Python Cartopy 映射
Python Cartopy mapping
我正在尝试根据来自 NASA 的 public 数据在全球地图上绘制 CO2 水平图，并根据数据并使用 Panoply 软件将这些值描绘为（long,lat,value）地形图，这是我的情节应该是这样的：
数据为 .nc4 格式，可以正确读取，但是我无法获取数据图 我正在使用 Cartopy API 并遵循以下示例：(https://scitools.org.uk/cartopy/docs/latest/gallery/waves.html#sphx-glr-gallery-waves-py).
我也不想用底图
尝试 # 1:
参见下面的Python代码：
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
import cartopy.crs as ccrs
"""
function that download each OCO - 2 data that is in .nc4 format from file "subset_OCO2_L2_ABand_V8_20180929_010345.txt"
which is list of links
for all data with date range 2015 - 09 - 01 to 2016 - 01 - 01# make sure that you have a valid user name & password by registering in https: //earthdata.nasa.gov/
#implementation based on http: //unidata.github.io/netcdf4-python/#section1"""
def download_oco2_nc4(username, password, filespath):
filespath = "C:\Users\Desktop\oco2\oco2_LtCO2_150831_B8100r_171009083146s.nc4"
dataset = Dataset(filespath)
print(dataset.file_format)
print(dataset.dimensions.keys())
print(dataset.variables['xco2'])
XCO2 = []
LONGITUDE = []
LATITUDE = []
# XCO2
XCO2 = dataset.variables['xco2'][:]
print("->", type(XCO2))
print(dataset.variables['latitude'])
# LATITUDE
LATITUDE = dataset.variables['latitude'][:]
print(dataset.variables['longitude'])
# LONGITUDE
LONGITUDE = dataset.variables['longitude'][:]
return XCO2, LONGITUDE, LATITUDE, dataset
def mapXoco2():
fig = plt.figure(figsize = (10, 5))
ax = fig.add_subplot(1, 1, 1, projection = ccrs.Mollweide())
XCO2, LONGITUDE, LATITUDE, dataset = download_oco2_nc4(1, 2, 3)
dataset.close()
XCO2_subset = list()
counter = 0
for xco2 in XCO2:
if counter < 10:
XCO2_subset.append(xco2)
counter = counter + 1
else:
break
print("XCO2_subset="+str(len(XCO2_subset)))
counter = 0
LONGITUDE_subset = list()
for longitude in LONGITUDE:
if counter < 10:
LONGITUDE_subset.append(longitude)
counter = counter + 1
else:
break
print("LONGITUDE_subset="+str(len(LONGITUDE_subset)))
counter = 0
LATITUDE_subset = list()
for latitude in LATITUDE:
if counter < 10:
LATITUDE_subset.append(latitude)
counter = counter + 1
else:
break
print("LATITUDE_subset="+str(len(LATITUDE_subset)))
XCO2_subset = np.array(XCO2_subset)
LONGITUDE_subset = np.array(LONGITUDE_subset)
LATITUDE_subset = np.array(LATITUDE_subset)
#LONGITUDE_subset, LATITUDE_subset = np.meshgrid(LONGITUDE_subset, LATITUDE_subset)
#XCO2_subset,XCO2_subset = np.meshgrid(XCO2_subset,XCO2_subset)
ax.contourf(LONGITUDE_subset,LATITUDE_subset,XCO2_subset,
transform = ccrs.Mollweide(central_longitude=0, globe=None),
cmap = 'nipy_spectral')
ax.coastlines()
ax.set_global()
plt.show()
print(XCO2_subset)
mapXoco2()
当我注释掉这些行时：
#LONGITUDE_subset, LATITUDE_subset = np.meshgrid(LONGITUDE_subset, LATITUDE_subset)
#XCO2_subset,XCO2_subset = np.meshgrid(XCO2_subset,XCO2_subset)
我得到一个错误：
引发 TypeError("Input z must be a 2D array.")
类型错误：输入 z 必须是二维数组。
但是当我不评论这些行时：
LONGITUDE_subset, LATITUDE_subset = np.meshgrid(LONGITUDE_subset, LATITUDE_subset)
XCO2_subset,XCO2_subset = np.meshgrid(XCO2_subset,XCO2_subset
)
我得到一张空地图，我看到大陆但没有标绘值 C02 值。
我认为我对输入的 1D 到 2D 转换的解释不正确。
尝试 # 2（更新）：
我没有处理 why/what API 中的这些二维变换，而是使用循环将每个点 1 乘 1 地绘制出来。问题是虽然我可以看到更多数据（我只绘制了大约 10% 的数据）我看不到 MAP/CONTINENTS 我看到白色背景上的值图？？？，请参见代码：
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
import cartopy.crs as ccrs
from random import sample
"""
function that download each OCO - 2 data that is in .nc4 format from file "subset_OCO2_L2_ABand_V8_20180929_010345.txt"
which is list of links
for all data with date range 2015 - 09 - 01 to 2016 - 01 - 01# make sure that you have a valid user name & password by registering in https: //earthdata.nasa.gov/
#implementation based on http: //unidata.github.io/netcdf4-python/#section1"""
filespath = "C:\Users\Downloads\oco2_LtCO2_150830_B7305Br_160712072205s.nc4"
def download_oco2_nc4(filespath):
dataset = Dataset(filespath)
print("file format:"+str(dataset.file_format))
print("dimensions.keys():"+str(dataset.dimensions.keys()))
print("variables['xco2']:"+str(dataset.variables['xco2']))
XCO2 = []
LONGITUDE = []
LATITUDE = []
# XCO2
XCO2 = dataset.variables['xco2'][:]
print("->", type(XCO2))
print(dataset.variables['latitude'])
# LATITUDE
LATITUDE = dataset.variables['latitude'][:]
print(dataset.variables['longitude'])
# LONGITUDE
LONGITUDE = dataset.variables['longitude'][:]
return XCO2, LONGITUDE, LATITUDE, dataset
def mapXoco2():
fig = plt.figure(figsize = (10, 5))
ax = fig.add_subplot(1, 1, 1, projection = ccrs.Mollweide())
XCO2, LONGITUDE, LATITUDE, dataset = download_oco2_nc4(filespath)
dataset.close()
XCO2_subset = np.array(XCO2)
LONGITUDE_subset = np.array(LONGITUDE)
LATITUDE_subset = np.array(LATITUDE)
"""each of the arrays has over 80,000 of data therefore its taking to long to map, after 10,000 rows its to slow, and 10,000 isnt sufficient.
Because oco-2 gathers data from trajectory the 1st 10% or whatever precent of the data will not be a good representation of the overal data.
We must sample from X number of slices across the data.
"""
#XCO2 attempt to get ten ranges, we need to check 10 ranges therefore we need if statements not if/else
if (len(XCO2_subset)>=10000):
first_XCO2_subset=XCO2_subset[0:1000]
if (len(XCO2_subset)>=20000):
second_XCO2_subset=XCO2_subset[20000:21000]
if (len(XCO2_subset)>=30000):
third_XCO2_subset=XCO2_subset[30000:31000]
if (len(XCO2_subset)>=40000):
fourth_XCO2_subset=XCO2_subset[40000:41000]
if (len(XCO2_subset)>=50000):
fifth_XCO2_subset=XCO2_subset[50000:51000]
if (len(XCO2_subset)>=60000):
sixth_XCO2_subset=XCO2_subset[60000:61000]
if (len(XCO2_subset)>=70000):
seventh_XCO2_subset=XCO2_subset[70000:71000]
if (len(XCO2_subset)>=80000):
eight_XCO2_subset=XCO2_subset[80000:81000]
sampled_xco2 = first_XCO2_subset + second_XCO2_subset + third_XCO2_subset + fourth_XCO2_subset + fifth_XCO2_subset + sixth_XCO2_subset + seventh_XCO2_subset + eight_XCO2_subset
#LONGITUDE attempt to get ten ranges, we need to check 10 ranges therefore we need if statements not if/else
if (len(LONGITUDE_subset)>=10000):
first_LONGITUDE_subset=LONGITUDE_subset[0:1000]
if (len(LONGITUDE_subset)>=20000):
second_LONGITUDE_subset=LONGITUDE_subset[20000:21000]
if (len(LONGITUDE_subset)>=30000):
third_LONGITUDE_subset=LONGITUDE_subset[30000:31000]
if (len(LONGITUDE_subset)>=40000):
fourth_LONGITUDE_subset=LONGITUDE_subset[40000:41000]
if (len(LONGITUDE_subset)>=50000):
fifth_LONGITUDE_subset=LONGITUDE_subset[50000:51000]
if (len(LONGITUDE_subset)>=60000):
sixth_LONGITUDE_subset=LONGITUDE_subset[60000:61000]
if (len(LONGITUDE_subset)>=70000):
seventh_LONGITUDE_subset=LONGITUDE_subset[70000:71000]
if (len(LONGITUDE_subset)>=80000):
eight_LONGITUDE_subset=LONGITUDE_subset[80000:81000]
sampled_LONGITUDE = first_LONGITUDE_subset + second_LONGITUDE_subset + third_LONGITUDE_subset + fourth_LONGITUDE_subset + fifth_LONGITUDE_subset + sixth_LONGITUDE_subset + seventh_LONGITUDE_subset + eight_LONGITUDE_subset
#LATITUDE attempt to get ten ranges, we need to check 10 ranges therefore we need if statements not if/else
if (len(LATITUDE_subset)>=10000):
first_LATITUDE_subset=LATITUDE_subset[0:1000]
if (len(LATITUDE_subset)>=20000):
second_LATITUDE_subset=LATITUDE_subset[20000:21000]
if (len(LATITUDE_subset)>=30000):
third_LATITUDE_subset=LATITUDE_subset[30000:31000]
if (len(LATITUDE_subset)>=40000):
fourth_LATITUDE_subset=LATITUDE_subset[40000:41000]
if (len(LATITUDE_subset)>=50000):
fifth_LATITUDE_subset=LATITUDE_subset[50000:51000]
if (len(LATITUDE_subset)>=60000):
sixth_LATITUDE_subset=LATITUDE_subset[60000:61000]
if (len(LATITUDE_subset)>=70000):
seventh_LATITUDE_subset=LATITUDE_subset[70000:71000]
if (len(LATITUDE_subset)>=80000):
eight_LATITUDE_subset=LATITUDE_subset[80000:81000]
sampled_LATITUDE = first_LATITUDE_subset + second_LATITUDE_subset + third_LATITUDE_subset + fourth_LATITUDE_subset + fifth_LATITUDE_subset + sixth_LATITUDE_subset + seventh_LATITUDE_subset + eight_LATITUDE_subset
ax = plt.axes(projection=ccrs.Mollweide())
#plt.contourf(LONGITUDE_subset, LATITUDE_subset, XCO2_subset, 60,transform=ccrs.PlateCarree())
for long, lat, value in zip(sampled_LONGITUDE, sampled_LATITUDE,sampled_xco2):
#print(long, lat, value)
if value >= 0 and value < 370:
ax.plot(long,lat,marker='o',color='blue', markersize=1, transform=ccrs.PlateCarree())
elif value >= 370 and value < 390:
ax.plot(long,lat,marker='o',color='cyan', markersize=1, transform=ccrs.PlateCarree())
elif value >= 390 and value < 402:
ax.plot(long,lat,marker='o',color='yellow', markersize=1, transform=ccrs.PlateCarree())
elif value >= 402 and value < 410:
ax.plot(long,lat,marker='o',color='orange', markersize=1, transform=ccrs.PlateCarree())
elif value >= 410 and value < 415:
ax.plot(long,lat,marker='o',color='red', markersize=1, transform=ccrs.PlateCarree())
else:
ax.plot(long,lat,marker='o',color='brown', markersize=1, transform=ccrs.PlateCarree())
ax.coastlines()
plt.show()
mapXoco2()
输出：
文件format:NETCDF4
dimensions.keys():odict_keys(['sounding_id', 'levels', 'bands', 'vertices', 'epoch_dimension', 'source_files'])
变量['xco2']：
float32 xco2(sounding_id)
单位：ppm
long_name：XCO2
missing_value：-999999.0
评论：CO2 的列平均干空气摩尔分数（包括偏差校正）
无限维度：
当前形状 = (82776,)
填充，使用默认 _FillValue 9.969209968386869e+36
->
float32 纬度(sounding_id)
单位：degrees_north
long_name: 纬度
missing_value：-999999.0
评论：测量的中心纬度
无限维度：
当前形状 = (82776,)
填充，使用默认 _FillValue 9.969209968386869e+36
float32 经度(sounding_id)
单位：degrees_east
long_name: 经度
missing_value：-999999.0
注释：测量的中心经度
无限维度：
当前形状 = (82776,)
填充，使用默认 _FillValue 9.969209968386869e+36
1) 地图和大陆怎么了？
谢谢，感谢任何有用的帮助。
看起来你的转换参数不正确。如果您有 latitude/longitude 数据，则转换参数的值应为 ccrs.PlateCarree()。有关详细信息，请参阅 cartopy 文档中的本指南：https://scitools.org.uk/cartopy/docs/latest/tutorials/understanding_transform.html.
我无法 运行 你的例子来验证这是解决方案。要充分利用 Stack Overflow，您应该提供一个其他人可以自己 运行 的最小工作示例。有关提示，请参阅 https://whosebug.com/help/mcve and https://whosebug.com/help/how-to-ask。
我正在尝试根据来自 NASA 的 public 数据在全球地图上绘制 CO2 水平图，并根据数据并使用 Panoply 软件将这些值描绘为（long,lat,value）地形图，这是我的情节应该是这样的：
数据为 .nc4 格式，可以正确读取，但是我无法获取数据图 我正在使用 Cartopy API 并遵循以下示例：(https://scitools.org.uk/cartopy/docs/latest/gallery/waves.html#sphx-glr-gallery-waves-py).
我也不想用底图
尝试 # 1:
参见下面的Python代码：
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
import cartopy.crs as ccrs
"""
function that download each OCO - 2 data that is in .nc4 format from file "subset_OCO2_L2_ABand_V8_20180929_010345.txt"
which is list of links
for all data with date range 2015 - 09 - 01 to 2016 - 01 - 01# make sure that you have a valid user name & password by registering in https: //earthdata.nasa.gov/
#implementation based on http: //unidata.github.io/netcdf4-python/#section1"""
def download_oco2_nc4(username, password, filespath):
filespath = "C:\Users\Desktop\oco2\oco2_LtCO2_150831_B8100r_171009083146s.nc4"
dataset = Dataset(filespath)
print(dataset.file_format)
print(dataset.dimensions.keys())
print(dataset.variables['xco2'])
XCO2 = []
LONGITUDE = []
LATITUDE = []
# XCO2
XCO2 = dataset.variables['xco2'][:]
print("->", type(XCO2))
print(dataset.variables['latitude'])
# LATITUDE
LATITUDE = dataset.variables['latitude'][:]
print(dataset.variables['longitude'])
# LONGITUDE
LONGITUDE = dataset.variables['longitude'][:]
return XCO2, LONGITUDE, LATITUDE, dataset
def mapXoco2():
fig = plt.figure(figsize = (10, 5))
ax = fig.add_subplot(1, 1, 1, projection = ccrs.Mollweide())
XCO2, LONGITUDE, LATITUDE, dataset = download_oco2_nc4(1, 2, 3)
dataset.close()
XCO2_subset = list()
counter = 0
for xco2 in XCO2:
if counter < 10:
XCO2_subset.append(xco2)
counter = counter + 1
else:
break
print("XCO2_subset="+str(len(XCO2_subset)))
counter = 0
LONGITUDE_subset = list()
for longitude in LONGITUDE:
if counter < 10:
LONGITUDE_subset.append(longitude)
counter = counter + 1
else:
break
print("LONGITUDE_subset="+str(len(LONGITUDE_subset)))
counter = 0
LATITUDE_subset = list()
for latitude in LATITUDE:
if counter < 10:
LATITUDE_subset.append(latitude)
counter = counter + 1
else:
break
print("LATITUDE_subset="+str(len(LATITUDE_subset)))
XCO2_subset = np.array(XCO2_subset)
LONGITUDE_subset = np.array(LONGITUDE_subset)
LATITUDE_subset = np.array(LATITUDE_subset)
#LONGITUDE_subset, LATITUDE_subset = np.meshgrid(LONGITUDE_subset, LATITUDE_subset)
#XCO2_subset,XCO2_subset = np.meshgrid(XCO2_subset,XCO2_subset)
ax.contourf(LONGITUDE_subset,LATITUDE_subset,XCO2_subset,
transform = ccrs.Mollweide(central_longitude=0, globe=None),
cmap = 'nipy_spectral')
ax.coastlines()
ax.set_global()
plt.show()
print(XCO2_subset)
mapXoco2()
当我注释掉这些行时：
#LONGITUDE_subset, LATITUDE_subset = np.meshgrid(LONGITUDE_subset, LATITUDE_subset)
#XCO2_subset,XCO2_subset = np.meshgrid(XCO2_subset,XCO2_subset)
我得到一个错误：
引发 TypeError("Input z must be a 2D array.")
类型错误：输入 z 必须是二维数组。
但是当我不评论这些行时：
LONGITUDE_subset, LATITUDE_subset = np.meshgrid(LONGITUDE_subset, LATITUDE_subset)
XCO2_subset,XCO2_subset = np.meshgrid(XCO2_subset,XCO2_subset
)
我得到一张空地图，我看到大陆但没有标绘值 C02 值。
我认为我对输入的 1D 到 2D 转换的解释不正确。
尝试 # 2（更新）：
我没有处理 why/what API 中的这些二维变换，而是使用循环将每个点 1 乘 1 地绘制出来。问题是虽然我可以看到更多数据（我只绘制了大约 10% 的数据）我看不到 MAP/CONTINENTS 我看到白色背景上的值图？？？，请参见代码：
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
import cartopy.crs as ccrs
from random import sample
"""
function that download each OCO - 2 data that is in .nc4 format from file "subset_OCO2_L2_ABand_V8_20180929_010345.txt"
which is list of links
for all data with date range 2015 - 09 - 01 to 2016 - 01 - 01# make sure that you have a valid user name & password by registering in https: //earthdata.nasa.gov/
#implementation based on http: //unidata.github.io/netcdf4-python/#section1"""
filespath = "C:\Users\Downloads\oco2_LtCO2_150830_B7305Br_160712072205s.nc4"
def download_oco2_nc4(filespath):
dataset = Dataset(filespath)
print("file format:"+str(dataset.file_format))
print("dimensions.keys():"+str(dataset.dimensions.keys()))
print("variables['xco2']:"+str(dataset.variables['xco2']))
XCO2 = []
LONGITUDE = []
LATITUDE = []
# XCO2
XCO2 = dataset.variables['xco2'][:]
print("->", type(XCO2))
print(dataset.variables['latitude'])
# LATITUDE
LATITUDE = dataset.variables['latitude'][:]
print(dataset.variables['longitude'])
# LONGITUDE
LONGITUDE = dataset.variables['longitude'][:]
return XCO2, LONGITUDE, LATITUDE, dataset
def mapXoco2():
fig = plt.figure(figsize = (10, 5))
ax = fig.add_subplot(1, 1, 1, projection = ccrs.Mollweide())
XCO2, LONGITUDE, LATITUDE, dataset = download_oco2_nc4(filespath)
dataset.close()
XCO2_subset = np.array(XCO2)
LONGITUDE_subset = np.array(LONGITUDE)
LATITUDE_subset = np.array(LATITUDE)
"""each of the arrays has over 80,000 of data therefore its taking to long to map, after 10,000 rows its to slow, and 10,000 isnt sufficient.
Because oco-2 gathers data from trajectory the 1st 10% or whatever precent of the data will not be a good representation of the overal data.
We must sample from X number of slices across the data.
"""
#XCO2 attempt to get ten ranges, we need to check 10 ranges therefore we need if statements not if/else
if (len(XCO2_subset)>=10000):
first_XCO2_subset=XCO2_subset[0:1000]
if (len(XCO2_subset)>=20000):
second_XCO2_subset=XCO2_subset[20000:21000]
if (len(XCO2_subset)>=30000):
third_XCO2_subset=XCO2_subset[30000:31000]
if (len(XCO2_subset)>=40000):
fourth_XCO2_subset=XCO2_subset[40000:41000]
if (len(XCO2_subset)>=50000):
fifth_XCO2_subset=XCO2_subset[50000:51000]
if (len(XCO2_subset)>=60000):
sixth_XCO2_subset=XCO2_subset[60000:61000]
if (len(XCO2_subset)>=70000):
seventh_XCO2_subset=XCO2_subset[70000:71000]
if (len(XCO2_subset)>=80000):
eight_XCO2_subset=XCO2_subset[80000:81000]
sampled_xco2 = first_XCO2_subset + second_XCO2_subset + third_XCO2_subset + fourth_XCO2_subset + fifth_XCO2_subset + sixth_XCO2_subset + seventh_XCO2_subset + eight_XCO2_subset
#LONGITUDE attempt to get ten ranges, we need to check 10 ranges therefore we need if statements not if/else
if (len(LONGITUDE_subset)>=10000):
first_LONGITUDE_subset=LONGITUDE_subset[0:1000]
if (len(LONGITUDE_subset)>=20000):
second_LONGITUDE_subset=LONGITUDE_subset[20000:21000]
if (len(LONGITUDE_subset)>=30000):
third_LONGITUDE_subset=LONGITUDE_subset[30000:31000]
if (len(LONGITUDE_subset)>=40000):
fourth_LONGITUDE_subset=LONGITUDE_subset[40000:41000]
if (len(LONGITUDE_subset)>=50000):
fifth_LONGITUDE_subset=LONGITUDE_subset[50000:51000]
if (len(LONGITUDE_subset)>=60000):
sixth_LONGITUDE_subset=LONGITUDE_subset[60000:61000]
if (len(LONGITUDE_subset)>=70000):
seventh_LONGITUDE_subset=LONGITUDE_subset[70000:71000]
if (len(LONGITUDE_subset)>=80000):
eight_LONGITUDE_subset=LONGITUDE_subset[80000:81000]
sampled_LONGITUDE = first_LONGITUDE_subset + second_LONGITUDE_subset + third_LONGITUDE_subset + fourth_LONGITUDE_subset + fifth_LONGITUDE_subset + sixth_LONGITUDE_subset + seventh_LONGITUDE_subset + eight_LONGITUDE_subset
#LATITUDE attempt to get ten ranges, we need to check 10 ranges therefore we need if statements not if/else
if (len(LATITUDE_subset)>=10000):
first_LATITUDE_subset=LATITUDE_subset[0:1000]
if (len(LATITUDE_subset)>=20000):
second_LATITUDE_subset=LATITUDE_subset[20000:21000]
if (len(LATITUDE_subset)>=30000):
third_LATITUDE_subset=LATITUDE_subset[30000:31000]
if (len(LATITUDE_subset)>=40000):
fourth_LATITUDE_subset=LATITUDE_subset[40000:41000]
if (len(LATITUDE_subset)>=50000):
fifth_LATITUDE_subset=LATITUDE_subset[50000:51000]
if (len(LATITUDE_subset)>=60000):
sixth_LATITUDE_subset=LATITUDE_subset[60000:61000]
if (len(LATITUDE_subset)>=70000):
seventh_LATITUDE_subset=LATITUDE_subset[70000:71000]
if (len(LATITUDE_subset)>=80000):
eight_LATITUDE_subset=LATITUDE_subset[80000:81000]
sampled_LATITUDE = first_LATITUDE_subset + second_LATITUDE_subset + third_LATITUDE_subset + fourth_LATITUDE_subset + fifth_LATITUDE_subset + sixth_LATITUDE_subset + seventh_LATITUDE_subset + eight_LATITUDE_subset
ax = plt.axes(projection=ccrs.Mollweide())
#plt.contourf(LONGITUDE_subset, LATITUDE_subset, XCO2_subset, 60,transform=ccrs.PlateCarree())
for long, lat, value in zip(sampled_LONGITUDE, sampled_LATITUDE,sampled_xco2):
#print(long, lat, value)
if value >= 0 and value < 370:
ax.plot(long,lat,marker='o',color='blue', markersize=1, transform=ccrs.PlateCarree())
elif value >= 370 and value < 390:
ax.plot(long,lat,marker='o',color='cyan', markersize=1, transform=ccrs.PlateCarree())
elif value >= 390 and value < 402:
ax.plot(long,lat,marker='o',color='yellow', markersize=1, transform=ccrs.PlateCarree())
elif value >= 402 and value < 410:
ax.plot(long,lat,marker='o',color='orange', markersize=1, transform=ccrs.PlateCarree())
elif value >= 410 and value < 415:
ax.plot(long,lat,marker='o',color='red', markersize=1, transform=ccrs.PlateCarree())
else:
ax.plot(long,lat,marker='o',color='brown', markersize=1, transform=ccrs.PlateCarree())
ax.coastlines()
plt.show()
mapXoco2()
输出：
文件format:NETCDF4
dimensions.keys():odict_keys(['sounding_id', 'levels', 'bands', 'vertices', 'epoch_dimension', 'source_files'])
变量['xco2']： float32 xco2(sounding_id) 单位：ppm long_name：XCO2 missing_value：-999999.0 评论：CO2 的列平均干空气摩尔分数（包括偏差校正）
无限维度： 当前形状 = (82776,) 填充，使用默认 _FillValue 9.969209968386869e+36
-> float32 纬度(sounding_id) 单位：degrees_north long_name: 纬度 missing_value：-999999.0 评论：测量的中心纬度 无限维度： 当前形状 = (82776,) 填充，使用默认 _FillValue 9.969209968386869e+36
float32 经度(sounding_id) 单位：degrees_east long_name: 经度 missing_value：-999999.0 注释：测量的中心经度 无限维度： 当前形状 = (82776,) 填充，使用默认 _FillValue 9.969209968386869e+36
1) 地图和大陆怎么了？
谢谢，感谢任何有用的帮助。
看起来你的转换参数不正确。如果您有 latitude/longitude 数据，则转换参数的值应为 ccrs.PlateCarree()。有关详细信息，请参阅 cartopy 文档中的本指南：https://scitools.org.uk/cartopy/docs/latest/tutorials/understanding_transform.html.
我无法 运行 你的例子来验证这是解决方案。要充分利用 Stack Overflow，您应该提供一个其他人可以自己 运行 的最小工作示例。有关提示，请参阅 https://whosebug.com/help/mcve and https://whosebug.com/help/how-to-ask。