Differences between when transform_first is True and False #2233
Description
Description
I'm using Cartopy to create spatial maps of 24 hour precipitation accumulations from NCEP's GFS model. I'm plotting the data that is on a 0.25 degree lat-lon grid (cartopy.crs.PlateCarree) to Lambert Conformal (cartopy.crs.LambertConformal) projection. I started noticing some oddities in the images. After some testing, I traced it back to the value of transform_first. With transform_first=False things look fine; with transform_first=True it looks like something odd is happening. I would think the images would look the same as I thought transform_first was a setting to help speedup runtime. This appears to be a bug in transform_first=True.
Attached are two images displaying the issue. Additionally text files with the data needed to use the code.
gfs_init2023080412_f24_APCP_A24.txt
gfs_init2023080412_f24_lat.txt
gfs_init2023080412_f24_lon.txt
Code to reproduce
#!/usr/bin/env python3
'''
Name: global_det_atmos_plots_precip_spatial_map.py
Contact(s): Mallory Row
Abstract: This script generates a spatial map for precip
'''
import netCDF4 as netcdf
import pyproj
import numpy as np
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.util import add_cyclic_point
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from cartopy import config
# Set contour levels and color map
clevs = [0.01, 0.10, 0.25, 0.50, 0.75, 1.00, 1.25, 1.50,
1.75, 2.00, 2.50, 3.00, 4.00, 5.00, 7.00, 10.00,
15.00, 20.00]
colorlist = ['#7fff00', '#00cd00', '#008b00', '#104e8b',
'#1e90ff', '#00b2ee', '#00eeee', '#8968cd',
'#912cee', '#8b008b', '#8b0000', '#cd0000',
'#ee4000', '#ff7f00', '#cd8500', '#ffd700',
'#ffff00', '#ffff02']
cmap_over_color = '#ffaeb9'
cmap = matplotlib.colors.ListedColormap(colorlist)
cmap_over_color = cmap_over_color
norm = matplotlib.colors.BoundaryNorm(clevs, cmap.N)
# Read in data
precip_lat = np.loadtxt('gfs_init2023080412_f24_lat.txt')
precip_lon = np.loadtxt('gfs_init2023080412_f24_lon.txt')
precip_APCP_A24 = np.loadtxt('gfs_init2023080412_f24_APCP_A24.txt')
x, y = np.meshgrid(precip_lon, precip_lat)
precip_APCP_A24 = precip_APCP_A24 * 0.0393701 # convert to inches
# Set projection
myproj=ccrs.LambertConformal()
# Make transform_first=False
fig = plt.figure(figsize=(8.,6.))
gs_hspace, gs_wspace = 0, 0
gs_bottom, gs_top = 0.125, 0.85
gs = gridspec.GridSpec(1,1, bottom=gs_bottom, top=gs_top,
hspace=gs_hspace, wspace=gs_wspace)
ax1 = fig.add_subplot(gs[0], projection=myproj)
ax1.add_feature(cfeature.COASTLINE.with_scale('50m'),
zorder=2, linewidth=1)
ax1.add_feature(cfeature.BORDERS.with_scale('50m'),
zorder=2, linewidth=1)
ax1.add_feature(cfeature.STATES.with_scale('50m'),
zorder=2, linewidth=1)
fig.suptitle('transform_first=False')
CF1 = ax1.contourf(x, y, precip_APCP_A24,
transform=ccrs.PlateCarree(),
levels=clevs, norm=norm,
cmap=cmap, extend='max',
transform_first=False)
CF1.cmap.set_over(cmap_over_color)
plt.savefig('transform_first_False.png')
# Make transform_first=True
fig = plt.figure(figsize=(8.,6.))
gs_hspace, gs_wspace = 0, 0
gs_bottom, gs_top = 0.125, 0.85
gs = gridspec.GridSpec(1,1, bottom=gs_bottom, top=gs_top,
hspace=gs_hspace, wspace=gs_wspace)
ax1 = fig.add_subplot(gs[0], projection=myproj)
ax1.add_feature(cfeature.COASTLINE.with_scale('50m'),
zorder=2, linewidth=1)
ax1.add_feature(cfeature.BORDERS.with_scale('50m'),
zorder=2, linewidth=1)
ax1.add_feature(cfeature.STATES.with_scale('50m'),
zorder=2, linewidth=1)
fig.suptitle('transform_first=True')
CF1 = ax1.contourf(x, y, precip_APCP_A24,
transform=ccrs.PlateCarree(),
levels=clevs, norm=norm,
cmap=cmap, extend='max',
transform_first=True)
CF1.cmap.set_over(cmap_over_color)
plt.savefig('transform_first_True.png')
plt.close('all')
Traceback
Full environment definition
Operating system
NCEP's WCOSS2
Cartopy version
0.21.1
conda list
pip list
Package Version
------------------ ----------
asttokens 2.2.1
attrs 22.1.0
backcall 0.2.0
Cartopy 0.21.1
certifi 2022.9.24
cftime 1.6.2
charset-normalizer 3.1.0
click 8.1.3
contourpy 1.0.5
cycler 0.11.0
dateutils 0.6.12
decorator 5.1.1
distlib 0.3.6
executing 1.2.0
filelock 3.8.0
Flask 2.2.3
fonttools 4.37.4
geos 0.2.3
idna 3.4
imageio 2.22.1
iniconfig 1.1.1
ipython 8.9.0
itsdangerous 2.1.2
jedi 0.18.2
Jinja2 3.1.2
joblib 1.2.0
kiwisolver 1.4.4
lxml 4.9.2
MarkupSafe 2.1.2
matplotlib 3.7.1
matplotlib-inline 0.1.6
metplus 4.1.4
MetPy 1.4.1
netCDF4 1.6.3
numpy 1.25.2
packaging 21.3
pandas 1.5.3
parso 0.8.3
pexpect 4.8.0
pickleshare 0.7.5
Pillow 9.2.0
Pint 0.20.1
pip 23.1.2
pipdeptree 2.3.1
pipenv 2022.10.11
platformdirs 2.5.2
plotly 5.14.1
pluggy 1.0.0
pooch 1.7.0
prompt-toolkit 3.0.36
ptyprocess 0.7.0
pure-eval 0.2.2
py 1.11.0
Pygments 2.14.0
pyparsing 3.0.9
pyproj 3.4.0
pyshp 2.3.1
pytest 7.1.3
python-dateutil 2.8.2
pytz 2022.4
PyYAML 6.0
requests 2.28.2
scikit-learn 1.2.2
scipy 1.10.1
setuptools 68.0.0
shapely 2.0.1
six 1.16.0
stack-data 0.6.2
tenacity 8.2.2
threadpoolctl 3.1.0
tomli 2.0.1
traitlets 5.9.0
tzdata 2023.3
urllib3 1.26.15
virtualenv 20.16.5
virtualenv-clone 0.5.7
wcwidth 0.2.6
Werkzeug 2.2.3
wheel 0.40.0
xarray 2023.3.0