```python
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
pi = np.pi
x = np.linspace(-4*pi, 4*pi, 1000)
plt.plot(x, np.sin(x)/x)
plt.show()
```


```python
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
pi = np.pi
x = np.linspace(-4*pi, 4*pi, 1000)
plt.plot(x, np.cos(x)/x)
plt.show()
```


```python
%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt
pi = np.pi
x = np.linspace(-4*pi, 4*pi, 1000)
plt.plot(x, np.tan(x)/x)
plt.show()
```


```python
%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt
pi = np.pi
x = np.linspace(-4*pi, 4*pi, 1000)
plt.plot(x, np.sin(x))
plt.show()
```


```python
from scipy.misc import derivative
import sympy as sp
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
x = sp.Symbol('x')
print(sp.diff(3*x**2 +2*x +3,x))
def f(x):
    return 4*x**3-5*x**2+8
print(derivative(f,2.0))
y=np.linspace(-3,3)
plt.plot(y,f(y))

```


```python
#def func(x):
#f(x) =np.e^{-ax^2}
# return f
#f = derivative(func, x0=2.)
#rint(df)
```


```python
import matplotlib.pyplot as plt
import numpy as np 
x=np.linspace(0,10,100)
plt.plot(x, np.sin(x))
plt.plot(x,np.cos(x))
plt.show
```


```python
%matplotlib inline
x=np.linspace(-10,10, 100)
def func(x):
    return exp(-ax**2)
plt.plot(x, np.func)
plt.plot(x,np.df)
```


```python
 # Derivative:
    #. f(x) = x^2 + 5x -4
    #. df/dx = 2x + 5
   # evaluation at (x=1) : df/dx at (x=1) = 7
    
    #import numpy as np
   # import matplotlib.pyplot as plt
    from scipy.misc import derivative
    def func(x):
        return x**2 + 5*x - 4
    df = derivative(func, x0=2.)
    print (df)
```


```python
import numpy as np
import matplotlib.pyplot as plt

# this tells Jupyter to embed matplotlib plots in the notebook
%matplotlib notebook
def sinc(x):
    return np.sin(x) / x

def d_sinc(x):
    "derivative of sinc-function"
    return np.cos(x)/x - np.sin(x)/x**2
# evaluate functions at 1000 points evenly spaced in [-15, 15]
x = np.linspace(-15, 15, 1000)
f = sinc(x)
df = d_sinc(x)
# plot the sinc-function and its derivative
fig, ax = plt.subplots()
ax.plot(x, f, color="red", label=r"$sinc(x)$")
ax.plot(x, df, color="blue", ls="--", label=r"$\frac{d(sinc(x))}{dx}$")


ax.set_title("Example Notebook Plot")
ax.set_xlabel(r"$x$ [radians]")

ax.grid(True)
ax.legend()
```


    <IPython.core.display.Javascript object>









    <matplotlib.legend.Legend at 0x21a9edeccc8>




    <IPython.core.display.Javascript object>







```python

```


```python

```

37d970f9b795e0b01af98c5bc23f3fbb3caa3e1a
ipynb
Jupyter Notebook
graph sinc.ipynb
kchari29/HKC
42acb71a26d2eb515afe0579a8994d2b06ae83f7
MIT
graph sinc.ipynb
kchari29/HKC
42acb71a26d2eb515afe0579a8994d2b06ae83f7
MIT
graph sinc.ipynb
kchari29/HKC
42acb71a26d2eb515afe0579a8994d2b06ae83f7
MIT
Qwen/Qwen-72B
1. YES
2. YES