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Revert "MAINT: Upgrade anaconda and README (#396)" (#397)
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This reverts commit a84f8c3.
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mmcky authored Mar 12, 2024
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46 changes: 36 additions & 10 deletions README.md
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# A First Course in Quantitative Economics with Python
# lecture-python-intro

An Undergraduate Lecture Series for the Foundations of Computational Economics
An undergraduate lecture series for the foundations of computational economics

## Jupyter notebooks
## Content ideas

Jupyter notebook versions of each lecture are available for download
via the website.
Content ideas in no particular order.

## Contributions
Open individual issues and PRs for the ones we decide to add.

To comment on the lectures please add to or open an issue in the issue tracker (see above).
1. Geometric Series (existing lecture)
2. Leontief Systems (from Networks Book)
3. Luenberger
4. IO Visualizations (from Networks Book)
5. PyPGM (Eileen Nielson ... Youtube Star)
6. Baby Version of https://python.quantecon.org/re_with_feedback.html (Cagan Model)
7. Baby Version of "unpleasant arithmetic and Friedmans optimal quantity of money"
8. Schelling Segregation Model
9. Solow Model
10. Simulations of Wealth Distribution
11. Baby model of Lake Model (Eigenvalue Extension)
12. Diamond Dybvig Model
13. Moral Harzard - Wallace
14. Philips Curve and Nairu
15. Baby version of the Markov Chain Lecture
16. Baby linear programming lecture
17. Basic Nonlinear Demand and Supply (non-linear solver) OOP lecture
18. Asset Pricing (Harrison/Kreps Model)
19. Two Models of Asset Bubbles
20. cobweb model -- start people thinking about expectations
21. social mobility lecture
22. Baby version of cattle cycles model
23. Bi-matrix games.
24. Shortest path lecture (existing)
25. Pricing an American option
26. Baby version of LLN / CLT lecture --- less maths, more simulation, all in one dimension
27. Baby version of heavy tails lecture
30. Lecture on solving linear equations and matrix algebra
31. Lecture on eigenvalues, Perron-Frobenius and the Neumann series lemma
32. Overlapping generations

We welcome pull requests!

Please read the [QuantEcon style guide](https://manual.quantecon.org/intro.html) first, so that you can match our style.
Get Tom's network intermediary paper.
2 changes: 1 addition & 1 deletion environment.yml
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Expand Up @@ -4,7 +4,7 @@ channels:
- conda-forge
dependencies:
- python=3.11
- anaconda=2024.02
- anaconda=2023.09
- pip
- pip:
- jupyter-book==0.15.1
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1 change: 0 additions & 1 deletion lectures/_config.yml
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Expand Up @@ -37,7 +37,6 @@ latex:
sphinx:
extra_extensions: [sphinx_multitoc_numbering, sphinxext.rediraffe, sphinx_exercise, sphinx_togglebutton, sphinx.ext.intersphinx, sphinx_proof, sphinx_tojupyter]
config:
bibtex_reference_style: author_year
# false-positive links
linkcheck_ignore: ['https://doi.org/https://doi.org/10.2307/1235116', 'https://math.stackexchange.com/*', 'https://stackoverflow.com/*']
# myst-nb config
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17 changes: 3 additions & 14 deletions lectures/status.md
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Expand Up @@ -18,17 +18,6 @@ This table contains the latest execution statistics.

(status:machine-details)=

These lectures are built on `linux` instances through `github actions`.

These lectures are using the following python version

```{code-cell} ipython
!python --version
```

and the following package versions

```{code-cell} ipython
:tags: [hide-output]
!conda list
```
These lectures are built on `linux` instances through `github actions` and `amazon web services (aws)` to
enable access to a `gpu`. These lectures are built on a [p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)
that has access to `8 vcpu's`, a `V100 NVIDIA Tesla GPU`, and `61 Gb` of memory.

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