import math
import warnings
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import numpy as np
import pyart
from .. import retrieval
from matplotlib.axes import Axes
from datatree import DataTree
try:
from cartopy.mpl.geoaxes import GeoAxes
CARTOPY_AVAILABLE = True
except ImportError:
CARTOPY_AVAILABLE = False
GeoAxes._pcolormesh_patched = Axes.pcolormesh
[docs]
def plot_horiz_xsection_streamlines(
Grids,
ax=None,
background_field="reflectivity",
level=1,
cmap="ChaseSpectral",
vmin=0,
vmax=70,
u_vel_contours=None,
v_vel_contours=None,
w_vel_contours=None,
wind_vel_contours=None,
u_field="u",
v_field="v",
w_field="w",
show_lobes=True,
title_flag=True,
axes_labels_flag=True,
colorbar_flag=True,
colorbar_contour_flag=False,
bg_grid_no=0,
contour_alpha=0.7,
arrowsize=1.0,
linewidth=None,
):
"""
This procedure plots a horizontal cross section of winds from wind fields
generated by PyDDA using streamlines. The density of streamlines varies
with horizontal wind speed.
Parameters
----------
Grids: list or DataTree
List of Py-DDA Grids to visualize
ax: matplotlib axis handle
The axis handle to place the plot on. Set to None to plot on the
current axis.
background_field: str
The name of the background field to plot the windbarbs on.
level: int
The number of the vertical level to plot the cross section through.
cmap: str or matplotlib colormap
The name of the matplotlib colormap to use for the background field.
vmin: float
The minimum bound to use for plotting the background field. None will
automatically detect the background field minimum.
vmax: float
The maximum bound to use for plotting the background field. None will
automatically detect the background field maximum.
u_vel_contours: 1-D array
The contours to use for plotting contours of u. Set to None to not
display such contours.
v_vel_contours: 1-D array
The contours to use for plotting contours of v. Set to None to not
display such contours.
w_vel_contours: 1-D array
The contours to use for plotting contours of w. Set to None to not
display such contours.
wind_vel_contours: 1-D array
The contours to use for plotting contours of horizontal wind speed.
Set to None to not display such contours
u_field: str
Name of zonal wind (u) field in Grids.
v_field: str
Name of meridional wind (v) field in Grids.
w_field: str
Name of vertical wind (w) field in Grids.
show_lobes: bool
If True, the dual doppler lobes from each pair of radars will be shown.
title_flag: bool
If True, PyDDA will generate a title for the plot.
axes_labels_flag: bool
If True, PyDDA will generate axes labels for the plot.
colorbar_flag: bool
If True, PyDDA will generate a colorbar for the plot background field.
colorbar_contour_flag: bool
If True, PyDDA will generate a colorbar for the contours.
bg_grid_no: int
Number of grid in Grids to take background field from.
Set to -1 to use maximum value from all grids.
contour_alpha: float
Alpha (transparency) of velocity contours. 0 = transparent, 1 = opaque
arrowsize: float
Factor scale arrow size for streamlines.
linewidth: numeric or 2d array
Linewidths for streamlines.
Returns
-------
ax: matplotlib axis
Axis handle to output axis
"""
if isinstance(Grids, DataTree):
child_list = list(Grids.children.keys())
grid_list = []
rad_names = []
for child in child_list:
if "radar" in child:
grid_list.append(Grids[child].to_dataset())
rad_names.append(child)
bca_min = math.radians(Grids[rad_names[0]][u_field].attrs["min_bca"])
bca_max = math.radians(Grids[rad_names[0]][u_field].attrs["max_bca"])
else:
grid_list = Grids
bca_min = math.radians(grid_list[0][u_field].attrs["min_bca"])
bca_max = math.radians(grid_list[0][u_field].attrs["max_bca"])
grid_bg = grid_list[bg_grid_no][background_field].values.squeeze()
if vmin is None:
vmin = grid_bg.min()
if vmax is None:
vmax = grid_bg.max()
grid_h = grid_list[0]["point_altitude"].values / 1e3
grid_x = grid_list[0]["point_x"].values / 1e3
grid_y = grid_list[0]["point_y"].values / 1e3
np.diff(grid_x, axis=2)[0, 0, 0]
np.diff(grid_y, axis=1)[0, 0, 0]
u = grid_list[0][u_field].values.squeeze()
v = grid_list[0][v_field].values.squeeze()
w = grid_list[0][w_field].values.squeeze()
if isinstance(u, np.ma.MaskedArray):
u = u.filled(np.nan)
if isinstance(v, np.ma.MaskedArray):
v = v.filled(np.nan)
if isinstance(w, np.ma.MaskedArray):
w = w.filled(np.nan)
if ax is None:
ax = plt.gca()
the_mesh = ax.pcolormesh(
grid_x[level, :, :],
grid_y[level, :, :],
grid_bg[level, :, :],
cmap=cmap,
vmin=vmin,
vmax=vmax,
)
np.ma.sqrt(u**2 + v**2)
ax.streamplot(
grid_x[level, :, :],
grid_y[level, :, :],
u[level, :, :],
v[level, :, :],
color="k",
linewidth=linewidth,
arrowsize=arrowsize,
)
if colorbar_flag is True:
cp = grid_list[bg_grid_no][background_field].attrs["long_name"]
cp.replace(" ", "_")
cp = cp + " [" + grid_list[bg_grid_no][background_field].attrs["units"]
cp = cp + "]"
plt.colorbar(the_mesh, ax=ax, label=(cp))
if u_vel_contours is not None:
u_filled = np.ma.filled(u[level, :, :], fill_value=np.nan)
cs = ax.contour(
grid_x[level, :, :],
grid_y[level, :, :],
u_filled,
levels=u_vel_contours,
linewidths=2,
)
ax.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(cs, ax=ax, label="U [m/s]")
if v_vel_contours is not None:
v_filled = np.ma.filled(v[level, :, :], fill_value=np.nan)
cs = ax.contour(
grid_x[level, :, :],
grid_y[level, :, :],
v_filled,
levels=u_vel_contours,
linewidths=2,
)
ax.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(cs, ax=ax, label="V [m/s]")
if w_vel_contours is not None:
w_filled = np.ma.filled(w[level, :, :], fill_value=np.nan)
cs = ax.contour(
grid_x[level, :, :],
grid_y[level, :, :],
w_filled,
levels=w_vel_contours,
linewidths=2,
)
ax.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(cs, ax=ax, label="W [m/s]")
if wind_vel_contours is not None:
vel = np.ma.sqrt(u[level, :, :] ** 2 + v[level, :, :] ** 2)
# vel = vel.filled(fill_value=np.nan)
cs = ax.contour(
grid_x[level, :, :],
grid_y[level, :, :],
vel,
levels=wind_vel_contours,
linewidths=2,
)
cs.set_clim([np.min(wind_vel_contours), np.max(wind_vel_contours)])
cs.cmap.set_under(color="white", alpha=0)
cs.cmap.set_bad(color="white", alpha=0)
ax.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(cs, ax=ax, label="|V| [m/s]")
if show_lobes is True:
for i in range(len(grid_list)):
for j in range(len(grid_list)):
if i != j:
bca = retrieval.get_bca(
grid_list[i],
grid_list[j],
)
ax.contour(
grid_x[level, :, :],
grid_y[level, :, :],
bca,
levels=[bca_min, bca_max],
color="k",
)
if axes_labels_flag is True:
ax.set_xlabel(("X [km]"))
ax.set_ylabel(("Y [km]"))
if title_flag is True:
ax.set_title(("PyDDA retreived winds @" + str(grid_h[level, 0, 0]) + " km"))
ax.set_xlim([grid_x.min(), grid_x.max()])
ax.set_ylim([grid_y.min(), grid_y.max()])
return ax
[docs]
def plot_horiz_xsection_streamlines_map(
Grids,
ax=None,
background_field="reflectivity",
level=1,
cmap="ChaseSpectral",
vmin=0,
vmax=70,
u_vel_contours=None,
v_vel_contours=None,
w_vel_contours=None,
wind_vel_contours=None,
u_field="u",
v_field="v",
w_field="w",
show_lobes=True,
title_flag=True,
axes_labels_flag=True,
colorbar_flag=True,
colorbar_contour_flag=False,
bg_grid_no=0,
contour_alpha=0.7,
coastlines=True,
gridlines=True,
arrowsize=1.0,
linewidth=None,
):
"""
This procedure plots a horizontal cross section of winds from wind fields
generated by PyDDA using streamlines. The density of streamlines varies
with horizontal wind speed.
Parameters
----------
Grids: list or DataTree
List of Py-DDA Grids to visualize
ax: matplotlib axis handle (with cartopy ccrs)
The axis handle to place the plot on. Set to None to create a new map.
Note: the axis needs to be in a PlateCarree() projection.
background_field: str
The name of the background field to plot the windbarbs on.
level: int
The number of the vertical level to plot the cross section through.
cmap: str or matplotlib colormap
The name of the matplotlib colormap to use for the background field.
vmin: float
The minimum bound to use for plotting the background field. None will
automatically detect the background field minimum.
vmax: float
The maximum bound to use for plotting the background field. None will
automatically detect the background field maximum.
u_vel_contours: 1-D array
The contours to use for plotting contours of u. Set to None to not
display such contours.
v_vel_contours: 1-D array
The contours to use for plotting contours of v. Set to None to not
display such contours.
w_vel_contours: 1-D array
The contours to use for plotting contours of w. Set to None to not
display such contours.
wind_vel_contours: 1-D array
The contours to use for plotting contours of horizontal wind speed.
Set to None to not display such contours.
u_field: str
Name of zonal wind (u) field in Grids.
v_field: str
Name of meridional wind (v) field in Grids.
w_field: str
Name of vertical wind (w) field in Grids.
show_lobes: bool
If True, the dual doppler lobes from each pair of radars will be shown.
title_flag: bool
If True, PyDDA will generate a title for the plot.
axes_labels_flag: bool
If True, PyDDA will generate axes labels for the plot.
colorbar_flag: bool
If True, PyDDA will generate a colorbar for the plot background field.
colorbar_contour_flag: bool
If True, PyDDA will generate a colorbar for the contours.
bg_grid_no: int
Number of grid in Grids to take background field from.
Set to -1 to use maximum value from all grids.
contour_alpha: float
Alpha (transparency) of velocity contours. 0 = transparent, 1 = opaque
coastlines: bool
Set to true to display coastlines
gridlines: bool
Set to true to show grid lines.
arrowsize: float
Factor scale arrow size for streamlines.
linewidth: numeric or 2d array
Linewidths for streamlines.
Returns
-------
ax: matplotlib axis
Axis handle to output axis
"""
if isinstance(Grids, DataTree):
child_list = list(Grids.children.keys())
grid_list = []
rad_names = []
for child in child_list:
if "radar" in child:
grid_list.append(Grids[child].to_dataset())
rad_names.append(child)
bca_min = math.radians(Grids[rad_names[0]][u_field].attrs["min_bca"])
bca_max = math.radians(Grids[rad_names[0]][u_field].attrs["max_bca"])
else:
grid_list = Grids
bca_min = math.radians(grid_list[0][u_field].attrs["min_bca"])
bca_max = math.radians(grid_list[0][u_field].attrs["max_bca"])
if not CARTOPY_AVAILABLE:
raise ModuleNotFoundError(
"Cartopy needs to be installed in order to use plotting module!"
)
if bg_grid_no > -1:
grid_bg = grid_list[bg_grid_no][background_field].values.squeeze()
else:
grid_array = np.ma.stack(
[x[background_field].values.squeeze() for x in grid_list]
)
grid_bg = grid_array.max(axis=0)
if vmin is None:
vmin = grid_bg.min()
if vmax is None:
vmax = grid_bg.max()
grid_h = grid_list[0]["point_altitude"].values / 1e3
grid_x = grid_list[0]["point_x"].values / 1e3
grid_y = grid_list[0]["point_y"].values / 1e3
grid_lat = grid_list[0].point_latitude.values[level]
grid_lon = grid_list[0].point_longitude.values[level]
np.diff(grid_x, axis=2)[0, 0, 0]
np.diff(grid_y, axis=1)[0, 0, 0]
if isinstance(Grids, DataTree):
u = Grids[u_field].values.squeeze()
v = Grids[v_field].values.squeeze()
w = Grids[w_field].values.squeeze()
else:
u = grid_list[0][u_field].values.squeeze()
v = grid_list[0][v_field].values.squeeze()
w = grid_list[0][w_field].values.squeeze()
if isinstance(u, np.ma.MaskedArray):
u = u.filled(np.nan)
if isinstance(v, np.ma.MaskedArray):
v = v.filled(np.nan)
if isinstance(w, np.ma.MaskedArray):
w = w.filled(np.nan)
transform = ccrs.PlateCarree()
if ax is None:
ax = plt.axes(projection=transform)
the_mesh = ax.pcolormesh(
grid_lon[:, :],
grid_lat[:, :],
grid_bg[level, :, :],
cmap=cmap,
transform=transform,
zorder=0,
vmin=vmin,
vmax=vmax,
)
np.ma.sqrt(u**2 + v**2)
ax.streamplot(
grid_lon,
grid_lat,
u[level],
v[level],
transform=transform,
zorder=1,
color="k",
arrowsize=arrowsize,
linewidth=linewidth,
)
if colorbar_flag is True:
cp = grid_list[bg_grid_no][background_field].attrs["long_name"]
cp.replace(" ", "_")
cp = cp + " [" + grid_list[bg_grid_no][background_field].attrs["units"]
cp = cp + "]"
plt.colorbar(the_mesh, ax=ax, label=(cp))
if u_vel_contours is not None:
u_filled = np.ma.masked_where(
u[level, :, :] < np.min(u_vel_contours), u[level, :, :]
)
try:
cs = ax.contourf(
grid_lon[:, :],
grid_lat[:, :],
u_filled,
levels=u_vel_contours,
linewidths=2,
alpha=contour_alpha,
zorder=2,
extend="both",
)
cs.set_clim([np.min(u_vel_contours), np.max(u_vel_contours)])
cs.cmap.set_under(color="white", alpha=0)
cs.cmap.set_over(color="white", alpha=0)
cs.cmap.set_bad(color="white", alpha=0)
ax.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(
cs, ax=ax, label="U [m/s]", extend="both", spacing="proportional"
)
except ValueError:
warnings.warn(
(
"Cartopy does not support blank contour plots, "
+ "contour color map not drawn!"
),
RuntimeWarning,
)
if v_vel_contours is not None:
v_filled = np.ma.masked_where(
v[level, :, :] < np.min(v_vel_contours), v[level, :, :]
)
try:
cs = ax.contour(
grid_lon[:, :],
grid_lat[:, :],
v_filled,
levels=u_vel_contours,
linewidths=2,
zorder=2,
extend="both",
)
cs.set_clim([np.min(v_vel_contours), np.max(v_vel_contours)])
cs.cmap.set_under(color="white", alpha=0)
cs.cmap.set_over(color="white", alpha=0)
cs.cmap.set_bad(color="white", alpha=0)
ax.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(
cs, ax=ax, label="V [m/s]", extend="both", spacing="proportional"
)
except ValueError:
warnings.warn(
(
"Cartopy does not support blank contour plots, "
+ "contour color map not drawn!"
),
RuntimeWarning,
)
if w_vel_contours is not None:
w_filled = np.ma.masked_where(
w[level, :, :] < np.min(w_vel_contours), w[level, :, :]
)
try:
cs = ax.contour(
grid_lon[::, ::],
grid_lat[::, ::],
w_filled,
levels=w_vel_contours,
linewidths=2,
zorder=2,
extend="both",
)
cs.set_clim([np.min(w_vel_contours), np.max(w_vel_contours)])
cs.cmap.set_under(color="white", alpha=0)
cs.cmap.set_over(color="white", alpha=0)
cs.cmap.set_bad(color="white", alpha=0)
ax.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(
cs,
ax=ax,
label="W [m/s]",
extend="both",
spacing="proportional",
ticks=w_vel_contours,
)
except ValueError:
warnings.warn(
(
"Cartopy does not support color maps on blank "
+ "contour plots, contour color map not drawn!"
),
RuntimeWarning,
)
if wind_vel_contours is not None:
vel = np.ma.sqrt(u[level, :, :] ** 2 + v[level, :, :] ** 2)
vel = vel.filled(fill_value=np.nan)
try:
cs = ax.contour(
grid_x[level, :, :],
grid_y[level, :, :],
vel,
levels=wind_vel_contours,
linewidths=2,
)
cs.cmap.set_under(color="white", alpha=0)
cs.cmap.set_bad(color="white", alpha=0)
ax.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(
cs,
ax=ax,
label="|V\ [m/s]",
extend="both",
spacing="proportional",
ticks=w_vel_contours,
)
except ValueError:
warnings.warn(
(
"Cartopy does not support color maps on blank "
+ "contour plots, contour color map not drawn!"
),
RuntimeWarning,
)
if show_lobes is True:
for i in range(len(grid_list)):
for j in range(len(grid_list)):
if i != j:
bca = retrieval.get_bca(grid_list[i], grid_list[j])
ax.contour(
grid_lon[:, :],
grid_lat[:, :],
bca,
levels=[bca_min, bca_max],
color="k",
zorder=1,
)
if axes_labels_flag is True:
ax.set_xlabel(("Latitude [$^{\circ}$]"))
ax.set_ylabel(("Longitude [$^{\circ}$]"))
if title_flag is True:
ax.set_title(("PyDDA retreived winds @" + str(grid_h[level, 0, 0]) + " km"))
if coastlines is True:
ax.coastlines(resolution="10m")
if gridlines is True:
ax.gridlines()
ax.set_extent([grid_lon.min(), grid_lon.max(), grid_lat.min(), grid_lat.max()])
num_tenths = int(round((grid_lon.max() - grid_lon.min()) * 10) + 1)
the_ticks_x = np.round(np.linspace(grid_lon.min(), grid_lon.max(), num_tenths), 1)
num_tenths = int(round((grid_lat.max() - grid_lat.min()) * 10) + 1)
the_ticks_y = np.round(np.linspace(grid_lat.min(), grid_lat.max(), num_tenths), 1)
ax.set_xticks(the_ticks_x)
ax.set_yticks(the_ticks_y)
return ax
[docs]
def plot_xz_xsection_streamlines(
Grids,
ax=None,
background_field="reflectivity",
level=1,
cmap="ChaseSpectral",
vmin=0,
vmax=70,
u_vel_contours=None,
v_vel_contours=None,
w_vel_contours=None,
wind_vel_contours=None,
u_field="u",
v_field="v",
w_field="w",
title_flag=True,
axes_labels_flag=True,
colorbar_flag=True,
colorbar_contour_flag=False,
bg_grid_no=0,
contour_alpha=0.7,
arrowsize=1.0,
linewidth=None,
):
"""
This procedure plots a cross section of winds from wind fields
generated by PyDDA in the X-Z plane using streamlines.
The density of streamlines varies with horizontal wind speed.
Parameters
----------
Grids: list or DataTree
List of Py-DDA Grids to visualize
ax: matplotlib axis handle
The axis handle to place the plot on. Set to None to plot on the
current axis.
background_field: str
The name of the background field to plot the streamlines on.
level: int
The number of the Y level to plot the cross section through.
cmap: str or matplotlib colormap
The name of the matplotlib colormap to use for the background field.
vmin: float
The minimum bound to use for plotting the background field. None will
automatically detect the background field minimum.
vmax: float
The maximum bound to use for plotting the background field. None will
automatically detect the background field maximum.
u_vel_contours: 1-D array
The contours to use for plotting contours of u. Set to None to not
display such contours.
v_vel_contours: 1-D array
The contours to use for plotting contours of v. Set to None to not
display such contours.
w_vel_contours: 1-D array
The contours to use for plotting contours of w. Set to None to not
display such contours.
wind_vel_contours: 1-D array
The contours to use for plotting contours of horizontal wind speed.
Set to None to not display such contours.
u_field: str
Name of zonal wind (u) field in Grids.
v_field: str
Name of meridional wind (v) field in Grids.
w_field: str
Name of vertical wind (w) field in Grids.
show_lobes: bool
If True, the dual doppler lobes from each pair of radars will be shown.
title_flag: bool
If True, PyDDA will generate a title for the plot.
axes_labels_flag: bool
If True, PyDDA will generate axes labels for the plot
colorbar_flag: bool
If True, PyDDA will generate a colorbar for the plot background field.
colorbar_contour_flag: bool
If True, PyDDA will generate a colorbar for the contours.
bg_grid_no: int
Number of grid in Grids to take background field from.
Set to -1 to use maximum value from all grids.
contour_alpha: float
Alpha (transparency) of velocity contours. 0 = transparent, 1 = opaque
arrowsize: float
Factor scale arrow size for streamlines.
linewidth: numeric or 2d array
Linewidths for streamlines.
Returns
-------
ax: matplotlib axis
Axis handle to output axis
"""
if isinstance(Grids, DataTree):
child_list = list(Grids.children.keys())
grid_list = []
rad_names = []
for child in child_list:
if "radar" in child:
grid_list.append(Grids[child].to_dataset())
rad_names.append(child)
else:
grid_list = Grids
grid_bg = grid_list[bg_grid_no][background_field].values.squeeze()
if vmin is None:
vmin = grid_bg.min()
if vmax is None:
vmax = grid_bg.max()
grid_h = grid_list[0]["point_altitude"].values / 1e3
grid_x = grid_list[0]["point_x"].values / 1e3
grid_y = grid_list[0]["point_y"].values / 1e3
if isinstance(Grids, DataTree):
u = Grids[u_field].values.squeeze()
v = Grids[v_field].values.squeeze()
w = Grids[w_field].values.squeeze()
else:
u = grid_list[0][u_field].values.squeeze()
v = grid_list[0][v_field].values.squeeze()
w = grid_list[0][w_field].values.squeeze()
if isinstance(u, np.ma.MaskedArray):
u = u.filled(np.nan)
if isinstance(v, np.ma.MaskedArray):
v = v.filled(np.nan)
if isinstance(w, np.ma.MaskedArray):
w = w.filled(np.nan)
if ax is None:
ax = plt.gca()
the_mesh = ax.pcolormesh(
grid_x[:, level, :],
grid_h[:, level, :],
grid_bg[:, level, :],
cmap=cmap,
vmin=vmin,
vmax=vmax,
)
ax.streamplot(
grid_x[:, level, :],
grid_h[:, level, :],
u[:, level, :],
w[:, level, :],
color="k",
arrowsize=arrowsize,
linewidth=linewidth,
)
if colorbar_flag is True:
cp = grid_list[bg_grid_no][background_field].attrs["long_name"]
cp.replace(" ", "_")
cp = cp + " [" + grid_list[bg_grid_no][background_field].attrs["units"]
cp = cp + "]"
plt.colorbar(the_mesh, ax=ax, label=(cp))
if u_vel_contours is not None:
u_filled = np.ma.filled(u[:, level, :], fill_value=np.nan)
cs = ax.contour(
grid_x[:, level, :],
grid_h[:, level, :],
u_filled,
levels=u_vel_contours,
linewidths=2,
)
ax.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(cs, ax=ax, label="U [m/s]")
if v_vel_contours is not None:
v_filled = np.ma.filled(w[:, level, :], fill_value=np.nan)
cs = ax.contour(
grid_x[:, level, :],
grid_h[:, level, :],
v_filled,
levels=v_vel_contours,
linewidths=2,
)
ax.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(cs, ax=ax, label="V [m/s]")
if w_vel_contours is not None:
w_filled = np.ma.filled(w[:, level, :], fill_value=np.nan)
cs = ax.contour(
grid_x[:, level, :],
grid_h[:, level, :],
w_filled,
levels=w_vel_contours,
linewidths=2,
)
ax.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(cs, ax=ax, label="W [m/s]")
if wind_vel_contours is not None:
vel = np.ma.sqrt(u[:, level, :] ** 2 + v[:, level, :] ** 2)
vel = vel.filled(fill_value=np.nan)
cs = ax.contour(
grid_x[:, level, :],
grid_h[:, level, :],
vel,
levels=wind_vel_contours,
linewidths=2,
)
cs.set_clim([np.min(wind_vel_contours), np.max(wind_vel_contours)])
cs.cmap.set_under(color="white", alpha=0)
cs.cmap.set_bad(color="white", alpha=0)
ax.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(cs, ax=ax, label="|V| [m/s]")
if axes_labels_flag is True:
ax.set_xlabel(("X [km]"))
ax.set_ylabel(("Z [km]"))
if title_flag is True:
if grid_y[0, level, 0] > 0:
ax.set_title(
(
"PyDDA retreived winds @"
+ str(grid_y[0, level, 0])
+ " km north of origin."
)
)
else:
ax.set_title(
(
"PyDDA retreived winds @"
+ str(-grid_y[0, level, 0])
+ " km south of origin."
)
)
ax.set_xlim([grid_x.min(), grid_x.max()])
ax.set_ylim([grid_h.min(), grid_h.max()])
return ax
[docs]
def plot_yz_xsection_streamlines(
Grids,
ax=None,
background_field="reflectivity",
level=1,
cmap="ChaseSpectral",
vmin=0,
vmax=70,
u_vel_contours=None,
v_vel_contours=None,
w_vel_contours=None,
wind_vel_contours=None,
u_field="u",
v_field="v",
w_field="w",
title_flag=True,
axes_labels_flag=True,
colorbar_flag=True,
colorbar_contour_flag=False,
bg_grid_no=0,
contour_alpha=0.7,
arrowsize=1.0,
linewidth=None,
):
"""
This procedure plots a cross section of winds from wind fields
generated by PyDDA in the Y-Z plane using streamlines.
The density of streamlines varies with horizontal wind speed.
Parameters
----------
Grids: list or DataTree
List of Py-DDA Grids to visualize
ax: matplotlib axis handle
The axis handle to place the plot on. Set to None to plot on the
current axis.
background_field: str
The name of the background field to plot the streamlines on.
level: int
The number of the X level to plot the cross section through.
cmap: str or matplotlib colormap
The name of the matplotlib colormap to use for the background field.
vmin: float
The minimum bound to use for plotting the background field. None will
automatically detect the background field minimum.
vmax: float
The maximum bound to use for plotting the background field. None will
automatically detect the background field maximum.
u_vel_contours: 1-D array
The contours to use for plotting contours of u. Set to None to not
display such contours.
v_vel_contours: 1-D array
The contours to use for plotting contours of v. Set to None to not
display such contours.
w_vel_contours: 1-D array
The contours to use for plotting contours of w. Set to None to not
display such contours.
wind_vel_contours: 1-D aray
The contours to use for plotting contours of horizontal wind speed.
Set to None to not display such contours.
u_field: str
Name of zonal wind (u) field in Grids.
v_field: str
Name of meridional wind (v) field in Grids.
w_field: str
Name of vertical wind (w) field in Grids.
show_lobes: bool
If True, the dual doppler lobes from each pair of radars will be shown.
title_flag: bool
If True, PyDDA will generate a title for the plot.
axes_labels_flag: bool
If True, PyDDA will generate axes labels for the plot.
colorbar_flag: bool
If True, PyDDA will generate a colorbar for the plot background field.
colorbar_contour_flag: bool
If True, PyDDA will generate a colorbar for the contours.
bg_grid_no: int
Number of grid in Grids to take background field from.
Set to -1 to use maximum value from all grids.
contour_alpha: float
Alpha (transparency) of velocity contours. 0 = transparent, 1 = opaque
arrowsize: float
Factor scale arrow size for streamlines.
linewidth: numeric or 2d array
Linewidths for streamlines.
Returns
-------
ax: Matplotlib axis handle
The matplotlib axis handle corresponding to the plot
"""
if isinstance(Grids, DataTree):
child_list = list(Grids.children.keys())
grid_list = []
rad_names = []
for child in child_list:
if "radar" in child:
grid_list.append(Grids[child].to_dataset())
rad_names.append(child)
else:
grid_list = Grids
grid_bg = grid_list[bg_grid_no][background_field].values.squeeze()
if vmin is None:
vmin = grid_bg.min()
if vmax is None:
vmax = grid_bg.max()
grid_h = grid_list[0]["point_altitude"].values / 1e3
grid_x = grid_list[0]["point_x"].values / 1e3
grid_y = grid_list[0]["point_y"].values / 1e3
np.diff(grid_x, axis=2)[0, 0, 0]
np.diff(grid_y, axis=1)[0, 0, 0]
if isinstance(Grids, DataTree):
u = Grids[u_field].values.squeeze()
v = Grids[v_field].values.squeeze()
w = Grids[w_field].values.squeeze()
else:
u = grid_list[0][u_field].values.squeeze()
v = grid_list[0][v_field].values.squeeze()
w = grid_list[0][w_field].values.squeeze()
if isinstance(u, np.ma.MaskedArray):
u = u.filled(np.nan)
if isinstance(v, np.ma.MaskedArray):
v = v.filled(np.nan)
if isinstance(w, np.ma.MaskedArray):
w = w.filled(np.nan)
if ax is None:
ax = plt.gca()
the_mesh = ax.pcolormesh(
grid_y[:, :, level],
grid_h[:, :, level],
grid_bg[:, :, level],
cmap=cmap,
vmin=vmin,
vmax=vmax,
)
ax.streamplot(
grid_y[:, :, level],
grid_h[:, :, level],
v[:, :, level],
w[:, :, level],
color="k",
cmap="coolwarm",
arrowsize=arrowsize,
linewidth=linewidth,
)
if colorbar_flag is True:
cp = grid_list[bg_grid_no][background_field].attrs["long_name"]
cp.replace(" ", "_")
cp = cp + " [" + grid_list[bg_grid_no][background_field].attrs["units"]
cp = cp + "]"
plt.colorbar(the_mesh, ax=ax, label=(cp))
if u_vel_contours is not None:
u_filled = np.ma.filled(u[:, :, level], fill_value=np.nan)
cs = ax.contour(
grid_y[:, :, level], grid_h[:, :, level], u_filled, levels=u_vel_contours
)
plt.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(cs, ax=ax, label="U [m/s]")
if v_vel_contours is not None:
v_filled = np.ma.filled(v[:, :, level], fill_value=np.nan)
cs = ax.contour(
grid_y[:, :, level], grid_h[:, :, level], v_filled, levels=w_vel_contours
)
plt.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(cs, ax=ax, label="V [m/s]")
if w_vel_contours is not None:
w_filled = np.ma.filled(w[:, :, level], fill_value=np.nan)
cs = ax.contour(
grid_y[:, :, level],
grid_h[:, :, level],
w_filled,
levels=w_vel_contours,
linewidths=2,
)
plt.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(cs, ax=ax, label="W [m/s]")
if wind_vel_contours is not None:
vel = np.ma.sqrt(u[:, :, level] ** 2 + v[:, :, level] ** 2)
vel = vel.filled(fill_value=np.nan)
cs = ax.contour(
grid_y[:, :, level],
grid_h[:, :, level],
vel,
levels=wind_vel_contours,
linewidths=2,
alpha=contour_alpha,
)
cs.set_clim([np.min(wind_vel_contours), np.max(wind_vel_contours)])
cs.cmap.set_under(color="white", alpha=0)
cs.cmap.set_bad(color="white", alpha=0)
ax.clabel(cs)
if colorbar_contour_flag is True:
plt.colorbar(cs, ax=ax, label="|V| [m/s]")
if axes_labels_flag is True:
ax.set_xlabel(("Y [km]"))
ax.set_ylabel(("Z [km]"))
if title_flag is True:
if grid_x[0, 0, level] > 0:
ax.set_title(
(
"PyDDA retreived winds @"
+ str(grid_x[0, level, 0])
+ " km east of origin."
)
)
else:
ax.set_title(
(
"PyDDA retreived winds @"
+ str(-grid_x[0, level, 0])
+ " km west of origin."
)
)
ax.set_xlim([grid_y.min(), grid_y.max()])
ax.set_ylim([grid_h.min(), grid_h.max()])
return ax