```python
import sys, os
sys.path.insert(0, os.path.join(os.pardir, 'src'))
from fe_approx1D_numint import approximate
from sympy import tanh, Symbol
x = Symbol('x')

steepness = 20
arg = steepness*(x-0.5)
approximate(tanh(arg), symbolic=False, numint='GaussLegendre2',
            d=1, N_e=4, filename='fe_p1_tanh_4e')
approximate(tanh(arg), symbolic=False, numint='GaussLegendre2',
            d=1, N_e=8, filename='fe_p1_tanh_8e')
approximate(tanh(arg), symbolic=False, numint='GaussLegendre2',
            d=1, N_e=16, filename='fe_p1_tanh_16e')
approximate(tanh(arg), symbolic=False, numint='GaussLegendre3',
            d=2, N_e=2, filename='fe_p2_tanh_2e')
approximate(tanh(arg), symbolic=False, numint='GaussLegendre3',
            d=2, N_e=4, filename='fe_p2_tanh_4e')
approximate(tanh(arg), symbolic=False, numint='GaussLegendre3',
            d=2, N_e=8, filename='fe_p2_tanh_8e')


```

89a24f3726e414306988e6e5fd1779de261df930
ipynb
Jupyter Notebook
Data Science and Machine Learning/Machine-Learning-In-Python-THOROUGH/EXAMPLES/FINITE_ELEMENTS/INTRO/EXERCICES/13_FE_TANH_P1P2.ipynb
okara83/Becoming-a-Data-Scientist
f09a15f7f239b96b77a2f080c403b2f3e95c9650
MIT
Data Science and Machine Learning/Machine-Learning-In-Python-THOROUGH/EXAMPLES/FINITE_ELEMENTS/INTRO/EXERCICES/13_FE_TANH_P1P2.ipynb
okara83/Becoming-a-Data-Scientist
f09a15f7f239b96b77a2f080c403b2f3e95c9650
MIT
Data Science and Machine Learning/Machine-Learning-In-Python-THOROUGH/EXAMPLES/FINITE_ELEMENTS/INTRO/EXERCICES/13_FE_TANH_P1P2.ipynb
okara83/Becoming-a-Data-Scientist
f09a15f7f239b96b77a2f080c403b2f3e95c9650
MIT
2022-02-09T15:41:33.000Z
2022-02-11T07:47:40.000Z
Qwen/Qwen-72B
1. YES
2. YES