def _scalar2raster(self, d, var, tndx):
"""
Convert a single variable into a raster and colorbar.
:param d: the netcdf file
:param var: the variable name
:param tndx: the time index
:return: two StringIO objects, first is the PNG raster, second is PNG colorbar
"""
# gather wisdom about the variable
wisdom = get_wisdom(var).copy()
wisdom.update(self.wisdom_update.get(var, {}))
native_unit = wisdom['native_unit']
cmap_name = wisdom['colormap']
cmap = mpl.cm.get_cmap(cmap_name)
# extract variable
fa = wisdom['retrieve_as'](
d,
tndx) # this calls a lambda defined to read the required 2d field
lat, lon = wisdom['grid'](d)
if lat.shape != fa.shape:
raise PostprocError(
"Variable %s size does not correspond to grid size." % var)
# look at mins and maxes
fa_min, fa_max = np.nanmin(fa), np.nanmax(fa)
# determine if we will use the range in the variable or a fixed range
scale = wisdom['scale']
if scale != 'original':
fa_min, fa_max = scale[0], scale[1]
fa[fa < fa_min] = fa_min
fa[fa > fa_max] = fa_max
# only create the colorbar if requested
cb_png_data = None
if wisdom['colorbar'] is not None:
cb_unit = wisdom['colorbar']
cbu_min, cbu_max = convert_value(native_unit, cb_unit,
fa_min), convert_value(
native_unit, cb_unit, fa_max)
# colorbar + add it to the KMZ as a screen overlay
cb_png_data = make_colorbar([cbu_min, cbu_max], 'vertical', 2,
cmap, wisdom['name'] + ' ' + cb_unit)
# check for 'transparent' color value and replace with nans
if 'transparent_values' in wisdom:
rng = wisdom['transparent_values']
fa = np.ma.masked_array(fa,
np.logical_and(fa >= rng[0], fa <= rng[1]))
# create the raster & get coordinate bounds
raster_png_data, corner_coords = basemap_raster_mercator(
lon, lat, fa, fa_min, fa_max, cmap)
return raster_png_data, corner_coords, cb_png_data
def scalar_field_to_raster(fa, lats, lons, wisdom):
"""
Render a scalar variable into a geolocated raster and colorbar.
:param fa: the field to render
:param lats: the latitudes
:param lons: the longitudes
:param wisdom: a configuration dictionary controlling the visualization
:return: a tuple with the raster as StringIO, its geolocation and the PNG colorbar StringIO (None if not requested)
"""
# gather wisdom about the variable
native_unit = wisdom['native_unit']
cmap_name = wisdom['colormap']
cmap = mpl.cm.get_cmap(cmap_name)
if lats.shape != fa.shape:
raise PostprocError(
"Variable size %s does not correspond to grid size %s." %
(fa.shape, lats.shape))
# look at mins and maxes
fa_min, fa_max = np.nanmin(fa), np.nanmax(fa)
# determine if we will use the range in the variable or a fixed range
scale = wisdom['scale']
if scale != 'original':
fa_min, fa_max = scale[0], scale[1]
fa[fa < fa_min] = fa_min
fa[fa > fa_max] = fa_max
# only create the colorbar if requested
cb_png_data = None
if wisdom['colorbar'] is not None:
cb_unit = wisdom['colorbar']
cbu_min, cbu_max = convert_value(native_unit, cb_unit,
fa_min), convert_value(
native_unit, cb_unit, fa_max)
# colorbar + add it to the KMZ as a screen overlay
cb_png_data = make_colorbar([cbu_min, cbu_max], 'vertical', 2, cmap,
wisdom['name'] + ' ' + cb_unit)
# check for 'transparent' color value and replace with nans
if 'transparent_values' in wisdom:
rng = wisdom['transparent_values']
fa = np.ma.masked_array(fa, np.logical_and(fa >= rng[0], fa <= rng[1]))
# create the raster & get coordinate bounds
raster_png_data, corner_coords = basemap_raster_mercator(
lons, lats, fa, fa_min, fa_max, cmap)
return raster_png_data, corner_coords, cb_png_data
def _scalar2raster(self, d, var, tndx):
"""
Convert a single variable into a raster and colorbar.
:param d: the netcdf file
:param var: the variable name
:param tndx: the time index
:return: two StringIO objects, first is the PNG raster, second is PNG colorbar
"""
# gather wisdom about the variable
wisdom = get_wisdom(var).copy()
wisdom.update(self.wisdom_update.get(var, {}))
native_unit = wisdom['native_unit']
cmap_name = wisdom['colormap']
cmap = mpl.cm.get_cmap(cmap_name)
# extract variable
fa = wisdom['retrieve_as'](d,tndx) # this calls a lambda defined to read the required 2d field
lat, lon = wisdom['grid'](d)
if lat.shape != fa.shape:
raise PostprocError("Variable %s size does not correspond to grid size." % var)
# look at mins and maxes
fa_min,fa_max = np.nanmin(fa),np.nanmax(fa)
# determine if we will use the range in the variable or a fixed range
scale = wisdom['scale']
if scale != 'original':
fa_min, fa_max = scale[0], scale[1]
fa[fa < fa_min] = fa_min
fa[fa > fa_max] = fa_max
# only create the colorbar if requested
cb_png_data = None
if wisdom['colorbar'] is not None:
cb_unit = wisdom['colorbar']
cbu_min,cbu_max = convert_value(native_unit, cb_unit, fa_min), convert_value(native_unit, cb_unit, fa_max)
# colorbar + add it to the KMZ as a screen overlay
cb_png_data = make_colorbar([cbu_min, cbu_max],'vertical',2,cmap,wisdom['name'] + ' ' + cb_unit)
# check for 'transparent' color value and replace with nans
if 'transparent_values' in wisdom:
rng = wisdom['transparent_values']
fa = np.ma.masked_array(fa, np.logical_and(fa >= rng[0], fa <= rng[1]))
# create the raster & get coordinate bounds
raster_png_data,corner_coords = basemap_raster_mercator(lon,lat,fa,fa_min,fa_max,cmap)
return raster_png_data, corner_coords, cb_png_data
def scalar_field_to_raster(fa, lats, lons, wisdom):
"""
Render a scalar variable into a geolocated raster and colorbar.
:param fa: the field to render
:param lats: the latitudes
:param lons: the longitudes
:param wisdom: a configuration dictionary controlling the visualization
:return: a tuple with the raster as StringIO, its geolocation and the PNG colorbar StringIO (None if not requested)
"""
# gather wisdom about the variable
native_unit = wisdom['native_unit']
cmap_name = wisdom['colormap']
cmap = mpl.cm.get_cmap(cmap_name)
if lats.shape != fa.shape:
raise PostprocError("Variable size %s does not correspond to grid size %s." % (fa.shape, lats.shape))
# look at mins and maxes
fa_min,fa_max = np.nanmin(fa),np.nanmax(fa)
# determine if we will use the range in the variable or a fixed range
scale = wisdom['scale']
if scale != 'original':
fa_min, fa_max = scale[0], scale[1]
fa[fa < fa_min] = fa_min
fa[fa > fa_max] = fa_max
# only create the colorbar if requested
cb_png_data = None
if wisdom['colorbar'] is not None:
cb_unit = wisdom['colorbar']
cbu_min,cbu_max = convert_value(native_unit, cb_unit, fa_min), convert_value(native_unit, cb_unit, fa_max)
# colorbar + add it to the KMZ as a screen overlay
cb_png_data = make_colorbar([cbu_min, cbu_max],'vertical',2,cmap,wisdom['name'] + ' ' + cb_unit)
# check for 'transparent' color value and replace with nans
if 'transparent_values' in wisdom:
rng = wisdom['transparent_values']
fa = np.ma.masked_array(fa, np.logical_and(fa >= rng[0], fa <= rng[1]))
# create the raster & get coordinate bounds
raster_png_data,corner_coords = basemap_raster_mercator(lons,lats,fa,fa_min,fa_max,cmap)
return raster_png_data, corner_coords, cb_png_data
def scalar_field_to_raster(fa, lats, lons, wisdom):
"""
Render a scalar variable into a geolocated raster and colorbar.
:param fa: the field to render
:param lats: the latitudes
:param lons: the longitudes
:param wisdom: a configuration dictionary controlling the visualization
:return: a tuple with the raster as StringIO, its geolocation and the PNG colorbar StringIO (None if not requested)
"""
# gather wisdom about the variable
native_unit = wisdom['native_unit']
cmap_name = wisdom['colormap']
cmap = mpl.cm.get_cmap(cmap_name)
if lats.shape != fa.shape:
raise PostprocError(
"Variable size %s does not correspond to grid size %s." %
(fa.shape, lats.shape))
logging.info('scalar_field_to_raster: variable %s min %s max %s' %
(var, np.nanmin(fa), np.nanmax(fa)))
# mask 'transparent' color value
if 'transparent_values' in wisdom:
rng = wisdom['transparent_values']
fa = np.ma.masked_array(fa, np.logical_and(fa >= rng[0], fa <= rng[1]))
logging.info(
'scalar_field_to_raster: transparent from %s to %, elements %s n))ot masked %s'
% (rng[0], rng[1], fa.size, fa.count()))
logging.info(
'scalar_field_to_raster: masked variable %s min %s max %s' %
(var, np.nanmin(fa), np.nanmax(fa)))
# look at mins and maxes, transparent don't count
if fa.count():
fa_min, fa_max = np.nanmin(fa), np.nanmax(fa)
else:
fa_min, fa_max = 0.0, 0.0
# determine if we will use the range in the variable or a fixed range
scale = wisdom['scale']
if scale != 'original':
fa_min, fa_max = scale[0], scale[1]
fa[fa < fa_min] = fa_min
fa[fa > fa_max] = fa_max
# only create the colorbar if requested
cb_png_data = None
if wisdom['colorbar'] is not None:
if scale == 'original':
logging.warning(
'postprocessor: Colorbar %s %s specified with scaling original'
% (wisdom['name'], wisdom['colorbar']))
logging.warning(
'postprocessor: Colorbar is not updated by the online visualization system correctly, use only with explicit scaling'
)
cb_unit = wisdom['colorbar']
cbu_min, cbu_max = convert_value(native_unit, cb_unit,
fa_min), convert_value(
native_unit, cb_unit, fa_max)
# colorbar + add it to the KMZ as a screen overlay
cb_png_data = make_colorbar([cbu_min, cbu_max], 'vertical', 2, cmap,
wisdom['name'] + ' ' + cb_unit)
# create the raster & get coordinate bounds
raster_png_data, corner_coords = basemap_raster_mercator(
lons, lats, fa, fa_min, fa_max, cmap)
return raster_png_data, corner_coords, cb_png_data
def _scalar2raster(self, d, var, tndx):
"""
Convert a single variable into a raster and colorbar.
:param d: the netcdf file
:param var: the variable name
:param tndx: the time index
:return: two StringIO objects, first is the PNG raster, second is PNG colorbar
"""
# gather wisdom about the variable
wisdom = get_wisdom(var).copy()
wisdom.update(self.wisdom_update.get(var, {}))
native_unit = wisdom['native_unit']
cmap_name = wisdom['colormap']
cmap = mpl.cm.get_cmap(cmap_name)
# extract variable
fa = wisdom['retrieve_as'](
d,
tndx) # this calls a lambda defined to read the required 2d field
lat, lon = wisdom['grid'](d)
if lat.shape != fa.shape:
raise PostprocError(
"Variable %s size does not correspond to grid size." % var)
# check for 'transparent' color value and mask
if 'transparent_values' in wisdom:
rng = wisdom['transparent_values']
logging.info(
'_scalar2raster: variable %s min %s max %s masking transparent from %s to %s'
% (var, np.nanmin(fa), np.nanmax(fa), rng[0], rng[1]))
fa = np.ma.masked_array(fa,
np.logical_and(fa >= rng[0], fa <= rng[1]))
else:
fa = np.ma.masked_array(fa)
# create the raster & get coordinate bounds
# look at mins and maxes
# fa_min,fa_max = np.nanmin(fa),np.nanmax(fa)
# look at mins and maxes, transparent don't count
fa_min, fa_max = np.nanmin(fa), np.nanmax(fa)
# determine if we will use the range in the variable or a fixed range
scale = wisdom['scale']
if scale != 'original':
m = fa.mask.copy(
) # save mask, resetting values below will destroy the mask
fa_min, fa_max = scale[0], scale[1]
fa[fa < fa_min] = fa_min
fa[fa > fa_max] = fa_max
fa.mask = m # restore the mask
# only create the colorbar if requested
cb_png_data = None
if wisdom['colorbar'] is not None:
cb_unit = wisdom['colorbar']
cbu_min, cbu_max = convert_value(native_unit, cb_unit,
fa_min), convert_value(
native_unit, cb_unit, fa_max)
# colorbar + add it to the KMZ as a screen overlay
legend = wisdom['name'] + ' ' + cb_unit
logging.info(
'_scalar2raster: variable %s colorbar from %s to %s %s' %
(var, cbu_min, cbu_max, legend))
cb_png_data = make_colorbar([cbu_min, cbu_max], 'vertical', 2,
cmap, legend)
# replace masked values by nans just in case
fa.data[fa.mask] = np.nan
fa.fill_value = np.nan
logging.info(
'_scalar2raster: variable %s elements %s count %s not masked %s min %s max %s'
% (var, fa.size, fa.count(), np.count_nonzero(fa.mask == False),
np.nanmin(fa), np.nanmax(fa)))
raster_png_data, corner_coords = basemap_raster_mercator(
lon, lat, fa, fa_min, fa_max, cmap)
return raster_png_data, corner_coords, cb_png_data
