Refactor info to allow datetime inputs from xarray.Dataset and pandas…

….DataFrame tables (#619)

Changes the backend mechanism of `info`
from using lib.virtualfile_from_matrix()
(which only supports single non-datetime dtypes)
to using lib.virtualfile_from_vectors()
(which supports datetime inputs as of #464).

* Refactor info to use virtualfile_from_vectors to support datetime inputs
* Test that xarray.Dataset inputs into pygmt.info works too
* Expect failures on test_info_*_time_column on GMT 6.1.1
* Document xarray.Datasets with 1D data_vars as allowed inputs to info

Co-authored-by: Dongdong Tian <[email protected]>

pygmt/modules.py

@@ -78,9 +78,10 @@ def info(table, **kwargs):
 
     Parameters
     ----------
-    table : pandas.DataFrame or np.ndarray or str
-        Either a pandas dataframe, a 1D/2D numpy.ndarray or a file name to an
-        ASCII data table.
+    table : str or np.ndarray or pandas.DataFrame or xarray.Dataset
+        Pass in either a file name to an ASCII data table, a 1D/2D numpy array,
+        a pandas dataframe, or an xarray dataset made up of 1D xarray.DataArray
+        data variables.
     per_column : bool
         Report the min/max values per column in separate columns.
     spacing : str
@@ -107,10 +108,13 @@ def info(table, **kwargs):
         if kind == "file":
             file_context = dummy_context(table)
         elif kind == "matrix":
-            _table = np.asanyarray(table)
-            if table.ndim == 1:  # 1D arrays need to be 2D and transposed
-                _table = np.transpose(np.atleast_2d(_table))
-            file_context = lib.virtualfile_from_matrix(_table)
+            try:
+                # pandas.DataFrame and xarray.Dataset types
+                arrays = [array for _, array in table.items()]
+            except AttributeError:
+                # Python lists, tuples, and numpy ndarray types
+                arrays = np.atleast_2d(np.asanyarray(table).T)
+            file_context = lib.virtualfile_from_vectors(*arrays)
         else:
             raise GMTInvalidInput(f"Unrecognized data type: {type(table)}")
 

pygmt/tests/test_info.py

@@ -8,13 +8,17 @@
 import pandas as pd
 import pytest
 import xarray as xr
+from packaging.version import Version
 
-from .. import info
+from .. import clib, info
 from ..exceptions import GMTInvalidInput
 
 TEST_DATA_DIR = os.path.join(os.path.dirname(__file__), "data")
 POINTS_DATA = os.path.join(TEST_DATA_DIR, "points.txt")
 
+with clib.Session() as _lib:
+    gmt_version = Version(_lib.info["version"])
+
 
 def test_info():
     "Make sure info works on file name inputs"
@@ -33,7 +37,48 @@ def test_info_dataframe():
     table = pd.read_csv(POINTS_DATA, sep=" ", header=None)
     output = info(table=table)
     expected_output = (
-        "<matrix memory>: N = 20 <11.5309/61.7074> <-2.9289/7.8648> <0.1412/0.9338>\n"
+        "<vector memory>: N = 20 <11.5309/61.7074> <-2.9289/7.8648> <0.1412/0.9338>\n"
+    )
+    assert output == expected_output
+
+
+@pytest.mark.xfail(
+    condition=gmt_version <= Version("6.1.1"),
+    reason="UNIX timestamps returned instead of ISO datetime, should work on GMT 6.2.0 "
+    "after https://github.com/GenericMappingTools/gmt/issues/4241 is resolved",
+)
+def test_info_pandas_dataframe_time_column():
+    "Make sure info works on pandas.DataFrame inputs with a time column"
+    table = pd.DataFrame(
+        data={
+            "z": [10, 13, 12, 15, 14],
+            "time": pd.date_range(start="2020-01-01", periods=5),
+        }
+    )
+    output = info(table=table)
+    expected_output = (
+        "<vector memory>: N = 5 <10/15> <2020-01-01T00:00:00/2020-01-05T00:00:00>\n"
+    )
+    assert output == expected_output
+
+
+@pytest.mark.xfail(
+    condition=gmt_version <= Version("6.1.1"),
+    reason="UNIX timestamp returned instead of ISO datetime, should work on GMT 6.2.0 "
+    "after https://github.com/GenericMappingTools/gmt/issues/4241 is resolved",
+)
+def test_info_xarray_dataset_time_column():
+    "Make sure info works on xarray.Dataset 1D inputs with a time column"
+    table = xr.Dataset(
+        coords={"index": [0, 1, 2, 3, 4]},
+        data_vars={
+            "z": ("index", [10, 13, 12, 15, 14]),
+            "time": ("index", pd.date_range(start="2020-01-01", periods=5)),
+        },
+    )
+    output = info(table=table)
+    expected_output = (
+        "<vector memory>: N = 5 <10/15> <2020-01-01T00:00:00/2020-01-05T00:00:00>\n"
     )
     assert output == expected_output
 
@@ -43,15 +88,15 @@ def test_info_2d_array():
     table = np.loadtxt(POINTS_DATA)
     output = info(table=table)
     expected_output = (
-        "<matrix memory>: N = 20 <11.5309/61.7074> <-2.9289/7.8648> <0.1412/0.9338>\n"
+        "<vector memory>: N = 20 <11.5309/61.7074> <-2.9289/7.8648> <0.1412/0.9338>\n"
     )
     assert output == expected_output
 
 
 def test_info_1d_array():
     "Make sure info works on 1D numpy.ndarray inputs"
     output = info(table=np.arange(20))
-    expected_output = "<matrix memory>: N = 20 <0/19>\n"
+    expected_output = "<vector memory>: N = 20 <0/19>\n"
     assert output == expected_output