class Plotter():
""" Create plot objects based on output from the FVCOM.
Class to assist in the creation of plots and animations based on output
from the FVCOM.
Provides
--------
plot_field
plot_quiver
plot_lines
plot_scatter
remove_line_plots (N.B., this is mostly specific to PyLag-tools)
Author(s)
---------
James Clark (Plymouth Marine Laboratory)
Pierre Cazenave (Plymouth Marine Laboratory)
"""
def __init__(self,
dataset,
figure=None,
axes=None,
stations=None,
extents=None,
vmin=None,
vmax=None,
mask=None,
res='c',
fs=10,
title=None,
cmap='viridis',
figsize=(10., 10.),
axis_position=None,
edgecolors='none',
s_stations=20,
s_particles=20,
linewidth=1.0,
tick_inc=None,
cb_label=None,
extend='neither',
norm=None,
m=None):
"""
Parameters:
-----------
dataset : Dataset, PyFVCOM.read.FileReader
netCDF4 Dataset or PyFVCOM.read.FileReader object.
stations : 2D array, optional
List of station coordinates to be plotted ([[lons], [lats]])
extents : 1D array, optional
Four element numpy array giving lon/lat limits (e.g. [-4.56, -3.76,
49.96, 50.44])
vmin : float, optional
Lower bound to be used on colour bar (plot_field only).
vmax : float, optional
Upper bound to be used colour bar (plot_field only).
mask : float, optional
Mask out values < mask (plot_field only).
res : string, optional
Resolution to use when drawing Basemap object
fs : int, optional
Font size to use when rendering plot text
title : str, optional
Title to use when creating the plot
cmap : string, optional
Colormap to use when shading field data (plot_field only).
figure : Figure, optional
Matplotlib figure object. A figure object is created if not
provided.
figsize : tuple(float), optional
Figure size in cm. This is only used if a new Figure object is
created.
axes : Axes, optional
Matplotlib Axes object. An Axes object is created if not
provided.
axis_position : 1D array, optional
Array giving axis dimensions
s_stations : int, optional
Symbol size used when producing scatter plot of station locations
s_particles : int, optional
Symbol size used when producing scatter plot of particle locations
linewidth : float, optional
Linewidth to be used when generating line plots
tick_inc : list, optional
Add coordinate axes (i.e. lat/long) at the intervals specified in
the list ([lon_spacing, lat_spacing]).
cb_label : str, optional
Set the colour bar label.
extend : str, optional
Set the colour bar extension ('neither', 'both', 'min', 'max').
Defaults to 'neither').
norm : matplotlib.colors.Normalize, optional
Normalise the luminance to 0,1. For example, use from
matplotlib.colors.LogNorm to do log plots of fields.
m : mpl_toolkits.basemap.Basemap, optional
Pass a Basemap object rather than creating one on each invocation.
Author(s):
-------
James Clark (PML)
Pierre Cazenave (PML)
"""
self.ds = dataset
self.figure = figure
self.axes = axes
self.stations = stations
self.extents = extents
self.vmin = vmin
self.vmax = vmax
self.mask = mask
self.res = res
self.fs = fs
self.title = title
self.cmap = cmap
self.figsize = figsize
self.axis_position = axis_position
self.edgecolors = edgecolors
self.s_stations = s_stations
self.s_particles = s_particles
self.linewidth = linewidth
self.tick_inc = tick_inc
self.cb_label = cb_label
self.extend = extend
self.norm = norm
self.m = m
# Plot instances (initialise to None for truthiness test later)
self.quiver_plot = None
self.scat_plot = None
self.tripcolor_plot = None
self.tri = None
self.masked_tris = None
self.cbar = None
self.line_plot = None
# Are we working with a FileReader object or a bog-standard netCDF4 Dataset?
self._FileReader = False
if isinstance(dataset, FileReader):
self._FileReader = True
# Initialise the figure
self._init_figure()
def _init_figure(self):
# Read in required grid variables
if self._FileReader:
self.n_nodes = getattr(self.ds.dims, 'node')
self.n_elems = getattr(self.ds.dims, 'nele')
self.lon = self.ds.grid.lon
self.lat = self.ds.grid.lat
self.lonc = self.ds.grid.lonc
self.latc = self.ds.grid.latc
self.nv = self.ds.grid.nv
else:
self.n_nodes = len(self.ds.dimensions['node'])
self.n_elems = len(self.ds.dimensions['nele'])
self.lon = self.ds.variables['lon'][:]
self.lat = self.ds.variables['lat'][:]
self.lonc = self.ds.variables['lonc'][:]
self.latc = self.ds.variables['latc'][:]
self.nv = self.ds.variables['nv'][:]
if self.nv.min() != 1:
self.nv -= self.nv.min()
# Triangles
self.triangles = self.nv.transpose() - 1
# Initialise the figure
if self.figure is None:
figsize = (cm2inch(self.figsize[0]), cm2inch(self.figsize[1]))
self.figure = plt.figure(figsize=figsize)
self.figure.set_facecolor('white')
# Create plot axes
if not self.axes:
self.axes = self.figure.add_subplot(1, 1, 1)
if self.axis_position:
self.axes.set_position(self.axis_position)
# If plot extents were not given, use min/max lat/lon values
if self.extents is None:
self.extents = np.array([
self.lon.min(),
self.lon.max(),
self.lat.min(),
self.lat.max()
])
# Create basemap object
if not self.m:
if have_basemap:
self.m = Basemap(llcrnrlon=self.extents[:2].min(),
llcrnrlat=self.extents[-2:].min(),
urcrnrlon=self.extents[:2].max(),
urcrnrlat=self.extents[-2:].max(),
rsphere=(6378137.00, 6356752.3142),
resolution=self.res,
projection='merc',
area_thresh=0.1,
lat_0=self.extents[-2:].mean(),
lon_0=self.extents[:2].mean(),
lat_ts=self.extents[-2:].mean(),
ax=self.axes)
else:
raise RuntimeError(
'mpl_toolkits is not available in this Python.')
self.m.drawmapboundary()
self.m.drawcoastlines(zorder=2)
self.m.fillcontinents(color='0.6', zorder=2)
if self.title:
self.axes.set_title(self.title)
# Add coordinate labels to the x and y axes.
if self.tick_inc:
meridians = np.arange(np.floor(np.min(self.extents[:2])),
np.ceil(np.max(self.extents[:2])),
self.tick_inc[0])
parallels = np.arange(np.floor(np.min(self.extents[2:])),
np.ceil(np.max(self.extents[2:])),
self.tick_inc[1])
self.m.drawparallels(parallels,
labels=[1, 0, 0, 0],
fontsize=self.fs,
linewidth=0,
ax=self.axes)
self.m.drawmeridians(meridians,
labels=[0, 0, 0, 1],
fontsize=self.fs,
linewidth=0,
ax=self.axes)
def replot(self):
self.axes.cla()
self._init_figure()
def plot_field(self, field):
""" Map the given field.
Parameters:
-----------
field : 1D array TOCHECK
Field to plot.
"""
if self.mask is not None:
field = np.ma.masked_where(field <= self.mask, field)
# Update array values if the plot has already been initialised
if self.tripcolor_plot:
field = field[self.masked_tris].mean(axis=1)
self.tripcolor_plot.set_array(field)
return
# Create tripcolor plot
x, y = self.m(self.lon, self.lat)
self.tri = Triangulation(x, y, self.triangles)
self.masked_tris = self.tri.get_masked_triangles()
field = field[self.masked_tris].mean(axis=1)
self.tripcolor_plot = self.axes.tripcolor(self.tri,
field,
vmin=self.vmin,
vmax=self.vmax,
cmap=self.cmap,
edgecolors=self.edgecolors,
zorder=1,
norm=self.norm)
# Overlay the grid
# self.axes.triplot(self.tri, zorder=2)
# Overlay stations in the first instance
if self.stations is not None:
mx, my = self.m(self.stations[0, :], self.stations[1, :])
self.axes.scatter(mx,
my,
marker='*',
c='k',
s=self.s_stations,
edgecolors='none',
zorder=4)
# Add colorbar scaled to axis width
divider = make_axes_locatable(self.axes)
cax = divider.append_axes("right", size="5%", pad=0.05)
self.cbar = self.figure.colorbar(self.tripcolor_plot,
cax=cax,
extend=self.extend)
self.cbar.ax.tick_params(labelsize=self.fs)
if self.cb_label:
self.cbar.set_label(self.cb_label)
return
def plot_quiver(self,
u,
v,
field=False,
add_key=True,
scale=1.0,
label=None):
""" Produce quiver plot using u and v velocity components.
Parameters:
-----------
u : 1D or 2D array
u-component of the velocity field.
v : 1D or 2D array
v-component of the velocity field
field : 1D or 2D array
velocity magnitude field. Used to colour the vectors. Also adds a colour bar which uses the cb_label and
cmap, if provided.
add_key : bool, optional
Add key for the quiver plot. Defaults to True.
scale : float, optional
Scaling to be provided to arrows with scale_units of inches. Defaults to 1.0.
label : str, optional
Give label to use for the quiver key (defaults to "`scale' ms^{-1}").
"""
if self.quiver_plot:
if np.any(field):
self.quiver_plot.set_UVC(u, v, field)
else:
self.quiver_plot.set_UVC(u, v)
return
if not label:
label = '{} '.format(scale) + r'$\mathrm{ms^{-1}}$'
x, y = self.m(self.lonc, self.latc)
if np.any(field):
self.quiver_plot = self.axes.quiver(x,
y,
u,
v,
field,
cmap=self.cmap,
units='inches',
scale_units='inches',
scale=scale)
divider = make_axes_locatable(self.axes)
cax = divider.append_axes("right", size="5%", pad=0.05)
self.cbar = self.figure.colorbar(self.quiver_plot, cax=cax)
self.cbar.ax.tick_params(labelsize=self.fs)
if self.cb_label:
self.cbar.set_label(self.cb_label)
else:
self.quiver_plot = self.axes.quiver(x,
y,
u,
v,
units='inches',
scale_units='inches',
scale=scale)
if add_key:
self.quiver_key = plt.quiverkey(self.quiver_plot,
0.9,
0.9,
scale,
label,
coordinates='axes')
return
def plot_lines(self,
x,
y,
group_name='Default',
colour='r',
zone_number='30N'):
""" Plot path lines.
Parameters:
-----------
x : 1D array TOCHECK
Array of x coordinates to plot.
y : 1D array TOCHECK
Array of y coordinates to plot.
group_name : str, optional
Group name for this set of particles - a separate plot object is
created for each group name passed in.
Default `None'
color : string, optional
Colour to use when making the plot.
Default `r'
zone_number : string, optional
See PyFVCOM documentation for a full list of supported codes.
"""
if not self.line_plot:
self.line_plot = dict()
# Remove current line plots for this group, if they exist
if group_name in self.line_plot:
if self.line_plot[group_name]:
self.remove_line_plots(group_name)
lon, lat = lonlat_from_utm(x, y, zone_number)
mx, my = self.m(lon, lat)
self.line_plot[group_name] = self.axes.plot(mx,
my,
color=colour,
linewidth=self.linewidth,
alpha=0.25,
zorder=2)
def remove_line_plots(self, group_name):
""" Remove line plots for group `group_name'
Parameters:
-----------
group_name : str
Name of the group for which line plots should be deleted.
"""
if self.line_plot:
while self.line_plot[group_name]:
self.line_plot[group_name].pop(0).remove()
def plot_scatter(self,
x,
y,
group_name='Default',
colour='r',
zone_number='30N'):
""" Plot scatter.
Parameters:
-----------
x : 1D array TOCHECK
Array of x coordinates to plot.
y : 1D array TOCHECK
Array of y coordinates to plot.
group_name : str, optional
Group name for this set of particles - a separate plot object is
created for each group name passed in.
Default `None'
color : string, optional
Colour to use when making the plot.
Default `r'
zone_number : string, optional
See PyFVCOM documentation for a full list of supported codes.
Default `30N'
"""
if not self.scat_plot:
self.scat_plot = dict()
lon, lat = lonlat_from_utm(x, y, zone_number)
mx, my = self.m(lon, lat)
try:
data = np.array([mx, my])
self.scat_plot[group_name].set_offsets(data.transpose())
except KeyError:
self.scat_plot[group_name] = self.axes.scatter(mx,
my,
s=self.s_particles,
color=colour,
edgecolors='none',
zorder=3)
def set_title(self, title):
""" Set the title for the current axis. """
self.axes.set_title(title, fontsize=self.fs)
def close(self):
""" Close the current figure. """
plt.close(self.figure)
