pygmt.binstats: Let the parameter 'statistic' support descriptive arguments

pygmt/src/binstats.py

@@ -2,15 +2,17 @@
 binstats - Bin spatial data and determine statistics per bin.
 """
 
+from typing import Literal
+
 import xarray as xr
 from pygmt._typing import PathLike, TableLike
+from pygmt.alias import Alias, AliasSystem
 from pygmt.clib import Session
 from pygmt.helpers import build_arg_list, fmt_docstring, kwargs_to_strings, use_alias
 
 
 @fmt_docstring
 @use_alias(
-    C="statistic",
     E="empty",
     I="spacing",
     N="normalize",
@@ -26,7 +28,28 @@
 )
 @kwargs_to_strings(I="sequence", R="sequence", i="sequence_comma")
 def binstats(
-    data: PathLike | TableLike, outgrid: PathLike | None = None, **kwargs
+    data: PathLike | TableLike,
+    outgrid: PathLike | None = None,
+    statistic: Literal[
+        "mean",
+        "mad",
+        "full",
+        "interquartile",
+        "min",
+        "minpos",
+        "median",
+        "number",
+        "lms",
+        "mode",
+        "quantile",
+        "rms",
+        "stddev",
+        "max",
+        "maxneg",
+        "sum",
+    ] = "number",
+    quantile_value: float = 50,
+    **kwargs,
 ) -> xr.DataArray | None:
     r"""
     Bin spatial data and determine statistics per bin.
@@ -48,29 +71,29 @@ def binstats(
     data
         A file name of an ASCII data table or a 2-D {table-classes}.
     {outgrid}
-    statistic : str
-        **a**\|\ **d**\|\ **g**\|\ **i**\|\ **l**\|\ **L**\|\ **m**\|\ **n**\
-        \|\ **o**\|\ **p**\|\ **q**\ [*quant*]\|\ **r**\|\ **s**\|\ **u**\
-        \|\ **U**\|\ **z**.
-        Choose the statistic that will be computed per node based on the
-        points that are within *radius* distance of the node. Select one of:
+    statistic
+        Choose the statistic that will be computed per node based on the points that are
+        within *radius* distance of the node. Select one of:
 
-        - **a**: mean (average)
-        - **d**: median absolute deviation (MAD)
-        - **g**: full (max-min) range
-        - **i**: 25-75% interquartile range
-        - **l**: minimum (low)
-        - **L**: minimum of positive values only
-        - **m**: median
-        - **n**: number of values
-        - **o**: LMS scale
-        - **p**: mode (maximum likelihood)
-        - **q**: selected quantile (append desired quantile in 0-100% range [50])
-        - **r**: root mean square (RMS)
-        - **s**: standard deviation
-        - **u**: maximum (upper)
-        - **U**: maximum of negative values only
-        - **z**: sum
+        - **mean**: Compute the mean value (average).
+        - **mad**: Compute the median absolute deviation (MAD).
+        - **full**: Compute the full (max-min) range.
+        - **interquartile**: Compute the 25-75% interquartile range.
+        - **min**: Compute the minimum value.
+        - **minpos**: Compute the minimum of positive values only.
+        - **median**: Compute the median value.
+        - **number**: Compute the number of values.
+        - **lms**: Compute the LMS scale.
+        - **mode**: Compute the mode (maximum likelihood).
+        - **quantile**: Compute the selected quantile. The quantile value is in 0-100%
+          range and is specified by the ``quantile_value`` parameter.
+        - **rms**: Compute the root mean square (RMS).
+        - **stddev**: Compute the standard deviation.
+        - **max**: Compute the maximum value.
+        - **maxneg**: Compute the maximum of negative values only.
+        - **sum**: Compute the sum of values.
+    quantile_value
+        The quantile value if ``statistic="quantile"``.
     empty : float
         Set the value assigned to empty nodes [Default is NaN].
     normalize : bool
@@ -104,13 +127,40 @@ def binstats(
         - ``None`` if ``outgrid`` is set (grid output will be stored in the file set by
           ``outgrid``)
     """
+    aliasdict = AliasSystem(
+        C=Alias(
+            statistic,
+            name="statistic",
+            mapping={
+                "mean": "a",
+                "mad": "d",
+                "full": "g",
+                "interquartile": "i",
+                "min": "l",
+                "minpos": "L",
+                "median": "m",
+                "number": "n",
+                "lms": "o",
+                "mode": "p",
+                "quantile": "q",
+                "rms": "r",
+                "stddev": "s",
+                "max": "u",
+                "maxneg": "U",
+                "sum": "z",
+            },
+        ),
+    ).merge(kwargs)
+    if statistic == "quantile":
+        aliasdict["C"] += f"{quantile_value}"
+
     with Session() as lib:
         with (
             lib.virtualfile_in(check_kind="vector", data=data) as vintbl,
             lib.virtualfile_out(kind="grid", fname=outgrid) as voutgrd,
         ):
-            kwargs["G"] = voutgrd
+            aliasdict["G"] = voutgrd
             lib.call_module(
-                module="binstats", args=build_arg_list(kwargs, infile=vintbl)
+                module="binstats", args=build_arg_list(aliasdict, infile=vintbl)
             )
             return lib.virtualfile_to_raster(vfname=voutgrd, outgrid=outgrid)

pygmt/tests/test_binstats.py

@@ -20,7 +20,7 @@ def test_binstats_outgrid():
             data="@capitals.gmt",
             outgrid=tmpfile.name,
             spacing=5,
-            statistic="z",
+            statistic="sum",
             search_radius="1000k",
             aspatial="2=population",
             region="g",
@@ -37,7 +37,7 @@ def test_binstats_no_outgrid():
     temp_grid = binstats(
         data="@capitals.gmt",
         spacing=5,
-        statistic="z",
+        statistic="sum",
         search_radius="1000k",
         aspatial="2=population",
         region="g",
@@ -49,3 +49,25 @@ def test_binstats_no_outgrid():
     npt.assert_allclose(temp_grid.min(), 53)
     npt.assert_allclose(temp_grid.median(), 1232714.5)
     npt.assert_allclose(temp_grid.mean(), 4227489)
+
+
+def test_binstats_quantile():
+    """
+    Test binstats with no set outgrid.
+    """
+    temp_grid = binstats(
+        data="@capitals.gmt",
+        spacing=5,
+        statistic="quantile",
+        quantile_value=75,
+        search_radius="1000k",
+        aspatial="2=population",
+        region="g",
+    )
+    assert temp_grid.dims == ("y", "x")
+    assert temp_grid.gmt.gtype is GridType.CARTESIAN
+    assert temp_grid.gmt.registration is GridRegistration.GRIDLINE
+    npt.assert_allclose(temp_grid.max(), 15047685)
+    npt.assert_allclose(temp_grid.min(), 53)
+    npt.assert_allclose(temp_grid.median(), 543664.5)
+    npt.assert_allclose(temp_grid.mean(), 1661363.6)