Refactor blockm* to use virtualfile_from_data and improve i/o

pygmt/src/blockm.py

@@ -3,12 +3,9 @@
 """
 import pandas as pd
 from pygmt.clib import Session
-from pygmt.exceptions import GMTInvalidInput
 from pygmt.helpers import (
     GMTTempFile,
     build_arg_string,
-    data_kind,
-    dummy_context,
     fmt_docstring,
     kwargs_to_strings,
     use_alias,
@@ -41,29 +38,24 @@ def _blockm(block_method, table, outfile, **kwargs):
           set by ``outfile``)
     """
 
-    kind = data_kind(table)
     with GMTTempFile(suffix=".csv") as tmpfile:
         with Session() as lib:
-            if kind == "matrix":
-                if not hasattr(table, "values"):
-                    raise GMTInvalidInput(f"Unrecognized data type: {type(table)}")
-                file_context = lib.virtualfile_from_matrix(table.values)
-            elif kind == "file":
-                if outfile is None:
-                    raise GMTInvalidInput("Please pass in a str to 'outfile'")
-                file_context = dummy_context(table)
-            else:
-                raise GMTInvalidInput(f"Unrecognized data type: {type(table)}")
-
-            with file_context as infile:
+            # Choose how data will be passed into the module
+            table_context = lib.virtualfile_from_data(check_kind="vector", data=table)
+            # Run blockm* on data table
+            with table_context as infile:
                 if outfile is None:
                     outfile = tmpfile.name
                 arg_str = " ".join([infile, build_arg_string(kwargs), "->" + outfile])
                 lib.call_module(module=block_method, args=arg_str)
 
         # Read temporary csv output to a pandas table
         if outfile == tmpfile.name:  # if user did not set outfile, return pd.DataFrame
-            result = pd.read_csv(tmpfile.name, sep="\t", names=table.columns)
+            try:
+                column_names = table.columns.to_list()
+                result = pd.read_csv(tmpfile.name, sep="\t", names=column_names)
+            except AttributeError:  # 'str' object has no attribute 'columns'
+                result = pd.read_csv(tmpfile.name, sep="\t", header=None, comment=">")
         elif outfile != tmpfile.name:  # return None if outfile set, output in outfile
             result = None
 
@@ -110,8 +102,7 @@ def blockmean(table, outfile=None, **kwargs):
         Specify the region of interest.
 
     outfile : str
-        Required if ``table`` is a file. The file name for the output ASCII
-        file.
+        The file name for the output ASCII file.
 
     {V}
     {a}
@@ -171,8 +162,7 @@ def blockmedian(table, outfile=None, **kwargs):
         Specify the region of interest.
 
     outfile : str
-        Required if ``table`` is a file. The file name for the output ASCII
-        file.
+        The file name for the output ASCII file.
 
     {V}
     {a}

pygmt/tests/test_blockmean.py

@@ -23,19 +23,18 @@ def test_blockmean_input_dataframe():
     assert output.shape == (5849, 3)
     npt.assert_allclose(output.iloc[0], [245.888877, 29.978707, -384.0])
 
-    return output
 
-
-def test_blockmean_wrong_kind_of_input_table_matrix():
+def test_blockmean_input_table_matrix():
     """
     Run blockmean using table input that is not a pandas.DataFrame but still a
     matrix.
     """
     dataframe = load_sample_bathymetry()
-    invalid_table = dataframe.values
-    assert data_kind(invalid_table) == "matrix"
-    with pytest.raises(GMTInvalidInput):
-        blockmean(table=invalid_table, spacing="5m", region=[245, 255, 20, 30])
+    table = dataframe.values
+    output = blockmean(table=table, spacing="5m", region=[245, 255, 20, 30])
+    assert isinstance(output, pd.DataFrame)
+    assert output.shape == (5849, 3)
+    npt.assert_allclose(output.iloc[0], [245.888877, 29.978707, -384.0])
 
 
 def test_blockmean_wrong_kind_of_input_table_grid():
@@ -67,12 +66,12 @@ def test_blockmean_input_filename():
         assert output.shape == (5849, 3)
         npt.assert_allclose(output.iloc[0], [245.888877, 29.978707, -384.0])
 
-    return output
-
 
 def test_blockmean_without_outfile_setting():
     """
     Run blockmean by not passing in outfile parameter setting.
     """
-    with pytest.raises(GMTInvalidInput):
-        blockmean(table="@tut_ship.xyz", spacing="5m", region=[245, 255, 20, 30])
+    output = blockmean(table="@tut_ship.xyz", spacing="5m", region=[245, 255, 20, 30])
+    assert isinstance(output, pd.DataFrame)
+    assert output.shape == (5849, 3)
+    npt.assert_allclose(output.iloc[0], [245.888877, 29.978707, -384.0])

pygmt/tests/test_blockmedian.py

@@ -23,19 +23,18 @@ def test_blockmedian_input_dataframe():
     assert output.shape == (5849, 3)
     npt.assert_allclose(output.iloc[0], [245.88819, 29.97895, -385.0])
 
-    return output
-
 
 def test_blockmedian_wrong_kind_of_input_table_matrix():
     """
     Run blockmedian using table input that is not a pandas.DataFrame but still
     a matrix.
     """
     dataframe = load_sample_bathymetry()
-    invalid_table = dataframe.values
-    assert data_kind(invalid_table) == "matrix"
-    with pytest.raises(GMTInvalidInput):
-        blockmedian(table=invalid_table, spacing="5m", region=[245, 255, 20, 30])
+    table = dataframe.values
+    output = blockmedian(table=table, spacing="5m", region=[245, 255, 20, 30])
+    assert isinstance(output, pd.DataFrame)
+    assert output.shape == (5849, 3)
+    npt.assert_allclose(output.iloc[0], [245.88819, 29.97895, -385.0])
 
 
 def test_blockmedian_wrong_kind_of_input_table_grid():
@@ -67,12 +66,12 @@ def test_blockmedian_input_filename():
         assert output.shape == (5849, 3)
         npt.assert_allclose(output.iloc[0], [245.88819, 29.97895, -385.0])
 
-    return output
-
 
 def test_blockmedian_without_outfile_setting():
     """
     Run blockmedian by not passing in outfile parameter setting.
     """
-    with pytest.raises(GMTInvalidInput):
-        blockmedian(table="@tut_ship.xyz", spacing="5m", region=[245, 255, 20, 30])
+    output = blockmedian(table="@tut_ship.xyz", spacing="5m", region=[245, 255, 20, 30])
+    assert isinstance(output, pd.DataFrame)
+    assert output.shape == (5849, 3)
+    npt.assert_allclose(output.iloc[0], [245.88819, 29.97895, -385.0])