def test_call_module():
"""
Run a command to see if call_module works.
"""
data_fname = os.path.join(TEST_DATA_DIR, "points.txt")
out_fname = "test_call_module.txt"
with clib.Session() as lib:
with GMTTempFile() as out_fname:
lib.call_module("info",
"{} -C ->{}".format(data_fname, out_fname.name))
assert os.path.exists(out_fname.name)
output = out_fname.read().strip()
assert output == "11.5309 61.7074 -2.9289 7.8648 0.1412 0.9338"
def test_create_data_grid_range():
"""
Create a grid specifying range and inc instead of dim.
"""
with clib.Session() as lib:
# Grids from matrices using range and int
lib.create_data(
family="GMT_IS_GRID|GMT_VIA_MATRIX",
geometry="GMT_IS_SURFACE",
mode="GMT_CONTAINER_ONLY",
ranges=[150.0, 250.0, -20.0, 20.0],
inc=[0.1, 0.2],
)
def test_parse_constant_composite():
"""
Parsing a composite constant argument (separated by |) correctly.
"""
lib = clib.Session()
test_cases = ((family, via) for family in FAMILIES for via in VIAS)
for family, via in test_cases:
composite = "|".join([family, via])
expected = lib[family] + lib[via]
parsed = lib._parse_constant(composite,
valid=FAMILIES,
valid_modifiers=VIAS)
assert parsed == expected
def test_create_destroy_session():
"Test that create and destroy session are called without errors"
# Create two session and make sure they are not pointing to the same memory
session1 = clib.Session()
session1.create(name="test_session1")
assert session1.session_pointer is not None
session2 = clib.Session()
session2.create(name="test_session2")
assert session2.session_pointer is not None
assert session2.session_pointer != session1.session_pointer
session1.destroy()
session2.destroy()
# Create and destroy a session twice
ses = clib.Session()
for __ in range(2):
with pytest.raises(GMTCLibNoSessionError):
ses.session_pointer # pylint: disable=pointless-statement
ses.create("session1")
assert ses.session_pointer is not None
ses.destroy()
with pytest.raises(GMTCLibNoSessionError):
ses.session_pointer # pylint: disable=pointless-statement
def test_put_matrix_grid():
"Check that assigning a numpy 2d array to an ASCII and NetCDF grid works"
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
wesn = [10, 15, 30, 40, 0, 0]
inc = [1, 1]
shape = ((wesn[3] - wesn[2]) // inc[1] + 1, (wesn[1] - wesn[0]) // inc[0] + 1)
for dtype in dtypes:
with clib.Session() as lib:
grid = lib.create_data(
family="GMT_IS_GRID|GMT_VIA_MATRIX",
geometry="GMT_IS_SURFACE",
mode="GMT_CONTAINER_ONLY",
ranges=wesn[:4],
inc=inc,
registration="GMT_GRID_NODE_REG",
)
data = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
lib.put_matrix(grid, matrix=data)
# Save the data to a file to see if it's being accessed correctly
with GMTTempFile() as tmp_file:
lib.write_data(
"GMT_IS_MATRIX",
"GMT_IS_POINT",
"GMT_CONTAINER_AND_DATA",
wesn,
tmp_file.name,
grid,
)
# Load the data and check that it's correct
newdata = tmp_file.loadtxt(dtype=dtype)
npt.assert_allclose(newdata, data)
# Save the data to a netCDF grid and check that xarray can load it
with GMTTempFile() as tmp_grid:
lib.write_data(
"GMT_IS_MATRIX",
"GMT_IS_SURFACE",
"GMT_CONTAINER_AND_DATA",
wesn,
tmp_grid.name,
grid,
)
with xr.open_dataarray(tmp_grid.name) as dataarray:
assert dataarray.shape == shape
npt.assert_allclose(dataarray.data, np.flipud(data))
npt.assert_allclose(
dataarray.coords["x"].actual_range, np.array(wesn[0:2])
)
npt.assert_allclose(
dataarray.coords["y"].actual_range, np.array(wesn[2:4])
)
def test_put_vector_2d_fails():
"""
Check that it fails with an exception for multidimensional arrays.
"""
with clib.Session() as lib:
dataset = lib.create_data(
family="GMT_IS_DATASET|GMT_VIA_VECTOR",
geometry="GMT_IS_POINT",
mode="GMT_CONTAINER_ONLY",
dim=[1, 6, 1, 0], # columns, rows, layers, dtype
)
data = np.array([[37, 12, 556], [37, 12, 556]], dtype="int32")
with pytest.raises(GMTInvalidInput):
lib.put_vector(dataset, column=0, vector=data)
def test_put_vector_wrong_column():
"""
Check that it fails with an exception when giving an invalid column.
"""
with clib.Session() as lib:
dataset = lib.create_data(
family="GMT_IS_DATASET|GMT_VIA_VECTOR",
geometry="GMT_IS_POINT",
mode="GMT_CONTAINER_ONLY",
dim=[1, 3, 1, 0], # columns, rows, layers, dtype
)
data = np.array([37, 12, 556], dtype="float32")
with pytest.raises(GMTCLibError):
lib.put_vector(dataset, column=1, vector=data)
def test_virtual_file_bad_direction():
"""
Test passing an invalid direction argument.
"""
with clib.Session() as lib:
vfargs = (
"GMT_IS_DATASET|GMT_VIA_MATRIX",
"GMT_IS_POINT",
"GMT_IS_GRID", # The invalid direction argument
0,
)
with pytest.raises(GMTInvalidInput):
with lib.open_virtual_file(*vfargs):
print("This should have failed")
def test_put_vector_invalid_dtype():
"""
Check that it fails with an exception for invalid data types.
"""
with clib.Session() as lib:
dataset = lib.create_data(
family="GMT_IS_DATASET|GMT_VIA_VECTOR",
geometry="GMT_IS_POINT",
mode="GMT_CONTAINER_ONLY",
dim=[2, 3, 1, 0], # columns, rows, layers, dtype
)
data = np.array([37, 12, 556], dtype="object")
with pytest.raises(GMTInvalidInput):
lib.put_vector(dataset, column=1, vector=data)
def test_create_data_fails():
"""
Check that create_data raises exceptions for invalid input and output.
"""
# Passing in invalid mode
with pytest.raises(GMTInvalidInput):
with clib.Session() as lib:
lib.create_data(
family="GMT_IS_DATASET",
geometry="GMT_IS_SURFACE",
mode="Not_a_valid_mode",
dim=[0, 0, 1, 0],
ranges=[150.0, 250.0, -20.0, 20.0],
inc=[0.1, 0.2],
)
# Passing in invalid geometry
with pytest.raises(GMTInvalidInput):
with clib.Session() as lib:
lib.create_data(
family="GMT_IS_GRID",
geometry="Not_a_valid_geometry",
mode="GMT_CONTAINER_ONLY",
dim=[0, 0, 1, 0],
ranges=[150.0, 250.0, -20.0, 20.0],
inc=[0.1, 0.2],
)
# If the data pointer returned is None (NULL pointer)
with pytest.raises(GMTCLibError):
with clib.Session() as lib:
with mock(lib, "GMT_Create_Data", returns=None):
lib.create_data(
family="GMT_IS_DATASET",
geometry="GMT_IS_SURFACE",
mode="GMT_CONTAINER_ONLY",
dim=[11, 10, 2, 0],
)
def test_virtualfile_from_vectors_arraylike():
"Pass array-like vectors to a dataset"
size = 13
x = list(range(0, size, 1))
y = tuple(range(size, size * 2, 1))
z = range(size * 2, size * 3, 1)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(x, y, z) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", "{} ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join(
["<{:.0f}/{:.0f}>".format(min(i), max(i)) for i in (x, y, z)])
expected = "<vector memory>: N = {}\t{}\n".format(size, bounds)
assert output == expected
def test_virtualfile_from_vectors_arraylike():
"""
Pass array-like vectors to a dataset.
"""
size = 13
x = list(range(0, size, 1))
y = tuple(range(size, size * 2, 1))
z = range(size * 2, size * 3, 1)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(x, y, z) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", f"{vfile} ->{outfile.name}")
output = outfile.read(keep_tabs=True)
bounds = "\t".join([f"<{min(i):.0f}/{max(i):.0f}>" for i in (x, y, z)])
expected = f"<vector memory>: N = {size}\t{bounds}\n"
assert output == expected
def test_write_data_fails():
"Check that write data raises an exception for non-zero return codes"
# It's hard to make the C API function fail without causing a Segmentation
# Fault. Can't test this if by giving a bad file name because if
# output=='', GMT will just write to stdout and spaces are valid file
# names. Use a mock instead just to exercise this part of the code.
with clib.Session() as lib:
with mock(lib, "GMT_Write_Data", returns=1):
with pytest.raises(GMTCLibError):
lib.write_data(
"GMT_IS_VECTOR",
"GMT_IS_POINT",
"GMT_WRITE_SET",
[1] * 6,
"some-file-name",
None,
)
def test_virtualfile_from_vectors_one_string_or_object_column(dtype):
"""
Test passing in one column with string or object dtype into virtual file
dataset.
"""
size = 5
x = np.arange(size, dtype=np.int32)
y = np.arange(size, size * 2, 1, dtype=np.int32)
strings = np.array(["a", "bc", "defg", "hijklmn", "opqrst"], dtype=dtype)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(x, y, strings) as vfile:
with GMTTempFile() as outfile:
lib.call_module("convert", f"{vfile} ->{outfile.name}")
output = outfile.read(keep_tabs=True)
expected = "".join(f"{i}\t{j}\t{k}\n"
for i, j, k in zip(x, y, strings))
assert output == expected
def test_virtualfile_from_matrix():
"""
Test transforming a matrix to virtual file dataset.
"""
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
shape = (7, 5)
for dtype in dtypes:
data = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
with clib.Session() as lib:
with lib.virtualfile_from_matrix(data) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", f"{vfile} ->{outfile.name}")
output = outfile.read(keep_tabs=True)
bounds = "\t".join(
[f"<{col.min():.0f}/{col.max():.0f}>" for col in data.T])
expected = f"<matrix memory>: N = {shape[0]}\t{bounds}\n"
assert output == expected
def test_virtualfile_from_vectors_transpose():
"Test transforming matrix columns to virtual file dataset"
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
shape = (7, 5)
for dtype in dtypes:
data = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(*data.T) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info",
"{} -C ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join([
"{:.0f}\t{:.0f}".format(col.min(), col.max()) for col in data.T
])
expected = "{}\n".format(bounds)
assert output == expected
def test_create_data_dataset():
"Run the function to make sure it doesn't fail badly."
with clib.Session() as lib:
# Dataset from vectors
data_vector = lib.create_data(
family="GMT_IS_DATASET|GMT_VIA_VECTOR",
geometry="GMT_IS_POINT",
mode="GMT_CONTAINER_ONLY",
dim=[10, 20, 1, 0], # columns, rows, layers, dtype
)
# Dataset from matrices
data_matrix = lib.create_data(
family="GMT_IS_DATASET|GMT_VIA_MATRIX",
geometry="GMT_IS_POINT",
mode="GMT_CONTAINER_ONLY",
dim=[10, 20, 1, 0],
)
assert data_vector != data_matrix
def test_virtualfile_from_vectors_two_string_or_object_columns(dtype):
"""
Test passing in two columns of string or object dtype into virtual file
dataset.
"""
size = 5
x = np.arange(size, dtype=np.int32)
y = np.arange(size, size * 2, 1, dtype=np.int32)
strings1 = np.array(["a", "bc", "def", "ghij", "klmno"], dtype=dtype)
strings2 = np.array(["pqrst", "uvwx", "yz!", "@#", "$"], dtype=dtype)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(x, y, strings1, strings2) as vfile:
with GMTTempFile() as outfile:
lib.call_module("convert", f"{vfile} ->{outfile.name}")
output = outfile.read(keep_tabs=True)
expected = "".join(f"{h}\t{i}\t{j} {k}\n"
for h, i, j, k in zip(x, y, strings1, strings2))
assert output == expected
def test_virtualfile_from_vectors():
"Test the automation for transforming vectors to virtual file dataset"
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
size = 10
for dtype in dtypes:
x = np.arange(size, dtype=dtype)
y = np.arange(size, size * 2, 1, dtype=dtype)
z = np.arange(size * 2, size * 3, 1, dtype=dtype)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(x, y, z) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info",
"{} ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join([
"<{:.0f}/{:.0f}>".format(i.min(), i.max()) for i in (x, y, z)
])
expected = "<vector memory>: N = {}\t{}\n".format(size, bounds)
assert output == expected
def test_virtualfile_from_vectors():
"""
Test the automation for transforming vectors to virtual file dataset.
"""
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
size = 10
for dtype in dtypes:
x = np.arange(size, dtype=dtype)
y = np.arange(size, size * 2, 1, dtype=dtype)
z = np.arange(size * 2, size * 3, 1, dtype=dtype)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(x, y, z) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", f"{vfile} ->{outfile.name}")
output = outfile.read(keep_tabs=True)
bounds = "\t".join(
[f"<{i.min():.0f}/{i.max():.0f}>" for i in (x, y, z)])
expected = f"<vector memory>: N = {size}\t{bounds}\n"
assert output == expected
def test_fails_for_wrong_version():
"Make sure the clib.Session raises an exception if GMT is too old"
# Mock GMT_Get_Default to return an old version
def mock_defaults(api, name, value): # pylint: disable=unused-argument
"Return an old version"
if name == b"API_VERSION":
value.value = b"5.4.3"
else:
value.value = b"bla"
return 0
lib = clib.Session()
with mock(lib, "GMT_Get_Default", mock_func=mock_defaults):
with pytest.raises(GMTVersionError):
with lib:
assert lib.info["version"] != "5.4.3"
# Make sure the session is closed when the exception is raised.
with pytest.raises(GMTCLibNoSessionError):
assert lib.session_pointer
def test_virtualfile_from_matrix_slice():
"Test transforming a slice of a larger array to virtual file dataset"
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
shape = (10, 6)
for dtype in dtypes:
full_data = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
rows = 5
cols = 3
data = full_data[:rows, :cols]
with clib.Session() as lib:
with lib.virtualfile_from_matrix(data) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info",
"{} ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join([
"<{:.0f}/{:.0f}>".format(col.min(), col.max())
for col in data.T
])
expected = "<matrix memory>: N = {}\t{}\n".format(rows, bounds)
assert output == expected
def test_virtualfile_from_data_required_z_matrix(array_func, kind):
"""
Test that function works when third z column in a matrix is needed and
provided.
"""
shape = (5, 3)
dataframe = pd.DataFrame(data=np.arange(shape[0] *
shape[1]).reshape(shape),
columns=["x", "y", "z"])
data = array_func(dataframe)
with clib.Session() as lib:
with lib.virtualfile_from_data(data=data, required_z=True) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", f"{vfile} ->{outfile.name}")
output = outfile.read(keep_tabs=True)
bounds = "\t".join([
f"<{i.min():.0f}/{i.max():.0f}>"
for i in (dataframe.x, dataframe.y, dataframe.z)
])
expected = f"<{kind} memory>: N = {shape[0]}\t{bounds}\n"
assert output == expected
def test_put_strings():
"""
Check that assigning a numpy array of dtype str to a dataset works.
"""
with clib.Session() as lib:
dataset = lib.create_data(
family="GMT_IS_DATASET|GMT_VIA_VECTOR",
geometry="GMT_IS_POINT",
mode="GMT_CONTAINER_ONLY",
dim=[2, 5, 1, 0], # columns, rows, layers, dtype
)
x = np.array([1, 2, 3, 4, 5], dtype=np.int32)
y = np.array([6, 7, 8, 9, 10], dtype=np.int32)
strings = np.array(["a", "bc", "defg", "hijklmn", "opqrst"],
dtype=np.str)
lib.put_vector(dataset, column=lib["GMT_X"], vector=x)
lib.put_vector(dataset, column=lib["GMT_Y"], vector=y)
lib.put_strings(dataset,
family="GMT_IS_VECTOR|GMT_IS_DUPLICATE",
strings=strings)
# Turns out wesn doesn't matter for Datasets
wesn = [0] * 6
# Save the data to a file to see if it's being accessed correctly
with GMTTempFile() as tmp_file:
lib.write_data(
"GMT_IS_VECTOR",
"GMT_IS_POINT",
"GMT_WRITE_SET",
wesn,
tmp_file.name,
dataset,
)
# Load the data and check that it's correct
newx, newy, newstrings = tmp_file.loadtxt(unpack=True,
dtype=[("x", np.int32),
("y", np.int32),
("text", "<U7")])
npt.assert_array_equal(newx, x)
npt.assert_array_equal(newy, y)
npt.assert_array_equal(newstrings, strings)
def test_put_vector_mixed_dtypes():
"""
Passing a numpy array of mixed dtypes to a dataset.
See https://github.com/GenericMappingTools/pygmt/issues/255
"""
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
for dtypex, dtypey in itertools.permutations(dtypes, r=2):
with clib.Session() as lib:
dataset = lib.create_data(
family="GMT_IS_DATASET|GMT_VIA_VECTOR",
geometry="GMT_IS_POINT",
mode="GMT_CONTAINER_ONLY",
dim=[2, 5, 1, 0], # columns, rows, layers, dtype
)
x = np.array([1, 2, 3, 4, 5], dtype=dtypex)
y = np.array([6, 7, 8, 9, 10], dtype=dtypey)
lib.put_vector(dataset, column=lib["GMT_X"], vector=x)
lib.put_vector(dataset, column=lib["GMT_Y"], vector=y)
# Turns out wesn doesn't matter for Datasets
wesn = [0] * 6
# Save the data to a file to see if it's being accessed correctly
with GMTTempFile() as tmp_file:
lib.write_data(
"GMT_IS_VECTOR",
"GMT_IS_POINT",
"GMT_WRITE_SET",
wesn,
tmp_file.name,
dataset,
)
# Load the data and check that it's correct
newx, newy = tmp_file.loadtxt(unpack=True,
dtype=[("x", dtypex),
("y", dtypey)])
assert x.dtype == newx.dtype
assert y.dtype == newy.dtype
npt.assert_allclose(newx, x)
npt.assert_allclose(newy, y)
def test_virtualfile_from_vectors_pandas():
"Pass vectors to a dataset using pandas Series"
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
size = 13
for dtype in dtypes:
data = pd.DataFrame(data=dict(
x=np.arange(size, dtype=dtype),
y=np.arange(size, size * 2, 1, dtype=dtype),
z=np.arange(size * 2, size * 3, 1, dtype=dtype),
))
with clib.Session() as lib:
with lib.virtualfile_from_vectors(data.x, data.y, data.z) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info",
"{} ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join([
"<{:.0f}/{:.0f}>".format(i.min(), i.max())
for i in (data.x, data.y, data.z)
])
expected = "<vector memory>: N = {}\t{}\n".format(size, bounds)
assert output == expected
def test_put_vector():
"""
Check that assigning a numpy array to a dataset works.
"""
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
for dtype in dtypes:
with clib.Session() as lib:
dataset = lib.create_data(
family="GMT_IS_DATASET|GMT_VIA_VECTOR",
geometry="GMT_IS_POINT",
mode="GMT_CONTAINER_ONLY",
dim=[3, 5, 1, 0], # columns, rows, layers, dtype
)
x = np.array([1, 2, 3, 4, 5], dtype=dtype)
y = np.array([6, 7, 8, 9, 10], dtype=dtype)
z = np.array([11, 12, 13, 14, 15], dtype=dtype)
lib.put_vector(dataset, column=lib["GMT_X"], vector=x)
lib.put_vector(dataset, column=lib["GMT_Y"], vector=y)
lib.put_vector(dataset, column=lib["GMT_Z"], vector=z)
# Turns out wesn doesn't matter for Datasets
wesn = [0] * 6
# Save the data to a file to see if it's being accessed correctly
with GMTTempFile() as tmp_file:
lib.write_data(
"GMT_IS_VECTOR",
"GMT_IS_POINT",
"GMT_WRITE_SET",
wesn,
tmp_file.name,
dataset,
)
# Load the data and check that it's correct
newx, newy, newz = tmp_file.loadtxt(unpack=True, dtype=dtype)
npt.assert_allclose(newx, x)
npt.assert_allclose(newy, y)
npt.assert_allclose(newz, z)
def test_get_default_fails():
"Make sure get_default raises an exception for invalid names"
with clib.Session() as lib:
with pytest.raises(GMTCLibError):
lib.get_default("NOT_A_VALID_NAME")
def test_put_vector_string_dtype():
"""
Passing string type vectors to a dataset.
"""
# input string vectors: numbers, longitudes, latitudes, and datetimes
vectors = np.array([
["10", "20.0", "-30.0", "3.5e1"],
["10W", "30.50E", "30:30W", "40:30:30.500E"],
["10N", "30.50S", "30:30N", "40:30:30.500S"],
[
"2021-02-03", "2021-02-03T04", "2021-02-03T04:05:06.700",
"T04:50:06.700"
],
])
# output vectors in double or string type
# Notes:
# 1. longitudes and latitudes are stored in double in GMT
# 2. The default output format for datetime is YYYY-mm-ddTHH:MM:SS
expected_vectors = [
[10.0, 20.0, -30.0, 35],
[-10, 30.5, -30.5, 40.508472],
[10, -30.50, 30.5, -40.508472],
[
"2021-02-03T00:00:00",
"2021-02-03T04:00:00",
"2021-02-03T04:05:06",
f"{datetime.utcnow().strftime('%Y-%m-%d')}T04:50:06",
],
]
# loop over all possible combinations of input types
for i, j in itertools.combinations_with_replacement(range(4), r=2):
with clib.Session() as lib:
dataset = lib.create_data(
family="GMT_IS_DATASET|GMT_VIA_VECTOR",
geometry="GMT_IS_POINT",
mode="GMT_CONTAINER_ONLY",
dim=[2, 4, 1, 0], # columns, rows, layers, dtype
)
lib.put_vector(dataset, column=lib["GMT_X"], vector=vectors[i])
lib.put_vector(dataset, column=lib["GMT_Y"], vector=vectors[j])
# Turns out wesn doesn't matter for Datasets
wesn = [0] * 6
# Save the data to a file to see if it's being accessed correctly
with GMTTempFile() as tmp_file:
lib.write_data(
"GMT_IS_VECTOR",
"GMT_IS_POINT",
"GMT_WRITE_SET",
wesn,
tmp_file.name,
dataset,
)
# Load the data
output = np.genfromtxt(tmp_file.name,
dtype=None,
names=("x", "y"),
encoding=None)
# check that the output is correct
# Use npt.assert_allclose for numeric arrays
# and npt.assert_array_equal for string arrays
if i != 3:
npt.assert_allclose(output["x"], expected_vectors[i])
else:
npt.assert_array_equal(output["x"], expected_vectors[i])
if j != 3:
npt.assert_allclose(output["y"], expected_vectors[j])
else:
npt.assert_array_equal(output["y"], expected_vectors[j])
import xarray as xr
from packaging.version import Version
from pygmt import Figure, clib
from pygmt.clib.conversion import dataarray_to_matrix
from pygmt.clib.session import FAMILIES, VIAS
from pygmt.exceptions import (
GMTCLibError,
GMTCLibNoSessionError,
GMTInvalidInput,
GMTVersionError,
)
from pygmt.helpers import GMTTempFile
TEST_DATA_DIR = os.path.join(os.path.dirname(__file__), "data")
with clib.Session() as _lib:
gmt_version = Version(_lib.info["version"])
@contextmanager
def mock(session, func, returns=None, mock_func=None):
"""
Mock a GMT C API function to make it always return a given value.
Used to test that exceptions are raised when API functions fail by
producing a NULL pointer as output or non-zero status codes.
Needed because it's not easy to get some API functions to fail without
inducing a Segmentation Fault (which is a good thing because libgmt usually
only fails with errors).
"""
