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The area of np.nan is filled with the same color as the surrounding area
Description
When drawing a diagram using contourf together with cartopy, the area of np.nan is filled with the same color as the surrounding area under certain conditions.
Code to reproduce
I have a 2D grid point value and I'm trying to plot it.
lon = np.load("lon.npy") # lon axis
lat = np.load("lat.npy") # lat axis
gpi = np.load("gpi.npy") # 2D array
To make this problem easier to understand, I will artificially fill two square areas with np.nan for this data.
i = np.argmin(np.abs(lon - 140))
j = np.argmin(np.abs(lat - 30))
gpi[j:j+30, i:i+30] = np.nan
i = np.argmin(np.abs(lon - 150))
j = np.argmin(np.abs(lat - 40))
gpi[j:j+30, i:i+30] = np.nan
Plotting this data using pcolormesh together with cartopy gives an image where the area filled with np.nan is correctly painted white.
ax.pcolormesh(
lon, lat, gpi,
norm=norm,
transform=ccrs.PlateCarree())
In contrast, if I plot this data using contourf together with cartopy, the area filled with np.nan that does not cross the contour line (e.g. left-bottom box and Taiwan) is painted in the same color as the surrounding area.
ax.contourf(
lon, lat, gpi,
levels=levels,
extend="max",
transform=ccrs.PlateCarree())
Plotting this data using contourf without cartopy, the area filled with np.nan is correctly painted white.
plt.contourf(
lon, lat, gpi,
levels=levels,
extend="both")
This bug does not occur all the time, but seems to happen under certain conditions. However, I do not know under what conditions it happens.
Sample data and the complete code are available in the following repository: https://github.com/wm-ytakano/contourf_nan_filled
Full environment definition
Operating system
macOS Big Sur version 11.2.3
Cartopy version
0.18.0
conda list
# packages in environment at /Users/ytakano/miniconda3/envs/cartopy-env:
#
# Name Version Build Channel
appnope 0.1.2 py39hecd8cb5_1001
argon2-cffi 20.1.0 py39h9ed2024_1
async_generator 1.10 pyhd3eb1b0_0
attrs 20.3.0 pyhd3eb1b0_0
autopep8 1.5.6 pyhd3eb1b0_0
backcall 0.2.0 pyhd3eb1b0_0
blas 1.0 openblas
bleach 3.3.0 pyhd3eb1b0_0
ca-certificates 2021.1.19 hecd8cb5_1
cartopy 0.18.0 py39he6a1819_5 conda-forge
certifi 2020.12.5 py39hecd8cb5_0
cffi 1.14.5 py39h2125817_0
cftime 1.4.1 py39he3068b8_0
curl 7.71.1 hb0a8c7a_1
cycler 0.10.0 py39hecd8cb5_0
decorator 5.0.3 pyhd3eb1b0_0
defusedxml 0.7.1 pyhd3eb1b0_0
entrypoints 0.3 py39hecd8cb5_0
flake8 3.9.0 pyhd3eb1b0_0
freetype 2.10.4 ha233b18_0
geos 3.8.1 hb1e8313_0
hdf4 4.2.13 h39711bb_2
hdf5 1.10.6 hdbbcd12_0
icu 58.2 h0a44026_3
importlib-metadata 3.7.3 py39hecd8cb5_1
importlib_metadata 3.7.3 hd3eb1b0_1
ipykernel 5.3.4 py39h01d92e1_0
ipython 7.22.0 py39h71a6800_0 conda-forge
ipython_genutils 0.2.0 pyhd3eb1b0_1
ipywidgets 7.6.3 pyhd3eb1b0_1
jedi 0.18.0 py39hecd8cb5_1
jinja2 2.11.3 pyhd3eb1b0_0
jpeg 9b he5867d9_2
jsonschema 3.2.0 py_2
jupyter 1.0.0 py39hecd8cb5_7
jupyter_client 6.1.12 pyhd3eb1b0_0
jupyter_console 6.4.0 pyhd3eb1b0_0
jupyter_core 4.7.1 py39hecd8cb5_0
jupyterlab_pygments 0.1.2 py_0
jupyterlab_widgets 1.0.0 pyhd3eb1b0_1
kiwisolver 1.3.1 py39h23ab428_0
krb5 1.18.2 h75d18d8_0
lcms2 2.11 h92f6f08_0
libcurl 7.71.1 h8a08a2b_1
libcxx 11.0.0 h439d374_0 conda-forge
libedit 3.1.20210216 h9ed2024_1
libffi 3.3 hb1e8313_2
libgfortran 3.0.1 h93005f0_2
libnetcdf 4.6.1 hfd9a460_3
libopenblas 0.3.13 h7ddc91c_0
libpng 1.6.37 ha441bb4_0
libsodium 1.0.18 h1de35cc_0
libssh2 1.9.0 ha12b0ac_1
libtiff 4.1.0 hcb84e12_1
lz4-c 1.9.3 h23ab428_0
markupsafe 1.1.1 py39h9ed2024_0
matplotlib 3.3.4 py39hecd8cb5_0
matplotlib-base 3.3.4 py39h8b3ea08_0
mccabe 0.6.1 py39hecd8cb5_1
mistune 0.8.4 py39h9ed2024_1000
nbclient 0.5.3 pyhd3eb1b0_0
nbconvert 6.0.7 py39hecd8cb5_0
nbformat 5.1.3 pyhd3eb1b0_0
ncurses 6.2 h0a44026_1
nest-asyncio 1.5.1 pyhd3eb1b0_0
netcdf4 1.5.6 py39h1695cb1_0
notebook 6.3.0 py39hecd8cb5_0
numpy 1.19.2 py39h0fa1045_0
numpy-base 1.19.2 py39h3a452eb_0
olefile 0.46 py_0
openssl 1.1.1k h9ed2024_0
packaging 20.9 pyhd3eb1b0_0
pandas 1.2.3 py39hb2f4e1b_0
pandoc 2.12 hecd8cb5_0
pandocfilters 1.4.3 py39hecd8cb5_1
parso 0.8.2 pyhd3eb1b0_0
pexpect 4.8.0 pyhd3eb1b0_3
pickleshare 0.7.5 pyhd3eb1b0_1003
pillow 8.2.0 py39h5270095_0
pip 21.0.1 py39hecd8cb5_0
proj 7.1.1 h45baca5_3 conda-forge
prometheus_client 0.10.0 pyhd3eb1b0_0
prompt-toolkit 3.0.17 pyh06a4308_0
prompt_toolkit 3.0.17 hd3eb1b0_0
ptyprocess 0.7.0 pyhd3eb1b0_2
pycodestyle 2.6.0 pyhd3eb1b0_0
pycparser 2.20 py_2
pyflakes 2.2.0 pyhd3eb1b0_0
pygments 2.8.1 pyhd3eb1b0_0
pyparsing 2.4.7 pyhd3eb1b0_0
pyqt 5.9.2 py39h23ab428_6
pyrsistent 0.17.3 py39h9ed2024_0
pyshp 2.1.3 pyhd3eb1b0_0
python 3.9.2 h88f2d9e_0
python-dateutil 2.8.1 pyhd3eb1b0_0
python_abi 3.9 1_cp39 conda-forge
pytz 2021.1 pyhd3eb1b0_0
pyzmq 20.0.0 py39h23ab428_1
qt 5.9.7 h468cd18_1
qtconsole 5.0.3 pyhd3eb1b0_0
qtpy 1.9.0 py_0
readline 8.1 h9ed2024_0
scipy 1.6.2 py39h4420a3a_0
seawater 3.3.4 py_1 conda-forge
send2trash 1.5.0 pyhd3eb1b0_1
setuptools 52.0.0 py39hecd8cb5_0
shapely 1.7.1 py39h7edaf14_1 conda-forge
sip 4.19.13 py39h23ab428_0
six 1.15.0 py39hecd8cb5_0
sqlite 3.35.3 hce871da_0
terminado 0.9.4 py39hecd8cb5_0
testpath 0.4.4 pyhd3eb1b0_0
tk 8.6.10 hb0a8c7a_0
toml 0.10.2 pyhd3eb1b0_0
tornado 6.1 py39h9ed2024_0
traitlets 5.0.5 pyhd3eb1b0_0
tzdata 2020f h52ac0ba_0
wcwidth 0.2.5 py_0
webencodings 0.5.1 py39hecd8cb5_1
wheel 0.36.2 pyhd3eb1b0_0
widgetsnbextension 3.5.1 py39hecd8cb5_0
xarray 0.17.0 pyhd3eb1b0_0
xz 5.2.5 h1de35cc_0
zeromq 4.3.4 h23ab428_0
zipp 3.4.1 pyhd3eb1b0_0
zlib 1.2.11 h1de35cc_3
zstd 1.4.9 h322a384_0
pip list
Package Version
------------------- -------------------
appnope 0.1.2
argon2-cffi 20.1.0
async-generator 1.10
attrs 20.3.0
autopep8 1.5.6
backcall 0.2.0
bleach 3.3.0
Cartopy 0.18.0
certifi 2020.12.5
cffi 1.14.5
cftime 1.4.1
cycler 0.10.0
decorator 5.0.3
defusedxml 0.7.1
entrypoints 0.3
flake8 3.9.0
importlib-metadata 3.7.3
ipykernel 5.3.4
ipython 7.22.0
ipython-genutils 0.2.0
ipywidgets 7.6.3
jedi 0.18.0
Jinja2 2.11.3
jsonschema 3.2.0
jupyter 1.0.0
jupyter-client 6.1.12
jupyter-console 6.4.0
jupyter-core 4.7.1
jupyterlab-pygments 0.1.2
jupyterlab-widgets 1.0.0
kiwisolver 1.3.1
MarkupSafe 1.1.1
matplotlib 3.3.4
mccabe 0.6.1
mistune 0.8.4
nbclient 0.5.3
nbconvert 6.0.7
nbformat 5.1.3
nest-asyncio 1.5.1
netCDF4 1.5.6
notebook 6.3.0
numpy 1.19.2
olefile 0.46
packaging 20.9
pandas 1.2.3
pandocfilters 1.4.3
parso 0.8.2
pexpect 4.8.0
pickleshare 0.7.5
Pillow 8.2.0
pip 21.0.1
prometheus-client 0.10.0
prompt-toolkit 3.0.17
ptyprocess 0.7.0
pycodestyle 2.6.0
pycparser 2.20
pyflakes 2.2.0
Pygments 2.8.1
pyparsing 2.4.7
pyrsistent 0.17.3
pyshp 2.1.3
python-dateutil 2.8.1
pytz 2021.1
pyzmq 20.0.0
qtconsole 5.0.3
QtPy 1.9.0
scipy 1.6.2
seawater 3.3.4
Send2Trash 1.5.0
setuptools 52.0.0.post20210125
Shapely 1.7.1
sip 4.19.13
six 1.15.0
terminado 0.9.4
testpath 0.4.4
toml 0.10.2
tornado 6.1
traitlets 5.0.5
wcwidth 0.2.5
webencodings 0.5.1
wheel 0.36.2
widgetsnbextension 3.5.1
xarray 0.17.0
zipp 3.4.1
Affects my work too, except with masked arrays. MWE:
import matplotlib.pyplot as plt
import numpy as np
import cartopy.crs as ccrs
lons = [-180, -179]
lats = [-90, -89]
lonsm, latsm = np.meshgrid(lons, lats)
lonsm = np.ma.masked_array(lonsm, mask=[[False, False], [False, True]])
latsm = np.ma.masked_array(latsm, mask=[[False, False], [False, True]])
fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
ax1.scatter(lonsm, latsm)
ax2 = fig.add_subplot(3, 1, 2, projection=ccrs.PlateCarree())
ax2.scatter(lonsm, latsm)
ax3 = fig.add_subplot(3, 1, 3, projection=ccrs.PlateCarree())
ax3.scatter(lonsm, latsm, transform=ccrs.PlateCarree())
Perhaps related to transform_points not respecting a mask:
>>> ccrs.PlateCarree().transform_points(ccrs.PlateCarree(), lonsm, latsm)
array([[[-180., -90., 0.],
[-179., -90., 0.]],
[[-180., -89., 0.],
[-179., -89., 0.]]])
@kdpenner your problem is not related with the original problem. Masked arrays in numpy are very fragile and only the mask-aware operations can reserve masks. So mask information can be lost very easily. The best way is to use NaN.
import matplotlib.pyplot as plt
import numpy as np
import cartopy.crs as ccrs
lons = [-180., -179.]
lats = [-90., -89.]
lonsm, latsm = np.meshgrid(lons, lats)
lonsm[1,1] = np.nan # = np.ma.masked_array(lonsm, mask=[[False, False], [False, True]])
latsm[1,1] = np.nan # = np.ma.masked_array(latsm, mask=[[False, False], [False, True]])
fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
ax1.scatter(lonsm, latsm)
ax2 = fig.add_subplot(3, 1, 2, projection=ccrs.PlateCarree())
ax2.scatter(lonsm, latsm)
ax3 = fig.add_subplot(3, 1, 3, projection=ccrs.PlateCarree())
ax3.scatter(lonsm, latsm, transform=ccrs.PlateCarree())
plt.show()
The output is
With Cartopy 0.22 the OP contour code gives me this, so I guess the problem has been fixed at some point.
