def plot_v1_0():
if request.method == 'GET':
args = request.args
else:
args = request.form
query = json.loads(args.get('query'))
with open_dataset(get_dataset_url(query.get('dataset'))) as dataset:
if 'time' in query:
query['time'] = dataset.convert_to_timestamp(query.get('time'))
else:
query['starttime'] = dataset.convert_to_timestamp(
query.get('starttime'))
query['endtime'] = dataset.convert_to_timestamp(
query.get('endtime'))
resp = routes.routes_impl.plot_impl(args, query)
m = hashlib.md5()
m.update(str(resp).encode())
if 'data' in request.args:
plotData = {
'data': str(resp),
'shape': resp.shape,
'mask': str(resp.mask)
}
plotData = json.dumps(plotData)
return Response(plotData, status=200, mimetype='application/json')
return resp
def range_query_v1_0(dataset, variable, interp, radius, neighbours, projection,
extent, depth, time):
with open_dataset(get_dataset_url(dataset)) as ds:
date = ds.convert_to_timestamp(time)
return routes.routes_impl.range_query_impl(interp, radius, neighbours,
dataset, projection, extent,
variable, depth, date)
def load_data(self):
with open_dataset(get_dataset_url(self.dataset_name)) as d:
if self.time < 0:
self.time += len(d.timestamps)
time = np.clip(self.time, 0, len(d.timestamps) - 1)
timestamp = d.timestamps[time]
try:
self.load_misc(d, self.variables)
except IndexError as e:
raise ClientError(
gettext(
"The selected variable(s) were not found in the dataset. \
Most likely, this variable is a derived product from existing dataset variables. \
Please select another variable. ") + str(e))
point_data, point_depths = self.get_data(d, self.variables, time)
point_data = self.apply_scale_factors(point_data)
self.variable_units, point_data = self.kelvin_to_celsius(
self.variable_units, point_data)
self.data = self.subtract_other(point_data)
self.depths = point_depths
self.timestamp = timestamp
def tile_v1_0(projection, interp, radius, neighbours, dataset, variable, time,
depth, scale, zoom, x, y):
with open_dataset(get_dataset_url(dataset)) as ds:
date = ds.convert_to_timestamp(time)
return routes.routes_impl.tile_impl(projection, interp, radius,
neighbours, dataset, variable,
date, depth, scale, zoom, x, y)
def load_data(self):
if not isinstance(self.depth, list):
self.depth = [self.depth]
self.depth = sorted(self.depth)
with open_dataset(get_dataset_url(self.dataset_name)) as dataset:
if self.starttime < 0:
self.starttime += len(dataset.timestamps)
if self.endtime < 0:
self.endtime += len(dataset.timestamps)
start = np.clip(self.starttime, 0, len(dataset.timestamps) - 1)
end = np.clip(self.endtime, 0, len(dataset.timestamps) - 1)
timestamp = dataset.timestamps[start:end + 1]
self.load_misc(dataset, self.variables)
point_data = []
point_depth = []
for p in self.points:
data = []
depth = []
for v in self.variables:
dd = []
jj = []
for d in self.depth:
da, dp = dataset.get_timeseries_point(
float(p[0]),
float(p[1]),
d,
start,
end,
v,
return_depth=True)
dd.append(da)
jj.append(dp)
data.append(np.ma.array(dd))
depth.append(np.ma.array(jj))
point_data.append(np.ma.array(data))
point_depth.append(np.ma.array(depth))
point_data = np.ma.array(point_data)
point_depth = np.ma.array(point_depth)
for idx, factor in enumerate(self.scale_factors):
if factor != 1.0:
point_data[idx] = np.multiply(point_data[idx], factor)
self.variable_units, point_data = self.kelvin_to_celsius(
self.variable_units, point_data)
self.data = self.subtract_other(point_data)
self.data_depth = point_depth
self.timestamp = timestamp
def load_data(self):
with open_dataset(get_dataset_url(self.dataset_name)) as dataset:
if self.time < 0:
self.time += len(dataset.timestamps)
time = np.clip(self.time, 0, len(dataset.timestamps) - 1)
self.timestamp = dataset.timestamps[time]
self.load_temp_sal(dataset, time)
self.variable_units[0], self.temperature = \
super(plPoint.PointPlotter, self).kelvin_to_celsius(
self.variable_units[0], self.temperature
)
def stats_v1_0():
if request.method == 'GET':
args = request.args
else:
args = request.form
query = json.loads(args.get('query'))
with open_dataset(get_dataset_url(query.get('dataset'))) as dataset:
date = dataset.convert_to_timestamp(query.get('time'))
date = {'time': date}
query.update(date)
return routes.routes_impl.stats_impl(args, query)
def subtract_other(self, data):
if self.compare:
with Dataset(
get_dataset_url(self.compare['dataset']), 'r'
) as dataset:
cli = self.get_data(
dataset, self.compare['variables'], self.compare['time']
)
for idx, v in enumerate(self.variables):
data[:, idx, :] = \
data[:, idx, :] - cli[:, idx, :]
return data
def timestamp_for_date_impl(old_dataset, date, new_dataset):
"""
API Format: /api/timestamp/<string:old_dataset>/<int:date>/<string:new_dataset>
<string:old_dataset> : Previous dataset used
<int:date> : Date of desired data - Can be found using /api/timestamps/?datasets='...'
<string:new_dataset> : Dataset to extract data - Can be found using /api/datasets
**Used when Changing datasets**
"""
with open_dataset(get_dataset_url(old_dataset)) as ds:
timestamp = ds.timestamps[date]
with open_dataset(get_dataset_url(new_dataset)) as ds:
timestamps = ds.timestamps
diffs = np.vectorize(lambda x: x.total_seconds())(timestamps - timestamp)
idx = np.where(diffs <= 0)[0]
res = 0
if len(idx) > 0:
res = idx.max().item() # https://stackoverflow.com/a/11389998/2231969
return Response(json.dumps(res), status=200, mimetype='application/json')
def get_scale(dataset, variable, depth, time, projection, extent, interp,
radius, neighbours):
x = np.linspace(extent[0], extent[2], 50)
y = np.linspace(extent[1], extent[3], 50)
xx, yy = np.meshgrid(x, y)
dest = Proj(init=projection)
lon, lat = dest(xx, yy, inverse=True)
variables_anom = variable.split(",")
variables = [re.sub('_anom$', '', v) for v in variables_anom]
with open_dataset(get_dataset_url(dataset)) as ds:
timestamp = ds.timestamps[time]
d = ds.get_area(np.array([lat, lon]), depth, time, variables[0],
interp, radius, neighbours)
if len(variables) > 1:
d0 = d
d1 = ds.get_area(np.array([lat, lon]), depth, time, variables[1],
interp, radius, neighbours)
d = np.sqrt(d0**2 + d1**2)
variable_unit = get_variable_unit(dataset, ds.variables[variables[0]])
if variable_unit.startswith("Kelvin"):
variable_unit = "Celsius"
d = np.add(d, -273.15)
if variables != variables_anom:
with open_dataset(get_dataset_climatology(dataset), 'r') as ds:
c = ds.get_area(np.array([lat, lon]), depth, timestamp.month - 1,
variables[0], interp, radius, neighbours)
if len(variables) > 1:
c0 = c
c1 = ds.get_area(np.array([lat,
lon]), depth, timestamp.month - 1,
variables[1], interp, radius, neighbours)
c = np.sqrt(c0**2 + c1**2)
d = d - c
m = max(abs(d.nanmin()), abs(d.nanmax()))
return -m, m
# Return min and max values of selected variable, while ignoring
# nan values
return np.nanmin(d), np.nanmax(d)
def subset_query_impl(args):
"""
API Format: /subset/?query='...'
**Query must be written in JSON and converted to encodedURI**
**Not all components of query are required
"""
working_dir = None
subset_filename = None
with open_dataset(get_dataset_url(args.get('dataset_name'))) as dataset:
working_dir, subset_filename = dataset.subset(args)
return send_from_directory(working_dir,
subset_filename,
as_attachment=True)
def time_query_conversion(dataset, index):
"""
API Format: /api/timestamps/?dataset=' '
dataset : Dataset to extract data - Can be found using /api/datasets
Finds all data timestamps available for a specific dataset
"""
with open_dataset(get_dataset_url(dataset)) as ds:
try:
date = ds.timestamps[index]
return date.replace(tzinfo=pytz.UTC).isoformat()
except IndexError:
return ClientError("Timestamp does not exist")
def depth_impl(args):
"""
API Format: /api/depth/?dataset=''&variable=' '
dataset : Dataset to extract data - Can be found using /api/datasets
variable : Type of data to retrieve - found using /api/variables/?dataset='...'
Returns all depths available for that variable in the dataset
"""
#Checking for valid Query
if 'variable' not in args or ('dataset' not in args):
if 'dataset' in args:
raise APIError("Please Specify a variable using &variable='...' ")
if 'variable' in args:
raise APIError("Please Specify a Dataset using &dataset='...' ")
raise APIError(
"Please Specify a Dataset and Variable using ?dataset='...'&variable='...' "
)
#~~~~~~~~~~~~~~~~~~~~~~~~
var = args.get('variable')
variables = var.split(',')
variables = [re.sub('_anom$', '', v) for v in variables]
data = []
dataset = args['dataset']
with open_dataset(get_dataset_url(dataset)) as ds:
for variable in variables:
if variable and \
variable in ds.variables and \
set(ds.depth_dimensions) & \
set(ds.variables[variable].dimensions):
if str(args.get('all')).lower() in ['true', 'yes', 'on']:
data.append({'id': 'all', 'value': gettext('All Depths')})
for idx, value in enumerate(np.round(ds.depths)):
data.append({'id': idx, 'value': "%d m" % (value)})
if len(data) > 0:
data.insert(0, {'id': 'bottom', 'value': gettext('Bottom')})
data = [e for i, e in enumerate(data) if data.index(e) == i]
resp = jsonify(data)
return resp
def timestamp_outOfBounds(dataset, time):
"""
API Format: /api/timestamps/?dataset=' '
dataset : Dataset to extract data - Can be found using /api/datasets
Finds all data timestamps available for a specific dataset
"""
with open_dataset(get_dataset_url(dataset)) as ds:
length = len(ds.timestamps)
if time < length:
return False #Valid timestamp
else:
print("Timestamp out of bounds")
return True
def get_point_data(dataset, variable, time, depth, location):
variables_anom = variable.split(",")
variables = [re.sub('_anom$', '', v) for v in variables_anom]
data = []
names = []
units = []
with open_dataset(get_dataset_url(dataset)) as ds:
timestamp = ds.timestamps[time]
for v in variables:
d = ds.get_point(location[0], location[1], depth, time, v)
variable_name = get_variable_name(dataset, ds.variables[v])
variable_unit = get_variable_unit(dataset, ds.variables[v])
if variable_unit.startswith("Kelvin"):
variable_unit = "Celsius"
d = np.add(d, -273.15)
data.append(d)
names.append(variable_name)
units.append(variable_unit)
if variables != variables_anom:
with open_dataset(get_dataset_climatology(dataset)) as ds:
for idx, v in enumerate(variables):
d = ds.get_point(location[0], location[1], depth,
timestamp.month, v)
data[idx] = data[idx] - d
names[idx] = names[idx] + " Anomaly"
result = {
'value': ['%s' % float('%.4g' % f) for f in data],
'location': [round(f, 4) for f in location],
'name': names,
'units': units,
}
return result
def time_query_impl(args):
"""
API Format: /api/timestamps/?dataset=' '
dataset : Dataset to extract data - Can be found using /api/datasets
Finds all data timestamps available for a specific dataset
"""
if 'dataset' not in args:
raise APIError("Please Specify a Dataset Using ?dataset='...' ")
#~~~~~~~~~~~~~~~~~~~~~~~
data = []
dataset = args['dataset']
quantum = args.get('quantum')
with open_dataset(get_dataset_url(dataset)) as ds:
for idx, date in enumerate(ds.timestamps):
if quantum == 'month':
date = datetime.datetime(date.year, date.month, 15)
data.append({'id': idx, 'value': date.replace(tzinfo=pytz.UTC)})
data = sorted(data, key=lambda k: k['id'])
class DateTimeEncoder(json.JSONEncoder):
def default(self, o):
if isinstance(o, datetime.datetime):
return o.isoformat()
return json.JSONEncoder.default(self, o)
js = json.dumps(data, cls=DateTimeEncoder)
resp = Response(js, status=200, mimetype='application/json')
return resp
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
def load_data(self):
if isinstance(self.observation[0], numbers.Number):
self.observation_variable_names = []
self.observation_variable_units = []
with Dataset(current_app.config["OBSERVATION_AGG_URL"], 'r') as ds:
t = netcdftime.utime(ds['time'].units)
for idx, o in enumerate(self.observation):
observation = {}
ts = t.num2date(ds['time'][o]).replace(tzinfo=pytz.UTC)
observation['time'] = ts.isoformat()
observation['longitude'] = ds['lon'][o]
observation['latitude'] = ds['lat'][o]
observation['depth'] = ds['z'][:]
observation['depthunit'] = ds['z'].units
observation['datatypes'] = []
data = []
for v in sorted(ds.variables):
if v in ['z', 'lat', 'lon', 'profile', 'time']:
continue
var = ds[v]
if var.datatype == '|S1':
continue
observation['datatypes'].append(
"%s [%s]" % (var.long_name, var.units))
data.append(var[o, :])
if idx == 0:
self.observation_variable_names.append(
var.long_name)
self.observation_variable_units.append(var.units)
observation['data'] = np.ma.array(data).transpose()
self.observation[idx] = observation
self.points = [[o['latitude'], o['longitude']]
for o in self.observation]
with open_dataset(get_dataset_url(self.dataset_name)) as dataset:
ts = dataset.timestamps
observation_times = []
timestamps = []
for o in self.observation:
observation_time = dateutil.parser.parse(o['time'])
observation_times.append(observation_time)
deltas = [(x.replace(tzinfo=pytz.UTC) -
observation_time).total_seconds() for x in ts]
time = np.abs(deltas).argmin()
timestamp = ts[time]
timestamps.append(timestamp)
try:
self.load_misc(dataset, self.variables)
except IndexError as e:
raise ClientError(
gettext(
"The selected variable(s) were not found in the dataset. \
Most likely, this variable is a derived product from existing dataset variables. \
Please select another variable.") + str(e))
point_data, self.depths = self.get_data(dataset, self.variables,
time)
point_data = np.ma.array(point_data)
point_data = self.apply_scale_factors(point_data)
self.variable_units, point_data = self.kelvin_to_celsius(
self.variable_units, point_data)
self.data = point_data
self.observation_time = observation_time
self.observation_times = observation_times
self.timestamps = timestamps
self.timestamp = timestamp
def load_data(self):
with open_dataset(get_dataset_url(self.dataset_name)) as dataset:
self.load_misc(dataset, self.variables)
self.fix_startend_times(dataset, self.starttime, self.endtime)
if len(self.variables) == 1:
self.variable_unit = get_variable_unit(
self.dataset_name, dataset.variables[self.variables[0]])
self.variable_name = get_variable_name(
self.dataset_name, dataset.variables[self.variables[0]])
else:
self.variable_name = self.vector_name(self.variable_names[0])
self.variable_unit = self.variable_units[0]
var = self.variables[0]
if self.depth != 'all' and self.depth != 'bottom' and \
(set(dataset.variables[var].dimensions) &
set(dataset.depth_dimensions)):
self.depth_label = " at %d m" % (np.round(
dataset.depths[self.depth]))
elif self.depth == 'bottom':
self.depth_label = ' at Bottom'
else:
self.depth_label = ''
if not (set(dataset.variables[var].dimensions)
& set(dataset.depth_dimensions)):
self.depth = 0
times = None
point_data = []
for p in self.points:
data = []
for v in self.variables:
if self.depth == 'all':
d, dep = dataset.get_timeseries_profile(
float(p[0]), float(p[1]), self.starttime,
self.endtime, v)
else:
d, dep = dataset.get_timeseries_point(
float(p[0]),
float(p[1]),
self.depth,
self.starttime,
self.endtime,
v,
return_depth=True)
data.append(d)
point_data.append(np.ma.array(data))
point_data = np.ma.array(point_data)
for idx, factor in enumerate(self.scale_factors):
if factor != 1.0:
point_data[idx] = np.multiply(point_data[idx], factor)
times = dataset.timestamps[self.starttime:self.endtime + 1]
if self.query.get('dataset_quantum') == 'month':
times = [datetime.date(x.year, x.month, 1) for x in times]
# depths = dataset.depths
depths = dep
# TODO: pint
if self.variable_unit.startswith("Kelvin"):
self.variable_unit = "Celsius"
for idx, v in enumerate(self.variables):
point_data[:, idx, :] = point_data[:, idx, :] - 273.15
if point_data.shape[1] == 2:
# Under the current API this indicates that velocity data is being
# loaded. Save each velocity component (X and Y) for possible CSV
# export later.
self.quiver_data = [point_data[:, 0, :], point_data[:, 1, :]]
point_data = np.ma.expand_dims(
np.sqrt(point_data[:, 0, :]**2 + point_data[:, 1, :]**2), 1)
self.times = times
self.data = point_data
self.depths = depths
self.depth_unit = "m"
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(get_dataset_url(self.dataset_name)) 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_variable_names(
dataset, self.variables)[0]
self.variable_name = self.vector_name(self.variable_name)
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, self.data = self.kelvin_to_celsius(
variable_units[0], 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:
with open_dataset(get_dataset_url(
self.compare['dataset'])) 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_variable_names(
dataset, self.compare['variables'])[0]
self.compare['variable_name'] = self.vector_name(
self.compare['variable_name'])
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'], self.compare[
'data'] = self.kelvin_to_celsius(variable_units[0], 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 get_data_v1_0(dataset, variable, time, depth, location):
with open_dataset(get_dataset_url(dataset)) as ds:
date = ds.convert_to_timestamp(time)
#print(date)
return routes.routes_impl.get_data_impl(dataset, variable, date, depth,
location)
def get_values(self, area_info, dataset_name, variables):
with open_dataset(get_dataset_url(dataset_name)) as dataset:
if self.time is None or (type(self.time) == str
and len(self.time) == 0):
time = -1
else:
time = int(self.time)
if time < 0:
time += len(dataset.timestamps)
time = np.clip(time, 0, len(dataset.timestamps) - 1)
depth = 0
depthm = 0
if self.depth:
if self.depth == 'bottom':
depth = 'bottom'
depthm = 'Bottom'
if len(self.depth) > 0 and \
self.depth != 'bottom':
depth = int(self.depth)
depth = np.clip(depth, 0, len(dataset.depths) - 1)
depthm = dataset.depths[depth]
lat, lon = np.meshgrid(
np.linspace(area_info.bounds[0], area_info.bounds[2], 50),
area_info.spaced_points)
output_fmtstr = "%6.5g"
for v_idx, v in enumerate(variables):
var = dataset.variables[v]
variable_name = get_variable_name(dataset_name, var)
variable_unit = get_variable_unit(dataset_name, var)
scale_factor = get_variable_scale_factor(dataset_name, var)
lat, lon, d = dataset.get_raw_point(lat.ravel(), lon.ravel(),
depth, time, v)
if scale_factor != 1.0:
d = np.multiply(d, scale_factor)
if variable_unit.startswith("Kelvin"):
variable_unit = "Celsius"
d = d - 273.15
lon[np.where(lon > 180)] -= 360
if len(var.dimensions) == 3:
variable_depth = ""
elif depth == 'bottom':
variable_depth = "(@ Bottom)"
else:
variable_depth = "(@%d m)" % np.round(depthm)
points = [
Point(p)
for p in zip(lat.values.ravel(), lon.values.ravel())
]
for i, a in enumerate(area_info.area_query):
indices = np.where(
map(lambda p, poly=area_info.area_polys[i]: poly.
contains(p),
points))
selection = np.ma.array(d.values.ravel()[indices])
if len(selection) > 0 and not selection.mask.all():
area_info.output[i]['variables'].append({
'name': ("%s %s" %
(variable_name, variable_depth)).strip(),
'unit':
variable_unit,
'min':
output_fmtstr %
(np.ma.amin(selection).astype(float)),
'max':
output_fmtstr %
(np.ma.amax(selection).astype(float)),
'mean':
output_fmtstr %
(np.ma.mean(selection).astype(float)),
'median':
output_fmtstr %
(np.ma.median(selection).astype(float)),
'stddev':
output_fmtstr %
(np.ma.std(selection).astype(float)),
'num':
"%d" % selection.count(),
})
else:
area_info.output[i]['variables'].append({
'name': ("%s %s" %
(variable_name, variable_depth)).strip(),
'unit':
variable_unit,
'min':
gettext("No Data"),
'max':
gettext("No Data"),
'mean':
gettext("No Data"),
'median':
gettext("No Data"),
'stddev':
gettext("No Data"),
'num':
"0",
})
ClientError(
gettext(
"there are no datapoints in the area you selected. \
you may have selected a area on land or you may \
have an ara that is smallenough to fit between \
the datapoints try selection a different area or \
a larger area"))
area_info.stats = area_info.output
return
raise ServerError(
gettext("An Error has occurred. When opening the dataset. \
Please try again or try a different dataset. \
If you would like to report this error please \
contact [email protected]"))
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):
with open_dataset(get_dataset_url(self.dataset_name)) 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])
# Get variable units and convert to Celsius if needed
variable_units[0], value = self.kelvin_to_celsius(
variable_units[0],
value
)
if len(self.variables) == 2:
variable_names[0] = self.vector_name(variable_names[0])
# 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,
)
surface_unit = get_variable_unit(
self.dataset_name,
dataset.variables[self.surface]
)
surface_name = get_variable_name(
self.dataset_name,
dataset.variables[self.surface]
)
surface_factor = get_variable_scale_factor(
self.dataset_name,
dataset.variables[self.surface]
)
surface_value = np.multiply(surface_value, surface_factor)
surface_unit, surface_value = self.kelvin_to_celsius(
surface_unit,
surface_value
)
self.surface_data = {
"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()
with open_dataset(get_dataset_url(self.compare['dataset'])) 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])
# Get variable units and convert to Celsius if needed
self.compare['unit'], climate_data = self.kelvin_to_celsius(
dataset.variables[self.compare['variables'][0]].unit,
climate_data
)
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_variable_names(dataset, self.compare['variables'])[0]
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 load_data(self):
ds_url = current_app.config['DRIFTER_URL']
data_names = []
data_units = []
with Dataset(ds_url % self.drifter, 'r') as ds:
self.name = ds.buoyid
self.imei = str(chartostring(ds['imei'][0]))
self.wmo = str(chartostring(ds['wmo'][0]))
t = netcdftime.utime(ds['data_date'].units)
d = []
for v in self.buoyvariables:
d.append(ds[v][:])
if "long_name" in ds[v].ncattrs():
data_names.append(ds[v].long_name)
else:
data_names.append(v)
if "units" in ds[v].ncattrs():
data_units.append(ds[v].units)
else:
data_units.append(None)
self.data = d
self.times = t.num2date(ds['data_date'][:])
self.points = np.array([
ds['latitude'][:],
ds['longitude'][:],
]).transpose()
data_names = data_names[:len(self.buoyvariables)]
data_units = data_units[:len(self.buoyvariables)]
for i, t in enumerate(self.times):
if t.tzinfo is None:
self.times[i] = t.replace(tzinfo=pytz.UTC)
self.data_names = data_names
self.data_units = data_units
if self.starttime is not None:
d = dateutil.parser.parse(self.starttime)
self.start = np.where(self.times >= d)[0].min()
else:
self.start = 0
if self.endtime is not None:
d = dateutil.parser.parse(self.endtime)
self.end = np.where(self.times <= d)[0].max() + 1
else:
self.end = len(self.times) - 1
if self.start < 0:
self.start += len(self.times)
self.start = np.clip(self.start, 0, len(self.times) - 1)
if self.end < 0:
self.end += len(self.times)
self.end = np.clip(self.end, 0, len(self.times) - 1)
with open_dataset(get_dataset_url(self.dataset_name)) as dataset:
depth = int(self.depth)
try:
model_start = np.where(
dataset.timestamps <= self.times[self.start]
)[0][-1]
except IndexError:
model_start = 0
model_start -= 1
model_start = np.clip(model_start, 0, len(dataset.timestamps) - 1)
try:
model_end = np.where(
dataset.timestamps >= self.times[self.end]
)[0][0]
except IndexError:
model_end = len(dataset.timestamps) - 1
model_end += 1
model_end = np.clip(
model_end,
model_start,
len(dataset.timestamps) - 1
)
model_times = [time.mktime(t.timetuple()) for t in dataset.timestamps[model_start:model_end + 1]]
output_times = [time.mktime(t.timetuple()) for t in self.times[self.start:self.end + 1]]
d = []
for v in self.variables:
pts, dist, mt, md = dataset.get_path(
self.points[self.start:self.end + 1],
depth,
list(range(model_start, model_end + 1)),
v,
times=output_times
)
f = interp1d(
model_times,
md,
assume_sorted=True,
bounds_error=False,
)
d.append(np.diag(f(mt)))
model_data = np.ma.array(d)
variable_names = []
variable_units = []
scale_factors = []
for v in self.variables:
variable_units.append(get_variable_unit(self.dataset_name,
dataset.variables[v]))
variable_names.append(get_variable_name(self.dataset_name,
dataset.variables[v]))
scale_factors.append(
get_variable_scale_factor(self.dataset_name,
dataset.variables[v])
)
for idx, sf in enumerate(scale_factors):
model_data[idx, :] = np.multiply(model_data[idx, :], sf)
for idx, u in enumerate(variable_units):
variable_units[idx], model_data[idx, :] = \
self.kelvin_to_celsius(u, model_data[idx, :])
self.model_data = model_data
self.model_times = list(map(datetime.datetime.utcfromtimestamp, mt))
self.variable_names = variable_names
self.variable_units = variable_units
def vars_query_impl(args):
"""
API Format: /api/variables/?dataset='...'&3d_only='...'&vectors_only='...'&vectors='...'
**Only use variables required for your specific request**
dataset : Dataset to extract data - Can be found using /api/datasets
3d_only : Boolean Value; When True, only variables with depth will be shown
vectors_only : Boolean Value; When True, ONLY variables with magnitude will be shown
vectors : Boolean Value; When True, magnitude components will be included
**Boolean: True / False**
"""
if 'dataset' not in args.keys():
raise APIError("Please Specify a Dataset Using ?dataset='...' ")
data = [] #Initializes empty data list
dataset = args['dataset'] #Dataset Specified in query
#Queries config files
if get_dataset_climatology(
dataset
) != "" and 'anom' in args: #If a url exists for the dataset and an anomaly
with open_dataset(get_dataset_climatology(dataset)) as ds:
climatology_variables = list(map(str, ds.variables))
else:
climatology_variables = []
#three_d = '3d_only' in args #Checks if 3d_only is in args
#If three_d is true - Only 3d variables will be returned
with open_dataset(get_dataset_url(dataset)) as ds:
if 'vectors_only' not in args: #Vectors_only -> Magnitude Only
# 'v' is a Variable in the Dataset
# v Contains: dimensions, key, name, unit, valid_min, valid_max
for v in ds.variables: #Iterates through all the variables in the dataset
#If a time period and at least one other unit type is specified
if ('time_counter' in v.dimensions or
'time' in v.dimensions) \
and ('y' in v.dimensions or
'yc' in v.dimensions or
'node' in v.dimensions or
'nele' in v.dimensions or
'latitude' in v.dimensions or
'lat' in v.dimensions):
if ('3d_only' in args) and not (set(ds.depth_dimensions)
& set(v.dimensions)):
continue
else:
if not is_variable_hidden(dataset, v):
data.append({
'id': v.key,
'value': get_variable_name(dataset, v),
'scale': get_variable_scale(dataset, v)
})
if v.key in climatology_variables:
data.append({
'id':
v.key + "_anom",
'value':
get_variable_name(dataset, v) + " Anomaly",
'scale': [-10, 10]
})
VECTOR_MAP = {
'vozocrtx':
'vozocrtx,vomecrty',
'itzocrtx':
'itzocrtx,itmecrty',
'iicevelu':
'iicevelu,iicevelv',
'u_wind':
'u_wind,v_wind',
'u':
'u,v',
'ua':
'ua,va',
'u-component_of_wind_height_above_ground':
'u-component_of_wind_height_above_ground,v-component_of_wind_height_above_ground',
}
#If Vectors are needed
if 'vectors' in args or 'vectors_only' in args:
rxp = r"(?i)(x |y |zonal |meridional |northward |eastward |East |North)"
for key, value in list(VECTOR_MAP.items()):
if key in ds.variables:
n = get_variable_name(
dataset,
ds.variables[key]) #Returns a normal variable type
data.append({
'id':
value,
'value':
re.sub(r" +", " ", re.sub(rxp, " ", n)),
'scale':
[0,
get_variable_scale(dataset, ds.variables[key])[1]]
})
data = sorted(
data,
key=lambda k: k['value']) #Sorts data alphabetically using the value
#Data is set of scale, id, value objects
resp = jsonify(data)
return resp
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
