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‎01-introduction.md‎

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:::::::::::::::::::::::::::::::::::::::::::::::
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::::::::::::::::::::::::::::::::::::::::: callout
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## Formatting Variables and Objects in Strings
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Throughout this course, we will be printing the values of variables inside strings to demonstrate what the code does. There are a number of ways to this in Python; in this course we will be using `f-strings` which are the recommended string interpolation syntax from version 3.6 onwards.
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To use `f-strings` simply start a string literal with `f` or `F`, then embed whatever you want interpolating into the string within curly braces `{}`:
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```python
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two_int = 2
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two_str = "2"
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f_string = f"{two_int} + {two_str} = {2 + 2}"
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print(f_string)
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```
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```output
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2 + 2 = 4
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```
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Numerical values can be formatted using `:` inside the curly braces. For example, an integer `i` can be made to have a width `n` using `{i:nd}`, or `{i:0nd}` to pad it out with zeros:
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```python
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print(f"One with four leading spaces {1:5d}")
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print(f"One with four leading zeros {1:05d}")
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```
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```output
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One with four leading spaces 1
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One with four leading zeros 00001
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```
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Floating point variables can have their precision specified with a number after a decimal point and an `f`:
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```python
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pi = 3.141592653589793
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print(pi)
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print(f"pi to three decimal places is {pi:.3f}")
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```
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```output
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3.141592653589793
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pi to three decimal places is 3.142
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```
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Or made to use scientific notation with an `e`:
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```python
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print(1e9)
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print(f"A billion in scientific notation is {1e9:.1e}")
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```
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```output
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1000000000.0
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A billion in scientific notation is 1.0e+09
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```
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This is only scratching the surface of what you can do with `f-strings`, they are often the most powerful and concise way to format variables inside strings. However, there are occasions when other methods are preferable, and you should be careful using them with Python [before version 3.12](https://realpython.com/python-f-strings/#upgrading-f-strings-python-312-and-beyond).
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To learn more, see: [Python f-strings](https://realpython.com/python-f-strings/).
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::::::::::::::::::::::::::::::::::::::::::::::::::
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## Generative AI
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We would like to reiterate the [Carpentries](https://carpentries.org/) stance on [Generative AI in coding](https://swcarpentry.github.io/python-novice-inflammation/01-intro.html#generative-ai) which you may have seen when preparing for this course.

‎02-dictionaries.md‎

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@@ -117,9 +117,6 @@ Following the previous example, we can create a python dictionary using the name
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```python
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d = {'alice': 35, 'bob': 18}
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```
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```python
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print(d)
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```
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To access an element of the dictionary we must use the *key*:
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```python
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print('The age of alice is :', d['alice'])
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print(f'The age of alice is: {d["alice"]}')
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```
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```output
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```python
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key = 'alice'
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print('The name of the person is used as key:', key)
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print('The value associated to that key is:', d[key])
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print(f'The name of the person is used as key: {key}')
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print(f'The value associated to that key is: {d[key]}')
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```
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```output
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Adding an element to a dictionary is done by creating a new key and attaching a value to it.
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```python
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print('Original dictionary:', d)
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print(f'Original dictionary: {d}')
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d['jane'] = 24
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print('New dictionary:', d)
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print(f'New dictionary: {d}')
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```
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```output
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d_extra = {'tom': 54, 'david': 87}
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d.update(d_extra)
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print('Updated dictionary:', d)
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print(f'Updated dictionary: {d}')
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```
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```output
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```python
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del d['tom']
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print('Dictionary with item deleted:', d)
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print(f'Dictionary with item deleted: {d}')
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```
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```output
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```python
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david_age = d.pop('david')
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print('Age of David:', david_age)
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print('Dictionary with david popped out:', d)
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print(f'Age of David: {david_age}')
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print(f'Dictionary with david popped out: {d}')
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```
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```output
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Keys have to be unique; you cannot have two keys with the same name. If you try to add an item using a key already present in the dictionary you will overwrite the previous value.
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```python
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print('Original dictionary:', d)
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print(f'Original dictionary: {d}')
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d['alice'] = 12
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print('New dictionary:', d)
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print(f'New dictionary: {d}')
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```
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```output
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d2 = {'tom': 54, 'david': 87}
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d3 = {'bob': 18, 'alice': 35, 'jane': 24}
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d4 = {'alice': 35, 'bob': 18, 'jane': 24}
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print('Dictionary 1 and dictionary 2 are equal:', d1 == d2)
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print('Dictionary 1 and dictionary 3 are equal:', d1 == d3)
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print('Dictionary 3 and dictionary 4 are equal:', d3 == d4)
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print(f'Dictionary 1 and dictionary 2 are equal: {d1 == d2}')
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print(f'Dictionary 1 and dictionary 3 are equal: {d1 == d3}')
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print(f'Dictionary 3 and dictionary 4 are equal: {d3 == d4}')
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```
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```output
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```
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It is also possible to iterate through the keys and items in the dictionary at the same time using the `items` function,
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which returns a *dict\_items* object containing `key, value` pairs:
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which returns a *dict\_items* object containing *key, value* pairs:
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```python
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d.items()
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```python
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for key, value in d.items():
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print("Name:", key, " Age:", value)
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print(f'Name: {key}, Age: {value}')
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```
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```output

‎03-numpy_essential.md‎

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```python
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stddev_noisy = np.std(noisy)
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mean_noisy = np.mean(noisy)
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print(f'standard deviation is: {stddev_noisy}')
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print(f'mean value is: {mean_noisy}')
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print(f'standard deviation is: {stddev_noisy:.5f}')
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print(f'mean value is: {mean_noisy:.5f}')
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```
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```output
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standard deviation is: 0.011592652442611553
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mean value is: 0.005047252119578472
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standard deviation is: 0.01159
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mean value is: 0.00505
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```
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We will create a mask for the data, by selecting all data points below this threshold value (we'll assume here that any signal we might be interested in is positive):
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First we examine the general stats of the data (using built-in methods, except for the median, which has to be called from NumPy directly):
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```python
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print('mean value im1:', imdata.mean())
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print('median value im1:', np.median(imdata))
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print('max value im1:', imdata.max())
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print('min value im1:', imdata.min())
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print(f'mean value im1: {imdata.mean()}')
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print(f'median value im1: {np.median(imdata)}')
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print(f'max value im1: {imdata.max()}')
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print(f'min value im1: {imdata.min()}')
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```
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```output
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This mask is applied to the data for all built-in functions. But where we have to directly use a NumPy function we have to make sure we use the equivalent function in the mask (`ma`) library:
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```python
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print('original average:', imdata.mean())
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print('Masked average:', immasked.mean())
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print()
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print('original max:', imdata.max())
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print('Masked max:', immasked.max())
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print()
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print('original min:', imdata.min())
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print('Masked min:', immasked.min())
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print()
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print('original median:', np.mean(imdata))
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print('Masked median:', np.ma.median(immasked))
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print(f'original average: {imdata.mean()}')
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print(f'Masked average: {immasked.mean()}\n')
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print(f'original max: {imdata.max()}')
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print(f'Masked max: {immasked.max()}\n')
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print(f'original min: {imdata.min()}')
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print(f'Masked min: {immasked.min()}\n')
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print(f'original median: {np.mean(imdata)}')
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print(f'Masked median: {np.ma.median(immasked)}')
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```
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```output

‎05-defensive_programming.md‎

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val = 1
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if val > 0:
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print('Value:', val, 'is positive.')
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print(f'Value: {val} is positive.')
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elif val == 0:
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print('Value:', val, 'is zero.')
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print(f'Value: {val} is zero.')
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else:
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print('Value:', val, 'is negative.')
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print(f'Value: {val} is negative.')
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if val > 0:
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print('Value:', val, 'is positive.')
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print(f'Value: {val} is positive.')
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print('Value:', val, 'is zero.')
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print(f'Value: {val} is zero.')
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else:
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print('Value:', val, 'is negative.')
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print(f'Value: {val} is negative.')
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```
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```output
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<ipython-input-2-99c0e25bf5e9> in <module>()
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1 def check_sign(val):
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----> 2 if val > 0:
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3 print('Value:', val, 'is positive.')
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3 print(f'Value: {val} is positive.')
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4 elif val == 0:
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5 print('Value:', val, 'is zero.')
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5 print(f'Value: {val} is zero.')
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TypeError: '>' not supported between instances of 'str' and 'int'
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def check_sign(val):
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if val > 0:
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print('Value:', val, 'is positive.')
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print(f'Value: {val} is positive.')
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elif val == 0:
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print('Value:', val, 'is zero.')
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print(f'Value: {val} is zero.')
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else:
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print('Value:', val, 'is negative.')
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print(f'Value: {val} is negative.')
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```
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Then wrap the function call in an `if` statement:
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except TypeError as err:
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print('Val is not a number')
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print('But our code does not crash anymore')
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print('The run-time error is:', err)
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print(f'The run-time error is: {err}')
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```
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As with `if` statements, multiple `except` statements can be used, each with a different error test. These can be followed by an (optional) catch-all bare `except` statement (as we started with) to catch any unexpected errors. Note that only one `try` statement is allowed in the structure.
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reciprocal = 1/val
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except TypeError as err:
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print('Val is not a number')
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print('The run-time error is:', err)
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print(f'The run-time error is: {err}')
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except Exception as err:
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print('Some error other than a TypeError occured')
140-
print('The run-time error is:', err)
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print(f'The run-time error is: {err}')
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else:
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print('The reciprocal of the value =', reciprocal)
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print(f'The reciprocal of the value = {reciprocal}')
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finally:
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print('release memory')
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```
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```python
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assert type(val) is float or type(val) is int, "Variable has to be a numerical object"
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assert type(val) is float or type(val) is int, 'Variable has to be a numerical object'
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check_sign(val)
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```

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