Merge pull request #248 from AaltoSciComp/rkdarst/linkcheck

Run `make linkcheck` and fix major issues

Author: rkdarst

content/data-formats.rst

@@ -587,7 +587,7 @@ NetCDF4 (Network Common Data Form version 4)
     Using NetCDF4 requires `netCDF4 <https://unidata.github.io/netcdf4-python>`__- or `h5netcdf <https://github.com/h5netcdf/h5netcdf>`__-package to be installed.
     h5netcdf is often mentioned as being faster to the official netCDF4-package, so we'll be using it in the example.
 
-    A great NetCDF4 interface is provided by a `xarray-package <https://xarray.pydata.org/en/stable/getting-started-guide/quick-overview.html#read-write-netcdf-files>`__.
+    A great NetCDF4 interface is provided by a `xarray-package <https://docs.xarray.dev/en/stable/getting-started-guide/quick-overview.html#read-write-netcdf-files>`__.
 
     You can try installing these packages with
 

content/data-visualization.md

@@ -45,7 +45,7 @@ From [Claus O. Wilke: "Fundamentals of Data Visualization"](https://clauswilke.c
     R users will be more at home
   - [PyNGL](https://www.pyngl.ucar.edu/Examples/gallery.shtml):
     used in the weather forecast community
-  - [K3D](https://k3d-jupyter.org/showcase/):
+  - [K3D](https://k3d-jupyter.org/gallery/):
     Jupyter notebook extension for 3D visualization
   - ...
 - Two main families of libraries: procedural (e.g. Matplotlib) and declarative

content/dependencies.rst

@@ -164,7 +164,7 @@ Exercises 2
 
   Conda environments can also be managed (create, update, delete) from the
   **anaconda-navigator**. Check out the corresponding documentation `here
-  <https://docs.anaconda.com/anaconda/navigator/getting-started/#navigator-managing-environments>`_.
+  <https://docs.anaconda.com/navigator/getting-started/#navigator-managing-environments>`_.
 
   2. Activate the environment::
 
@@ -340,7 +340,7 @@ Here are the two files again, but this time with versions pinned:
 - Conda can also read and write ``requirements.txt``.
 - ``requirements.txt`` can also refer to packages on Github.
 - ``environments.yml`` can also contain a ``pip`` section.
-- See also: https://coderefinery.github.io/reproducible-research/03-dependencies/#dependencies.
+- See also: https://coderefinery.github.io/reproducible-research/dependencies/ .
 
 .. admonition:: Putting too strict requirements can be counter-productive
 

content/installation.rst

@@ -64,7 +64,7 @@ Python
 We expect you to have a working Python installation with some common
 libraries.  We recommend that you install the `Anaconda python
 distribution <https://docs.continuum.io/anaconda/install/>`__.  The
-`Anaconda Navigator <https://docs.anaconda.com/anaconda/navigator/>`__
+`Anaconda Navigator <https://docs.anaconda.com/navigator/>`__
 provides a convenient way to access the software.
 
 .. admonition:: Other options
@@ -227,6 +227,6 @@ See also
   <https://www.youtube.com/watch?v=ddCde5Nu2qo&list=PLpLblYHCzJAB6blBBa0O2BEYadVZV3JYf>`__
 * `Conda manual <https://docs.conda.io/en/latest/>`__ (technical)
 * `Anaconda individual edition home
-  <https://www.anaconda.com/products/individual>`__
+  <https://www.anaconda.com/products/distribution>`__
 * `Anaconda getting started
   <https://docs.anaconda.com/anaconda/user-guide/getting-started/>`__

content/libraries.rst

@@ -59,7 +59,7 @@ but searching for these repositories is a starting point of a lot of
 future work.
 
 The "core" packages `could be considered
-<https://www.scipy.org/about.html>`__.  Many other packages build on
+<https://www.scipy.org/about/>`__.  Many other packages build on
 these, and others that try to do similar things often try to conform
 to their interfaces (especially numpy):
 
@@ -102,7 +102,7 @@ Data analysis and other important core packages
   data analysis
 * `statsmodels <https://www.statsmodels.org/stable/>`__ - just what it says
 * `SymPy <https://www.sympy.org/>`__ - symbolic math
-* `networkx <https://networkx.github.io/>`__ - graph and network analysis
+* `networkx <https://networkx.org/>`__ - graph and network analysis
 * `h5py <https://www.h5py.org/>`__ and `PyTables <https://www.pytables.org/>`__ - interfaces to
   the `HDF5 <https://en.wikipedia.org/wiki/Hierarchical_Data_Format>`__ on-disk file format
 * `dateutil <https://dateutil.readthedocs.io/>`__ and `pytz
@@ -115,8 +115,8 @@ Interactive computing and human interface
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 * Interactive computing
 
-  * `IPython <http://ipython.org/>`__ - nicer interactive interpreter
-  * `Jupyter <http://jupyter.org/>`__ (notebook, lab, hub, ...) -
+  * `IPython <https://ipython.org/>`__ - nicer interactive interpreter
+  * `Jupyter <https://jupyter.org/>`__ (notebook, lab, hub, ...) -
     web-based interface to IPython and other languages
 
 * Testing
@@ -142,13 +142,13 @@ Speeding up code and parallelism
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 * `PyMPI <https://sourceforge.net/projects/pympi/>`__ - Message
   Passing Interface (MPI) in Python for parallelizing jobs.
-* `cython <http://cython.org/>`__ - easily make C extensions for
+* `cython <https://cython.org/>`__ - easily make C extensions for
   Python, also interface to C libraries
 * `numba <https://numba.pydata.org/>`__ - just in time compiling of
   functions for speed-up
 * `PyPy <https://www.pypy.org/>`__ - Python written in Python so that
   it can internally optimize more.
-* `Dask <https://dask.org/>`__ - distributed array data structure for
+* `Dask <https://www.dask.org/>`__ - distributed array data structure for
   distributed computation
 * `Joblib <https://joblib.readthedocs.io/>`__ - easy embarrassingly
   parallel computing
@@ -211,7 +211,7 @@ These days, one rarely directly extends the Python interpreter, but uses
   Python, also interface to C libraries
 * `f2py <https://numpy.org/doc/stable/f2py/>`__ - interface to Fortran
   code
-* `swig <http://swig.org/>`__ - connect to a variety of programming languages.
+* `swig <https://swig.org/>`__ - connect to a variety of programming languages.
 * ``Boost.python`` - Another Python/C++ interface
 * TODO: Julia modules for Python?
 
@@ -341,7 +341,7 @@ See also
 --------
 
 * `Topical Software in the SciPy ecosystem
-  <https://www.scipy.org/topical-software.html>`__ - relatively
+  <https://new.scipy.org/topical-software.html>`__ - relatively
   detailed (but not comprehensive) list of projects
 
 

content/packaging.rst

@@ -186,7 +186,7 @@ trend goes towards using ``pyproject.toml`` as more general alternative to
 There are at least two tools which try to make the packaging and PyPI interaction easier:
 
 - `Poetry <https://python-poetry.org/>`__
-- `Flit <https://flit.readthedocs.io/>`__
+- `Flit <https://flit.pypa.io/>`__
 
 
 Building a conda package and share it

content/parallel.rst

@@ -449,7 +449,7 @@ distribute operations across multiple CPUs.
 
 Dask is very popular for data analysis and is used by a number of high-level Python libraries:
 
-- Dask arrays scale NumPy (see also `xarray <http://xarray.pydata.org/en/stable/>`__ 
+- Dask arrays scale NumPy (see also `xarray <https://xarray.pydata.org/en/stable/>`__ 
 - Dask dataframes scale Pandas workflows
 - Dask-ML scales Scikit-Learn
 

content/quick-reference.rst

@@ -15,12 +15,12 @@ Quick reference
   <https://www.datacamp.com/community/data-science-cheatsheets>`__)
 
 * `JupyterLab cheatsheet
-  <https://cs.anu.edu.au/courses/comp2420/labs/lab-1/helpManuals/JupyterLab-Cheatsheet.pdf>`__
+  <https://comp.anu.edu.au/courses/comp2420/labs/lab-1/helpManuals/JupyterLab-Cheatsheet.pdf>`__
 
 * `Matplotlib cheatsheet
   <https://datacamp-community-prod.s3.amazonaws.com/e1a8f39d-71ad-4d13-9a6b-618fe1b8c9e9>`__
   (via `Datacamp
-  <https://www.datacamp.com/community/data-science-cheatsheets>`__)
+  <https://www.datacamp.com/cheat-sheet>`__)
 
 * `Numpy, Pandas, Matplotlib, Scikit-learn all together
   <https://web.itu.edu.tr/iguzel/files/Python_Cheat_Sheets.pdf>`__

content/scipy.rst

@@ -169,7 +169,7 @@ eventually.
 See also
 --------
 
-* `SciPy general introduction <https://docs.scipy.org/doc/scipy/reference/tutorial/general.html>`__
+* `SciPy general introduction <https://docs.scipy.org/doc/scipy/tutorial/general.html>`__
 * `SciPy documentation
   <https://docs.scipy.org/doc/scipy/reference/>`__