html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/7388#issuecomment-1519536791,https://api.github.com/repos/pydata/xarray/issues/7388,1519536791,IC_kwDOAMm_X85akkqX,6883049,2023-04-24T07:32:26Z,2023-04-24T07:32:26Z,CONTRIBUTOR,"Good question. Right now ci/requirements/environment.yml is resolving libnetcdf 4.9.1, so fixing 4.9.2 would not work. I am not sure why or how to change this, as few package versions are pinned.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1503046820
https://github.com/pydata/xarray/issues/7388#issuecomment-1517820737,https://api.github.com/repos/pydata/xarray/issues/7388,1517820737,IC_kwDOAMm_X85aeBtB,6883049,2023-04-21T13:15:03Z,2023-04-21T13:15:03Z,CONTRIBUTOR,"I think it is about these two issues only, so backporting the fixes it should work.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1503046820
https://github.com/pydata/xarray/issues/7388#issuecomment-1511596653,https://api.github.com/repos/pydata/xarray/issues/7388,1511596653,IC_kwDOAMm_X85aGSJt,6883049,2023-04-17T15:28:21Z,2023-04-17T15:29:43Z,CONTRIBUTOR,Thanks. It looks like the errors are related to this bug https://github.com/Unidata/netcdf-c/issues/2674 The fix has been merged so I hope they include it in the next netcdf-c release. For the moment I prefer not to merge this as netcdf 4.9.2 and dask do not seem to play well together.,"{""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1503046820
https://github.com/pydata/xarray/issues/7388#issuecomment-1503827455,https://api.github.com/repos/pydata/xarray/issues/7388,1503827455,IC_kwDOAMm_X85ZopX_,6883049,2023-04-11T17:38:51Z,2023-04-11T17:38:51Z,CONTRIBUTOR,"Hi. I updated the branch and created a fresh python environment with the idea of writing another, final test for this.
However before doing it I run the test suite, and got some bad HDF5 errors in test_backends.py::test_open_mfdataset_manyfiles[netcdf4-20-True-None-5]
```
#000: H5A.c line 528 in H5Aopen_by_name(): can't open attribute
major: Attribute
minor: Can't open object
#001: H5VLcallback.c line 1091 in H5VL_attr_open(): attribute open failed
major: Virtual Object Layer
minor: Can't open object
#002: H5VLcallback.c line 1058 in H5VL__attr_open(): attribute open failed
major: Virtual Object Layer
minor: Can't open object
#003: H5VLnative_attr.c line 130 in H5VL__native_attr_open(): can't open attribute
major: Attribute
minor: Can't open object
#004: H5Aint.c line 545 in H5A__open_by_name(): unable to load attribute info from object header
major: Attribute
minor: Unable to initialize object
#005: H5Oattribute.c line 494 in H5O__attr_open_by_name(): can't locate attribute: '_QuantizeBitGroomNumberOfSignificantDigits'
major: Attribute
minor: Object not found
HDF5-DIAG: Error detected in HDF5 (1.12.2) thread 1:
#000: H5A.c line 528 in H5Aopen_by_name(): can't open attribute
major: Attribute
minor: Can't open object
#001: H5VLcallback.c line 1091 in H5VL_attr_open(): attribute open failed
major: Virtual Object Layer
minor: Can't open object
#002: H5VLcallback.c line 1058 in H5VL__attr_open(): attribute open failed
major: Virtual Object Layer
minor: Can't open object
#003: H5VLnative_attr.c line 130 in H5VL__native_attr_open(): can't open attribute
major: Attribute
minor: Can't open object
#004: H5Aint.c line 545 in H5A__open_by_name(): unable to load attribute info from object header
major: Attribute
minor: Unable to initialize object
#005: H5Oattribute.c line 494 in H5O__attr_open_by_name(): can't locate attribute: '_QuantizeGranularBitRoundNumberOfSignificantDigits'
major: Attribute
minor: Object not found
HDF5-DIAG: Error detected in HDF5 (1.12.2) thread 1:
#000: H5A.c line 528 in H5Aopen_by_name(): can't open attribute
major: Attribute
minor: Can't open object
#001: H5VLcallback.c line 1091 in H5VL_attr_open(): attribute open failed
major: Virtual Object Layer
minor: Can't open object
#002: H5VLcallback.c line 1058 in H5VL__attr_open(): attribute open failed
major: Virtual Object Layer
minor: Can't open object
#003: H5VLnative_attr.c line 130 in H5VL__native_attr_open(): can't open attribute
major: Attribute
minor: Can't open object
#004: H5Aint.c line 545 in H5A__open_by_name(): unable to load attribute info from object header
major: Attribute
minor: Unable to initialize object
#005: H5Oattribute.c line 494 in H5O__attr_open_by_name(): can't locate attribute: '_QuantizeBitRoundNumberOfSignificantBits'
major: Attribute
minor: Object not found
```
I am not sure what is going on. It seems that the currently resolved netcdf4-hdf5 versions do not like the default parameters we are supplying. My environment is
```
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
affine 2.4.0 pyhd8ed1ab_0 conda-forge
aiobotocore 2.5.0 pyhd8ed1ab_0 conda-forge
aiohttp 3.8.4 py310h1fa729e_0 conda-forge
aioitertools 0.11.0 pyhd8ed1ab_0 conda-forge
aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge
antlr-python-runtime 4.7.2 py310hff52083_1003 conda-forge
asciitree 0.3.3 py_2 conda-forge
async-timeout 4.0.2 pyhd8ed1ab_0 conda-forge
attrs 22.2.0 pyh71513ae_0 conda-forge
backports.zoneinfo 0.2.1 py310hff52083_7 conda-forge
beautifulsoup4 4.12.2 pyha770c72_0 conda-forge
blosc 1.21.3 hafa529b_0 conda-forge
boost-cpp 1.78.0 h5adbc97_2 conda-forge
boto3 1.26.76 pyhd8ed1ab_0 conda-forge
botocore 1.29.76 pyhd8ed1ab_0 conda-forge
bottleneck 1.3.7 py310h0a54255_0 conda-forge
brotli 1.0.9 h166bdaf_8 conda-forge
brotli-bin 1.0.9 h166bdaf_8 conda-forge
brotlipy 0.7.0 py310h5764c6d_1005 conda-forge
bzip2 1.0.8 h7f98852_4 conda-forge
c-ares 1.18.1 h7f98852_0 conda-forge
ca-certificates 2022.12.7 ha878542_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
cairo 1.16.0 ha61ee94_1014 conda-forge
cartopy 0.21.1 py310hcb7e713_0 conda-forge
cdat_info 8.2.1 pyhd8ed1ab_2 conda-forge
cdms2 3.1.5 py310hb9168da_16 conda-forge
cdtime 3.1.4 py310h87e304a_8 conda-forge
certifi 2022.12.7 pyhd8ed1ab_0 conda-forge
cf-units 3.1.1 py310hde88566_2 conda-forge
cffi 1.15.1 py310h255011f_3 conda-forge
cfgrib 0.9.10.3 pyhd8ed1ab_0 conda-forge
cfgv 3.3.1 pyhd8ed1ab_0 conda-forge
cfitsio 4.2.0 hd9d235c_0 conda-forge
cftime 1.6.2 py310hde88566_1 conda-forge
charset-normalizer 2.1.1 pyhd8ed1ab_0 conda-forge
click 8.1.3 unix_pyhd8ed1ab_2 conda-forge
click-plugins 1.1.1 py_0 conda-forge
cligj 0.7.2 pyhd8ed1ab_1 conda-forge
cloudpickle 2.2.1 pyhd8ed1ab_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
contourpy 1.0.7 py310hdf3cbec_0 conda-forge
coverage 7.2.3 py310h1fa729e_0 conda-forge
cryptography 40.0.1 py310h34c0648_0 conda-forge
curl 7.88.1 hdc1c0ab_1 conda-forge
cycler 0.11.0 pyhd8ed1ab_0 conda-forge
cytoolz 0.12.0 py310h5764c6d_1 conda-forge
dask-core 2023.3.2 pyhd8ed1ab_0 conda-forge
distarray 2.12.2 pyh050c7b8_4 conda-forge
distlib 0.3.6 pyhd8ed1ab_0 conda-forge
distributed 2023.3.2.1 pyhd8ed1ab_0 conda-forge
docopt 0.6.2 py_1 conda-forge
eccodes 2.29.0 h54fcba4_0 conda-forge
entrypoints 0.4 pyhd8ed1ab_0 conda-forge
esmf 8.4.1 nompi_he2e5181_0 conda-forge
esmpy 8.4.1 pyhc1e730c_0 conda-forge
exceptiongroup 1.1.1 pyhd8ed1ab_0 conda-forge
execnet 1.9.0 pyhd8ed1ab_0 conda-forge
expat 2.5.0 hcb278e6_1 conda-forge
fasteners 0.17.3 pyhd8ed1ab_0 conda-forge
filelock 3.11.0 pyhd8ed1ab_0 conda-forge
findlibs 0.0.2 pyhd8ed1ab_0 conda-forge
flox 0.6.10 pyhd8ed1ab_0 conda-forge
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge
font-ttf-inconsolata 3.000 h77eed37_0 conda-forge
font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge
font-ttf-ubuntu 0.83 hab24e00_0 conda-forge
fontconfig 2.14.2 h14ed4e7_0 conda-forge
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
fonttools 4.39.3 py310h1fa729e_0 conda-forge
freeglut 3.2.2 h9c3ff4c_1 conda-forge
freetype 2.12.1 hca18f0e_1 conda-forge
freexl 1.0.6 h166bdaf_1 conda-forge
frozenlist 1.3.3 py310h5764c6d_0 conda-forge
fsspec 2023.4.0 pyh1a96a4e_0 conda-forge
future 0.18.3 pyhd8ed1ab_0 conda-forge
g2clib 1.6.3 hbecde78_1 conda-forge
geos 3.11.1 h27087fc_0 conda-forge
geotiff 1.7.1 h7a142b4_6 conda-forge
gettext 0.21.1 h27087fc_0 conda-forge
giflib 5.2.1 h0b41bf4_3 conda-forge
h5netcdf 1.1.0 pyhd8ed1ab_1 conda-forge
h5py 3.8.0 nompi_py310h0311031_100 conda-forge
hdf4 4.2.15 h9772cbc_5 conda-forge
hdf5 1.12.2 nompi_h4df4325_101 conda-forge
heapdict 1.0.1 py_0 conda-forge
hypothesis 6.71.0 pyha770c72_0 conda-forge
icu 70.1 h27087fc_0 conda-forge
identify 2.5.22 pyhd8ed1ab_0 conda-forge
idna 3.4 pyhd8ed1ab_0 conda-forge
importlib-metadata 6.3.0 pyha770c72_0 conda-forge
importlib_metadata 6.3.0 hd8ed1ab_0 conda-forge
importlib_resources 5.12.0 pyhd8ed1ab_0 conda-forge
iniconfig 2.0.0 pyhd8ed1ab_0 conda-forge
iris 3.4.1 pyhd8ed1ab_0 conda-forge
jasper 2.0.33 h0ff4b12_1 conda-forge
jinja2 3.1.2 pyhd8ed1ab_1 conda-forge
jmespath 1.0.1 pyhd8ed1ab_0 conda-forge
jpeg 9e h0b41bf4_3 conda-forge
json-c 0.16 hc379101_0 conda-forge
jsonschema 4.17.3 pyhd8ed1ab_0 conda-forge
jupyter_core 5.3.0 py310hff52083_0 conda-forge
kealib 1.5.0 ha7026e8_0 conda-forge
keyutils 1.6.1 h166bdaf_0 conda-forge
kiwisolver 1.4.4 py310hbf28c38_1 conda-forge
krb5 1.20.1 h81ceb04_0 conda-forge
lazy-object-proxy 1.9.0 py310h1fa729e_0 conda-forge
lcms2 2.15 hfd0df8a_0 conda-forge
ld_impl_linux-64 2.40 h41732ed_0 conda-forge
lerc 4.0.0 h27087fc_0 conda-forge
libaec 1.0.6 hcb278e6_1 conda-forge
libblas 3.9.0 16_linux64_openblas conda-forge
libbrotlicommon 1.0.9 h166bdaf_8 conda-forge
libbrotlidec 1.0.9 h166bdaf_8 conda-forge
libbrotlienc 1.0.9 h166bdaf_8 conda-forge
libcblas 3.9.0 16_linux64_openblas conda-forge
libcdms 3.1.2 h9366c0b_120 conda-forge
libcf 1.0.3 py310h71500c5_116 conda-forge
libcurl 7.88.1 hdc1c0ab_1 conda-forge
libdeflate 1.17 h0b41bf4_0 conda-forge
libdrs 3.1.2 h01ed8d5_119 conda-forge
libdrs_f 3.1.2 h059c5b8_115 conda-forge
libedit 3.1.20191231 he28a2e2_2 conda-forge
libev 4.33 h516909a_1 conda-forge
libexpat 2.5.0 hcb278e6_1 conda-forge
libffi 3.4.2 h7f98852_5 conda-forge
libgcc-ng 12.2.0 h65d4601_19 conda-forge
libgdal 3.6.2 h6c674c2_9 conda-forge
libgfortran-ng 12.2.0 h69a702a_19 conda-forge
libgfortran5 12.2.0 h337968e_19 conda-forge
libglib 2.74.1 h606061b_1 conda-forge
libglu 9.0.0 he1b5a44_1001 conda-forge
libgomp 12.2.0 h65d4601_19 conda-forge
libiconv 1.17 h166bdaf_0 conda-forge
libkml 1.3.0 h37653c0_1015 conda-forge
liblapack 3.9.0 16_linux64_openblas conda-forge
libllvm11 11.1.0 he0ac6c6_5 conda-forge
libnetcdf 4.9.1 nompi_h34a3ff0_101 conda-forge
libnghttp2 1.52.0 h61bc06f_0 conda-forge
libnsl 2.0.0 h7f98852_0 conda-forge
libopenblas 0.3.21 pthreads_h78a6416_3 conda-forge
libpng 1.6.39 h753d276_0 conda-forge
libpq 15.2 hb675445_0 conda-forge
librttopo 1.1.0 ha49c73b_12 conda-forge
libspatialite 5.0.1 h221c8f1_23 conda-forge
libsqlite 3.40.0 h753d276_0 conda-forge
libssh2 1.10.0 hf14f497_3 conda-forge
libstdcxx-ng 12.2.0 h46fd767_19 conda-forge
libtiff 4.5.0 h6adf6a1_2 conda-forge
libuuid 2.38.1 h0b41bf4_0 conda-forge
libwebp-base 1.3.0 h0b41bf4_0 conda-forge
libxcb 1.13 h7f98852_1004 conda-forge
libxml2 2.10.3 hca2bb57_4 conda-forge
libxslt 1.1.37 h873f0b0_0 conda-forge
libzip 1.9.2 hc929e4a_1 conda-forge
libzlib 1.2.13 h166bdaf_4 conda-forge
llvmlite 0.39.1 py310h58363a5_1 conda-forge
locket 1.0.0 pyhd8ed1ab_0 conda-forge
lxml 4.9.2 py310hbdc0903_0 conda-forge
lz4-c 1.9.4 hcb278e6_0 conda-forge
markupsafe 2.1.2 py310h1fa729e_0 conda-forge
matplotlib-base 3.7.1 py310he60537e_0 conda-forge
msgpack-python 1.0.5 py310hdf3cbec_0 conda-forge
multidict 6.0.4 py310h1fa729e_0 conda-forge
munkres 1.1.4 pyh9f0ad1d_0 conda-forge
nbformat 5.8.0 pyhd8ed1ab_0 conda-forge
nc-time-axis 1.4.1 pyhd8ed1ab_0 conda-forge
ncurses 6.3 h27087fc_1 conda-forge
netcdf-fortran 4.6.0 nompi_heb5813c_103 conda-forge
netcdf4 1.6.3 nompi_py310h0feb132_100 conda-forge
nodeenv 1.7.0 pyhd8ed1ab_0 conda-forge
nomkl 1.0 h5ca1d4c_0 conda-forge
nspr 4.35 h27087fc_0 conda-forge
nss 3.89 he45b914_0 conda-forge
numba 0.56.4 py310h0e39c9b_1 conda-forge
numbagg 0.2.2 pyhd8ed1ab_1 conda-forge
numcodecs 0.11.0 py310heca2aa9_1 conda-forge
numexpr 2.8.4 py310h690d005_100 conda-forge
numpy 1.23.5 py310h53a5b5f_0 conda-forge
numpy_groupies 0.9.20 pyhd8ed1ab_0 conda-forge
openblas 0.3.21 pthreads_h320a7e8_3 conda-forge
openjpeg 2.5.0 hfec8fc6_2 conda-forge
openssl 3.1.0 h0b41bf4_0 conda-forge
packaging 23.0 pyhd8ed1ab_0 conda-forge
pandas 1.5.3 py310h9b08913_1 conda-forge
partd 1.3.0 pyhd8ed1ab_0 conda-forge
patsy 0.5.3 pyhd8ed1ab_0 conda-forge
pcre2 10.40 hc3806b6_0 conda-forge
pillow 9.4.0 py310h023d228_1 conda-forge
pint 0.20.1 pyhd8ed1ab_0 conda-forge
pip 23.0.1 pyhd8ed1ab_0 conda-forge
pixman 0.40.0 h36c2ea0_0 conda-forge
pkgutil-resolve-name 1.3.10 pyhd8ed1ab_0 conda-forge
platformdirs 3.2.0 pyhd8ed1ab_0 conda-forge
pluggy 1.0.0 pyhd8ed1ab_5 conda-forge
pooch 1.7.0 pyha770c72_3 conda-forge
poppler 23.03.0 h091648b_0 conda-forge
poppler-data 0.4.12 hd8ed1ab_0 conda-forge
postgresql 15.2 h3248436_0 conda-forge
pre-commit 3.2.2 pyha770c72_0 conda-forge
proj 9.1.1 h8ffa02c_2 conda-forge
pseudonetcdf 3.2.2 pyhd8ed1ab_0 conda-forge
psutil 5.9.4 py310h5764c6d_0 conda-forge
pthread-stubs 0.4 h36c2ea0_1001 conda-forge
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pydap 3.4.0 pyhd8ed1ab_0 conda-forge
pyopenssl 23.1.1 pyhd8ed1ab_0 conda-forge
pyparsing 3.0.9 pyhd8ed1ab_0 conda-forge
pyproj 3.5.0 py310h15e2413_0 conda-forge
pyrsistent 0.19.3 py310h1fa729e_0 conda-forge
pyshp 2.3.1 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 pyha2e5f31_6 conda-forge
pytest 7.3.0 pyhd8ed1ab_0 conda-forge
pytest-cov 4.0.0 pyhd8ed1ab_0 conda-forge
pytest-env 0.8.1 pyhd8ed1ab_0 conda-forge
pytest-xdist 3.2.1 pyhd8ed1ab_0 conda-forge
python 3.10.10 he550d4f_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-eccodes 1.5.1 py310h0a54255_0 conda-forge
python-fastjsonschema 2.16.3 pyhd8ed1ab_0 conda-forge
python-xxhash 3.2.0 py310h1fa729e_0 conda-forge
python_abi 3.10 3_cp310 conda-forge
pytz 2023.3 pyhd8ed1ab_0 conda-forge
pyyaml 6.0 py310h5764c6d_5 conda-forge
rasterio 1.3.6 py310h3e853a9_0 conda-forge
readline 8.2 h8228510_1 conda-forge
requests 2.28.2 pyhd8ed1ab_1 conda-forge
s3transfer 0.6.0 pyhd8ed1ab_0 conda-forge
scipy 1.10.1 py310h8deb116_0 conda-forge
seaborn 0.12.2 hd8ed1ab_0 conda-forge
seaborn-base 0.12.2 pyhd8ed1ab_0 conda-forge
setuptools 67.6.1 pyhd8ed1ab_0 conda-forge
shapely 2.0.1 py310h8b84c32_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
snappy 1.1.10 h9fff704_0 conda-forge
snuggs 1.4.7 py_0 conda-forge
sortedcontainers 2.4.0 pyhd8ed1ab_0 conda-forge
soupsieve 2.3.2.post1 pyhd8ed1ab_0 conda-forge
sparse 0.14.0 pyhd8ed1ab_0 conda-forge
sqlite 3.40.0 h4ff8645_0 conda-forge
statsmodels 0.13.5 py310hde88566_2 conda-forge
tblib 1.7.0 pyhd8ed1ab_0 conda-forge
tiledb 2.13.2 hd532e3d_0 conda-forge
tk 8.6.12 h27826a3_0 conda-forge
toml 0.10.2 pyhd8ed1ab_0 conda-forge
tomli 2.0.1 pyhd8ed1ab_0 conda-forge
toolz 0.12.0 pyhd8ed1ab_0 conda-forge
tornado 6.2 py310h5764c6d_1 conda-forge
traitlets 5.9.0 pyhd8ed1ab_0 conda-forge
typing-extensions 4.5.0 hd8ed1ab_0 conda-forge
typing_extensions 4.5.0 pyha770c72_0 conda-forge
tzcode 2023c h0b41bf4_0 conda-forge
tzdata 2023c h71feb2d_0 conda-forge
udunits2 2.2.28 hc3e0081_0 conda-forge
ukkonen 1.0.1 py310hbf28c38_3 conda-forge
unicodedata2 15.0.0 py310h5764c6d_0 conda-forge
urllib3 1.26.15 pyhd8ed1ab_0 conda-forge
virtualenv 20.21.0 pyhd8ed1ab_0 conda-forge
webob 1.8.7 pyhd8ed1ab_0 conda-forge
wheel 0.40.0 pyhd8ed1ab_0 conda-forge
wrapt 1.15.0 py310h1fa729e_0 conda-forge
xarray 2023.3.0 pyhd8ed1ab_0 conda-forge
xerces-c 3.2.4 h55805fa_1 conda-forge
xorg-fixesproto 5.0 h7f98852_1002 conda-forge
xorg-inputproto 2.3.2 h7f98852_1002 conda-forge
xorg-kbproto 1.0.7 h7f98852_1002 conda-forge
xorg-libice 1.0.10 h7f98852_0 conda-forge
xorg-libsm 1.2.3 hd9c2040_1000 conda-forge
xorg-libx11 1.8.4 h0b41bf4_0 conda-forge
xorg-libxau 1.0.9 h7f98852_0 conda-forge
xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge
xorg-libxext 1.3.4 h0b41bf4_2 conda-forge
xorg-libxfixes 5.0.3 h7f98852_1004 conda-forge
xorg-libxi 1.7.10 h7f98852_0 conda-forge
xorg-libxrender 0.9.10 h7f98852_1003 conda-forge
xorg-renderproto 0.11.1 h7f98852_1002 conda-forge
xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge
xorg-xproto 7.0.31 h7f98852_1007 conda-forge
xxhash 0.8.1 h0b41bf4_0 conda-forge
xz 5.2.6 h166bdaf_0 conda-forge
yaml 0.2.5 h7f98852_2 conda-forge
yarl 1.8.2 py310h5764c6d_0 conda-forge
zarr 2.14.2 pyhd8ed1ab_0 conda-forge
zict 2.2.0 pyhd8ed1ab_0 conda-forge
zipp 3.15.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 h166bdaf_4 conda-forge
zstd 1.5.2 h3eb15da_6 conda-forge
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1503046820
https://github.com/pydata/xarray/issues/7388#issuecomment-1441513675,https://api.github.com/repos/pydata/xarray/issues/7388,1441513675,IC_kwDOAMm_X85V68DL,6883049,2023-02-23T10:23:21Z,2023-02-23T10:23:21Z,CONTRIBUTOR,"With the PR the test above works, and also bzip2. I can't get it to apply blosc filters for some reason, it works but it does not really apply them. This is the full snippet I am using:
```
import xarray as xr
import numpy as np
shape = (10, 20)
chunksizes = (1, 10)
encoding = {
'compression': 'bzip2',
'shuffle': True,
'complevel': 8,
'fletcher32': False,
'contiguous': False,
'chunksizes': chunksizes
}
da = xr.DataArray(
data=np.random.rand(*shape),
dims=['y', 'x'],
name=""foo"",
attrs={""bar"": ""baz""}
)
da.encoding = encoding
ds = da.to_dataset()
fname = ""test.nc""
ds.to_netcdf(fname, engine=""netcdf4"", mode=""w"")
with xr.open_dataset(fname, engine=""netcdf4"") as ds1:
print(ds1.foo.encoding)
```
Also, I was not able to make the conda environment in ci/environment.yml to resolve libnetcdf 4.9.1. I had to build an environment on my own. I also added the hdf5 filters
```
name: xarray-tests
channels:
- conda-forge
dependencies:
- _libgcc_mutex=0.1=conda_forge
- _openmp_mutex=4.5=2_gnu
- blosc=1.21.3=hafa529b_0
- blosc-hdf5-plugin=1.0.0=h8b9aba8_4
- bzip2=1.0.8=h7f98852_4
- c-ares=1.18.1=h7f98852_0
- ca-certificates=2022.12.7=ha878542_0
- cached-property=1.5.2=hd8ed1ab_1
- cached_property=1.5.2=pyha770c72_1
- cftime=1.6.2=py311h4c7f6c3_1
- curl=7.88.1=hdc1c0ab_0
- h5py=3.8.0=nompi_py311h1db17ec_100
- hdf4=4.2.15=h9772cbc_5
- hdf5=1.12.2=nompi_h4df4325_101
- hdf5-external-filter-plugins=0.1.0=ha770c72_9
- hdf5-external-filter-plugins-bitshuffle=0.1.0=h6ca952b_9
- hdf5-external-filter-plugins-bzip2=0.1.0=hd13e76c_9
- hdf5-external-filter-plugins-lz4=0.1.0=h6ca952b_9
- hdf5plugin=4.1.1=py311hc7375e3_0
- icu=70.1=h27087fc_0
- jpeg=9e=h0b41bf4_3
- keyutils=1.6.1=h166bdaf_0
- krb5=1.20.1=h81ceb04_0
- ld_impl_linux-64=2.40=h41732ed_0
- libaec=1.0.6=hcb278e6_1
- libblas=3.9.0=16_linux64_openblas
- libcblas=3.9.0=16_linux64_openblas
- libcurl=7.88.1=hdc1c0ab_0
- libedit=3.1.20191231=he28a2e2_2
- libev=4.33=h516909a_1
- libffi=3.4.2=h7f98852_5
- libgcc-ng=12.2.0=h65d4601_19
- libgfortran-ng=12.2.0=h69a702a_19
- libgfortran5=12.2.0=h337968e_19
- libgomp=12.2.0=h65d4601_19
- libiconv=1.17=h166bdaf_0
- liblapack=3.9.0=16_linux64_openblas
- libnetcdf=4.9.1=nompi_h34a3ff0_100
- libnghttp2=1.51.0=hff17c54_0
- libnsl=2.0.0=h7f98852_0
- libopenblas=0.3.21=pthreads_h78a6416_3
- libsqlite=3.40.0=h753d276_0
- libssh2=1.10.0=hf14f497_3
- libstdcxx-ng=12.2.0=h46fd767_19
- libuuid=2.32.1=h7f98852_1000
- libxml2=2.10.3=h7463322_0
- libzip=1.9.2=hc929e4a_1
- libzlib=1.2.13=h166bdaf_4
- lz4-c=1.9.4=hcb278e6_0
- ncurses=6.3=h27087fc_1
- netcdf4=1.6.2=nompi_py311ha396515_101
- numpy=1.24.2=py311h8e6699e_0
- openssl=3.0.8=h0b41bf4_0
- pip=23.0.1=pyhd8ed1ab_0
- python=3.11.0=he550d4f_1_cpython
- python_abi=3.11=3_cp311
- readline=8.1.2=h0f457ee_0
- setuptools=67.4.0=pyhd8ed1ab_0
- snappy=1.1.9=hbd366e4_2
- tk=8.6.12=h27826a3_0
- tzdata=2022g=h191b570_0
- wheel=0.38.4=pyhd8ed1ab_0
- xz=5.2.6=h166bdaf_0
- zlib=1.2.13=h166bdaf_4
- zstd=1.5.2=h3eb15da_6
- pip:
- packaging==23.0
- pandas==1.5.3
- python-dateutil==2.8.2
- pytz==2022.7.1
- six==1.16.0
- xarray==0.1.dev4485+gf8a0014
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1503046820
https://github.com/pydata/xarray/issues/7388#issuecomment-1383643668,https://api.github.com/repos/pydata/xarray/issues/7388,1383643668,IC_kwDOAMm_X85SeLoU,6883049,2023-01-16T08:15:41Z,2023-01-16T08:15:41Z,CONTRIBUTOR,"I'll have a look. Sorry about that, I guess we assumed that encoding was passed with ""**kwargs"". I did not try it with netcdf-c 4.9.x since is it not yet available in conda-forge and I did not find the time to compile it.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1503046820
https://github.com/pydata/xarray/issues/7344#issuecomment-1351398850,https://api.github.com/repos/pydata/xarray/issues/7344,1351398850,IC_kwDOAMm_X85QjLXC,6883049,2022-12-14T13:54:06Z,2022-12-14T13:54:06Z,CONTRIBUTOR,"I fully agree that correctness is the priority. Note however that some functions now require bottleneck, like ffill and bfill (I am not sure if there are more cases). They may need to be modified so they can run without bottleneck.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1471685307
https://github.com/pydata/xarray/pull/6981#issuecomment-1240394972,https://api.github.com/repos/pydata/xarray/issues/6981,1240394972,IC_kwDOAMm_X85J7uzc,6883049,2022-09-08T08:27:05Z,2022-09-08T08:27:05Z,CONTRIBUTOR,"I think this is ready, unfortunately it does not make sense to merge it yet, and one test is failing, because netcdf-c 4.9.0 is not yet available in conda-forge (see https://github.com/conda-forge/libnetcdf-feedstock/pull/140). So I would put it on hold.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1359914824
https://github.com/pydata/xarray/issues/6929#issuecomment-1235267423,https://api.github.com/repos/pydata/xarray/issues/6929,1235267423,IC_kwDOAMm_X85JoK9f,6883049,2022-09-02T09:16:47Z,2022-09-02T09:16:47Z,CONTRIBUTOR,"Thank you @dcherian. I opened a pull request, please take a look. I added ""compression"" to the list, but it will be removed if a version of netCDF4 below 1.6.0 is detected. It may be a good idea also to check for the latest versions of netcdf-c.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1343038233
https://github.com/pydata/xarray/issues/4710#issuecomment-749403542,https://api.github.com/repos/pydata/xarray/issues/4710,749403542,MDEyOklzc3VlQ29tbWVudDc0OTQwMzU0Mg==,6883049,2020-12-22T07:58:57Z,2020-12-22T07:58:57Z,CONTRIBUTOR,Perhaps this is related to #3961? Did you try to call open_mfdataset with lock=False? ,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,771127744
https://github.com/pydata/xarray/issues/3961#issuecomment-623402330,https://api.github.com/repos/pydata/xarray/issues/3961,623402330,MDEyOklzc3VlQ29tbWVudDYyMzQwMjMzMA==,6883049,2020-05-04T11:12:25Z,2020-05-06T08:47:46Z,CONTRIBUTOR,"Thanks @jessicaaustin. We have run into the same issue. Setting lock=False works, but as hdf5 is not thread safe, we are not sure if this could have unexpected consequences.
Edit: Actually, I have checked, and the hdf5 version we are using (from conda-forge) is build in thread safe mode. This means that concurrent reads are possible, and that the lock=False in open_mfdataset would be safe. In fact it is more efficient as it does not make sense to handle locks if hdf5 is already thread safe. Am I right?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,597657663
https://github.com/pydata/xarray/issues/422#issuecomment-428855722,https://api.github.com/repos/pydata/xarray/issues/422,428855722,MDEyOklzc3VlQ29tbWVudDQyODg1NTcyMg==,6883049,2018-10-11T07:48:36Z,2018-10-11T07:48:36Z,CONTRIBUTOR,"Hi,
This would be a really nice feature to have. I'd be happy to help too.
Thank you","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,84127296
https://github.com/pydata/xarray/issues/557#issuecomment-427291294,https://api.github.com/repos/pydata/xarray/issues/557,427291294,MDEyOklzc3VlQ29tbWVudDQyNzI5MTI5NA==,6883049,2018-10-05T08:46:12Z,2018-10-05T08:46:12Z,CONTRIBUTOR,"I though this issue was long forgotten ; ) The fact is that today I still call this seltime function very often in my code so I am glad to see it back, thank you.
I like the .isin(elnino_years) syntax, and I see that it is consistent with pandas. Something like dataset.time.year.isin(elnino_years) would be very nice too, right now is just a ""to_index()"" away, as this works: dataset.time.to_index().year.isin(elnino_years). However I am not sure about how this is related with multidimensional indexing, as time is only one dimension.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,104484316
https://github.com/pydata/xarray/issues/2368#issuecomment-415402600,https://api.github.com/repos/pydata/xarray/issues/2368,415402600,MDEyOklzc3VlQ29tbWVudDQxNTQwMjYwMA==,6883049,2018-08-23T12:50:26Z,2018-08-23T12:50:26Z,CONTRIBUTOR,"I found this problem too long ago (see #457). Back then the workaround we implemented is to exclude the offending variable (""siglay"" or ""isobaric"" in the examples above) with the ""drop_variables"" optional argument. Of course this is not great if you want to actually use the values in the variable you are dropping.
I personally don't like the notion of a ""two dimensional coordinate"", I find it confusing. However this kind of netCDFs are common, so fully supporting them in xarray would be nice. But I don't know how. Maybe just renaming the variable instead of dropping it with a ""rename_variables""? This is the only thing that comes to my mind.","{""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,350899839
https://github.com/pydata/xarray/issues/1849#issuecomment-366255764,https://api.github.com/repos/pydata/xarray/issues/1849,366255764,MDEyOklzc3VlQ29tbWVudDM2NjI1NTc2NA==,6883049,2018-02-16T14:50:21Z,2018-02-16T15:10:40Z,CONTRIBUTOR,"This happened to me today after introducing some modifications in a code that was working fine. I have tried to trace it without success. Finally, I found a workaround which consist on removing the ""contiguous"" entry from the .encoding attributes. This works with gerritholl's file:
```
import xarray as xr
ds = xr.open_dataset(""sample.nc"")
del ds.x.encoding[""contiguous""]
del ds.y.encoding[""contiguous""]
ds.to_netcdf(""sample2.nc"", unlimited_dims=[""y""])
```
So it seems that this entry in the encoding dictionaries is triggering the error.
OK, so I guess that this explains it, from the netCDF4 documentation:
""contiguous: if True (default False), the variable data is stored contiguously on disk. Default False. Setting to True for a variable with an unlimited dimension will trigger an error.""
This is quite an obscure error right now, so maybe we could force contiguous to be False when unlimited_dims is being used, or either raise a more informative error.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,290572700
https://github.com/pydata/xarray/issues/631#issuecomment-149458659,https://api.github.com/repos/pydata/xarray/issues/631,149458659,MDEyOklzc3VlQ29tbWVudDE0OTQ1ODY1OQ==,6883049,2015-10-20T07:15:20Z,2015-10-20T07:15:20Z,CONTRIBUTOR,"This is related to #457. In the first case, I think that the error message must be improved, but I do not think that Error is itself is a bug. From the netCDF data model point of view, it does not make sense to define a two dimensional variable sharing the name with a one-dimensional coordinate.
The second example does look like a bug, as it should raise an error too, in my opinion, as the function Dataset is given contradictory information (two different sets of values for the same variable ""a""). In netCDF files you cannot have two variables with the same name, and coordinate variables are just regular variables.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,112254767
https://github.com/pydata/xarray/issues/557#issuecomment-137478562,https://api.github.com/repos/pydata/xarray/issues/557,137478562,MDEyOklzc3VlQ29tbWVudDEzNzQ3ODU2Mg==,6883049,2015-09-03T15:05:35Z,2015-09-04T09:49:54Z,CONTRIBUTOR,"I agree with your arguments against creating too many datetime specific methods. However, I am not convinced about using .sel with 'time.year', it looks a bit hackish to me. Maybe all ""CDO methods"" can be integrated in one single ""seltimes"" method. Please look at the function below. The time_coord arguments lets you choose the time coordinate that you want to filter, and by using numpy.logical_and is it possible to do the whole operation in one single function call. It could be turned into a method. What do you think?
``` python
%matplotlib inline
import xray
import numpy as np
from matplotlib import pyplot as plt
ifile = ""HadCRUT.4.3.0.0.median.nc""
ixd = xray.open_dataset(ifile)
```
You can download the example file from here http://www.metoffice.gov.uk/hadobs/hadcrut4/data/4.3.0.0/gridded_fields/HadCRUT.4.3.0.0.median_netcdf.zip Now comes the function.
``` python
def seltime(ixd, time_coord, **kwargs):
""""""
Select time steps by groups of years, months, etc.
Parameters
----------
ixd : xray.Dataset or xray.DataArray, input dataset. Will be ""self"" if
the function is turned into a method.
time_coord: str, name of the time coordinate to use. Needed to avoid ambiguities.
**kwargs: String or interable: Currently years, months, days and hours are supported.
Returns
----------
xray.Dataset or xrat.DataArray filtered by the constraints defined
by the kwargs.
""""""
ntimes = len(ixd.coords[time_coord])
time_mask = np.repeat(True, ntimes)
for time_unit, time_values in kwargs.iteritems():
if time_unit not in (""year"", ""month"", ""day"", ""hour""):
raise KeyError(time_unit)
time_str = ""{}.{}"".format(time_coord, time_unit)
time_mask_temp = np.in1d(ixd[time_str], time_values)
time_mask = np.logical_and(time_mask, time_mask_temp)
oxd = ixd.sel(**{time_coord : time_mask})
return oxd
```
It works, and it looks fast enough.
``` python
elnino_years = (1958, 1973, 1983, 1987, 1998)
months = (1, 2, 3, 4)
%timeit elnino_xd = seltime(ixd, ""time"", year=elnino_years, month=months)
elnino_xd.time
```
```
1000 loops, best of 3: 1.05 ms per loop
array(['1958-01-16T12:00:00.000000000Z', '1958-02-15T00:00:00.000000000Z',
'1958-03-16T12:00:00.000000000Z', '1958-04-16T00:00:00.000000000Z',
'1973-01-16T13:00:00.000000000+0100',
'1973-02-15T01:00:00.000000000+0100',
'1973-03-16T13:00:00.000000000+0100',
'1973-04-16T01:00:00.000000000+0100',
'1983-01-16T13:00:00.000000000+0100',
'1983-02-15T01:00:00.000000000+0100',
'1983-03-16T13:00:00.000000000+0100',
'1983-04-16T02:00:00.000000000+0200',
'1987-01-16T13:00:00.000000000+0100',
'1987-02-15T01:00:00.000000000+0100',
'1987-03-16T13:00:00.000000000+0100',
'1987-04-16T02:00:00.000000000+0200',
'1998-01-16T13:00:00.000000000+0100',
'1998-02-15T01:00:00.000000000+0100',
'1998-03-16T13:00:00.000000000+0100',
'1998-04-16T02:00:00.000000000+0200'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1958-01-16T12:00:00 1958-02-15 ...
Attributes:
start_month: 1
start_year: 1850
end_month: 4
end_year: 2015
long_name: time
standard_name: time
axis: T
```
If we average in time we can see the warm tonge in the pacific.
``` python
plt.figure(figsize=(12, 5))
elnino_xd.temperature_anomaly.mean(""time"").plot()
plt.gca().invert_yaxis()
```
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,104484316
https://github.com/pydata/xarray/pull/532#issuecomment-131854997,https://api.github.com/repos/pydata/xarray/issues/532,131854997,MDEyOklzc3VlQ29tbWVudDEzMTg1NDk5Nw==,6883049,2015-08-17T15:02:14Z,2015-08-17T15:02:14Z,CONTRIBUTOR,"I am not able to reproduce the error found by Travis on a virtualenv with the same python version and packages, so I don't know why that specific run failed : (
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,101050642
https://github.com/pydata/xarray/pull/532#issuecomment-131817957,https://api.github.com/repos/pydata/xarray/issues/532,131817957,MDEyOklzc3VlQ29tbWVudDEzMTgxNzk1Nw==,6883049,2015-08-17T13:26:54Z,2015-08-17T13:26:54Z,CONTRIBUTOR,"I updated it following your advice, now the logic is in the loop of decode_cf_variables, and it supports single strings. I added the test and the documentation too. I successfully run nosetests.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,101050642