显示NOAA高程数据
我找到了一个示例,并且想复制它:
import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset
data = Dataset("Path/ETOPO1_Bed_g_gmt4.grd",'r')
print(data.variables.keys())
lon_range = data.variables['x_range'][:]
lat_range = data.variables['y_range'][:]
topo_range = data.variables['z_range'][:]
spacing = data.variables['spacing'][:]
dimension = data.variables['dimension'][:]
z = data.variables['z'][:]
lon_num = dimension[0]
lat_num = dimension[1]
lon = np.linspace(lon_range[0],lon_range[1],dimension[0])
lat = np.linspace(lat_range[0],lat_range[1],dimension[1])
topo = np.reshape(z, (lat_num, lon_num))
plt.imshow(topo, vmax=0)
但是,我不知道x_range
来自哪里,因为当我打印时:
print(data.variables.keys())
dict_keys(['x', 'y', 'z'])
如何解决这个问题?
I have found the example and would like to copy it:
import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset
data = Dataset("Path/ETOPO1_Bed_g_gmt4.grd",'r')
print(data.variables.keys())
lon_range = data.variables['x_range'][:]
lat_range = data.variables['y_range'][:]
topo_range = data.variables['z_range'][:]
spacing = data.variables['spacing'][:]
dimension = data.variables['dimension'][:]
z = data.variables['z'][:]
lon_num = dimension[0]
lat_num = dimension[1]
lon = np.linspace(lon_range[0],lon_range[1],dimension[0])
lat = np.linspace(lat_range[0],lat_range[1],dimension[1])
topo = np.reshape(z, (lat_num, lon_num))
plt.imshow(topo, vmax=0)
However, I don't know where x_range
comes from because when I print:
print(data.variables.keys())
dict_keys(['x', 'y', 'z'])
How can I get around this problem?
the source: https://earthscience.stackexchange.com/questions/23904/etopo1-region-selection-in-python/23924#23924
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您可以使用 xarray 为此?这似乎是一个很棒的用例:)
没有看到您的数据,很难确定这是否可以解决,但这应该很简单:
can you use xarray for this? it seems like a great use case :)
Without seeing your data it's hard to know for sure if this will work out of the box, but it should be something as simple as this: