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Merged
merged 8 commits into from
Jun 15, 2023
Merged

Edits to "Xarray and Dask" #177

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8d5871f
mean var used in an exercise before intro
Jun 12, 2023
88fc75e
deleting note to self
Jun 12, 2023
86ba621
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jun 12, 2023
d2b6113
cleared cells output
yutik-nn Jun 12, 2023
3e6b72d
added a sentence clarifying /tmp
yutik-nn Jun 15, 2023
8e706d1
Merge branch 'first_edit' of https://github.com/yutik-nn/xarray-tutor…
yutik-nn Jun 15, 2023
e39aa15
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jun 15, 2023
0ab5bb3
Merge branch 'main' into first_edit
yutik-nn Jun 15, 2023
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33 changes: 14 additions & 19 deletions intermediate/xarray_and_dask.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -230,9 +230,6 @@
"cell_type": "code",
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"outputs_hidden": true
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"tags": []
},
"outputs": [],
Expand All @@ -246,16 +243,13 @@
"source": [
"### Exercise\n",
"\n",
"Try calling `mean.values` and `mean.data`. Do you understand the difference?"
"Try calling `ds.air.values` and `ds.air.data`. Do you understand the difference?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
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"tags": []
},
"outputs": [],
Expand Down Expand Up @@ -331,7 +325,7 @@
"2. `.load()` replaces the dask array in the xarray object with a numpy array.\n",
" This is equivalent to `ds = ds.compute()`\n",
" \n",
"**Tip:** There is a third option : \"persisting\". `.persist()` loads the values into distributed RAM. The values are computed but remain distributed across workers. So `ds.air.persist()` still returns a dask array. This is useful if you will be repeatedly using a dataset for computation but it is too large to load into local memory. You will see a persistent task on the dashboard. See the [dask user guide](https://docs.dask.org/en/latest/api.html#dask.persist) for more on persisting\n"
"**Tip:** There is a third option : \"persisting\". `.persist()` loads the values into distributed RAM. The values are computed but remain distributed across workers. So `ds.air.persist()` still returns a dask array. This is useful if you will be repeatedly using a dataset for computation but it is too large to load into local memory. You will see a persistent task on the dashboard. See the [dask user guide](https://docs.dask.org/en/latest/api.html#dask.persist) for more on persisting"
]
},
{
Expand All @@ -347,9 +341,6 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"outputs": [],
Expand Down Expand Up @@ -446,7 +437,10 @@
"\n",
"You can use any kind of Dask cluster. This step is completely independent of\n",
"xarray. While not strictly necessary, the dashboard provides a nice learning\n",
"tool."
"tool.\n",
"\n",
"By default, Dask uses the current working directory for writing temporary files.\n",
"We choose to use a temporary scratch folder `local_directory='/tmp'` in the example below instead."
]
},
{
Expand All @@ -464,10 +458,17 @@
"# if os.environ.get('JUPYTERHUB_USER'):\n",
"# dask.config.set(**{\"distributed.dashboard.link\": \"/user/{JUPYTERHUB_USER}/proxy/{port}/status\"})\n",
"\n",
"client = Client(local_directory='/tmp')\n",
"client = Client()\n",
"client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
Expand All @@ -483,9 +484,6 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"outputs": [],
Expand Down Expand Up @@ -539,9 +537,6 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"outputs": [],
Expand Down