Refactor _validate_data_input

pygmt/helpers/utils.py

@@ -15,6 +15,7 @@
 from pathlib import Path
 from typing import Any, Literal
 
+import numpy as np
 import xarray as xr
 from pygmt.encodings import charset
 from pygmt.exceptions import GMTInvalidInput
@@ -39,10 +40,20 @@
     "ISO-8859-15",
     "ISO-8859-16",
 ]
+# Type hints for the list of possible data kinds.
+Kind = Literal[
+    "arg", "empty", "file", "geojson", "grid", "image", "matrix", "stringio", "vectors"
+]
 
 
 def _validate_data_input(
-    data=None, x=None, y=None, z=None, required_z=False, required_data=True, kind=None
+    data=None,
+    x=None,
+    y=None,
+    z=None,
+    required_z: bool = False,
+    required_data: bool = True,
+    kind: Kind | None = None,
 ) -> None:
     """
     Check if the combination of data/x/y/z is valid.
@@ -76,23 +87,23 @@ def _validate_data_input(
     >>> _validate_data_input(data=data, required_z=True, kind="matrix")
     Traceback (most recent call last):
         ...
-    pygmt.exceptions.GMTInvalidInput: data must provide x, y, and z columns.
+    pygmt.exceptions.GMTInvalidInput: Need at least 3 columns but 2 column(s) are given.
     >>> _validate_data_input(
     ...     data=pd.DataFrame(data, columns=["x", "y"]),
     ...     required_z=True,
     ...     kind="vectors",
     ... )
     Traceback (most recent call last):
         ...
-    pygmt.exceptions.GMTInvalidInput: data must provide x, y, and z columns.
+    pygmt.exceptions.GMTInvalidInput: Need at least 3 columns but 2 column(s) are given.
     >>> _validate_data_input(
     ...     data=xr.Dataset(pd.DataFrame(data, columns=["x", "y"])),
     ...     required_z=True,
     ...     kind="vectors",
     ... )
     Traceback (most recent call last):
         ...
-    pygmt.exceptions.GMTInvalidInput: data must provide x, y, and z columns.
+    pygmt.exceptions.GMTInvalidInput: Need at least 3 columns but 2 column(s) are given.
     >>> _validate_data_input(data="infile", x=[1, 2, 3])
     Traceback (most recent call last):
         ...
@@ -115,34 +126,52 @@ def _validate_data_input(
     GMTInvalidInput
         If the data input is not valid.
     """
-    if data is None:  # data is None
-        if x is None and y is None:  # both x and y are None
-            if required_data:  # data is not optional
+    # Check if too much data is provided.
+    if data is not None and any(v is not None for v in (x, y, z)):
+        msg = "Too much data. Use either data or x/y/z."
+        raise GMTInvalidInput(msg)
+
+    # Determine the data kind if not provided.
+    kind = kind or data_kind(data, required=required_data)
+
+    # Determine the required number of columns based on the required_z flag.
+    required_cols = 3 if required_z else 1
+
+    # Check based on the data kind.
+    match kind:
+        case "empty":  # data is given via a series vectors like x/y/z.
+            if x is None and y is None:
                 msg = "No input data provided."
                 raise GMTInvalidInput(msg)
-        elif x is None or y is None:  # either x or y is None
-            msg = "Must provide both x and y."
-            raise GMTInvalidInput(msg)
-        if required_z and z is None:  # both x and y are not None, now check z
-            msg = "Must provide x, y, and z."
-            raise GMTInvalidInput(msg)
-    else:  # data is not None
-        if x is not None or y is not None or z is not None:
-            msg = "Too much data. Use either data or x/y/z."
-            raise GMTInvalidInput(msg)
-        # check if data has the required z column
-        if required_z:
-            msg = "data must provide x, y, and z columns."
-            if kind == "matrix" and data.shape[1] < 3:
+            if x is None or y is None:
+                msg = "Must provide both x and y."
+                raise GMTInvalidInput(msg)
+            if required_z and z is None:
+                msg = "Must provide x, y, and z."
+                raise GMTInvalidInput(msg)
+        case "matrix":  # 2-D numpy.ndarray
+            if (actual_cols := data.shape[1]) < required_cols:
+                msg = (
+                    f"Need at least {required_cols} columns but {actual_cols} column(s) "
+                    "are given."
+                )
+                raise GMTInvalidInput(msg)
+        case "vectors":
+            # The if-else block should match the codes in the virtualfile_in function.
+            if hasattr(data, "items") and not hasattr(data, "to_frame"):
+                # Dict, pandas.DataFrame, or xarray.Dataset, but not pd.Series.
+                _data = [array for _, array in data.items()]
+            else:
+                # Python list, tuple, numpy.ndarray, and pandas.Series types
+                _data = np.atleast_2d(np.asanyarray(data).T)
+
+            # Check if the number of columns is sufficient.
+            if (actual_cols := len(_data)) < required_cols:
+                msg = (
+                    f"Need at least {required_cols} columns but {actual_cols} "
+                    "column(s) are given."
+                )
                 raise GMTInvalidInput(msg)
-            if kind == "vectors":
-                if hasattr(data, "shape") and (
-                    (len(data.shape) == 1 and data.shape[0] < 3)
-                    or (len(data.shape) > 1 and data.shape[1] < 3)
-                ):  # np.ndarray or pd.DataFrame
-                    raise GMTInvalidInput(msg)
-                if hasattr(data, "data_vars") and len(data.data_vars) < 3:  # xr.Dataset
-                    raise GMTInvalidInput(msg)
 
 
 def _is_printable_ascii(argstr: str) -> bool:
@@ -261,11 +290,7 @@ def _check_encoding(argstr: str) -> Encoding:
     return "ISOLatin1+"
 
 
-def data_kind(
-    data: Any, required: bool = True
-) -> Literal[
-    "arg", "empty", "file", "geojson", "grid", "image", "matrix", "stringio", "vectors"
-]:
+def data_kind(data: Any, required: bool = True) -> Kind:
     r"""
     Check the kind of data that is provided to a module.