Week 1: Signals in Python

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# Know your TA

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* My name is Asem Alaa, graduate of 2016 class.

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* Eng. Eman Ibraheem, graduate of 2017 class.

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* e-mail: `asem.a.abdelaziz@gmail.com`

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* Office hours and materials are avaiable on the course page.

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## Python Basics

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### Variables

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Variables in python are very flexible, unlike C++:

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* Interpreted language.

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* No need to declare the variable type.

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* The same variable can be assigned to different types.

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```python
x = 5
y = 'Hello SBME'
z = 5
z = "Hello SBME"
```

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### Lists

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```python
# List construction
subjects = ['DSP','Computer Graphics']

# Append an element
subjects.append('Biochemistry')

# Append another list
subjects.extend(['Bioelectronics','Clinical'])

print( subjects ) # ??
print( subjects[0] ) # ??
print( subjects[1] ) # ??
```

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### Arithmetic Operations

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```python

x = 19
y = 18

z = x / y
z = x * y
z = x + y
z = x - y
```

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### Logical Opertations

```python
x = 17 % 2 == 1

y = 9 / 3 < 1

b = x or y ## True

b = x and y ## False
```

- `and` => `&&` in C++

- `or` => `||` in C++

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### If, elif, else

```python

x = 23 
y = 22

if x < y:
  z = 13 # Local scope

elif x % 2 == 1 and x > y :
  h = 17 # Local scope

else:
  v = 80 # Local scope

print( z ) # Error: z is out of scope
print( h ) # Error: h is out of scope
```

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### Loops

```python

for i in range(10):
  print( i )

i = 0
while i < 10 :
  print( i )
  i += 1
```

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### Functions

```python
def mean( list ):
  sum = 0
  for element in list:
    sum += element
  return sum / len( sum )

m = mean([1,12,42,1,23,12])
print( m )
```

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### Importing Libraries

```python 
import numpy as np
import scipy as sp
```

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### Numpy

Reference: [{NumPy Reference}](https://docs.scipy.org/doc/numpy-1.15.0/reference/#numpy-reference)

```python
import numpy as np

a = np.array([12,23,44,21,23])

print( np.mean( a ))
print( np.std( a ))
```

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### Scipy & Matplotlib

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* Scipy is very rich signal processing library.

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* Scipy works on `numpy` arrays.

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* `Matplotlib` is Matlab-like visualization library.

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```python
from scipy import signal
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

sig = np.repeat([0., 1., 0.], 100)

highpass = [-1,0,1]
hanning = signal.hann(50)

filtered_hanning = signal.convolve(sig, hanning)
filtered_highpass = signal.convolve(sig, highpass)
```

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## Cont'd

```python
## Plotting

mpl.style.use('seaborn')

plt.figure(1)
plt.subplot(311)
plt.plot( sig , lw = 2 )

plt.subplot(312)
plt.plot( filtered_highpass , lw = 2 )

plt.subplot(313)
plt.plot( filtered_hanning , lw = 2 )
```

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### Plotly - Dash

* Web based visualization.

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* Many features (used in Data Science).

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* Declarative Style.

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* Web-based visualization.

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## Declarative Programming

This snippet is from [{Dash by plotly}](https://plot.ly/products/dash/)

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#### Imports

```python
import dash
from dash.dependencies import Input, Output
import dash_core_components as dcc
import dash_html_components as html
from pandas_datareader import data as web
from datetime import datetime as dt

```

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### Layout

```python
app = dash.Dash()

app.layout = html.Div([
    html.H1('Stock Tickers'),
    dcc.Dropdown(
        id='my-dropdown',
        options=[
            {'label': 'Coke', 'value': 'COKE'},
            {'label': 'Tesla', 'value': 'TSLA'},
            {'label': 'Apple', 'value': 'AAPL'}
        ],
        value='COKE'
    ),
    dcc.Graph(id='my-graph')
])
```

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### Callbacks

```python
@app.callback(Output('my-graph', 'figure'), [Input('my-dropdown', 'value')])
def update_graph(selected_dropdown_value):
    df = web.DataReader(
        selected_dropdown_value, data_source='google',
        start=dt(2017, 1, 1), end=dt.now())
    return {
        'data': [{
            'x': df.index,
            'y': df.Close
        }]
    }

if __name__ == '__main__':
    app.run_server()
```

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Output

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## Signals: Examples

### Electrocardiography (ECG)

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### Electroencephalography (EEG)

[{CC BY-SA 4.0}](https://creativecommons.org/licenses/by-sa/4.0) (by *Andrii Cherninskyi*)

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## Task Objective

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* Signal Viewer Application.

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* 3 Signals X 3 Cases.

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* Load the signals dataset from the hard disk.

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* Live sound signal visualization from Microphone.

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* Matlab or Python Implementation.

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* Apply filters on your signal (convolution).

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* User designed filters (windows).

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## Getting Started with Python: Installing Anaconda

### Anaconda

* Shipped +1000 Data Science Packages (DSP, Machine Learning, AI, Statistics).

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* Shipped with Python IDE (Spyder).

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* Shipped with Jupyter Notebook.

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[{Download Anaconda 3 (Python 3)}](https://www.anaconda.com/download)