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--- | ||
name: estimagic | ||
channels: | ||
- conda-forge | ||
- nodefaults | ||
dependencies: | ||
- pandas<2.0.0 | ||
- nlopt # dev, tests | ||
- pip # dev, tests, docs | ||
- pytest # dev, tests | ||
- pytest-cov # tests | ||
- pytest-xdist # dev, tests | ||
- statsmodels # dev, tests | ||
- bokeh<=2.4.3 # run, tests | ||
- click # run, tests | ||
- cloudpickle # run, tests | ||
- joblib # run, tests | ||
- numpy>=1.17.0 # run, tests | ||
- plotly # run, tests | ||
- pybaum >= 0.1.2 # run, tests | ||
- scipy>=1.2.1 # run, tests | ||
- sqlalchemy # run, tests | ||
- tranquilo>=0.0.4 # dev, tests | ||
- seaborn # dev, tests | ||
- pip: # dev, tests, docs | ||
- DFO-LS # dev, tests | ||
- Py-BOBYQA # dev, tests | ||
- fides==0.7.4 # dev, tests | ||
- kaleido # dev, tests | ||
- simoptlib==1.0.1 # dev, tests | ||
- -e ../ |
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(list_of_videos)= | ||
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# Videos | ||
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Check out our tutorials, talks and screencasts about estimagic. | ||
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## Talks and tutorials | ||
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### EuroSciPy 2023 (Talk) | ||
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```{raw} html | ||
<iframe | ||
src="https://www.youtube.com/embed/5xYn0v1zEsY" | ||
style="width: 100%; aspect-ratio: 16 / 9;" | ||
allowfullscreen> | ||
</iframe> | ||
``` | ||
|
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### EuroSciPy 2023 (Tutorial) | ||
|
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```{raw} html | ||
<iframe | ||
src="https://www.youtube.com/embed/LQo5NDFKH1Q" | ||
style="width: 100%; aspect-ratio: 16 / 9;" | ||
allowfullscreen> | ||
</iframe> | ||
``` | ||
|
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### SciPy 2022 (Tutorial) | ||
|
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```{raw} html | ||
<iframe | ||
src="https://www.youtube.com/embed/ftlw0rARrtI" | ||
style="width: 100%; aspect-ratio: 16 / 9;" | ||
allowfullscreen> | ||
</iframe> | ||
``` | ||
|
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## Screencasts | ||
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The screencasts are part of the course _Effective Programming Practices for Economists_, | ||
taught at the University of Bonn by | ||
[Hans-Martin von Gaudecker](https://www.wiwi.uni-bonn.de/gaudecker/), and previously | ||
also [Janoś Gabler](https://github.com/janosg). You can find all screencasts of the | ||
course on the | ||
[course webite](https://effective-programming-practices.vercel.app/landing-page.html). | ||
Here, we show the screencasts about numerical optimization and estimagic. | ||
|
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### Introduction to numerical optimization | ||
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```{raw} html | ||
<iframe | ||
src="https://www.youtube.com/embed/hOZueB4Cn1Y" | ||
style="width: 100%; aspect-ratio: 16 / 9;" | ||
allowfullscreen> | ||
</iframe> | ||
``` | ||
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### Using estimagic’s minimize and maximize | ||
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```{raw} html | ||
<iframe | ||
src="https://www.youtube.com/embed/QqTGE3nq0q8" | ||
style="width: 100%; aspect-ratio: 16 / 9;" | ||
allowfullscreen> | ||
</iframe> | ||
``` | ||
|
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### Visualizing optimizer histories | ||
|
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```{raw} html | ||
<iframe | ||
src="https://www.youtube.com/embed/wQWWW8rlxmY" | ||
style="width: 100%; aspect-ratio: 16 / 9;" | ||
allowfullscreen> | ||
</iframe> | ||
``` | ||
|
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### Choosing optimization algorithms | ||
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```{raw} html | ||
<iframe | ||
src="https://www.youtube.com/embed/tJ7Xba3wcxY" | ||
style="width: 100%; aspect-ratio: 16 / 9;" | ||
allowfullscreen> | ||
</iframe> | ||
``` |
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"""Compatibility module. | ||
Contains wrapper functions to handle compatibility issues between different versions of | ||
external libraries. | ||
""" | ||
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from estimagic.config import IS_PANDAS_VERSION_NEWER_OR_EQUAL_TO_2_1_0 | ||
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def pd_df_map(df, func, na_action=None, **kwargs): | ||
"""Apply a function to a Dataframe elementwise. | ||
pandas has depricated the .applymap() function with version 2.1.0. This function | ||
calls either .map() (if pandas version is greater or equal to 2.1.0) or .applymap() | ||
(if pandas version is smaller than 2.1.0). | ||
Args: | ||
df (pd.DataFrame): A pandas DataFrame. | ||
func (callable): Python function, returns a single value from a single value. | ||
na_action (str): If 'ignore', propagate NaN values, without passing them to | ||
func. If None, pass NaN values to func. Default is None. | ||
**kwargs: Additional keyword arguments to pass as keywords arguments to func. | ||
Returns: | ||
pd.DataFrame: Transformed DataFrame. | ||
""" | ||
if IS_PANDAS_VERSION_NEWER_OR_EQUAL_TO_2_1_0: | ||
out = df.map(func, na_action=na_action, **kwargs) | ||
else: | ||
out = df.applymap(func, na_action=na_action, **kwargs) | ||
return out |
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