def _vector2raster(self, d, var, tndx):
"""
Postprocess a vector field into barbs (used for wind) and contains hacks for WRF staggered grids.
:param d: the open netCDF file
:param var: the name of the variable in var_wisdom
:param tndx: the time index
:return: the raster png as a StringIO, and the coordinates
"""
# gather wisdom about the variable
wisdom = get_wisdom(var)
wisdom.update(self.wisdom_update.get(var, {}))
native_unit = wisdom['native_unit']
u_name, v_name = wisdom['components']
lat, lon = wisdom['grid'](d)
# extract variable
uw, vw = get_wisdom(u_name), get_wisdom(v_name)
uw.update(self.wisdom_update.get(u_name, {}))
vw.update(self.wisdom_update.get(v_name, {}))
u = uw['retrieve_as'](d, tndx)
v = vw['retrieve_as'](d, tndx)
if u.shape != lat.shape:
raise PostprocError(
"Variable %s size does not correspond to grid size: var %s grid %s."
% (u_name, u.shape, u_lat.shape))
if v.shape != lat.shape:
raise PostprocError(
"Variable %s size does not correspond to grid size." % v_name)
# look at mins and maxes
fa_min, fa_max = min(np.nanmin(u),
np.nanmin(v)), max(np.nanmax(u), np.nanmax(v))
# 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]
u[u < fa_min] = fa_min
u[u > fa_max] = fa_max
v[v < fa_min] = fa_min
v[v > fa_max] = fa_max
# create the raster & get coordinate bounds, HACK to get better quiver resolution
s = 3
raster_png_data, corner_coords = basemap_barbs_mercator(
u[::s, ::s], v[::s, ::s], lat[::s, ::s], lon[::s, ::s])
return raster_png_data, corner_coords
def _vector2raster(self, d, var, tndx):
"""
Postprocess a vector field into barbs (used for wind) and contains hacks for WRF staggered grids.
:param d: the open netCDF file
:param var: the name of the variable in var_wisdom
:param tndx: the time index
:return: the raster png as a StringIO, and the coordinates
"""
# gather wisdom about the variable
wisdom = get_wisdom(var)
wisdom.update(self.wisdom_update.get(var, {}))
native_unit = wisdom['native_unit']
u_name, v_name = wisdom['components']
lat, lon = wisdom['grid'](d)
# extract variable
uw, vw = get_wisdom(u_name), get_wisdom(v_name)
uw.update(self.wisdom_update.get(u_name, {}))
vw.update(self.wisdom_update.get(v_name, {}))
u = uw['retrieve_as'](d, tndx)
v = vw['retrieve_as'](d, tndx)
if u.shape != lat.shape:
raise PostprocError("Variable %s size does not correspond to grid size: var %s grid %s." % (u_name, u.shape, u_lat.shape))
if v.shape != lat.shape:
raise PostprocError("Variable %s size does not correspond to grid size." % v_name)
# look at mins and maxes
fa_min,fa_max = min(np.nanmin(u), np.nanmin(v)),max(np.nanmax(u), np.nanmax(v))
# 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]
u[u < fa_min] = fa_min
u[u > fa_max] = fa_max
v[v < fa_min] = fa_min
v[v > fa_max] = fa_max
# create the raster & get coordinate bounds, HACK to get better quiver resolution
s = 3
raster_png_data,corner_coords = basemap_barbs_mercator(u[::s,::s],v[::s,::s],lat[::s,::s],lon[::s,::s])
return raster_png_data, corner_coords
def vector_field_to_raster(u, v, lats, lons, wisdom):
"""
Postprocess a vector field into arrows (used for wind).
:param u: the vector in the U direction
:param v: the vector in the V direction
:param lats: the latitudes
:param lons: the longitudes
:param wisdom: a configuration dictionary controlling the visualization
:return: the raster png as a StringIO, and the coordinates
"""
native_unit = wisdom['native_unit']
if u.shape != lats.shape:
raise PostprocError(
"U field does not correspond to grid size: field %s grid %s." %
(u.shape, lats.shape))
if v.shape != lats.shape:
raise PostprocError(
"V field does not correspond to grid size: field %s grid %s." %
(v.shape, lats.shape))
# look at mins and maxes
fa_min, fa_max = min(np.nanmin(u),
np.nanmin(v)), max(np.nanmax(u), np.nanmax(v))
# 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]
u[u < fa_min] = fa_min
u[u > fa_max] = fa_max
v[v < fa_min] = fa_min
v[v > fa_max] = fa_max
# create the raster & get coordinate bounds, HACK to get better quiver resolution
s = 3
raster_png_data, corner_coords = basemap_barbs_mercator(
u[::s, ::s], v[::s, ::s], lats[::s, ::s], lons[::s, ::s])
return raster_png_data, corner_coords
def vector_field_to_raster(u, v, lats, lons, wisdom):
"""
Postprocess a vector field into arrows (used for wind).
:param u: the vector in the U direction
:param v: the vector in the V direction
:param lats: the latitudes
:param lons: the longitudes
:param wisdom: a configuration dictionary controlling the visualization
:return: the raster png as a StringIO, and the coordinates
"""
native_unit = wisdom['native_unit']
if u.shape != lats.shape:
raise PostprocError("U field does not correspond to grid size: field %s grid %s." % (u.shape, lats.shape))
if v.shape != lats.shape:
raise PostprocError("V field does not correspond to grid size: field %s grid %s." % (v.shape, lats.shape))
# look at mins and maxes
fa_min,fa_max = min(np.nanmin(u), np.nanmin(v)),max(np.nanmax(u), np.nanmax(v))
# 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]
u[u < fa_min] = fa_min
u[u > fa_max] = fa_max
v[v < fa_min] = fa_min
v[v > fa_max] = fa_max
# create the raster & get coordinate bounds, HACK to get better quiver resolution
s = 3
raster_png_data,corner_coords = basemap_barbs_mercator(u[::s,::s],v[::s,::s],lats[::s,::s],lons[::s,::s])
return raster_png_data, corner_coords
