Fix broken and deprecated URLs causing link checker failures (#380)

* Initial plan

* Fix broken and deprecated URLs in about_py.md

Co-authored-by: mmcky <8263752+mmcky@users.noreply.github.com>

* Fix HTTP to HTTPS conversions and update deprecated URLs across all lecture files

Co-authored-by: mmcky <8263752+mmcky@users.noreply.github.com>

* Complete link fixing - resolve all remaining HTTP to HTTPS issues and update config

Co-authored-by: mmcky <8263752+mmcky@users.noreply.github.com>

* Final cleanup: fix remaining problematic HTTP links

* [numpy] fix execution error

---------

Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
Co-authored-by: mmcky <8263752+mmcky@users.noreply.github.com>
Co-authored-by: mmcky <mamckay@gmail.com>

Author: Copilot

lectures/_config.yml

@@ -33,7 +33,7 @@ sphinx:
     html_theme_options:
       authors:
         - name: Thomas J. Sargent
-          url: http://www.tomsargent.com/
+          url: https://www.tomsargent.com/
         - name: John Stachurski
           url: https://johnstachurski.net/      
       dark_logo: quantecon-logo-transparent.png

lectures/about_py.md

@@ -235,7 +235,7 @@ Even if you don't yet know Python, you can see that the code is far simpler and
 import csv
 
 total, count = 0, 0
-with open(data.csv, mode='r') as file:
+with open('data.csv', mode='r') as file:
     reader = csv.reader(file)
     for row in reader:
         try:
@@ -311,7 +311,7 @@ But when we want to work with larger arrays in real programs we need more effici
 For this we need to use libraries for working with arrays.
 
 For Python, the most important matrix and array processing library is
-[NumPy](http://www.numpy.org/) library.
+[NumPy](https://www.numpy.org/) library.
 
 For example, let's build a NumPy array with 100 elements
 
@@ -365,7 +365,7 @@ This lecture series will provide you with extensive background in NumPy.
 
 ### SciPy
 
-The [SciPy](http://www.scipy.org) library is built on top of NumPy and provides additional functionality.
+The [SciPy](https://www.scipy.org) library is built on top of NumPy and provides additional functionality.
 
 (tuple_unpacking_example)=
 For example, let's calculate $\int_{-2}^2 \phi(z) dz$ where $\phi$ is the standard normal density.
@@ -381,14 +381,14 @@ value
 
 SciPy includes many of the standard routines used in
 
-* [linear algebra](http://docs.scipy.org/doc/scipy/reference/linalg.html)
-* [integration](http://docs.scipy.org/doc/scipy/reference/integrate.html)
-* [interpolation](http://docs.scipy.org/doc/scipy/reference/interpolate.html)
-* [optimization](http://docs.scipy.org/doc/scipy/reference/optimize.html)
-* [distributions and statistical techniques](http://docs.scipy.org/doc/scipy/reference/stats.html)
-* [signal processing](http://docs.scipy.org/doc/scipy/reference/signal.html)
+* [linear algebra](https://docs.scipy.org/doc/scipy/reference/linalg.html)
+* [integration](https://docs.scipy.org/doc/scipy/reference/integrate.html)
+* [interpolation](https://docs.scipy.org/doc/scipy/reference/interpolate.html)
+* [optimization](https://docs.scipy.org/doc/scipy/reference/optimize.html)
+* [distributions and statistical techniques](https://docs.scipy.org/doc/scipy/reference/stats.html)
+* [signal processing](https://docs.scipy.org/doc/scipy/reference/signal.html)
 
-See them all [here](http://docs.scipy.org/doc/scipy/reference/index.html).
+See them all [here](https://docs.scipy.org/doc/scipy/reference/index.html).
 
 Later we'll discuss SciPy in more detail.
 
@@ -400,7 +400,7 @@ Later we'll discuss SciPy in more detail.
 
 A major strength of Python is data visualization.
 
-The most popular and comprehensive Python library for creating figures and graphs is [Matplotlib](http://matplotlib.org/), with functionality including
+The most popular and comprehensive Python library for creating figures and graphs is [Matplotlib](https://matplotlib.org/), with functionality including
 
 * plots, histograms, contour images, 3D graphs, bar charts etc.
 * output in many formats (PDF, PNG, EPS, etc.)
@@ -427,10 +427,10 @@ More examples can be found in the [Matplotlib thumbnail gallery](https://matplot
 
 Other graphics libraries include
 
-* [Plotly](https://plot.ly/python/)
+* [Plotly](https://plotly.com/python/)
 * [seaborn](https://seaborn.pydata.org/) --- a high-level interface for matplotlib
 * [Altair](https://altair-viz.github.io/)
-* [Bokeh](http://bokeh.pydata.org/en/latest/)
+* [Bokeh](https://bokeh.pydata.org/en/latest/)
 
 You can visit the [Python Graph Gallery](https://www.python-graph-gallery.com/) for more example plots drawn using a variety of libraries.
 
@@ -452,7 +452,7 @@ Python has many libraries for studying networks and graphs.
 ```{index} single: NetworkX
 ```
 
-One well-known example is [NetworkX](http://networkx.github.io/).
+One well-known example is [NetworkX](https://networkx.org/).
 
 Its features include, among many other things:
 
@@ -503,14 +503,14 @@ firms.
 Here's a short list of some important scientific libraries for Python not
 mentioned above.
 
-* [SymPy](http://www.sympy.org/) for symbolic algebra, including limits, derivatives and integrals
-* [statsmodels](http://statsmodels.sourceforge.net/) for statistical routines
-* [scikit-learn](http://scikit-learn.org/) for machine learning 
+* [SymPy](https://www.sympy.org/) for symbolic algebra, including limits, derivatives and integrals
+* [statsmodels](https://www.statsmodels.org/) for statistical routines
+* [scikit-learn](https://scikit-learn.org/) for machine learning 
 * [Keras](https://keras.io/) for machine learning
-* [Pyro](https://pyro.ai/) and [PyStan](https://pystan.readthedocs.org/en/latest/) for Bayesian data analysis 
+* [Pyro](https://pyro.ai/) and [PyStan](https://pystan.readthedocs.io/en/latest/) for Bayesian data analysis 
 * [GeoPandas](https://geopandas.org/en/stable/) for spatial data analysis
 * [Dask](https://docs.dask.org/en/stable/) for parallelization
-* [Numba](http://numba.pydata.org/) for making Python run at the same speed as native machine code
+* [Numba](https://numba.pydata.org/) for making Python run at the same speed as native machine code
 * [CVXPY](https://www.cvxpy.org/) for convex optimization 
 * [scikit-image](https://scikit-image.org/) and [OpenCV](https://opencv.org/) for processing and analyzing image data
 * [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/) for extracting data from HTML and XML files

lectures/debugging.md

@@ -254,7 +254,7 @@ There are many other useful magics:
 * `%whos` gives a list of variables and their values
 * `%quickref` gives a list of magics
 
-The full list of magics is [here](http://ipython.readthedocs.org/en/stable/interactive/magics.html).
+The full list of magics is [here](https://ipython.readthedocs.io/en/stable/interactive/magics.html).
 
 
 ## Handling Errors

lectures/getting_started.md

@@ -139,7 +139,7 @@ For more information on conda, type conda help in a terminal.
 ```{index} single: Jupyter
 ```
 
-[Jupyter](http://jupyter.org/) notebooks are one of the many possible ways to interact with Python and the scientific libraries.
+[Jupyter](https://jupyter.org/) notebooks are one of the many possible ways to interact with Python and the scientific libraries.
 
 They use  a *browser-based* interface to Python with
 
@@ -149,7 +149,7 @@ They use  a *browser-based* interface to Python with
 
 Because of these features, Jupyter is now a major player in the scientific computing ecosystem.
 
-Here's an image showing execution of some code (borrowed from [here](http://matplotlib.org/examples/pylab_examples/hexbin_demo.html)) in a Jupyter notebook
+Here's an image showing execution of some code (borrowed from [here](https://matplotlib.org/stable/gallery/statistics/hexbin_demo.html)) in a Jupyter notebook
 
 ```{figure} /_static/lecture_specific/getting_started/jp_demo.png
 :figclass: auto
@@ -269,7 +269,7 @@ In a code cell, try typing `\alpha` and then hitting the tab key on your keyboar
 
 Let's run a test program.
 
-Here's an arbitrary program we can use: [http://matplotlib.org/3.1.1/gallery/pie_and_polar_charts/polar_bar.html](http://matplotlib.org/3.1.1/gallery/pie_and_polar_charts/polar_bar.html).
+Here's an arbitrary program we can use: [https://matplotlib.org/stable/gallery/pie_and_polar_charts/polar_bar.html](https://matplotlib.org/stable/gallery/pie_and_polar_charts/polar_bar.html).
 
 On that page, you'll see the following code
 
@@ -350,7 +350,7 @@ In addition to executing code, the Jupyter notebook allows you to embed text, eq
 For example, we can enter a mixture of plain text and LaTeX instead of code.
 
 Next we `Esc` to enter command mode and then type `m` to indicate that we
-are writing [Markdown](http://daringfireball.net/projects/markdown/), a mark-up language similar to (but simpler than) LaTeX.
+are writing [Markdown](https://daringfireball.net/projects/markdown/), a mark-up language similar to (but simpler than) LaTeX.
 
 (You can also use your mouse to select `Markdown` from the `Code` drop-down box just below the list of menu items)
 
@@ -410,7 +410,7 @@ You can explore more functionality of the debugger in the [Jupyter documentation
 
 Notebook files are just text files structured in [JSON](https://en.wikipedia.org/wiki/JSON) and typically ending with `.ipynb`.
 
-You can share them in the usual way that you share files --- or by using web services such as [nbviewer](http://nbviewer.jupyter.org/).
+You can share them in the usual way that you share files --- or by using web services such as [nbviewer](https://nbviewer.jupyter.org/).
 
 The notebooks you see on that site are **static** html representations.
 
@@ -510,7 +510,7 @@ print("foobar")
 
 This writes the line `print("foobar")` into a file called `foo.py` in the local directory.
 
-Here `%%writefile` is an example of a [cell magic](http://ipython.readthedocs.org/en/stable/interactive/magics.html#cell-magics).
+Here `%%writefile` is an example of a [cell magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#cell-magics).
 
 ### Editing and Execution
 
@@ -553,7 +553,7 @@ Right now, an extremely popular text editor for coding is [VS Code](https://code
 
 VS Code is easy to use out of the box and has many high quality extensions.
 
-Alternatively, if you want an outstanding free text editor and don't mind a seemingly vertical learning curve plus long days of pain and suffering while all your neural pathways are rewired, try [Vim](http://www.vim.org/).
+Alternatively, if you want an outstanding free text editor and don't mind a seemingly vertical learning curve plus long days of pain and suffering while all your neural pathways are rewired, try [Vim](https://www.vim.org/).
 
 ## Exercises
 

lectures/matplotlib.md

@@ -25,7 +25,7 @@ kernelspec:
 
 ## Overview
 
-We've already generated quite a few figures in these lectures using [Matplotlib](http://matplotlib.org/).
+We've already generated quite a few figures in these lectures using [Matplotlib](https://matplotlib.org/).
 
 Matplotlib is an outstanding graphics library, designed for scientific computing, with
 
@@ -438,9 +438,9 @@ plt.rcParams['figure.figsize'] = (10, 6)
 
 ## Further Reading
 
-* The [Matplotlib gallery](http://matplotlib.org/gallery.html) provides many examples.
-* A nice [Matplotlib tutorial](http://scipy-lectures.org/intro/matplotlib/index.html) by Nicolas Rougier, Mike Muller and Gael Varoquaux.
-* [mpltools](http://tonysyu.github.io/mpltools/index.html) allows easy
+* The [Matplotlib gallery](https://matplotlib.org/stable/gallery/index.html) provides many examples.
+* A nice [Matplotlib tutorial](https://scipy-lectures.org/intro/matplotlib/index.html) by Nicolas Rougier, Mike Muller and Gael Varoquaux.
+* [mpltools](https://tonysyu.github.io/mpltools/index.html) allows easy
   switching between plot styles.
 * [Seaborn](https://github.com/mwaskom/seaborn) facilitates common statistics plots in Matplotlib.
 

lectures/numba.md

@@ -56,7 +56,7 @@ Another is that the set of algorithms that can be entirely vectorized is not uni
 
 In fact, for some algorithms, vectorization is ineffective.
 
-Fortunately, a new Python library called [Numba](http://numba.pydata.org/)
+Fortunately, a new Python library called [Numba](https://numba.pydata.org/)
 solves many of these problems.
 
 It does so through something called **just in time (JIT) compilation**.

lectures/numpy.md

@@ -27,6 +27,14 @@ kernelspec:
 "Let's be clear: the work of science has nothing whatever to do with consensus.  Consensus is the business of politics. Science, on the contrary, requires only one investigator who happens to be right, which means that he or she has results that are verifiable by reference to the real world. In science consensus is irrelevant. What is relevant is reproducible results." -- Michael Crichton
 ```
 
+In addition to what's in Anaconda, this lecture will need the following libraries:
+
+```{code-cell} ipython3
+:tags: [hide-output]
+
+!pip install quantecon
+```
+
 ## Overview
 
 [NumPy](https://en.wikipedia.org/wiki/NumPy) is a first-rate library for numerical programming
@@ -42,7 +50,7 @@ In this lecture, we will start a more systematic discussion of
 1. the fundamental array processing operations provided by NumPy.
 
 
-(For an alternative reference, see [the official NumPy documentation](http://docs.scipy.org/doc/numpy/reference/).)
+(For an alternative reference, see [the official NumPy documentation](https://docs.scipy.org/doc/numpy/reference/).)
 
 We will use the following imports.
 
@@ -64,11 +72,11 @@ from matplotlib import cm
 The essential problem that NumPy solves is fast array processing.
 
 The most important structure that NumPy defines is an array data type, formally
-called a [numpy.ndarray](http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html).
+called a [numpy.ndarray](https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html).
 
 NumPy arrays power a very large proportion of the scientific Python ecosystem.
 
-To create a NumPy array containing only zeros we use  [np.zeros](http://docs.scipy.org/doc/numpy/reference/generated/numpy.zeros.html#numpy.zeros)
+To create a NumPy array containing only zeros we use  [np.zeros](https://docs.scipy.org/doc/numpy/reference/generated/numpy.zeros.html#numpy.zeros)
 
 ```{code-cell} python3
 a = np.zeros(3)
@@ -212,7 +220,7 @@ na is np.array(na)     # Does make a new copy --- perhaps unnecessarily
 ```
 
 To read in the array data from a text file containing numeric data use `np.loadtxt`
-or `np.genfromtxt`---see [the documentation](http://docs.scipy.org/doc/numpy/reference/routines.io.html) for details.
+or `np.genfromtxt`---see [the documentation](https://docs.scipy.org/doc/numpy/reference/routines.io.html) for details.
 
 ### Array Indexing
 
@@ -456,7 +464,7 @@ B = np.ones((2, 2))
 A @ B
 ```
 
-(For older versions of Python and NumPy you need to use the [np.dot](http://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html) function)
+(For older versions of Python and NumPy you need to use the [np.dot](https://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html) function)
 
 We can also use `@` to take the inner product of two flat arrays
 
@@ -526,7 +534,7 @@ Here is a visual representation of this broadcasting operation:
 tags: [hide-input]
 ---
 # Adapted and modified based on the code in the book written by Jake VanderPlas (see https://jakevdp.github.io/PythonDataScienceHandbook/06.00-figure-code.html#Broadcasting)
-# Originally from astroML: see http://www.astroml.org/book_figures/appendix/fig_broadcast_visual.html
+# Originally from astroML: see https://www.astroml.org/book_figures/appendix/fig_broadcast_visual.html
 
 
 def draw_cube(ax, xy, size, depth=0.4,
@@ -1155,7 +1163,7 @@ np.linalg.inv(A)           # Compute the inverse
 ```{index} single: Python; SciPy
 ```
 
-Much of this functionality is also available in [SciPy](http://www.scipy.org/), a collection of modules that are built on top of NumPy.
+Much of this functionality is also available in [SciPy](https://www.scipy.org/), a collection of modules that are built on top of NumPy.
 
 We'll cover the SciPy versions in more detail {doc}`soon <scipy>`.
 

lectures/pandas.md

@@ -36,7 +36,7 @@ In addition to what’s in Anaconda, this lecture will need the following librar
 
 ## Overview
 
-[Pandas](http://pandas.pydata.org/) is a package of fast, efficient data analysis tools for Python.
+[Pandas](https://pandas.pydata.org/) is a package of fast, efficient data analysis tools for Python.
 
 Its popularity has surged in recent years, coincident with the rise
 of fields such as data science and machine learning.
@@ -47,7 +47,7 @@ Here's a popularity comparison over time against Matlab and STATA courtesy of St
 :scale: 100
 ```
 
-Just as [NumPy](http://www.numpy.org/) provides the basic array data type plus core array operations, pandas
+Just as [NumPy](https://www.numpy.org/) provides the basic array data type plus core array operations, pandas
 
 1. defines fundamental structures for working with data and
 1. endows them with methods that facilitate operations such as
@@ -58,7 +58,7 @@ Just as [NumPy](http://www.numpy.org/) provides the basic array data type plus c
     * dealing with missing values, etc., etc.
 
 More sophisticated statistical functionality is left to other packages, such
-as [statsmodels](http://www.statsmodels.org/) and [scikit-learn](http://scikit-learn.org/), which are built on top of pandas.
+as [statsmodels](https://www.statsmodels.org/) and [scikit-learn](https://scikit-learn.org/), which are built on top of pandas.
 
 This lecture will provide a basic introduction to pandas.
 
@@ -536,7 +536,7 @@ In the second case, you can either
 * switch to another machine
 * solve your proxy problem by reading [the documentation](https://requests.readthedocs.io/en/master/)
 
-Assuming that all is working, you can now proceed to use the `source` object returned by the call `requests.get('http://research.stlouisfed.org/fred2/series/UNRATE/downloaddata/UNRATE.csv')`
+Assuming that all is working, you can now proceed to use the `source` object returned by the call `requests.get('https://research.stlouisfed.org/fred2/series/UNRATE/downloaddata/UNRATE.csv')`
 
 ```{code-cell} ipython3
 url = 'https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1318&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=UNRATE&scale=left&cosd=1948-01-01&coed=2024-06-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2020-02-01&line_index=1&transformation=lin&vintage_date=2024-07-29&revision_date=2024-07-29&nd=1948-01-01'
@@ -604,9 +604,9 @@ in the exercises.
 For now let's work through one example of downloading and plotting data --- this
 time from the World Bank.
 
-The World Bank [collects and organizes data](http://data.worldbank.org/indicator) on a huge range of indicators.
+The World Bank [collects and organizes data](https://data.worldbank.org/indicator) on a huge range of indicators.
 
-For example, [here's](http://data.worldbank.org/indicator/GC.DOD.TOTL.GD.ZS/countries) some data on government debt as a ratio to GDP.
+For example, [here's](https://data.worldbank.org/indicator/GC.DOD.TOTL.GD.ZS/countries) some data on government debt as a ratio to GDP.
 
 The next code example fetches the data for you and plots time series for the US and Australia
 

lectures/parallelization.md

@@ -185,7 +185,7 @@ To get some basis for comparison for the last example, let's try the same
 thing with Numba.
 
 In fact there is an easy way to do this, since Numba can also be used to
-create custom {ref}`ufuncs <ufuncs>` with the [@vectorize](http://numba.pydata.org/numba-doc/dev/user/vectorize.html) decorator.
+create custom {ref}`ufuncs <ufuncs>` with the [@vectorize](https://numba.pydata.org/numba-doc/dev/user/vectorize.html) decorator.
 
 ```{code-cell} python3
 from numba import vectorize