使用 cartopy 在其他投影仪中绘制投影数据 [英] Plotting projected data in other projectons using cartopy

问题描述

这个问题与绘制一些我使用兰伯特保形(LCC)CRS的数据有关.虽然这些问题特别涉及在多个投影中绘制LCC数据，但它也适用于一般使用Cartopy的情况，因为我想更好地了解使用Cartopy进行绘制的逻辑/过程.

以下是我正在尝试做的一些代码示例.第一个示例只是绘制一些 LCC 数据.在链接的答案，似乎我对问题2的答案是不需要transform_points .可以在许多 matplotlib 绘图方法中简单地使用 transform 关键字参数.这就是我最初的想法.但是，跳过 transform_points 对我来说不起作用.请参见下面的示例:

  ax = plt.axes(投影= ccrs.Orthographic(265,25))ax.pcolormesh(lon，lat，dat，transform = proj)ax.add_feature(cf.NaturalEarthFeature(category ='文化'，name ='admin_1_states_provinces_lines'，规模='50m'，facecolor='无'))ax.coastlines('50m')ax.add_feature(cf.BORDERS)ax.set_global()

产生以下情节的地方:

没有transform_points步骤的正交图

问题似乎是 lat 和 lon 输入没有转换为网格坐标，因此它们只绘制在图的极小区域中.因此，为了扩展问题2，如果您应该跳过 transform_points ，那么根据我上面的示例，cartopy的绘制方法是否存在错误?还是我还缺一步?

解决方案

需要在地理坐标和投影(或网格)坐标之间进行重要区分.有关这些内容的详细说明，请参见here.

import cartopy.crs as ccrs
import cartopy.feature as cf
import matplotlib.pyplot as plt
import numpy as np

proj = ccrs.LambertConformal(central_latitude = 25, 
                             central_longitude = 265, 
                             standard_parallels = (25, 25))

# Data and coordinates (from download link above)
with np.load('nam_218_20120414_1200_006.npz') as nam:
   dat = nam['dpc']
   lat = nam['lat']
   lon = nam['lon']

ax = plt.axes(projection = proj)
ax.pcolormesh(lon, lat, dat, transform = ccrs.PlateCarree())
ax.add_feature(cf.NaturalEarthFeature(
               category='cultural',
               name='admin_1_states_provinces_lines',
               scale='50m',
               facecolor='none'))
ax.coastlines('50m')
ax.add_feature(cf.BORDERS)
plt.show()

The plot produced can be seen here:

US Dewpoints on LCC Map

My first confusion when using cartopy was why I always have to transform to PlateCarree when plotting? My initial thought was the transform keyword of the pcolormesh call needed the LCC projection information and not PlateCarree.

Next, if I want to plot my LCC data in another projection, e.g. Orthographic, would I go about doing so like below?

# First, transform from LCC to Orthographic
transform = proj.transform_points(ccrs.Orthographic(265,25), lon, lat)
x = transform[..., 0]
y = transform[..., 1]

ax = plt.axes(projection = ccrs.Orthographic(265,25))
ax.pcolormesh(x, y, dat, transform = ccrs.PlateCarree())
ax.add_feature(cf.NaturalEarthFeature(
               category='cultural',
               name='admin_1_states_provinces_lines',
               scale='50m',
               facecolor='none'))
ax.coastlines('50m')
ax.add_feature(cf.BORDERS)
ax.set_global()

The plot produced can be seen here:

US Dewpoints on Orthographic Map

I think the Orthographic map looks right, but I'd like to be sure that I understand the process of re-projection with cartopy correctly.

In summary, I would like to know the following things:

1. Do you always have to transform to PlateCarree when plotting? Why or why not?
2. Does re-projecting simply require a call to the transform_points method or are there other steps involved?

Update 1

Based on the answer from @swatchai, it seems as though the answer to my Question 2 is that transform_points is not required. One can simply use the transform keyword argument in many matplotlib plotting methods. This is what I thought originally. However, skipping the transform_points has not worked for me. See example below:

ax = plt.axes(projection = ccrs.Orthographic(265,25))
ax.pcolormesh(lon, lat, dat, transform = proj)
ax.add_feature(cf.NaturalEarthFeature(
               category='cultural',
               name='admin_1_states_provinces_lines',
               scale='50m',
               facecolor='none'))
ax.coastlines('50m')
ax.add_feature(cf.BORDERS)
ax.set_global()

Which produces this plot:

Orthographic Plot Without transform_points Step

The problem appears to be that the lat and lon input does not get transformed into the grid coordinates so they only get plotted in an extremely small area of the plot. So, to expand upon Question 2, if you are supposed to skip transform_points is there a bug in cartopy's plotting methods based on my above example? Or am I still missing a step?

解决方案

An important distinction needs to be made between geographic and projected (or grid) coordinates. A more detailed description of those can be found here. The important thing, and what helps to answer Question 1, is that latitude and longitude are geographic coordinates whereas points that have units in meters are projected coordinates.

The numerical weather model where the example data came from uses the Lambert Conformal projection in its calculations (more here). However, the coordinates that get output are latitude and longitude. If you are inexperienced with spatial data, you can end up thinking that the lat/lon pairs are LCC projected coordinates when they are in fact geogrphic coordinates; the LCC stuff is used during model integration.

To answer Question 1, no, you do not always have to use PlateCarree as the source CRS. You do, however, always use PlateCarree for latitude and longitude data (which was the case here). This way cartopy will correctly transform your lat/lon values into projected coordinates (in meters) and be able to easily transform your data to other projections during plotting. This issue is ultimately the reason for the seemingly blank plot in Update 1. By saying the source data have LCC projected coordinates in transform, cartopy took the lat/lon input and interpreted them as having units of meters. The data did get plotted, but the extent was so small that it was impossible to see them without changing the plot extent to be the same as the data.

With regard to Question 2, no, using transform_points is not a requirement. cartopy was set up in such a way to make is easy to plot in multiple projections with minimal intermediary steps. As @swatchai mentioned, sometimes you may want to use the actual projected coordinates and using the transform_points method will allow you to do that. When transform_points was used to produce the second plot in the original post it essentially was manually doing what would have automatically been done had the input coordinates been handled properly with PlateCarree in transform.

Finally, an important clarification was made by @ajdawson with regard to how to use projection and transform when plotting. Once you understand what you have for source coordinates, this information is also useful. The comment is quoted below:

In general, projection tells cartopy what the drawn map should look like, and transform tells cartopy what coordinate system your data is represented in. You can set projection to any projection you like, but transform needs to match whatever coordinate system your data uses.

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