```python
from matplotlib import pyplot as plt, ticker as tk
%matplotlib inline
%config InlineBackend.figure_format='retina'

import numpy as np
import pandas as pd

from sympy import *
init_printing()

from fun_expr import Function_from_Expression as FE
```


```python
k_0 = 400
k_inf = 1100
beta = 1/20

t = Symbol('t')

k = FE(t, k_inf + (k_0-k_inf)*exp(-beta*t),name='k')
k.equation()
```




$\displaystyle k\left(t\right) = 1100 - 700 e^{- 0.05 t}$




```python
fig,ax = plt.subplots()

ax.axhline(k_inf,c='r')
lx = np.linspace(0,120)
ax.plot(lx,k.lambdified(lx))

ax.set(
    xlim=(0,120), xlabel='Zeit $t$ in Minuten',
    ylim=(0,1200), ylabel='CO_2-Konzentration in ppM'
)

ax.grid(lw=0.25,c='k')
plt.savefig('U03-A02-grafik.pdf',bbox_inches='tight')
```

e25fb0c30ba6580cef0170f252bd5598ac44dd8b
ipynb
Jupyter Notebook
Aufgaben/Untitled.ipynb
w-meiners/rlt-rlq
215594ebdbcc364d81d29fa620389e006864cc4d
MIT
Aufgaben/Untitled.ipynb
w-meiners/rlt-rlq
215594ebdbcc364d81d29fa620389e006864cc4d
MIT
Aufgaben/Untitled.ipynb
w-meiners/rlt-rlq
215594ebdbcc364d81d29fa620389e006864cc4d
MIT
Qwen/Qwen-72B
1. YES
2. YES