Update output shape and mean values from some x2sys_cross tests (#2986)

pygmt/tests/test_x2sys_cross.py

@@ -1,6 +1,7 @@
 """
 Test pygmt.x2sys_cross.
 """
+import copy
 import os
 from pathlib import Path
 from tempfile import TemporaryDirectory
@@ -65,7 +66,7 @@ def test_x2sys_cross_input_file_output_dataframe():
  output = x2sys_cross(tracks=["@tut_ship.xyz"], tag=tag, coe="i")
 
  assert isinstance(output, pd.DataFrame)
- assert output.shape == (14294, 12)
+ assert output.shape == (14338, 12)
  columns = list(output.columns)
  assert columns[:6] == ["x", "y", "i_1", "i_2", "dist_1", "dist_2"]
  assert columns[6:] == ["head_1", "head_2", "vel_1", "vel_2", "z_X", "z_M"]
@@ -142,8 +143,9 @@ def test_x2sys_cross_input_dataframe_with_nan(tracks):
  tag=tag, fmtfile="xyz", suffix="xyzt", units=["de", "se"], force=True
  )
 
- tracks[0].loc[tracks[0]["z"] < -15, "z"] = np.nan # set some values to NaN
- output = x2sys_cross(tracks=tracks, tag=tag, coe="i")
+ newtracks = copy.deepcopy(x=tracks)
+ newtracks[0].loc[newtracks[0]["z"] < -15, "z"] = np.nan # set some NaN values
+ output = x2sys_cross(tracks=newtracks, tag=tag, coe="i")
 
  assert isinstance(output, pd.DataFrame)
  assert output.shape == (3, 12)
@@ -212,10 +214,10 @@ def test_x2sys_cross_region_interpolation_numpoints():
  )
 
  assert isinstance(output, pd.DataFrame)
- assert output.shape == (3867, 12)
+ assert output.shape == (3882, 12)
  # Check crossover errors (z_X) and mean value of observables (z_M)
- npt.assert_allclose(output.z_X.mean(), -139.2, rtol=1e-4)
- npt.assert_allclose(output.z_M.mean(), -2890.465813)
+ npt.assert_allclose(output.z_X.mean(), -138.66, rtol=1e-4)
+ npt.assert_allclose(output.z_M.mean(), -2896.875915)
 
 
 @pytest.mark.usefixtures("mock_x2sys_home")
@@ -229,7 +231,7 @@ def test_x2sys_cross_trackvalues():
  output = x2sys_cross(tracks=["@tut_ship.xyz"], tag=tag, trackvalues=True)
 
  assert isinstance(output, pd.DataFrame)
- assert output.shape == (14294, 12)
+ assert output.shape == (14338, 12)
  # Check mean of track 1 values (z_1) and track 2 values (z_2)
- npt.assert_allclose(output.z_1.mean(), -2420.569767)
- npt.assert_allclose(output.z_2.mean(), -2400.357549)
+ npt.assert_allclose(output.z_1.mean(), -2422.418556)
+ npt.assert_allclose(output.z_2.mean(), -2402.268364)