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Fixed typos in linear-algebra and added JLD2 to data-science.rst #20

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Fixed typos in linear-algebra and added JLD2 to data-science.rst #20

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96 changes: 32 additions & 64 deletions content/data-science.rst
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Expand Up @@ -23,14 +23,12 @@
Working with data
-----------------

Via Data Formats and Dataframes lesson, we explored a Julian approach
to manipulating and visualization of data.
In the Data Formats and Dataframes lesson, we explored a Julian approach
to manipulation and visualisation of data.

Julia is a good language to use for data science problems as
it will perform well and alleviate the need to translate
computationally demanding parts to another language.

Here we will learn and clustering, classification, machine learning and deep learning (toy example). Use penguin data.machine learning.
Here we will learn and clustering, classification, machine learning and deep learning with some toy examples.


Download a dataset
^^^^^^^^^^^^^^^^^^
Expand All @@ -55,50 +53,6 @@
using PalmerPenguins


Dataframes
^^^^^^^^^^

.. todo:: Dataframes

We will use `DataFrames.jl <https://dataframes.juliadata.org/stable/>`_
package function here to analyze the penguins dataset, but first we need to install it:

.. code-block:: julia

Pkg.add("DataFrames")
using DataFrames

We now create a dataframe containing the PalmerPenguins dataset.

.. code-block:: julia

# using PalmerPenguins
table = PalmerPenguins.load()
df = DataFrame(table)

# the raw data can be loaded by
#tableraw = PalmerPenguins.load(; raw = true)

Summary statistics can be displayed with the ``describe`` function:

.. code-block:: julia

describe(df)

.. code-block:: text

7×7 DataFrame
Row │ variable mean min median max nmissing eltype
│ Symbol Union… Any Union… Any Int64 Type
─────┼──────────────────────────────────────────────────────────────────────────────────────────
1 │ species Adelie Gentoo 0 String
2 │ island Biscoe Torgersen 0 String
3 │ bill_length_mm 43.9219 32.1 44.45 59.6 2 Union{Missing, Float64}
4 │ bill_depth_mm 17.1512 13.1 17.3 21.5 2 Union{Missing, Float64}
5 │ flipper_length_mm 200.915 172 197.0 231 2 Union{Missing, Int64}
6 │ body_mass_g 4201.75 2700 4050.0 6300 2 Union{Missing, Int64}
7 │ sex female male 11 Union{Missing, String}

As it was done in the Data Formats and Dataframes lesson, we can

.. code-block:: julia
Expand All @@ -119,7 +73,8 @@
------------------------

There are several ways to save the current setup in Julia.
This section will cover three methods: saving the environment, saving data as a CSV file, and saving data using JLD.jl.
This section will cover three parts: saving the environment to
have reproducible code and saving data using CSV files or ``JLD``.

1. Saving the Environment
^^^^^^^^^^^^^^^^^^^^^^^^^
Expand All @@ -144,7 +99,7 @@
This will display the list of packages in the current environment along with their versions.

To save the state of your environment, Julia uses two files: ``Project.toml`` and ``Manifest.toml``.
The ``Project.tom`` file specifies the packages that you explicitly added to your environment,
The ``Project.toml`` file specifies the packages that you explicitly added to your environment,
while the ``Manifest.toml`` file records the exact versions of these packages and all their dependencies1.

When you add packages using ``Pkg.add()``, Julia automatically updates these files.
Expand All @@ -162,12 +117,12 @@
2. Saving Data as a CSV File
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

(The way we use in this lesson).
As shown in the Data Formats and DataFrames lesson, a DataFrame can easily dumped into a CSV file using
the ``CSV.jl`` package, which also allows for reading tabular data.

.. todo::
(Include the content about saving data as a CSV file here)

You can use the CSV.jl package to save your DataFrame as a CSV file, which can be loaded later.
You can use the CSV.jl package to save a DataFrame as a CSV file, which can be re-read later.

.. code-block:: julia

Expand All @@ -182,35 +137,48 @@

df = CSV.read("penguins.csv", DataFrame)

3. Saving Data Using JLD.jl
3. Saving Data Using JLD/JLD2
^^^^^^^^^^^^^^^^^^^^^^^^^^^

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Another option is to use `JLD.jl <https://github.com/JuliaIO/JLD.jl>`_
The `JLD.jl` package provides a way to save and load Julia variables while preserving native types.
It is a specific "dialect" of HDF5, a cross-platform, multi-language data storage format most frequently used for scientific data.
The ``JLD.jl`` package provides a way to save and load Julia variables while preserving native types.
It is based on HDF5, a cross-platform, multi-language data storage format most frequently used for scientific data.
However, it is written in pure Julia and does not require any of the original C HDF5 implementation.

To use the `JLD.jl` module, you can start your code with `using JLD`.
If you want to save a few variables and don't care to use the more advanced features, then a simple syntax is:
The ``JLD`` package can be imported in the usual way:

.. code-block:: julia

using Pkg
Pkg.add("JLD")

Now, we can save our DataFrame `df` to a JLD file.
A DataFrame can be saved to file in the following way:

.. code-block:: julia

using JLD
save("penguins.jld", "df", df)

Here we're saving `df` as "df" within `penguins.jld`. You can load this DataFrame back in with:
Here we're saving ``df`` as "df" within ``penguins.jld``. You can load this DataFrame back in with:

.. code-block:: julia

df = load("penguins.jld", "df")

This will return the DataFrame `df` from the file and assign it back to `df`.
This will return the DataFrame ``df`` from the file and assign it back to ``df``.
In the past years, the ``JLD2.jl`` package came forward as an alternative to ``JLD``. It
is also based on HDF5 and can read h5 files saved by other HDF5 implementations. It exposes an interface
similar to ``JLD`` with ``save()`` and ``load()`` functions, but the more user-friendly function ``jldsave()``
is also available:

.. code-block:: julia

using JLD2
jldsave("penguins.jld2"; df) # This is equivalent to the save command above
df = load("penguins.jld2", "df")

Moreover, a ``jldopen()`` function provides a file-like interface. More information can be found
`here <https://github.com/JuliaIO/JLD2.jl>`__.

Machine learning
----------------
Expand Down Expand Up @@ -609,4 +577,4 @@
^^^^^^^

- https://juliapackages.com/c/quantum-mechanics
- Swedish Quantum Society | SQS – https://swedishquantumsociety.vercel.app/
- Swedish Quantum Society | SQS – https://swedishquantumsociety.vercel.app/
22 changes: 11 additions & 11 deletions content/linear-algebra.rst
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Expand Up @@ -22,12 +22,12 @@ Linear algebra
Vectors and matrices in Julia
-----------------------------

We will start with a breif look at how we can form arrays
We will start with a brief look at how we can create arrays
and vectors in Julia and how to perform vector and matrix operations.

.. code-block:: julia

# range notation, list from 1 to 10
# lazy range notation, list from 1 to 10
1:10

# make into vector
Expand All @@ -36,7 +36,7 @@ and vectors in Julia and how to perform vector and matrix operations.
# another way to make ranges
range(1, 10)

.. code-block:: text
.. code-block:: julia-repl

julia> Vector(1:10)
10-element Vector{Int64}:
Expand All @@ -49,7 +49,7 @@ and vectors in Julia and how to perform vector and matrix operations.
9
10

Picking out elements or parts of vectors and matrices can be done with sclicing as in Python or Matlab.
Indexing elements or parts of vectors and matrices can be done with slicing as in Python or Matlab.

.. code-block:: julia

Expand All @@ -75,7 +75,7 @@ Picking out elements or parts of vectors and matrices can be done with sclicing
ones(5) # [1,1,1,1,1]
ones(5,5) # 5x5-matrix of ones

.. code-block:: text
.. code-block:: julia-repl

julia> u
4-element Vector{Int64}:
Expand Down Expand Up @@ -106,7 +106,7 @@ Picking out elements or parts of vectors and matrices can be done with sclicing
1.0 1.0 1.0 1.0 1.0
1.0 1.0 1.0 1.0 1.0

To perform vector and matrix operations we can use syntax similar to Matlab och Python.
To perform vector and matrix operations we can use a syntax similar to Matlab or Python.

.. code-block:: julia

Expand Down Expand Up @@ -145,7 +145,7 @@ To perform vector and matrix operations we can use syntax similar to Matlab och
# vector matrix multiplication
A*v

# matrix multiplicaiton
# matrix multiplication
B = A*A

# Matrix multiplication
Expand All @@ -165,9 +165,9 @@ Below we will discuss Principal Component Analysis and in that context we
recall here the notion of eigenvectors and eigenvalues of a square matrix
:math:`M`.

.. callout::
.. callout:: Eigendecomposition

A vector :math:`u \neq 0` is called an eigenvector of :math:`M`
A vector :math:`u \neq 0` is called an eigenvector of a square matrix :math:`M`
with eigenvalue :math:`\lambda \in \mathbb{R}` if :math:`Mu=\lambda u`.
Let us for illustration say that :math:`\lambda=2`. Then
:math:`Mu=2u` and the linear map :math:`M` maps :math:`u` to a vector
Expand Down Expand Up @@ -217,11 +217,11 @@ it down to a smaller dimensional space.
that approximates the dataset in a least squares sense. This means that the
points are as close to the linear space as possible measured in the sum of
squared distances. The approximating linear space is spanned by so-called
principal components which are ordered in terms of imporance: the first
principal components which are ordered in terms of importance: the first
principal component, the second principal component and so on.

It turns out the principal components are eigenvectors of the so-called
covaraince matrix of the data. The corresponding eigenvalues rank the principal
covariance matrix of the data. The corresponding eigenvalues rank the principal
components in importance, where the biggest eigenvalue marks the first principal
component.

Expand Down
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