Closed bradyrx closed 4 years ago
andersy005
commented
5 years ago Perhaps this would be solved with the idea that there should be a preprocess=func(*args, **kwargs) feature for intake-esm. This was mentioned in the discussion at the NCAR hub but I'm not seeing a formal issue for it.
See #155
andersy005
commented
5 years ago @bradyrx,
I did further investigation regarding this chunking issue, and I couldn't get xarray+dask to chunk data in uniform chunks:
import intake
col = intake.open_esm_datastore("/glade/collections/cmip/catalog/intake-esm-datastore/catalogs/glade-cmip6.json")
cat = col.search(experiment_id='historical',
activity_id='CMIP',
table_id='Omon',
variable_id='spco2',
grid_label='gn',
source_id='CESM2')
dsets = cat.to_dataset_dict(cdf_kwargs={"chunks": {"time": -1}}, aggregate=False)--> The keys in the returned dictionary of datasets are constructed as follows:
'activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version.time_range'
--> There will be 26 group(s)for key, ds in dsets.items():
print(f"member_id={key.split('.')[4]} --> {ds.dims}")member_id=r10i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r10i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r10i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r10i1p1f1 --> Frozen(SortedKeysDict({'time': 180, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r11i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r11i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r11i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r11i1p1f1 --> Frozen(SortedKeysDict({'time': 180, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r1i1p1f1 --> Frozen(SortedKeysDict({'time': 1980, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r2i1p1f1 --> Frozen(SortedKeysDict({'time': 1980, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r3i1p1f1 --> Frozen(SortedKeysDict({'time': 1980, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r4i1p1f1 --> Frozen(SortedKeysDict({'time': 1980, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r5i1p1f1 --> Frozen(SortedKeysDict({'time': 1980, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r6i1p1f1 --> Frozen(SortedKeysDict({'time': 1980, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r7i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r7i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r7i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r7i1p1f1 --> Frozen(SortedKeysDict({'time': 180, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r8i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r8i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r8i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r8i1p1f1 --> Frozen(SortedKeysDict({'time': 180, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r9i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r9i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r9i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r9i1p1f1 --> Frozen(SortedKeysDict({'time': 180, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))I tried different chunking schemes without success:
for chunks in [-1, 11, 180, 200, 500, 800, 2000]:
dsets = cat.to_dataset_dict(cdf_kwargs={"chunks": {"time": chunks}})
_, ds = dsets.popitem()
print(f"specified_chunks={chunks} --> {ds.spco2.chunks}")
print("*"*80)--> The keys in the returned dictionary of datasets are constructed as follows:
'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'
--> There will be 1 group(s)
specified_chunks=-1 --> ((1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), (600, 600, 600, 180), (384,), (320,))
********************************************************************************
--> The keys in the returned dictionary of datasets are constructed as follows:
'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'
--> There will be 1 group(s)
specified_chunks=11 --> ((1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), (11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 6, 5, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 10, 1, 6, 4, 7, 4, 7, 4, 7, 4, 7, 4, 7, 4, 7, 4, 7, 4, 7, 4, 7, 4, 7, 4, 7, 4, 7, 4, 7, 4, 7, 4, 7, 4, 7, 4), (384,), (320,))
********************************************************************************
--> The keys in the returned dictionary of datasets are constructed as follows:
'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'
--> There will be 1 group(s)
specified_chunks=180 --> ((1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), (180, 180, 180, 60, 120, 60, 120, 60, 120, 60, 60, 60, 120, 60, 120, 60, 120, 60, 180), (384,), (320,))
********************************************************************************
--> The keys in the returned dictionary of datasets are constructed as follows:
'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'
--> There will be 1 group(s)
specified_chunks=200 --> ((1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), (200, 200, 200, 200, 200, 200, 200, 200, 200, 180), (384,), (320,))
********************************************************************************
--> The keys in the returned dictionary of datasets are constructed as follows:
'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'
--> There will be 1 group(s)
specified_chunks=500 --> ((1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), (500, 100, 400, 100, 100, 300, 200, 100, 180), (384,), (320,))
********************************************************************************
--> The keys in the returned dictionary of datasets are constructed as follows:
'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'
--> There will be 1 group(s)
specified_chunks=800 --> ((1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), (600, 200, 400, 400, 200, 180), (384,), (320,))
********************************************************************************
--> The keys in the returned dictionary of datasets are constructed as follows:
'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'
--> There will be 1 group(s)
specified_chunks=2000 --> ((1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), (600, 600, 600, 180), (384,), (320,))
********************************************************************************In the future, we would like to enable automatic alignment of Dask chunksizes (but not the other way around). We might also require that all arrays in a dataset share the same chunking alignment. Neither of these are currently done.
I am not sure this note explains the behavior
Ccing @dcherian as he might know why xarray doesn't uniformly chunk the data.
Perhaps this would be solved with the idea that there should be a preprocess=func(*args, **kwargs)
When I tried using the preprocess, I was getting this error:
def preprocess(ds):
return ds.chunk({"time": 180})
dsets = cat.to_dataset_dict(preprocess=preprocess)
_, ds = dsets.popitem()
print(ds.chunks)
ValueError Traceback (most recent call last)
<ipython-input-28-abfadfb85ab7> in <module>
1 _, ds = dsets.popitem()
----> 2 print(ds.chunks)
/glade/work/abanihi/devel/pangeo/xarray/xarray/core/dataset.py in chunks(self)
1671 if dim in chunks and c != chunks[dim]:
1672 raise ValueError(
-> 1673 f"Object has inconsistent chunks along dimension {dim}. "
1674 "This can be fixed by calling unify_chunks()."
1675 )
ValueError: Object has inconsistent chunks along dimension time. This can be fixed by calling unify_chunks().
```
So far, the only solution to this issue that I am aware of, involves re-chunking the data with `ds.rechunk()`
dcherian
commented
5 years ago Are you reading files that contain different lengths of time?
The chunks kwargs is passed down to open_dataset so when you say "time": -1, it means that (chunksize for each file) == (length of time in each file). You should rechunk afterward to get what you want.
dcherian
commented
5 years ago It would be great if one of you could update the xarray documentation to make that clear. (see issue https://github.com/pydata/xarray/issues/1795)
andersy005
commented
5 years ago Are you reading files that contain different lengths of time?
Yes. Here are the dimension sizes found in each file:
member_id=r10i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r10i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r10i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r10i1p1f1 --> Frozen(SortedKeysDict({'time': 180, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r11i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r11i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r11i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r11i1p1f1 --> Frozen(SortedKeysDict({'time': 180, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r1i1p1f1 --> Frozen(SortedKeysDict({'time': 1980, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r2i1p1f1 --> Frozen(SortedKeysDict({'time': 1980, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r3i1p1f1 --> Frozen(SortedKeysDict({'time': 1980, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r4i1p1f1 --> Frozen(SortedKeysDict({'time': 1980, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r5i1p1f1 --> Frozen(SortedKeysDict({'time': 1980, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r6i1p1f1 --> Frozen(SortedKeysDict({'time': 1980, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r7i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r7i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r7i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r7i1p1f1 --> Frozen(SortedKeysDict({'time': 180, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r8i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r8i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r8i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r8i1p1f1 --> Frozen(SortedKeysDict({'time': 180, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r9i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r9i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r9i1p1f1 --> Frozen(SortedKeysDict({'time': 600, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))
member_id=r9i1p1f1 --> Frozen(SortedKeysDict({'time': 180, 'nlat': 384, 'nlon': 320, 'd2': 2, 'vertices': 4}))It would be great if one of you could update the xarray documentation to make that clear. (see issue pydata/xarray#1795)
Sounds good. I will take a look in the coming days
Description
I am using kwargs to chunk my multiple file datasets as they come in as in the
intake-esmdemos:dsets = cat.to_dataset_dict(cdf_kwargs={"chunks": {"time": -1}}), but after they are concatenated and loaded into a dictionary, the chunking doesn't come through as expected.Note the chunk size on
spco2for time is 600 and not 1980. I think this is an issue I've encountered withopen_mfdatasetbefore in the past, so maybe this is not anintake-esmissue.*Perhaps this would be solved with the idea that there should be a `preprocess=func(args, kwargs)
feature forintake-esm`. This was mentioned in the discussion at the NCAR hub but I'm not seeing a formal issue for it.Output of
intake_esm.__version__'0.0.post698'
Output of
conda listpackages in environment at /glade/u/home/rbrady/miniconda3/envs/analysis:
#
Name Version Build Channel
_libgcc_mutex 0.1 main
affine 2.2.2 py_0 conda-forge aiohttp 3.6.1 py36h516909a_0 conda-forge ansiwrap 0.8.4 py_0 conda-forge appdirs 1.4.3 py_1 conda-forge asciitree 0.3.3 py_2 conda-forge asn1crypto 0.24.0 py36_1003 conda-forge async-timeout 3.0.1 py_1000 conda-forge atomicwrites 1.3.0 py_0 conda-forge attrs 19.1.0 py_0 conda-forge backcall 0.1.0 py_0 conda-forge backports 1.0 py_2 conda-forge backports.tempfile 1.0 py_0 conda-forge backports.weakref 1.0.post1 py36_1000 conda-forge basemap 1.2.1 py36hd759880_1 conda-forge black 19.3b0 py_0 conda-forge blas 2.12 openblas conda-forge bleach 3.1.0 py_0 conda-forge blosc 1.17.0 he1b5a44_0 conda-forge bokeh 1.3.4 py36_0 conda-forge boost-cpp 1.68.0 h11c811c_1000 conda-forge boto3 1.9.238 pypi_0 pypi botocore 1.12.238 pypi_0 pypi bottleneck 1.2.1 py36h3010b51_1001 conda-forge bzip2 1.0.8 h516909a_0 conda-forge ca-certificates 2019.9.11 hecc5488_0 conda-forge cached-property 1.5.1 py_0 conda-forge cachetools 3.1.1 pypi_0 pypi cairo 1.16.0 h18b612c_1001 conda-forge cartopy 0.17.0 py36hd759880_1006 conda-forge certifi 2019.9.11 py36_0 conda-forge cf_units 2.0.1 py36h3010b51_1002 conda-forge cffi 1.12.3 py36h8022711_0 conda-forge cfitsio 3.470 hb60a0a2_2 conda-forge cftime 1.0.3.4 py36hd352d35_1001 conda-forge chardet 3.0.4 py36_1003 conda-forge click 7.0 py_0 conda-forge click-plugins 1.1.1 py_0 conda-forge cligj 0.5.0 py_0 conda-forge climpred 1.1.0 py_1 conda-forge cloudpickle 1.2.1 py_0 conda-forge cmocean 2.0 py_1 conda-forge colorcet 2.0.1 py_0 conda-forge colorspacious 1.1.2 pyh24bf2e0_0 conda-forge cryptography 2.7 py36h72c5cf5_0 conda-forge curl 7.65.3 hf8cf82a_0 conda-forge cycler 0.10.0 py_1 conda-forge cython 0.29.13 py36he1b5a44_0 conda-forge cytoolz 0.10.0 py36h516909a_0 conda-forge dask 2.4.0 py_0 conda-forge dask-core 2.4.0 py_0 conda-forge dask-glm 0.2.0 pypi_0 pypi dask-jobqueue 0.6.3 py_0 conda-forge dask-ml 1.0.0 pypi_0 pypi dask-mpi 1.0.3 py36_0 conda-forge datashader 0.7.0 py_0 conda-forge datashape 0.5.4 py_1 conda-forge dbus 1.13.6 he372182_0 conda-forge decorator 4.4.0 py_0 conda-forge defusedxml 0.5.0 py_1 conda-forge distributed 2.4.0 py_0 conda-forge docrep 0.2.7 py_0 conda-forge docutils 0.15.2 py36_0 conda-forge entrypoints 0.3 py36_1000 conda-forge eofs 1.4.0 py_0 conda-forge esmf 7.1.0 h9eb252b_1005 conda-forge esmlab 2019.4.27 py_0 conda-forge esmpy 7.1.0 py36h24bf2e0_3 conda-forge esmtools 1.1.0 pypi_0 pypi expat 2.2.5 he1b5a44_1003 conda-forge fasteners 0.14.1 py_3 conda-forge fastparquet 0.3.2 py36hc1659b7_0 conda-forge fiona 1.8.6 py36hf242f0b_3 conda-forge flake8 3.7.8 py36_1 conda-forge fontconfig 2.13.1 he4413a7_1000 conda-forge freetype 2.10.0 he983fc9_1 conda-forge freexl 1.0.5 h14c3975_1002 conda-forge fribidi 1.0.5 h516909a_1002 conda-forge fsspec 0.4.1 py_0 conda-forge future 0.17.1 py36_1000 conda-forge g2clib 1.6.0 hf3f1b0b_9 conda-forge gcsfs 0.3.1 pypi_0 pypi gdal 2.4.1 py36h5f563d9_10 conda-forge geopandas 0.5.1 py_0 conda-forge geos 3.7.2 he1b5a44_1 conda-forge geotiff 1.4.3 hb6868eb_1001 conda-forge geoviews 1.6.3 py_0 conda-forge geoviews-core 1.6.3 py_0 conda-forge gettext 0.19.8.1 hc5be6a0_1002 conda-forge giflib 5.1.7 h516909a_1 conda-forge git 2.22.0 pl526hce37bd2_0 conda-forge glib 2.58.3 h6f030ca_1002 conda-forge google-auth 1.6.3 pypi_0 pypi google-auth-oauthlib 0.4.1 pypi_0 pypi graphite2 1.3.13 hf484d3e_1000 conda-forge graphviz 2.40.1 h5933667_1 conda-forge gst-plugins-base 1.14.5 h0935bb2_0 conda-forge gstreamer 1.14.5 h36ae1b5_0 conda-forge gsw 3.3.1 py36h516909a_0 conda-forge h5netcdf 0.7.4 py_0 conda-forge h5py 2.9.0 nompi_py36hcafd542_1103 conda-forge harfbuzz 2.4.0 h37c48d4_1 conda-forge hdf4 4.2.13 h9a582f1_1002 conda-forge hdf5 1.10.4 nompi_h3c11f04_1106 conda-forge hdfeos2 2.20 h64bfcee_1000 conda-forge hdfeos5 5.1.16 hccfc538_4 conda-forge heapdict 1.0.0 py36_1000 conda-forge holoviews 1.12.5 py_0 conda-forge hvplot 0.4.0 py_1 conda-forge icu 58.2 hf484d3e_1000 conda-forge idna 2.8 py36_1000 conda-forge idna_ssl 1.1.0 py36_1000 conda-forge imageio 2.5.0 py36_0 conda-forge importlib_metadata 0.23 py36_0 conda-forge intake 0.5.3 py_0 conda-forge intake-esm 0.0.post698 pypi_0 pypi intake-xarray 0.3.1 py_0 conda-forge ipykernel 5.1.2 py36h5ca1d4c_0 conda-forge ipython 7.8.0 py36h5ca1d4c_0 conda-forge ipython_genutils 0.2.0 py_1 conda-forge ipywidgets 7.5.1 py_0 conda-forge isort 4.3.21 py36_0 conda-forge jasper 1.900.1 h07fcdf6_1006 conda-forge jedi 0.15.1 py36_0 conda-forge jinja2 2.10.1 py_0 conda-forge jmespath 0.9.4 py_0 conda-forge joblib 0.13.2 py_0 conda-forge jpeg 9c h14c3975_1001 conda-forge json-c 0.13.1 h14c3975_1001 conda-forge json5 0.8.5 py_0 conda-forge jsonschema 3.0.2 py36_0 conda-forge jupyter 1.0.0 py_2 conda-forge jupyter-server-proxy 1.1.0 py_0 conda-forge jupyter_client 5.3.1 py_0 conda-forge jupyter_console 6.0.0 py_0 conda-forge jupyter_core 4.4.0 py_0 conda-forge jupyterlab 1.1.4 py_0 conda-forge jupyterlab_server 1.0.5 py_0 conda-forge kealib 1.4.10 h1978553_1003 conda-forge 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