diff --git a/decode/convert.py b/decode/convert.py
index 98a0c15..659f2bb 100644
--- a/decode/convert.py
+++ b/decode/convert.py
@@ -112,10 +112,10 @@ def to_brightness(
     if dems.long_name == "Brightness" or dems.units == "K":
         LOGGER.warning("DEMS may already be on the brightness temperature scale.")
 
-    if all(np.isnan(T_room_ := dems.aste_cabin_temperature)):
+    if np.isnan(T_room_ := dems.aste_cabin_temperature.mean().data):
         T_room_ = T_room
 
-    if all(np.isnan(T_amb_ := dems.temperature)):
+    if np.isnan(T_amb_ := dems.temperature.mean().data):
         T_amb_ = T_amb
 
     fwd = dems.d2_resp_fwd.data
@@ -158,10 +158,10 @@ def to_dfof(
     if dems.long_name == "df/f" or dems.units == "dimensionless":
         LOGGER.warning("DEMS may already be on the df/f scale.")
 
-    if all(np.isnan(T_room_ := dems.aste_cabin_temperature)):
+    if np.isnan(T_room_ := dems.aste_cabin_temperature.mean().data):
         T_room_ = T_room
 
-    if all(np.isnan(T_amb_ := dems.temperature)):
+    if np.isnan(T_amb_ := dems.temperature.mean().data):
         T_amb_ = T_amb
 
     fwd = dems.d2_resp_fwd.data
diff --git a/decode/correct.py b/decode/correct.py
index ab09b80..78461a6 100644
--- a/decode/correct.py
+++ b/decode/correct.py
@@ -44,9 +44,9 @@ def for_atmosphere(
         DEMS DataArray of the on-source samples in the Ta* scale.
 
     """
-    Tb_on = select.by(dems, "state", include=include_on)
-    Tb_off = select.by(dems, "state", include=include_off)
-    Tb_r = select.by(dems, "state", include=include_r)
+    Tb_on = dems.sel(time=dems.state.isin(include_on))
+    Tb_off = dems.sel(time=dems.state.isin(include_off))
+    Tb_r = dems.sel(time=dems.state.isin(include_r))
 
     Tb_off_mean = Tb_off.groupby("scan").map(mean_in_time)
     Tb_r_mean = Tb_r.groupby("scan").map(mean_in_time)
diff --git a/decode/load.py b/decode/load.py
index 6e1102a..c026bd7 100644
--- a/decode/load.py
+++ b/decode/load.py
@@ -2,8 +2,10 @@
 
 
 # standard library
+from collections.abc import Sequence
+from os import PathLike
 from pathlib import Path
-from typing import Any, Literal, Union
+from typing import Any, Literal, Optional, Union
 from warnings import catch_warnings, simplefilter
 
 
@@ -11,6 +13,9 @@
 import numpy as np
 import pandas as pd
 import xarray as xr
+from astropy.units import Quantity
+from ndtools import Range
+from . import convert
 
 
 # constants
@@ -66,25 +71,64 @@ def atm(*, type: Literal["eta", "tau"] = "tau") -> xr.DataArray:
         raise ValueError("Type must be either eta or tau.")
 
 
-def dems(dems: Union[Path, str], /, **options: Any) -> xr.DataArray:
+def dems(
+    dems: Union[PathLike[str], str],
+    /,
+    *,
+    # options for data selection
+    include_mkid_types: Optional[Sequence[str]] = ("filter",),
+    exclude_mkid_types: Optional[Sequence[str]] = None,
+    include_mkid_ids: Optional[Sequence[int]] = None,
+    exclude_mkid_ids: Optional[Sequence[int]] = None,
+    min_frequency: Optional[str] = None,
+    max_frequency: Optional[str] = None,
+    # options for coordinate conversion
+    frequency_units: Optional[str] = "GHz",
+    skycoord_units: Optional[str] = "arcsec",
+    skycoord_frame: Optional[str] = None,
+    # options for data conversion
+    data_scaling: Optional[Literal["brightness", "df/f"]] = None,
+    T_amb: float = 273.0,  # K
+    T_room: float = 293.0,  # K
+    # other options for loading
+    **options: Any,
+) -> xr.DataArray:
     """Load a DEMS file as a DataArray.
 
     Args:
         dems: Path of the DEMS file.
-
-    Keyword Args:
-        options: Arguments to be passed to ``xarray.open_dataarray``.
+        include_mkid_types: MKID types to be included.
+            Defaults to filter-only.
+        exclude_mkid_types: MKID types to be excluded.
+            Defaults to no MKID types.
+        include_mkid_ids: MKID IDs to be included.
+            Defaults to all MKID IDs.
+        exclude_mkid_ids: MKID IDs to be excluded.
+            Defaults to no MKID IDs.
+        min_frequency: Minimum frequency to be included.
+            Defaults to no minimum frequency bound.
+        max_frequency: Maximum frequency to be included.
+            Defaults to no maximum frequency bound.
+        frequency_units: Units of the frequency-related coordinates.
+            Defaults to GHz.
+        skycoord_units: Units of the skycoord-related coordinates.
+            Defaults to arcsec.
+        skycoord_frame: Frame of the skycoord.
+            Defaults to the skycoord of the input DEMS file.
+        data_scaling: Data scaling (either brightness or df/f).
+            Defaults to the data scaling of the input DEMS file.
+        T_amb: Default ambient temperature value for the data scaling
+            to be used when the ``temperature`` coordinate is all-NaN.
+        T_room: Default room temperature value for the data scaling
+            to be used when the ``aste_cabin_temperature`` coordinate is all-NaN.
+        **options: Other options for loading (e.g. ``chunks=None``, etc).
 
     Return:
-        Loaded DEMS DataArray.
-
-    Raises:
-        ValueError: Raised if the file type is not supported.
+        DataArray of the loaded DEMS file.
 
     """
-    suffixes = Path(dems).suffixes
-
-    if NETCDF_SUFFIX in suffixes:
+    # load DEMS as DataArray
+    if NETCDF_SUFFIX in (suffixes := Path(dems).suffixes):
         options = {
             "engine": NETCDF_ENGINE,
             **options,
@@ -101,4 +145,59 @@ def dems(dems: Union[Path, str], /, **options: Any) -> xr.DataArray:
             "Use netCDF (.nc) or Zarr (.zarr, .zarr.zip)."
         )
 
-    return xr.open_dataarray(dems, **options)
+    da = xr.open_dataarray(dems, **options)
+
+    # load DataArray coordinates on memory
+    for name in da.coords:
+        da.coords[name].load()
+
+    # select DataArray by MKID types
+    if include_mkid_types is not None:
+        da = da.sel(chan=da.d2_mkid_type.isin(include_mkid_types))
+
+    if exclude_mkid_types is not None:
+        da = da.sel(chan=~da.d2_mkid_type.isin(exclude_mkid_types))
+
+    # select DataArray by MKID master IDs
+    if include_mkid_ids is not None:
+        da = da.sel(chan=da.d2_mkid_id.isin(include_mkid_ids))
+
+    if exclude_mkid_ids is not None:
+        da = da.sel(chan=~da.d2_mkid_id.isin(exclude_mkid_ids))
+
+    # select DataArray by frequency range
+    if min_frequency is not None:
+        min_frequency = Quantity(min_frequency).to(da.frequency.units).value
+
+    if max_frequency is not None:
+        max_frequency = Quantity(max_frequency).to(da.frequency.units).value
+
+    if min_frequency is not None or max_frequency is not None:
+        da = da.sel(chan=da.frequency == Range(min_frequency, max_frequency))
+
+    # convert frequency units
+    if frequency_units is not None:
+        da = convert.coord_units(
+            da,
+            ["bandwidth", "frequency", "d2_mkid_frequency"],
+            frequency_units,
+        )
+
+    # convert skycoord units and frame
+    if skycoord_units is not None:
+        da = convert.coord_units(
+            da,
+            ["lat", "lat_origin", "lon", "lon_origin"],
+            skycoord_units,
+        )
+
+    if skycoord_frame is not None:
+        da = convert.frame(da, skycoord_frame)
+
+    # convert data scaling
+    if data_scaling == "brightness":
+        return convert.to_brightness(da, T_amb=T_amb, T_room=T_room)
+    elif data_scaling == "df/f":
+        return convert.to_dfof(da, T_amb=T_amb, T_room=T_room)
+    else:
+        return da
diff --git a/decode/qlook.py b/decode/qlook.py
index cac0aac..4de2aa4 100644
--- a/decode/qlook.py
+++ b/decode/qlook.py
@@ -17,7 +17,7 @@
 from contextlib import contextmanager
 from logging import DEBUG, basicConfig, getLogger
 from pathlib import Path
-from typing import Any, Literal, Optional, Sequence, Union, cast
+from typing import Any, Literal, Optional, Sequence, Union
 from warnings import catch_warnings, simplefilter
 from datetime import datetime
 
@@ -37,7 +37,7 @@
 ABBA_PHASES = {0, 1, 2, 3}
 DATA_FORMATS = "csv", "nc", "zarr", "zarr.zip"
 TEXT_FORMATS = ("toml",)
-DEFAULT_DATA_TYPE = "auto"
+DEFAULT_DATA_SCALING = None
 DEFAULT_DEBUG = False
 DEFAULT_FIGSIZE = 12, 4
 DEFAULT_FORMAT = "png"
@@ -85,7 +85,7 @@ def auto(dems: Path, /, **options: Any) -> Path:
     """
     with xr.set_options(keep_attrs=True):
         da = load.dems(dems, chunks=None)
-        obs: str = da.attrs["observation"]
+        obs: str = da.aste_obs_file
 
         if "daisy" in obs:
             return daisy(dems, **options)
@@ -126,7 +126,7 @@ def daisy(
     exclude_mkid_ids: Optional[Sequence[int]] = DEFAULT_EXCL_MKID_IDS,
     min_frequency: Optional[str] = DEFAULT_MIN_FREQUENCY,
     max_frequency: Optional[str] = DEFAULT_MAX_FREQUENCY,
-    data_type: Literal["auto", "brightness", "df/f"] = DEFAULT_DATA_TYPE,
+    data_scaling: Optional[Literal["brightness", "df/f"]] = DEFAULT_DATA_SCALING,
     # options for analysis
     rolling_time: int = DEFAULT_ROLLING_TIME,
     source_radius: str = "60 arcsec",
@@ -155,8 +155,8 @@ def daisy(
             Defaults to no minimum frequency bound.
         max_frequency: Maximum frequency to be included in analysis.
             Defaults to no maximum frequency bound.
-        data_type: Data type of the input DEMS file.
-            Defaults to the ``long_name`` attribute in it.
+        data_scaling: Data scaling to be used in analysis.
+            Defaults to the data scaling of the input DEMS file.
         rolling_time: Moving window size.
         source_radius: Radius of the on-source area.
             Other areas are considered off-source in sky subtraction.
@@ -185,16 +185,18 @@ def daisy(
             LOGGER.debug(f"{key}: {val!r}")
 
     with xr.set_options(keep_attrs=True):
-        da = load_dems(
+        da = load.dems(
             dems,
             include_mkid_ids=include_mkid_ids,
             exclude_mkid_ids=exclude_mkid_ids,
             min_frequency=min_frequency,
             max_frequency=max_frequency,
-            data_type=data_type,
+            data_scaling=data_scaling,
             skycoord_units=skycoord_units,
+            skycoord_frame="relative",
+            chunks=None,
         )
-        da = select.by(da, "state", exclude="GRAD")
+        da = da.sel(time=da.state != "GRAD")
 
         ### Rolling
         da_rolled = da.rolling(time=int(rolling_time), center=True).mean()
@@ -210,8 +212,8 @@ def daisy(
         assign.scan(da, by="state", inplace=True)
 
         # subtract temporal baseline
-        da_on = select.by(da, "state", include="SCAN@ON")
-        da_off = select.by(da, "state", exclude="SCAN@ON")
+        da_on = da.sel(time=da.state == "SCAN@ON")
+        da_off = da.sel(time=da.state != "SCAN@ON")
         da_base = (
             da_off.groupby("scan")
             .map(mean_in_time)
@@ -277,7 +279,7 @@ def daisy(
 
         ax = axes[0]  # type: ignore
         plot.data(series, ax=ax)
-        ax.set_title(f"{Path(dems).name}\n({da.observation})")
+        ax.set_title(f"{Path(dems).name}\n({da.aste_obs_file})")
 
         ax = axes[1]  # type: ignore
         map_lim = max(abs(cube.lon).max(), abs(cube.lat).max())
@@ -335,7 +337,7 @@ def pswsc(
     exclude_mkid_ids: Optional[Sequence[int]] = DEFAULT_EXCL_MKID_IDS,
     min_frequency: Optional[str] = DEFAULT_MIN_FREQUENCY,
     max_frequency: Optional[str] = DEFAULT_MAX_FREQUENCY,
-    data_type: Literal["auto", "brightness", "df/f"] = DEFAULT_DATA_TYPE,
+    data_scaling: Optional[Literal["brightness", "df/f"]] = DEFAULT_DATA_SCALING,
     frequency_units: str = DEFAULT_FREQUENCY_UNITS,
     # options for saving
     format: str = DEFAULT_FORMAT,
@@ -358,8 +360,8 @@ def pswsc(
             Defaults to no minimum frequency bound.
         max_frequency: Maximum frequency to be included in analysis.
             Defaults to no maximum frequency bound.
-        data_type: Data type of the input DEMS file.
-            Defaults to the ``long_name`` attribute in it.
+        data_scaling: Data scaling to be used in analysis.
+            Defaults to the data scaling of the input DEMS file.
         frequency_units: Units of the frequency axis.
         format: Output data format of the quick-look result.
         outdir: Output directory for the quick-look result.
@@ -377,20 +379,22 @@ def pswsc(
             LOGGER.debug(f"{key}: {val!r}")
 
     with xr.set_options(keep_attrs=True):
-        da = load_dems(
+        da = load.dems(
             dems,
             include_mkid_ids=include_mkid_ids,
             exclude_mkid_ids=exclude_mkid_ids,
             min_frequency=min_frequency,
             max_frequency=max_frequency,
-            data_type=data_type,
+            data_scaling=data_scaling,
             frequency_units=frequency_units,
+            skycoord_frame="relative",
+            chunks=None,
         )
 
         da_despiked = despike(da)
 
         # calculate ABBA cycles and phases
-        da_onoff = select.by(da_despiked, "state", ["ON", "OFF"])
+        da_onoff = da_despiked.sel(time=da_despiked.state.isin(["ON", "OFF"]))
         scan_onoff = utils.phaseof(da_onoff.state)
         chop_per_scan = da_onoff.beam.groupby(scan_onoff).apply(utils.phaseof)
         is_second_half = chop_per_scan.groupby(scan_onoff).apply(
@@ -424,7 +428,7 @@ def pswsc(
         ax.set_ylim(get_robust_lim(spec))
 
         for ax in axes:  # type: ignore
-            ax.set_title(f"{Path(dems).name}\n({da.observation})")
+            ax.set_title(f"{Path(dems).name}\n({da.aste_obs_file})")
             ax.grid(True)
 
         fig.tight_layout()
@@ -440,7 +444,7 @@ def raster(
     exclude_mkid_ids: Optional[Sequence[int]] = DEFAULT_EXCL_MKID_IDS,
     min_frequency: Optional[str] = DEFAULT_MIN_FREQUENCY,
     max_frequency: Optional[str] = DEFAULT_MAX_FREQUENCY,
-    data_type: Literal["auto", "brightness", "df/f"] = DEFAULT_DATA_TYPE,
+    data_scaling: Optional[Literal["brightness", "df/f"]] = DEFAULT_DATA_SCALING,
     # options for analysis
     chan_weight: Literal["uniform", "std", "std/tx"] = "std/tx",
     pwv: Literal["0.5", "1.0", "2.0", "3.0", "4.0", "5.0"] = "5.0",
@@ -467,8 +471,8 @@ def raster(
             Defaults to no minimum frequency bound.
         max_frequency: Maximum frequency to be included in analysis.
             Defaults to no maximum frequency bound.
-        data_type: Data type of the input DEMS file.
-            Defaults to the ``long_name`` attribute in it.
+        data_scaling: Data scaling to be used in analysis.
+            Defaults to the data scaling of the input DEMS file.
         chan_weight: Weighting method along the channel axis.
             uniform: Uniform weight (i.e. no channel dependence).
             std: Inverse square of temporal standard deviation of sky.
@@ -494,19 +498,21 @@ def raster(
             LOGGER.debug(f"{key}: {val!r}")
 
     with xr.set_options(keep_attrs=True):
-        da = load_dems(
+        da = load.dems(
             dems,
             include_mkid_ids=include_mkid_ids,
             exclude_mkid_ids=exclude_mkid_ids,
             min_frequency=min_frequency,
             max_frequency=max_frequency,
-            data_type=data_type,
+            data_scaling=data_scaling,
             skycoord_units=skycoord_units,
+            skycoord_frame="relative",
+            chunks=None,
         )
 
         # subtract temporal baseline
-        da_on = select.by(da, "state", include="SCAN")
-        da_off = select.by(da, "state", exclude="SCAN")
+        da_on = da.sel(time=da.state == "SCAN")
+        da_off = da.sel(time=da.state != "SCAN")
         da_base = (
             da_off.groupby("scan")
             .map(mean_in_time)
@@ -572,7 +578,7 @@ def raster(
 
         ax = axes[0]  # type: ignore
         plot.data(series, ax=ax)
-        ax.set_title(f"{Path(dems).name}\n({da.observation})")
+        ax.set_title(f"{Path(dems).name}\n({da.aste_obs_file})")
 
         ax = axes[1]  # type: ignore
         map_lim = max(abs(cube.lon).max(), abs(cube.lat).max())
@@ -630,7 +636,7 @@ def skydip(
     exclude_mkid_ids: Optional[Sequence[int]] = DEFAULT_EXCL_MKID_IDS,
     min_frequency: Optional[str] = DEFAULT_MIN_FREQUENCY,
     max_frequency: Optional[str] = DEFAULT_MAX_FREQUENCY,
-    data_type: Literal["auto", "brightness", "df/f"] = DEFAULT_DATA_TYPE,
+    data_scaling: Optional[Literal["brightness", "df/f"]] = DEFAULT_DATA_SCALING,
     # options for analysis
     chan_weight: Literal["uniform", "std", "std/tx"] = "std/tx",
     pwv: Literal["0.5", "1.0", "2.0", "3.0", "4.0", "5.0"] = "5.0",
@@ -655,8 +661,8 @@ def skydip(
             Defaults to no minimum frequency bound.
         max_frequency: Maximum frequency to be included in analysis.
             Defaults to no maximum frequency bound.
-        data_type: Data type of the input DEMS file.
-            Defaults to the ``long_name`` attribute in it.
+        data_scaling: Data scaling to be used in analysis.
+            Defaults to the data scaling of the input DEMS file.
         chan_weight: Weighting method along the channel axis.
             uniform: Uniform weight (i.e. no channel dependence).
             std: Inverse square of temporal standard deviation of sky.
@@ -680,18 +686,32 @@ def skydip(
             LOGGER.debug(f"{key}: {val!r}")
 
     with xr.set_options(keep_attrs=True):
-        da = load_dems(
+        da = load.dems(
             dems,
             include_mkid_ids=include_mkid_ids,
             exclude_mkid_ids=exclude_mkid_ids,
             min_frequency=min_frequency,
             max_frequency=max_frequency,
-            data_type=data_type,
+            data_scaling=data_scaling,
+            skycoord_frame="relative",
+            chunks=None,
         )
 
+        # add airmass as a coordinate
+        # fmt: off
+        airmass = (
+            xr.DataArray(1 / np.sin(convert.units(da.lat, "rad")))
+            .assign_attrs(
+                long_name="Airmass",
+                units="dimensionless",
+            )
+        )
+        da = da.assign_coords(airmass=airmass)
+        # fmt: on
+
         # make continuum series
-        da_on = select.by(da, "state", include="SCAN")
-        da_off = select.by(da, "state", exclude="SCAN")
+        da_on = da.sel(time=da.state == "SCAN")
+        da_off = da.sel(time=da.state != "SCAN")
         weight = get_chan_weight(da_off, method=chan_weight, pwv=pwv)
         series = da_on.weighted(weight.fillna(0)).mean("chan")
 
@@ -711,7 +731,7 @@ def skydip(
         plot.data(series, x="secz", ax=ax)
 
         for ax in axes:  # type: ignore
-            ax.set_title(f"{Path(dems).name}\n({da.observation})")
+            ax.set_title(f"{Path(dems).name}\n({da.aste_obs_file})")
             ax.grid(True)
 
         fig.tight_layout()
@@ -727,7 +747,7 @@ def still(
     exclude_mkid_ids: Optional[Sequence[int]] = DEFAULT_EXCL_MKID_IDS,
     min_frequency: Optional[str] = DEFAULT_MIN_FREQUENCY,
     max_frequency: Optional[str] = DEFAULT_MAX_FREQUENCY,
-    data_type: Literal["auto", "brightness", "df/f"] = DEFAULT_DATA_TYPE,
+    data_scaling: Optional[Literal["brightness", "df/f"]] = DEFAULT_DATA_SCALING,
     # options for analysis
     chan_weight: Literal["uniform", "std", "std/tx"] = "std/tx",
     pwv: Literal["0.5", "1.0", "2.0", "3.0", "4.0", "5.0"] = "5.0",
@@ -752,8 +772,8 @@ def still(
             Defaults to no minimum frequency bound.
         max_frequency: Maximum frequency to be included in analysis.
             Defaults to no maximum frequency bound.
-        data_type: Data type of the input DEMS file.
-            Defaults to the ``long_name`` attribute in it.
+        data_scaling: Data scaling to be used in analysis.
+            Defaults to the data scaling of the input DEMS file.
         chan_weight: Weighting method along the channel axis.
             uniform: Uniform weight (i.e. no channel dependence).
             std: Inverse square of temporal standard deviation of sky.
@@ -777,17 +797,19 @@ def still(
             LOGGER.debug(f"{key}: {val!r}")
 
     with xr.set_options(keep_attrs=True):
-        da = load_dems(
+        da = load.dems(
             dems,
             include_mkid_ids=include_mkid_ids,
             exclude_mkid_ids=exclude_mkid_ids,
             min_frequency=min_frequency,
             max_frequency=max_frequency,
-            data_type=data_type,
+            data_scaling=data_scaling,
+            skycoord_frame="relative",
+            chunks=None,
         )
 
         # make continuum series
-        da_off = select.by(da, "state", exclude=["ON", "SCAN"])
+        da_off = da.sel(time=~da.state.isin(["ON", "OFF"]))
         weight = get_chan_weight(da_off, method=chan_weight, pwv=pwv)
         series = da.weighted(weight.fillna(0)).mean("chan")
 
@@ -807,7 +829,7 @@ def still(
         plot.data(series, add_colorbar=False, ax=ax)
 
         for ax in axes:  # type: ignore
-            ax.set_title(f"{Path(dems).name}\n({da.observation})")
+            ax.set_title(f"{Path(dems).name}\n({da.aste_obs_file})")
             ax.grid(True)
 
         fig.tight_layout()
@@ -823,7 +845,7 @@ def xscan(
     exclude_mkid_ids: Optional[Sequence[int]] = DEFAULT_EXCL_MKID_IDS,
     min_frequency: Optional[str] = DEFAULT_MIN_FREQUENCY,
     max_frequency: Optional[str] = DEFAULT_MAX_FREQUENCY,
-    data_type: Literal["auto", "brightness", "df/f"] = DEFAULT_DATA_TYPE,
+    data_scaling: Optional[Literal["brightness", "df/f"]] = DEFAULT_DATA_SCALING,
     # options for analysis
     chan_weight: Literal["uniform", "std", "std/tx"] = "std/tx",
     pwv: Literal["0.5", "1.0", "2.0", "3.0", "4.0", "5.0"] = "5.0",
@@ -848,8 +870,8 @@ def xscan(
             Defaults to no minimum frequency bound.
         max_frequency: Maximum frequency to be included in analysis.
             Defaults to no maximum frequency bound.
-        data_type: Data type of the input DEMS file.
-            Defaults to the ``long_name`` attribute in it.
+        data_scaling: Data scaling to be used in analysis.
+            Defaults to the data scaling of the input DEMS file.
         chan_weight: Weighting method along the channel axis.
             uniform: Uniform weight (i.e. no channel dependence).
             std: Inverse square of temporal standard deviation of sky.
@@ -880,7 +902,7 @@ def xscan(
         exclude_mkid_ids=exclude_mkid_ids,
         min_frequency=min_frequency,
         max_frequency=max_frequency,
-        data_type=data_type,
+        data_scaling=data_scaling,
         # options for analysis
         chan_weight=chan_weight,
         pwv=pwv,
@@ -902,7 +924,7 @@ def yscan(
     exclude_mkid_ids: Optional[Sequence[int]] = DEFAULT_EXCL_MKID_IDS,
     min_frequency: Optional[str] = DEFAULT_MIN_FREQUENCY,
     max_frequency: Optional[str] = DEFAULT_MAX_FREQUENCY,
-    data_type: Literal["auto", "brightness", "df/f"] = DEFAULT_DATA_TYPE,
+    data_scaling: Optional[Literal["brightness", "df/f"]] = DEFAULT_DATA_SCALING,
     # options for analysis
     chan_weight: Literal["uniform", "std", "std/tx"] = "std/tx",
     pwv: Literal["0.5", "1.0", "2.0", "3.0", "4.0", "5.0"] = "5.0",
@@ -927,8 +949,8 @@ def yscan(
             Defaults to no minimum frequency bound.
         max_frequency: Maximum frequency to be included in analysis.
             Defaults to no maximum frequency bound.
-        data_type: Data type of the input DEMS file.
-            Defaults to the ``long_name`` attribute in it.
+        data_scaling: Data scaling to be used in analysis.
+            Defaults to the data scaling of the input DEMS file.
         chan_weight: Weighting method along the channel axis.
             uniform: Uniform weight (i.e. no channel dependence).
             std: Inverse square of temporal standard deviation of sky.
@@ -959,7 +981,7 @@ def yscan(
         exclude_mkid_ids=exclude_mkid_ids,
         min_frequency=min_frequency,
         max_frequency=max_frequency,
-        data_type=data_type,
+        data_scaling=data_scaling,
         # options for analysis
         chan_weight=chan_weight,
         pwv=pwv,
@@ -981,7 +1003,7 @@ def zscan(
     exclude_mkid_ids: Optional[Sequence[int]] = DEFAULT_EXCL_MKID_IDS,
     min_frequency: Optional[str] = DEFAULT_MIN_FREQUENCY,
     max_frequency: Optional[str] = DEFAULT_MAX_FREQUENCY,
-    data_type: Literal["auto", "brightness", "df/f"] = DEFAULT_DATA_TYPE,
+    data_scaling: Optional[Literal["brightness", "df/f"]] = DEFAULT_DATA_SCALING,
     # options for analysis
     chan_weight: Literal["uniform", "std", "std/tx"] = "std/tx",
     pwv: Literal["0.5", "1.0", "2.0", "3.0", "4.0", "5.0"] = "5.0",
@@ -1006,8 +1028,8 @@ def zscan(
             Defaults to no minimum frequency bound.
         max_frequency: Maximum frequency to be included in analysis.
             Defaults to no maximum frequency bound.
-        data_type: Data type of the input DEMS file.
-            Defaults to the ``long_name`` attribute in it.
+        data_scaling: Data scaling to be used in analysis.
+            Defaults to the data scaling of the input DEMS file.
         chan_weight: Weighting method along the channel axis.
             uniform: Uniform weight (i.e. no channel dependence).
             std: Inverse square of temporal standard deviation of sky.
@@ -1038,7 +1060,7 @@ def zscan(
         exclude_mkid_ids=exclude_mkid_ids,
         min_frequency=min_frequency,
         max_frequency=max_frequency,
-        data_type=data_type,
+        data_scaling=data_scaling,
         # options for analysis
         chan_weight=chan_weight,
         pwv=pwv,
@@ -1062,7 +1084,7 @@ def _scan(
     exclude_mkid_ids: Optional[Sequence[int]] = DEFAULT_EXCL_MKID_IDS,
     min_frequency: Optional[str] = DEFAULT_MIN_FREQUENCY,
     max_frequency: Optional[str] = DEFAULT_MAX_FREQUENCY,
-    data_type: Literal["auto", "brightness", "df/f"] = DEFAULT_DATA_TYPE,
+    data_scaling: Optional[Literal["brightness", "df/f"]] = DEFAULT_DATA_SCALING,
     # options for analysis
     chan_weight: Literal["uniform", "std", "std/tx"] = "std/tx",
     pwv: Literal["0.5", "1.0", "2.0", "3.0", "4.0", "5.0"] = "5.0",
@@ -1088,8 +1110,8 @@ def _scan(
             Defaults to no minimum frequency bound.
         max_frequency: Maximum frequency to be included in analysis.
             Defaults to no maximum frequency bound.
-        data_type: Data type of the input DEMS file.
-            Defaults to the ``long_name`` attribute in it.
+        data_scaling: Data scaling to be used in analysis.
+            Defaults to the data scaling of the input DEMS file.
         chan_weight: Weighting method along the channel axis.
             uniform: Uniform weight (i.e. no channel dependence).
             std: Inverse square of temporal standard deviation of sky.
@@ -1113,18 +1135,20 @@ def _scan(
             LOGGER.debug(f"{key}: {val!r}")
 
     with xr.set_options(keep_attrs=True):
-        da = load_dems(
+        da = load.dems(
             dems,
             include_mkid_ids=include_mkid_ids,
             exclude_mkid_ids=exclude_mkid_ids,
             min_frequency=min_frequency,
             max_frequency=max_frequency,
-            data_type=data_type,
+            data_scaling=data_scaling,
+            skycoord_frame="relative",
+            chunks=None,
         )
 
         # make continuum series
-        da_on = select.by(da, "state", include="ON")
-        da_off = select.by(da, "state", exclude="ON")
+        da_on = da.sel(time=da.state == "ON")
+        da_off = da.sel(time=da.state != "ON")
         weight = get_chan_weight(da_off, method=chan_weight, pwv=pwv)
         series = da_on.weighted(weight.fillna(0)).mean("chan")
 
@@ -1144,7 +1168,7 @@ def _scan(
         plot.data(series, x=f"aste_subref_{axis}", ax=ax)
 
         for ax in axes:  # type: ignore
-            ax.set_title(f"{Path(dems).name}\n({da.observation})")
+            ax.set_title(f"{Path(dems).name}\n({da.aste_obs_file})")
             ax.grid(True)
 
         fig.tight_layout()
@@ -1209,11 +1233,11 @@ def subtract_per_abba_phase(dems: xr.DataArray, /) -> xr.DataArray:
         raise ValueError("State must be unique.")
 
     if states[0] == "ON":
-        src = select.by(dems, "beam", include="A").mean("time")
-        sky = select.by(dems, "beam", include="B").mean("time")
+        src = dems.sel(time=dems.beam == "A").mean("time")
+        sky = dems.sel(time=dems.beam == "B").mean("time")
     elif states[0] == "OFF":
-        src = select.by(dems, "beam", include="B").mean("time")
-        sky = select.by(dems, "beam", include="A").mean("time")
+        src = dems.sel(time=dems.beam == "B").mean("time")
+        sky = dems.sel(time=dems.beam == "A").mean("time")
     else:
         raise ValueError("State must be either ON or OFF.")
 
@@ -1272,92 +1296,6 @@ def get_robust_lim(da: xr.DataArray) -> tuple[float, float]:
     )
 
 
-def load_dems(
-    dems: Path,
-    /,
-    *,
-    include_mkid_ids: Optional[Sequence[int]] = DEFAULT_INCL_MKID_IDS,
-    exclude_mkid_ids: Optional[Sequence[int]] = DEFAULT_EXCL_MKID_IDS,
-    min_frequency: Optional[str] = DEFAULT_MIN_FREQUENCY,
-    max_frequency: Optional[str] = DEFAULT_MAX_FREQUENCY,
-    data_type: Literal["auto", "brightness", "df/f"] = DEFAULT_DATA_TYPE,
-    frequency_units: str = DEFAULT_FREQUENCY_UNITS,
-    skycoord_units: str = DEFAULT_SKYCOORD_UNITS,
-) -> xr.DataArray:
-    """Load a DEMS with given conversions and selections.
-
-    Args:
-        dems: Input DEMS file (netCDF or Zarr).
-        include_mkid_ids: MKID IDs to be included in analysis.
-            Defaults to all MKID IDs.
-        exclude_mkid_ids: MKID IDs to be excluded in analysis.
-            Defaults to no MKID IDs.
-        min_frequency: Minimum frequency to be included in analysis.
-            Defaults to no minimum frequency bound.
-        max_frequency: Maximum frequency to be included in analysis.
-            Defaults to no maximum frequency bound.
-        data_type: Data type of the input DEMS file.
-            Defaults to the ``long_name`` attribute in it.
-        frequency_units: Units of the frequency.
-        skycoord_units: Units of the sky coordinate axes.
-
-    Returns:
-        DEMS as a DataArray with given conversion and selections.
-
-    """
-    da = load.dems(dems, chunks=None)
-
-    if min_frequency is not None:
-        min_frequency = Quantity(min_frequency).to(frequency_units).value
-
-    if max_frequency is not None:
-        max_frequency = Quantity(max_frequency).to(frequency_units).value
-
-    if da.frame == "altaz":
-        z = np.pi / 2 - convert.units(da.lat, "rad")
-        secz = cast(xr.DataArray, 1 / np.cos(z))
-        da = da.assign_coords(
-            secz=secz.assign_attrs(long_name="sec(Z)", units="dimensionless")
-        )
-
-    da = convert.coord_units(
-        da,
-        ["d2_mkid_frequency", "frequency"],
-        frequency_units,
-    )
-    da = convert.coord_units(
-        da,
-        ["lat", "lat_origin", "lon", "lon_origin"],
-        skycoord_units,
-    )
-    da = assign.scan(da, by="state")
-    da = convert.frame(da, "relative")
-    da = select.by(da, "d2_mkid_type", "filter")
-    da = select.by(
-        da,
-        "chan",
-        include=include_mkid_ids,
-        exclude=exclude_mkid_ids,
-    )
-    da = select.by(
-        da,
-        "frequency",
-        min=min_frequency,
-        max=max_frequency,
-    )
-
-    if data_type == "auto" and "units" in da.attrs:
-        return da
-
-    if data_type == "brightness":
-        return da.assign_attrs(long_name="Brightness", units="K")
-
-    if data_type == "df/f":
-        return da.assign_attrs(long_name="df/f", units="dimensionless")
-
-    raise ValueError("Data type could not be inferred.")
-
-
 def save_qlook(
     qlook: Union[Figure, xr.DataArray, str],
     file: Path,
@@ -1391,7 +1329,7 @@ def save_qlook(
         return path
     elif isinstance(qlook, xr.DataArray):
         if path.name.endswith(".csv"):
-            name = qlook.attrs["data_type"]
+            name = qlook.attrs["data_scaling"]
             ds = qlook.to_dataset(name=name)
             ds.to_pandas().to_csv(path, **options)
             return path
diff --git a/decode/select.py b/decode/select.py
index 7ef2057..fe33fef 100644
--- a/decode/select.py
+++ b/decode/select.py
@@ -4,6 +4,7 @@
 # standard library
 from collections.abc import Sequence
 from datetime import datetime, timedelta
+from logging import getLogger
 from typing import Optional, TypeVar, Union
 
 
@@ -11,6 +12,10 @@
 import xarray as xr
 
 
+# constants
+LOGGER = getLogger(__name__)
+
+
 # type hints
 T = TypeVar("T", bool, int, float, str, datetime, timedelta)
 Multiple = Union[Sequence[T], T]
@@ -47,7 +52,21 @@ def by(
     Returns:
         Selected DEMS.
 
+    Important:
+        This function will be deprecated in a future release.
+        For ``include`` and ``exclude`` options, use ``xarray.DataArray.isin``
+        like ``dems.sel(time=dems.state.isin(['ON', 'OFF']))``.
+        For ``min`` and ``max`` options, use ``ndtools.Range`` like
+        ``dems.sel(chan=dems.frequency == Range(250e9, 300e9))``.
+
     """
+    LOGGER.warning(
+        "This function will be deprecated in a future release. "
+        "For include and exclude options, use DataArray.isin like "
+        "dems.sel(time=dems.state.isin(['ON', 'OFF'])). "
+        "For min and max options, use ndtools.Range like "
+        "dems.sel(chan=dems.frequency == Range(250e9, 300e9))."
+    )
     coord = dems[coord_name]
 
     if not isinstance(coord, xr.DataArray):
diff --git a/pyproject.toml b/pyproject.toml
index a249aa4..56b3409 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,9 +13,11 @@ keywords = [
 requires-python = ">=3.9,<3.13"
 dependencies = [
     "astropy>=6,<8",
+    "dask>=2024,<2026",
     "dems>=2025.6,<2026.0",
     "fire>=0.5,<1.0",
     "matplotlib>=3,<4",
+    "ndtools>=1,<2",
     "numpy>=1.23,<3.0",
     "pandas>=2,<3",
     "pandas-stubs>=2,<3",
diff --git a/tests/test_assign.py b/tests/test_assign.py
index c033ec8..355f633 100644
--- a/tests/test_assign.py
+++ b/tests/test_assign.py
@@ -8,7 +8,10 @@
 
 # constants
 DEMS_DIR = Path(__file__).parents[1] / "data" / "dems"
-DEMS = load.dems(DEMS_DIR / "dems_20171111110002.nc.gz").compute()
+DEMS = load.dems(
+    DEMS_DIR / "dems_20171111110002.nc.gz",
+    include_mkid_types=None,
+)
 
 
 def test_assign_scan() -> None:
diff --git a/tests/test_convert.py b/tests/test_convert.py
index db335a2..4b2c378 100644
--- a/tests/test_convert.py
+++ b/tests/test_convert.py
@@ -8,7 +8,10 @@
 
 # constants
 DEMS_DIR = Path(__file__).parents[1] / "data" / "dems"
-DEMS = load.dems(DEMS_DIR / "dems_20171111110002.nc.gz").compute()
+DEMS = load.dems(
+    DEMS_DIR / "dems_20171111110002.nc.gz",
+    include_mkid_types=None,
+)
 
 
 def test_convert_frame() -> None:
diff --git a/tests/test_load.py b/tests/test_load.py
index 3b6f8a5..3c83320 100644
--- a/tests/test_load.py
+++ b/tests/test_load.py
@@ -20,4 +20,4 @@ def test_load_atm() -> None:
 
 @mark.parametrize("dems", DEMS_ALL)
 def test_load_dems(dems: Path) -> None:
-    load.dems(dems)
+    load.dems(dems, include_mkid_types=None)
diff --git a/tests/test_plot.py b/tests/test_plot.py
index b5e2c47..c376500 100644
--- a/tests/test_plot.py
+++ b/tests/test_plot.py
@@ -9,7 +9,10 @@
 
 # constants
 DEMS_DIR = Path(__file__).parents[1] / "data" / "dems"
-DEMS = load.dems(DEMS_DIR / "dems_20171111110002.nc.gz").compute()
+DEMS = load.dems(
+    DEMS_DIR / "dems_20171111110002.nc.gz",
+    include_mkid_types=None,
+)
 
 
 def test_plot_data_1d_time() -> None:
diff --git a/tests/test_select.py b/tests/test_select.py
index c086dfd..f6d6763 100644
--- a/tests/test_select.py
+++ b/tests/test_select.py
@@ -9,7 +9,10 @@
 
 # constants
 DEMS_DIR = Path(__file__).parents[1] / "data" / "dems"
-DEMS = load.dems(DEMS_DIR / "dems_20171111110002.nc.gz").compute()
+DEMS = load.dems(
+    DEMS_DIR / "dems_20171111110002.nc.gz",
+    include_mkid_types=None,
+)
 
 
 def test_select_by_min() -> None:
diff --git a/uv.lock b/uv.lock
index bba91df..4a1cc8e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -347,6 +347,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/85/32/10bb5764d90a8eee674e9dc6f4db6a0ab47c8c4d0d83c27f7c39ac415a4d/click-8.2.1-py3-none-any.whl", hash = "sha256:61a3265b914e850b85317d0b3109c7f8cd35a670f963866005d6ef1d5175a12b", size = 102215, upload-time = "2025-05-20T23:19:47.796Z" },
 ]
 
+[[package]]
+name = "cloudpickle"
+version = "3.1.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/52/39/069100b84d7418bc358d81669d5748efb14b9cceacd2f9c75f550424132f/cloudpickle-3.1.1.tar.gz", hash = "sha256:b216fa8ae4019d5482a8ac3c95d8f6346115d8835911fd4aefd1a445e4242c64", size = 22113, upload-time = "2025-01-14T17:02:05.085Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/e8/64c37fadfc2816a7701fa8a6ed8d87327c7d54eacfbfb6edab14a2f2be75/cloudpickle-3.1.1-py3-none-any.whl", hash = "sha256:c8c5a44295039331ee9dad40ba100a9c7297b6f988e50e87ccdf3765a668350e", size = 20992, upload-time = "2025-01-14T17:02:02.417Z" },
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -478,6 +487,52 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" },
 ]
 
+[[package]]
+name = "dask"
+version = "2024.8.0"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.10'",
+]
+dependencies = [
+    { name = "click", version = "8.1.8", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "cloudpickle", marker = "python_full_version < '3.10'" },
+    { name = "fsspec", marker = "python_full_version < '3.10'" },
+    { name = "importlib-metadata", marker = "python_full_version < '3.10'" },
+    { name = "packaging", marker = "python_full_version < '3.10'" },
+    { name = "partd", marker = "python_full_version < '3.10'" },
+    { name = "pyyaml", marker = "python_full_version < '3.10'" },
+    { name = "toolz", marker = "python_full_version < '3.10'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/1f/2e/568a422d907745a3a897a732c83d05a3923d8cfa2511a6abea2a0e19994e/dask-2024.8.0.tar.gz", hash = "sha256:f1fec39373d2f101bc045529ad4e9b30e34e6eb33b7aa0fa7073aec7b1bf9eee", size = 9895684, upload-time = "2024-08-06T20:23:54.464Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/db/47/136a5dd68a33089f96f8aa1178ccd545d325ec9ab2bb42a3038711a935c0/dask-2024.8.0-py3-none-any.whl", hash = "sha256:250ea3df30d4a25958290eec4f252850091c6cfaed82d098179c3b25bba18309", size = 1233681, upload-time = "2024-08-06T20:23:42.258Z" },
+]
+
+[[package]]
+name = "dask"
+version = "2025.5.1"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version == '3.10.*'",
+]
+dependencies = [
+    { name = "click", version = "8.2.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "cloudpickle", marker = "python_full_version >= '3.10'" },
+    { name = "fsspec", marker = "python_full_version >= '3.10'" },
+    { name = "importlib-metadata", marker = "python_full_version >= '3.10' and python_full_version < '3.12'" },
+    { name = "packaging", marker = "python_full_version >= '3.10'" },
+    { name = "partd", marker = "python_full_version >= '3.10'" },
+    { name = "pyyaml", marker = "python_full_version >= '3.10'" },
+    { name = "toolz", marker = "python_full_version >= '3.10'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3d/29/05feb8e2531c46d763547c66b7f5deb39b53d99b3be1b4ddddbd1cec6567/dask-2025.5.1.tar.gz", hash = "sha256:979d9536549de0e463f4cab8a8c66c3a2ef55791cd740d07d9bf58fab1d1076a", size = 10969324, upload-time = "2025-05-20T19:54:30.688Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/38/30/53b0844a7a4c6b041b111b24ca15cc9b8661a86fe1f6aaeb2d0d7f0fb1f2/dask-2025.5.1-py3-none-any.whl", hash = "sha256:3b85fdaa5f6f989dde49da6008415b1ae996985ebdfb1e40de2c997d9010371d", size = 1474226, upload-time = "2025-05-20T19:54:20.309Z" },
+]
+
 [[package]]
 name = "decode"
 version = "2025.4.2"
@@ -486,10 +541,13 @@ dependencies = [
     { name = "astropy", version = "6.0.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
     { name = "astropy", version = "6.1.7", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
     { name = "astropy", version = "7.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "dask", version = "2024.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "dask", version = "2025.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
     { name = "dems" },
     { name = "fire" },
     { name = "matplotlib", version = "3.9.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
     { name = "matplotlib", version = "3.10.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "ndtools" },
     { name = "numpy", version = "1.26.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
     { name = "numpy", version = "2.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
@@ -527,9 +585,11 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "astropy", specifier = ">=6,<8" },
+    { name = "dask", specifier = ">=2024,<2026" },
     { name = "dems", specifier = ">=2025.6,<2026.0" },
     { name = "fire", specifier = ">=0.5,<1.0" },
     { name = "matplotlib", specifier = ">=3,<4" },
+    { name = "ndtools", specifier = ">=1,<2" },
     { name = "numpy", specifier = ">=1.23,<3.0" },
     { name = "pandas", specifier = ">=2,<3" },
     { name = "pandas-stubs", specifier = ">=2,<3" },
@@ -673,6 +733,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0b/2f/c536b5b9bb3c071e91d536a4d11f969e911dbb6b227939f4c5b0bca090df/fonttools-4.58.4-py3-none-any.whl", hash = "sha256:a10ce13a13f26cbb9f37512a4346bb437ad7e002ff6fa966a7ce7ff5ac3528bd", size = 1114660, upload-time = "2025-06-13T17:25:13.321Z" },
 ]
 
+[[package]]
+name = "fsspec"
+version = "2025.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/00/f7/27f15d41f0ed38e8fcc488584b57e902b331da7f7c6dcda53721b15838fc/fsspec-2025.5.1.tar.gz", hash = "sha256:2e55e47a540b91843b755e83ded97c6e897fa0942b11490113f09e9c443c2475", size = 303033, upload-time = "2025-05-24T12:03:23.792Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/bb/61/78c7b3851add1481b048b5fdc29067397a1784e2910592bc81bb3f608635/fsspec-2025.5.1-py3-none-any.whl", hash = "sha256:24d3a2e663d5fc735ab256263c4075f374a174c3410c0b25e5bd1970bceaa462", size = 199052, upload-time = "2025-05-24T12:03:21.66Z" },
+]
+
 [[package]]
 name = "idna"
 version = "3.10"
@@ -696,7 +765,7 @@ name = "importlib-metadata"
 version = "8.7.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "zipp", marker = "python_full_version < '3.10'" },
+    { name = "zipp", marker = "python_full_version < '3.12'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/76/66/650a33bd90f786193e4de4b3ad86ea60b53c89b669a5c7be931fac31cdb0/importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000", size = 56641, upload-time = "2025-04-27T15:29:01.736Z" }
 wheels = [
@@ -996,6 +1065,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3a/1d/50ad811d1c5dae091e4cf046beba925bcae0a610e79ae4c538f996f63ed5/kiwisolver-1.4.8-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:65ea09a5a3faadd59c2ce96dc7bf0f364986a315949dc6374f04396b0d60e09b", size = 71762, upload-time = "2024-12-24T18:30:48.903Z" },
 ]
 
+[[package]]
+name = "locket"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2f/83/97b29fe05cb6ae28d2dbd30b81e2e402a3eed5f460c26e9eaa5895ceacf5/locket-1.0.0.tar.gz", hash = "sha256:5c0d4c052a8bbbf750e056a8e65ccd309086f4f0f18a2eac306a8dfa4112a632", size = 4350, upload-time = "2022-04-20T22:04:44.312Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/db/bc/83e112abc66cd466c6b83f99118035867cecd41802f8d044638aa78a106e/locket-1.0.0-py2.py3-none-any.whl", hash = "sha256:b6c819a722f7b6bd955b80781788e4a66a55628b858d347536b7e81325a3a5e3", size = 4398, upload-time = "2022-04-20T22:04:42.23Z" },
+]
+
 [[package]]
 name = "markdown-it-py"
 version = "3.0.0"
@@ -1238,6 +1316,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/5f/df/76d0321c3797b54b60fef9ec3bd6f4cfd124b9e422182156a1dd418722cf/myst_parser-4.0.1-py3-none-any.whl", hash = "sha256:9134e88959ec3b5780aedf8a99680ea242869d012e8821db3126d427edc9c95d", size = 84579, upload-time = "2025-02-12T10:53:02.078Z" },
 ]
 
+[[package]]
+name = "ndtools"
+version = "1.0.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy", version = "1.26.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "pandas" },
+    { name = "pandas-stubs", version = "2.2.2.240807", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "pandas-stubs", version = "2.2.3.250527", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/40/c4/db95382830ae5b0e538ba0d37f4de8713a9736624c0d4dac7ceca624974c/ndtools-1.0.1.tar.gz", hash = "sha256:44d6b2da9e3f508772145e65035b91e3a510fc44054835caee0a2c569087c322", size = 63830, upload-time = "2025-04-29T06:47:52.795Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9e/74/7c15ce8fa6bc9eea35c19299a150502f8af55efa90816f5f4914ced7972e/ndtools-1.0.1-py3-none-any.whl", hash = "sha256:464aaed3ffd79770d200a89b041749454155da99afef3d3f359f6eab2431c965", size = 11114, upload-time = "2025-04-29T06:47:51.479Z" },
+]
+
 [[package]]
 name = "nodeenv"
 version = "1.9.1"
@@ -1555,6 +1651,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c6/ac/dac4a63f978e4dcb3c6d3a78c4d8e0192a113d288502a1216950c41b1027/parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18", size = 103650, upload-time = "2024-04-05T09:43:53.299Z" },
 ]
 
+[[package]]
+name = "partd"
+version = "1.4.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "locket" },
+    { name = "toolz" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b2/3a/3f06f34820a31257ddcabdfafc2672c5816be79c7e353b02c1f318daa7d4/partd-1.4.2.tar.gz", hash = "sha256:d022c33afbdc8405c226621b015e8067888173d85f7f5ecebb3cafed9a20f02c", size = 21029, upload-time = "2024-05-06T19:51:41.945Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/71/e7/40fb618334dcdf7c5a316c0e7343c5cd82d3d866edc100d98e29bc945ecd/partd-1.4.2-py3-none-any.whl", hash = "sha256:978e4ac767ec4ba5b86c6eaa52e5a2a3bc748a2ca839e8cc798f1cc6ce6efb0f", size = 18905, upload-time = "2024-05-06T19:51:39.271Z" },
+]
+
 [[package]]
 name = "pathspec"
 version = "0.12.1"
@@ -2271,6 +2380,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c7/18/c86eb8e0202e32dd3df50d43d7ff9854f8e0603945ff398974c1d91ac1ef/tomli_w-1.2.0-py3-none-any.whl", hash = "sha256:188306098d013b691fcadc011abd66727d3c414c571bb01b1a174ba8c983cf90", size = 6675, upload-time = "2025-01-15T12:07:22.074Z" },
 ]
 
+[[package]]
+name = "toolz"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8a/0b/d80dfa675bf592f636d1ea0b835eab4ec8df6e9415d8cfd766df54456123/toolz-1.0.0.tar.gz", hash = "sha256:2c86e3d9a04798ac556793bced838816296a2f085017664e4995cb40a1047a02", size = 66790, upload-time = "2024-10-04T16:17:04.001Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/03/98/eb27cc78ad3af8e302c9d8ff4977f5026676e130d28dd7578132a457170c/toolz-1.0.0-py3-none-any.whl", hash = "sha256:292c8f1c4e7516bf9086f8850935c799a874039c8bcf959d47b600e4c44a6236", size = 56383, upload-time = "2024-10-04T16:17:01.533Z" },
+]
+
 [[package]]
 name = "traitlets"
 version = "5.14.3"