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Add note for installation of missing packages in xarray lecture #304

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merged 3 commits into from
Nov 4, 2024
Merged

Add note for installation of missing packages in xarray lecture #304

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6cd3241
Add note for installation of missing packages
gregordecristoforo Nov 4, 2024
90b58cf
Add conda install for all packages
gregordecristoforo Nov 4, 2024
c3234ab
explicitly using conda-forge channel to help Windows installation
eglerean Nov 4, 2024
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14 changes: 8 additions & 6 deletions content/xarray.rst
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Expand Up @@ -45,6 +45,12 @@ Xarray is a powerful Python library that introduces labelled multidimensional ar

We will first download a dataset similar to the example above to illustrate the advantages of Xarray. We will cover how to transform your own data into an Xarray Dataset later in this lecture.

.. Note::

If you have set up your ``python-for-scicomp`` environment yesterday or earlier, you need to install the packages ``netcdf4`` and ``pythia_datasets`` manually. You can do this by running the following command in your (JupyterLab) terminal: ::

conda install netcdf4 pythia-datasets -c conda-forge

Let us open a python shell and download a public dataset: ::

>>> from pythia_datasets import DATASETS
Expand Down Expand Up @@ -328,7 +334,7 @@ Creating your own Xarray Dataset is quite simple. We can create a Dataset from s

# Put everything together to create the Dataset
ds = xr.Dataset(
{
data_vars = {
"pressure": (["time", "location"], pressure_data),
"humidity": (["time", "location"], humidity_data)
},
Expand All @@ -338,10 +344,6 @@ Creating your own Xarray Dataset is quite simple. We can create a Dataset from s
},
attrs={
"description": "Weather data",
"units": {
"pressure": "hPa",
"humidity": "%"
},
"creation_date": "2023-01-01",
"author": "Data Scientist"
}
Expand Down Expand Up @@ -403,7 +405,7 @@ Exercises 2
stock_prices = np.random.normal(loc=[100, 1500, 200], scale=[10, 50, 20], size=(10, 3))
trading_volumes = np.random.randint(1000, 10000, size=(10, 3))
ds = xr.Dataset(
{
data_vars = {
"stock_price": (["time", "company"], stock_prices),
"trading_volume": (["time", "company"], trading_volumes),
},
Expand Down
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