From 9ab76c14df8f308ffaf12b5ad9826dec65884239 Mon Sep 17 00:00:00 2001 From: Copilot <198982749+Copilot@users.noreply.github.com> Date: Tue, 12 Aug 2025 00:23:18 +0000 Subject: [PATCH 1/6] Initial plan From b7ead8dd8679b1b8b8d08445b5ede01df5fe5d8d Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Tue, 12 Aug 2025 00:28:32 +0000 Subject: [PATCH 2/6] Fix broken and deprecated URLs in about_py.md Co-authored-by: mmcky <8263752+mmcky@users.noreply.github.com> --- lectures/about_py.md | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/lectures/about_py.md b/lectures/about_py.md index 61c450b0..5f616779 100644 --- a/lectures/about_py.md +++ b/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.) @@ -427,7 +427,7 @@ 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/) @@ -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 From 638f41f0334d7b47631b2b45bb08a9abc1f7eccb Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Tue, 12 Aug 2025 00:32:06 +0000 Subject: [PATCH 3/6] Fix HTTP to HTTPS conversions and update deprecated URLs across all lecture files Co-authored-by: mmcky <8263752+mmcky@users.noreply.github.com> --- lectures/debugging.md | 2 +- lectures/getting_started.md | 2 +- lectures/matplotlib.md | 6 +++--- lectures/pandas.md | 12 ++++++------ lectures/parallelization.md | 2 +- lectures/scipy.md | 28 ++++++++++++++-------------- lectures/writing_good_code.md | 2 +- 7 files changed, 27 insertions(+), 27 deletions(-) diff --git a/lectures/debugging.md b/lectures/debugging.md index ea40fc29..a9356866 100644 --- a/lectures/debugging.md +++ b/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 diff --git a/lectures/getting_started.md b/lectures/getting_started.md index ecb19613..693d033d 100644 --- a/lectures/getting_started.md +++ b/lectures/getting_started.md @@ -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 diff --git a/lectures/matplotlib.md b/lectures/matplotlib.md index 5e80dfd3..798f26eb 100644 --- a/lectures/matplotlib.md +++ b/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 @@ -438,8 +438,8 @@ 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. +* 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](http://tonysyu.github.io/mpltools/index.html) allows easy switching between plot styles. * [Seaborn](https://github.com/mwaskom/seaborn) facilitates common statistics plots in Matplotlib. diff --git a/lectures/pandas.md b/lectures/pandas.md index e2f93b75..b43396c0 100644 --- a/lectures/pandas.md +++ b/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 diff --git a/lectures/parallelization.md b/lectures/parallelization.md index ec12690a..41f23835 100644 --- a/lectures/parallelization.md +++ b/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 ` with the [@vectorize](http://numba.pydata.org/numba-doc/dev/user/vectorize.html) decorator. +create custom {ref}`ufuncs ` with the [@vectorize](https://numba.pydata.org/numba-doc/dev/user/vectorize.html) decorator. ```{code-cell} python3 from numba import vectorize diff --git a/lectures/scipy.md b/lectures/scipy.md index 8a666ae5..5b916f39 100644 --- a/lectures/scipy.md +++ b/lectures/scipy.md @@ -25,14 +25,14 @@ kernelspec: ## Overview -[SciPy](http://www.scipy.org) builds on top of NumPy to provide common tools for scientific programming such as - -* [linear algebra](http://docs.scipy.org/doc/scipy/reference/linalg.html) -* [numerical 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 random number generation](http://docs.scipy.org/doc/scipy/reference/stats.html) -* [signal processing](http://docs.scipy.org/doc/scipy/reference/signal.html) +[SciPy](https://www.scipy.org) builds on top of NumPy to provide common tools for scientific programming such as + +* [linear algebra](https://docs.scipy.org/doc/scipy/reference/linalg.html) +* [numerical 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 random number generation](https://docs.scipy.org/doc/scipy/reference/stats.html) +* [signal processing](https://docs.scipy.org/doc/scipy/reference/signal.html) * etc., etc Like NumPy, SciPy is stable, mature and widely used. @@ -41,7 +41,7 @@ Many SciPy routines are thin wrappers around industry-standard Fortran libraries It's not really necessary to "learn" SciPy as a whole. -A more common approach is to get some idea of what's in the library and then look up [documentation](http://docs.scipy.org/doc/scipy/reference/index.html) as required. +A more common approach is to get some idea of what's in the library and then look up [documentation](https://docs.scipy.org/doc/scipy/reference/index.html) as required. In this lecture, we aim only to highlight some useful parts of the package. @@ -141,7 +141,7 @@ The general syntax for creating these objects that represent distributions (of t > `name = scipy.stats.distribution_name(shape_parameters, loc=c, scale=d)` -Here `distribution_name` is one of the distribution names in [scipy.stats](http://docs.scipy.org/doc/scipy/reference/stats.html). +Here `distribution_name` is one of the distribution names in [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html). The `loc` and `scale` parameters transform the original random variable $X$ into $Y = c + d X$. @@ -334,7 +334,7 @@ Here the correct solution is found and the speed is better than bisection: Use `scipy.optimize.fsolve`, a wrapper for a hybrid method in MINPACK. -See the [documentation](http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fsolve.html) for details. +See the [documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fsolve.html) for details. ### Fixed Points @@ -387,7 +387,7 @@ Multivariate local optimizers include `minimize`, `fmin`, `fmin_powell`, `fmin_c Constrained multivariate local optimizers include `fmin_l_bfgs_b`, `fmin_tnc`, `fmin_cobyla`. -See the [documentation](http://docs.scipy.org/doc/scipy/reference/optimize.html) for details. +See the [documentation](https://docs.scipy.org/doc/scipy/reference/optimize.html) for details. ## {index}`Integration ` @@ -417,7 +417,7 @@ There are other options for univariate integration---a useful one is `fixed_quad There are also functions for multivariate integration. -See the [documentation](http://docs.scipy.org/doc/scipy/reference/integrate.html) for more details. +See the [documentation](https://docs.scipy.org/doc/scipy/reference/integrate.html) for more details. ## {index}`Linear Algebra ` @@ -430,7 +430,7 @@ SciPy also provides a module for linear algebra with the same name. The latter is not an exact superset of the former, but overall it has more functionality. -We leave you to investigate the [set of available routines](http://docs.scipy.org/doc/scipy/reference/linalg.html). +We leave you to investigate the [set of available routines](https://docs.scipy.org/doc/scipy/reference/linalg.html). ## Exercises diff --git a/lectures/writing_good_code.md b/lectures/writing_good_code.md index a22c15f9..153eb741 100644 --- a/lectures/writing_good_code.md +++ b/lectures/writing_good_code.md @@ -206,7 +206,7 @@ But global variables are dangerous, especially in medium to large size programs, This makes it much harder to be certain about what some small part of a given piece of code actually commands. -Here's a [useful discussion on the topic](http://wiki.c2.com/?GlobalVariablesAreBad). +Here's a [useful discussion on the topic](https://wiki.c2.com/?GlobalVariablesAreBad). While the odd global in small scripts is no big deal, we recommend that you teach yourself to avoid them. From 28140dfe07dcb1297a1b96d1cca86747d9263f28 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Tue, 12 Aug 2025 00:35:12 +0000 Subject: [PATCH 4/6] Complete link fixing - resolve all remaining HTTP to HTTPS issues and update config Co-authored-by: mmcky <8263752+mmcky@users.noreply.github.com> --- lectures/_config.yml | 2 +- lectures/about_py.md | 2 +- lectures/getting_started.md | 4 ++-- lectures/matplotlib.md | 2 +- lectures/numpy.md | 14 +++++++------- 5 files changed, 12 insertions(+), 12 deletions(-) diff --git a/lectures/_config.yml b/lectures/_config.yml index e4a8a23d..fccfa67a 100644 --- a/lectures/_config.yml +++ b/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 diff --git a/lectures/about_py.md b/lectures/about_py.md index 5f616779..4f9c883c 100644 --- a/lectures/about_py.md +++ b/lectures/about_py.md @@ -430,7 +430,7 @@ Other graphics libraries include * [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. diff --git a/lectures/getting_started.md b/lectures/getting_started.md index 693d033d..df3c8d7d 100644 --- a/lectures/getting_started.md +++ b/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 diff --git a/lectures/matplotlib.md b/lectures/matplotlib.md index 798f26eb..4f860d22 100644 --- a/lectures/matplotlib.md +++ b/lectures/matplotlib.md @@ -440,7 +440,7 @@ plt.rcParams['figure.figsize'] = (10, 6) * 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](http://tonysyu.github.io/mpltools/index.html) allows easy +* [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. diff --git a/lectures/numpy.md b/lectures/numpy.md index 173433cb..18428c5e 100644 --- a/lectures/numpy.md +++ b/lectures/numpy.md @@ -42,7 +42,7 @@ 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 +64,11 @@ 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 +212,7 @@ 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 +456,7 @@ 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 +526,7 @@ 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 +1155,7 @@ 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 `. From a02fea3a5a6a7675dfab1bbb4920fe3231965c73 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Tue, 12 Aug 2025 00:36:31 +0000 Subject: [PATCH 5/6] Final cleanup: fix remaining problematic HTTP links --- lectures/getting_started.md | 8 ++++---- lectures/numba.md | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/lectures/getting_started.md b/lectures/getting_started.md index df3c8d7d..e6c19685 100644 --- a/lectures/getting_started.md +++ b/lectures/getting_started.md @@ -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. @@ -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 diff --git a/lectures/numba.md b/lectures/numba.md index 736b1b52..9580196f 100644 --- a/lectures/numba.md +++ b/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**. From 9478765d58e367490d9831dc008c19c3dead7135 Mon Sep 17 00:00:00 2001 From: mmcky Date: Tue, 12 Aug 2025 11:05:59 +1000 Subject: [PATCH 6/6] [numpy] fix execution error --- lectures/numpy.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/lectures/numpy.md b/lectures/numpy.md index 18428c5e..564db8a9 100644 --- a/lectures/numpy.md +++ b/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