def minput_2d(data, lon, lat, metadata, map_region=None):
"""
Put the data into magics minput function.
minput is used 2 define matrices as input for contouring or
list of values as input of curve or symbol plotting.
Args:
data (np.float64): 2D array, [nlat, nlon]
lon (np.float64): 1D vector, [nlon]
lat (np.float64): 1D vector, [nlat]
metadata (dictionary): variable name and units information,
like {'units': 'K', 'long_name': '2 metre temperature'}. Defaults to None.
map_region (list, optional): if set, the data will fit to the map_region.
[lonmin, lonmax, latmin, latmax]
"""
lat = np.squeeze(lat.astype(np.float64))
lon = np.squeeze(lon.astype(np.float64))
input_field_values = np.squeeze(data.astype(np.float64))
if map_region is not None:
loni, lonj, lati, latj = grid_subset(lon, lat, map_region)
lon = lon[loni:lonj]
lat = lat[lati:latj]
input_field_values = input_field_values[lati:latj, loni:lonj]
# put values into magics
return magics.minput(input_type="geographical",
input_field=input_field_values,
input_latitudes_list=lat,
input_longitudes_list=lon,
input_metadata=metadata)
def minput_2d_vector(udata,
vdata,
lon,
lat,
skip=1,
metadata=None,
map_region=None):
"""
Put the data into magics minput function.
minput is used 2 define matrices as input for contouring or
list of values as input of curve or symbol plotting.
Args:
udata (np.array): 2D array, [nlat, nlon]
vdata (np.array): 2D array, [nlat, nlon]
lon (np.array): 1D vector, [nlon]
lat (np.array): 1D vector, [nlat]
skip (int, optional): grid skip point number. Defaults to 1.
metadata (dictionary): variable name and units information,
like {'units': 'm/s', 'long_name': 'wind field'}. Defaults to None.
map_region (list, optional): if set, the data will fit to the map_region.
[lonmin, lonmax, latmin, latmax]
"""
# extract values
lat = np.squeeze(lat.astype(np.float64)) # 1D vector
lon = np.squeeze(lon.astype(np.float64)) # 1D vector
uwind_field = np.squeeze(udata.astype(
np.float64)) # 2D array, [nlat, nlon]
vwind_field = np.squeeze(vdata.astype(
np.float64)) # 2D array, [nlat, nlon]
if map_region is not None:
loni, lonj, lati, latj = grid_subset(lon, lat, map_region)
lon = lon[loni:lonj]
lat = lat[lati:latj]
uwind_field = uwind_field[lati:latj, loni:lonj]
vwind_field = vwind_field[lati:latj, loni:lonj]
lon, lat = np.meshgrid(lon, lat)
# skip values and flatten
uwind_field = uwind_field[::skip, ::skip].flatten()
vwind_field = vwind_field[::skip, ::skip].flatten()
lat = lat[::skip, ::skip].flatten()
lon = lon[::skip, ::skip].flatten()
# remove nan values
index = (~np.isnan(uwind_field)) & (~np.isnan(vwind_field))
uwind_field = uwind_field[index]
vwind_field = vwind_field[index]
if uwind_field.size == 0:
return None
lat = lat[index]
lon = lon[index]
# check meta information
if metadata is None:
metadata = {'units': 'm/s', 'long_name': 'wind field'}
# put into magics minput function
return magics.minput(input_type="geographical",
input_x_component_values=uwind_field,
input_y_component_values=vwind_field,
input_latitude_values=lat,
input_longitude_values=lon,
input_metadata=metadata)