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 load_data(self):
def find_depth(depth, clip_length, dataset):
"""
Calculates and returns the depth, depth-value, and depth unit from a given dataset
Args:
* depth: Stored depth information (self.depth or self.compare['depth'])
* clip_length: How many depth values to clip (usually len(dataset.depths) - 1)
* dataset: Opened dataset
Returns:
(depth, depth_value, depth_unit)
"""
depth_value = 0
depth_unit = "m"
if depth:
if depth == "bottom":
depth_value = "Bottom"
depth_unit = ""
return (depth, depth_value, depth_unit)
else:
depth = np.clip(int(depth), 0, clip_length)
depth_value = np.round(dataset.depths[depth])
depth_unit = "m"
return (depth, depth_value, depth_unit)
return (depth, depth_value, depth_unit)
# Load left/Main Map
with open_dataset(
self.dataset_config,
timestamp=self.starttime,
endtime=self.endtime,
variable=self.variables,
) as dataset:
self.depth, self.depth_value, self.depth_unit = find_depth(
self.depth,
len(dataset.depths) - 1, dataset)
self.path_points, self.distance, times, data = dataset.get_path(
self.points,
self.depth,
self.variables[0],
self.starttime,
self.endtime,
tile_time=False,
)
self.variable_name = self.get_variable_names(
dataset, self.variables)[0]
variable_units = self.get_variable_units(dataset, self.variables)
self.variable_unit = variable_units[0]
self.data = data.T
self.iso_timestamps = times
# Get colourmap
if self.cmap is None:
self.cmap = colormap.find_colormap(self.variable_name)
# Load data sent from Right Map (if in compare mode)
if self.compare:
compare_config = DatasetConfig(self.compare["dataset"])
with open_dataset(
compare_config,
timestamp=self.compare["starttime"],
endtime=self.compare["endtime"],
variable=self.compare["variables"],
) as dataset:
(
self.compare["depth"],
self.compare["depth_value"],
self.compare["depth_unit"],
) = find_depth(self.compare["depth"],
len(dataset.depths) - 1, dataset)
path, distance, times, data = dataset.get_path(
self.points,
self.compare["depth"],
self.compare["variables"][0],
self.compare["starttime"],
self.compare["endtime"],
tile_time=False,
)
self.compare["variable_name"] = self.get_variable_names(
dataset, self.compare["variables"])[0]
# Colourmap
if self.compare["colormap"] == "default":
self.compare["colormap"] = colormap.find_colormap(
self.compare["variable_name"])
else:
self.compare["colormap"] = colormap.find_colormap(
self.compare["colormap"])
variable_units = self.get_variable_units(
dataset, self.compare["variables"])
self.compare["variable_unit"] = variable_units[0]
self.compare["data"] = data.T
self.compare["times"] = times
def plot(self):
def get_depth_label(depthValue, depthUnit):
if depthValue == "bottom":
return " at Bottom"
return " at %s %s" % (depthValue, depthUnit)
# Figure size
figuresize = list(map(float, self.size.split("x")))
# Vertical scaling of figure
figuresize[1] *= 1.5 if self.compare else 1
fig = plt.figure(figsize=figuresize, dpi=self.dpi)
if self.showmap:
width = 2 # 2 columns
width_ratios = [2, 7]
else:
width = 1 # 1 column
width_ratios = [1]
# Setup grid (rows, columns, column/row ratios) depending on view mode
if self.compare:
# Don't show a difference plot if variables are different
if self.compare["variables"][0] == self.variables[0]:
gs = gridspec.GridSpec(3,
width,
width_ratios=width_ratios,
height_ratios=[1, 1, 1])
else:
gs = gridspec.GridSpec(2,
width,
width_ratios=width_ratios,
height_ratios=[1, 1])
else:
gs = gridspec.GridSpec(1, width, width_ratios=width_ratios)
if self.showmap:
# Plot the path on a map
utils.path_plot(self.path_points, gs[:, 0])
# Calculate variable range
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 len(self.variables) > 1:
vmin = 0
# Render
self._hovmoller_plot(
gs,
[0, 1],
[0, 0],
gettext(self.variable_name),
vmin,
vmax,
self.data,
self.iso_timestamps,
self.cmap,
self.variable_unit,
gettext(self.variable_name) +
gettext(get_depth_label(self.depth_value, self.depth_unit)),
)
# If in compare mode
if self.compare:
# Calculate variable range
if self.compare["scale"]:
vmin = self.compare["scale"][0]
vmax = self.compare["scale"][1]
else:
vmin = np.amin(self.compare["data"])
vmax = np.amax(self.compare["data"])
if np.any([
re.search(x, self.compare["variable_name"], re.IGNORECASE)
for x in ["velocity", "surface height", "wind"]
]):
vmin = min(vmin, -vmax)
vmax = max(vmax, -vmin)
if len(self.compare["variables"]) > 1:
vmin = 0
self._hovmoller_plot(
gs,
[1, 1],
[1, 0],
gettext(self.compare["variable_name"]),
vmin,
vmax,
self.compare["data"],
self.compare["times"],
self.compare["colormap"],
self.compare["variable_unit"],
gettext(self.compare["variable_name"]) + gettext(
get_depth_label(self.compare["depth"],
self.compare["depth_unit"])),
)
# Difference plot
if self.compare["variables"][0] == self.variables[0]:
data_difference = self.data - self.compare["data"]
vmin = np.amin(data_difference)
vmax = np.amax(data_difference)
self._hovmoller_plot(
gs,
[2, 1],
[2, 0],
gettext(self.compare["variable_name"]),
vmin,
vmax,
data_difference,
self.compare["times"],
colormap.find_colormap("anomaly"),
self.compare["variable_unit"],
gettext(self.compare["variable_name"]) +
gettext(" Difference") + gettext(
get_depth_label(self.compare["depth"],
self.compare["depth_unit"])),
)
# Image title
if self.plotTitle:
fig.suptitle(gettext("Hovm\xf6ller Diagram(s) for:\n%s") %
(self.name),
fontsize=15)
else:
fig.suptitle(self.plotTitle, fontsize=15)
# Subplot padding
fig.tight_layout(pad=0, w_pad=4, h_pad=2)
fig.subplots_adjust(top=0.9 if self.compare else 0.85)
return super(HovmollerPlotter, self).plot(fig)
def load_data(self):
def find_depth(depth, clip_length, dataset):
"""
Calculates and returns the depth, depth-value, and depth unit from a given dataset
Args:
* depth: Stored depth information (self.depth or self.compare['depth'])
* clip_length: How many depth values to clip (usually len(dataset.depths) - 1)
* dataset: Opened dataset
Returns:
(depth, depth_value, depth_unit)
"""
depth_value = 0
depth_unit = "m"
if depth:
if depth == 'bottom':
depth_value = 'Bottom'
depth_unit = ''
return (depth, depth_value, depth_unit)
else:
depth = np.clip(int(depth), 0, clip_length)
depth_value = np.round(dataset.depths[depth])
depth_unit = "m"
return (depth, depth_value, depth_unit)
return (depth, depth_value, depth_unit)
# Load left/Main Map
with open_dataset(self.dataset_config,
timestamp=self.starttime,
endtime=self.endtime,
variable=self.variables) as dataset:
self.depth, self.depth_value, self.depth_unit = find_depth(
self.depth,
len(dataset.depths) - 1, dataset)
self.path_points, self.distance, times, data = dataset.get_path(
self.points,
self.depth,
self.variables[0],
self.starttime,
self.endtime,
tile_time=False)
self.variable_name = self.get_variable_names(
dataset, self.variables)[0]
variable_units = self.get_variable_units(dataset, self.variables)
scale_factors = self.get_variable_scale_factors(
dataset, self.variables)
self.variable_unit = variable_units[0]
self.data = np.multiply(data, scale_factors[0]).T
self.iso_timestamps = times
# Get colourmap
if self.cmap is None:
self.cmap = colormap.find_colormap(self.variable_name)
# Load data sent from Right Map (if in compare mode)
if self.compare:
compare_config = DatasetConfig(self.compare['dataset'])
with open_dataset(compare_config,
timestamp=self.compare['starttime'],
endtime=self.compare['endtime'],
variable=self.compare['variables']) as dataset:
self.compare['depth'], self.compare[
'depth_value'], self.compare['depth_unit'] = find_depth(
self.compare['depth'],
len(dataset.depths) - 1, dataset)
path, distance, times, data = dataset.get_path(
self.points,
self.compare['depth'],
self.compare['variables'][0],
self.compare['starttime'],
self.compare['endtime'],
tile_time=False)
self.compare['variable_name'] = self.get_variable_names(
dataset, self.compare['variables'])[0]
# Colourmap
if (self.compare['colormap'] == 'default'):
self.compare['colormap'] = colormap.find_colormap(
self.compare['variable_name'])
else:
self.compare['colormap'] = colormap.find_colormap(
self.compare['colormap'])
variable_units = self.get_variable_units(
dataset, self.compare['variables'])
scale_factors = self.get_variable_scale_factors(
dataset, self.compare['variables'])
self.compare['variable_unit'] = variable_units[0]
self.compare['data'] = np.multiply(data, scale_factors[0]).T
self.compare['times'] = times
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, vmax = utils.normalize_scale(
self.data,
self.dataset_config.variable[f"{self.variables[0]}"])
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)
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 = self.basemap.quiver(
x,
y,
qx,
qy,
self.quiver_magnitude,
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(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,
.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='Greys',
levels=[100, 200, 500, 1000, 2000, 3000, 4000, 5000, 6000])
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()
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.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 = 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(projection, x, y, z, args):
lat, lon = get_latlon_coords(projection, x, y, z)
if len(lat.shape) == 1:
lat, lon = np.meshgrid(lat, lon)
dataset_name = args.get('dataset')
variable = args.get('variable')
if variable.endswith('_anom'):
variable = variable[0:-5]
anom = True
else:
anom = False
variable = variable.split(',')
depth = args.get('depth')
scale = args.get('scale')
scale = [float(component) for component in scale.split(',')]
data = []
with open_dataset(get_dataset_url(dataset_name)) as dataset:
if args.get('time') is None or (type(args.get('time')) == str
and len(args.get('time')) == 0):
time = -1
else:
time = int(args.get('time'))
t_len = len(dataset.timestamps)
while time >= t_len:
time -= t_len
while time < 0:
time += len(dataset.timestamps)
timestamp = dataset.timestamps[time]
for v in variable:
data.append(
dataset.get_area(np.array([lat, lon]), depth, time, v,
args.get('interp'), args.get('radius'),
args.get('neighbours')))
variable_name = get_variable_name(dataset_name,
dataset.variables[variable[0]])
variable_unit = get_variable_unit(dataset_name,
dataset.variables[variable[0]])
scale_factor = get_variable_scale_factor(
dataset_name, dataset.variables[variable[0]])
if anom:
cmap = colormap.colormaps['anomaly']
else:
cmap = colormap.find_colormap(variable_name)
if depth != 'bottom':
depthm = dataset.depths[depth]
else:
depthm = 0
if scale_factor != 1.0:
for idx, val in enumerate(data):
data[idx] = np.multiply(val, scale_factor)
if variable_unit.startswith("Kelvin"):
variable_unit = "Celsius"
for idx, val in enumerate(data):
data[idx] = np.add(val, -273.15)
if len(data) == 1:
data = data[0]
if len(data) == 2:
data = np.sqrt(data[0]**2 + data[1]**2)
if not anom:
cmap = colormap.colormaps.get('speed')
if anom:
with open_dataset(get_dataset_climatology(dataset_name)) as dataset:
a = dataset.get_area(np.array([lat, lon]), depth,
timestamp.month - 1, v, args.get('interp'),
args.get('radius'), args.get('neighbours'))
data -= a
data = data.transpose()
xpx = x * 256
ypx = y * 256
with Dataset(current_app.config['ETOPO_FILE'] % (projection, z),
'r') as dataset:
bathymetry = dataset["z"][ypx:(ypx + 256), xpx:(xpx + 256)]
bathymetry = gaussian_filter(bathymetry, 0.5)
data[np.where(bathymetry > -depthm)] = np.ma.masked
sm = matplotlib.cm.ScalarMappable(matplotlib.colors.Normalize(
vmin=scale[0], vmax=scale[1]),
cmap=cmap)
img = sm.to_rgba(np.ma.masked_invalid(np.squeeze(data)))
im = Image.fromarray((img * 255.0).astype(np.uint8))
buf = BytesIO()
im.save(buf, format='PNG', optimize=True)
return buf
def plot(self):
gs, fig, velocity = self.gridSetup()
# Plot the transect on a map
if self.showmap:
plt.subplot(gs[0, 0])
utils.path_plot(self.transect_data['points'])
# Args:
# subplots: a GridSpec object (gs)
# map_subplot: Row number (Note: don't use consecutive rows to allow
# for expanding figure height)
# data: Data to be plotted
# name: subplot title
# cmapLabel: label for colourmap legend
# vmin: minimum value for a variable (grabbed from the lowest value of some data)
# vmax: maxmimum value for a variable (grabbed from the highest value of some data)onstrate a networked Ope
# units: units for variable (PSU, Celsius, etc)
# cmap: colormap for variable
#
def do_plot(subplots, map_subplot, data, name, cmapLabel, vmin, vmax,
units, cmap):
plt.subplot(subplots[map_subplot[0], map_subplot[1]])
divider = self._transect_plot(data, self.depth, name, vmin, vmax,
cmapLabel, units, cmap)
if self.surface:
self._surface_plot(divider)
"""
Finds and returns the correct min/max values for the variable scale
Args:
scale: scale for the left or Right Map (self.scale or self.compare['scale])
data: transect_data
Returns:
(min, max)
"""
def find_minmax(scale, data):
if scale:
return (scale[0], scale[1])
else:
return (np.amin(data), np.amax(data))
# Creates and places the plots
def velocity_plot():
Row = 0
if self.showmap:
Col = 1
else:
Col = 0
if self.selected_velocity_plots[0] == 1:
do_plot(
gs, [Row, Col], self.transect_data['magnitude'],
gettext("Magnitude") +
gettext(" for ") + self.date_formatter(self.timestamp),
gettext("Magnitude"), vmin, vmax,
self.transect_data['unit'], self.cmap)
Row += 1
if self.selected_velocity_plots[1] == 1:
do_plot(
gs, [Row, Col], self.transect_data['parallel'],
self.transect_data['name'] + " (" + gettext("Parallel") +
")" +
gettext(" for ") + self.date_formatter(self.timestamp),
gettext("Parallel"), vmin, vmax,
self.transect_data['unit'], self.cmap)
Row += 1
if self.selected_velocity_plots[2] == 1:
do_plot(
gs, [Row, Col], self.transect_data['perpendicular'],
self.transect_data['name'] + " (" +
gettext("Perpendicular") + ")" + gettext(" for ") +
self.date_formatter(self.timestamp),
gettext("Perpendicular"), vmin, vmax,
self.transect_data['unit'], self.cmap)
# Plot Transects
# If in compare mode
Type = ['magnitude', 'parallel', 'perpendicular']
if self.compare:
# Velocity has 2 components
if velocity:
if self.scale:
vmin = self.scale[0]
vmax = self.scale[1]
else:
vmin = min(np.amin(self.transect_data['parallel']),
np.amin(self.transect_data['perpendicular']))
vmax = max(np.amax(self.transect_data['parallel']),
np.amin(self.transect_data['perpendicular']))
vmin = min(vmin, -vmax)
vmax = max(vmax, -vmin)
# Get colormap for variable
if self.showmap:
Col = 1
else:
Col = 0
do_plot(
gs, [0, Col], self.transect_data['parallel'],
self.transect_data['name'] + " (" + gettext("Parallel") +
")" +
gettext(" for ") + self.date_formatter(self.timestamp),
gettext("Parallel"), vmin, vmax,
self.transect_data['unit'], self.cmap)
Col += 1
do_plot(
gs, [0, Col], self.transect_data['perpendicular'],
self.transect_data['name'] + " (" +
gettext("Perpendicular") + ")" + gettext(" for ") +
self.date_formatter(self.timestamp),
gettext("Perpendicular"), vmin, vmax,
self.transect_data['unit'], self.cmap)
if len(self.compare['variables']) == 2:
if self.compare['scale']:
vmin = self.compare['scale'][0]
vmax = self.compare['scale'][1]
else:
vmin = min(np.amin(self.compare['parallel']),
np.amin(self.compare['perpendicular']))
vmax = max(np.amax(self.compare['parallel']),
np.amin(self.compare['perpendicular']))
vmin = min(vmin, -vmax)
vmax = max(vmax, -vmin)
# Get colormap for variable
cmap = colormap.find_colormap(self.compare['colormap'])
if self.showmap:
Col = 1
else:
Col = 0
do_plot(
gs, [1, Col], self.compare['parallel'],
self.transect_data['name'] + " (" +
gettext("Parallel") + ")" + gettext(" for ") +
self.date_formatter(self.compare['date']),
gettext("Parallel"), vmin, vmax,
self.transect_data['unit'], cmap)
Col += 1
do_plot(
gs, [1, Col], self.compare['perpendicular'],
self.transect_data['name'] + " (" +
gettext("Perpendicular") + ")" + gettext(" for ") +
self.date_formatter(self.compare['date']),
gettext("Perpendicular"), vmin, vmax,
self.transect_data['unit'], cmap)
else:
vmin, vmax = utils.normalize_scale(
self.transect_data['data'],
self.dataset_config.variable[self.variables[0]])
# Render primary/Left Map
if self.showmap:
Col = 1
else:
Col = 0
do_plot(
gs, [0, Col], self.transect_data['data'],
self.transect_data['name'] + gettext(" for ") +
self.date_formatter(self.timestamp),
self.transect_data['name'], vmin, vmax,
self.transect_data['unit'], self.cmap)
# Render Right Map
vmin, vmax = utils.normalize_scale(
self.transect_data['compare_data'],
self.compare_config.variable[",".join(
self.compare['variables'])])
if self.showmap:
Col = 1
else:
Col = 0
do_plot(
gs, [1, Col], self.transect_data['compare_data'],
self.compare['name'] + gettext(" for ") +
self.date_formatter(self.compare['date']),
self.compare['name'], vmin, vmax, self.compare['unit'],
self.compare['colormap'])
# Show a difference plot if both variables and datasets are the same
if self.variables[0] == self.compare['variables'][0]:
self.transect_data['difference'] = self.transect_data['data'] - \
self.transect_data['compare_data']
# Calculate variable range
if self.compare['scale_diff'] is not None:
vmin = self.compare['scale_diff'][0]
vmax = self.compare['scale_diff'][1]
else:
vmin, vmax = find_minmax(
self.compare['scale_diff'],
self.transect_data['difference'])
vmin = min(vmin, -vmax)
vmax = max(vmax, -vmin)
if self.showmap:
Col = 1
else:
Col = 0
do_plot(
gs,
[2, Col],
self.transect_data['difference'],
self.transect_data['name'] + gettext(" Difference"),
self.transect_data['name'],
vmin,
vmax,
self.transect_data[
'unit'], # Since both variables are the same doesn't matter which view we reference
colormap.find_colormap(
self.compare['colormap_diff']
) # Colormap for difference graphs
)
# Not comparing
else:
# Velocity has 3 possible components
if velocity:
if self.scale:
vmin = self.scale[0]
vmax = self.scale[1]
else:
vmin = min(np.amin(self.transect_data['magnitude']),
np.amin(self.transect_data['parallel']),
np.amin(self.transect_data['perpendicular']))
vmax = max(np.amax(self.transect_data['magnitude']),
np.amax(self.transect_data['parallel']),
np.amin(self.transect_data['perpendicular']))
vmin = min(vmin, -vmax)
vmax = max(vmax, -vmin)
Row = 0
velocity_plot()
# All other variables have 1 component
else:
if self.showmap:
Col = 1
else:
Col = 0
if self.scale:
vmin = self.scale[0]
vmax = self.scale[1]
else:
vmin, vmax = utils.normalize_scale(
self.transect_data['data'],
self.dataset_config.variable[self.variables[0]])
do_plot(
gs, [0, Col], self.transect_data['data'],
self.transect_data['name'] + " for " +
self.date_formatter(self.timestamp),
self.transect_data['name'], vmin, vmax,
self.transect_data['unit'], self.cmap)
# Figure title
if self.plotTitle is None or self.plotTitle == "":
fig.suptitle("Transect Data for:\n%s" % (self.name), fontsize=15)
else:
fig.suptitle(self.plotTitle, fontsize=15)
# Subplot padding
fig.tight_layout(pad=2, w_pad=2, h_pad=2)
fig.subplots_adjust(top=0.90 if self.compare else 0.85)
return super(TransectPlotter, self).plot(fig)
def load_data(self):
width_scale = 1.25
height_scale = 1.25
if self.projection == "EPSG:32661": # north pole projection
near_pole, covers_pole = self.pole_proximity(self.points[0])
blat = min(self.bounds[0], self.bounds[2])
blat = 5 * np.floor(blat / 5)
if self.centroid[0] > 80 or near_pole or covers_pole:
self.plot_projection = ccrs.Stereographic(
central_latitude=self.centroid[0],
central_longitude=self.centroid[1],
)
width_scale = 1.5
else:
self.plot_projection = ccrs.LambertConformal(
central_latitude=self.centroid[0],
central_longitude=self.centroid[1],
)
elif self.projection == "EPSG:3031": # south pole projection
near_pole, covers_pole = self.pole_proximity(self.points[0])
blat = max(self.bounds[0], self.bounds[2])
blat = 5 * np.ceil(blat / 5)
# is centerered close to the south pole
if ((self.centroid[0] < -80 or self.bounds[1] < -80
or self.bounds[3] < -80) or covers_pole) or near_pole:
self.plot_projection = ccrs.Stereographic(
central_latitude=self.centroid[0],
central_longitude=self.centroid[1],
)
width_scale = 1.5
else:
self.plot_projection = ccrs.LambertConformal(
central_latitude=self.centroid[0],
central_longitude=self.centroid[1],
)
elif abs(self.centroid[1] - self.bounds[1]) > 90:
if abs(self.bounds[3] - self.bounds[1]) > 360:
raise ClientError(
gettext(
"You have requested an area that exceeds the width \
of the world. Thinking big is good but plots need to \
be less than 360 deg wide."))
self.plot_projection = ccrs.Mercator(
central_longitude=self.centroid[1])
else:
self.plot_projection = ccrs.LambertConformal(
central_latitude=self.centroid[0],
central_longitude=self.centroid[1])
proj_bounds = self.plot_projection.transform_points(
self.pc_projection,
np.array([self.bounds[1], self.bounds[3]]),
np.array([self.bounds[0], self.bounds[2]]),
)
proj_size = np.diff(proj_bounds, axis=0)
width = proj_size[0][0] * width_scale
height = proj_size[0][1] * height_scale
aspect_ratio = height / width
if aspect_ratio < 1:
gridx = 500
gridy = int(500 * aspect_ratio)
else:
gridy = 500
gridx = int(500 / aspect_ratio)
self.plot_res = basemap.get_resolution(height, width)
x_grid, y_grid, self.plot_extent = cimg_transform.mesh_projection(
self.plot_projection,
gridx,
gridy,
x_extents=(-width / 2, width / 2),
y_extents=(-height / 2, height / 2),
)
latlon_grid = self.pc_projection.transform_points(
self.plot_projection, x_grid, y_grid)
self.longitude = latlon_grid[:, :, 0]
self.latitude = latlon_grid[:, :, 1]
variables_to_load = self.variables[:] # we don't want to change self,variables so copy it
if self.__load_quiver():
variables_to_load.append(self.quiver["variable"])
with open_dataset(self.dataset_config,
variable=variables_to_load,
timestamp=self.time) as dataset:
self.variable_unit = self.get_variable_units(
dataset, self.variables)[0]
self.variable_name = self.get_variable_names(
dataset, self.variables)[0]
if self.cmap is None:
self.cmap = colormap.find_colormap(self.variable_name)
if self.depth == "bottom":
depth_value_map = "Bottom"
else:
self.depth = np.clip(int(self.depth), 0,
len(dataset.depths) - 1)
depth_value = dataset.depths[self.depth]
depth_value_map = depth_value
data = []
var = dataset.variables[self.variables[0]]
if self.filetype in ["csv", "odv", "txt"]:
d, depth_value_map = dataset.get_area(
np.array([self.latitude, self.longitude]),
self.depth,
self.time,
self.variables[0],
self.interp,
self.radius,
self.neighbours,
return_depth=True,
)
else:
d = dataset.get_area(
np.array([self.latitude, self.longitude]),
self.depth,
self.time,
self.variables[0],
self.interp,
self.radius,
self.neighbours,
)
data.append(d)
if self.filetype not in ["csv", "odv", "txt"]:
if len(var.dimensions) == 3:
self.depth_label = ""
elif self.depth == "bottom":
self.depth_label = " at Bottom"
else:
self.depth_label = (" at " +
str(int(np.round(depth_value_map))) +
" m")
self.data = data[0]
quiver_data = []
# Store the quiver data on the same grid as the main variable. This
# will only be used for CSV export.
quiver_data_fullgrid = []
if self.__load_quiver():
var = dataset.variables[self.quiver["variable"]]
quiver_unit = self.dataset_config.variable[var].unit
quiver_name = self.dataset_config.variable[var].name
quiver_x_var = self.dataset_config.variable[
var].east_vector_component
quiver_y_var = self.dataset_config.variable[
var].north_vector_component
quiver_x, quiver_y, _ = cimg_transform.mesh_projection(
self.plot_projection,
50,
50,
self.plot_extent[:2],
self.plot_extent[2:],
)
quiver_coords = self.pc_projection.transform_points(
self.plot_projection, quiver_x, quiver_y)
quiver_lon = quiver_coords[:, :, 0]
quiver_lat = quiver_coords[:, :, 1]
x_vals = dataset.get_area(
np.array([quiver_lat, quiver_lon]),
self.depth,
self.time,
quiver_x_var,
self.interp,
self.radius,
self.neighbours,
)
quiver_data.append(x_vals)
y_vals = dataset.get_area(
np.array([quiver_lat, quiver_lon]),
self.depth,
self.time,
quiver_y_var,
self.interp,
self.radius,
self.neighbours,
)
quiver_data.append(y_vals)
mag_data = dataset.get_area(
np.array([quiver_lat, quiver_lon]),
self.depth,
self.time,
self.quiver["variable"],
self.interp,
self.radius,
self.neighbours,
)
self.quiver_magnitude = mag_data
# Get the quiver data on the same grid as the main
# variable.
x_vals = dataset.get_area(
np.array([self.latitude, self.longitude]),
self.depth,
self.time,
quiver_x_var,
self.interp,
self.radius,
self.neighbours,
)
quiver_data_fullgrid.append(x_vals)
y_vals = dataset.get_area(
np.array([self.latitude, self.longitude]),
self.depth,
self.time,
quiver_y_var,
self.interp,
self.radius,
self.neighbours,
)
quiver_data_fullgrid.append(y_vals)
self.quiver_name = self.get_variable_names(
dataset, [self.quiver["variable"]])[0]
self.quiver_longitude = quiver_lon
self.quiver_latitude = quiver_lat
self.quiver_unit = quiver_unit
self.quiver_data = quiver_data
self.quiver_data_fullgrid = quiver_data_fullgrid
if all([
dataset.variables[v].is_surface_only()
for v in variables_to_load
]):
self.depth = 0
contour_data = []
if (self.contour is not None and self.contour["variable"] != ""
and self.contour["variable"] != "none"):
d = dataset.get_area(
np.array([self.latitude, self.longitude]),
self.depth,
self.time,
self.contour["variable"],
self.interp,
self.radius,
self.neighbours,
)
vc = self.dataset_config.variable[self.contour["variable"]]
contour_unit = vc.unit
contour_name = vc.name
contour_data.append(d)
self.contour_unit = contour_unit
self.contour_name = contour_name
self.contour_data = contour_data
self.timestamp = dataset.nc_data.timestamp_to_iso_8601(self.time)
if self.compare:
self.variable_name += " Difference"
compare_config = DatasetConfig(self.compare["dataset"])
with open_dataset(
compare_config,
variable=self.compare["variables"],
timestamp=self.compare["time"],
) as dataset:
data = []
for v in self.compare["variables"]:
var = dataset.variables[v]
d = dataset.get_area(
np.array([self.latitude, self.longitude]),
self.compare["depth"],
self.compare["time"],
v,
self.interp,
self.radius,
self.neighbours,
)
data.append(d)
data = data[0]
self.data -= data
# Load bathymetry data
self.bathymetry = overlays.bathymetry(self.latitude,
self.longitude,
blur=2)
if self.depth != "bottom" and self.depth != 0:
if quiver_data:
quiver_bathymetry = overlays.bathymetry(quiver_lat, quiver_lon)
self.data[np.where(
self.bathymetry < depth_value_map)] = np.ma.masked
for d in self.quiver_data:
d[np.where(quiver_bathymetry < depth_value)] = np.ma.masked
for d in self.contour_data:
d[np.where(self.bathymetry < depth_value_map)] = np.ma.masked
else:
mask = maskoceans(self.longitude, self.latitude, self.data, True,
"h", 1.25).mask
self.data[~mask] = np.ma.masked
for d in self.quiver_data:
mask = maskoceans(self.quiver_longitude, self.quiver_latitude,
d).mask
d[~mask] = np.ma.masked
for d in contour_data:
mask = maskoceans(self.longitude, self.latitude, d).mask
d[~mask] = np.ma.masked
if self.area and self.filetype in ["csv", "odv", "txt", "geotiff"]:
area_polys = []
for a in self.area:
rings = [LinearRing(p) for p in a["polygons"]]
innerrings = [LinearRing(p) for p in a["innerrings"]]
polygons = []
for r in rings:
inners = []
for ir in innerrings:
if r.contains(ir):
inners.append(ir)
polygons.append(Poly(r, inners))
area_polys.append(MultiPolygon(polygons))
points = [
Point(p)
for p in zip(self.latitude.ravel(), self.longitude.ravel())
]
indicies = []
for a in area_polys:
indicies.append(
np.where(
list(map(lambda p, poly=a: poly.contains(p),
points)))[0])
indicies = np.unique(np.array(indicies).ravel())
newmask = np.ones(self.data.shape, dtype=bool)
newmask[np.unravel_index(indicies, newmask.shape)] = False
self.data.mask |= newmask
self.depth_value_map = depth_value_map
def load_data(self):
distance = VincentyDistance()
height = distance.measure(
(self.bounds[0], self.centroid[1]),
(self.bounds[2], self.centroid[1])) * 1000 * 1.25
width = distance.measure(
(self.centroid[0], self.bounds[1]),
(self.centroid[0], self.bounds[3])) * 1000 * 1.25
if self.projection == 'EPSG:32661': # north pole projection
near_pole, covers_pole = self.pole_proximity(self.points[0])
blat = min(self.bounds[0], self.bounds[2])
blat = 5 * np.floor(blat / 5)
if self.centroid[0] > 80 or near_pole or covers_pole:
self.basemap = basemap.load_map(
'npstere', self.centroid, height, width,
min(self.bounds[0], self.bounds[2]))
else:
self.basemap = basemap.load_map('lcc', self.centroid, height,
width)
elif self.projection == 'EPSG:3031': # south pole projection
near_pole, covers_pole = self.pole_proximity(self.points[0])
blat = max(self.bounds[0], self.bounds[2])
blat = 5 * np.ceil(blat / 5)
if ((self.centroid[0] < -80 or self.bounds[1] < -80
or self.bounds[3] < -80) or covers_pole
) or near_pole: # is centerered close to the south pole
self.basemap = basemap.load_map(
'spstere', self.centroid, height, width,
max(self.bounds[0], self.bounds[2]))
else:
self.basemap = basemap.load_map('lcc', self.centroid, height,
width)
elif abs(self.centroid[1] - self.bounds[1]) > 90:
height_bounds = [self.bounds[0], self.bounds[2]]
width_bounds = [self.bounds[1], self.bounds[3]]
height_buffer = (abs(height_bounds[1] - height_bounds[0])) * 0.1
width_buffer = (abs(width_bounds[0] - width_bounds[1])) * 0.1
if abs(width_bounds[1] - width_bounds[0]) > 360:
raise ClientError(
gettext(
"You have requested an area that exceeds the width of the world. \
Thinking big is good but plots need to be less than 360 deg wide."
))
if height_bounds[1] < 0:
height_bounds[1] = height_bounds[1] + height_buffer
else:
height_bounds[1] = height_bounds[1] + height_buffer
if height_bounds[0] < 0:
height_bounds[0] = height_bounds[0] - height_buffer
else:
height_bounds[0] = height_bounds[0] - height_buffer
new_width_bounds = []
new_width_bounds.append(width_bounds[0] - width_buffer)
new_width_bounds.append(width_bounds[1] + width_buffer)
if abs(new_width_bounds[1] - new_width_bounds[0]) > 360:
width_buffer = np.floor(
(360 - abs(width_bounds[1] - width_bounds[0])) / 2)
new_width_bounds[0] = width_bounds[0] - width_buffer
new_width_bounds[1] = width_bounds[1] + width_buffer
if new_width_bounds[0] < -360:
new_width_bounds[0] = -360
if new_width_bounds[1] > 720:
new_width_bounds[1] = 720
self.basemap = basemap.load_map(
'merc', self.centroid, (height_bounds[0], height_bounds[1]),
(new_width_bounds[0], new_width_bounds[1]))
else:
self.basemap = basemap.load_map('lcc', self.centroid, height,
width)
if self.basemap.aspect < 1:
gridx = 500
gridy = int(500 * self.basemap.aspect)
else:
gridy = 500
gridx = int(500 / self.basemap.aspect)
self.longitude, self.latitude = self.basemap.makegrid(gridx, gridy)
with open_dataset(get_dataset_url(self.dataset_name)) as dataset:
if self.time < 0:
self.time += len(dataset.timestamps)
self.time = np.clip(self.time, 0, len(dataset.timestamps) - 1)
self.variable_unit = self.get_variable_units(
dataset, self.variables)[0]
self.variable_name = self.get_variable_names(
dataset, self.variables)[0]
scale_factor = self.get_variable_scale_factors(
dataset, self.variables)[0]
if self.cmap is None:
if len(self.variables) == 1:
self.cmap = colormap.find_colormap(self.variable_name)
else:
self.cmap = colormap.colormaps.get('speed')
if len(self.variables) == 2:
self.variable_name = self.vector_name(self.variable_name)
if self.depth == 'bottom':
depth_value = 'Bottom'
else:
self.depth = np.clip(int(self.depth), 0,
len(dataset.depths) - 1)
depth_value = dataset.depths[self.depth]
data = []
allvars = []
for v in self.variables:
var = dataset.variables[v]
allvars.append(v)
if self.filetype in ['csv', 'odv', 'txt']:
d, depth_value = dataset.get_area(np.array(
[self.latitude, self.longitude]),
self.depth,
self.time,
v,
self.interp,
self.radius,
self.neighbours,
return_depth=True)
else:
d = dataset.get_area(
np.array([self.latitude,
self.longitude]), self.depth, self.time, v,
self.interp, self.radius, self.neighbours)
d = np.multiply(d, scale_factor)
self.variable_unit, d = self.kelvin_to_celsius(
self.variable_unit, d)
data.append(d)
if self.filetype not in ['csv', 'odv', 'txt']:
if len(var.dimensions) == 3:
self.depth_label = ""
elif self.depth == 'bottom':
self.depth_label = " at Bottom"
else:
self.depth_label = " at " + \
str(int(np.round(depth_value))) + " m"
if len(data) == 2:
data[0] = np.sqrt(data[0]**2 + data[1]**2)
self.data = data[0]
quiver_data = []
# Store the quiver data on the same grid as the main variable. This
# will only be used for CSV export.
quiver_data_fullgrid = []
if self.quiver is not None and \
self.quiver['variable'] != '' and \
self.quiver['variable'] != 'none':
for v in self.quiver['variable'].split(','):
allvars.append(v)
var = dataset.variables[v]
quiver_unit = get_variable_unit(self.dataset_name, var)
quiver_name = get_variable_name(self.dataset_name, var)
quiver_lon, quiver_lat = self.basemap.makegrid(50, 50)
d = dataset.get_area(
np.array([quiver_lat, quiver_lon]),
self.depth,
self.time,
v,
self.interp,
self.radius,
self.neighbours,
)
quiver_data.append(d)
# Get the quiver data on the same grid as the main
# variable.
d = dataset.get_area(
np.array([self.latitude, self.longitude]),
self.depth,
self.time,
v,
self.interp,
self.radius,
self.neighbours,
)
quiver_data_fullgrid.append(d)
self.quiver_name = self.vector_name(quiver_name)
self.quiver_longitude = quiver_lon
self.quiver_latitude = quiver_lat
self.quiver_unit = quiver_unit
self.quiver_data = quiver_data
self.quiver_data_fullgrid = quiver_data_fullgrid
if all(
[len(dataset.variables[v].dimensions) == 3 for v in allvars]):
self.depth = 0
contour_data = []
if self.contour is not None and \
self.contour['variable'] != '' and \
self.contour['variable'] != 'none':
d = dataset.get_area(
np.array([self.latitude, self.longitude]),
self.depth,
self.time,
self.contour['variable'],
self.interp,
self.radius,
self.neighbours,
)
contour_unit = get_variable_unit(
self.dataset_name,
dataset.variables[self.contour['variable']])
contour_name = get_variable_name(
self.dataset_name,
dataset.variables[self.contour['variable']])
contour_factor = get_variable_scale_factor(
self.dataset_name,
dataset.variables[self.contour['variable']])
contour_unit, d = self.kelvin_to_celsius(contour_unit, d)
d = np.multiply(d, contour_factor)
contour_data.append(d)
self.contour_unit = contour_unit
self.contour_name = contour_name
self.contour_data = contour_data
self.timestamp = dataset.timestamps[self.time]
if self.compare:
self.variable_name += " Difference"
with open_dataset(get_dataset_url(
self.compare['dataset'])) as dataset:
data = []
for v in self.compare['variables']:
var = dataset.variables[v]
d = dataset.get_area(
np.array([self.latitude, self.longitude]),
self.compare['depth'],
self.compare['time'],
v,
self.interp,
self.radius,
self.neighbours,
)
data.append(d)
if len(data) == 2:
data = np.sqrt(data[0]**2 + data[1]**2)
else:
data = data[0]
u, data = self.kelvin_to_celsius(
dataset.variables[self.compare['variables'][0]].unit, data)
self.data -= data
# Load bathymetry data
self.bathymetry = overlays.bathymetry(self.basemap,
self.latitude,
self.longitude,
blur=2)
if self.depth != 'bottom' and self.depth != 0:
if len(quiver_data) > 0:
quiver_bathymetry = overlays.bathymetry(
self.basemap, quiver_lat, quiver_lon)
self.data[np.where(self.bathymetry < depth_value)] = np.ma.masked
for d in self.quiver_data:
d[np.where(quiver_bathymetry < depth_value)] = np.ma.masked
for d in self.contour_data:
d[np.where(self.bathymetry < depth_value)] = np.ma.masked
else:
mask = maskoceans(self.longitude, self.latitude, self.data, True,
'h', 1.25).mask
self.data[~mask] = np.ma.masked
for d in self.quiver_data:
mask = maskoceans(self.quiver_longitude, self.quiver_latitude,
d).mask
d[~mask] = np.ma.masked
for d in contour_data:
mask = maskoceans(self.longitude, self.latitude, d).mask
d[~mask] = np.ma.masked
if self.area and self.filetype in ['csv', 'odv', 'txt', 'geotiff']:
area_polys = []
for a in self.area:
rings = [LinearRing(p) for p in a['polygons']]
innerrings = [LinearRing(p) for p in a['innerrings']]
polygons = []
for r in rings:
inners = []
for ir in innerrings:
if r.contains(ir):
inners.append(ir)
polygons.append(Poly(r, inners))
area_polys.append(MultiPolygon(polygons))
points = [
Point(p)
for p in zip(self.latitude.ravel(), self.longitude.ravel())
]
indicies = []
for a in area_polys:
indicies.append(
np.where(
list(map(lambda p, poly=a: poly.contains(p),
points)))[0])
indicies = np.unique(np.array(indicies).ravel())
newmask = np.ones(self.data.shape, dtype=bool)
newmask[np.unravel_index(indicies, newmask.shape)] = False
self.data.mask |= newmask
self.depth_value = depth_value
def load_data(self):
platform = db.session.query(Platform).get(self.platform)
self.name = platform.unique_id
# First get the variable
st0 = db.session.query(Station).filter(
Station.platform == platform).first()
datatype_keys = db.session.query(db.func.distinct(
Sample.datatype_key)).filter(Sample.station == st0).all()
datatypes = db.session.query(DataType).filter(
DataType.key.in_(datatype_keys)).order_by(DataType.key).all()
variables = [datatypes[int(x)] for x in self.trackvariables]
self.data_names = [dt.name for dt in variables]
self.data_units = [dt.unit for dt in variables]
self.track_cmaps = [
colormap.find_colormap(dt.name) for dt in variables
]
d = []
for v in variables:
d.append(
get_platform_variable_track(
db.session,
platform,
v.key,
self.track_quantum,
starttime=self.starttime,
endtime=self.endtime,
))
d = np.array(d)
self.points = d[0, :, 1:3].astype(float)
add_tz_utc = np.vectorize(lambda x: x.replace(tzinfo=pytz.UTC))
self.times = add_tz_utc(d[0, :, 0])
self.data = d[:, :, 4].astype(float)
self.depth = d[0, :, 3].astype(float)
d_delta = [
distance(p0, p1).km
for p0, p1 in zip(self.points[0:-1], self.points[1:])
]
d_delta.insert(0, 0)
self.distances = np.cumsum(d_delta)
start = int(
datetime_to_timestamp(self.times[0],
self.dataset_config.time_dim_units))
end = int(
datetime_to_timestamp(self.times[-1],
self.dataset_config.time_dim_units))
points_simplified = self.points
if len(self.points) > 100:
points_simplified = np.array(vw.simplify(self.points, number=100))
if len(self.variables) > 0:
with open_dataset(self.dataset_config,
timestamp=start,
endtime=end,
variable=self.variables,
nearest_timestamp=True) as dataset:
# Make distance -> time function
dist_to_time = interp1d(
self.distances,
[time.mktime(t.timetuple()) for t in self.times],
assume_sorted=True,
bounds_error=False,
)
output_times = dist_to_time(
np.linspace(0, self.distances[-1], 100))
model_times = sorted([
time.mktime(t.timetuple())
for t in dataset.nc_data.timestamps
])
self.model_depths = dataset.depths
d = []
depth = 0
for v in self.variables:
if len(np.unique(self.depth)) > 1:
pts, dist, md, dep = dataset.get_path_profile(
points_simplified,
v,
int(
datetime_to_timestamp(
dataset.nc_data.timestamps[0],
self.dataset_config.time_dim_units)),
endtime=int(
datetime_to_timestamp(
dataset.nc_data.timestamps[-1],
self.dataset_config.time_dim_units)),
)
if len(model_times) > 1:
f = interp1d(
model_times,
md.filled(np.nan),
assume_sorted=True,
bounds_error=False,
)
ot = dist_to_time(dist)
od = f(ot).diagonal(0, 0, 2).copy()
else:
od = md
# Clear model data beneath observed data
od[np.where(self.model_depths > max(self.depth)
)[0][1:], :] = np.nan
d.append(od)
mt = [
int(
datetime_to_timestamp(
t, self.dataset_config.time_dim_units))
for t in dataset.nc_data.timestamps
]
model_dist = dist
else:
pts, dist, mt, md = dataset.get_path(
self.points,
depth,
v,
datetime_to_timestamp(
dataset.nc_data.timestamps[0],
self.dataset_config.time_dim_units),
endtime=datetime_to_timestamp(
dataset.nc_data.timestamps[-1],
self.dataset_config.time_dim_units),
times=output_times)
model_dist = dist
if len(model_times) > 1:
f = interp1d(
model_times,
md,
assume_sorted=True,
bounds_error=False,
)
d.append(np.diag(f(mt)))
else:
d.append(md)
model_data = np.ma.array(d)
variable_units = []
variable_names = []
scale_factors = []
cmaps = []
for v in self.variables:
vc = self.dataset_config.variable[v]
variable_units.append(vc.unit)
variable_names.append(vc.name)
scale_factors.append(vc.scale_factor)
cmaps.append(colormap.find_colormap(vc.name))
for idx, sf in enumerate(scale_factors):
model_data[idx, :] = np.multiply(model_data[idx, :], sf)
self.model_data = model_data
self.model_dist = model_dist
self.model_times = list(
map(datetime.datetime.utcfromtimestamp, model_times))
self.variable_names = variable_names
self.variable_units = variable_units
self.cmaps = cmaps
def plot(projection, x, y, z, args):
lat, lon = get_latlon_coords(projection, x, y, z)
if len(lat.shape) == 1:
lat, lon = np.meshgrid(lat, lon)
dataset_name = args.get("dataset")
config = DatasetConfig(dataset_name)
variable = args.get("variable")
variable = variable.split(",")
depth = args.get("depth")
scale = args.get("scale")
scale = [float(component) for component in scale.split(",")]
time = args.get("time")
data = []
with open_dataset(config, variable=variable, timestamp=time) as dataset:
for v in variable:
data.append(
dataset.get_area(
np.array([lat, lon]),
depth,
time,
v,
args.get("interp"),
args.get("radius"),
args.get("neighbours"),
))
vc = config.variable[dataset.variables[variable[0]]]
variable_name = vc.name
variable_unit = vc.unit
cmap = colormap.find_colormap(variable_name)
if depth != "bottom":
depthm = dataset.depths[depth]
else:
depthm = 0
if len(data) == 1:
data = data[0]
if len(data) == 2:
data = np.sqrt(data[0]**2 + data[1]**2)
cmap = colormap.colormaps.get("speed")
data = data.transpose()
xpx = x * 256
ypx = y * 256
# Mask out any topography if we're below the vector-tile threshold
if z < 8:
with Dataset(current_app.config["ETOPO_FILE"] % (projection, z),
"r") as dataset:
bathymetry = dataset["z"][ypx:(ypx + 256), xpx:(xpx + 256)]
bathymetry = gaussian_filter(bathymetry, 0.5)
data[np.where(bathymetry > -depthm)] = np.ma.masked
sm = matplotlib.cm.ScalarMappable(matplotlib.colors.Normalize(
vmin=scale[0], vmax=scale[1]),
cmap=cmap)
img = sm.to_rgba(np.ma.masked_invalid(np.squeeze(data)))
im = Image.fromarray((img * 255.0).astype(np.uint8))
buf = BytesIO()
im.save(buf, format="PNG", optimize=True)
return buf
def load_data(self):
vars_to_load = self.variables
if self.surface:
vars_to_load.append(self.surface)
with open_dataset(self.dataset_config,
timestamp=self.time,
variable=vars_to_load) as dataset:
for idx, v in enumerate(self.variables):
var = dataset.variables[v]
if not (set(var.dimensions)
& set(dataset.nc_data.depth_dimensions)):
for potential in dataset.variables:
if potential in self.variables:
continue
pot = dataset.variables[potential]
if set(pot.dimensions) & set(
dataset.nc_data.depth_dimensions):
if len(pot.dimensions) > 3:
self.variables[idx] = potential.key
value = parallel = perpendicular = magnitude = None
variable_names = self.get_variable_names(dataset, self.variables)
variable_units = self.get_variable_units(dataset, self.variables)
# Load data sent from primary/Left Map
if len(self.variables) > 1:
# Only velocity has 2 variables
v = []
for name in self.variables:
v.append(dataset.variables[name])
distances, times, lat, lon, bearings = geo.path_to_points(
self.points, 100)
# Calculate vector components
transect_pts, distance, x, dep = dataset.get_path_profile(
self.points, self.variables[0], self.time, numpoints=100)
transect_pts, distance, y, dep = dataset.get_path_profile(
self.points, self.variables[1], self.time, numpoints=100)
r = np.radians(np.subtract(90, bearings))
theta = np.arctan2(y, x) - r
magnitude = np.sqrt(x**2 + y**2)
parallel = magnitude * np.cos(theta)
perpendicular = magnitude * np.sin(theta)
else:
# Get data for one variable
transect_pts, distance, value, dep = dataset.get_path_profile(
self.points, self.variables[0], self.time)
if len(self.variables) == 2:
variable_names = [
self.get_vector_variable_name(dataset, self.variables)
]
variable_units = [
self.get_vector_variable_unit(dataset, self.variables)
]
# If a colourmap has not been manually specified by the
# Navigator...
if self.cmap is None:
self.cmap = colormap.find_colormap(variable_names[0])
self.iso_timestamp = dataset.nc_data.timestamp_to_iso_8601(
self.time)
self.depth = dep
self.depth_unit = "m"
self.transect_data = {
"points": transect_pts,
"distance": distance,
"data": value,
"name": variable_names[0],
"unit": variable_units[0],
"parallel": parallel,
"perpendicular": perpendicular,
"magnitude": magnitude,
}
if self.surface:
surface_pts, surface_dist, _, surface_value = dataset.get_path(
self.points, 0, self.surface, self.time)
vc = self.dataset_config.variable[dataset.variables[
self.surface]]
surface_unit = vc.unit
surface_name = vc.name
surface_value = np.multiply(surface_value, surface_factor)
self.surface_data = {
"config": vc,
"points": surface_pts,
"distance": surface_dist,
"data": surface_value,
"name": surface_name,
"unit": surface_unit,
}
# Load data sent from Right Map (if in compare mode)
if self.compare:
def interpolate_depths(data, depth_in, depth_out):
output = []
for i in range(0, depth_in.shape[0]):
f = interp1d(
depth_in[i],
data[:, i],
bounds_error=False,
assume_sorted=True,
)
output.append(
f(depth_out[i].view(np.ma.MaskedArray).filled()))
return np.ma.masked_invalid(output).transpose()
self.compare_config = DatasetConfig(self.compare["dataset"])
self.compare["time"] = int(self.compare["time"])
with open_dataset(
self.compare_config,
timestamp=self.compare["time"],
variable=self.compare["variables"],
) as dataset:
self.compare[
"iso_timestamp"] = dataset.nc_data.timestamp_to_iso_8601(
self.compare["time"])
# 1 variable
if len(self.compare["variables"]) == 1:
# Get and store the "nicely formatted" string for the variable name
self.compare["name"] = self.get_variable_names(
dataset, self.compare["variables"])[0]
# Find correct colourmap
if self.compare["colormap"] == "default":
self.compare["colormap"] = colormap.find_colormap(
self.compare["name"])
else:
self.compare["colormap"] = colormap.find_colormap(
self.compare["colormap"])
(
climate_points,
climate_distance,
climate_data,
cdep,
) = dataset.get_path_profile(self.points,
self.compare["variables"][0],
self.compare["time"])
self.compare["unit"] = dataset.variables[
self.compare["variables"][0]].unit
self.__fill_invalid_shift(climate_data)
if (self.depth.shape != cdep.shape) or (self.depth !=
cdep).any():
# Need to interpolate the depths
climate_data = interpolate_depths(
climate_data, cdep, self.depth)
if self.transect_data["data"] is None:
self.transect_data["magnitude"] -= climate_data
self.transect_data["parallel"] -= climate_data
self.transect_data["perpendicular"] -= climate_data
else:
self.transect_data["compare_data"] = climate_data
# Velocity variables
else:
# Get and store the "nicely formatted" string for the variable name
self.compare["name"] = self.get_vector_variable_name(
dataset, self.compare["variables"])
(
climate_pts,
climate_distance,
climate_x,
cdep,
) = dataset.get_path_profile(
self.points,
self.compare["variables"][0],
self.compare["time"],
numpoints=100,
)
(
climate_pts,
climate_distance,
climate_y,
cdep,
) = dataset.get_path_profile(
self.points,
self.compare["variables"][0],
self.compare["time"],
numpoints=100,
)
(
climate_distances,
ctimes,
clat,
clon,
bearings,
) = geo.path_to_points(self.points, 100)
r = np.radians(np.subtract(90, bearings))
theta = np.arctan2(climate_y, climate_x) - r
mag = np.sqrt(climate_x**2 + climate_y**2)
if np.all(self.depth != cdep):
theta = interpolate_depths(theta, cdep, self.depth)
self.__fill_invalid_shift(theta)
mag = interpolate_depths(mag, cdep, self.depth)
self.__fill_invalid_shift(mag)
self.compare["parallel"] = mag * np.cos(theta)
self.compare["perpendicular"] = mag * np.sin(theta)
"""
if self.transect_data['parallel'] is None:
self.transect_data['data'] -= mag
else:
self.transect_data['parallel'] -= climate_parallel
self.transect_data['perpendicular'] -= climate_perpendicular
"""
# Bathymetry
with Dataset(current_app.config["BATHYMETRY_FILE"], "r") as dataset:
bath_x, bath_y = bathymetry(
dataset.variables["y"],
dataset.variables["x"],
dataset.variables["z"],
self.points,
)
self.bathymetry = {"x": bath_x, "y": bath_y}
def load_data(self):
"""
Calculates and returns the depth, depth-value, and depth unit from a given dataset
Args:
depth: Stored depth information (self.depth or self.compare['depth'])
clip_length: How many depth values to clip (usually len(dataset.depths) - 1)
dataset: Opened dataset
Returns:
(depth, depth_value, depth_unit)
"""
def find_depth(depth, clip_length, dataset):
depth_value = 0
depth_unit = "m"
if depth:
if depth == 'bottom':
depth_value = 'Bottom'
depth_unit = ''
return (depth, depth_value, depth_unit)
else:
depth = np.clip(int(depth), 0, clip_length)
depth_value = np.round(dataset.depths[depth])
depth_unit = "m"
return (depth, depth_value, depth_unit)
return (depth, depth_value, depth_unit)
# Load left/Main Map
with open_dataset(self.dataset_config) as dataset:
latvar, lonvar = utils.get_latlon_vars(dataset)
self.depth, self.depth_value, self.depth_unit = find_depth(
self.depth,
len(dataset.depths) - 1, dataset)
self.fix_startend_times(dataset, self.starttime, self.endtime)
time = list(range(self.starttime, self.endtime + 1))
if len(self.variables) > 1:
v = []
for name in self.variables:
self.path_points, self.distance, t, value = dataset.get_path(
self.points, self.depth, time, name)
v.append(value**2)
value = np.sqrt(np.ma.sum(v, axis=0))
self.variable_name = self.get_vector_variable_name(
dataset, self.variables)
else:
self.path_points, self.distance, t, value = dataset.get_path(
self.points, self.depth, time, self.variables[0])
self.variable_name = self.get_variable_names(
dataset, self.variables)[0]
variable_units = self.get_variable_units(dataset, self.variables)
scale_factors = self.get_variable_scale_factors(
dataset, self.variables)
self.variable_unit = variable_units[0]
self.data = value
self.times = dataset.timestamps[self.starttime:self.endtime + 1]
self.data = np.multiply(self.data, scale_factors[0])
self.data = self.data.transpose()
# Get colourmap
if self.cmap is None:
self.cmap = colormap.find_colormap(self.variable_name)
# Load data sent from Right Map (if in compare mode)
if self.compare:
compare_config = DatasetConfig(self.compare['dataset'])
with open_dataset(compare_config) as dataset:
latvar, lonvar = utils.get_latlon_vars(dataset)
self.compare['depth'], self.compare[
'depth_value'], self.compare['depth_unit'] = find_depth(
self.compare['depth'],
len(dataset.depths) - 1, dataset)
self.fix_startend_times(dataset, self.compare['starttime'],
self.compare['endtime'])
time = list(
range(self.compare['starttime'],
self.compare['endtime'] + 1))
if len(self.compare['variables']) > 1:
v = []
for name in self.compare['variables']:
path, distance, t, value = dataset.get_path(
self.points, self.compare['depth'], time, name)
v.append(value**2)
value = np.sqrt(np.ma.sum(v, axis=0))
self.compare['variable_name'] = \
self.get_vector_variable_name(dataset,
self.compare['variables'])
else:
path, distance, t, value = dataset.get_path(
self.points, self.compare['depth'], time,
self.compare['variables'][0])
self.compare['variable_name'] = self.get_variable_names(
dataset, self.compare['variables'])[0]
# Colourmap
if (self.compare['colormap'] == 'default'):
self.compare['colormap'] = colormap.find_colormap(
self.compare['variable_name'])
else:
self.compare['colormap'] = colormap.find_colormap(
self.compare['colormap'])
variable_units = self.get_variable_units(
dataset, self.compare['variables'])
scale_factors = self.get_variable_scale_factors(
dataset, self.compare['variables'])
self.compare['variable_unit'] = variable_units[0]
self.compare['data'] = value
self.compare['times'] = dataset.timestamps[
self.compare['starttime']:self.compare['endtime'] + 1]
self.compare['data'] = np.multiply(self.compare['data'],
scale_factors[0])
self.compare['data'] = self.compare['data'].transpose()
# Comparison over different time ranges makes no sense
if self.starttime != self.compare['starttime'] or\
self.endtime != self.compare['endtime']:
raise ClientError(
gettext(
"Please ensure the Start Time and End Time for the Left and Right maps are identical."
))
def plot(projection, x, y, z, args):
lat, lon = get_latlon_coords(projection, x, y, z)
if len(lat.shape) == 1:
lat, lon = np.meshgrid(lat, lon)
dataset_name = args.get('dataset')
config = DatasetConfig(dataset_name)
variable = args.get('variable')
variable = variable.split(',')
depth = args.get('depth')
scale = args.get('scale')
scale = [float(component) for component in scale.split(',')]
data = []
with open_dataset(config) as dataset:
if args.get('time') is None or (type(args.get('time')) == str
and len(args.get('time')) == 0):
time = -1
else:
time = int(args.get('time'))
t_len = len(dataset.timestamps)
while time >= t_len:
time -= t_len
while time < 0:
time += len(dataset.timestamps)
timestamp = dataset.timestamps[time]
for v in variable:
data.append(
dataset.get_area(np.array([lat, lon]), depth, time, v,
args.get('interp'), args.get('radius'),
args.get('neighbours')))
vc = config.variable[dataset.variables[variable[0]]]
variable_name = vc.name
variable_unit = vc.unit
scale_factor = vc.scale_factor
cmap = colormap.find_colormap(variable_name)
if depth != 'bottom':
depthm = dataset.depths[depth]
else:
depthm = 0
if scale_factor != 1.0:
for idx, val in enumerate(data):
data[idx] = np.multiply(val, scale_factor)
if len(data) == 1:
data = data[0]
if len(data) == 2:
data = np.sqrt(data[0]**2 + data[1]**2)
cmap = colormap.colormaps.get('speed')
data = data.transpose()
xpx = x * 256
ypx = y * 256
# Mask out any topography if we're below the vector-tile threshold
if z < 8:
with Dataset(current_app.config['ETOPO_FILE'] % (projection, z),
'r') as dataset:
bathymetry = dataset["z"][ypx:(ypx + 256), xpx:(xpx + 256)]
bathymetry = gaussian_filter(bathymetry, 0.5)
data[np.where(bathymetry > -depthm)] = np.ma.masked
sm = matplotlib.cm.ScalarMappable(matplotlib.colors.Normalize(
vmin=scale[0], vmax=scale[1]),
cmap=cmap)
img = sm.to_rgba(np.ma.masked_invalid(np.squeeze(data)))
im = Image.fromarray((img * 255.0).astype(np.uint8))
buf = BytesIO()
im.save(buf, format='PNG', optimize=True)
return buf
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.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 load_data(self):
distance = VincentyDistance()
height = distance.measure(
(self.bounds[0], self.centroid[1]),
(self.bounds[2], self.centroid[1])) * 1000 * 1.25
width = distance.measure(
(self.centroid[0], self.bounds[1]),
(self.centroid[0], self.bounds[3])) * 1000 * 1.25
if self.projection == 'EPSG:32661': # north pole projection
near_pole, covers_pole = self.pole_proximity(self.points[0])
blat = min(self.bounds[0], self.bounds[2])
blat = 5 * np.floor(blat / 5)
if self.centroid[0] > 80 or near_pole or covers_pole:
self.basemap = basemap.load_map(
'npstere', self.centroid, height, width,
min(self.bounds[0], self.bounds[2]))
else:
self.basemap = basemap.load_map('lcc', self.centroid, height,
width)
elif self.projection == 'EPSG:3031': # south pole projection
near_pole, covers_pole = self.pole_proximity(self.points[0])
blat = max(self.bounds[0], self.bounds[2])
blat = 5 * np.ceil(blat / 5)
# is centerered close to the south pole
if ((self.centroid[0] < -80 or self.bounds[1] < -80
or self.bounds[3] < -80) or covers_pole) or near_pole:
self.basemap = basemap.load_map(
'spstere', self.centroid, height, width,
max(self.bounds[0], self.bounds[2]))
else:
self.basemap = basemap.load_map('lcc', self.centroid, height,
width)
elif abs(self.centroid[1] - self.bounds[1]) > 90:
height_bounds = [self.bounds[0], self.bounds[2]]
width_bounds = [self.bounds[1], self.bounds[3]]
height_buffer = (abs(height_bounds[1] - height_bounds[0])) * 0.1
width_buffer = (abs(width_bounds[0] - width_bounds[1])) * 0.1
if abs(width_bounds[1] - width_bounds[0]) > 360:
raise ClientError(
gettext(
"You have requested an area that exceeds the width of the world. \
Thinking big is good but plots need to be less than 360 deg wide."
))
if height_bounds[1] < 0:
height_bounds[1] = height_bounds[1] + height_buffer
else:
height_bounds[1] = height_bounds[1] + height_buffer
if height_bounds[0] < 0:
height_bounds[0] = height_bounds[0] - height_buffer
else:
height_bounds[0] = height_bounds[0] - height_buffer
new_width_bounds = []
new_width_bounds.append(width_bounds[0] - width_buffer)
new_width_bounds.append(width_bounds[1] + width_buffer)
if abs(new_width_bounds[1] - new_width_bounds[0]) > 360:
width_buffer = np.floor(
(360 - abs(width_bounds[1] - width_bounds[0])) / 2)
new_width_bounds[0] = width_bounds[0] - width_buffer
new_width_bounds[1] = width_bounds[1] + width_buffer
if new_width_bounds[0] < -360:
new_width_bounds[0] = -360
if new_width_bounds[1] > 720:
new_width_bounds[1] = 720
self.basemap = basemap.load_map(
'merc', self.centroid, (height_bounds[0], height_bounds[1]),
(new_width_bounds[0], new_width_bounds[1]))
else:
self.basemap = basemap.load_map('lcc', self.centroid, height,
width)
if self.basemap.aspect < 1:
gridx = 500
gridy = int(500 * self.basemap.aspect)
else:
gridy = 500
gridx = int(500 / self.basemap.aspect)
self.longitude, self.latitude = self.basemap.makegrid(gridx, gridy)
variables_to_load = self.variables[:] # we don't want to change self,variables so copy it
if self.__load_quiver():
variables_to_load.append(self.quiver['variable'])
with open_dataset(self.dataset_config,
variable=variables_to_load,
timestamp=self.time) as dataset:
self.variable_unit = self.get_variable_units(
dataset, self.variables)[0]
self.variable_name = self.get_variable_names(
dataset, self.variables)[0]
if self.cmap is None:
self.cmap = colormap.find_colormap(self.variable_name)
if self.depth == 'bottom':
depth_value_map = 'Bottom'
else:
self.depth = np.clip(int(self.depth), 0,
len(dataset.depths) - 1)
depth_value = dataset.depths[self.depth]
depth_value_map = depth_value
data = []
var = dataset.variables[self.variables[0]]
if self.filetype in ['csv', 'odv', 'txt']:
d, depth_value_map = dataset.get_area(np.array(
[self.latitude, self.longitude]),
self.depth,
self.time,
self.variables[0],
self.interp,
self.radius,
self.neighbours,
return_depth=True)
else:
d = dataset.get_area(np.array([self.latitude, self.longitude]),
self.depth, self.time, self.variables[0],
self.interp, self.radius, self.neighbours)
data.append(d)
if self.filetype not in ['csv', 'odv', 'txt']:
if len(var.dimensions) == 3:
self.depth_label = ""
elif self.depth == 'bottom':
self.depth_label = " at Bottom"
else:
self.depth_label = " at " + \
str(int(np.round(depth_value_map))) + " m"
self.data = data[0]
quiver_data = []
# Store the quiver data on the same grid as the main variable. This
# will only be used for CSV export.
quiver_data_fullgrid = []
if self.__load_quiver():
var = dataset.variables[self.quiver['variable']]
quiver_unit = self.dataset_config.variable[var].unit
quiver_name = self.dataset_config.variable[var].name
quiver_x_var = self.dataset_config.variable[
var].east_vector_component
quiver_y_var = self.dataset_config.variable[
var].north_vector_component
quiver_lon, quiver_lat = self.basemap.makegrid(50, 50)
x_vals = dataset.get_area(
np.array([quiver_lat, quiver_lon]),
self.depth,
self.time,
quiver_x_var,
self.interp,
self.radius,
self.neighbours,
)
quiver_data.append(x_vals)
y_vals = dataset.get_area(
np.array([quiver_lat, quiver_lon]),
self.depth,
self.time,
quiver_y_var,
self.interp,
self.radius,
self.neighbours,
)
quiver_data.append(y_vals)
mag_data = dataset.get_area(
np.array([quiver_lat, quiver_lon]),
self.depth,
self.time,
self.quiver['variable'],
self.interp,
self.radius,
self.neighbours,
)
self.quiver_magnitude = mag_data
# Get the quiver data on the same grid as the main
# variable.
x_vals = dataset.get_area(
np.array([self.latitude, self.longitude]),
self.depth,
self.time,
quiver_x_var,
self.interp,
self.radius,
self.neighbours,
)
quiver_data_fullgrid.append(x_vals)
y_vals = dataset.get_area(
np.array([self.latitude, self.longitude]),
self.depth,
self.time,
quiver_y_var,
self.interp,
self.radius,
self.neighbours,
)
quiver_data_fullgrid.append(y_vals)
self.quiver_name = self.get_variable_names(
dataset, [self.quiver['variable']])[0]
self.quiver_longitude = quiver_lon
self.quiver_latitude = quiver_lat
self.quiver_unit = quiver_unit
self.quiver_data = quiver_data
self.quiver_data_fullgrid = quiver_data_fullgrid
if all([
dataset.variables[v].is_surface_only()
for v in variables_to_load
]):
self.depth = 0
contour_data = []
if self.contour is not None and \
self.contour['variable'] != '' and \
self.contour['variable'] != 'none':
d = dataset.get_area(
np.array([self.latitude, self.longitude]),
self.depth,
self.time,
self.contour['variable'],
self.interp,
self.radius,
self.neighbours,
)
vc = self.dataset_config.variable[self.contour['variable']]
contour_unit = vc.unit
contour_name = vc.name
contour_data.append(d)
self.contour_unit = contour_unit
self.contour_name = contour_name
self.contour_data = contour_data
self.timestamp = dataset.nc_data.timestamp_to_iso_8601(self.time)
if self.compare:
self.variable_name += " Difference"
compare_config = DatasetConfig(self.compare['dataset'])
with open_dataset(compare_config,
variable=self.compare['variables'],
timestamp=self.compare['time']) as dataset:
data = []
for v in self.compare['variables']:
var = dataset.variables[v]
d = dataset.get_area(
np.array([self.latitude, self.longitude]),
self.compare['depth'],
self.compare['time'],
v,
self.interp,
self.radius,
self.neighbours,
)
data.append(d)
data = data[0]
self.data -= data
# Load bathymetry data
self.bathymetry = overlays.bathymetry(self.basemap,
self.latitude,
self.longitude,
blur=2)
if self.depth != 'bottom' and self.depth != 0:
if quiver_data:
quiver_bathymetry = overlays.bathymetry(
self.basemap, quiver_lat, quiver_lon)
self.data[np.where(
self.bathymetry < depth_value_map)] = np.ma.masked
for d in self.quiver_data:
d[np.where(quiver_bathymetry < depth_value)] = np.ma.masked
for d in self.contour_data:
d[np.where(self.bathymetry < depth_value_map)] = np.ma.masked
else:
mask = maskoceans(self.longitude, self.latitude, self.data, True,
'h', 1.25).mask
self.data[~mask] = np.ma.masked
for d in self.quiver_data:
mask = maskoceans(self.quiver_longitude, self.quiver_latitude,
d).mask
d[~mask] = np.ma.masked
for d in contour_data:
mask = maskoceans(self.longitude, self.latitude, d).mask
d[~mask] = np.ma.masked
if self.area and self.filetype in ['csv', 'odv', 'txt', 'geotiff']:
area_polys = []
for a in self.area:
rings = [LinearRing(p) for p in a['polygons']]
innerrings = [LinearRing(p) for p in a['innerrings']]
polygons = []
for r in rings:
inners = []
for ir in innerrings:
if r.contains(ir):
inners.append(ir)
polygons.append(Poly(r, inners))
area_polys.append(MultiPolygon(polygons))
points = [
Point(p)
for p in zip(self.latitude.ravel(), self.longitude.ravel())
]
indicies = []
for a in area_polys:
indicies.append(
np.where(
list(map(lambda p, poly=a: poly.contains(p),
points)))[0])
indicies = np.unique(np.array(indicies).ravel())
newmask = np.ones(self.data.shape, dtype=bool)
newmask[np.unravel_index(indicies, newmask.shape)] = False
self.data.mask |= newmask
self.depth_value_map = depth_value_map
def load_data(self):
with open_dataset(self.dataset_config) as dataset:
if self.time < 0:
self.time += len(dataset.timestamps)
time = np.clip(self.time, 0, len(dataset.timestamps) - 1)
for idx, v in enumerate(self.variables):
var = dataset.variables[v]
if not (set(var.dimensions) & set(dataset.depth_dimensions)):
for potential in dataset.variables:
if potential in self.variables:
continue
pot = dataset.variables[potential]
if (set(pot.dimensions)
& set(dataset.depth_dimensions)):
if len(pot.dimensions) > 3:
self.variables[idx] = potential.key
value = parallel = perpendicular = magnitude = None
variable_names = self.get_variable_names(dataset, self.variables)
variable_units = self.get_variable_units(dataset, self.variables)
scale_factors = self.get_variable_scale_factors(
dataset, self.variables)
# Load data sent from primary/Left Map
if len(self.variables) > 1:
# Only velocity has 2 variables
v = []
for name in self.variables:
v.append(dataset.variables[name])
distances, times, lat, lon, bearings = geo.path_to_points(
self.points, 100)
transect_pts, distance, x, dep = dataset.get_path_profile(
self.points, time, self.variables[0], 100)
transect_pts, distance, y, dep = dataset.get_path_profile(
self.points, time, self.variables[1], 100)
# Calculate vector components
x = np.multiply(x, scale_factors[0])
y = np.multiply(y, scale_factors[1])
r = np.radians(np.subtract(90, bearings))
theta = np.arctan2(y, x) - r
magnitude = np.sqrt(x**2 + y**2)
parallel = magnitude * np.cos(theta)
perpendicular = magnitude * np.sin(theta)
else:
# Get data for one variable
transect_pts, distance, value, dep = dataset.get_path_profile(
self.points, time, self.variables[0])
value = np.multiply(value, scale_factors[0])
if len(self.variables) == 2:
variable_names = [
self.get_vector_variable_name(dataset, self.variables)
]
variable_units = [
self.get_vector_variable_unit(dataset, self.variables)
]
# If a colourmap has not been manually specified by the
# Navigator...
if self.cmap is None:
self.cmap = colormap.find_colormap(variable_names[0])
self.timestamp = dataset.timestamps[int(time)]
self.depth = dep
self.depth_unit = "m"
self.transect_data = {
"points": transect_pts,
"distance": distance,
"data": value,
"name": variable_names[0],
"unit": variable_units[0],
"parallel": parallel,
"perpendicular": perpendicular,
"magnitude": magnitude,
}
if self.surface is not None:
surface_pts, surface_dist, t, surface_value = \
dataset.get_path(
self.points,
0,
time,
self.surface,
)
vc = self.dataset_config.variable[dataset.variables[
self.surface]]
surface_unit = vc.unit
surface_name = vc.name
surface_factor = vc.scale_factor
surface_value = np.multiply(surface_value, surface_factor)
self.surface_data = {
"config": vc,
"points": surface_pts,
"distance": surface_dist,
"data": surface_value,
"name": surface_name,
"unit": surface_unit
}
# Load data sent from Right Map (if in compare mode)
if self.compare:
def interpolate_depths(data, depth_in, depth_out):
output = []
for i in range(0, depth_in.shape[0]):
f = interp1d(
depth_in[i],
data[:, i],
bounds_error=False,
assume_sorted=True,
)
output.append(
f(depth_out[i].view(np.ma.MaskedArray).filled()))
return np.ma.masked_invalid(output).transpose()
self.compare_config = DatasetConfig(self.compare['dataset'])
with open_dataset(self.compare_config) as dataset:
# Get and format date
self.compare['date'] = np.clip(np.int64(self.compare['time']),
0,
len(dataset.timestamps) - 1)
self.compare['date'] = dataset.timestamps[int(
self.compare['date'])]
# 1 variable
if len(self.compare['variables']) == 1:
# Get and store the "nicely formatted" string for the variable name
self.compare['name'] = self.get_variable_names(
dataset, self.compare['variables'])[0]
# Find correct colourmap
if (self.compare['colormap'] == 'default'):
self.compare['colormap'] = colormap.find_colormap(
self.compare['name'])
else:
self.compare['colormap'] = colormap.find_colormap(
self.compare['colormap'])
climate_points, climate_distance, climate_data, cdep = \
dataset.get_path_profile(self.points,
self.compare['time'],
self.compare['variables'][0])
self.compare['unit'] = dataset.variables[
self.compare['variables'][0]].unit
self.__fill_invalid_shift(climate_data)
if (self.depth.shape != cdep.shape) or \
(self.depth != cdep).any():
# Need to interpolate the depths
climate_data = interpolate_depths(
climate_data, cdep, self.depth)
if self.transect_data['data'] is None:
self.transect_data['magnitude'] -= climate_data
self.transect_data['parallel'] -= climate_data
self.transect_data['perpendicular'] -= climate_data
else:
self.transect_data['compare_data'] = climate_data
# Velocity variables
else:
# Get and store the "nicely formatted" string for the variable name
self.compare['name'] = self.get_vector_variable_name(
dataset, self.compare['variables'])
climate_pts, climate_distance, climate_x, cdep = \
dataset.get_path_profile(
self.points,
self.compare['time'],
self.compare['variables'][0],
100
)
climate_pts, climate_distance, climate_y, cdep = \
dataset.get_path_profile(
self.points,
self.compare['time'],
self.compare['variables'][0],
100
)
climate_distances, ctimes, clat, clon, bearings = \
geo.path_to_points(self.points, 100)
r = np.radians(np.subtract(90, bearings))
theta = np.arctan2(climate_y, climate_x) - r
mag = np.sqrt(climate_x**2 + climate_y**2)
if np.all(self.depth != cdep):
theta = interpolate_depths(theta, cdep, self.depth)
self.__fill_invalid_shift(theta)
mag = interpolate_depths(mag, cdep, self.depth)
self.__fill_invalid_shift(mag)
self.compare['parallel'] = mag * np.cos(theta)
self.compare['perpendicular'] = mag * np.sin(theta)
"""
if self.transect_data['parallel'] is None:
self.transect_data['data'] -= mag
else:
self.transect_data['parallel'] -= climate_parallel
self.transect_data['perpendicular'] -= climate_perpendicular
"""
# Bathymetry
with Dataset(current_app.config['BATHYMETRY_FILE'], 'r') as dataset:
bath_x, bath_y = bathymetry(dataset.variables['y'],
dataset.variables['x'],
dataset.variables['z'], self.points)
self.bathymetry = {'x': bath_x, 'y': bath_y}
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
