def test_grdtrack_without_newcolname_setting(dataarray):
"""
Run grdtrack by not passing in newcolname parameter setting.
"""
dataframe = load_ocean_ridge_points()
with pytest.raises(GMTInvalidInput):
grdtrack(points=dataframe, grid=dataarray)
def test_grdtrack_without_outfile_setting(dataarray):
"""
Run grdtrack by not passing in outfile parameter setting.
"""
csvfile = which("@ridge.txt", download="c")
with pytest.raises(GMTInvalidInput):
grdtrack(points=csvfile, grid=dataarray)
def test_grdtrack_wrong_kind_of_points_input(dataarray, dataframe):
"""
Run grdtrack using points input that is not a pandas.DataFrame (matrix) or
file.
"""
invalid_points = dataframe.longitude.to_xarray()
assert data_kind(invalid_points) == "grid"
with pytest.raises(GMTInvalidInput):
grdtrack(points=invalid_points, grid=dataarray, newcolname="bathymetry")
def test_grdtrack_wrong_kind_of_grid_input(dataarray, dataframe):
"""
Run grdtrack using grid input that is not as xarray.DataArray (grid) or
file.
"""
invalid_grid = dataarray.to_dataset()
assert data_kind(invalid_grid) == "matrix"
with pytest.raises(GMTInvalidInput):
grdtrack(points=dataframe, grid=invalid_grid, newcolname="bathymetry")
def gridtrack(Longitude='', Latitude='', geotif='', geotif_name='', geotif_epsg=''):
import pygmt
import rioxarray
import pandas as pd
Longitude = Longitude
Latitude = Latitude
geotif = geotif
geotif_name = geotif_name
geotif_epsg = geotif_epsg
df = pd.DataFrame(Longitude)
df['Latitude'] = pd.DataFrame(Latitude)
df.columns = ['Longitude', 'Latitude']
rds = rioxarray.open_rasterio(geotif)
rds.rio.set_crs(geotif_epsg)
rds = rds.rio.reproject("epsg:4326")
rds = rds.squeeze('band')
rds = rds.astype(float)
df = pygmt.grdtrack(df, rds, newcolname=geotif_name)
raster_vals = df[geotif_name].values
return raster_vals
del df, Latitude, Longitude
del rds
def get_crustal_thickness_points(points,
grid=None,
top_name='CRUST1-TOP',
bottom_name='CRUST3-BOTTOM'):
# if no grid is provided, we take the layer thickness from litho1.0
if not grid:
import litho1pt0 as litho
ptlats = np.array([pt.to_lat_lon()[0] for pt in points])
ptlons = np.array([pt.to_lat_lon()[1] for pt in points])
top_depth = litho.layer_depth(ptlats, ptlons, top_name)
bottom_depth = litho.layer_depth(ptlats, ptlons, bottom_name)
layer_thickness = bottom_depth - top_depth
return layer_thickness
# if a grid is provided, sample using grdtrack
else:
ptlats = np.array([pt.to_lat_lon()[0] for pt in points])
ptlons = np.array([pt.to_lat_lon()[1] for pt in points])
layer_thickness = pygmt.grdtrack(points=np.vstack((ptlons, ptlats)),
grid=grid,
newcolname='z')
return np.array(layer_thickness['z'])
def test_grdtrack_input_csvfile_and_ncfile_to_dataframe(expected_array):
"""
Run grdtrack by passing in a csv file and netcdf file as inputs with a
pandas.DataFrame output.
"""
output = grdtrack(points=POINTS_DATA, grid="@static_earth_relief.nc")
assert isinstance(output, pd.DataFrame)
npt.assert_allclose(np.array(output), expected_array)
def test_grdtrack_without_outfile_setting(csvfile, ncfile):
"""
Run grdtrack by not passing in outfile parameter setting.
"""
output = grdtrack(points=csvfile, grid=ncfile)
npt.assert_allclose(output.iloc[0], [-32.2971, 37.4118, -1939.748245])
return output
def test_grdtrack_input_dataframe_and_ncfile(dataframe, expected_array):
"""
Run grdtrack by passing in a pandas.DataFrame and netcdf file as inputs.
"""
output = grdtrack(points=dataframe,
grid="@static_earth_relief.nc",
newcolname="bathymetry")
assert isinstance(output, pd.DataFrame)
assert output.columns.to_list() == ["longitude", "latitude", "bathymetry"]
npt.assert_allclose(np.array(output), expected_array)
def test_grdtrack_without_outfile_setting():
"""
Run grdtrack by not passing in outfile parameter setting.
"""
csvfile = which("@ridge.txt", download="c")
ncfile = which("@earth_relief_01d", download="a")
output = grdtrack(points=csvfile, grid=ncfile)
npt.assert_allclose(output.iloc[0], [-32.2971, 37.4118, -1939.748245])
return output
def test_grdtrack_input_dataframe_and_dataarray(dataarray, dataframe):
"""
Run grdtrack by passing in a pandas.DataFrame and xarray.DataArray as
inputs.
"""
output = grdtrack(points=dataframe, grid=dataarray, newcolname="bathymetry")
assert isinstance(output, pd.DataFrame)
assert output.columns.to_list() == ["longitude", "latitude", "bathymetry"]
npt.assert_allclose(output.iloc[0], [-110.9536, -42.2489, -2797.394987])
return output
def test_grdtrack_input_dataframe_and_ncfile(dataframe, ncfile):
"""
Run grdtrack by passing in a pandas.DataFrame and netcdf file as inputs.
"""
output = grdtrack(points=dataframe, grid=ncfile, newcolname="bathymetry")
assert isinstance(output, pd.DataFrame)
assert output.columns.to_list() == ["longitude", "latitude", "bathymetry"]
npt.assert_allclose(output.iloc[0], [-32.2971, 37.4118, -1939.748245])
return output
def test_grdtrack_input_dataframe_and_dataarray(dataarray, dataframe,
expected_array):
"""
Run grdtrack by passing in a pandas.DataFrame and xarray.DataArray as
inputs.
"""
output = grdtrack(points=dataframe,
grid=dataarray,
newcolname="bathymetry")
assert isinstance(output, pd.DataFrame)
assert output.columns.to_list() == ["longitude", "latitude", "bathymetry"]
npt.assert_allclose(np.array(output), expected_array)
def visualize_grid_in_3D(
df_lake: pd.DataFrame, azimuth: float, elevation: float, context
):
"""
Create 3D plots of gridded ice surface elevation over time.
"""
# Get 3D grid_region (xmin/xmax/ymin/ymax/zmin/zmax),
# and calculate normalized z-values as Elevation delta relative to Cycle 3
z_limits: tuple = (
float(context.ds_lake.z.min()),
float(context.ds_lake.z.max()),
) # original z limits
grid_region: tuple = context.region.bounds() + z_limits
ds_lake_diff: xr.Dataset = (
context.ds_lake - context.ds_lake.sel(cycle_number=context.cycles[0]).z
)
z_diff_limits: tuple = (float(ds_lake_diff.z.min()), float(ds_lake_diff.z.max()))
diff_grid_region: np.ndarray = np.append(arr=grid_region[:4], values=z_diff_limits)
print(f"Elevation limits are: {z_limits}")
# 3D plots of gridded ice surface elevation over time
for cycle in tqdm.tqdm(iterable=context.cycles):
time_nsec: pd.Timestamp = df_lake[f"utc_time_{cycle}"].mean()
time_sec: str = np.datetime_as_string(arr=time_nsec.to_datetime64(), unit="s")
grid = (
f"figures/{context.placename}/h_corr_{context.placename}_cycle_{cycle}.nc"
)
points = pd.DataFrame(
data=np.vstack(context.lake.geometry.boundary.coords.xy).T,
columns=("x", "y"),
)
outline_points = pygmt.grdtrack(points=points, grid=grid, newcolname="z")
outline_points["z"] = outline_points.z.fillna(value=outline_points.z.median())
fig = deepicedrain.plot_icesurface(
grid=grid, # ds_lake.sel(cycle_number=cycle).z
grid_region=grid_region,
diff_grid=ds_lake_diff.sel(cycle_number=cycle).z,
diff_grid_region=diff_grid_region,
track_points=df_lake[["x", "y", f"h_corr_{cycle}"]].dropna().to_numpy(),
outline_points=outline_points,
azimuth=azimuth,
elevation=elevation,
title=f"{context.region.name} at Cycle {cycle} ({time_sec})",
)
fig.savefig(
f"figures/{context.placename}/dsm_{context.placename}_cycle_{cycle}.png"
)
return fig
def test_grdtrack_input_dataframe_and_ncfile():
"""
Run grdtrack by passing in a pandas.DataFrame and netcdf file as inputs.
"""
dataframe = load_ocean_ridge_points()
ncfile = which("@earth_relief_01d", download="a")
output = grdtrack(points=dataframe, grid=ncfile, newcolname="bathymetry")
assert isinstance(output, pd.DataFrame)
assert output.columns.to_list() == ["longitude", "latitude", "bathymetry"]
npt.assert_allclose(output.iloc[0], [-32.2971, 37.4118, -1939.748245])
return output
def test_grdtrack_input_csvfile_and_dataarray(dataarray, expected_array):
"""
Run grdtrack by passing in a csvfile and xarray.DataArray as inputs.
"""
with GMTTempFile() as tmpfile:
output = grdtrack(points=POINTS_DATA,
grid=dataarray,
outfile=tmpfile.name)
assert output is None # check that output is None since outfile is set
assert os.path.exists(
path=tmpfile.name) # check that outfile exists at path
output = np.loadtxt(tmpfile.name)
npt.assert_allclose(np.array(output), expected_array)
def test_grdtrack_input_csvfile_and_dataarray(dataarray, csvfile):
"""
Run grdtrack by passing in a csvfile and xarray.DataArray as inputs.
"""
try:
output = grdtrack(points=csvfile, grid=dataarray, outfile=TEMP_TRACK)
assert output is None # check that output is None since outfile is set
assert os.path.exists(path=TEMP_TRACK) # check that outfile exists at path
track = pd.read_csv(TEMP_TRACK, sep="\t", header=None, comment=">")
npt.assert_allclose(track.iloc[0], [-110.9536, -42.2489, -2797.394987])
finally:
os.remove(path=TEMP_TRACK)
return output
def test_grdtrack_input_csvfile_and_ncfile(csvfile, ncfile):
"""
Run grdtrack by passing in a csvfile and netcdf file as inputs.
"""
try:
output = grdtrack(points=csvfile, grid=ncfile, outfile=TEMP_TRACK)
assert output is None # check that output is None since outfile is set
assert os.path.exists(path=TEMP_TRACK) # check that outfile exists at path
track = pd.read_csv(TEMP_TRACK, sep="\t", header=None, comment=">")
npt.assert_allclose(track.iloc[0], [-32.2971, 37.4118, -1939.748245])
finally:
os.remove(path=TEMP_TRACK)
return output
def filter_vectors_to_land_only(region, elon, nlat, e, n):
maskfile = pygmt.grdlandmask(region=region, spacing='10m', resolution='i');
points, newelon, newnlat, newe, newn = [], [], [], [], [];
for i in range(len(elon)):
points.append(np.array([elon[i], nlat[i]])); # build np array of points
points = np.array(points);
if len(points) == 0:
return [], [], [], [];
values = pygmt.grdtrack(points, maskfile); # values is a pandas DataFrame
for i, item in enumerate(values[2]): # column 2 is the grdtrack output
if item > 0.8: # if grdtrack gives something on land
newelon.append(elon[i]);
newnlat.append(nlat[i]);
newe.append(e[i]);
newn.append(n[i]);
return newelon, newnlat, newe, newn;
# always_xy=True,
# )
#
# start_xy = reprj_func.transform(xx=start_lonlat[0], yy=start_lonlat[1])
# stop_xy = reprj_func.transform(xx=stop_lonlat[0], yy=stop_lonlat[1])
# start_stop_inc = f"{start_xy[0]}/{start_xy[1]}/{stop_xy[0]}/{stop_xy[1]}/+i125"
# print(start_stop_inc)
# %%
elevpoints = {}
# Get elevation (z) values
for name, grid in gridDict.items():
elevpoints[name] = gmt.grdtrack(
points=oibpoints[["x", "y"]],
grid=grid,
newcolname="elevation",
R="/".join(str(r) for r in region),
# E="-1573985/-470866/-993375/-464996+i250",
)
elevpoints[name] = elevpoints[name].dropna()
elevpoints[name].x = elevpoints[name].x.astype(float)
print(len(elevpoints[name]), name)
# Get roughness values
for name, grid in roughDict.items():
roughness = gmt.grdtrack(
points=oibpoints[["x", "y"]],
grid=grid,
newcolname="roughness",
R="/".join(str(r) for r in region),
# E=start_stop_inc,
).roughness
# - DeepBedMap3 grid (predicted from our [Super Resolution Generative Adversarial Network model](/srgan_train.ipynb))
# - CubicBedMap grid (interpolated from BEDMAP2 using a [bicubic spline algorithm](http://scikit-image.org/docs/dev/api/skimage.transform.html#skimage.transform.rescale))
# - Synthetic High Res grid (created by [Graham et al. 2017](https://doi.org/10.5194/essd-9-267-2017))
#
# References:
#
# - Wessel, P., Smith, W. H. F., Scharroo, R., Luis, J., & Wobbe, F. (2013). Generic Mapping Tools: Improved Version Released. Eos, Transactions American Geophysical Union, 94(45), 409–410. https://doi.org/10.1002/2013EO450001
# - Wessel, P. (2010). Tools for analyzing intersecting tracks: The x2sys package. Computers & Geosciences, 36(3), 348–354. https://doi.org/10.1016/j.cageo.2009.05.009
# %%
tracks = [data_prep.ascii_to_xyz(pipeline_file=f"{pf}.json") for pf in test_filepaths]
points: pd.DataFrame = pd.concat(objs=tracks) # concatenate all tracks into one table
# %%
df_groundtruth = gmt.grdtrack(
points=points, grid=groundtruth, newcolname="z_interpolated"
)
# df_deepbedmap3 = gmt.grdtrack(
# points=points, grid=deepbedmap3_grid, newcolname="z_interpolated"
# )
df_deepbedmap3 = gmt.grdtrack(
points=points, grid="model/deepbedmap3.nc", newcolname="z_interpolated"
)
df_cubicbedmap = gmt.grdtrack(
points=points, grid="model/cubicbedmap.nc", newcolname="z_interpolated"
)
df_synthetichr = gmt.grdtrack(
points=points, grid="model/synthetichr.nc", newcolname="z_interpolated"
)
# %% [markdown]
number_of_samples = 100000
filin = '/home/sberton2/tmp/LDAM_8.GRD' # '/home/sberton2/Works/NASA/Mercury_tides/aux/HDEM_64.GRD' #
rng = np.random.default_rng()
lon = rng.random((number_of_samples)) * 360. # if grd lon is [0,360)
lat = (rng.random(
(number_of_samples)) * 2 - 1) * 90. # if grd lat is [-90,90)
if method == 'xarray':
import xarray as xr
import pyproj
if filin.split('.')[-1] == 'GRD': # to read grd/netcdf files
z = get_demz_grd(filin, lon, lat)
elif filin.split('.')[-1] == 'TIF': # to read geotiffs usgs
z = np.squeeze(get_demz_tiff(filin, lon, lat))
out = pd.DataFrame([lon, lat, z], index=['lon', 'lat', 'z']).T
elif method == 'pygmt':
import pygmt # needs GMT 6.1.1 installed, plus linking of GMTdir/lib64/libgmt.so to some general xovutil dir (see bottom of https://www.pygmt.org/dev/install.html)
points = pd.DataFrame([lon, lat], index=['lon', 'lat']).T
out = pygmt.grdtrack(points, filin, newcolname='z')
print(out)
end = time.time()
print("## Reading/interpolation of", number_of_samples,
"samples finished after", str(np.round(end - start, 2)), "sec!")
exit()
diff_grid_region: np.ndarray = np.append(arr=grid_region[:4],
values=z_diff_limits)
print(f"Elevation limits are: {z_limits}")
# %%
# 3D plots of gridded ice surface elevation over time
for cycle in tqdm.tqdm(iterable=cycles):
time_nsec: pd.Timestamp = df_lake[f"utc_time_{cycle}"].to_pandas().mean()
time_sec: str = np.datetime_as_string(arr=time_nsec.to_datetime64(),
unit="s")
grid = f"figures/{placename}/h_corr_{placename}_cycle_{cycle}.nc"
points = pd.DataFrame(data=np.vstack(lake.geometry.boundary.coords.xy).T,
columns=("x", "y"))
outline_points = pygmt.grdtrack(points=points, grid=grid, newcolname="z")
outline_points["z"] = outline_points.z.fillna(
value=outline_points.z.median())
fig = deepicedrain.plot_icesurface(
grid=grid, # ds_lake.sel(cycle_number=cycle).z
grid_region=grid_region,
diff_grid=ds_lake_diff.sel(cycle_number=cycle).z,
diff_grid_region=diff_grid_region,
track_points=df_lake[["x", "y",
f"h_corr_{cycle}"]].dropna().as_matrix(),
outline_points=outline_points,
azimuth=157.5, # 202.5 # 270
elevation=45, # 60
title=f"{region.name} at Cycle {cycle} ({time_sec})",
)
I=250,
G=f"Models/{bedname.lower()}_{z_attribute}.nc",
)
# %%
# Gridded plots, and transect plot of friction/velocity/rheologyB
grid = xr.open_dataarray(f"Models/{bedname.lower()}_{z_attribute}.nc")
pointXY = collections.namedtuple(typename="pointXY", field_names="x y")
pointA = pointXY(x=-1590_000, y=-99_000)
pointB = pointXY(x=-1580_000, y=-255_000)
points = pd.DataFrame(
data=np.linspace(start=pointA, stop=pointB, num=50),
columns=["x", "y"],
)
transect = pygmt.grdtrack(points=points, grid=grid, newcolname=z_attribute)
fig = pygmt.Figure()
# Plot 2D grid and transect line on map
pygmt.makecpt(
cmap="hawaii",
series=[z.min(), z.max(), (z.max() - z.min()) / 10],
reverse=True,
continuous=True,
)
with pygmt.config(FONT_TITLE="18p"):
fig.grdimage(
grid=grid,
cmap=True,
frame=["af", f'WSne+t"Mean {z_attribute}={grid.mean().item():.4e}"'],
projection="X8c/12c",
reverse=True,
continuous=True,
)
with pygmt.clib.Session() as session:
subplot = lambda args: session.call_module(module="subplot", args=args)
# Begin subplot
subplot(args="begin 2x3 -BWSne -Bxaf -Byaf -Fs10c/4c,14c -M0c/0.5c -SRl")
# Plot transect line graph
subplot(args=f"set 0,0")
transectproj = "X32c/4c"
_xyz = pd.concat(
pygmt.grdtrack(
points=points,
grid=grid,
newcolname=z_attr.varname,
) for grid in grids)
fig.basemap(
region=pygmt.info(table=_xyz[["x", z_attr.varname]],
per_column=True,
spacing=10),
projection=transectproj,
frame=[
"af",
f'WSne+t"{z_attr.varname.title().replace("_", " ")} ({z_attr.symbol}) at Pine Island Glacier"',
],
)
for bedname in ("DeepBedMap", "BedMachine"):
grid = f"Models/{bedname.lower()}_{z_attr.varname}.nc"
transect = pygmt.grdtrack(points=points,
def raster_topological_reconstruction(grid,
topological_model,
reconstruction_time,
reverse=True,
region=None,
spacing=1.):
"""
Generate a reconstructed raster based on a topological reconstruction
By default, the operation will use the input raster to determine the extent and sampling
of the output raster (Which would work for global grids).
Optionally, a different region and sampling for the output grid can be specified.
"""
coord_keys = [key for key in grid.coords.keys()]
if 'lon' in coord_keys[0].lower():
latitude_key = 1
longitude_key = 0
elif 'x' in coord_keys[0].lower():
latitude_key = 1
longitude_key = 0
else:
latitude_key = 0
longitude_key = 1
if region:
coords = [('lat', np.arange(region[2], region[3] + spacing, spacing)),
('lon', np.arange(region[0], region[1] + spacing, spacing))]
XX, YY = np.meshgrid(
np.arange(region[0], region[1] + spacing, spacing),
np.arange(region[2], region[3] + spacing, spacing))
else:
coords = [('lat', grid.coords[coord_keys[latitude_key]].data),
('lon', grid.coords[coord_keys[longitude_key]].data)]
XX, YY = np.meshgrid(grid.coords[coord_keys[longitude_key]].data,
grid.coords[coord_keys[latitude_key]].data)
geometry_points = [(lat, lon)
for lat, lon in zip(YY.flatten(), XX.flatten())]
if reverse:
pts, valid_index = topological_reconstruction(
topological_model,
geometry_points,
0.,
initial_time=reconstruction_time,
oldest_time=reconstruction_time,
return_inactive_points=True,
deactivate_points=False)
res = pygmt.grdtrack(grid=grid,
points=pd.DataFrame(data={
'x': pts[1],
'y': pts[0]
}),
newcolname='z')
x = XX.flatten()[valid_index]
y = YY.flatten()[valid_index]
resg = xyz2grd(x, y, np.array(res['z']), XX, YY)
reconstructed_raster = xr.DataArray(resg, coords=coords, name='z')
return reconstructed_raster
else:
print('Forward reconstruction not yet implemented')
:class:`pandas.DataFrame` table where the first two columns are x and y (or longitude
and latitude). Note also that there is a ``newcolname`` parameter that will be used to
name the new column of values sampled from the grid.
Alternatively, a NetCDF file path can be passed to ``grid``. An ASCII file path can
also be accepted for ``points``. To save an output ASCII file, a file name argument
needs to be passed to the ``outfile`` parameter.
"""
import pygmt
# Load sample grid and point datasets
grid = pygmt.datasets.load_earth_relief()
points = pygmt.datasets.load_ocean_ridge_points()
# Sample the bathymetry along the world's ocean ridges at specified track points
track = pygmt.grdtrack(points=points, grid=grid, newcolname="bathymetry")
fig = pygmt.Figure()
# Plot the earth relief grid on Cylindrical Stereographic projection, masking land areas
fig.basemap(region="g", frame=True, projection="Cyl_stere/150/-20/15c")
fig.grdimage(grid=grid, cmap="gray")
fig.coast(land="#666666")
# Plot using circles (c) of 0.15 cm, the sampled bathymetry points
# Points are colored using elevation values (normalized for visual purposes)
fig.plot(
x=track.longitude,
y=track.latitude,
style="c0.15c",
cmap="terra",
color=(track.bathymetry - track.bathymetry.mean()) /
track.bathymetry.std(),
def test_grdtrack_without_outfile_setting(dataarray, dataframe):
"""
Run grdtrack by not passing in outfile parameter setting.
"""
with pytest.raises(GMTInvalidInput):
grdtrack(points=dataframe, grid=dataarray)
