def contour_temp(ds: xr.core.dataset.Dataset,
in_year: int,
ax: mpl.axes.Axes,
title: str,
central_longitude: int = 0) -> mpl.axes.Axes:
"""Takes in a Dataset for Temperature, and creates a contour plot for
the data for the given year. Adds a darkgrey landmask, grindlines,
coastlines, a title, and a colorbar to the plot."""
clevs = np.arange(260, 320, 10)
# Can also choose to define colormap
colors = ['blue', 'green', 'yellow', 'orange', 'red']
crs = ccrs.PlateCarree(central_longitude=central_longitude)
# Specify variables
X = ds['xt_ocean']
Y = ds['yt_ocean']
# Already grouped by year
Z = ds['temp'].sel(year=in_year).squeeze()
Z, X = add_cyclic_point(Z, coord=X)
# can also use cmap='plasma','inferno',etc...
im = ax.contourf(X, Y, Z, levels=clevs, transform=crs)
if ax == plt.gcf().get_axes()[0]:
cbar = plt.colorbar(im,
ax=plt.gcf().get_axes(),
orientation='horizontal',
fraction=0.05,
pad=0.05)
cbar.set_label('Temeprature ($^\circ\,K$)', fontsize=18)
# Zoom in on a region
# ax.set_extent([x0,x1,y0,y1])
# Add land mask, gridlines, coastlines, and title
ax.add_feature(cfeature.LAND, zorder=10, facecolor='darkgray')
ax.gridlines()
ax.coastlines()
ax.set_title(title + " " + str(in_year), fontsize=18, loc='center')
return ax
def contour_oa(ds: xr.core.dataset.Dataset,
in_year: int,
ax: mpl.axes.Axes,
title: str,
central_longitude: int = 0) -> mpl.axes.Axes:
"""Takes in a Dataset for Omega Aragonite, and creates a contour plot
for the data for the given year. Adds a darkgrey landmask, grindlines,
coastlines, a title, and a colorbar to the plot."""
clevs = np.array([0, 1, 2, 3, 4])
# Can also choose to define colormap
# colors = ['red', 'orange', 'yellow','green','blue']
crs = ccrs.PlateCarree(central_longitude=central_longitude)
# Specify variables
X = ds['XT_OCEAN']
Y = ds['YT_OCEAN']
# Already grouped by year
Z = ds['OMEGA_ARAG'].sel(year=in_year).squeeze()
Z, X = add_cyclic_point(Z, coord=X)
im = ax.contourf(X, Y, Z, clevs, transform=crs)
if ax == plt.gcf().get_axes()[0]:
cbar = plt.colorbar(im,
ax=plt.gcf().get_axes(),
orientation='horizontal',
fraction=0.05,
pad=0.05)
cbar.set_label('$\Omega$ Aragonite', fontsize=18)
# Zoom in on a region
# ax.set_extent([x0,x1,y0,y1])
# Add land mask, gridlines, coastlines, and title
ax.add_feature(cfeature.LAND, zorder=10, facecolor='darkgray')
ax.gridlines()
ax.coastlines()
ax.set_title(title + " " + str(in_year), fontsize=18, loc='center')
return ax
def southern_ocean_axes_setup(
ax: matplotlib.axes.Axes,
fig: matplotlib.figure.Figure,
add_gridlines: bool = True,
) -> None:
"""
This function sets up the subplot so that it is a cartopy map of the southern ocean.
returns void as the ax and figure objects are pointers not data.
Args:
ax (matplotlib.axes.Axes): The axis object to add the map to.
fig (matplotlib.figure.Figure): The figure object for the figure in general.
add_gridlines (bool): whether or not to add gridlines to the plot.
"""
carree = ccrs.PlateCarree()
ax.set_extent([-180, 180, -90, -30], carree)
fig.subplots_adjust(bottom=0.05,
top=0.95,
left=0.04,
right=0.95,
wspace=0.02)
def plot_boundary() -> None:
"""
Makes SO plot boundary into a nice circle
of the right size.
"""
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.45
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
ax.set_boundary(circle, transform=ax.transAxes)
plot_boundary()
# add coastlines and gridlines
ax.coastlines(resolution="50m", linewidth=0.3)
if add_gridlines:
ax.gridlines(linewidth=0.5)
# Add 2000m isobath (or whatever the max depth is).
@jit(cache=True) # significant performance enhancement.
def find_isobath(tmp_bathymetry: np.ndarray,
crit_depth=cst.MAX_DEPTH) -> List[list]:
"""
Find isobath.
Args:
tmp_bathymetry (np.ndarray): Bathymetry np array.
crit_depth ([type], optional): Critical depth. Defaults to cst.MAX_DEPTH.
Returns:
List[list]: List of index pairs.
"""
isobath_index_list = []
shape_bathymetry = np.shape(tmp_bathymetry)
for i in range(0, shape_bathymetry[0] - 1):
for j in range(0, shape_bathymetry[1] - 1):
if tmp_bathymetry[i, j] >= crit_depth:
if tmp_bathymetry[i - 1, j] < crit_depth:
isobath_index_list.append([i, j])
if tmp_bathymetry[i, j - 1] < crit_depth:
isobath_index_list.append([i, j])
if tmp_bathymetry[i + 1, j] < crit_depth:
isobath_index_list.append([i, j])
if tmp_bathymetry[i, j + 1] < crit_depth:
isobath_index_list.append([i, j])
return isobath_index_list
i_list = find_isobath(
xr.open_dataset(cst.SALT_FILE)[cst.DEPTH_NAME].values,
crit_depth=cst.MAX_DEPTH,
)
index_npa: np.ndarray = np.array(i_list)
lons = xr.open_dataset(cst.SALT_FILE)[cst.X_COORD].values[index_npa[:, 1]]
lats = xr.open_dataset(cst.SALT_FILE)[cst.Y_COORD].values[index_npa[:, 0]]
ax.plot(lons,
lats,
",",
markersize=0.4,
color="grey",
transform=ccrs.Geodetic())
