def get_range(standard_name, level="total", typ=None):
"""
Returns valid range of values for target at given level
Args:
standard_name: string of CF standard_name
level (optional): horizontal level of data
type (optional): type of data (pl, ml, tl, ...)
Returns:
Tuple of lowest and highest valid value
"""
if standard_name in Targets.RANGES:
if level == "total" and "total" in Targets.RANGES[standard_name]:
unit, values = Targets.RANGES[standard_name]["total"]
return convert_to(values, unit,
Targets.get_unit(standard_name))
if typ in Targets.RANGES[standard_name]:
if level in Targets.RANGES[standard_name][typ]:
unit, values = Targets.RANGES[standard_name][typ][level]
return convert_to(values, unit,
Targets.get_unit(standard_name))
elif level is None:
return 0, 0
if standard_name.startswith("surface_origin_tracer_from_"):
return 0, 100
return None, None
def _prepare_datafields(self):
"""
Computes vertical velocity in cm/s.
"""
self.data["upward_wind"] = convert_to(
thermolib.omega_to_w(self.data["lagrangian_tendency_of_air_pressure"],
self.data['air_pressure'], self.data["air_temperature"]),
"m/s", "cm/s")
self.variable = "upward_wind"
self.y_values = self.data[self.variable]
self.unit = "cm/s"
def verticalunitsclicked(self, index):
new_unit = self._suffixes[index]
old_unit = self.sbPbot.suffix().strip()
if new_unit == old_unit:
return
self.setBotTopLimits("maximum")
for sb in (self.sbPbot, self.sbPtop):
sb.setSuffix(" " + new_unit)
if new_unit == "hPa":
sb.setValue(
thermolib.flightlevel2pressure(
convert_to(sb.value(), old_unit, "hft", 1) *
units.hft).to(units.hPa).magnitude)
elif old_unit == "hPa":
sb.setValue(
convert_to(
thermolib.pressure2flightlevel(
sb.value() * units.hPa).magnitude, "hft",
new_unit))
else:
sb.setValue(convert_to(sb.value(), old_unit, new_unit, 1))
self.setBotTopLimits(self.cbVerticalAxis.currentText())
def get_thresholds(standard_name):
"""
Returns a list of meaningful values for a BoundaryNorm for plotting.
Args:
standard_name: string of CF standard_name
level (optional): horizontal level of data
type (optional): type of data (pl, ml, tl, ...)
Returns:
Tuple of threshold values to be supplied to a BoundaryNorm.
"""
try:
threshold_unit, thresholds = Targets.THRESHOLDS[standard_name]
return convert_to(thresholds, threshold_unit,
Targets.get_unit(standard_name))
except KeyError:
return None
def test_convert_to():
assert convert_to(10, "km", "m", None) == 10000
assert convert_to(1000, "m", "km", None) == 1
assert convert_to(1000, "Pa", "hPa", None) == 10
assert convert_to(1000, "hPa", "Pa", None) == 100000
assert convert_to(10, "degC", "K", None) == 283.15
assert convert_to(10 * 9.81, "m^2s^-2", "m", None) == pytest.approx(10)
assert convert_to(10 * 9.81, "m**2s**-2", "m", None) == pytest.approx(10)
with pytest.raises(TypeError):
assert convert_to(10, "m", "m**2s**-2", None) == 10
assert convert_to(10, "m", "m**2s**-2", 1) == 10
assert convert_to(10, "m", "m**2s**-2", 9.81) == pytest.approx(98.1)
assert convert_to(1000, "", "Pa", 999) == 999000
assert convert_to(1000, "Pa", "", 999) == 999000
assert convert_to(1000, "whattheheck", "Pa", 999) == 999000
assert convert_to(1000, "hPa", "whattheheck", 999) == 999000
assert convert_to(1000, "whattheheck", "whatthehock", 999) == 999000
assert convert_to(10, "percent", "dimensionless", None) == 0.1
assert convert_to(10, "permille", "dimensionless", None) == 0.01
assert convert_to(10, "ppm", "dimensionless", None) == pytest.approx(10e-6)
assert convert_to(10, "ppb", "dimensionless", None) == pytest.approx(10e-9)
assert convert_to(10, "ppt", "dimensionless",
None) == pytest.approx(10e-12)
assert convert_to(10, "ppm", "ppt", None) == pytest.approx(10e6)
assert convert_to(10, "ppb", "ppm", None) == pytest.approx(10e-3)
def plot_hsection(self,
data,
lats,
lons,
bbox=(-180, -90, 180, 90),
level=None,
figsize=(960, 640),
crs=None,
proj_params=None,
valid_time=None,
init_time=None,
style=None,
resolution=-1,
noframe=False,
show=False,
transparent=False):
"""
EPSG overrides proj_params!
"""
if proj_params is None:
proj_params = {"projection": "cyl"}
bbox_units = "latlon"
# Projection parameters from EPSG code.
if crs is not None:
proj_params, bbox_units = [
get_projection_params(crs)[_x] for _x in ("basemap", "bbox")
]
logging.debug("plotting data..")
# Check if required data is available.
self.data_units = self.driver.data_units.copy()
for datatype, dataitem, dataunit in self.required_datafields:
if dataitem not in data:
raise KeyError(f"required data field '{dataitem}' not found")
origunit = self.driver.data_units[dataitem]
if dataunit is not None:
data[dataitem] = convert_to(data[dataitem], origunit, dataunit)
self.data_units[dataitem] = dataunit
else:
logging.debug("Please add units to plot variables")
# Copy parameters to properties.
self.data = data
self.lats = lats
self.lons = lons
self.level = level
self.valid_time = valid_time
self.init_time = init_time
self.style = style
self.resolution = resolution
self.noframe = noframe
self.crs = crs
# Derive additional data fields and make the plot.
logging.debug("preparing additional data fields..")
self._prepare_datafields()
logging.debug("creating figure..")
dpi = 80
figsize = (figsize[0] / dpi), (figsize[1] / dpi)
facecolor = "white"
fig = mpl.figure.Figure(figsize=figsize, dpi=dpi, facecolor=facecolor)
logging.debug(
"\twith frame and legends" if not noframe else "\twithout frame")
if noframe:
ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
else:
ax = fig.add_axes([0.05, 0.05, 0.9, 0.88])
# The basemap instance is created with a fixed aspect ratio for framed
# plots; the aspect ratio is not fixed for frameless plots (standard
# WMS). This means that for WMS plots, the map will always fill the
# entire image area, no matter of whether this stretches or shears the
# map. This is the behaviour specified by the WMS standard (WMS Spec
# 1.1.1, Section 7.2.3.8):
# "The returned picture, regardless of its return format, shall have
# exactly the specified width and height in pixels. In the case where
# the aspect ratio of the BBOX and the ratio width/height are different,
# the WMS shall stretch the returned map so that the resulting pixels
# could themselves be rendered in the aspect ratio of the BBOX. In
# other words, it should be possible using this definition to request a
# map for a device whose output pixels are themselves non-square, or to
# stretch a map into an image area of a different aspect ratio."
# NOTE: While the MSUI always requests image sizes that match the aspect
# ratio, for instance the Metview 4 client does not (mr, 2011Dec16).
# Some additional code to store the last 20 coastlines in memory for quicker
# access.
key = repr((proj_params, bbox, bbox_units))
basemap_use_cache = getattr(mswms_settings, "basemap_use_cache", False)
basemap_request_size = getattr(mswms_settings, "basemap_request_size ",
200)
basemap_cache_size = getattr(mswms_settings, "basemap_cache_size", 20)
bm_params = {
"area_thresh": 1000.,
"ax": ax,
"fix_aspect": (not noframe)
}
bm_params.update(proj_params)
if bbox_units == "degree":
bm_params.update({
"llcrnrlon": bbox[0],
"llcrnrlat": bbox[1],
"urcrnrlon": bbox[2],
"urcrnrlat": bbox[3]
})
elif bbox_units.startswith("meter"):
# convert meters to degrees
try:
bm_p = basemap.Basemap(resolution=None, **bm_params)
except ValueError: # projection requires some extent
bm_p = basemap.Basemap(resolution=None,
width=1e7,
height=1e7,
**bm_params)
bm_center = [float(_x) for _x in bbox_units[6:-1].split(",")]
center_x, center_y = bm_p(*bm_center)
bbox_0, bbox_1 = bm_p(bbox[0] + center_x,
bbox[1] + center_y,
inverse=True)
bbox_2, bbox_3 = bm_p(bbox[2] + center_x,
bbox[3] + center_y,
inverse=True)
bm_params.update({
"llcrnrlon": bbox_0,
"llcrnrlat": bbox_1,
"urcrnrlon": bbox_2,
"urcrnrlat": bbox_3
})
elif bbox_units == "no":
pass
else:
raise ValueError(f"bbox_units '{bbox_units}' not known.")
if basemap_use_cache and key in BASEMAP_CACHE:
bm = basemap.Basemap(resolution=None, **bm_params)
(bm.resolution, bm.coastsegs, bm.coastpolygontypes,
bm.coastpolygons, bm.coastsegs, bm.landpolygons, bm.lakepolygons,
bm.cntrysegs) = BASEMAP_CACHE[key]
logging.debug("Loaded '%s' from basemap cache", key)
else:
bm = basemap.Basemap(resolution='l', **bm_params)
# read in countries manually, as those are laoded only on demand
bm.cntrysegs, _ = bm._readboundarydata("countries")
if basemap_use_cache:
BASEMAP_CACHE[key] = (bm.resolution, bm.coastsegs,
bm.coastpolygontypes, bm.coastpolygons,
bm.coastsegs, bm.landpolygons,
bm.lakepolygons, bm.cntrysegs)
if basemap_use_cache:
BASEMAP_REQUESTS.append(key)
BASEMAP_REQUESTS[:] = BASEMAP_REQUESTS[-basemap_request_size:]
if len(BASEMAP_CACHE) > basemap_cache_size:
useful = {}
for idx, key in enumerate(BASEMAP_REQUESTS):
useful[key] = useful.get(key, 0) + idx
least_useful = sorted([
(value, key) for key, value in useful.items()
])[:-basemap_cache_size]
for _, key in least_useful:
del BASEMAP_CACHE[key]
BASEMAP_REQUESTS[:] = [
_x for _x in BASEMAP_REQUESTS if key != _x
]
if self._plot_countries:
# Set up the map appearance.
try:
bm.drawcoastlines(color='0.25')
except ValueError as ex:
logging.error(
"Error in basemap/matplotlib call of drawcoastlines: %s",
ex)
bm.drawcountries(color='0.5')
bm.drawmapboundary(fill_color='white')
# zorder = 0 is necessary to paint over the filled continents with
# scatter() for drawing the flight tracks and trajectories.
# Curiously, plot() works fine without this setting, but scatter()
# doesn't.
bm.fillcontinents(color='0.98', lake_color='white', zorder=0)
self._draw_auto_graticule(bm)
if noframe:
ax.axis('off')
self.bm = bm # !! BETTER PASS EVERYTHING AS PARAMETERS?
self.fig = fig
self.shift_data()
self.mask_data()
self.lonmesh, self.latmesh = bm(*np.meshgrid(self.lons, self.lats))
self._plot_style()
# Set transparency for the output image.
if transparent:
fig.patch.set_alpha(0.)
# Return the image as png embedded in a StringIO stream.
canvas = FigureCanvas(fig)
output = io.BytesIO()
canvas.print_png(output)
if show:
logging.debug("saving figure to mpl_hsec.png ..")
canvas.print_png("mpl_hsec.png")
# Convert the image to an 8bit palette image with a significantly
# smaller file size (~factor 4, from RGBA to one 8bit value, plus the
# space to store the palette colours).
# NOTE: PIL at the current time can only create an adaptive palette for
# RGB images, hence alpha values are lost here. If transparency is
# requested, the figure face colour is stored as the "transparent"
# colour in the image. This works in most cases, but might lead to
# visible artefacts in some cases.
logging.debug("converting image to indexed palette.")
# Read the above stored png into a PIL image and create an adaptive
# colour palette.
output.seek(0) # necessary for PIL.Image.open()
palette_img = PIL.Image.open(output).convert(mode="RGB").convert(
"P", palette=PIL.Image.ADAPTIVE)
output = io.BytesIO()
if not transparent:
logging.debug("saving figure as non-transparent PNG.")
palette_img.save(
output,
format="PNG") # using optimize=True doesn't change much
else:
# If the image has a transparent background, we need to find the
# index of the background colour in the palette. See the
# documentation for PIL's ImagePalette module
# (http://www.pythonware.com/library/pil/handbook/imagepalette.htm). The
# idea is to create a 256 pixel image with the same colour palette
# as the original image and use it as a lookup-table. Converting the
# lut image back to RGB gives us a list of all colours in the
# palette. (Why doesn't PIL provide a method to directly access the
# colours in a palette??)
lut = palette_img.resize((256, 1))
lut.putdata(list(range(256)))
lut = [c[1] for c in lut.convert("RGB").getcolors()]
facecolor_rgb = list(
mpl.colors.hex2color(mpl.colors.cnames[facecolor]))
for i in [0, 1, 2]:
facecolor_rgb[i] = int(facecolor_rgb[i] * 255)
try:
facecolor_index = lut.index(tuple(facecolor_rgb))
logging.debug(
"saving figure as transparent PNG with transparency index %s.",
facecolor_index)
palette_img.save(output,
format="PNG",
transparency=facecolor_index)
except ValueError:
logging.debug(
"transparency requested but not possible, saving non-transparent instead"
)
palette_img.save(output, format="PNG")
logging.debug("returning figure..")
return output.getvalue()
def plot_lsection(self, data, lats, lons, valid_time, init_time):
"""
"""
# Check if required data is available.
self.data_units = self.driver.data_units.copy()
for datatype, dataitem, dataunit in self.required_datafields:
if dataitem not in data:
raise KeyError(f"required data field '{dataitem}' not found")
origunit = self.driver.data_units[dataitem]
if dataunit is not None:
data[dataitem] = convert_to(data[dataitem], origunit, dataunit)
self.data_units[dataitem] = dataunit
else:
logging.debug("Please add units to plot variables")
# Copy parameters to properties.
self.data = data
self.lats = lats
self.lons = lons
self.valid_time = valid_time
self.init_time = init_time
# Derive additional data fields and make the plot.
self._prepare_datafields()
impl = getDOMImplementation()
xmldoc = impl.createDocument(None, "MSS_LinearSection_Data", None)
# Title of this section.
node = xmldoc.createElement("Title")
node.appendChild(xmldoc.createTextNode(self.title))
xmldoc.documentElement.appendChild(node)
# Time information of this section.
node = xmldoc.createElement("ValidTime")
node.appendChild(
xmldoc.createTextNode(
self.valid_time.strftime("%Y-%m-%dT%H:%M:%SZ")))
xmldoc.documentElement.appendChild(node)
node = xmldoc.createElement("InitTime")
node.appendChild(
xmldoc.createTextNode(
self.init_time.strftime("%Y-%m-%dT%H:%M:%SZ")))
xmldoc.documentElement.appendChild(node)
# Longitude data.
node = xmldoc.createElement("Longitude")
node.setAttribute("num_waypoints", f"{len(self.lons)}")
data_str = ",".join([str(lon) for lon in self.lons])
node.appendChild(xmldoc.createTextNode(data_str))
xmldoc.documentElement.appendChild(node)
# Latitude data.
node = xmldoc.createElement("Latitude")
node.setAttribute("num_waypoints", f"{len(self.lats)}")
data_str = ",".join([str(lat) for lat in self.lats])
node.appendChild(xmldoc.createTextNode(data_str))
xmldoc.documentElement.appendChild(node)
# Variable data.
node = xmldoc.createElement("Data")
node.setAttribute("num_waypoints", f"{len(self.y_values)}")
node.setAttribute("unit", self.unit)
if isinstance(self.y_values[0], Quantity):
data_str = ",".join([str(val.magnitude) for val in self.y_values])
else:
data_str = ",".join([str(val) for val in self.y_values])
node.appendChild(xmldoc.createTextNode(data_str))
xmldoc.documentElement.appendChild(node)
# Return the XML document as formatted string.
return xmldoc.toprettyxml(indent=" ")
def plot_vsection(self,
data,
lats,
lons,
valid_time,
init_time,
resolution=(-1, -1),
bbox=(-1, 1050, -1, 200),
style=None,
show=False,
highlight=None,
noframe=False,
figsize=(960, 480),
draw_verticals=False,
numlabels=10,
orography_color='k',
transparent=False,
return_format="image/png"):
"""
"""
# Check if required data is available.
self.data_units = self.driver.data_units.copy()
for datatype, dataitem, dataunit in self.required_datafields:
if dataitem not in data:
raise KeyError(f"required data field '{dataitem}' not found")
origunit = self.driver.data_units[dataitem]
if dataunit is not None:
data[dataitem] = convert_to(data[dataitem], origunit, dataunit)
self.data_units[dataitem] = dataunit
else:
logging.debug("Please add units to plot variables")
# Copy parameters to properties.
self.data = data
self.lats = lats
self.lat_inds = np.arange(len(lats))
self.lons = lons
self.valid_time = valid_time
self.init_time = init_time
self.resolution = resolution
self.style = style
self.highlight = highlight
self.noframe = noframe
self.draw_verticals = draw_verticals
self.p_bot = bbox[1] * 100
self.p_top = bbox[3] * 100
self.numlabels = numlabels
self.orography_color = orography_color
# Provide an air_pressured 2-D field in 'Pa' from vertical axis
if (("air_pressure" not in self.data)
and units(self.driver.vert_units).check("[pressure]")):
self.data_units["air_pressure"] = "Pa"
self.data["air_pressure"] = convert_to(
self.driver.vert_data[::-self.driver.vert_order,
np.newaxis], self.driver.vert_units,
self.data_units["air_pressure"]).repeat(len(self.lats), axis=1)
if (("air_potential_temperature" not in self.data)
and units(self.driver.vert_units).check("[temperature]")):
self.data_units["air_potential_temperature"] = "K"
self.data["air_potential_temperature"] = convert_to(
self.driver.vert_data[::-self.driver.vert_order,
np.newaxis], self.driver.vert_units,
self.data_units["air_potential_temperature"]).repeat(len(
self.lats),
axis=1)
# Derive additional data fields and make the plot.
self._prepare_datafields()
if "air_pressure" not in self.data:
raise KeyError(
"'air_pressure' need to be available for VSEC plots."
"Either provide as data or compute in _prepare_datafields")
# Code for producing a png image with Matplotlib.
# ===============================================
if return_format == "image/png":
logging.debug("creating figure..")
dpi = 80
figsize = (figsize[0] / dpi), (figsize[1] / dpi)
facecolor = "white"
self.fig = mpl.figure.Figure(figsize=figsize,
dpi=dpi,
facecolor=facecolor)
logging.debug("\twith frame and legends"
if not noframe else "\twithout frame")
if noframe:
self.ax = self.fig.add_axes([0.0, 0.0, 1.0, 1.0])
else:
self.ax = self.fig.add_axes([0.07, 0.17, 0.9, 0.72])
# prepare horizontal axis
self.horizontal_coordinate = self.lat_inds[np.newaxis, :].repeat(
self.data["air_pressure"].shape[0], axis=0)
self._plot_style()
# Set transparency for the output image.
if transparent:
self.fig.patch.set_alpha(0.)
# Return the image as png embedded in a StringIO stream.
canvas = FigureCanvas(self.fig)
output = io.BytesIO()
canvas.print_png(output)
if show:
logging.debug("saving figure to mpl_vsec.png ..")
canvas.print_png("mpl_vsec.png")
# Convert the image to an 8bit palette image with a significantly
# smaller file size (~factor 4, from RGBA to one 8bit value, plus the
# space to store the palette colours).
# NOTE: PIL at the current time can only create an adaptive palette for
# RGB images, hence alpha values are lost here. If transparency is
# requested, the figure face colour is stored as the "transparent"
# colour in the image. This works in most cases, but might lead to
# visible artefacts in some cases.
logging.debug("converting image to indexed palette.")
# Read the above stored png into a PIL image and create an adaptive
# colour palette.
output.seek(0) # necessary for PIL.Image.open()
palette_img = PIL.Image.open(output).convert(mode="RGB").convert(
"P", palette=PIL.Image.ADAPTIVE)
output = io.BytesIO()
if not transparent:
logging.debug("saving figure as non-transparent PNG.")
palette_img.save(
output,
format="PNG") # using optimize=True doesn't change much
else:
# If the image has a transparent background, we need to find the
# index of the background colour in the palette. See the
# documentation for PIL's ImagePalette module
# (http://www.pythonware.com/library/pil/handbook/imagepalette.htm). The
# idea is to create a 256 pixel image with the same colour palette
# as the original image and use it as a lookup-table. Converting the
# lut image back to RGB gives us a list of all colours in the
# palette. (Why doesn't PIL provide a method to directly access the
# colours in a palette??)
lut = palette_img.resize((256, 1))
lut.putdata(list(range(256)))
lut = [c[1] for c in lut.convert("RGB").getcolors()]
facecolor_rgb = list(
mpl.colors.hex2color(mpl.colors.cnames[facecolor]))
for i in [0, 1, 2]:
facecolor_rgb[i] = int(facecolor_rgb[i] * 255)
facecolor_index = lut.index(tuple(facecolor_rgb))
logging.debug(
"saving figure as transparent PNG with transparency index %i.",
facecolor_index)
palette_img.save(output,
format="PNG",
transparency=facecolor_index)
logging.debug("returning figure..")
return output.getvalue()
# Code for generating an XML document with the data values in ASCII format.
# =========================================================================
elif return_format == "text/xml":
impl = getDOMImplementation()
xmldoc = impl.createDocument(None, "MSS_VerticalSection_Data",
None)
# Title of this section.
node = xmldoc.createElement("Title")
node.appendChild(xmldoc.createTextNode(self.title))
xmldoc.documentElement.appendChild(node)
# Time information of this section.
node = xmldoc.createElement("ValidTime")
node.appendChild(
xmldoc.createTextNode(
self.valid_time.strftime("%Y-%m-%dT%H:%M:%SZ")))
xmldoc.documentElement.appendChild(node)
node = xmldoc.createElement("InitTime")
node.appendChild(
xmldoc.createTextNode(
self.init_time.strftime("%Y-%m-%dT%H:%M:%SZ")))
xmldoc.documentElement.appendChild(node)
# Longitude data.
node = xmldoc.createElement("Longitude")
node.setAttribute("num_waypoints", f"{len(self.lons)}")
data_str = ""
for value in self.lons:
data_str += str(value) + ","
data_str = data_str[:-1]
node.appendChild(xmldoc.createTextNode(data_str))
xmldoc.documentElement.appendChild(node)
# Latitude data.
node = xmldoc.createElement("Latitude")
node.setAttribute("num_waypoints", f"{len(self.lats)}")
data_str = ""
for value in self.lats:
data_str += str(value) + ","
data_str = data_str[:-1]
node.appendChild(xmldoc.createTextNode(data_str))
xmldoc.documentElement.appendChild(node)
# Variable data.
data_node = xmldoc.createElement("Data")
for var in self.data:
node = xmldoc.createElement(var)
data_shape = self.data[var].shape
node.setAttribute("num_levels", f"{data_shape[0]}")
node.setAttribute("num_waypoints", f"{data_shape[1]}")
data_str = ""
for data_row in self.data[var]:
for value in data_row:
data_str += str(value) + ","
data_str = data_str[:-1] + "\n"
data_str = data_str[:-1]
node.appendChild(xmldoc.createTextNode(data_str))
data_node.appendChild(node)
xmldoc.documentElement.appendChild(data_node)
# Return the XML document as formatted string.
return xmldoc.toprettyxml(indent=" ")
