pygmt.grdhisteq: Improve performance by storing output in virtual files

Author: seisman

pygmt/src/grdhisteq.py

@@ -2,6 +2,8 @@
 grdhisteq - Perform histogram equalization for a grid.
 """
 
+from typing import Literal
+
 import numpy as np
 import pandas as pd
 from pygmt.clib import Session
@@ -135,15 +137,19 @@ def equalize_grid(grid, **kwargs):
     @fmt_docstring
     @use_alias(
         C="divisions",
-        D="outfile",
         R="region",
         N="gaussian",
         Q="quadratic",
         V="verbose",
         h="header",
     )
     @kwargs_to_strings(R="sequence")
-    def compute_bins(grid, output_type="pandas", **kwargs):
+    def compute_bins(
+        grid,
+        output_type: Literal["pandas", "numpy", "file"] = "pandas",
+        outfile: str | None = None,
+        **kwargs,
+    ) -> pd.DataFrame | np.ndarray | None:
         r"""
         Perform histogram equalization for a grid.
 
@@ -168,16 +174,8 @@ def compute_bins(grid, output_type="pandas", **kwargs):
         Parameters
         ----------
         {grid}
-        outfile : str or bool or None
-            The name of the output ASCII file to store the results of the
-            histogram equalization in.
-        output_type : str
-            Determine the format the xyz data will be returned in [Default is
-            ``pandas``]:
-
-                - ``numpy`` - :class:`numpy.ndarray`
-                - ``pandas``- :class:`pandas.DataFrame`
-                - ``file`` - ASCII file (requires ``outfile``)
+        {output_type}
+        {outfile}
         divisions : int
             Set the number of divisions of the data range.
         quadratic : bool
@@ -188,13 +186,13 @@ def compute_bins(grid, output_type="pandas", **kwargs):
 
         Returns
         -------
-        ret : pandas.DataFrame or numpy.ndarray or None
+        ret
             Return type depends on ``outfile`` and ``output_type``:
 
             - None if ``outfile`` is set (output will be stored in file set by
               ``outfile``)
-            - :class:`pandas.DataFrame` or :class:`numpy.ndarray` if
-              ``outfile`` is not set (depends on ``output_type``)
+            - :class:`pandas.DataFrame` or :class:`numpy.ndarray` if ``outfile`` is not
+              set (depends on ``output_type``)
 
         Example
         -------
@@ -225,39 +223,33 @@ def compute_bins(grid, output_type="pandas", **kwargs):
         This method does a weighted histogram equalization for geographic
         grids to account for node area varying with latitude.
         """
-        outfile = kwargs.get("D")
         output_type = validate_output_table_type(output_type, outfile=outfile)
 
         if kwargs.get("h") is not None and output_type != "file":
             raise GMTInvalidInput("'header' is only allowed with output_type='file'.")
 
-        with GMTTempFile(suffix=".txt") as tmpfile:
-            with Session() as lib:
-                with lib.virtualfile_in(check_kind="raster", data=grid) as vingrd:
-                    if outfile is None:
-                        kwargs["D"] = outfile = tmpfile.name  # output to tmpfile
-                    lib.call_module(
-                        module="grdhisteq", args=build_arg_string(kwargs, infile=vingrd)
-                    )
+        with Session() as lib:
+            with (
+                lib.virtualfile_in(check_kind="raster", data=grid) as vingrd,
+                lib.virtualfile_out(kind="dataset", fname=outfile) as vouttbl,
+            ):
+                kwargs["D"] = vouttbl  # -D for output file name
+                lib.call_module(
+                    module="grdhisteq", args=build_arg_string(kwargs, infile=vingrd)
+                )
 
-            if outfile == tmpfile.name:
-                # if user did not set outfile, return pd.DataFrame
-                result = pd.read_csv(
-                    filepath_or_buffer=outfile,
-                    sep="\t",
-                    header=None,
-                    names=["start", "stop", "bin_id"],
-                    dtype={
+            result = lib.virtualfile_to_dataset(
+                output_type=output_type,
+                vfile=vouttbl,
+                column_names=["start", "stop", "bin_id"],
+            )
+            if output_type == "pandas":
+                result = result.astype(
+                    {
                         "start": np.float32,
                         "stop": np.float32,
                         "bin_id": np.uint32,
-                    },
+                    }
                 )
-            elif outfile != tmpfile.name:
-                # return None if outfile set, output in outfile
-                return None
-
-            if output_type == "numpy":
-                return result.to_numpy()
-
-            return result.set_index("bin_id")
+                return result.set_index("bin_id")
+            return result