def test_contour_incols_transposed_data(region):
"""
Make sure that transposing the data matrix still produces a correct result
with incols reordering the columns.
This is a regression test for
https://github.com/GenericMappingTools/pygmt/issues/1313
Modified from the test_contour_vec() test.
"""
fig = Figure()
x, y = np.meshgrid(np.linspace(region[0], region[1]),
np.linspace(region[2], region[3]))
x = x.flatten()
y = y.flatten()
z = (x - 0.5 * (region[0] + region[1]))**2 + 4 * y**2
z = np.exp(-z / 10**2 * np.log(2))
# generate dataframe
# switch x and y from here onwards to simulate different column order
data = np.array([y, x, z]).T
fig.contour(
data,
projection="X10c",
region=region,
frame="a",
pen=True,
incols=[1, 0, 2],
)
return fig
def test_contour_matrix(data, region):
"""
Plot data.
"""
fig = Figure()
fig.contour(data=data, projection="X3i", region=region, frame="ag", pen="")
return fig
def test_contour_deprecate_columns_to_incols(region):
"""
Make sure that the old parameter "columns" is supported and it reports an
warning.
Modified from the test_contour_vec() test.
"""
fig = Figure()
x, y = np.meshgrid(np.linspace(region[0], region[1]),
np.linspace(region[2], region[3]))
x = x.flatten()
y = y.flatten()
z = (x - 0.5 * (region[0] + region[1]))**2 + 4 * y**2
z = np.exp(-z / 10**2 * np.log(2))
# generate dataframe
# switch x and y from here onwards to simulate different column order
data = np.array([y, x, z]).T
with pytest.warns(expected_warning=FutureWarning) as record:
fig.contour(
data,
projection="X10c",
region=region,
frame="a",
pen=True,
columns=[1, 0, 2],
)
assert len(record) == 1 # check that only one warning was raised
return fig
def test_contour_from_file(region):
"Plot using the data file name instead of loaded data"
fig = Figure()
fig.contour(
data=POINTS_DATA, projection="X4i", region=region, frame="af", pen="#ffcb87"
)
return fig
def test_contour_matrix(array_func, data, region):
"""
Plot data.
"""
fig = Figure()
fig.contour(data=array_func(data),
projection="X10c",
region=region,
frame="ag",
pen=True)
return fig
def test_contour_vec(region):
"Plot an x-centered gaussian kernel with different y scale"
fig = Figure()
x, y = np.meshgrid(
np.linspace(region[0], region[1]), np.linspace(region[2], region[3])
)
x = x.flatten()
y = y.flatten()
z = (x - 0.5 * (region[0] + region[1])) ** 2 + 4 * y ** 2
z = np.exp(-z / 10 ** 2 * np.log(2))
fig.contour(x=x, y=y, z=z, projection="X4i", region=region, frame="a", pen="")
return fig
def test_contour_fail_no_data(data):
"""
Should raise an exception if no data is given.
"""
# Contour should raise an exception if no or not sufficient data
# is given
fig = Figure()
# Test all combinations where at least one data variable
# is not given:
for variable in product([None, data[:, 0]], repeat=3):
# Filter one valid configuration:
if not any(item is None for item in variable):
continue
with pytest.raises(GMTInvalidInput):
fig.contour(
x=variable[0],
y=variable[1],
z=variable[2],
region=region,
projection="X4i",
color="red",
frame="afg",
pen="",
)
# Should also fail if given too much data
with pytest.raises(GMTInvalidInput):
fig.contour(
x=data[:, 0],
y=data[:, 1],
z=data[:, 2],
data=data,
region=region,
projection="X4i",
style="c0.2c",
color="red",
frame="afg",
pen="",
)
fig.plot(data=trench_fid, pen="thicker,black,solid",
style="f1c/0.3c+r+t") # carrots denoting subduction direction
# Plot major SSEs as contours
if sses:
sse = ("/Users/Chow/Documents/academic/vuw/data/carto/slow_slip_events/"
"filter_SSE2002_2014.grd")
fig.grdcontour(sse,
pen=sse_pen,
limit=[100, 550],
annotation=(100, sse_fontsize))
# Deep 2006, 2008 slip
if deep_sses:
deep = ("/Users/Chow/Documents/academic/vuw/data/carto/slow_slip_events/"
"sum_06_08_deep.txt")
fig.contour(data=deep, levels=20, pen=deep_sse_pen, annotation=(20, "+f8"))
if seismicity:
fig.plot(data=seis_fid, style="a", color="white",
pen="1.,black") # style='c'
if moment_tensors and mt_fid:
mts = open(mt_fid, "r").readlines()
for mt in mts[1:]:
_, _, lat, lon, strike, dip, rake, *_ = mt.split(",")
*_, ml, mw, m0 = mt.split(",")
fig.meca(dict(strike=float(strike),
dip=float(dip),
rake=float(rake),
magnitude=float(mw) / 2),
scale="1c",
