# Plot Data in Real Time Using Matplotlib

To plot data in real-time using Matplotlib, or make an animation in Matplotlib, we constantly update the variables to be plotted by iterating in a loop and then plotting the updated values. To view the updated plot in real-time through animation, we use various methods such as `FuncAnimation()`

function, `canvas.draw()`

along with `canvas_flush_events()`

.

`FuncAnimation()`

Function

We can update the plot in real-time by updating the variables `x`

and `y`

and then displaying updates through animation using `matplotlib.animation.FuncAnimation`

.

Syntax:

```
matplotlib.animation.FuncAnimation(fig,
func,
frames=None,
init_func=None,
fargs=None,
save_count=None,
*,
cache_frame_data=True,
**kwargs)
```

Code:

```
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
x = []
y = []
figure, ax = plt.subplots(figsize=(4,3))
line, = ax.plot(x, y)
plt.axis([0, 4*np.pi, -1, 1])
def func_animate(i):
x = np.linspace(0, 4*np.pi, 1000)
y = np.sin(2 * (x - 0.1 * i))
line.set_data(x, y)
return line,
ani = FuncAnimation(figure,
func_animate,
frames=10,
interval=50)
ani.save(r'animation.gif', fps=10)
plt.show()
```

```
ani = FuncAnimation(figure,
func_animate,
frames=10,
interval=50)
```

`figure`

is the figure object whose plot will be updated.

`func_animate`

is the function to be called at each frame. Its first argument comes from the next value `frames`

.

`frames=10`

is equal to `range(10)`

. Values from 0 to 9 is passed to the `func_animate`

at each frame. We could also assign an interalbe to `frames`

, like a list `[0, 1, 3, 7, 12]`

.

`interval`

is the delay between frames in the unit of `ms`

.

```
ani.save('animation.gif', fps=10)
```

We could save the animation to a `gif`

or `mp4`

with the parameters like `fps`

and `dpi`

.

`canvas.draw()`

Along With `canvas_flush_events()`

We can update the plot in real-time by updating the variables `x`

and `y`

with `set_xdata()`

and `set_ydata()`

and then displaying updates through animation using `canvas.draw()`

, which is a method based on Javascript.

```
import numpy as np
import time
import matplotlib.pyplot as plt
x = np.linspace(0, 10, 100)
y = np.cos(x)
plt.ion()
figure, ax = plt.subplots(figsize=(8,6))
line1, = ax.plot(x, y)
plt.title("Dynamic Plot of sinx",fontsize=25)
plt.xlabel("X",fontsize=18)
plt.ylabel("sinX",fontsize=18)
for p in range(100):
updated_y = np.cos(x-0.05*p)
line1.set_xdata(x)
line1.set_ydata(updated_y)
figure.canvas.draw()
figure.canvas.flush_events()
time.sleep(0.1)
```

Here the values of `x`

and `y`

get updated repeatedly and the plot also gets updated in real time.

`plt.ion()`

turns on the interactive mode. The plot will not be updated if it is not called.

`canvas.flush_events()`

is method based on Javascript to clear figures on every iterations so that successive figures might not overlap.

## Real Time Scatter Plot

However, to make a real-time scatter, we can just update the values of `x`

and `y`

and add scatter points in each iteration. In this case, we need not clear every figure as a scatter plot generally represents a distinct point in the plane and the points have very little chance of overlapping.

```
import numpy as np
import matplotlib.pyplot as plt
x=0
for i in range(100):
x=x+0.04
y = np.sin(x)
plt.scatter(x, y)
plt.title("Real Time plot")
plt.xlabel("x")
plt.ylabel("sinx")
plt.pause(0.05)
plt.show()
```