diff --git a/.prettierignore b/.prettierignore
index 4c100aeb..9b89f28d 100644
--- a/.prettierignore
+++ b/.prettierignore
@@ -1 +1 @@
-conda/**
+conda/
diff --git a/_config.yml b/_config.yml
index a0ae745b..85a12b15 100644
--- a/_config.yml
+++ b/_config.yml
@@ -68,7 +68,6 @@ sphinx:
# maintain old paths and redirect them (so google results dont go to 404)
# https://github.com/wpilibsuite/sphinxext-rediraffe
- sphinxext.rediraffe
- - sphinx_exercise
config:
language: en # accessibility
# application/vnd.holoviews_load.v0+json, application/vnd.holoviews_exec.v0+json
diff --git a/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb b/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb
index 6a18888e..4fd42fdc 100644
--- a/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb
+++ b/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb
@@ -63,18 +63,17 @@
" out[index, :] = np.interp(..., array[index, :], ...)\n",
"```\n",
"\n",
- "\n",
- "```{exercise}\n",
- ":label: coreloopdims\n",
- "\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"Consider the example problem of interpolating a 2D array with dimensions `space` and `time` along the `time` dimension.\n",
"Which dimension is the core dimension, and which is the \"loop dimension\"?\n",
- "```\n",
- "```{solution} coreloopdims\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"`time` is the core dimension, and `space` is the loop dimension.\n",
- "```\n",
+ ":::\n",
+ "::::\n",
"\n",
"## Vectorization\n",
"\n",
diff --git a/advanced/apply_ufunc/complex-output-numpy.ipynb b/advanced/apply_ufunc/complex-output-numpy.ipynb
index d993fe0e..e7336a77 100644
--- a/advanced/apply_ufunc/complex-output-numpy.ipynb
+++ b/advanced/apply_ufunc/complex-output-numpy.ipynb
@@ -138,8 +138,8 @@
"tags": []
},
"source": [
- "```{exercise}\n",
- ":label: newdim\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Apply the following function using `apply_ufunc`. It adds a new dimension to the input array, let's call it `newdim`. Specify the new dimension using `output_core_dims`. Do you need any `input_core_dims`?\n",
"\n",
@@ -147,10 +147,11 @@
"def add_new_dim(array):\n",
" return np.expand_dims(array, axis=-1)\n",
"```\n",
- "````{solution} newdim\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
- "``` python\n",
+ "```python\n",
"def add_new_dim(array):\n",
" return np.expand_dims(array, axis=-1)\n",
"\n",
@@ -161,7 +162,8 @@
" output_core_dims=[[\"newdim\"]],\n",
")\n",
"```\n",
- "````"
+ ":::\n",
+ "::::"
]
},
{
@@ -327,8 +329,8 @@
"tags": []
},
"source": [
- "````{exercise}\n",
- ":label: generalize\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"We presented the concept of \"core dimensions\" as the \"smallest unit of data the function could handle.\" Do you understand how the above use of `apply_ufunc` generalizes to an array with more than one dimension? \n",
"\n",
@@ -337,9 +339,8 @@
"air3d = xr.tutorial.load_dataset(\"air_temperature\").air)\n",
"``` \n",
"Your goal is to have a minimum and maximum value of temperature across all latitudes for a given time and longitude.\n",
- "````\n",
"\n",
- "````{solution} generalize\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"We want to use `minmax` to compute the minimum and maximum along the \"lat\" dimension always, regardless of how many dimensions are on the input. So we specify `input_core_dims=[[\"lat\"]]`. The output does not contain the \"lat\" dimension, but we expect two returned variables. So we pass an empty list `[]` for each returned array, so `output_core_dims=[[], []]` just as before.\n",
@@ -352,8 +353,8 @@
" input_core_dims=[[\"lat\"]],\n",
" output_core_dims=[[],[]],\n",
")\n",
- "```\n",
- "````"
+ ":::\n",
+ "::::"
]
}
],
diff --git a/advanced/apply_ufunc/core-dimensions.ipynb b/advanced/apply_ufunc/core-dimensions.ipynb
index 82cb11ed..7a009782 100644
--- a/advanced/apply_ufunc/core-dimensions.ipynb
+++ b/advanced/apply_ufunc/core-dimensions.ipynb
@@ -335,13 +335,12 @@
"tags": []
},
"source": [
- "```{exercise}\n",
- ":label: trapezoid\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Use `apply_ufunc` to apply `scipy.integrate.trapezoid` along the `time` axis.\n",
- "```\n",
"\n",
- "````{solution} trapezoid\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"```python\n",
@@ -350,7 +349,8 @@
"\n",
"xr.apply_ufunc(scipy.integrate.trapezoid, ds, input_core_dims=[[\"time\"]], kwargs={\"axis\": -1})\n",
"```\n",
- "````"
+ ":::\n",
+ "::::"
]
}
],
diff --git a/advanced/apply_ufunc/dask_apply_ufunc.ipynb b/advanced/apply_ufunc/dask_apply_ufunc.ipynb
index c38f614e..768674c5 100644
--- a/advanced/apply_ufunc/dask_apply_ufunc.ipynb
+++ b/advanced/apply_ufunc/dask_apply_ufunc.ipynb
@@ -267,15 +267,15 @@
"Such functions involve the concept of \"core dimensions\". This concept is independent of the underlying array type, and is a property of the applied function. See the [core dimensions with NumPy](core-dimensions) tutorial for more.\n",
"\n",
"\n",
- "```{exercise}\n",
- ":label: daskmean\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Use `dask.array.mean` as an example of a function that can handle dask\n",
"arrays and uses an `axis` kwarg. \n",
- "```\n",
"\n",
- "````{solution} daskmean\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
+ "\n",
"```python\n",
"def time_mean(da):\n",
" return xr.apply_ufunc(\n",
@@ -285,10 +285,11 @@
" dask=\"allowed\",\n",
" kwargs={\"axis\": -1}, # core dimensions are moved to the end\n",
" )\n",
- "\n",
- "\n",
+ " \n",
"time_mean(ds.air)\n",
- "````\n"
+ "```\n",
+ ":::\n",
+ "::::\n"
]
},
{
@@ -493,12 +494,11 @@
"tags": []
},
"source": [
- "```{exercise} \n",
- ":label: rechunk\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"Apply the integrate function to `ds` after rechunking to have a different chunksize along `lon` using `ds.chunk(lon=4)` (for example). What happens?\n",
- "```\n",
"\n",
- "```{solution} rechunk\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"`apply_ufunc` complains that it cannot automatically parallelize because the dataset `ds` is now chunked along the core dimension `lon`. You should see the following error:\n",
@@ -509,7 +509,8 @@
" ``.chunk(dict(lon=-1))``, or pass ``allow_rechunk=True`` in ``dask_gufunc_kwargs`` \n",
" but beware that this may significantly increase memory usage.\n",
"\n",
- "```"
+ ":::\n",
+ "::::"
]
},
{
@@ -652,7 +653,7 @@
"source": [
"### Adding new dimensions\n",
"\n",
- "We use the [expand_dims example](newdim) that changes the size of the input along a single dimension.\n",
+ "We use the `np.expand_dims` to change the size of the input along a single dimension.\n",
"\n",
"```python\n",
"def add_new_dim(array):\n",
diff --git a/advanced/apply_ufunc/numba-vectorization.ipynb b/advanced/apply_ufunc/numba-vectorization.ipynb
index 91195214..6bcbf30f 100644
--- a/advanced/apply_ufunc/numba-vectorization.ipynb
+++ b/advanced/apply_ufunc/numba-vectorization.ipynb
@@ -7,9 +7,9 @@
"tags": []
},
"source": [
- "![](\"https://numba.pydata.org/_static/numba-blue-horizontal-rgb.svg\")
\n",
+ "# Fast vectorization with Numba\n",
"\n",
- "# Fast vectorization with Numba"
+ ""
]
},
{
@@ -241,12 +241,12 @@
"id": "18",
"metadata": {},
"source": [
- "```{exercise}\n",
- ":label: g\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Apply `g` to `da_dask`\n",
- "```\n",
- "````{solution} g\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"```python\n",
@@ -259,7 +259,8 @@
" dask=\"parallelized\",\n",
")\n",
"```\n",
- "````"
+ ":::\n",
+ "::::"
]
},
{
diff --git a/conda/conda-lock.yml b/conda/conda-lock.yml
index 9c74fa6f..d4ecf965 100644
--- a/conda/conda-lock.yml
+++ b/conda/conda-lock.yml
@@ -13,10 +13,10 @@
version: 1
metadata:
content_hash:
- osx-64: 66f5474d08c72922e1236c5da5c182fe36e41ba657a9a022a4b5bcbf96f4685c
- linux-64: f6315ebbea8a6cc47a0d6f17dcd5f238a892cd373cd85cc60faf6c60871d8e02
- win-64: 4fe3b0846ccb6d9eb0e04a33e7607a353980c6563375db24b4c808039d5f2695
- osx-arm64: 244ff697d401ecf39ac7ac674af0ff84e6ab36d203d6d3c2916036feff5ea088
+ osx-64: 04db034b972956d26ea5a2e216d203ad9ec50d1264db68fad8b8819687e2dbca
+ linux-64: 1bf1f9f5d61f47892c7aa44e23f0d353c0b37071f3dfd42e1a88a79e0bf96780
+ win-64: d2979a92577f588632cc336a9c1d5ca088886463ed8fbb6f6fd560c83c08442f
+ osx-arm64: 4f169409756d1bd8b7c8088061b8a81352704e1992ecda0baaad820a2facc9df
channels:
- url: conda-forge
used_env_vars: []
@@ -53,55 +53,55 @@ package:
category: main
optional: false
- name: accessible-pygments
- version: 0.0.4
+ version: 0.0.5
manager: conda
platform: linux-64
dependencies:
pygments: ''
- python: '>=3.6'
- url: https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.4-pyhd8ed1ab_0.conda
+ python: '>=3.9'
+ url: https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.5-pyhd8ed1ab_0.conda
hash:
- md5: 46a2e6e3dfa718ce3492018d5a110dd6
- sha256: ce532e2da08fb2b77df281e3d42e2b2131641bdd6a6e8127f3718ae6860bd70d
+ md5: 1bb1ef9806a9a20872434f58b3e7fc1a
+ sha256: 712c1875bcd32674e8ce2f418f0b2875ecb98e6bcbb21ec7502dae8ff4b0f73c
category: main
optional: false
- name: accessible-pygments
- version: 0.0.4
+ version: 0.0.5
manager: conda
platform: osx-64
dependencies:
pygments: ''
- python: '>=3.6'
- url: https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.4-pyhd8ed1ab_0.conda
+ python: '>=3.9'
+ url: https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.5-pyhd8ed1ab_0.conda
hash:
- md5: 46a2e6e3dfa718ce3492018d5a110dd6
- sha256: ce532e2da08fb2b77df281e3d42e2b2131641bdd6a6e8127f3718ae6860bd70d
+ md5: 1bb1ef9806a9a20872434f58b3e7fc1a
+ sha256: 712c1875bcd32674e8ce2f418f0b2875ecb98e6bcbb21ec7502dae8ff4b0f73c
category: main
optional: false
- name: accessible-pygments
- version: 0.0.4
+ version: 0.0.5
manager: conda
platform: osx-arm64
dependencies:
pygments: ''
- python: '>=3.6'
- url: https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.4-pyhd8ed1ab_0.conda
+ python: '>=3.9'
+ url: https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.5-pyhd8ed1ab_0.conda
hash:
- md5: 46a2e6e3dfa718ce3492018d5a110dd6
- sha256: ce532e2da08fb2b77df281e3d42e2b2131641bdd6a6e8127f3718ae6860bd70d
+ md5: 1bb1ef9806a9a20872434f58b3e7fc1a
+ sha256: 712c1875bcd32674e8ce2f418f0b2875ecb98e6bcbb21ec7502dae8ff4b0f73c
category: main
optional: false
- name: accessible-pygments
- version: 0.0.4
+ version: 0.0.5
manager: conda
platform: win-64
dependencies:
pygments: ''
- python: '>=3.6'
- url: https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.4-pyhd8ed1ab_0.conda
+ python: '>=3.9'
+ url: https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.5-pyhd8ed1ab_0.conda
hash:
- md5: 46a2e6e3dfa718ce3492018d5a110dd6
- sha256: ce532e2da08fb2b77df281e3d42e2b2131641bdd6a6e8127f3718ae6860bd70d
+ md5: 1bb1ef9806a9a20872434f58b3e7fc1a
+ sha256: 712c1875bcd32674e8ce2f418f0b2875ecb98e6bcbb21ec7502dae8ff4b0f73c
category: main
optional: false
- name: affine
@@ -162,13 +162,13 @@ package:
frozenlist: '>=1.1.1'
libgcc-ng: '>=12'
multidict: '>=4.5,<7.0'
- python: '>=3.11,<3.12.0a0'
- python_abi: 3.11.*
+ python: '>=3.12,<3.13.0a0'
+ python_abi: 3.12.*
yarl: '>=1.0,<2.0'
- url: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.9.5-py311h459d7ec_0.conda
+ url: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.9.5-py312h98912ed_0.conda
hash:
- md5: 0175d2636cc41dc019b51462c13ce225
- sha256: 2eb99d920ef0dcd608e195bb852a64634ecf13f74680796959f1b9d9a9650a7b
+ md5: edc01db954d139fe398a5f378f96ab4d
+ sha256: 4727041c2bf3ee90f1a3bba68f846f6ea34f59b292bf5bc390db3bc4ae56d315
category: main
optional: false
- name: aiohttp
@@ -180,13 +180,13 @@ package:
attrs: '>=17.3.0'
frozenlist: '>=1.1.1'
multidict: '>=4.5,<7.0'
- python: '>=3.11,<3.12.0a0'
- python_abi: 3.11.*
+ python: '>=3.12,<3.13.0a0'
+ python_abi: 3.12.*
yarl: '>=1.0,<2.0'
- url: https://conda.anaconda.org/conda-forge/osx-64/aiohttp-3.9.5-py311he705e18_0.conda
+ url: https://conda.anaconda.org/conda-forge/osx-64/aiohttp-3.9.5-py312h41838bb_0.conda
hash:
- md5: a955769e6187495614f719668695e28f
- sha256: 6e1c28d255830f350ccc135db4932153a978956d480e7bcd26c1663e19db4f9d
+ md5: 559e0083034b5313393798e733f784bf
+ sha256: fce6b95a067af67fc5bd04a7d3e95e49da32ee09585f36bfc9ecc0460d2044a6
category: main
optional: false
- name: aiohttp
@@ -198,13 +198,13 @@ package:
attrs: '>=17.3.0'
frozenlist: '>=1.1.1'
multidict: '>=4.5,<7.0'
- python: '>=3.11,<3.12.0a0'
- python_abi: 3.11.*
+ python: '>=3.12,<3.13.0a0'
+ python_abi: 3.12.*
yarl: '>=1.0,<2.0'
- url: https://conda.anaconda.org/conda-forge/osx-arm64/aiohttp-3.9.5-py311h05b510d_0.conda
+ url: https://conda.anaconda.org/conda-forge/osx-arm64/aiohttp-3.9.5-py312he37b823_0.conda
hash:
- md5: 69eee7117ab7f3ef9eb59a600a9079a3
- sha256: 63ee70099b66bfa62751d1eb82831438426e3cfc9671a0b836dd9b9d94c92bd6
+ md5: 5b7aa6817d365896c16910bf7e78c099
+ sha256: 98bebf4177a9b296eaac0e2b2541dc4e1d2c584a278cc15e7c03f910b75810f3
category: main
optional: false
- name: aiohttp
@@ -216,16 +216,16 @@ package:
attrs: '>=17.3.0'
frozenlist: '>=1.1.1'
multidict: '>=4.5,<7.0'
- python: '>=3.11,<3.12.0a0'
- python_abi: 3.11.*
+ python: '>=3.12,<3.13.0a0'
+ python_abi: 3.12.*
ucrt: '>=10.0.20348.0'
vc: '>=14.2,<15'
vc14_runtime: '>=14.29.30139'
yarl: '>=1.0,<2.0'
- url: https://conda.anaconda.org/conda-forge/win-64/aiohttp-3.9.5-py311ha68e1ae_0.conda
+ url: https://conda.anaconda.org/conda-forge/win-64/aiohttp-3.9.5-py312he70551f_0.conda
hash:
- md5: 9c350d73bdc0e3c68fd1d20afa9466a1
- sha256: 03e161ef1e710089630276964921bb6de9c9852d0b04a59e3fe528c608327767
+ md5: ed8105878c3c2a7d78f5d57ea63a8e14
+ sha256: 5fdb9b9670d7c6ff91163e4302f050d0d4a5813a44ad543cc05893099ac7808a
category: main
optional: false
- name: aiosignal
@@ -329,7 +329,7 @@ package:
category: main
optional: false
- name: anyio
- version: 4.3.0
+ version: 4.4.0
manager: conda
platform: linux-64
dependencies:
@@ -338,14 +338,14 @@ package:
python: '>=3.8'
sniffio: '>=1.1'
typing_extensions: '>=4.1'
- url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.3.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.4.0-pyhd8ed1ab_0.conda
hash:
- md5: ac95aa8ed65adfdde51132595c79aade
- sha256: 86aca4a31c09f9b4dbdb332cd9a6a7dbab62ca734d3f832651c0ab59c6a7f52e
+ md5: 1fa97c6e8db1f82c64ff17a5efc4ae8e
+ sha256: 84ac9429812495f12939ab4994f2634f7cacd254f6234a0c2c0243daed15a7ee
category: main
optional: false
- name: anyio
- version: 4.3.0
+ version: 4.4.0
manager: conda
platform: osx-64
dependencies:
@@ -354,14 +354,14 @@ package:
typing_extensions: '>=4.1'
idna: '>=2.8'
exceptiongroup: '>=1.0.2'
- url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.3.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.4.0-pyhd8ed1ab_0.conda
hash:
- md5: ac95aa8ed65adfdde51132595c79aade
- sha256: 86aca4a31c09f9b4dbdb332cd9a6a7dbab62ca734d3f832651c0ab59c6a7f52e
+ md5: 1fa97c6e8db1f82c64ff17a5efc4ae8e
+ sha256: 84ac9429812495f12939ab4994f2634f7cacd254f6234a0c2c0243daed15a7ee
category: main
optional: false
- name: anyio
- version: 4.3.0
+ version: 4.4.0
manager: conda
platform: osx-arm64
dependencies:
@@ -370,14 +370,14 @@ package:
typing_extensions: '>=4.1'
idna: '>=2.8'
exceptiongroup: '>=1.0.2'
- url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.3.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.4.0-pyhd8ed1ab_0.conda
hash:
- md5: ac95aa8ed65adfdde51132595c79aade
- sha256: 86aca4a31c09f9b4dbdb332cd9a6a7dbab62ca734d3f832651c0ab59c6a7f52e
+ md5: 1fa97c6e8db1f82c64ff17a5efc4ae8e
+ sha256: 84ac9429812495f12939ab4994f2634f7cacd254f6234a0c2c0243daed15a7ee
category: main
optional: false
- name: anyio
- version: 4.3.0
+ version: 4.4.0
manager: conda
platform: win-64
dependencies:
@@ -386,10 +386,58 @@ package:
typing_extensions: '>=4.1'
idna: '>=2.8'
exceptiongroup: '>=1.0.2'
- url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.3.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.4.0-pyhd8ed1ab_0.conda
+ hash:
+ md5: 1fa97c6e8db1f82c64ff17a5efc4ae8e
+ sha256: 84ac9429812495f12939ab4994f2634f7cacd254f6234a0c2c0243daed15a7ee
+ category: main
+ optional: false
+- name: appdirs
+ version: 1.4.4
+ manager: conda
+ platform: linux-64
+ dependencies:
+ python: ''
+ url: https://conda.anaconda.org/conda-forge/noarch/appdirs-1.4.4-pyh9f0ad1d_0.tar.bz2
+ hash:
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libcurl: ''
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libre2-11: '>=2023.9.1,<2024.0a0'
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vc14_runtime: '>=14.29.30139'
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libstdcxx-ng: '>=12'
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libstdcxx-ng: '>=12'
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- url: https://conda.anaconda.org/conda-forge/osx-64/yarl-1.9.4-py311he705e18_0.conda
+ python: '>=3.12,<3.13.0a0'
+ python_abi: 3.12.*
+ url: https://conda.anaconda.org/conda-forge/osx-64/yarl-1.9.4-py312h41838bb_0.conda
hash:
- md5: 6b7f34fc151c338cdaca4d4d6fb92d55
- sha256: 668ea9d1e0c7b4eaa769cc79de1ea4e8da22a61d4112e660ecbaca140f097109
+ md5: 5d15d92a788612cf319ecfac53e0c542
+ sha256: 748199e3d7725e8e50c6b3d87066de383a2bedd8248d89dd80c8c6e78f2bc8b9
category: main
optional: false
- name: yarl
@@ -25431,12 +25612,12 @@ package:
dependencies:
idna: '>=2.0'
multidict: '>=4.0'
- python: '>=3.11,<3.12.0a0'
- python_abi: 3.11.*
- url: https://conda.anaconda.org/conda-forge/osx-arm64/yarl-1.9.4-py311h05b510d_0.conda
+ python: '>=3.12,<3.13.0a0'
+ python_abi: 3.12.*
+ url: https://conda.anaconda.org/conda-forge/osx-arm64/yarl-1.9.4-py312he37b823_0.conda
hash:
- md5: 510eded0989b4ef17f3adeca6cb21b22
- sha256: 1da2a08c44e284d17156838d8207fde58dececde3c07626114df4d9a64ae9213
+ md5: 44ead39ed723937c4701dcb040ff8df6
+ sha256: 4ed261d50453813ceff0777b439d1646999ae3f8eba86775655a78d5411c9769
category: main
optional: false
- name: yarl
@@ -25446,19 +25627,19 @@ package:
dependencies:
idna: '>=2.0'
multidict: '>=4.0'
- python: '>=3.11,<3.12.0a0'
- python_abi: 3.11.*
+ python: '>=3.12,<3.13.0a0'
+ python_abi: 3.12.*
ucrt: '>=10.0.20348.0'
vc: '>=14.2,<15'
vc14_runtime: '>=14.29.30139'
- url: https://conda.anaconda.org/conda-forge/win-64/yarl-1.9.4-py311ha68e1ae_0.conda
+ url: https://conda.anaconda.org/conda-forge/win-64/yarl-1.9.4-py312he70551f_0.conda
hash:
- md5: 522f873d68d2557056d6cfed8335fe96
- sha256: 647a7f6395be44b82a3dd4ad58d02fd1ce56cca19083061cbb118f788c0f31e5
+ md5: 9dee8a153f1644fbc66c40bcda96bf59
+ sha256: 57089bd47731282b4f58c378b4711f397393e6e1c8252aa49af881e36685e07d
category: main
optional: false
- name: zarr
- version: 2.18.0
+ version: 2.18.2
manager: conda
platform: linux-64
dependencies:
@@ -25467,14 +25648,14 @@ package:
numcodecs: '>=0.10.0'
numpy: '>=1.23'
python: '>=3.9'
- url: https://conda.anaconda.org/conda-forge/noarch/zarr-2.18.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/zarr-2.18.2-pyhd8ed1ab_0.conda
hash:
- md5: 62b7eab69361dc3fe99186e296045882
- sha256: 97b627e14fcbdd7e617ef85a71de084b1dd429814a2513b431e04fb431cfda9f
+ md5: 02f53038910b6fbc9d36bd5f663318e8
+ sha256: 16685f7412d79345732e889595a881ee320b85abd44f7244c0f7e628d9d976ec
category: main
optional: false
- name: zarr
- version: 2.18.0
+ version: 2.18.2
manager: conda
platform: osx-64
dependencies:
@@ -25483,14 +25664,14 @@ package:
python: '>=3.9'
numpy: '>=1.23'
numcodecs: '>=0.10.0'
- url: https://conda.anaconda.org/conda-forge/noarch/zarr-2.18.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/zarr-2.18.2-pyhd8ed1ab_0.conda
hash:
- md5: 62b7eab69361dc3fe99186e296045882
- sha256: 97b627e14fcbdd7e617ef85a71de084b1dd429814a2513b431e04fb431cfda9f
+ md5: 02f53038910b6fbc9d36bd5f663318e8
+ sha256: 16685f7412d79345732e889595a881ee320b85abd44f7244c0f7e628d9d976ec
category: main
optional: false
- name: zarr
- version: 2.18.0
+ version: 2.18.2
manager: conda
platform: osx-arm64
dependencies:
@@ -25499,14 +25680,14 @@ package:
python: '>=3.9'
numpy: '>=1.23'
numcodecs: '>=0.10.0'
- url: https://conda.anaconda.org/conda-forge/noarch/zarr-2.18.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/zarr-2.18.2-pyhd8ed1ab_0.conda
hash:
- md5: 62b7eab69361dc3fe99186e296045882
- sha256: 97b627e14fcbdd7e617ef85a71de084b1dd429814a2513b431e04fb431cfda9f
+ md5: 02f53038910b6fbc9d36bd5f663318e8
+ sha256: 16685f7412d79345732e889595a881ee320b85abd44f7244c0f7e628d9d976ec
category: main
optional: false
- name: zarr
- version: 2.18.0
+ version: 2.18.2
manager: conda
platform: win-64
dependencies:
@@ -25515,10 +25696,10 @@ package:
python: '>=3.9'
numpy: '>=1.23'
numcodecs: '>=0.10.0'
- url: https://conda.anaconda.org/conda-forge/noarch/zarr-2.18.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/zarr-2.18.2-pyhd8ed1ab_0.conda
hash:
- md5: 62b7eab69361dc3fe99186e296045882
- sha256: 97b627e14fcbdd7e617ef85a71de084b1dd429814a2513b431e04fb431cfda9f
+ md5: 02f53038910b6fbc9d36bd5f663318e8
+ sha256: 16685f7412d79345732e889595a881ee320b85abd44f7244c0f7e628d9d976ec
category: main
optional: false
- name: zeromq
@@ -25631,103 +25812,105 @@ package:
category: main
optional: false
- name: zipp
- version: 3.17.0
+ version: 3.19.2
manager: conda
platform: linux-64
dependencies:
python: '>=3.8'
- url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.19.2-pyhd8ed1ab_0.conda
hash:
- md5: 2e4d6bc0b14e10f895fc6791a7d9b26a
- sha256: bced1423fdbf77bca0a735187d05d9b9812d2163f60ab426fc10f11f92ecbe26
+ md5: 49808e59df5535116f6878b2a820d6f4
+ sha256: e3e9c8501f581bfdc4700b83ea283395e237ec6b9b5cbfbedb556e1da6f4fdc9
category: main
optional: false
- name: zipp
- version: 3.17.0
+ version: 3.19.2
manager: conda
platform: osx-64
dependencies:
python: '>=3.8'
- url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.19.2-pyhd8ed1ab_0.conda
hash:
- md5: 2e4d6bc0b14e10f895fc6791a7d9b26a
- sha256: bced1423fdbf77bca0a735187d05d9b9812d2163f60ab426fc10f11f92ecbe26
+ md5: 49808e59df5535116f6878b2a820d6f4
+ sha256: e3e9c8501f581bfdc4700b83ea283395e237ec6b9b5cbfbedb556e1da6f4fdc9
category: main
optional: false
- name: zipp
- version: 3.17.0
+ version: 3.19.2
manager: conda
platform: osx-arm64
dependencies:
python: '>=3.8'
- url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.19.2-pyhd8ed1ab_0.conda
hash:
- md5: 2e4d6bc0b14e10f895fc6791a7d9b26a
- sha256: bced1423fdbf77bca0a735187d05d9b9812d2163f60ab426fc10f11f92ecbe26
+ md5: 49808e59df5535116f6878b2a820d6f4
+ sha256: e3e9c8501f581bfdc4700b83ea283395e237ec6b9b5cbfbedb556e1da6f4fdc9
category: main
optional: false
- name: zipp
- version: 3.17.0
+ version: 3.19.2
manager: conda
platform: win-64
dependencies:
python: '>=3.8'
- url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.19.2-pyhd8ed1ab_0.conda
hash:
- md5: 2e4d6bc0b14e10f895fc6791a7d9b26a
- sha256: bced1423fdbf77bca0a735187d05d9b9812d2163f60ab426fc10f11f92ecbe26
+ md5: 49808e59df5535116f6878b2a820d6f4
+ sha256: e3e9c8501f581bfdc4700b83ea283395e237ec6b9b5cbfbedb556e1da6f4fdc9
category: main
optional: false
- name: zlib
- version: 1.2.13
+ version: 1.3.1
manager: conda
platform: linux-64
dependencies:
libgcc-ng: '>=12'
- libzlib: 1.2.13
- url: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.13-hd590300_5.conda
+ libzlib: 1.3.1
+ url: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-h4ab18f5_1.conda
hash:
- md5: 68c34ec6149623be41a1933ab996a209
- sha256: 9887a04d7e7cb14bd2b52fa01858f05a6d7f002c890f618d9fcd864adbfecb1b
+ md5: 9653f1bf3766164d0e65fa723cabbc54
+ sha256: cee16ab07a11303de721915f0a269e8c7a54a5c834aa52f74b1cc3a59000ade8
category: main
optional: false
- name: zlib
- version: 1.2.13
+ version: 1.3.1
manager: conda
platform: osx-64
dependencies:
- libzlib: 1.2.13
- url: https://conda.anaconda.org/conda-forge/osx-64/zlib-1.2.13-h8a1eda9_5.conda
+ __osx: '>=10.13'
+ libzlib: 1.3.1
+ url: https://conda.anaconda.org/conda-forge/osx-64/zlib-1.3.1-h87427d6_1.conda
hash:
- md5: 75a8a98b1c4671c5d2897975731da42d
- sha256: d1f4c82fd7bd240a78ce8905e931e68dca5f523c7da237b6b63c87d5625c5b35
+ md5: 3ac9ef8975965f9698dbedd2a4cc5894
+ sha256: 41bd5fef28b2755d637e3a8ea5c84010628392fbcf80c7e3d7370aaced7ee4fe
category: main
optional: false
- name: zlib
- version: 1.2.13
+ version: 1.3.1
manager: conda
platform: osx-arm64
dependencies:
- libzlib: 1.2.13
- url: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.2.13-h53f4e23_5.conda
+ __osx: '>=11.0'
+ libzlib: 1.3.1
+ url: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-hfb2fe0b_1.conda
hash:
- md5: a08383f223b10b71492d27566fafbf6c
- sha256: de0ee1e24aa6867058d3b852a15c8d7f49f262f5828772700c647186d4a96bbe
+ md5: f27e021db7862b6ddbc1d3578f10d883
+ sha256: 87360c2dc662916aac37cf01e53324b4f4f78db6f399220818076752b093ede5
category: main
optional: false
- name: zlib
- version: 1.2.13
+ version: 1.3.1
manager: conda
platform: win-64
dependencies:
- libzlib: 1.2.13
+ libzlib: 1.3.1
ucrt: '>=10.0.20348.0'
vc: '>=14.2,<15'
vc14_runtime: '>=14.29.30139'
- url: https://conda.anaconda.org/conda-forge/win-64/zlib-1.2.13-hcfcfb64_5.conda
+ url: https://conda.anaconda.org/conda-forge/win-64/zlib-1.3.1-h2466b09_1.conda
hash:
- md5: a318e8622e11663f645cc7fa3260f462
- sha256: 0f91b719c7558046bcd37fdc7ae4b9eb2b7a8e335beb8b59ae7ccb285a46aa46
+ md5: f8e0a35bf6df768ad87ed7bbbc36ab04
+ sha256: 76409556e6c7cb91991cd94d7fc853c9272c2872bd7e3573ff35eb33d6fca5be
category: main
optional: false
- name: zstd
@@ -25737,7 +25920,7 @@ package:
dependencies:
libgcc-ng: '>=12'
libstdcxx-ng: '>=12'
- libzlib: '>=1.2.13,<1.3.0a0'
+ libzlib: '>=1.2.13,<2.0.0a0'
url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
hash:
md5: 4d056880988120e29d75bfff282e0f45
@@ -25750,7 +25933,7 @@ package:
platform: osx-64
dependencies:
__osx: '>=10.9'
- libzlib: '>=1.2.13,<1.3.0a0'
+ libzlib: '>=1.2.13,<2.0.0a0'
url: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.6-h915ae27_0.conda
hash:
md5: 4cb2cd56f039b129bb0e491c1164167e
@@ -25763,7 +25946,7 @@ package:
platform: osx-arm64
dependencies:
__osx: '>=11.0'
- libzlib: '>=1.2.13,<1.3.0a0'
+ libzlib: '>=1.2.13,<2.0.0a0'
url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
hash:
md5: d96942c06c3e84bfcc5efb038724a7fd
@@ -25775,7 +25958,7 @@ package:
manager: conda
platform: win-64
dependencies:
- libzlib: '>=1.2.13,<1.3.0a0'
+ libzlib: '>=1.2.13,<2.0.0a0'
ucrt: '>=10.0.20348.0'
vc: '>=14.2,<15'
vc14_runtime: '>=14.29.30139'
diff --git a/conda/environment-unpinned.yml b/conda/environment-unpinned.yml
index d8a8d458..26c60c46 100644
--- a/conda/environment-unpinned.yml
+++ b/conda/environment-unpinned.yml
@@ -2,9 +2,9 @@ name: xarray-tutorial
channels:
- conda-forge
dependencies:
- - python=3.11
+ - python
# for building website:
- - jupyter-book<1
+ - jupyter-book
- pre-commit
# for user interface
- dask-labextension
@@ -24,6 +24,7 @@ dependencies:
- ipython
- matplotlib-base
- netcdf4
+ - numpy<2
- pint-xarray
- pip
- pydap
@@ -32,7 +33,6 @@ dependencies:
- rioxarray
- scipy
- sphinx-codeautolink
- - sphinx-exercise
- sphinx-notfound-page
- sphinxext-rediraffe
- xarray
diff --git a/conda/environment.yml b/conda/environment.yml
index 298f46f9..154f15b2 100644
--- a/conda/environment.yml
+++ b/conda/environment.yml
@@ -1,39 +1,40 @@
# Generated by conda-lock.
# platform: linux-64
-# input_hash: f6315ebbea8a6cc47a0d6f17dcd5f238a892cd373cd85cc60faf6c60871d8e02
+# input_hash: 1bf1f9f5d61f47892c7aa44e23f0d353c0b37071f3dfd42e1a88a79e0bf96780
channels:
- conda-forge
dependencies:
- _libgcc_mutex=0.1=conda_forge
- _openmp_mutex=4.5=2_gnu
- - accessible-pygments=0.0.4=pyhd8ed1ab_0
+ - accessible-pygments=0.0.5=pyhd8ed1ab_0
- affine=2.4.0=pyhd8ed1ab_0
- - aiohttp=3.9.5=py311h459d7ec_0
+ - aiohttp=3.9.5=py312h98912ed_0
- aiosignal=1.3.1=pyhd8ed1ab_0
- alabaster=0.7.16=pyhd8ed1ab_0
- - anyio=4.3.0=pyhd8ed1ab_0
+ - anyio=4.4.0=pyhd8ed1ab_0
+ - appdirs=1.4.4=pyh9f0ad1d_0
- argon2-cffi=23.1.0=pyhd8ed1ab_0
- - argon2-cffi-bindings=21.2.0=py311h459d7ec_4
+ - argon2-cffi-bindings=21.2.0=py312h98912ed_4
- arrow=1.3.0=pyhd8ed1ab_0
- asciitree=0.3.3=py_2
- asttokens=2.4.1=pyhd8ed1ab_0
- async-lru=2.0.4=pyhd8ed1ab_0
- atk-1.0=2.38.0=h04ea711_2
- attrs=23.2.0=pyh71513ae_0
- - aws-c-auth=0.7.20=h5f1c8d9_0
- - aws-c-cal=0.6.12=h2ba76a8_0
- - aws-c-common=0.9.17=h4ab18f5_0
- - aws-c-compression=0.2.18=h36a0aea_4
- - aws-c-event-stream=0.4.2=h161de36_10
- - aws-c-http=0.8.1=h63f54a0_13
- - aws-c-io=0.14.8=h96d4d28_0
- - aws-c-mqtt=0.10.4=hcc7299c_2
- - aws-c-s3=0.5.8=h10bd90f_3
- - aws-c-sdkutils=0.1.16=h36a0aea_0
- - aws-checksums=0.1.18=h36a0aea_4
- - aws-crt-cpp=0.26.8=h02fd9b4_10
- - aws-sdk-cpp=1.11.267=h51dfee4_8
+ - aws-c-auth=0.7.22=h9137712_5
+ - aws-c-cal=0.6.15=h88a6e22_0
+ - aws-c-common=0.9.19=h4ab18f5_0
+ - aws-c-compression=0.2.18=h83b837d_6
+ - aws-c-event-stream=0.4.2=h0cbf018_13
+ - aws-c-http=0.8.2=h360477d_2
+ - aws-c-io=0.14.9=h2d549f9_2
+ - aws-c-mqtt=0.10.4=hf85b563_6
+ - aws-c-s3=0.5.10=h679ed35_3
+ - aws-c-sdkutils=0.1.16=h83b837d_2
+ - aws-checksums=0.1.18=h83b837d_6
+ - aws-crt-cpp=0.26.12=h8bc9c4d_0
+ - aws-sdk-cpp=1.11.329=hf74b5d1_5
- azure-core-cpp=1.11.1=h91d86a7_1
- azure-identity-cpp=1.6.0=hf1915f5_1
- azure-storage-blobs-cpp=12.10.0=h00ab1b0_1
@@ -46,21 +47,21 @@ dependencies:
- bokeh=3.4.1=pyhd8ed1ab_0
- brotli=1.1.0=hd590300_1
- brotli-bin=1.1.0=hd590300_1
- - brotli-python=1.1.0=py311hb755f60_1
+ - brotli-python=1.1.0=py312h30efb56_1
- bzip2=1.0.8=hd590300_5
- c-ares=1.28.1=hd590300_0
- - ca-certificates=2024.2.2=hbcca054_0
+ - ca-certificates=2024.6.2=hbcca054_0
- cached-property=1.5.2=hd8ed1ab_1
- cached_property=1.5.2=pyha770c72_1
- cachetools=5.3.3=pyhd8ed1ab_0
- - cairo=1.18.0=h3faef2a_0
- - cartopy=0.23.0=py311h320fe9a_0
- - certifi=2024.2.2=pyhd8ed1ab_0
- - cf_xarray=0.9.0=pyhd8ed1ab_0
- - cffi=1.16.0=py311hb3a22ac_0
+ - cairo=1.18.0=hbb29018_2
+ - cartopy=0.23.0=py312h1d6d2e6_1
+ - certifi=2024.6.2=pyhd8ed1ab_0
+ - cf_xarray=0.9.1=pyhd8ed1ab_0
+ - cffi=1.16.0=py312hf06ca03_0
- cfgv=3.3.1=pyhd8ed1ab_0
- cfitsio=4.4.0=hbdc6101_1
- - cftime=1.6.3=py311h1f0f07a_0
+ - cftime=1.6.4=py312h085067d_0
- charset-normalizer=3.3.2=pyhd8ed1ab_0
- click=8.1.7=unix_pyh707e725_0
- click-plugins=1.1.1=py_0
@@ -69,29 +70,31 @@ dependencies:
- colorama=0.4.6=pyhd8ed1ab_0
- colorcet=3.1.0=pyhd8ed1ab_0
- comm=0.2.2=pyhd8ed1ab_0
- - contourpy=1.2.1=py311h9547e67_0
- - cryptography=42.0.7=py311h4a61cc7_0
+ - contourpy=1.2.1=py312h8572e83_0
+ - cryptography=42.0.8=py312hbcc2302_0
- cycler=0.12.1=pyhd8ed1ab_0
- - cytoolz=0.12.3=py311h459d7ec_0
- - dask=2024.5.0=pyhd8ed1ab_0
- - dask-core=2024.5.0=pyhd8ed1ab_0
- - dask-expr=1.1.0=pyhd8ed1ab_0
+ - cytoolz=0.12.3=py312h98912ed_0
+ - dask=2024.6.2=pyhd8ed1ab_0
+ - dask-core=2024.6.2=pyhd8ed1ab_0
+ - dask-expr=1.1.6=pyhd8ed1ab_0
- dask-labextension=7.0.0=pyhd8ed1ab_0
- dataclasses=0.8=pyhc8e2a94_3
- - datashader=0.16.1=pyhd8ed1ab_0
- - debugpy=1.8.1=py311hb755f60_0
+ - datashader=0.16.2=pyhd8ed1ab_0
+ - debugpy=1.8.1=py312h30efb56_0
- decorator=5.1.1=pyhd8ed1ab_0
- defusedxml=0.7.1=pyhd8ed1ab_0
- distlib=0.3.8=pyhd8ed1ab_0
- - distributed=2024.5.0=pyhd8ed1ab_0
+ - distributed=2024.6.2=pyhd8ed1ab_0
- docopt=0.6.2=py_1
- - docutils=0.17.1=py311h38be061_5
+ - docutils=0.20.1=py312h7900ff3_3
- entrypoints=0.4=pyhd8ed1ab_0
- exceptiongroup=1.2.0=pyhd8ed1ab_2
- executing=2.0.1=pyhd8ed1ab_0
- expat=2.6.2=h59595ed_0
- fasteners=0.17.3=pyhd8ed1ab_0
- - filelock=3.14.0=pyhd8ed1ab_0
+ - filelock=3.15.3=pyhd8ed1ab_0
+ - flexcache=0.3=pyhd8ed1ab_0
+ - flexparser=0.3.1=pyhd8ed1ab_0
- fmt=10.2.1=h00ab1b0_0
- font-ttf-dejavu-sans-mono=2.37=hab24e00_0
- font-ttf-inconsolata=3.000=h77eed37_0
@@ -100,42 +103,42 @@ dependencies:
- fontconfig=2.14.2=h14ed4e7_0
- fonts-conda-ecosystem=1=0
- fonts-conda-forge=1=0
- - fonttools=4.51.0=py311h459d7ec_0
+ - fonttools=4.53.0=py312h9a8786e_0
- fqdn=1.5.1=pyhd8ed1ab_0
- freetype=2.12.1=h267a509_2
- freexl=2.0.0=h743c826_0
- fribidi=1.0.10=h36c2ea0_0
- - frozenlist=1.4.1=py311h459d7ec_0
- - fsspec=2024.3.1=pyhca7485f_0
- - gcsfs=2024.3.1=pyhd8ed1ab_0
+ - frozenlist=1.4.1=py312h98912ed_0
+ - fsspec=2024.6.0=pyhff2d567_0
+ - gcsfs=2024.6.0=pyhd8ed1ab_0
- gdk-pixbuf=2.42.12=hb9ae30d_0
- geos=3.12.1=h59595ed_0
- - geotiff=1.7.1=h6cf1f90_16
+ - geotiff=1.7.3=hf7fa9e8_1
- geoviews-core=1.12.0=pyha770c72_0
- gflags=2.2.2=he1b5a44_1004
- giflib=5.2.2=hd590300_0
- - glog=0.7.0=hed5481d_0
+ - glog=0.7.1=hbabe93e_0
- google-api-core=2.19.0=pyhd8ed1ab_0
- - google-auth=2.29.0=pyhca7485f_0
+ - google-auth=2.30.0=pyhff2d567_0
- google-auth-oauthlib=1.2.0=pyhd8ed1ab_0
- google-cloud-core=2.4.1=pyhd8ed1ab_0
- - google-cloud-storage=2.16.0=pyhca7485f_0
- - google-crc32c=1.1.2=py311h9b08b9c_5
+ - google-cloud-storage=2.17.0=pyhff2d567_0
+ - google-crc32c=1.1.2=py312h775cd15_5
- google-resumable-media=2.7.0=pyhd8ed1ab_0
- - googleapis-common-protos=1.63.0=pyhd8ed1ab_0
+ - googleapis-common-protos=1.63.1=pyhd8ed1ab_0
- graphite2=1.3.13=h59595ed_1003
- - graphviz=9.0.0=h78e8752_1
- - greenlet=3.0.3=py311hb755f60_0
- - grpcio=1.62.2=py311ha6695c7_0
- - gsw=3.6.17=py311h1f0f07a_1
+ - graphviz=11.0.0=hc68bbd7_0
+ - greenlet=3.0.3=py312h30efb56_0
+ - grpcio=1.62.2=py312hb06c811_0
+ - gsw=3.6.18=py312h085067d_0
- gtk2=2.24.33=h280cfa0_4
- gts=0.7.6=h977cf35_4
- h11=0.14.0=pyhd8ed1ab_0
- h2=4.1.0=pyhd8ed1ab_0
- harfbuzz=8.5.0=hfac3d4d_0
- hdf4=4.2.15=h2a13503_7
- - hdf5=1.14.3=nompi_h4f84152_101
- - holoviews=1.18.3=pyhd8ed1ab_0
+ - hdf5=1.14.3=nompi_hdf9ad27_105
+ - holoviews=1.19.0=pyhd8ed1ab_0
- hpack=4.0.0=pyh9f0ad1d_0
- httpcore=1.0.5=pyhd8ed1ab_0
- httpx=0.27.0=pyhd8ed1ab_0
@@ -145,222 +148,222 @@ dependencies:
- identify=2.5.36=pyhd8ed1ab_0
- idna=3.7=pyhd8ed1ab_0
- imagesize=1.4.1=pyhd8ed1ab_0
- - importlib-metadata=7.1.0=pyha770c72_0
- - importlib_metadata=7.1.0=hd8ed1ab_0
+ - importlib-metadata=7.2.0=pyha770c72_0
+ - importlib_metadata=7.2.0=hd8ed1ab_0
- importlib_resources=6.4.0=pyhd8ed1ab_0
- - ipykernel=6.29.3=pyhd33586a_0
- - ipython=8.24.0=pyh707e725_0
+ - ipykernel=6.29.4=pyh3099207_0
+ - ipython=8.25.0=pyh707e725_0
- isoduration=20.11.0=pyhd8ed1ab_0
- jedi=0.19.1=pyhd8ed1ab_0
- jinja2=3.1.4=pyhd8ed1ab_0
- json-c=0.17=h7ab15ed_0
- json5=0.9.25=pyhd8ed1ab_0
- - jsonpointer=2.4=py311h38be061_3
+ - jsonpointer=3.0.0=py312h7900ff3_0
- jsonschema=4.22.0=pyhd8ed1ab_0
- jsonschema-specifications=2023.12.1=pyhd8ed1ab_0
- jsonschema-with-format-nongpl=4.22.0=pyhd8ed1ab_0
- - jupyter-book=0.15.1=pyhd8ed1ab_0
- - jupyter-cache=0.6.1=pyhd8ed1ab_0
+ - jupyter-book=1.0.0=pyhd8ed1ab_0
+ - jupyter-cache=1.0.0=pyhd8ed1ab_0
- jupyter-lsp=2.2.5=pyhd8ed1ab_0
- jupyter-resource-usage=1.0.2=pyhd8ed1ab_0
- - jupyter-server-proxy=4.1.2=pyhd8ed1ab_0
- - jupyter_client=8.6.1=pyhd8ed1ab_0
- - jupyter_core=5.7.2=py311h38be061_0
+ - jupyter-server-proxy=4.2.0=pyhd8ed1ab_0
+ - jupyter_client=8.6.2=pyhd8ed1ab_0
+ - jupyter_core=5.7.2=py312h7900ff3_0
- jupyter_events=0.10.0=pyhd8ed1ab_0
- - jupyter_server=2.14.0=pyhd8ed1ab_0
+ - jupyter_server=2.14.1=pyhd8ed1ab_0
- jupyter_server_terminals=0.5.3=pyhd8ed1ab_0
- - jupyterlab=4.2.0=pyhd8ed1ab_1
+ - jupyterlab=4.2.2=pyhd8ed1ab_0
- jupyterlab-myst=2.4.2=pyhd8ed1ab_0
- jupyterlab_pygments=0.3.0=pyhd8ed1ab_1
- - jupyterlab_server=2.27.1=pyhd8ed1ab_0
- - kealib=1.5.3=h2f55d51_0
+ - jupyterlab_server=2.27.2=pyhd8ed1ab_0
+ - kealib=1.5.3=hee9dde6_1
- keyutils=1.6.1=h166bdaf_0
- - kiwisolver=1.4.5=py311h9547e67_1
+ - kiwisolver=1.4.5=py312h8572e83_1
- krb5=1.21.2=h659d440_0
- latexcodec=2.0.1=pyh9f0ad1d_0
- lcms2=2.16=hb7c19ff_0
- - ld_impl_linux-64=2.40=h55db66e_0
+ - ld_impl_linux-64=2.40=hf3520f5_7
- lerc=4.0.0=h27087fc_0
- libabseil=20240116.2=cxx17_h59595ed_0
- libaec=1.1.3=h59595ed_0
- - libarchive=3.7.2=h2aa1ff5_1
- - libarrow=16.0.0=hefa796f_1_cpu
- - libarrow-acero=16.0.0=hac33072_1_cpu
- - libarrow-dataset=16.0.0=hac33072_1_cpu
- - libarrow-substrait=16.0.0=h7e0c224_1_cpu
+ - libarchive=3.7.4=hfca40fe_0
+ - libarrow=16.1.0=h9102155_9_cpu
+ - libarrow-acero=16.1.0=hac33072_9_cpu
+ - libarrow-dataset=16.1.0=hac33072_9_cpu
+ - libarrow-substrait=16.1.0=h7e0c224_9_cpu
- libblas=3.9.0=22_linux64_openblas
- - libboost-headers=1.85.0=ha770c72_0
+ - libboost-headers=1.85.0=ha770c72_1
- libbrotlicommon=1.1.0=hd590300_1
- libbrotlidec=1.1.0=hd590300_1
- libbrotlienc=1.1.0=hd590300_1
- libcblas=3.9.0=22_linux64_openblas
- libcrc32c=1.1.2=h9c3ff4c_0
- - libcurl=8.7.1=hca28451_0
+ - libcurl=8.8.0=hca28451_0
- libdeflate=1.20=hd590300_0
- libedit=3.1.20191231=he28a2e2_2
- libev=4.33=hd590300_2
- libevent=2.1.12=hf998b51_1
- libexpat=2.6.2=h59595ed_0
- libffi=3.4.2=h7f98852_5
- - libgcc-ng=13.2.0=h77fa898_7
+ - libgcc-ng=13.2.0=h77fa898_11
- libgd=2.3.3=h119a65a_9
- - libgdal=3.8.5=h77540a9_5
- - libgfortran-ng=13.2.0=h69a702a_7
- - libgfortran5=13.2.0=hca663fb_7
+ - libgdal=3.9.0=h471f4ab_7
+ - libgfortran-ng=13.2.0=h69a702a_11
+ - libgfortran5=13.2.0=h3d2ce59_11
- libglib=2.80.2=hf974151_0
- - libgomp=13.2.0=h77fa898_7
- - libgoogle-cloud=2.23.0=h9be4e54_1
- - libgoogle-cloud-storage=2.23.0=hc7a4891_1
+ - libgomp=13.2.0=h77fa898_11
+ - libgoogle-cloud=2.25.0=h2736e30_0
+ - libgoogle-cloud-storage=2.25.0=h3d9a0c8_0
- libgrpc=1.62.2=h15f2491_0
- libiconv=1.17=hd590300_2
- libjpeg-turbo=3.0.0=hd590300_1
- libkml=1.3.0=h01aab08_1018
- liblapack=3.9.0=22_linux64_openblas
- libllvm14=14.0.6=hcd5def8_4
- - libnetcdf=4.9.2=nompi_h9612171_113
+ - libnetcdf=4.9.2=nompi_h135f659_114
- libnghttp2=1.58.0=h47da74e_1
- libnsl=2.0.1=hd590300_0
- libopenblas=0.3.27=pthreads_h413a1c8_0
- - libparquet=16.0.0=h6a7eafb_1_cpu
+ - libparquet=16.1.0=h6a7eafb_9_cpu
- libpng=1.6.43=h2797004_0
- libpq=16.3=ha72fbe1_0
- libprotobuf=4.25.3=h08a7969_0
- libre2-11=2023.09.01=h5a48ba9_2
- - librsvg=2.58.0=hadf69e7_1
+ - librsvg=2.58.1=hadf69e7_0
- librttopo=1.1.0=h8917695_15
- libsodium=1.0.18=h36c2ea0_1
- - libspatialite=5.1.0=h6f065fc_5
- - libsqlite=3.45.3=h2797004_0
+ - libspatialite=5.1.0=h6fbd9c4_7
+ - libsqlite=3.46.0=hde9e2c9_0
- libssh2=1.11.0=h0841786_0
- - libstdcxx-ng=13.2.0=hc0a3c3a_7
+ - libstdcxx-ng=13.2.0=hc0a3c3a_11
- libthrift=0.19.0=hb90f79a_1
- libtiff=4.6.0=h1dd3fc0_3
- libutf8proc=2.8.0=h166bdaf_0
- libuuid=2.38.1=h0b41bf4_0
- libwebp=1.4.0=h2c329e2_0
- libwebp-base=1.4.0=hd590300_0
- - libxcb=1.15=h0b41bf4_0
+ - libxcb=1.16=hd590300_0
- libxcrypt=4.4.36=hd590300_1
- - libxml2=2.12.7=hc051c1a_0
+ - libxml2=2.12.7=hc051c1a_1
- libzip=1.10.1=h2629f0a_3
- - libzlib=1.2.13=hd590300_5
+ - libzlib=1.3.1=h4ab18f5_1
- linkify-it-py=2.0.3=pyhd8ed1ab_0
- - llvmlite=0.42.0=py311ha6695c7_1
+ - llvmlite=0.43.0=py312h9c5d478_0
- locket=1.0.0=pyhd8ed1ab_0
- - lz4=4.3.3=py311h38e4bf4_0
+ - lz4=4.3.3=py312h03f37cb_0
- lz4-c=1.9.4=hcb278e6_0
- lzo=2.10=hd590300_1001
- markdown=3.6=pyhd8ed1ab_0
- - markdown-it-py=2.2.0=pyhd8ed1ab_0
- - markupsafe=2.1.5=py311h459d7ec_0
- - matplotlib-base=3.8.4=py311h54ef318_0
+ - markdown-it-py=3.0.0=pyhd8ed1ab_0
+ - markupsafe=2.1.5=py312h98912ed_0
+ - matplotlib-base=3.8.4=py312h20ab3a6_2
- matplotlib-inline=0.1.7=pyhd8ed1ab_0
- mdit-py-plugins=0.4.1=pyhd8ed1ab_0
- mdurl=0.1.2=pyhd8ed1ab_0
- - minizip=4.0.5=h0ab5242_0
+ - minizip=4.0.7=h401b404_0
- mistune=3.0.2=pyhd8ed1ab_0
- - msgpack-python=1.0.8=py311h52f7536_0
- - multidict=6.0.5=py311h459d7ec_0
+ - msgpack-python=1.0.8=py312h2492b07_0
+ - multidict=6.0.5=py312h98912ed_0
- multipledispatch=0.6.0=py_0
- munkres=1.1.4=pyh9f0ad1d_0
- - myst-nb=0.17.2=pyhd8ed1ab_0
- - myst-parser=0.18.1=pyhd8ed1ab_0
- - nbclient=0.7.4=pyhd8ed1ab_0
- - nbconvert-core=7.16.4=pyhd8ed1ab_0
+ - myst-nb=1.1.0=pyhd8ed1ab_0
+ - myst-parser=2.0.0=pyhd8ed1ab_0
+ - nbclient=0.10.0=pyhd8ed1ab_0
+ - nbconvert-core=7.16.4=pyhd8ed1ab_1
- nbformat=5.10.4=pyhd8ed1ab_0
- ncurses=6.5=h59595ed_0
- nest-asyncio=1.6.0=pyhd8ed1ab_0
- - netcdf4=1.6.5=nompi_py311he8ad708_100
- - nodeenv=1.8.0=pyhd8ed1ab_0
+ - netcdf4=1.7.1=nompi_py312h1ef7fb6_101
+ - nodeenv=1.9.1=pyhd8ed1ab_0
- notebook-shim=0.2.4=pyhd8ed1ab_0
- nspr=4.35=h27087fc_0
- - nss=3.100=hca3bf56_0
- - numba=0.59.1=py311h96b013e_0
- - numcodecs=0.12.1=py311h4332511_1
- - numpy=1.26.4=py311h64a7726_0
+ - nss=3.101=h593d115_0
+ - numba=0.60.0=py312h83e6fd3_0
+ - numcodecs=0.12.1=py312h7070661_1
+ - numpy=1.26.4=py312heda63a1_0
- oauthlib=3.2.2=pyhd8ed1ab_0
- openjpeg=2.5.2=h488ebb8_0
- - openssl=3.3.0=hd590300_0
- - orc=2.0.0=h17fec99_1
+ - openssl=3.3.1=h4ab18f5_0
+ - orc=2.0.1=h17fec99_1
- overrides=7.7.0=pyhd8ed1ab_0
- - packaging=24.0=pyhd8ed1ab_0
- - pandas=2.2.2=py311h320fe9a_0
+ - packaging=24.1=pyhd8ed1ab_0
+ - pandas=2.2.2=py312h1d6d2e6_1
- pandocfilters=1.5.0=pyhd8ed1ab_0
- - panel=1.4.2=pyhd8ed1ab_0
- - pango=1.52.2=ha41ecd1_0
+ - panel=1.4.4=pyhd8ed1ab_0
+ - pango=1.54.0=h84a9a3c_0
- param=2.1.0=pyhca7485f_0
- parso=0.8.4=pyhd8ed1ab_0
- partd=1.4.2=pyhd8ed1ab_0
- pcre2=10.43=hcad00b1_0
- pexpect=4.9.0=pyhd8ed1ab_0
- pickleshare=0.7.5=py_1003
- - pillow=10.3.0=py311h18e6fac_0
- - pint=0.23=pyhd8ed1ab_0
+ - pillow=10.3.0=py312h287a98d_1
+ - pint=0.24=pyhd8ed1ab_2
- pint-xarray=0.3=pyhd8ed1ab_0
- pip=24.0=pyhd8ed1ab_0
- pixman=0.43.2=h59595ed_0
- pkgutil-resolve-name=1.3.10=pyhd8ed1ab_1
- platformdirs=4.2.2=pyhd8ed1ab_0
- - pooch=1.8.1=pyhd8ed1ab_0
+ - pooch=1.8.2=pyhd8ed1ab_0
- poppler=24.04.0=hb6cd0d7_0
- poppler-data=0.4.12=hd8ed1ab_0
- postgresql=16.3=h8e811e2_0
- pre-commit=3.7.1=pyha770c72_0
- - proj=9.4.0=h1d62c97_1
+ - proj=9.4.1=hb784bbd_0
- prometheus_client=0.20.0=pyhd8ed1ab_0
- - prompt-toolkit=3.0.42=pyha770c72_0
+ - prompt-toolkit=3.0.47=pyha770c72_0
- proto-plus=1.23.0=pyhd8ed1ab_0
- - protobuf=4.25.3=py311h7b78aeb_0
- - psutil=5.9.8=py311h459d7ec_0
+ - protobuf=4.25.3=py312h72fbbdf_0
+ - psutil=5.9.8=py312h98912ed_0
- pthread-stubs=0.4=h36c2ea0_1001
- ptyprocess=0.7.0=pyhd3deb0d_0
- pure_eval=0.2.2=pyhd8ed1ab_0
- - pyarrow=16.0.0=py311h781c19f_0
- - pyarrow-core=16.0.0=py311h8e2c35d_0_cpu
+ - pyarrow=16.1.0=py312h9cebb41_3
+ - pyarrow-core=16.1.0=py312h70856f0_3_cpu
- pyarrow-hotfix=0.6=pyhd8ed1ab_0
- pyasn1=0.6.0=pyhd8ed1ab_0
- pyasn1-modules=0.4.0=pyhd8ed1ab_0
- pybtex=0.24.0=pyhd8ed1ab_2
- - pybtex-docutils=1.0.3=py311h38be061_1
+ - pybtex-docutils=1.0.3=py312h7900ff3_1
- pycparser=2.22=pyhd8ed1ab_0
- pyct=0.5.0=pyhd8ed1ab_0
- pydap=3.4.0=pyhd8ed1ab_0
- - pydata-sphinx-theme=0.15.2=pyhd8ed1ab_0
+ - pydata-sphinx-theme=0.15.3=pyhd8ed1ab_0
- pygments=2.18.0=pyhd8ed1ab_0
- pyjwt=2.8.0=pyhd8ed1ab_1
- pyopenssl=24.0.0=pyhd8ed1ab_0
- pyparsing=3.1.2=pyhd8ed1ab_0
- - pyproj=3.6.1=py311hb3a3e68_6
+ - pyproj=3.6.1=py312h5d05ceb_7
- pyshp=2.3.1=pyhd8ed1ab_0
- pysocks=1.7.1=pyha2e5f31_6
- - python=3.11.9=hb806964_0_cpython
+ - python=3.12.4=h194c7f8_0_cpython
- python-dateutil=2.9.0=pyhd8ed1ab_0
- - python-fastjsonschema=2.19.1=pyhd8ed1ab_0
+ - python-fastjsonschema=2.20.0=pyhd8ed1ab_0
- python-graphviz=0.20.3=pyh717bed2_0
- python-json-logger=2.0.7=pyhd8ed1ab_0
- python-tzdata=2024.1=pyhd8ed1ab_0
- - python_abi=3.11=4_cp311
+ - python_abi=3.12=4_cp312
- pytz=2024.1=pyhd8ed1ab_0
- pyu2f=0.1.5=pyhd8ed1ab_0
- pyviz_comms=3.0.2=pyhd8ed1ab_1
- - pyyaml=6.0.1=py311h459d7ec_1
- - pyzmq=26.0.3=py311h08a0b41_0
- - rasterio=1.3.10=py311h375a7ea_0
+ - pyyaml=6.0.1=py312h98912ed_1
+ - pyzmq=26.0.3=py312h8fd38d8_0
+ - rasterio=1.3.10=py312hc022a17_4
- re2=2023.09.01=h7f4b329_2
- readline=8.2=h8228510_1
- referencing=0.35.1=pyhd8ed1ab_0
- - requests=2.31.0=pyhd8ed1ab_0
+ - requests=2.32.3=pyhd8ed1ab_0
- requests-oauthlib=2.0.0=pyhd8ed1ab_0
- rfc3339-validator=0.1.4=pyhd8ed1ab_0
- rfc3986-validator=0.1.1=pyh9f0ad1d_0
- - rioxarray=0.15.5=pyhd8ed1ab_0
- - rpds-py=0.18.1=py311h5ecf98a_0
+ - rioxarray=0.15.6=pyhd8ed1ab_0
+ - rpds-py=0.18.1=py312h4413252_0
- rsa=4.9=pyhd8ed1ab_0
- - s2n=1.4.13=he19d79f_0
- - scipy=1.13.0=py311h517d4fd_1
+ - s2n=1.4.16=he19d79f_0
+ - scipy=1.13.1=py312hc2bc53b_0
- send2trash=1.8.3=pyh0d859eb_0
- - setuptools=69.5.1=pyhd8ed1ab_0
- - shapely=2.0.4=py311h2032efe_0
+ - setuptools=70.1.0=pyhd8ed1ab_0
+ - shapely=2.0.4=py312ha5b4d35_1
- simpervisor=1.0.0=pyhd8ed1ab_0
- six=1.16.0=pyh6c4a22f_0
- snappy=1.2.0=hdb0a2a9_1
@@ -370,65 +373,64 @@ dependencies:
- sortedcontainers=2.4.0=pyhd8ed1ab_0
- soupsieve=2.5=pyhd8ed1ab_1
- spdlog=1.13.0=hd2e6256_0
- - sphinx=5.0.2=pyh6c4a22f_0
- - sphinx-book-theme=1.0.1=pyhd8ed1ab_0
- - sphinx-codeautolink=0.15.1=pyhd8ed1ab_0
+ - sphinx=7.3.7=pyhd8ed1ab_0
+ - sphinx-book-theme=1.1.3=pyhd8ed1ab_0
+ - sphinx-codeautolink=0.15.2=pyhd8ed1ab_0
- sphinx-comments=0.0.3=pyh9f0ad1d_0
- sphinx-copybutton=0.5.2=pyhd8ed1ab_0
- - sphinx-design=0.3.0=pyhd8ed1ab_0
- - sphinx-exercise=0.4.0=pyhd8ed1ab_0
- - sphinx-external-toc=0.3.1=pyhd8ed1ab_1
- - sphinx-jupyterbook-latex=0.5.2=pyhd8ed1ab_0
+ - sphinx-design=0.6.0=pyhd8ed1ab_0
+ - sphinx-external-toc=1.0.1=pyhd8ed1ab_0
+ - sphinx-jupyterbook-latex=1.0.0=pyhd8ed1ab_0
- sphinx-multitoc-numbering=0.1.3=pyhd8ed1ab_0
- - sphinx-notfound-page=1.0.0=pyhd8ed1ab_0
- - sphinx-thebe=0.2.1=pyhd8ed1ab_0
+ - sphinx-notfound-page=1.0.2=pyhd8ed1ab_0
+ - sphinx-thebe=0.3.1=pyhd8ed1ab_0
- sphinx-togglebutton=0.3.2=pyhd8ed1ab_0
- sphinxcontrib-applehelp=1.0.8=pyhd8ed1ab_0
- - sphinxcontrib-bibtex=2.5.0=pyhd8ed1ab_0
+ - sphinxcontrib-bibtex=2.6.2=pyhd8ed1ab_0
- sphinxcontrib-devhelp=1.0.6=pyhd8ed1ab_0
- sphinxcontrib-htmlhelp=2.0.5=pyhd8ed1ab_0
- sphinxcontrib-jsmath=1.0.1=pyhd8ed1ab_0
- sphinxcontrib-qthelp=1.0.7=pyhd8ed1ab_0
- sphinxcontrib-serializinghtml=1.1.10=pyhd8ed1ab_0
- - sphinxext-rediraffe=0.2.7=pyhd8ed1ab_0
- - sqlalchemy=2.0.30=py311h331c9d8_0
- - sqlite=3.45.3=h2c6b66d_0
+ - sphinxext-rediraffe=0.2.7=pyhd8ed1ab_1
+ - sqlalchemy=2.0.31=py312h9a8786e_0
+ - sqlite=3.46.0=h6d4b2fc_0
- stack_data=0.6.2=pyhd8ed1ab_0
- tabulate=0.9.0=pyhd8ed1ab_1
- tblib=3.0.0=pyhd8ed1ab_0
- terminado=0.18.1=pyh0d859eb_0
- - tiledb=2.23.0=h27f064a_0
+ - tiledb=2.24.1=h73c5a7c_0
- tinycss2=1.3.0=pyhd8ed1ab_0
- tk=8.6.13=noxft_h4845f30_101
- tomli=2.0.1=pyhd8ed1ab_0
- toolz=0.12.1=pyhd8ed1ab_0
- - tornado=6.4=py311h459d7ec_0
+ - tornado=6.4.1=py312h9a8786e_0
- tqdm=4.66.4=pyhd8ed1ab_0
- traitlets=5.14.3=pyhd8ed1ab_0
- types-python-dateutil=2.9.0.20240316=pyhd8ed1ab_0
- - typing-extensions=4.11.0=hd8ed1ab_0
- - typing_extensions=4.11.0=pyha770c72_0
+ - typing-extensions=4.12.2=hd8ed1ab_0
+ - typing_extensions=4.12.2=pyha770c72_0
- typing_utils=0.1.0=pyhd8ed1ab_0
- tzcode=2024a=h3f72095_0
- tzdata=2024a=h0c530f3_0
- uc-micro-py=1.0.3=pyhd8ed1ab_0
- - ukkonen=1.0.1=py311h9547e67_4
+ - ukkonen=1.0.1=py312h8572e83_4
- uri-template=1.3.0=pyhd8ed1ab_0
- uriparser=0.9.8=hac33072_0
- - urllib3=2.2.1=pyhd8ed1ab_0
+ - urllib3=2.2.2=pyhd8ed1ab_0
- virtualenv=20.26.2=pyhd8ed1ab_0
- wcwidth=0.2.13=pyhd8ed1ab_0
- - webcolors=1.13=pyhd8ed1ab_0
+ - webcolors=24.6.0=pyhd8ed1ab_0
- webencodings=0.5.1=pyhd8ed1ab_2
- webob=1.8.7=pyhd8ed1ab_0
- websocket-client=1.8.0=pyhd8ed1ab_0
- wheel=0.43.0=pyhd8ed1ab_1
- - xarray=2024.5.0=pyhd8ed1ab_0
+ - xarray=2024.6.0=pyhd8ed1ab_1
- xerces-c=3.2.5=hac6953d_0
- xorg-kbproto=1.0.7=h7f98852_1002
- xorg-libice=1.1.1=hd590300_0
- xorg-libsm=1.2.4=h7391055_0
- - xorg-libx11=1.8.9=h8ee46fc_0
+ - xorg-libx11=1.8.9=hb711507_1
- xorg-libxau=1.0.11=hd590300_0
- xorg-libxdmcp=1.1.3=h7f98852_0
- xorg-libxext=1.3.4=h0b41bf4_2
@@ -436,13 +438,13 @@ dependencies:
- xorg-renderproto=0.11.1=h7f98852_1002
- xorg-xextproto=7.3.0=h0b41bf4_1003
- xorg-xproto=7.0.31=h7f98852_1007
- - xyzservices=2024.4.0=pyhd8ed1ab_0
+ - xyzservices=2024.6.0=pyhd8ed1ab_0
- xz=5.2.6=h166bdaf_0
- yaml=0.2.5=h7f98852_2
- - yarl=1.9.4=py311h459d7ec_0
- - zarr=2.18.0=pyhd8ed1ab_0
+ - yarl=1.9.4=py312h98912ed_0
+ - zarr=2.18.2=pyhd8ed1ab_0
- zeromq=4.3.5=h75354e8_4
- zict=3.0.0=pyhd8ed1ab_0
- - zipp=3.17.0=pyhd8ed1ab_0
- - zlib=1.2.13=hd590300_5
+ - zipp=3.19.2=pyhd8ed1ab_0
+ - zlib=1.3.1=h4ab18f5_1
- zstd=1.5.6=ha6fb4c9_0
diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb
index 6b98ee39..ef4f4d39 100644
--- a/fundamentals/02.1_indexing_Basic.ipynb
+++ b/fundamentals/02.1_indexing_Basic.ipynb
@@ -204,11 +204,6 @@
"da[:, 20, 40]"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -581,6 +576,13 @@
"```"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -590,6 +592,13 @@
"Another technique is to use a list of datetime objects or date strings for indexing. For example, you could select data for specific, non-contiguous dates like this:"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -602,6 +611,13 @@
"ds.sel(time=dates)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -663,54 +679,55 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "```{exercise}\n",
- ":label: indexing-1\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Select the first 30 entries of `latitude` and 30th to 40th entries of `longitude`:\n",
- "```\n",
"\n",
- "````{solution} indexing-1\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"```python\n",
"ds.isel(lat=slice(None, 30), lon=slice(30, 40))\n",
"```\n",
"\n",
- "````"
+ ":::\n",
+ "::::"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "```{exercise}\n",
- ":label: indexing-2\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
- "Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013 :\n",
- "```\n",
- "````{solution} indexing-2\n",
+ "Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"```python\n",
"ds.sel(lat=75, time=slice(\"2013-01-01\", \"2013-10-15\"))\n",
"```\n",
- "````"
+ ":::\n",
+ "::::"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "```{exercise}\n",
- ":label: indexing-3\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
- "Remove all entries at 260 and 270 degrees :\n",
+ "Remove all entries at 260 and 270 degrees\n",
"\n",
- "```\n",
- "````{solution} indexing-3\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"```python\n",
"ds.drop_sel(lon=[260, 270])\n",
"```\n",
- "````"
+ ":::\n",
+ "::::"
]
},
{
diff --git a/fundamentals/02.3_aligning_data_objects.ipynb b/fundamentals/02.3_aligning_data_objects.ipynb
index e3d8deec..490fbc88 100644
--- a/fundamentals/02.3_aligning_data_objects.ipynb
+++ b/fundamentals/02.3_aligning_data_objects.ipynb
@@ -251,8 +251,8 @@
"tags": []
},
"source": [
- "````{exercise}\n",
- ":label: ex1\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Consider the following 2D array. What are the dimensions of `array - array.mean(\"time\")`?\n",
"```python\n",
@@ -263,15 +263,16 @@
" name=\"array\",\n",
")\n",
"```\n",
- "````\n",
"\n",
- "````{solution} ex1\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"```python\n",
"(array - array.mean(\"time\")).dims\n",
"```\n",
- "````"
+ ":::\n",
+ "::::"
]
},
{
@@ -443,8 +444,8 @@
]
},
"source": [
- "````{exercise}\n",
- ":label: dims\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Consider the following two arrays. Write down the `x` and `y` coordinate locations for `da1 - da2`\n",
"```python\n",
@@ -459,13 +460,13 @@
" coords={\"space\": [\"b\", \"d\"]},\n",
")\n",
"```\n",
- "````\n",
"\n",
- "```{solution} dims\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"`x = [\"b\"], y=[0, 1, 2, 3]` . `da2` has been broadcasted to 2D (so dimension \"y\" has been inserted) and the two arrays are aligned using `join=\"inner\"` prior to subtraction.\n",
- "```"
+ ":::\n",
+ "::::"
]
},
{
diff --git a/fundamentals/03.1_computation_with_xarray.ipynb b/fundamentals/03.1_computation_with_xarray.ipynb
index 2f5c5075..9dbaadd0 100644
--- a/fundamentals/03.1_computation_with_xarray.ipynb
+++ b/fundamentals/03.1_computation_with_xarray.ipynb
@@ -265,15 +265,18 @@
"tags": []
},
"source": [
- "```{exercise}\n",
- ":label: sst-mean\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"Take the mean of `sst` in both longitude and latitude. Make a simple timeseries plot.\n",
- "```\n",
- "````{solution} sst-mean\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
+ "```python\n",
"sst.mean([\"lat\", \"lon\"]).plot();\n",
- "```\n"
+ "```\n",
+ ":::\n",
+ "::::\n"
]
}
],
diff --git a/fundamentals/03.2_groupby_with_xarray.ipynb b/fundamentals/03.2_groupby_with_xarray.ipynb
index f16d61ef..cb1ba252 100644
--- a/fundamentals/03.2_groupby_with_xarray.ipynb
+++ b/fundamentals/03.2_groupby_with_xarray.ipynb
@@ -479,17 +479,18 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "```{exercise} \n",
- ":label: annual-mean\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Using `groupby`, plot the annual mean time series of SST at 300°E, 50°N\n",
- "```\n",
- "````{solution} annual-mean\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"```python\n",
"ds.groupby(\"time.year\").mean().sst.sel(lon=300, lat=50).plot();\n",
"```\n",
- "````"
+ ":::\n",
+ "::::"
]
},
{
@@ -582,20 +583,21 @@
"tags": []
},
"source": [
- "```{exercise}\n",
- ":label: resample-mean\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Using `resample`, plot the annual mean time series of SST at 300°E, 50°N.\n",
"\n",
"Compare this output to the groupby output. What differences do you see?\n",
- "```\n",
- "````{solution} resample-mean\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"```python\n",
"resampled = ds.resample(time='Y').mean().sst.sel(lon=300, lat=50)\n",
"resampled.plot();\n",
"```\n",
- "````"
+ ":::\n",
+ "::::"
]
},
{
diff --git a/fundamentals/03.3_windowed.ipynb b/fundamentals/03.3_windowed.ipynb
index 504ee9b7..4454d50e 100644
--- a/fundamentals/03.3_windowed.ipynb
+++ b/fundamentals/03.3_windowed.ipynb
@@ -193,11 +193,11 @@
"id": "16",
"metadata": {},
"source": [
- "```{exercise} \n",
- ":label: rolling-center\n",
+ ":::{admonition} Exercise\n",
+ ":class: tip\n",
"Illustrate the difference between `center=True` and `center=False` for rolling by looking at the `construct`-ed array.\n",
- "```\n",
- "````{solution} rolling-center\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"```python\n",
"display(\"center=True\")\n",
@@ -206,7 +206,8 @@
"display(\"center=False\")\n",
"display(simple.rolling(time=5, center=False).construct(\"window\"))\n",
"```\n",
- "````"
+ ":::\n",
+ "::::"
]
},
{
@@ -336,8 +337,8 @@
"id": "27",
"metadata": {},
"source": [
- "````{exercise} \n",
- ":label: rolling-pad\n",
+ ":::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Imagine the array `months` was one element shorter. Use `boundary=\"pad\"` and the `side` kwarg to reshape `months.isel(time=slice(1, None))` to a 2D DataArray with the following values:\n",
"\n",
@@ -345,8 +346,8 @@
"array([[nan, 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.],\n",
" [ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.]])\n",
"```\n",
- "````\n",
- "````{solution} rolling-pad\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"```python\n",
@@ -354,7 +355,8 @@
" time=(\"year\", \"month\")\n",
")\n",
"```\n",
- "````"
+ ":::\n",
+ "::::"
]
},
{
diff --git a/intermediate/01-high-level-computation-patterns.ipynb b/intermediate/01-high-level-computation-patterns.ipynb
index 5aa3940f..e5f17c1c 100644
--- a/intermediate/01-high-level-computation-patterns.ipynb
+++ b/intermediate/01-high-level-computation-patterns.ipynb
@@ -457,39 +457,26 @@
"id": "26",
"metadata": {},
"source": [
- "```{exercise}\n",
- ":label: rolling-reduce\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Calculate the rolling mean in 5 point bins along both latitude and longitude using\n",
"[`rolling(...).reduce`](https://docs.xarray.dev/en/stable/generated/xarray.core.rolling.DataArrayRolling.reduce.html)\n",
"\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "27",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- },
- "tags": []
- },
- "source": [
- "````{solution} rolling-reduce\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"```python\n",
"# exactly equivalent to data.rolling(...).mean()\n",
"data.rolling(lat=5, lon=5, center=True).reduce(np.mean).plot();\n",
"```\n",
- "\n",
- "````"
+ ":::\n",
+ "::::"
]
},
{
"cell_type": "markdown",
- "id": "28",
+ "id": "27",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -505,7 +492,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "29",
+ "id": "28",
"metadata": {},
"outputs": [],
"source": [
@@ -516,7 +503,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "30",
+ "id": "29",
"metadata": {},
"outputs": [],
"source": [
@@ -526,38 +513,36 @@
},
{
"cell_type": "markdown",
- "id": "31",
+ "id": "30",
"metadata": {},
"source": [
"```{note}\n",
- ":class: dropdown\n",
"Because `.construct()` only returns a \"view\" (not a copy) of the original data object (i.e. it is not operating \"in-place\"), in order to \"save\" the results you would need to rewrite the original object: `simple = simple.rolling(time=5, center=True).construct(\"window\")`.\n",
"```"
]
},
{
"cell_type": "markdown",
- "id": "32",
+ "id": "31",
"metadata": {},
"source": [
- "```{exercise}\n",
- ":label: rolling-construct\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"Calculate the 5 point running mean in time using `rolling.construct`\n",
- "```\n",
"\n",
- "````{solution} rolling-construct\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"```python\n",
"simple.rolling(time=5, center=True).construct(\"window\").mean(\"window\")\n",
"```\n",
- "\n",
- "````\n"
+ ":::\n",
+ "::::"
]
},
{
"cell_type": "markdown",
- "id": "33",
+ "id": "32",
"metadata": {},
"source": [
"`construct` is clever.\n",
@@ -569,7 +554,7 @@
},
{
"cell_type": "markdown",
- "id": "34",
+ "id": "33",
"metadata": {
"tags": []
},
@@ -582,7 +567,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "35",
+ "id": "34",
"metadata": {},
"outputs": [],
"source": [
@@ -591,7 +576,7 @@
},
{
"cell_type": "markdown",
- "id": "36",
+ "id": "35",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -615,7 +600,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "37",
+ "id": "36",
"metadata": {},
"outputs": [],
"source": [
@@ -625,7 +610,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "38",
+ "id": "37",
"metadata": {},
"outputs": [],
"source": [
@@ -635,7 +620,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "39",
+ "id": "38",
"metadata": {},
"outputs": [],
"source": [
@@ -645,7 +630,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "40",
+ "id": "39",
"metadata": {},
"outputs": [],
"source": [
@@ -654,7 +639,7 @@
},
{
"cell_type": "markdown",
- "id": "41",
+ "id": "40",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -664,30 +649,23 @@
"source": [
"#### Coarsen supports `reduce` for custom reductions\n",
"\n",
- "```{exercise}\n",
- ":label: coarsen-reduce\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"Use `coarsen.reduce` to apply `np.ptp` in 5x5 (lat x lon) point blocks to `data`\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "42",
- "metadata": {},
- "source": [
- "````{solution} coarsen-reduce\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"```python\n",
"data.coarsen(lat=5, lon=5, boundary=\"trim\").reduce(np.mean).plot();\n",
"```\n",
- "\n",
- "````\n"
+ ":::\n",
+ "::::"
]
},
{
"cell_type": "markdown",
- "id": "43",
+ "id": "41",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -707,7 +685,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "44",
+ "id": "42",
"metadata": {},
"outputs": [],
"source": [
@@ -722,7 +700,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "45",
+ "id": "43",
"metadata": {},
"outputs": [],
"source": [
@@ -732,7 +710,7 @@
},
{
"cell_type": "markdown",
- "id": "46",
+ "id": "44",
"metadata": {},
"source": [
"Note two things:\n",
@@ -747,7 +725,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "47",
+ "id": "45",
"metadata": {},
"outputs": [],
"source": [
@@ -756,7 +734,7 @@
},
{
"cell_type": "markdown",
- "id": "48",
+ "id": "46",
"metadata": {},
"source": [
"This adds values at the end of the array (see the 'nan' at the end of the time coordinate?), which is not so sensible for this\n",
@@ -766,7 +744,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "49",
+ "id": "47",
"metadata": {},
"outputs": [],
"source": [
@@ -777,7 +755,7 @@
},
{
"cell_type": "markdown",
- "id": "50",
+ "id": "48",
"metadata": {},
"source": [
"Note that `coarsen` pads with NaNs. For more control over padding, use\n",
@@ -787,7 +765,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "51",
+ "id": "49",
"metadata": {},
"outputs": [],
"source": [
@@ -801,7 +779,7 @@
},
{
"cell_type": "markdown",
- "id": "52",
+ "id": "50",
"metadata": {},
"source": [
"```{note}\n",
@@ -812,22 +790,16 @@
},
{
"cell_type": "markdown",
- "id": "53",
+ "id": "51",
"metadata": {},
"source": [
- "```{exercise}\n",
- ":label: reshape\n",
+ ":::{admonition} Exercise\n",
+ ":class: tip\n",
"Reshape the `time` dimension of the DataArray `monthly` to year x\n",
"month and visualize the seasonal cycle for two years at 250°E\n",
- "```\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "54",
- "metadata": {},
- "source": [
- "````{solution} reshape\n",
+ "\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"```python\n",
@@ -856,29 +828,20 @@
"# seasonal cycle for two years\n",
"year_month.sel(lon=250).plot.contourf(col=\"year\", x=\"month\", y=\"lat\")\n",
"```\n",
- "\n",
- "````\n"
+ ":::\n",
+ "::::"
]
},
{
"cell_type": "markdown",
- "id": "55",
+ "id": "52",
"metadata": {},
"source": [
- "\n",
- "\n",
- "```{exercise}\n",
- ":label: rolling\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"Calculate the rolling 4 month average, averaged across years. (This exercise came up during a live lecture).\n",
- "```\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "56",
- "metadata": {},
- "source": [
- "````{solution} rolling\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"1. We first reshape using `coarsen.construct` to add `year` as a new dimension.\n",
@@ -890,13 +853,13 @@
"roll = reshaped.rolling(month=4, center=True)\n",
"roll.construct(\"window\").mean([\"window\", \"year\"])\n",
"```\n",
- "\n",
- "````"
+ ":::\n",
+ "::::"
]
},
{
"cell_type": "markdown",
- "id": "57",
+ "id": "53",
"metadata": {},
"source": [
"### Summary\n",
@@ -912,7 +875,7 @@
},
{
"cell_type": "markdown",
- "id": "58",
+ "id": "54",
"metadata": {
"slideshow": {
"slide_type": "slide"
@@ -958,7 +921,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "59",
+ "id": "55",
"metadata": {},
"outputs": [],
"source": [
@@ -969,7 +932,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "60",
+ "id": "56",
"metadata": {},
"outputs": [],
"source": [
@@ -982,7 +945,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "61",
+ "id": "57",
"metadata": {},
"outputs": [],
"source": [
@@ -993,7 +956,7 @@
},
{
"cell_type": "markdown",
- "id": "62",
+ "id": "58",
"metadata": {},
"source": [
"Notice that since we have averaged over all the years for each month, our resulting DataArray no longer has a \"year\" coordinate.\n",
@@ -1004,7 +967,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "63",
+ "id": "59",
"metadata": {},
"outputs": [],
"source": [
@@ -1013,7 +976,7 @@
},
{
"cell_type": "markdown",
- "id": "64",
+ "id": "60",
"metadata": {},
"source": [
"Similarly for binning (remember this is useful when the parameter you are binning over is not \"exact\", like a float),\n"
@@ -1022,7 +985,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "65",
+ "id": "61",
"metadata": {},
"outputs": [],
"source": [
@@ -1031,7 +994,7 @@
},
{
"cell_type": "markdown",
- "id": "66",
+ "id": "62",
"metadata": {},
"source": [
"and resampling...\n"
@@ -1040,7 +1003,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "67",
+ "id": "63",
"metadata": {},
"outputs": [],
"source": [
@@ -1049,7 +1012,7 @@
},
{
"cell_type": "markdown",
- "id": "68",
+ "id": "64",
"metadata": {},
"source": [
"```{note}\n",
@@ -1061,7 +1024,7 @@
},
{
"cell_type": "markdown",
- "id": "69",
+ "id": "65",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -1095,7 +1058,7 @@
},
{
"cell_type": "markdown",
- "id": "70",
+ "id": "66",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -1114,7 +1077,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "71",
+ "id": "67",
"metadata": {
"tags": []
},
@@ -1126,7 +1089,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "72",
+ "id": "68",
"metadata": {},
"outputs": [],
"source": [
@@ -1136,7 +1099,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "73",
+ "id": "69",
"metadata": {},
"outputs": [],
"source": [
@@ -1146,7 +1109,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "74",
+ "id": "70",
"metadata": {},
"outputs": [],
"source": [
@@ -1155,7 +1118,7 @@
},
{
"cell_type": "markdown",
- "id": "75",
+ "id": "71",
"metadata": {},
"source": [
"#### Construct and use custom labels\n",
@@ -1170,7 +1133,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "76",
+ "id": "72",
"metadata": {},
"outputs": [],
"source": [
@@ -1180,7 +1143,7 @@
},
{
"cell_type": "markdown",
- "id": "77",
+ "id": "73",
"metadata": {},
"source": [
"Create a new empty array\n"
@@ -1189,7 +1152,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "78",
+ "id": "74",
"metadata": {},
"outputs": [],
"source": [
@@ -1199,7 +1162,7 @@
},
{
"cell_type": "markdown",
- "id": "79",
+ "id": "75",
"metadata": {},
"source": [
"Use `isin` to assign custom seasons,\n"
@@ -1208,7 +1171,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "80",
+ "id": "76",
"metadata": {},
"outputs": [],
"source": [
@@ -1220,7 +1183,7 @@
},
{
"cell_type": "markdown",
- "id": "81",
+ "id": "77",
"metadata": {},
"source": [
"Turn our new seasonal group array into a DataArray."
@@ -1229,7 +1192,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "82",
+ "id": "78",
"metadata": {},
"outputs": [],
"source": [
@@ -1240,7 +1203,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "83",
+ "id": "79",
"metadata": {},
"outputs": [],
"source": [
@@ -1256,7 +1219,7 @@
},
{
"cell_type": "markdown",
- "id": "84",
+ "id": "80",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -1272,7 +1235,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "85",
+ "id": "81",
"metadata": {},
"outputs": [],
"source": [
@@ -1282,7 +1245,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "86",
+ "id": "82",
"metadata": {},
"outputs": [],
"source": [
@@ -1293,7 +1256,7 @@
},
{
"cell_type": "markdown",
- "id": "87",
+ "id": "83",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -1311,7 +1274,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "88",
+ "id": "84",
"metadata": {},
"outputs": [],
"source": [
@@ -1320,7 +1283,7 @@
},
{
"cell_type": "markdown",
- "id": "89",
+ "id": "85",
"metadata": {
"tags": []
},
@@ -1333,7 +1296,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "90",
+ "id": "86",
"metadata": {},
"outputs": [],
"source": [
@@ -1342,7 +1305,7 @@
},
{
"cell_type": "markdown",
- "id": "91",
+ "id": "87",
"metadata": {},
"source": [
"```{tip}\n",
@@ -1352,7 +1315,7 @@
},
{
"cell_type": "markdown",
- "id": "92",
+ "id": "88",
"metadata": {
"tags": []
},
@@ -1371,7 +1334,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "93",
+ "id": "89",
"metadata": {},
"outputs": [],
"source": [
@@ -1381,7 +1344,7 @@
},
{
"cell_type": "markdown",
- "id": "94",
+ "id": "90",
"metadata": {},
"source": [
"`group` is a DataArray containing data for all December days (because the last printed `label` value is `12`, so the last `group` value is for December)."
@@ -1390,7 +1353,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "95",
+ "id": "91",
"metadata": {
"tags": []
},
@@ -1401,7 +1364,7 @@
},
{
"cell_type": "markdown",
- "id": "96",
+ "id": "92",
"metadata": {},
"source": [
"Maybe you want a histogram of December temperatures?\n"
@@ -1410,7 +1373,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "97",
+ "id": "93",
"metadata": {},
"outputs": [],
"source": [
@@ -1419,7 +1382,7 @@
},
{
"cell_type": "markdown",
- "id": "98",
+ "id": "94",
"metadata": {},
"source": [
"Remember, this example is just to show how you could operate on each group object in a groupby operation. If we wanted to just explore the December (or March) data, we should just filter for it directly:"
@@ -1428,7 +1391,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "99",
+ "id": "95",
"metadata": {},
"outputs": [],
"source": [
@@ -1437,7 +1400,7 @@
},
{
"cell_type": "markdown",
- "id": "100",
+ "id": "96",
"metadata": {
"tags": []
},
@@ -1455,7 +1418,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "101",
+ "id": "97",
"metadata": {
"tags": []
},
@@ -1471,7 +1434,7 @@
},
{
"cell_type": "markdown",
- "id": "102",
+ "id": "98",
"metadata": {},
"source": [
"***"
@@ -1479,7 +1442,7 @@
},
{
"cell_type": "markdown",
- "id": "103",
+ "id": "99",
"metadata": {},
"source": [
"## Summary\n",
diff --git a/intermediate/indexing/boolean-masking-indexing.ipynb b/intermediate/indexing/boolean-masking-indexing.ipynb
index 2ee0a6e5..67aeced7 100644
--- a/intermediate/indexing/boolean-masking-indexing.ipynb
+++ b/intermediate/indexing/boolean-masking-indexing.ipynb
@@ -160,11 +160,20 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "```{exercise}\n",
- ":label: boolean-2\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Using the syntax you’ve learned so far, mask all the points with latitudes above 60° N.\n",
- "```\n"
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "\n",
+ "```python\n",
+ "da_masked = da.where(da.yc >= 60)\n",
+ "da_masked[:, :].plot();\n",
+ "```\n",
+ ":::\n",
+ "::::"
]
},
{
@@ -176,19 +185,6 @@
"# write your answer here!"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "````{solution} boolean-2\n",
- ":class: dropdown\n",
- "```python\n",
- "da_masked = da.where(da.yc >= 60)\n",
- "da_masked[:, :].plot();\n",
- "```\n",
- "````"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -323,8 +319,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "````{exercise}\n",
- ":label: boolean-1\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"If we load air temperature dataset from NCEP, we could use `sel` method for selecting a region:\n",
"\n",
@@ -336,9 +332,7 @@
"```\n",
"Can you use a similar method as above using `sel` to crop a region using the RASM dataset? Why?\n",
"\n",
- "````\n",
- "\n",
- "````{solution} boolean-1\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"This method will not work here as the dimensions are different from coordinates here. Specifically, the variables xc (longitude) and yc (latitude) are two-dimensional scalar fields, which differ from the logical coordinates represented by x and y.\n",
"\n",
@@ -347,7 +341,8 @@
"cropped_ds = ds.sel(x=slice(min_lat,max_lat), y=slice(min_lon,max_lon))\n",
"cropped_ds.Tair.plot()\n",
"```\n",
- "````\n"
+ ":::\n",
+ "::::"
]
},
{
diff --git a/intermediate/xarray_and_dask.ipynb b/intermediate/xarray_and_dask.ipynb
index 446002c1..2f025b69 100644
--- a/intermediate/xarray_and_dask.ipynb
+++ b/intermediate/xarray_and_dask.ipynb
@@ -250,10 +250,19 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "```{exercise} \n",
- ":label: data-values\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"Try calling `ds.air.values` and `ds.air.data`. Do you understand the difference?\n",
- "```"
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use this cell for the exercise"
]
},
{
@@ -344,32 +353,34 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "```{exercise}\n",
- ":label: compute\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Try running `mean.compute` and then examine `mean` after that. Is it still a dask array?\n",
- "```\n",
- "```{solution} compute\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"Computing returns a numpy array but does not modify in-place. So `mean` still contains a dask array.\n",
- "```"
+ ":::\n",
+ "::::"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "```{exercise}\n",
- ":label: load\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"\n",
"Now repeat that exercise with `mean.load`.\n",
- "```\n",
- "```{solution} load\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"`load` modifies an Xarray object in-place so `mean` now contains a numpy array.\n",
- "```"
+ ":::\n",
+ "::::"
]
},
{
@@ -545,9 +556,18 @@
"tags": []
},
"source": [
- "```{exercise}\n",
+ "::::{admonition} Exercise\n",
"Visualize the task graph for a few different computations on `ds.air`!\n",
- "```"
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Use this cell for the exercise"
]
},
{
diff --git a/intermediate/xarray_ecosystem.ipynb b/intermediate/xarray_ecosystem.ipynb
index a7b12b5a..017b9690 100644
--- a/intermediate/xarray_ecosystem.ipynb
+++ b/intermediate/xarray_ecosystem.ipynb
@@ -609,12 +609,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "```{exercise}\n",
- ":label: wrong-unit\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
"Write a function which will raise a `pint.DimensionalityError` if supplied with Xarray DataArray with the wrong units.\n",
- "```\n",
"\n",
- "````{solution} wrong-unit\n",
+ "\n",
+ ":::{admonition} Solution\n",
":class: dropdown\n",
"\n",
"```python\n",
@@ -627,21 +627,21 @@
" \"this function will only give the correct answer if the input is in units of miles\"\n",
" )\n",
"```\n",
- "\n",
- "````"
+ ":::\n",
+ "::::"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "```{exercise}\n",
+ "::::{admonition} Exercise\n",
"Try this on some of your data!\n",
"\n",
"After you have imported pint-xarray (and maybe cf-xarray) as above, start with something like\n",
"\n",
"`ds = xr.open_dataset(my_data).pint.quantify()`\n",
- "```"
+ "::::"
]
},
{