def _hovmoller_plot(self, subplot, map_subplot, nomap_subplot, name, vmin,
vmax, data, times, cmap, unit, title):
if self.showmap:
plt.subplot(subplot[map_subplot[0], map_subplot[1]])
else:
plt.subplot(subplot[nomap_subplot[0], nomap_subplot[1]])
c = plt.pcolormesh(
self.distance,
times,
data,
cmap=cmap,
shading='gouraud', # Smooth shading
vmin=vmin,
vmax=vmax)
ax = plt.gca()
ax.set_title(title, fontsize=14) # Set title of subplot
ax.yaxis_date()
ax.yaxis.grid(True)
ax.set_facecolor('dimgray')
plt.xlabel(gettext("Distance (km)"))
plt.xlim([self.distance[0], self.distance[-1]])
divider = make_axes_locatable(plt.gca())
cax = divider.append_axes("right", size="5%", pad=0.05)
bar = plt.colorbar(c, cax=cax)
bar.set_label("%s (%s)" % (name, utils.mathtext(unit)))
def scale(args):
"""
Draws the variable scale that is placed over the map.
Returns a BytesIO object.
"""
dataset_name = args.get("dataset")
config = DatasetConfig(dataset_name)
scale = args.get("scale")
scale = [float(component) for component in scale.split(",")]
variable = args.get("variable")
variable = variable.split(",")
if len(variable) > 1:
variable_unit = config.variable[",".join(variable)].unit
variable_name = config.variable[",".join(variable)].name
else:
variable_unit = config.variable[variable[0]].unit
variable_name = config.variable[variable[0]].name
cmap = colormap.find_colormap(variable_name)
if len(variable) == 2:
cmap = colormap.colormaps.get("speed")
fig = plt.figure(figsize=(2, 5), dpi=75)
ax = fig.add_axes([0.05, 0.05, 0.25, 0.9])
norm = matplotlib.colors.Normalize(vmin=scale[0], vmax=scale[1])
formatter = ScalarFormatter()
formatter.set_powerlimits((-3, 4))
bar = ColorbarBase(ax,
cmap=cmap,
norm=norm,
orientation="vertical",
format=formatter)
if variable_name == "Potential Sub Surface Channel":
bar.set_ticks([0, 1], True)
bar.set_label("%s (%s)" %
(variable_name.title(), utils.mathtext(variable_unit)),
fontsize=12)
# Increase tick font size
bar.ax.tick_params(labelsize=12)
buf = BytesIO()
plt.savefig(
buf,
format="png",
dpi="figure",
transparent=False,
bbox_inches="tight",
pad_inches=0.05,
)
plt.close(fig)
buf.seek(0) # Move buffer back to beginning
return buf
def scale(args):
dataset_name = args.get('dataset')
config = DatasetConfig(dataset_name)
scale = args.get('scale')
scale = [float(component) for component in scale.split(',')]
variable = args.get('variable')
variable = variable.split(',')
with open_dataset(config) as dataset:
if len(variable) > 1:
variable_unit = config.variable[",".join(variable)].unit
variable_name = config.variable[",".join(variable)].name
else:
variable_unit = config.variable[dataset.variables[
variable[0]]].unit
variable_name = config.variable[dataset.variables[
variable[0]]].name
cmap = colormap.find_colormap(variable_name)
if len(variable) == 2:
cmap = colormap.colormaps.get('speed')
fig = plt.figure(figsize=(2, 5), dpi=75)
ax = fig.add_axes([0.05, 0.05, 0.25, 0.9])
norm = matplotlib.colors.Normalize(vmin=scale[0], vmax=scale[1])
formatter = ScalarFormatter()
formatter.set_powerlimits((-3, 4))
bar = ColorbarBase(ax,
cmap=cmap,
norm=norm,
orientation='vertical',
format=formatter)
bar.set_label("%s (%s)" %
(variable_name.title(), utils.mathtext(variable_unit)),
fontsize=12)
# Increase tick font size
bar.ax.tick_params(labelsize=12)
buf = BytesIO()
plt.savefig(buf,
format='png',
dpi='figure',
transparent=False,
bbox_inches='tight',
pad_inches=0.05)
plt.close(fig)
buf.seek(0) # Move buffer back to beginning
return buf
def __add_surface_plot(self, axis_divider):
ax = axis_divider.append_axes("top", size="35%", pad=0.35)
ax.plot(self.surface_data['distance'],
self.surface_data['data'], color='r')
ax.locator_params(nbins=3)
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")
label = plt.ylabel(utils.mathtext(self.surface_data['unit']))
title = plt.title(self.surface_data['name'], y=1.1)
plt.setp(title, size='smaller')
plt.setp(label, size='smaller')
plt.setp(ax.get_yticklabels(), size='x-small')
plt.xlim([0, self.surface_data['distance'][-1]])
plt.ylim(utils.normalize_scale(self.surface_data['data'],
self.surface_data['config']))
ax.yaxis.grid(True)
ax.axes.get_xaxis().set_visible(False)
def _surface_plot(self, axis_divider):
ax = axis_divider.append_axes("top", size="35%", pad=0.35)
ax.plot(self.surface_data['distance'],
self.surface_data['data'], color='r')
ax.locator_params(nbins=3)
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")
label = plt.ylabel(utils.mathtext(self.surface_data['unit']))
title = plt.title(self.surface_data['name'], y=1.1)
plt.setp(title, size='smaller')
plt.setp(label, size='smaller')
plt.setp(ax.get_yticklabels(), size='x-small')
plt.xlim([0, self.surface_data['distance'][-1]])
if np.any([re.search(x, self.surface_data['name'], re.IGNORECASE) for x in [
"free surface",
"surface height"
]]):
ylim = plt.ylim()
plt.ylim([min(ylim[0], -ylim[1]), max([-ylim[0], ylim[1]])])
ax.yaxis.grid(True)
ax.axes.get_xaxis().set_visible(False)
def _transect_plot(self, values, depths, plotTitle, vmin, vmax, cmapLabel,
unit, cmap):
self.__fill_invalid_shift(values)
dist = np.tile(self.transect_data['distance'], (values.shape[0], 1))
# Plot the data
c = plt.pcolormesh(
dist,
depths.transpose(),
values,
cmap=cmap,
shading='gouraud', # Smooth shading
vmin=vmin,
vmax=vmax)
ax = plt.gca()
ax.set_title(plotTitle, fontsize=14) # Set title of subplot
ax.invert_yaxis()
if self.depth_limit is None or (self.depth_limit is not None and
self.linearthresh < self.depth_limit):
plt.yscale('symlog', linthreshy=self.linearthresh)
ax.yaxis.set_major_formatter(ScalarFormatter())
# Mask out the bottom
plt.fill_between(self.bathymetry['x'],
self.bathymetry['y'] * -1,
plt.ylim()[0],
facecolor='dimgray',
hatch='xx')
ax.set_facecolor('dimgray')
plt.xlabel(gettext("Distance (km)"))
plt.ylabel(gettext("Depth (m)"))
plt.xlim([
self.transect_data['distance'][0],
self.transect_data['distance'][-1]
])
# Tighten the y-limits
if self.depth_limit:
plt.ylim(self.depth_limit, 0)
else:
deep = np.amax(self.bathymetry['y'] * -1)
l = 10**np.floor(np.log10(deep))
plt.ylim(np.ceil(deep / l) * l, 0)
ticks = sorted(
set(list(plt.yticks()[0]) +
[self.linearthresh, plt.ylim()[0]]))
if self.depth_limit is not None:
ticks = [y for y in ticks if y <= self.depth_limit]
plt.yticks(ticks)
# Show the linear threshold
plt.plot([
self.transect_data['distance'][0],
self.transect_data['distance'][-1]
], [self.linearthresh, self.linearthresh],
'k:',
alpha=1.0)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
bar = plt.colorbar(c, cax=cax)
# Append variable units to color scale label
bar.set_label(cmapLabel + " (" + utils.mathtext(unit) + ")")
if len(self.points) > 2:
station_distances = []
current_dist = 0
d = VincentyDistance()
for idx, p in enumerate(self.points):
if idx == 0:
station_distances.append(0)
else:
current_dist += d.measure(p, self.points[idx - 1])
station_distances.append(current_dist)
ax2 = ax.twiny()
ax2.set_xticks(station_distances)
ax2.set_xlim([
self.transect_data['distance'][0],
self.transect_data['distance'][-1]
])
ax2.tick_params('x',
length=0,
width=0,
pad=-3,
labelsize='xx-small',
which='major')
ax2.xaxis.set_major_formatter(StrMethodFormatter("$\u25bc$"))
cax = make_axes_locatable(ax2).append_axes("right",
size="5%",
pad=0.05)
bar2 = plt.colorbar(c, cax=cax)
bar2.remove()
return divider
def plot(self):
v = set([])
for idx in self.observation_variable:
v.add(self.observation_variable_names[idx])
for n in self.variable_names:
v.add(n)
numplots = len(v)
fig, ax = self.setup_subplots(numplots)
data = []
for o in self.observation:
d = np.ma.MaskedArray(o['data'])
d[np.where(d == '')] = np.ma.masked
d = np.ma.masked_invalid(d.filled(np.nan).astype(np.float32))
data.append(d)
ureg = pint.UnitRegistry()
ax_idx = -1
axis_map = {}
unit_map = {}
for idx in self.observation_variable:
ax_idx += 1
for d in data:
if d.shape[1] == 1:
style = '.'
else:
style = '-'
ax[ax_idx].plot(d[idx, :, 1], d[idx, :, 0], style)
ax[ax_idx].xaxis.set_label_position('top')
ax[ax_idx].xaxis.set_ticks_position('top')
ax[ax_idx].set_xlabel("%s (%s)" % (
self.observation_variable_names[idx],
mathtext(self.observation_variable_units[idx]),
))
axis_map[self.observation_variable_names[idx]] = ax[ax_idx]
try:
if "_" in self.observation_variable_units[idx]:
u = self.observation_variable_units[idx].lower().split(
"_", 1)[1]
else:
u = self.observation_variable_units[idx].lower()
unit_map[
self.observation_variable_names[idx]] = ureg.parse_units(u)
except:
unit_map[
self.observation_variable_names[idx]] = ureg.dimensionless
for k, v in list(unit_map.items()):
if v == ureg.speed_of_light:
unit_map[k] = ureg.celsius
for idx, var in enumerate(self.variables):
if axis_map.get(self.variable_names[idx]) is not None:
axis = axis_map.get(self.variable_names[idx])
showlegend = True
destunit = unit_map.get(self.variable_names[idx])
else:
ax_idx += 1
axis = ax[ax_idx]
showlegend = False
try:
destunit = ureg.parse_units(
self.variable_units[idx].lower())
if destunit == ureg.speed_of_light:
destunit = ureg.celsius
except:
destunit = ureg.dimensionless
for j in range(0, self.data.shape[0]):
try:
u = ureg.parse_units(self.variable_units[idx].lower())
if u == ureg.speed_of_light:
u = ureg.celsius
quan = ureg.Quantity(self.data[j, idx, :], u)
except:
quan = ureg.Quantity(self.data[j, idx, :],
ureg.dimensionless)
axis.plot(quan.to(destunit).magnitude, self.depths[j, idx, :])
showlegend = showlegend or len(self.observation) > 1
if not showlegend:
axis.xaxis.set_label_position('top')
axis.xaxis.set_ticks_position('top')
axis.set_xlabel("%s (%s)" % (
self.variable_names[idx],
mathtext(self.variable_units[idx]),
))
else:
l = []
for j in [(gettext("Observed"), self.observation_times),
(gettext("Modelled"), self.timestamps)]:
for i, name in enumerate(self.names):
if len(self.names) == 1:
name = ""
else:
name = name + " "
l.append("%s%s (%s)" %
(name, j[0], format_datetime(j[1][i])))
leg = axis.legend(l, loc='best')
for legobj in leg.legendHandles:
legobj.set_linewidth(4.0)
ax[0].invert_yaxis()
ax[0].set_ylabel(gettext("Depth (m)"))
if not self.plotTitle:
if len(self.variables) > 0:
plt.suptitle("\n".join(
wrap(
gettext(
"Profile for %s, Observed at %s, Modelled at %s") %
(", ".join(self.names),
format_datetime(self.observation_time),
format_datetime(self.timestamp)), 80)))
else:
plt.suptitle("\n".join(
wrap(
gettext("Profile for %s (%s)") %
(", ".join(self.names),
format_datetime(self.observation_time)), 80)))
else:
plt.suptitle(self.plotTitle, fontsize=15)
fig.tight_layout()
fig.subplots_adjust(top=0.85)
return super(ObservationPlotter, self).plot()
def plot(self):
figuresize = list(map(float, self.size.split("x")))
figuresize[1] *= len(self.points) * len(self.depth)
fig, ax = plt.subplots(len(self.points) * len(self.depth),
1,
sharex=True,
figsize=figuresize,
dpi=self.dpi)
if len(self.points) * len(self.depth) == 1:
ax = [ax]
if self.data.shape[1] == 2:
for idx, p in enumerate(self.points):
magnitude = np.sqrt(self.data[idx, 0, :, :]**2 +
self.data[idx, 1, :, :]**2)
scale = np.mean(magnitude)
if scale != 0:
scale = np.round(scale, int(-np.floor(np.log10(scale))))
for idx2, d in enumerate(self.depth):
datenums = date2num(self.timestamp)
a = ax[idx * len(self.points) + idx2]
q = a.quiver(
datenums,
[0] * len(self.timestamp),
self.data[idx, 0, idx2, :],
self.data[idx, 1, idx2, :],
angles='uv',
width=0.002,
headwidth=0,
headlength=0,
headaxislength=0,
)
a.axes.get_yaxis().set_visible(False)
a.axes.get_xaxis().tick_bottom()
a.xaxis_date()
a.quiverkey(
q, 0.1, 0.75, scale, "%.1g %s" %
(scale, utils.mathtext(self.variable_units[0])))
dx = datenums[1] - datenums[0]
a.set_xlim(
[datenums[0] - dx / 2.0, datenums[-1] + dx / 2.0])
a.set_frame_on(False)
a.axhline(0, color='grey', ls=':')
if self.depth[idx2] == "bottom":
depth = "Bottom"
else:
depth = "%d m" % np.round(self.data_depth[idx, 0, idx2,
0])
if self.plotTitle is None or self.plotTitle == "":
a.set_title(gettext("%s at (%s)\n%s") %
(self.vector_name(self.variable_names[0]),
self.names[idx], depth),
fontsize=15)
else:
a.set_title(self.plotTitle, fontsize=15)
plt.setp(plt.gca().get_xticklabels(), rotation=30)
fig.tight_layout()
return super(StickPlotter, self).plot(fig)
def plot(self):
if self.scale:
vmin = self.scale[0]
vmax = self.scale[1]
else:
vmin, vmax = utils.normalize_scale(
self.data, self.dataset_config.variable[self.variables[0]])
if self.cmap is None:
self.cmap = colormap.find_colormap(self.variable_name)
datenum = matplotlib.dates.date2num(self.times)
if self.depth == 'all':
size = list(map(float, self.size.split("x")))
numpoints = len(self.points)
figuresize = (size[0], size[1] * numpoints)
fig, ax = plt.subplots(numpoints,
1,
sharex=True,
figsize=figuresize,
dpi=self.dpi)
if not isinstance(ax, np.ndarray):
ax = [ax]
for idx, p in enumerate(self.points):
d = self.data[idx, 0, :]
dlim = np.ma.flatnotmasked_edges(d[0, :])
maxdepth = self.depths[dlim[1]].max()
mindepth = self.depths[dlim[0]].min()
c = ax[idx].pcolormesh(datenum,
self.depths[:dlim[1] + 1],
d[:, :dlim[1] + 1].transpose(),
shading='gouraud',
cmap=self.cmap,
vmin=vmin,
vmax=vmax)
ax[idx].invert_yaxis()
if maxdepth > LINEAR:
ax[idx].set_yscale('symlog', linthreshy=LINEAR)
ax[idx].yaxis.set_major_formatter(ScalarFormatter())
if maxdepth > LINEAR:
l = 10**np.floor(np.log10(maxdepth))
ax[idx].set_ylim(np.ceil(maxdepth / l) * l, mindepth)
ax[idx].set_yticks(
list(ax[idx].get_yticks()) + [maxdepth, LINEAR])
else:
ax[idx].set_ylim(maxdepth, mindepth)
ax[idx].set_ylabel("Depth (%s)" %
utils.mathtext(self.depth_unit))
ax[idx].xaxis_date()
ax[idx].set_xlim(datenum[0], datenum[-1])
divider = make_axes_locatable(ax[idx])
cax = divider.append_axes("right", size="5%", pad=0.05)
bar = plt.colorbar(c, cax=cax)
bar.set_label("%s (%s)" % (self.variable_name.title(),
utils.mathtext(self.variable_unit)))
ax[idx].set_title("%s%s at %s" %
(self.variable_name.title(),
self.depth_label, self.names[idx]))
plt.setp(ax[idx].get_xticklabels(), rotation=30)
fig.autofmt_xdate()
else:
# Create base figure
figure_size = self.figuresize
figure_size[0] *= 1.5 if self.showmap else 1.0
fig = plt.figure(figsize=figure_size, dpi=self.dpi)
# Setup figure layout
width = 1
if self.showmap:
width += 1
# Horizontally scale the actual plots by 2x the size of
# the location map
width_ratios = [1, 2]
else:
width_ratios = None
# Create layout helper
gs = gridspec.GridSpec(1, width, width_ratios=width_ratios)
subplot = 0
# Render point location
if self.showmap:
plt.subplot(gs[0, 0])
subplot += 1
utils.point_plot(
np.array([
[x[0] for x in self.points], # Latitudes
[x[1] for x in self.points]
])) # Longitudes
plt.subplot(gs[:, subplot])
plt.plot_date(datenum,
np.squeeze(self.data),
fmt='-',
figure=fig,
xdate=True)
plt.ylabel(
f"{self.variable_name.title()} ({utils.mathtext(self.variable_unit)})",
fontsize=14)
plt.ylim(vmin, vmax)
# Title
if self.plotTitle is None or self.plotTitle == "":
wrapped_title = wrap(
"%s%s at %s" % (self.variable_name.title(),
self.depth_label, ", ".join(self.names)),
80)
plt.title("\n".join(wrapped_title), fontsize=15)
else:
plt.title(self.plotTitle, fontsize=15)
plt.gca().grid(True)
fig.autofmt_xdate()
self.plot_legend(fig, self.names)
return super(TimeseriesPlotter, self).plot(fig)
def plot(self):
# Create base figure
fig = plt.figure(figsize=self.figuresize(), dpi=self.dpi)
# Setup figure layout
width = len(self.variables)
if self.showmap:
width += 1
# Horizontally scale the actual plots by 2x the size of
# the location map
width_ratios = [1]
[width_ratios.append(2) for w in range(0, width - 1)]
else:
width_ratios = None
# Create layout helper
gs = gridspec.GridSpec(1, width, width_ratios=width_ratios)
subplot = 0
# Render point location
if self.showmap:
plt.subplot(gs[0, subplot])
subplot += 1
utils.point_plot(
np.array([
[x[0] for x in self.points], # Latitudes
[x[1] for x in self.points]
])) # Longitudes
is_y_label_plotted = False
# Create a subplot for each variable selected
# Each subplot has all points plotted
for idx, v in enumerate(self.variables):
plt.subplot(gs[:, subplot])
plt.plot(self.data[:, idx, :].transpose(),
self.depths[:, idx, :].transpose())
current_axis = plt.gca()
current_axis.xaxis.set_label_position('top')
current_axis.xaxis.set_ticks_position('top')
current_axis.invert_yaxis()
current_axis.grid(True)
current_axis.set_xlabel("%s (%s)" %
(self.variable_names[idx],
utils.mathtext(self.variable_units[idx])),
fontsize=14)
# Put y-axis label on left-most graph (but after the point location)
if not is_y_label_plotted and (subplot == 0 or subplot == 1):
current_axis.set_ylabel(gettext("Depth (m)"), fontsize=14)
is_y_label_plotted = True
if self.compare:
xlim = np.abs(plt.gca().get_xlim()).max()
plt.gca().set_xlim([-xlim, xlim])
subplot += 1
self.plot_legend(fig, self.names)
if self.plotTitle is None or self.plotTitle == "":
plt.suptitle("%s(%s)\n%s\n%s" % (gettext("Profile for "), \
", ".join(self.names), \
", ".join(self.variable_names), \
self.date_formatter(self.timestamp)), \
fontsize=15)
else:
plt.suptitle(self.plotTitle, fontsize=15)
fig.tight_layout()
fig.subplots_adjust(top=(0.8))
return super(ProfilePlotter, self).plot(fig)
def plot(self):
if self.showmap:
width = 2
width_ratios = [2, 7]
else:
width = 1
width_ratios = [1]
numplots = len(self.variables) + len(self.buoyvariables)
if "votemper" in self.variables and "sst" in self.buoyvariables:
numplots -= 1
if self.latlon:
numplots += 2
figuresize = list(map(float, self.size.split("x")))
figuresize[1] *= numplots
fig = plt.figure(figsize=figuresize, dpi=self.dpi)
gs = gridspec.GridSpec(numplots, width, width_ratios=width_ratios)
if self.showmap:
# Plot the path on a map
if numplots > 1:
plt.subplot(gs[:, 0])
else:
plt.subplot(gs[0])
utils.path_plot(self.points[self.start:self.end].transpose(),
False)
# Plot observed
if self.showmap:
subplot = 1
subplot_inc = 2
else:
subplot = 0
subplot_inc = 1
for j, v in enumerate(self.buoyvariables):
ax = plt.subplot(gs[subplot])
subplot += subplot_inc
ax.plot(self.times[self.start:self.end],
self.data[j][self.start:self.end])
if v == 'sst' and 'votemper' in self.variables:
i = self.variables.index('votemper')
plt.plot(self.model_times, self.model_data[i])
legend = [self.name]
if v == 'sst' and 'votemper' in self.variables:
legend = legend + ["%s (Modelled)" % self.name]
if 'votemper' in self.variables and v == 'sst':
legend = [gettext("Observed"), gettext("Modelled")]
if len(legend) > 1:
leg = plt.legend(legend, loc='best')
for legobj in leg.legendHandles:
legobj.set_linewidth(4.0)
if self.data_units[j] is not None:
plt.ylabel(
"%s (%s)" %
(self.data_names[j], utils.mathtext(self.data_units[j])))
else:
plt.ylabel(self.data_names[j]),
plt.setp(ax.get_xticklabels(), rotation=30)
for idx, v in enumerate(self.variables):
if v == 'votemper' and 'sst' in self.buoyvariables:
continue
if np.isnan(self.model_data[idx]).all():
continue
ax = plt.subplot(gs[subplot])
subplot += subplot_inc
ax.plot(self.model_times, self.model_data[idx])
plt.ylabel("%s (%s)" % (self.variable_names[idx],
utils.mathtext(self.variable_units[idx])))
plt.setp(ax.get_xticklabels(), rotation=30)
# latlon
if self.latlon:
for j, label in enumerate([
gettext("Latitude (degrees)"),
gettext("Longitude (degrees)")
]):
plt.subplot(gs[subplot])
subplot += subplot_inc
plt.plot(self.times[self.start:self.end],
self.points[self.start:self.end, j])
plt.ylabel(label)
plt.setp(plt.gca().get_xticklabels(), rotation=30)
fig.suptitle(
gettext("Drifter Plot (IMEI: %s, WMO: %s)") %
(self.imei, self.wmo))
fig.tight_layout(pad=3, w_pad=4)
return super(DrifterPlotter, self).plot(fig)
def plot(self):
figuresize = list(map(float, self.size.split("x")))
fig = plt.figure(figsize=figuresize, dpi=self.dpi)
width = len(self.variables)
if self.showmap:
width += 1 # Shift graphs to the right
gs = gridspec.GridSpec(2, width)
subplot = 0
# Render point location
if self.showmap:
plt.subplot(gs[0, subplot])
subplot += 1
utils.point_plot(np.array([self.latitude, self.longitude]))
if len(self.ids) > 1:
plt.legend(self.ids, loc='best')
plot_label = ""
giops_name = gettext("Model")
if len(self.additional_model_names) > 0:
giops_name = "GIOPS"
for idx, v in enumerate(self.variables):
plt.subplot(gs[:, subplot])
subplot += 1
handles = []
legend = []
for i in range(0, len(self.forecast_data)):
if len(self.ids) > 1:
id_label = self.ids[i] + " "
else:
id_label = ""
form = '-'
if self.observed_data[i, idx, :].count() < 3:
form = 'o-'
if self.error in ['climatology', 'observation']:
if self.error == 'climatology':
plot_label = gettext("Error wrt Climatology")
handles.append(
plt.plot(
self.observed_data[i, idx, :] -
self.climatology_data[i, idx, :],
self.depths[i], form))
legend.append("%s %s" %
(id_label, gettext("Observed")))
data = self.climatology_data
else:
plot_label = gettext("Error wrt Observation")
data = self.observed_data
handles.append(
plt.plot(
self.forecast_data[i, idx, :] - data[i, idx, :],
self.depths[i], form))
legend.append("%s %s" % (id_label, giops_name))
for j, m in enumerate(self.additional_model_names):
handles.append(
plt.plot(
self.additional_model_data[j, i, idx, :] -
data[i, idx, :], self.depths[i], form))
legend.append("%s %s" % (id_label, m))
if self.error == 'observation' and self.climatology:
handles.append(
plt.plot(
self.climatology_data[i, idx, :] -
self.observed_data[i, idx, :], self.depths[i],
form))
legend.append("%s %s" %
(id_label, gettext("Climatology")))
lim = np.abs(plt.xlim()).max()
plt.xlim([-lim, lim])
else:
plot_label = gettext("Class 4")
handles.append(
plt.plot(self.observed_data[i, idx, :], self.depths[i],
form))
legend.append("%s %s" % (id_label, gettext("Observed")))
handles.append(
plt.plot(self.forecast_data[i, idx, :], self.depths[i],
form))
legend.append("%s %s" % (id_label, giops_name))
for j, m in enumerate(self.additional_model_names):
handles.append(
plt.plot(self.additional_model_data[j, i, idx, :],
self.depths[i], form))
legend.append("%s %s" % (id_label, m))
if self.climatology:
handles.append(
plt.plot(self.climatology_data[i, idx, :],
self.depths[i], form))
legend.append("%s %s" %
(id_label, gettext("Climatology")))
plt.xlim([np.floor(plt.xlim()[0]), np.ceil(plt.xlim()[1])])
plt.gca().xaxis.set_label_position('top')
plt.gca().xaxis.set_ticks_position('top')
plt.xlabel("%s (%s)" %
(v, utils.mathtext(self.variable_units[idx])),
fontsize=14)
plt.gca().invert_yaxis()
plt.ylabel(gettext("Depth (%s)") % utils.mathtext(self.depth_unit),
fontsize=14)
plt.grid(True)
leg = fig.legend([x[0] for x in handles],
legend,
loc='lower left',
bbox_to_anchor=(0.05, 0.05))
for legobj in leg.legendHandles:
legobj.set_linewidth(4.0)
names = [
"%s (%0.2f, %0.2f)" % x
for x in zip(self.ids, self.latitude, self.longitude)
]
plt.suptitle("%s\n%s" %
("\n".join(wrap(", ".join(names), 60)), plot_label),
fontsize=15)
fig.tight_layout(pad=3, w_pad=4)
fig.subplots_adjust(top=0.85)
return super(Class4Plotter, self).plot(fig)
def plot(self):
if self.filetype == "geotiff":
f, fname = tempfile.mkstemp()
os.close(f)
driver = gdal.GetDriverByName("GTiff")
outRaster = driver.Create(
fname,
self.latitude.shape[1],
self.longitude.shape[0],
1,
gdal.GDT_Float64,
)
x = np.array([self.longitude[0, 0], self.longitude[-1, -1]])
y = np.array([self.latitude[0, 0], self.latitude[-1, -1]])
outRasterSRS = osr.SpatialReference()
pts = self.plot_projection.transform_points(
self.pc_projection, x, y)
x = pts[:, 0]
y = pts[:, 1]
outRasterSRS.ImportFromProj4(self.plot_projection.proj4_init)
pixelWidth = (x[-1] - x[0]) / self.longitude.shape[0]
pixelHeight = (y[-1] - y[0]) / self.latitude.shape[0]
outRaster.SetGeoTransform(
(x[0], pixelWidth, 0, y[0], 0, pixelHeight))
outband = outRaster.GetRasterBand(1)
d = self.data.astype(np.float64)
ndv = d.fill_value
outband.WriteArray(d.filled(ndv))
outband.SetNoDataValue(ndv)
outRaster.SetProjection(outRasterSRS.ExportToWkt())
outband.FlushCache()
outRaster = None
with open(fname, "r", encoding="latin-1") as f:
buf = f.read()
os.remove(fname)
return (buf, self.mime, self.filename.replace(".geotiff", ".tif"))
# Figure size
figuresize = list(map(float, self.size.split("x")))
fig, map_plot = basemap.load_map(
self.plot_projection,
self.plot_extent,
figuresize,
self.dpi,
self.plot_res,
)
ax = plt.gca()
if self.scale:
vmin = self.scale[0]
vmax = self.scale[1]
else:
vmin, vmax = utils.normalize_scale(
self.data,
self.dataset_config.variable[f"{self.variables[0]}"])
c = map_plot.imshow(
self.data,
vmin=vmin,
vmax=vmax,
cmap=self.cmap,
extent=self.plot_extent,
transform=self.plot_projection,
origin="lower",
zorder=0,
)
if len(self.quiver_data) == 2:
qx, qy = self.quiver_data
qx, qy = self.plot_projection.transform_vectors(
self.pc_projection, self.quiver_longitude,
self.quiver_latitude, qx, qy)
pts = self.plot_projection.transform_points(
self.pc_projection, self.quiver_longitude,
self.quiver_latitude)
x = pts[:, :, 0]
y = pts[:, :, 1]
qx = np.ma.masked_where(np.ma.getmask(self.quiver_data[0]), qx)
qy = np.ma.masked_where(np.ma.getmask(self.quiver_data[1]), qy)
if self.quiver["magnitude"] != "length":
qx = qx / self.quiver_magnitude
qy = qy / self.quiver_magnitude
qscale = 50
else:
qscale = None
if self.quiver["magnitude"] == "color":
if (self.quiver["colormap"] is None
or self.quiver["colormap"] == "default"):
qcmap = colormap.colormaps.get("speed")
else:
qcmap = colormap.colormaps.get(self.quiver["colormap"])
q = map_plot.quiver(
x,
y,
qx,
qy,
self.quiver_magnitude,
width=0.0035,
headaxislength=4,
headlength=4,
scale=qscale,
pivot="mid",
cmap=qcmap,
transform=self.plot_projection,
)
else:
q = map_plot.quiver(
x,
y,
qx,
qy,
width=0.0025,
headaxislength=4,
headlength=4,
scale=qscale,
pivot="mid",
transform=self.plot_projection,
zorder=6,
)
if self.quiver["magnitude"] == "length":
unit_length = np.mean(self.quiver_magnitude) * 2
unit_length = np.round(unit_length,
-int(np.floor(np.log10(unit_length))))
if unit_length >= 1:
unit_length = int(unit_length)
plt.quiverkey(
q,
0.65,
0.01,
unit_length,
self.quiver_name.title() + " " + str(unit_length) + " " +
utils.mathtext(self.quiver_unit),
coordinates="figure",
labelpos="E",
)
if self.show_bathymetry:
# Plot bathymetry on top
cs = map_plot.contour(
self.longitude,
self.latitude,
self.bathymetry,
linewidths=0.5,
norm=FuncNorm((lambda x: np.log10(x), lambda x: 10**x),
vmin=1,
vmax=6000),
cmap="Greys",
levels=[100, 200, 500, 1000, 2000, 3000, 4000, 5000, 6000],
transform=self.pc_projection,
zorder=4,
)
plt.clabel(cs, fontsize="x-large", fmt="%1.0fm")
if self.area and self.show_area:
for a in self.area:
polys = []
for co in a["polygons"] + a["innerrings"]:
coords = np.array(co).transpose()
coords_transform = self.plot_projection.transform_points(
self.pc_projection, coords[1], coords[0])
mx = coords_transform[:, 0]
my = coords_transform[:, 1]
map_coords = list(zip(mx, my))
polys.append(Polygon(map_coords))
paths = []
for poly in polys:
paths.append(poly.get_path())
path = Path.make_compound_path(*paths)
for ec, lw in zip(["w", "k"], [5, 3]):
poly = PathPatch(
path,
fill=None,
edgecolor=ec,
linewidth=lw,
transform=self.plot_projection,
zorder=3,
)
map_plot.add_patch(poly)
if self.names is not None and len(self.names) > 1:
for idx, name in enumerate(self.names):
pts = self.plot_projection.transform_points(
self.pc_projection, self.centroids[idx].x,
self.centroids[idx].y)
x = pts[:, 0]
y = pts[:, 1]
plt.annotate(
xy=(x, y),
s=name,
ha="center",
va="center",
size=12,
# weight='bold'
)
if len(self.contour_data) > 0:
if self.contour_data[0].min() != self.contour_data[0].max():
cmin, cmax = utils.normalize_scale(
self.contour_data[0],
self.dataset_config.variable[self.contour["variable"]],
)
levels = None
if (self.contour.get("levels") is not None
and self.contour["levels"] != "auto"
and self.contour["levels"] != ""):
try:
levels = list(
set([
float(xx)
for xx in self.contour["levels"].split(",")
if xx.strip()
]))
levels.sort()
except ValueError:
pass
if levels is None:
levels = np.linspace(cmin, cmax, 5)
cmap = self.contour["colormap"]
if cmap is not None:
cmap = colormap.colormaps.get(cmap)
if cmap is None:
cmap = colormap.find_colormap(self.contour_name)
if not self.contour.get("hatch"):
contours = map_plot.contour(
self.longitude,
self.latitude,
self.contour_data[0],
linewidths=2,
levels=levels,
cmap=cmap,
transform=self.pc_projection,
zorder=5,
)
else:
hatches = [
"//", "xx", "\\\\", "--", "||", "..", "oo", "**"
]
if len(levels) + 1 < len(hatches):
hatches = hatches[0:len(levels) + 2]
map_plot.contour(
self.longitude,
self.latitude,
self.contour_data[0],
linewidths=1,
levels=levels,
colors="k",
transform=self.pc_projection,
zorder=5,
)
contours = map_plot.contourf(
self.longitude,
self.latitude,
self.contour_data[0],
colors=["none"],
levels=levels,
hatches=hatches,
vmin=cmin,
vmax=cmax,
extend="both",
transform=self.pc_projection,
zorder=5,
)
if self.contour["legend"]:
handles, l = contours.legend_elements()
labels = []
for i, lab in enumerate(l):
if self.contour.get("hatch"):
if self.contour_unit == "fraction":
if i == 0:
labels.append(
"$x \\leq {0: .0f}\\%$".format(
levels[i] * 100))
elif i == len(levels):
labels.append("$x > {0: .0f}\\%$".format(
levels[i - 1] * 100))
else:
labels.append(
"${0:.0f}\\% < x \\leq {1:.0f}\\%$".
format(levels[i - 1] * 100,
levels[i] * 100))
else:
if i == 0:
labels.append("$x \\leq %.3g$" % levels[i])
elif i == len(levels):
labels.append("$x > %.3g$" % levels[i - 1])
else:
labels.append("$%.3g < x \\leq %.3g$" %
(levels[i - 1], levels[i]))
else:
if self.contour_unit == "fraction":
labels.append("{0:.0%}".format(levels[i]))
else:
labels.append(
"%.3g %s" %
(levels[i],
utils.mathtext(self.contour_unit)))
ax = plt.gca()
if self.contour_unit != "fraction" and not self.contour.get(
"hatch"):
contour_title = "%s (%s)" % (
self.contour_name,
utils.mathtext(self.contour_unit),
)
else:
contour_title = self.contour_name
leg = ax.legend(
handles[::-1],
labels[::-1],
loc="lower left",
fontsize="medium",
frameon=True,
framealpha=0.75,
title=contour_title,
)
leg.get_title().set_fontsize("medium")
if not self.contour.get("hatch"):
for legobj in leg.legendHandles:
legobj.set_linewidth(3)
title = self.plotTitle
if self.plotTitle is None or self.plotTitle == "":
area_title = "\n".join(wrap(", ".join(self.names), 60)) + "\n"
title = "%s %s %s, %s" % (
area_title,
self.variable_name.title(),
self.depth_label,
self.date_formatter(self.timestamp),
)
plt.title(title.strip())
axpos = map_plot.get_position()
pos_x = axpos.x0 + axpos.width + 0.01
pos_y = axpos.y0
cax = fig.add_axes([pos_x, pos_y, 0.03, axpos.height])
bar = plt.colorbar(c, cax=cax)
bar.set_label(
"%s (%s)" %
(self.variable_name.title(), utils.mathtext(self.variable_unit)),
fontsize=14,
)
if (self.quiver is not None and self.quiver["variable"] != ""
and self.quiver["variable"] != "none"
and self.quiver["magnitude"] == "color"):
pos_x = axpos.x0
pos_y = axpos.y0 - 0.05
bax = fig.add_axes([pos_x, pos_y, axpos.width, 0.03])
qbar = plt.colorbar(q, orientation="horizontal", cax=bax)
qbar.set_label(
self.quiver_name.title() + " " +
utils.mathtext(self.quiver_unit),
fontsize=14,
)
return super(MapPlotter, self).plot(fig)
def plot(self):
if self.filetype == 'geotiff':
f, fname = tempfile.mkstemp()
os.close(f)
driver = gdal.GetDriverByName('GTiff')
outRaster = driver.Create(fname, self.latitude.shape[1],
self.longitude.shape[0], 1,
gdal.GDT_Float64)
x = [self.longitude[0, 0], self.longitude[-1, -1]]
y = [self.latitude[0, 0], self.latitude[-1, -1]]
outRasterSRS = osr.SpatialReference()
x, y = self.basemap(x, y)
outRasterSRS.ImportFromProj4(self.basemap.proj4string)
pixelWidth = (x[-1] - x[0]) / self.longitude.shape[0]
pixelHeight = (y[-1] - y[0]) / self.latitude.shape[0]
outRaster.SetGeoTransform(
(x[0], pixelWidth, 0, y[0], 0, pixelHeight))
outband = outRaster.GetRasterBand(1)
d = self.data.astype("Float64")
ndv = d.fill_value
outband.WriteArray(d.filled(ndv))
outband.SetNoDataValue(ndv)
outRaster.SetProjection(outRasterSRS.ExportToWkt())
outband.FlushCache()
outRaster = None
with open(fname, 'r', encoding="latin-1") as f:
buf = f.read()
os.remove(fname)
return (buf, self.mime, self.filename.replace(".geotiff", ".tif"))
# Figure size
figuresize = list(map(float, self.size.split("x")))
fig = plt.figure(figsize=figuresize, dpi=self.dpi)
ax = plt.gca()
if self.scale:
vmin = self.scale[0]
vmax = self.scale[1]
else:
vmin = np.amin(self.data)
vmax = np.amax(self.data)
if self.compare:
vmax = max(abs(vmax), abs(vmin))
vmin = -vmax
c = self.basemap.imshow(self.data,
vmin=vmin,
vmax=vmax,
cmap=self.cmap)
if len(self.quiver_data) == 2:
qx, qy = self.quiver_data
qx, qy, x, y = self.basemap.rotate_vector(qx,
qy,
self.quiver_longitude,
self.quiver_latitude,
returnxy=True)
quiver_mag = np.sqrt(qx**2 + qy**2)
if self.quiver['magnitude'] != 'length':
qx = qx / quiver_mag
qy = qy / quiver_mag
qscale = 50
else:
qscale = None
if self.quiver['magnitude'] == 'color':
if self.quiver['colormap'] is None or \
self.quiver['colormap'] == 'default':
qcmap = colormap.colormaps.get('speed')
else:
qcmap = colormap.colormaps.get(self.quiver['colormap'])
q = self.basemap.quiver(
x,
y,
qx,
qy,
quiver_mag,
width=0.0035,
headaxislength=4,
headlength=4,
scale=qscale,
pivot='mid',
cmap=qcmap,
)
else:
q = self.basemap.quiver(
x,
y,
qx,
qy,
width=0.0025,
headaxislength=4,
headlength=4,
scale=qscale,
pivot='mid',
)
if self.quiver['magnitude'] == 'length':
unit_length = np.mean(quiver_mag) * 2
unit_length = np.round(unit_length,
-int(np.floor(np.log10(unit_length))))
if unit_length >= 1:
unit_length = int(unit_length)
plt.quiverkey(q,
.65,
.01,
unit_length,
self.quiver_name.title() + " " +
str(unit_length) + " " +
utils.mathtext(self.quiver_unit),
coordinates='figure',
labelpos='E')
if self.show_bathymetry:
# Plot bathymetry on top
cs = self.basemap.contour(
self.longitude,
self.latitude,
self.bathymetry,
latlon=True,
linewidths=0.5,
norm=LogNorm(vmin=1, vmax=6000),
cmap=mcolors.LinearSegmentedColormap.from_list(
'transparent_gray', [(0, 0, 0, 0.5), (0, 0, 0, 0.1)]),
levels=[100, 200, 500, 1000, 2000, 3000, 4000, 5000, 6000])
plt.clabel(cs, fontsize='xx-small', fmt='%1.0fm')
if self.area and self.show_area:
for a in self.area:
polys = []
for co in a['polygons'] + a['innerrings']:
coords = np.array(co).transpose()
mx, my = self.basemap(coords[1], coords[0])
map_coords = list(zip(mx, my))
polys.append(Polygon(map_coords))
paths = []
for poly in polys:
paths.append(poly.get_path())
path = concatenate_paths(paths)
poly = PathPatch(path,
fill=None,
edgecolor='#ffffff',
linewidth=5)
plt.gca().add_patch(poly)
poly = PathPatch(path, fill=None, edgecolor='k', linewidth=2)
plt.gca().add_patch(poly)
if self.names is not None and len(self.names) > 1:
for idx, name in enumerate(self.names):
x, y = self.basemap(self.centroids[idx].y,
self.centroids[idx].x)
plt.annotate(
xy=(x, y),
s=name,
ha='center',
va='center',
size=12,
# weight='bold'
)
if len(self.contour_data) > 0:
if (self.contour_data[0].min() != self.contour_data[0].max()):
cmin, cmax = utils.normalize_scale(self.contour_data[0],
self.contour_name,
self.contour_unit)
levels = None
if self.contour.get('levels') is not None and \
self.contour['levels'] != 'auto' and \
self.contour['levels'] != '':
try:
levels = list(
set([
float(xx)
for xx in self.contour['levels'].split(",")
if xx.strip()
]))
levels.sort()
except ValueError:
pass
if levels is None:
levels = np.linspace(cmin, cmax, 5)
cmap = self.contour['colormap']
if cmap is not None:
cmap = colormap.colormaps.get(cmap)
if cmap is None:
cmap = colormap.find_colormap(self.contour_name)
if not self.contour.get('hatch'):
contours = self.basemap.contour(self.longitude,
self.latitude,
self.contour_data[0],
latlon=True,
linewidths=2,
levels=levels,
cmap=cmap)
else:
hatches = [
'//', 'xx', '\\\\', '--', '||', '..', 'oo', '**'
]
if len(levels) + 1 < len(hatches):
hatches = hatches[0:len(levels) + 2]
self.basemap.contour(self.longitude,
self.latitude,
self.contour_data[0],
latlon=True,
linewidths=1,
levels=levels,
colors='k')
contours = self.basemap.contourf(self.longitude,
self.latitude,
self.contour_data[0],
latlon=True,
colors=['none'],
levels=levels,
hatches=hatches,
vmin=cmin,
vmax=cmax,
extend='both')
if self.contour['legend']:
handles, l = contours.legend_elements()
labels = []
for i, lab in enumerate(l):
if self.contour.get('hatch'):
if self.contour_unit == 'fraction':
if i == 0:
labels.append(
"$x \\leq {0: .0f}\\%$".format(
levels[i] * 100))
elif i == len(levels):
labels.append("$x > {0: .0f}\\%$".format(
levels[i - 1] * 100))
else:
labels.append(
"${0:.0f}\\% < x \\leq {1:.0f}\\%$".
format(levels[i - 1] * 100,
levels[i] * 100))
else:
if i == 0:
labels.append("$x \\leq %.3g$" % levels[i])
elif i == len(levels):
labels.append("$x > %.3g$" % levels[i - 1])
else:
labels.append("$%.3g < x \\leq %.3g$" %
(levels[i - 1], levels[i]))
else:
if self.contour_unit == 'fraction':
labels.append("{0:.0%}".format(levels[i]))
else:
labels.append(
"%.3g %s" %
(levels[i],
utils.mathtext(self.contour_unit)))
ax = plt.gca()
if self.contour_unit != 'fraction' and not \
self.contour.get('hatch'):
contour_title = "%s (%s)" % (self.contour_name,
utils.mathtext(
self.contour_unit))
else:
contour_title = self.contour_name
leg = ax.legend(handles[::-1],
labels[::-1],
loc='lower left',
fontsize='medium',
frameon=True,
framealpha=0.75,
title=contour_title)
leg.get_title().set_fontsize('medium')
if not self.contour.get('hatch'):
for legobj in leg.legendHandles:
legobj.set_linewidth(3)
# Map Info
self.basemap.drawmapboundary(fill_color=(0.3, 0.3, 0.3), zorder=-1)
self.basemap.drawcoastlines(linewidth=0.5)
self.basemap.fillcontinents(color='grey', lake_color='dimgrey')
def find_lines(values):
if np.amax(values) - np.amin(values) < 1:
return [values.mean()]
elif np.amax(values) - np.amin(values) < 25:
return np.round(
np.arange(np.amin(values), np.amax(values),
round(np.amax(values) - np.amin(values)) / 5))
else:
return np.arange(round(np.amin(values), -1),
round(np.amax(values), -1), 5)
parallels = find_lines(self.latitude)
meridians = find_lines(self.longitude)
self.basemap.drawparallels(parallels,
labels=[1, 0, 0, 0],
color=(0, 0, 0, 0.5))
self.basemap.drawmeridians(meridians,
labels=[0, 0, 0, 1],
color=(0, 0, 0, 0.5),
latmax=85)
title = self.plotTitle
if self.plotTitle is None or self.plotTitle == "":
area_title = "\n".join(wrap(", ".join(self.names), 60)) + "\n"
title = "%s %s %s, %s" % (area_title, self.variable_name.title(),
self.depth_label,
self.date_formatter(self.timestamp))
plt.title(title.strip())
ax = plt.gca()
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
bar = plt.colorbar(c, cax=cax)
bar.set_label(
"%s (%s)" %
(self.variable_name.title(), utils.mathtext(self.variable_unit)),
fontsize=14)
if self.quiver is not None and \
self.quiver['variable'] != '' and \
self.quiver['variable'] != 'none' and \
self.quiver['magnitude'] == 'color':
bax = divider.append_axes("bottom", size="5%", pad=0.35)
qbar = plt.colorbar(q, orientation='horizontal', cax=bax)
qbar.set_label(self.quiver_name.title() + " " +
utils.mathtext(self.quiver_unit),
fontsize=14)
fig.tight_layout(pad=3, w_pad=4)
return super(MapPlotter, self).plot(fig)
def plot(self):
if self.showmap:
width = 2
width_ratios = [3, 7]
else:
width = 1
width_ratios = [1]
numplots = len(self.variables) + len(self.trackvariables)
if "votemper" in self.variables and "sst" in self.trackvariables:
numplots -= 1
if self.latlon:
numplots += 2
figuresize = list(map(float, self.size.split("x")))
figuresize[1] *= numplots
fig = plt.figure(figsize=figuresize, dpi=self.dpi)
gs = gridspec.GridSpec(numplots, width, width_ratios=width_ratios)
if self.showmap:
# Plot the path on a map
if numplots > 1:
plt.subplot(gs[:, 0])
else:
plt.subplot(gs[0])
utils.path_plot(self.points.transpose(), False)
# Plot observed
if self.showmap:
subplot = 1
subplot_inc = 2
else:
subplot = 0
subplot_inc = 1
for j, v in enumerate(self.trackvariables):
ax = plt.subplot(gs[subplot])
subplot += subplot_inc
# Is the depth changing?
if len(np.unique(self.depth)) == 1:
ax.plot(self.distances, self.data[j])
ax.set_xlim(self.distances[0], self.distances[-1])
ax.set_xlabel("Distance (km)")
else:
self.data[j, np.where(self.depth <= 0)] = np.nan
RES = (50, 100)
dd = np.empty((RES[0] + 1, RES[1] + 1))
dd[:, :] = np.nan
x = np.linspace(0, max(self.distances), RES[1])
y = np.linspace(0, max(self.depth), RES[0])
di = np.digitize(self.distances, x)
de = np.digitize(self.depth, y)
co = np.array(list(zip(di, de)))
dd[co[:, 1], co[:, 0]] = self.data[j]
c = ax.pcolormesh(x,
y,
np.ma.masked_invalid(dd[:-1, :-1]),
cmap=self.track_cmaps[j],
shading='gouraud')
ax.set_xlim(0, max(self.distances))
ax.invert_yaxis()
ax.set_xlabel("Distance (km)")
ax.set_ylim(max(self.depth), 0)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad="5%")
bar = plt.colorbar(c, cax=cax)
legend = [self.name]
if len(legend) > 1:
leg = plt.legend(legend, loc='best')
for legobj in leg.legendHandles:
legobj.set_linewidth(4.0)
if len(np.unique(self.depth)) == 1:
if self.data_units[j] is not None:
ax.set_ylabel("%s (%s)" %
(self.data_names[j],
utils.mathtext(self.data_units[j])))
else:
ax.set_ylabel(self.data_names[j])
else:
if self.data_units[j] is not None:
bar.set_label("%s (%s)" %
(self.data_names[j],
utils.mathtext(self.data_units[j])))
else:
bar.set_label(self.data_names[j])
ax.set_ylabel("Depth (m)")
for idx, v in enumerate(self.variables):
if np.isnan(self.model_data[idx]).all():
continue
ax = plt.subplot(gs[subplot])
subplot += subplot_inc
if len(np.unique(self.depth)) > 1:
mdist = np.linspace(0, self.model_dist[-1], 100)
f = interp1d(
self.model_dist,
self.model_data[idx],
assume_sorted=True,
bounds_error=False,
)
mdata = f(mdist)
mdata = np.ma.masked_invalid(mdata)
mdata = np.ma.masked_greater(mdata, mdata.fill_value)
c = ax.pcolormesh(
mdist,
self.model_depths,
mdata,
cmap=self.cmaps[idx],
shading='gouraud',
)
ax.invert_yaxis()
ax.set_ylim(max(self.depth), 0)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad="5%")
bar = plt.colorbar(c, cax=cax)
else:
ax.plot(self.model_dist, self.model_data[idx])
ax.set_xlim(
self.model_dist[0],
self.model_dist[-1],
)
ax.set_xlabel("Distance (km)")
if len(np.unique(self.depth)) > 1:
ax.set_ylabel("Depth (m)")
bar.set_label("%s (%s)" %
(self.variable_names[idx],
utils.mathtext(self.variable_units[idx])))
else:
ax.set_ylabel("%s (%s)" %
(self.variable_names[idx],
utils.mathtext(self.variable_units[idx])))
plt.setp(ax.get_xticklabels(), rotation=30)
# latlon
if self.latlon:
for j, label in enumerate([
gettext("Latitude (degrees)"),
gettext("Longitude (degrees)")
]):
plt.subplot(gs[subplot])
subplot += subplot_inc
plt.plot(self.times, self.points[:, j])
plt.ylabel(label)
plt.setp(plt.gca().get_xticklabels(), rotation=30)
fig.suptitle(
gettext("Track Plot (Observed %s - %s, Modelled %s - %s)") %
(self.times[0].strftime("%Y-%m-%d"),
self.times[-1].strftime("%Y-%m-%d"),
self.model_times[0].strftime("%Y-%m-%d"),
self.model_times[-1].strftime("%Y-%m-%d")))
fig.tight_layout(pad=3, w_pad=4)
return super().plot(fig)
def scale(args):
dataset_name = args.get('dataset')
scale = args.get('scale')
scale = [float(component) for component in scale.split(',')]
variable = args.get('variable')
anom = False
if variable.endswith('_anom'):
variable = variable[0:-5]
anom = True
variable = variable.split(',')
with open_dataset(get_dataset_url(dataset_name)) as dataset:
variable_unit = get_variable_unit(dataset_name,
dataset.variables[variable[0]])
variable_name = get_variable_name(dataset_name,
dataset.variables[variable[0]])
if variable_unit.startswith("Kelvin"):
variable_unit = "Celsius"
if anom:
cmap = colormap.colormaps['anomaly']
variable_name = gettext("%s Anomaly") % variable_name
else:
cmap = colormap.find_colormap(variable_name)
if len(variable) == 2:
if not anom:
cmap = colormap.colormaps.get('speed')
variable_name = re.sub(
r"(?i)( x | y |zonal |meridional |northward |eastward )", " ",
variable_name)
variable_name = re.sub(r" +", " ", variable_name)
fig = plt.figure(figsize=(2, 5), dpi=75)
ax = fig.add_axes([0.05, 0.05, 0.25, 0.9])
norm = matplotlib.colors.Normalize(vmin=scale[0], vmax=scale[1])
formatter = ScalarFormatter()
formatter.set_powerlimits((-3, 4))
bar = ColorbarBase(ax,
cmap=cmap,
norm=norm,
orientation='vertical',
format=formatter)
bar.set_label("%s (%s)" %
(variable_name.title(), utils.mathtext(variable_unit)),
fontsize=12)
# Increase tick font size
bar.ax.tick_params(labelsize=12)
buf = BytesIO()
plt.savefig(buf,
format='png',
dpi='figure',
transparent=False,
bbox_inches='tight',
pad_inches=0.05)
plt.close(fig)
buf.seek(0) # Move buffer back to beginning
return buf
