def get_xy(ll_ul, ll_lr):
N6 = e2.Ease2Transform("EASE2_N6.25km")
N3 = e2.Ease2Transform("EASE2_N3.125km")
# get x,y for 6.25
row, col = N6.geographic_to_grid(ll_ul[0], ll_ul[1])
x6ul, y6ul = N6.grid_to_map(row, col)
row, col = N6.geographic_to_grid(ll_lr[0], ll_lr[1])
x6lr, y6lr = N6.grid_to_map(row, col)
# get x,y for 3.125
row, col = N6.geographic_to_grid(ll_ul[0], ll_ul[1])
x3ul, y3ul = N6.grid_to_map(row, col)
row, col = N6.geographic_to_grid(ll_lr[0], ll_lr[1])
x3lr, y3lr = N6.grid_to_map(row, col)
list_6 = [x6ul, y6ul, x6lr, y6lr]
list_3 = [x3ul, y3ul, x3lr, y3lr]
return list_3
def plot_a_day(file1, file2, path, token):
'''read tb,x,y data from final files,
with the purpose of plotting.'''
os.chdir(path + '/data/Subsetted_19H')
fid_19H = Dataset(file1, "r", format="NETCDF4")
os.chdir(path + '/data/Subsetted_37H')
fid_37H = Dataset(file2, "r", format="NETCDF4")
x = fid_19H.variables['x'][:]
y = fid_37H.variables['y'][:]
tb_19H = fid_19H.variables['TB'][:]
tb_37H = fid_37H.variables['TB'][:]
tb_37H = block_reduce(tb_37H, block_size = (1,2,2), func = np.mean)
tb= tb_19H - tb_37H
lats = np.zeros((len(y), len(x)), dtype=np.float64)
lons = np.zeros((len(y), len(x)), dtype=np.float64)
grid = e2.Ease2Transform(gridname=fid_19H.variables["crs"].long_name)
for i, xi in enumerate(x):
for j, yj in enumerate(y):
row, col = grid.map_to_grid(xi, yj)
lat, lon = grid.grid_to_geographic(row, col)
lats[j, i] = lat
lons[j, i] = lon
one_day = tb[0,:,:]
df = pd.DataFrame(columns = ['lat', 'lon', 'swe'])
for i in range(len(one_day[:,1])):
for j in range(1,:):
df = df.append({'lat': lats[i][j], 'lon': lons[i][j], 'swe':one_day[i][j]}, ignore_index = True)
def get_xy(ll_ul, ll_lr):
'''Use NSIDC scripts to convert user inputted
lat/lon into Ease grid 2.0 coordinates'''
N6 = e2.Ease2Transform("EASE2_N6.25km")
N3 = e2.Ease2Transform("EASE2_N3.125km")
# get x,y for 6.25
row, col = N6.geographic_to_grid(ll_ul[0], ll_ul[1])
x6ul, y6ul = N6.grid_to_map(row,col)
row, col = N6.geographic_to_grid(ll_lr[0], ll_lr[1])
x6lr, y6lr = N6.grid_to_map(row, col)
# get x,y for 3.125
row, col = N6.geographic_to_grid(ll_ul[0], ll_ul[1])
x3ul, y3ul = N6.grid_to_map(row, col)
row, col = N6.geographic_to_grid(ll_lr[0], ll_lr[1])
x3lr, y3lr = N6.grid_to_map(row, col)
list_6 = [x6ul, y6ul, x6lr, y6lr]
list_3 = [x3ul, y3ul, x3lr, y3lr]
return list_3
def plot_a_day(file1, file2, path, token):
'''read tb,x,y data from final files,
with the purpose of plotting.'''
os.chdir(path + '/data/Subsetted_19H')
fid_19H = Dataset(file1, "r", format="NETCDF4")
os.chdir(path + '/data/Subsetted_37H')
fid_37H = Dataset(file2, "r", format="NETCDF4")
x = fid_19H.variables['x'][:]
y = fid_37H.variables['y'][:]
tb_19H = fid_19H.variables['TB'][:]
tb_37H = fid_37H.variables['TB'][:]
tb_37H = block_reduce(tb_37H, block_size=(1, 2, 2), func=np.mean)
tb = tb_19H - tb_37H
lats = np.zeros((len(y), len(x)), dtype=np.float64)
lons = np.zeros((len(y), len(x)), dtype=np.float64)
grid = e2.Ease2Transform(gridname=fid_19H.variables["crs"].long_name)
for i, xi in enumerate(x):
for j, yj in enumerate(y):
row, col = grid.map_to_grid(xi, yj)
lat, lon = grid.grid_to_geographic(row, col)
lats[j, i] = lat
lons[j, i] = lon
one_day = tb[0, :, :]
df = pd.DataFrame(columns=['lat', 'lon', 'swe'])
for i in range(len(one_day[:, 1])):
for j in range(len(one_day[1, :])):
df = df.append(
{
'lat': lats[i][j],
'lon': lons[i][j],
'swe': one_day[i][j]
},
ignore_index=True)
df_to_geojson(df,
filename='swe_1day.geojson',
properties=['swe'],
lat='lat',
lon='lon')
measure = 'swe'
color_breaks = [
round(df[measure].quantile(q=x * 0.1), 2) for x in range(1, 9)
]
color_stops = create_color_stops(color_breaks, colors='YlGnBu')
# Create the viz from the dataframe
viz = CircleViz('swe_1day.geojson',
access_token=token,
color_property="swe",
color_stops=color_stops,
center=(-154, 67),
zoom=3,
below_layer='waterway-label')
viz.show()