.
"""
s = pint2cfunits(value)
if "delta_" in s:
is_difference = True
s = s.replace("delta_", "")
attrs = {"units": s}
if "[temperature]" in value.dimensionality:
if is_difference:
attrs["units_metadata"] = "temperature: difference"
elif is_difference is False:
attrs["units_metadata"] = "temperature: on_scale"
else:
attrs["units_metadata"] = "temperature: unknown"
return attrs
[docs]
def ensure_cf_units(ustr: str) -> str:
"""
Ensure the passed unit string is CF-compliant.
The string will be parsed to `pint` then recast to a string by :py:func:`xclim.core.units.pint2cfunits`.
Parameters
----------
ustr : str
A unit string.
Returns
-------
str
The unit string in CF-compliant form.
"""
return pint2cfunits(units2pint(ustr))
[docs]
def pint_multiply(da: xr.DataArray, q: Any, out_units: str | None = None) -> xr.DataArray:
"""
Multiply xarray.DataArray by pint.Quantity.
Parameters
----------
da : xr.DataArray
Input array.
q : pint.Quantity
Multiplicative factor.
out_units : str, optional
Units the output array should be converted into.
Returns
-------
xr.DataArray
The product DataArray.
"""
a = 1 * units2pint(da) # noqa
f = a * q.to_base_units()
if out_units:
f = f.to(out_units)
else:
f = f.to_reduced_units()
out: xr.DataArray = da * f.magnitude
out = out.assign_attrs(units=pint2cfunits(f.units))
return out
[docs]
def str2pint(val: str) -> pint.Quantity:
"""
Convert a string to a pint.Quantity, splitting the magnitude and the units.
Parameters
----------
val : str
A quantity in the form "[{magnitude} ]{units}", where magnitude can be cast to a float and
units is understood by :py:func:`xclim.core.units.units2pint`.
Returns
-------
pint.Quantity
Magnitude is 1 if no magnitude was present in the string.
"""
mstr, *ustr = val.split(" ", maxsplit=1)
try:
if ustr:
return units.Quantity(float(mstr), units=units2pint(ustr[0]))
return units.Quantity(float(mstr))
except ValueError:
return units.Quantity(1, units2pint(val))
# FIXME: The typing here is difficult to determine, as Generics cannot be used to track the type of the output.
[docs]
def convert_units_to( # noqa: C901
source: Quantified | xr.Dataset | DataTree,
target: Quantified | units.Unit | dict,
context: Literal["infer", "hydro", "none"] | None = None,
) -> xr.DataArray | float | xr.Dataset:
"""
Convert a mathematical expression into a value with the same units as a DataArray.
If the dimensionalities of source and target units differ, automatic CF conversions
will be applied when possible. See :py:func:`xclim.core.units.cf_conversion`.
Parameters
----------
source : str or xr.DataArray or units.Quantity or xr.Dataset or xr.DataTree
The value to be converted, e.g. '4C' or '1 mm/d'.
If a Dataset, `target` must also be a mapping from variable name to target units.
If a DataTree, this function will be applied over nodes with :py:func:`xarray.DataTree.map_over_datasets`.
target : str or xr.DataArray or units.Quantity or units.Unit or dict
Target array of values to which units must conform.
If `source` is a Dataset, it must be mapping from variable name to target units.
context : {"infer", "hydro", "none"}, optional
The unit definition context. Default: None.
If "infer", it will be inferred with :py:func:`xclim.core.units.infer_context` using
the standard name from the `source` or, if none is found, from the `target`.
This means that the "hydro" context could be activated if any one of the standard names allows it.
Returns
-------
xr.DataArray or float or xr.Dataset
The source value converted to target's units.
The outputted type is always similar to `source` initial type.
Attributes are preserved unless an automatic CF conversion is performed,
in which case only the new `standard_name` appears in the result.
See Also
--------
cf_conversion : Get the standard name of the specific conversion for the given standard name.
amount2rate : Convert an amount to a rate.
rate2amount : Convert a rate to an amount.
amount2lwethickness : Convert an amount to a liquid water equivalent thickness.
lwethickness2amount : Convert a liquid water equivalent thickness to an amount.
"""
if DataTree and isinstance(source, DataTree):
return source.map_over_datasets(convert_units_to, target, kwargs={"context": context})
if isinstance(source, xr.Dataset):
return source.assign({var: convert_units_to(source[var], tgt, context=context) for var, tgt in target.items()})
context = context or "none"
# Target units
target_unit = units2pint(target)
if context == "infer":
ctxs = []
if isinstance(source, xr.DataArray):
ctxs.append(infer_context(source.attrs.get("standard_name")))
if isinstance(target, xr.DataArray):
ctxs.append(infer_context(target.attrs.get("standard_name")))
# If any one of the target or source is compatible with the "hydro" context, use it.
if "hydro" in ctxs:
context = "hydro"
else:
context = "none"
m: float
if isinstance(source, str):
q = str2pint(source)
# Return magnitude of converted quantity. This is going to fail if units are not compatible.
m = q.to(target_unit, context).m
return m
if isinstance(source, units.Quantity):
m = source.to(target_unit, context).m
return m
if isinstance(source, xr.DataArray):
source_unit = units2pint(source)
target_cf_attrs = pint2cfattrs(target_unit)
# Automatic pre-conversions based on the dimensionalities and CF standard names
standard_name = source.attrs.get("standard_name")
if standard_name is not None and source_unit.dimensionality != target_unit.dimensionality:
dim_order_diff = source_unit.dimensionality / target_unit.dimensionality
for convname, convconf in CF_CONVERSIONS.items():
for direction, sign in [("to", 1), ("from", -1)]:
# If the dimensionality diff is compatible with this conversion
compatible = all(
dimdiff == sign * dim_order_diff.get(f"[{dim}]")
for dim, dimdiff in convconf["dimensionality"].items()
)
# Does the input cf standard name have an equivalent after conversion
valid = cf_conversion(standard_name, convname, direction)
if compatible and valid:
# The new cf standard name is inserted by the converter
try:
source = _CONVERSIONS[(convname, direction)](source)
except Exception as err:
raise ValueError(
f"There is a dimensionality incompatibility between the source and the target "
f"and no CF-based conversions have been found for this standard name: {standard_name}"
) from err
source_unit = units2pint(source)
out: xr.DataArray
if source_unit == target_unit:
# The units are the same, but the symbol may not be.
out = source.assign_attrs(**target_cf_attrs)
return out
with units.context(context or "none"):
out = source.copy(data=units.convert(source.data, source_unit, target_unit))
out = out.assign_attrs(**target_cf_attrs)
return out
# TODO remove backwards compatibility of int/float thresholds after v1.0 release
if isinstance(source, float | int):
raise TypeError("Please specify units explicitly.")
raise NotImplementedError(f"Source of type `{type(source)}` is not supported.")
[docs]
def cf_conversion(standard_name: str, conversion: str, direction: Literal["to", "from"]) -> str | None:
"""
Get the standard name of the specific conversion for the given standard name.
Parameters
----------
standard_name : str
Standard name of the input.
conversion : {'amount2rate', 'amount2lwethickness'}
Type of conversion. Available conversions are the keys of the `conversions` entry in `xclim/data/variables.yml`.
See :py:data:`xclim.core.units.CF_CONVERSIONS`. They also correspond to functions in this module.
direction : {'to', 'from'}
The direction of the requested conversion. "to" means the conversion as given by the `conversion` name,
while "from" means the reverse operation. For example `conversion="amount2rate"` and `direction="from"`
will search for a conversion from a rate or flux to an amount or thickness for the given standard name.
Returns
-------
str or None
If a string, this means the conversion is possible and the result should have this standard name.
If None, the conversion is not possible within the CF standards.
"""
i = ["to", "from"].index(direction)
for names in CF_CONVERSIONS[conversion]["valid_names"]:
if names[i] == standard_name:
cf_name: str = names[int(not i)]
return cf_name
return None
FREQ_UNITS = {
"Y": "year",
"M": "month",
"W": "week",
"D": "d",
"h": "h",
"min": "min",
"s": "s",
"ms": "ms",
"us": "us",
"ns": "ns",
}
"""
Resampling frequency units for :py:func:`xclim.core.units.infer_sampling_units`.
Mapping from offset base to CF-compliant unit.
"""
[docs]
def infer_sampling_units(
da: xr.DataArray,
deffreq: str | None = None,
dim: str = "time",
) -> tuple[int, str]:
"""
Infer a multiplier and the units corresponding to one sampling period.
Parameters
----------
da : xr.DataArray
A DataArray from which to take coordinate `dim`.
deffreq : str, optional
If no frequency is inferred from `da[dim]`, take this one.
dim : str
Dimension from which to infer the frequency.
Returns
-------
int
The magnitude (number of base periods per period).
str
Units as a string, understandable by pint.
Raises
------
ValueError
If the frequency has no corresponding units.
"""
da = da[dim]
freq = xr.infer_freq(da)
if freq is None:
if deffreq is None:
raise ValueError("Unable to find the sampling frequency of the data.")
freq = deffreq
multi, base, _, _ = parse_offset(freq)
if base == "Q":
multi = multi * 3
base = "M"
if base in FREQ_UNITS:
u = FREQ_UNITS[base]
else:
raise ValueError(f"Sampling frequency {freq} has no corresponding pint or CF units.")
if u == "d" and multi == 7:
# Special case for weekly frequency. xarray's CFTimeOffsets do not have "W".
u = "week"
multi = 1
return multi, u
DELTA_ABSOLUTE_TEMP = {
units.delta_degC: units.kelvin,
units.delta_degF: units.rankine,
}
[docs]
def ensure_absolute_temperature(units: str) -> str:
"""
Convert temperature units to their absolute counterpart, assuming they represented a difference (delta).
Celsius becomes Kelvin, Fahrenheit becomes Rankine. Does nothing for other units.
Parameters
----------
units : str
Units to transform.
Returns
-------
str
The transformed units.
See Also
--------
ensure_delta : Ensure a unit is a delta unit.
"""
a = str2pint(units)
# ensure a delta pint unit
a = a - 0 * a
if a.units in DELTA_ABSOLUTE_TEMP:
return pint2cfunits(DELTA_ABSOLUTE_TEMP[a.units])
return units
[docs]
def ensure_delta(unit: xr.DataArray | str | units.Quantity) -> str:
"""
Return delta units for temperature.
For dimensions where delta exist in pint (Temperature), it replaces the temperature unit by delta_degC or
delta_degF based on the input unit. For other dimensionality, it just gives back the input units.
Parameters
----------
unit : str
Unit to transform in delta (or not).
Returns
-------
str
The transformed units.
"""
u = units2pint(unit)
d = 1 * u
#
delta_unit = pint2cfunits(d - d)
# replace kelvin/rankine by delta_degC/F
# Note (DH): This will fail if dimension is [temperature]^-1 or [temperature]^2 (e.g. variance)
# Recent versions of pint have a `to_preferred` method that could be used here (untested).
if "kelvin" in u._units:
delta_unit = pint2cfunits(u / units2pint("K") * units2pint("delta_degC"))
if "degree_Rankine" in u._units:
delta_unit = pint2cfunits(u / units2pint("°R") * units2pint("delta_degF"))
return delta_unit
[docs]
def to_agg_units(
out: xr.DataArray,
orig: xr.DataArray,
op: Literal["min", "max", "mean", "std", "var", "doymin", "doymax", "count", "integral", "sum"],
dim: str = "time",
) -> xr.DataArray:
"""
Set and convert units of an array after an aggregation operation along the sampling dimension (time).
Parameters
----------
out : xr.DataArray
The output array of the aggregation operation, no units operation done yet.
orig : xr.DataArray
The original array before the aggregation operation,
used to infer the sampling units and get the variable units.
op : {'min', 'max', 'mean', 'std', 'var', 'doymin', 'doymax', 'count', 'integral', 'sum'}
The type of aggregation operation performed. "integral" is mathematically equivalent to "sum",
but the units are multiplied by the timestep of the data (requires an inferrable frequency).
dim : str
The time dimension along which the aggregation was performed.
Returns
-------
xr.DataArray
The DataArray with aggregated values.
Examples
--------
Take a daily array of temperature and count number of days above a threshold.
`to_agg_units` will infer the units from the sampling rate along "time", so
we ensure the final units are correct:
>>> time = xr.date_range("2001-01-01", freq="D", periods=365)
>>> tas = xr.DataArray(
... np.arange(365),
... dims=("time",),
... coords={"time": time},
... attrs={"units": "degC"},
... )
>>> cond = tas > 100 # Which days are boiling
>>> Ndays = cond.sum("time") # Number of boiling days
>>> Ndays.attrs.get("units")
None
>>> Ndays = to_agg_units(Ndays, tas, op="count")
>>> Ndays.units
'd'
Similarly, here we compute the total heating degree-days, but we have weekly data:
>>> time = xr.date_range("2001-01-01", freq="7D", periods=52)
>>> tas = xr.DataArray(
... np.arange(52) + 10,
... dims=("time",),
... coords={"time": time},
... )
>>> dt = (tas - 16).assign_attrs(units="degC", units_metadata="temperature: difference")
>>> degdays = dt.clip(0).sum("time") # Integral of temperature above a threshold
>>> degdays = to_agg_units(degdays, dt, op="integral")
>>> degdays.units
'degC week'
Which we can always convert to the more common "K days":
>>> degdays = convert_units_to(degdays, "K days")
>>> degdays.units
'd K'
"""
is_difference = True if op in ["std", "var"] else None
if op in ["amin", "min", "amax", "max", "mean", "sum", "std"]:
out.attrs["units"] = orig.attrs["units"]
elif op in ["var"]:
out.attrs["units"] = pint2cfunits(str2pint(orig.units) ** 2)
elif op in ["doymin", "doymax"]:
out.attrs.update(units="1", is_dayofyear=np.int32(1), calendar=get_calendar(orig))
elif op in ["count", "integral"]:
m, freq_u_raw = infer_sampling_units(orig[dim])
orig_u = units2pint(orig)
freq_u = str2pint(freq_u_raw)
with xr.set_options(keep_attrs=True):
out = out * m
if op == "count":
out.attrs["units"] = freq_u_raw
elif op == "integral":
if "[temperature]" in orig_u.dimensionality:
# ensure delta_temperature
orig_u = 1 * orig_u - 1 * orig_u
if "[time]" in orig_u.dimensionality:
# We need to simplify units after multiplication
out_units = (orig_u * freq_u).to_reduced_units()
with xr.set_options(keep_attrs=True):
out = out * out_units.magnitude
out.attrs.update(pint2cfattrs(out_units, is_difference))
else:
out.attrs.update(pint2cfattrs(orig_u * freq_u, is_difference))
else:
raise ValueError(
f"Unknown aggregation op {op}. "
"Known ops are [min, max, mean, std, var, doymin, doymax, count, integral, sum]."
)
# Remove units_metadata where it doesn't make sense
if op in ["doymin", "doymax", "count"]:
out.attrs.pop("units_metadata", None)
return out
def _rate_and_amount_converter(
da: Quantified,
dim: str | xr.DataArray = "time",
to: str = "amount",
sampling_rate_from_coord: bool = False,
out_units: str | None = None,
) -> xr.DataArray:
"""Internal converter for :py:func:`xclim.core.units.rate2amount` and :py:func:`xclim.core.units.amount2rate`."""
m = 1
u = None # Default to assume a non-uniform axis
label: Literal["lower", "upper"] = "lower" # Default to "lower" label for diff
if isinstance(dim, str):
if not isinstance(da, xr.DataArray):
raise ValueError("If `dim` is a string, the data to convert must be a DataArray.")
time = da[dim]
else:
time = dim
dim = time.name
# We accept str, Quantity or DataArray
# Ensure the code below has a DataArray, so its simpler
# We convert back at the end
orig_da = da
if isinstance(da, str):
da = str2pint(da)
if isinstance(da, units.Quantity):
da = xr.DataArray(da.magnitude, attrs={"units": f"{da.units:~cf}"})
try:
freq = xr.infer_freq(time)
except ValueError as err:
if sampling_rate_from_coord:
freq = None
else:
raise ValueError(
"The variables' sampling frequency could not be inferred, "
"which is needed for conversions between rates and amounts. "
f"If the derivative of the variables' {dim} coordinate "
"can be used as the sampling rate, pass `sampling_rate_from_coord=True`."
) from err
if freq is not None:
_, base, start_anchor, _ = parse_offset(freq)
if base in ["M", "Q", "A", "Y"]:
start = time.indexes[dim][0]
if not start_anchor:
# Anchor is on the end of the period, subtract 1 period.
if isinstance(start, pd.Timestamp):
start = start - pd.tseries.frequencies.to_offset(freq)
else:
start = start - xr.coding.cftime_offsets.to_offset(freq)
# In the diff below, assign to upper label!
label = "upper"
# We generate "time" with an extra element, so we do not need to repeat the last element below.
time = xr.DataArray(
xr.date_range(
start,
periods=len(time) + 1,
freq=freq,
calendar=get_calendar(time),
use_cftime=(time.dtype == "O"),
),
dims=(dim,),
name=dim,
attrs=time.attrs,
)
else:
m, u = infer_sampling_units(da, freq)
out: xr.DataArray
# Freq is month, season or year, which are not constant units, or simply freq is not inferrable.
if u is None:
# Get sampling period lengths in nanoseconds
# In the case with no freq, last period as the same length as the one before.
# In the case with freq in M, Q, A, this has been dealt with above in `time`
# and `label` has been updated accordingly.
dt = time.diff(dim, label=label).reindex({dim: time}, method="ffill").astype(float)
dt = dt / 1e9 # Convert to seconds
if to == "amount":
tu = (str2pint(da.units) * str2pint("s")).to_reduced_units()
out = da * dt * tu.m
elif to == "rate":
tu = (str2pint(da.units) / str2pint("s")).to_reduced_units()
out = (da / dt) * tu.m
else:
raise ValueError("Argument `to` must be one of 'amount' or 'rate'.")
out.attrs["units"] = pint2cfunits(tu)
else:
q = units.Quantity(m, u)
if to == "amount":
out = pint_multiply(da, q)
elif to == "rate":
out = pint_multiply(da, 1 / q)
else:
raise ValueError("Argument `to` must be one of 'amount' or 'rate'.")
old_name = da.attrs.get("standard_name")
if old_name and (new_name := cf_conversion(old_name, "amount2rate", "to" if to == "rate" else "from")):
out = out.assign_attrs(standard_name=new_name)
if out_units:
out = convert_units_to(out, out_units)
if not isinstance(orig_da, xr.DataArray):
out = units.Quantity(out.item(), out.attrs["units"])
return out
[docs]
@_register_conversion("amount2rate", "from")
def rate2amount(
rate: Quantified,
dim: str | xr.DataArray = "time",
sampling_rate_from_coord: bool = False,
out_units: str | None = None,
) -> xr.DataArray:
"""
Convert a rate variable to an amount by multiplying by the sampling period length.
If the sampling period length cannot be inferred, the rate values are multiplied by the duration
between their time coordinate and the next one. The last period is estimated with the duration of
the one just before.
This is the inverse operation of :py:func:`xclim.core.units.amount2rate`.
Parameters
----------
rate : xr.DataArray or pint.Quantity or str
"Rate" variable, with units of "amount" per time. Ex: Precipitation in "mm / d".
dim : str or DataArray
The name of time dimension or the coordinate itself.
sampling_rate_from_coord : bool
For data with irregular time coordinates.
If True, the diff of the time coordinate will be used as the sampling rate, meaning each data point
will be assumed to apply for the interval ending at the next point. See notes.
Defaults to False, which raises an error if the time coordinate is irregular.
out_units : str, optional
Specific output units, if needed.
Returns
-------
xr.DataArray or Quantity
The converted variable. The standard_name of `rate` is modified if a conversion is found.
Raises
------
ValueError
If the time coordinate is irregular and `sampling_rate_from_coord` is False (default).
See Also
--------
amount2rate : Convert an amount to a rate.
Examples
--------
The following converts a daily array of precipitation in mm/h to the daily amounts in mm:
>>> time = xr.date_range("2001-01-01", freq="D", periods=365)
>>> pr = xr.DataArray([1] * 365, dims=("time",), coords={"time": time}, attrs={"units": "mm/h"})
>>> pram = rate2amount(pr)
>>> pram.units
'mm'
>>> float(pram[0])
24.0
Also works if the time axis is irregular : the rates are assumed constant for the whole period starting on
the values timestamp to the next timestamp. This option is activated with `sampling_rate_from_coord=True`.
>>> time = time[[0, 9, 30]] # The time axis is Jan 1st, Jan 10th, Jan 31st
>>> pr = xr.DataArray([1] * 3, dims=("time",), coords={"time": time}, attrs={"units": "mm/h"})
>>> pram = rate2amount(pr, sampling_rate_from_coord=True)
>>> pram.values
array([216., 504., 504.])
Finally, we can force output units:
>>> pram = rate2amount(pr, out_units="pc") # Get rain amount in parsecs. Why not.
>>> pram.values
array([7.00008327e-18, 1.63335276e-17, 1.63335276e-17])
"""
return _rate_and_amount_converter(
rate,
dim=dim,
to="amount",
sampling_rate_from_coord=sampling_rate_from_coord,
out_units=out_units,
)
[docs]
@_register_conversion("amount2rate", "to")
def amount2rate(
amount: Quantified,
dim: str | xr.DataArray = "time",
sampling_rate_from_coord: bool = False,
out_units: str | None = None,
) -> xr.DataArray:
"""
Convert an amount variable to a rate by dividing by the sampling period length.
If the sampling period length cannot be inferred, the amount values
are divided by the duration between their time coordinate and the next one. The last period
is estimated with the duration of the one just before.
This is the inverse operation of :py:func:`xclim.core.units.rate2amount`.
Parameters
----------
amount : xr.DataArray or pint.Quantity or str
"amount" variable. Ex: Precipitation amount in "mm".
dim : str or xr.DataArray
The name of the time dimension or the time coordinate itself.
sampling_rate_from_coord : bool
For data with irregular time coordinates.
If True, the diff of the time coordinate will be used as the sampling rate,
meaning each data point will be assumed to span the interval ending at the next point.
See notes of :py:func:`xclim.core.units.rate2amount`.
Defaults to False, which raises an error if the time coordinate is irregular.
out_units : str, optional
Specific output units, if needed.
Returns
-------
xr.DataArray or Quantity
The converted variable. The standard_name of `amount` is modified if a conversion is found.
Raises
------
ValueError
If the time coordinate is irregular and `sampling_rate_from_coord` is False (default).
See Also
--------
rate2amount : Convert a rate to an amount.
"""
return _rate_and_amount_converter(
amount,
dim=dim,
to="rate",
sampling_rate_from_coord=sampling_rate_from_coord,
out_units=out_units,
)
[docs]
@_register_conversion("amount2lwethickness", "to")
def amount2lwethickness(amount: xr.DataArray, out_units: str | None = None) -> xr.DataArray | Quantified:
"""
Convert a liquid water amount (mass over area) to its equivalent area-averaged thickness (length).
This will simply divide the amount by the density of liquid water, 1000 kg/m³.
This is equivalent to using the "hydro" context of :py:data:`xclim.core.units.units`.
Parameters
----------
amount : xr.DataArray
A DataArray storing a liquid water amount quantity.
out_units : str, optional
Specific output units, if needed.
Returns
-------
xr.DataArray or Quantified
The standard_name of `amount` is modified if a conversion is found
(see :py:func:`xclim.core.units.cf_conversion`), it is removed otherwise.
Other attributes are left untouched.
See Also
--------
lwethickness2amount : Convert a liquid water equivalent thickness to an amount.
"""
water_density = str2pint("1000 kg m-3")
out = pint_multiply(amount, 1 / water_density)
old_name = amount.attrs.get("standard_name", None)
if old_name and (new_name := cf_conversion(old_name, "amount2lwethickness", "to")):
out.attrs["standard_name"] = new_name
if out_units:
out = cast(xr.DataArray, convert_units_to(out, out_units))
return out
[docs]
@_register_conversion("amount2lwethickness", "from")
def lwethickness2amount(thickness: xr.DataArray, out_units: str | None = None) -> xr.DataArray | Quantified:
"""
Convert a liquid water thickness (length) to its equivalent amount (mass over area).
This will simply multiply the thickness by the density of liquid water, 1000 kg/m³.
This is equivalent to using the "hydro" context of :py:data:`xclim.core.units.units`.
Parameters
----------
thickness : xr.DataArray
A DataArray storing a liquid water thickness quantity.
out_units : str, optional
Specific output units, if needed.
Returns
-------
xr.DataArray or Quantified
The standard_name of `amount` is modified if a conversion is found
(see :py:func:`xclim.core.units.cf_conversion`), it is removed otherwise. Other attributes are left untouched.
See Also
--------
amount2lwethickness : Convert an amount to a liquid water equivalent thickness.
"""
water_density = str2pint("1000 kg m-3")
out = pint_multiply(thickness, water_density)
old_name = thickness.attrs.get("standard_name")
if old_name and (new_name := cf_conversion(old_name, "amount2lwethickness", "from")):
out.attrs["standard_name"] = new_name
if out_units:
out = cast(xr.DataArray, convert_units_to(out, out_units))
return out
def _flux_and_rate_converter(
da: xr.DataArray,
density: Quantified,
to: str = "rate",
out_units: str | None = None,
) -> xr.DataArray:
"""Internal converter for :py:func:`xclim.core.units.flux2rate` and :py:func:`xclim.core.units.rate2flux`."""
if to == "rate":
# output: rate = da / density, with da = flux
density_exp = -1
elif to == "flux":
# output: flux = da * density, with da = rate
density_exp = 1
else:
raise ValueError("Argument `to` must be one of 'rate' or 'flux'.")
in_u = units2pint(da)
if isinstance(density, str):
density_u = str2pint(density).units
else:
density_u = units2pint(density)
if out_units:
out_u = str2pint(out_units).units
if (in_u * density_u**density_exp).dimensionality != out_u.dimensionality:
op = {1: "*", -1: "/"}[density_exp]
raise ValueError(
f"Dimensions incompatible for {to} = da {op} density:\n"
f"da: {in_u.dimensionality}\n"
f"density: {density_u.dimensionality}\n"
f"out_units ({to}): {out_u.dimensionality}\n"
)
else:
out_u = in_u * density_u**density_exp
density_conv = convert_units_to(density, (out_u / in_u) ** density_exp)
out: xr.DataArray = (da * density_conv**density_exp).assign_attrs(da.attrs)
out = out.assign_attrs(units=pint2cfunits(out_u))
if "standard_name" in out.attrs.keys():
out.attrs.pop("standard_name")
return out
[docs]
def rate2flux(
rate: xr.DataArray,
density: Quantified,
out_units: str | None = None,
) -> xr.DataArray:
"""
Convert a rate variable to a flux by multiplying with a density.
This is the inverse operation of :py:func:`xclim.core.units.flux2rate`.
Parameters
----------
rate : xr.DataArray
"Rate" variable, e.g. Snowfall rate in "mm / d".
density : Quantified
Density used to convert from a rate to a flux, e.g. Snowfall density "312 kg m-3".
Density can also be an array with the same shape as `rate`.
out_units : str, optional
Specific output units, if needed.
Returns
-------
xr.DataArray
The converted flux value.
See Also
--------
flux2rate : Convert a flux to a rate.
Examples
--------
The following converts an array of snowfall rate in mm/s to snowfall flux in kg m-2 s-1,
assuming a density of 100 kg m-3:
>>> time = xr.date_range("2001-01-01", freq="D", periods=365)
>>> prsnd = xr.DataArray([1] * 365, dims=("time",), coords={"time": time}, attrs={"units": "mm/s"})
>>> prsn = rate2flux(prsnd, density="100 kg m-3", out_units="kg m-2 s-1")
>>> prsn.units
'kg m-2 s-1'
>>> float(prsn[0])
0.1
"""
return _flux_and_rate_converter(
rate,
density=density,
to="flux",
out_units=out_units,
)
[docs]
def flux2rate(
flux: xr.DataArray,
density: Quantified,
out_units: str | None = None,
) -> xr.DataArray:
"""
Convert a flux variable to a rate by dividing with a density.
This is the inverse operation of :py:func:`xclim.core.units.rate2flux`.
Parameters
----------
flux : xr.DataArray
"flux" variable, e.g. Snowfall flux in "kg m-2 s-1".
density : Quantified
Density used to convert from a flux to a rate, e.g. Snowfall density "312 kg m-3".
Density can also be an array with the same shape as `flux`.
out_units : str, optional
Specific output units, if needed.
Returns
-------
xr.DataArray
The converted rate value.
See Also
--------
rate2flux : Convert a rate to a flux.
Examples
--------
The following converts an array of snowfall flux in kg m-2 s-1 to snowfall flux in mm/s,
assuming a density of 100 kg m-3:
>>> time = xr.date_range("2001-01-01", freq="D", periods=365)
>>> prsn = xr.DataArray(
... [0.1] * 365,
... dims=("time",),
... coords={"time": time},
... attrs={"units": "kg m-2 s-1"},
... )
>>> prsnd = flux2rate(prsn, density="100 kg m-3", out_units="mm/s")
>>> prsnd.units
'mm s-1'
>>> float(prsnd[0])
1.0
"""
return _flux_and_rate_converter(
flux,
density=density,
to="rate",
out_units=out_units,
)
[docs]
@datacheck
def check_units(val: str | xr.DataArray | None, dim: str | xr.DataArray | None = None) -> None:
"""
Check that units are compatible with dimensions, otherwise raise a `ValidationError`.
Parameters
----------
val : str or xr.DataArray, optional
Value to check.
dim : str or xr.DataArray, optional
Expected dimension, e.g. [temperature]. If a quantity or DataArray is given, the dimensionality is extracted.
"""
if dim is None or val is None:
return
if isinstance(dim, xr.DataArray):
_dim = str(dim.dims[0])
else:
_dim = dim
# In case val is a DataArray, we try to get a standard_name
if hasattr(val, "attrs"):
standard_name = val.attrs.get("standard_name", None)
else:
standard_name = None
context = infer_context(standard_name=standard_name, dimension=_dim)
# Issue originally introduced in https://github.com/hgrecco/pint/issues/1486
# Should be resolved in pint v0.24. See: https://github.com/hgrecco/pint/issues/1913
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=DeprecationWarning)
if str(val).startswith("UNSET "):
warnings.warn(
"This index calculation will soon require user-specified thresholds.",
FutureWarning,
stacklevel=4,
)
val = str(val).replace("UNSET ", "")
if isinstance(val, int | float):
raise TypeError("Please set units explicitly using a string.")
try:
dim_units: pint.Unit | pint.Quantity
if isinstance(dim, str):
dim_units = str2pint(dim)
else:
dim_units = units2pint(dim)
expected = dim_units.dimensionality
except pint.UndefinedUnitError:
# Raised when it is not understood, we assume it was a dimensionality
expected = units.get_dimensionality(dim.replace("dimensionless", ""))
val_units: pint.Unit | pint.Quantity
if isinstance(val, str):
val_units = str2pint(val)
else:
val_units = units2pint(val)
val_dim = val_units.dimensionality
if val_dim == expected:
return
# Check if there is a transformation available
with units.context(context):
start = pint.util.to_units_container(val_dim)
end = pint.util.to_units_container(expected)
graph = units._active_ctx.graph # noqa
if pint.util.find_shortest_path(graph, start, end):
return
# Issue originally introduced in https://github.com/hgrecco/pint/issues/1486
# Should be resolved in pint v0.24. See: https://github.com/hgrecco/pint/issues/1913
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=DeprecationWarning)
raise ValidationError(f"Data units {val_units} are not compatible with requested {dim}.")
def _check_output_has_units(
out: xr.DataArray | tuple[xr.DataArray] | xr.Dataset,
) -> None:
"""
Perform very basic sanity check on the output.
Indices are responsible for unit management. If this fails, it's a developer's error.
"""
if isinstance(out, xr.Dataset):
out = out.data_vars.values()
elif not isinstance(out, tuple):
out = (out,)
for outd in out:
if "units" not in outd.attrs:
raise ValueError("No units were assigned in one of the indice's outputs.")
outd.attrs["units"] = ensure_cf_units(outd.attrs["units"])
# FIXME: This needs to be properly annotated for mypy compliance.
# See: https://mypy.readthedocs.io/en/stable/generics.html#declaring-decorators
[docs]
def declare_relative_units(**units_by_name: str) -> Callable:
r"""
Function decorator checking the units of arguments.
The decorator checks that input values have units that are compatible with each other.
It also stores the input units as a 'relative_units' attribute.
Parameters
----------
**units_by_name : str
Mapping from the input parameter names to dimensions relative to other parameters.
The dimensions can be a single parameter name as `<other_var>` or more complex expressions,
such as `<other_var> * [time]`.
Returns
-------
Callable
The decorated function.
See Also
--------
declare_units : A decorator to check units of function arguments.
Examples
--------
In the following function definition:
.. code-block:: python
@declare_relative_units(thresh="<da>", thresh2="<da> / [time]")
def func(da, thresh, thresh2): ...
The decorator will check that `thresh` has units compatible with those of da
and that `thresh2` has units compatible with the time derivative of da.
Usually, the function would be decorated further by :py:func:`declare_units` to create
a unit-aware index:
.. code-block:: python
temperature_func = declare_units(da="[temperature]")(func)
This call will replace the "<da>" by "[temperature]" everywhere needed.
"""
def dec(func): # numpydoc ignore=GL08
sig = signature(func)
# Check if units are valid
for name, dim in units_by_name.items():
for ref, refparam in sig.parameters.items():
if f"<{ref}>" in dim:
if infer_kind_from_parameter(refparam) not in [
InputKind.QUANTIFIED,
InputKind.OPTIONAL_VARIABLE,
InputKind.VARIABLE,
]:
raise ValueError(
f"Dimensions of {name} are declared relative to {ref}, "
f"but that argument doesn't have a type that supports units. Got {refparam.annotation}."
)
# Put something simple to check validity
dim = dim.replace(f"<{ref}>", "(m)")
if "<" in dim:
raise ValueError(
f"Unit declaration of {name} relative to variables absent from the function's signature."
)
try:
str2pint(dim)
except pint.UndefinedUnitError:
# Raised when it is not understood, we assume it was a dimensionality
try:
units.get_dimensionality(dim.replace("dimensionless", ""))
except Exception as e:
raise ValueError(
f"Relative units for {name} are invalid. Got {dim}. (See stacktrace for more information)."
) from e
@wraps(func)
def wrapper(*args, **kwargs): # numpydoc ignore=GL08
# Match all passed values to their proper arguments, so we can check units
bound_args = sig.bind(*args, **kwargs)
for name, dim in units_by_name.items():
context = None
for ref, refvar in bound_args.arguments.items():
if f"<{ref}>" in dim:
dim = dim.replace(f"<{ref}>", f"({units2pint(refvar)})")
# check_units will guess the hydro context if "precipitation" appears in dim,
# but here we pass a real unit. It will also check the standard name of the arg,
# but we give it another chance by checking the ref arg.
context = context or infer_context(
standard_name=getattr(refvar, "attrs", {}).get("standard_name")
)
with units.context(context):
check_units(bound_args.arguments.get(name), dim)
out = func(*args, **kwargs)
_check_output_has_units(out)
return out
wrapper.relative_units = units_by_name
return wrapper
return dec
# FIXME: This needs to be properly annotated for mypy compliance.
# See: https://mypy.readthedocs.io/en/stable/generics.html#declaring-decorators
[docs]
def declare_units(**units_by_name) -> Callable:
r"""
Create a decorator to check units of function arguments.
The decorator checks that input and output values have units that are compatible with expected dimensions.
It also stores the input units as an 'in_units' attribute.
Parameters
----------
**units_by_name : str
Mapping from the input parameter names to their units or dimensionality ("[...]").
If this decorates a function previously decorated with :py:func:`declare_relative_units`,
the relative unit declarations are made absolute with the information passed here.
Returns
-------
Callable
The decorated function.
See Also
--------
declare_relative_units : A decorator to check for relative units of function arguments.
Examples
--------
In the following function definition:
.. code-block:: python
@declare_units(tas="[temperature]")
def func(tas): ...
The decorator will check that `tas` has units of temperature (C, K, F).
"""
def dec(func): # numpydoc ignore=GL08
# The `_in_units` attr denotes a previously partially-declared function, update with that info.
if hasattr(func, "relative_units"):
# Make relative declarations absolute if possible
for arg, dim in func.relative_units.items():
if arg in units_by_name:
continue
for ref, refdim in units_by_name.items():
if f"<{ref}>" in dim:
dim = dim.replace(f"<{ref}>", f"({refdim})")
if "<" in dim:
raise ValueError(
f"Units for {arg} are declared relative to arguments absent from this decorator ({dim})."
"Pass units for the missing arguments."
)
units_by_name[arg] = dim
# Check that all Quantified parameters have their dimension declared.
sig = signature(func)
for name, param in sig.parameters.items():
if infer_kind_from_parameter(param) == InputKind.QUANTIFIED and (name not in units_by_name):
raise ValueError(f"Argument {name} has no declared dimensions.")
@wraps(func)
def wrapper(*args, **kwargs): # numpydoc ignore=GL08
# Match all passed in value to their proper arguments, so we can check units
bound_args = sig.bind(*args, **kwargs)
for name, dim in units_by_name.items():
check_units(bound_args.arguments.get(name), dim)
out = func(*args, **kwargs)
_check_output_has_units(out)
return out
wrapper.in_units = units_by_name
return wrapper
return dec
[docs]
def infer_context(standard_name: str | None = None, dimension: str | None = None) -> str:
"""
Return units context based on either the variable's standard name or the pint dimension.
Valid standard names for the hydro context are those including the terms "rainfall",
"lwe" (liquid water equivalent) and "precipitation". The latter is technically incorrect,
as any phase of precipitation could be referenced. Standard names for evapotranspiration,
evaporation and canopy water amounts are also associated with the hydro context.
Parameters
----------
standard_name : str, optional
CF-Convention standard name.
dimension : str, optional
Pint dimension, e.g. '[time]'.
Returns
-------
str
"hydro" if variable is a liquid water flux, otherwise "none".
"""
csn = (
(
standard_name
in [
"water_potential_evapotranspiration_flux",
"canopy_water_amount",
"water_evaporation_amount",
]
or "rainfall" in standard_name
or "lwe" in standard_name
or "precipitation" in standard_name
)
if standard_name is not None
else False
)
c_dim = ("[precipitation]" in dimension) if dimension is not None else False
return "hydro" if csn or c_dim else "none"