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Begin working on SciPy.ipynb; added checkpoints folder for SciPy
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ExoCore/Exercise_Solutions/Module_3/SciPy/Checkpoints/questions.json
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,220 @@ | ||
| [ | ||
| { | ||
| "question": "Which of the following will invert the elements of an array, `arr`?", | ||
| "type": "multiple_choice", | ||
| "answers": [ | ||
| { | ||
| "answer": "arr = arr[-1]", | ||
| "correct": false, | ||
| "feedback": "Incorrect. This returns only the last element." | ||
| }, | ||
| { | ||
| "answer": "arr = arr[::-1]", | ||
| "correct": true, | ||
| "feedback": "Correct!" | ||
| }, | ||
| { | ||
| "answer": "arr = arr[1:]", | ||
| "correct": false, | ||
| "feedback": "Incorrect. This returns the same array, excluding the first value." | ||
| }, | ||
| { | ||
| "answer": "arr = arr[3::-1]", | ||
| "correct": false, | ||
| "feedback": "Incorrect. This returns an array from the fourth element back to the first element." | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "question": "Which list comprehension syntax will exclude all negative elements, and multiply the remaining elements in an array, `arr`, by 1.5 times?", | ||
| "type": "multiple_choice", | ||
| "answers": [ | ||
| { | ||
| "answer": "arr = [x*1.5 for x in arr if x < 0]", | ||
| "correct": false, | ||
| "feedback": "Incorrect. Almost! This will exclude all positive elements, not negative." | ||
| }, | ||
| { | ||
| "answer": "arr = [x/2 for x in arr if np.abs(x) <= 0]", | ||
| "correct": false, | ||
| "feedback": "Incorrect. This divides all elements by two, and doesn't exclude any values!" | ||
| }, | ||
| { | ||
| "answer": "arr = [x*1.5 for x in arr if x > 0]", | ||
| "correct": true, | ||
| "feedback": "Correct!" | ||
| }, | ||
| { | ||
| "answer": "arr = [x*1.5 for x in arr]", | ||
| "correct": false, | ||
| "feedback": "Incorrect. This gets close, but does not exclude negative values." | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "question": "What conditional would you pass to check if a value, x, is NAN?", | ||
| "type": "multiple_choice", | ||
| "answers": [ | ||
| { | ||
| "answer": "if x == np.nan", | ||
| "correct": false, | ||
| "feedback": "Incorrect. Since NANs are not a string, number, or int, checking exactness is not well defined." | ||
| }, | ||
| { | ||
| "answer": "if np.isnan(x) == 'True'", | ||
| "correct": false, | ||
| "feedback": "Incorrect. The returned value form np.isnan() is a Bool, not a string." | ||
| }, | ||
| { | ||
| "answer": "if np.isnan(x) == True", | ||
| "correct": true, | ||
| "feedback": "Correct!" | ||
| }, | ||
| { | ||
| "answer": "if x == 'nan'", | ||
| "correct": false, | ||
| "feedback": "Incorrect. The RHS is a string, not a np.nan object. Additionally, '==' conditional does not work to check if a value is NAN." | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "question": "Which array creation method will create a 4x30 array, with each element being `45`?", | ||
| "type": "multiple_choice", | ||
| "answers": [ | ||
| { | ||
| "answer": "np.ones((4, 30), value=45)", | ||
| "correct": false, | ||
| "feedback": "Incorrect. While np.ones is on the right track and the dimensions are correct, passing `value=45` will not work." | ||
| }, | ||
| { | ||
| "answer": "np.ones((4, 30))*45", | ||
| "correct": true, | ||
| "feedback": "Correct!." | ||
| }, | ||
| { | ||
| "answer": "np.linspace((4, 30))*40", | ||
| "correct": true, | ||
| "feedback": "Incorrect. Linspace creates evenly spaced arrays from the start and end value." | ||
| }, | ||
| { | ||
| "answer": "np.zeros((4,30)) + np.ones((4,30))", | ||
| "correct": false, | ||
| "feedback": "Incorrect. While this gives the right shape, the values will be 1s, and the zero array is unnecessary." | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "question": "np.argmax(array) will return the highest value in the array.", | ||
| "type": "multiple_choice", | ||
| "answers": [ | ||
| { | ||
| "answer": "True", | ||
| "correct": false, | ||
| "feedback": "Incorrect. It will return the index of the highest value." | ||
| }, | ||
| { | ||
| "answer": "False", | ||
| "correct": false, | ||
| "feedback": "Correct!" | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "question": "What method allows you to take three arrays, arr1, arr2, arr3, and export them as a three columned csv file?", | ||
| "type": "multiple_choice", | ||
| "answers": [ | ||
| { | ||
| "answer": "np.hstack((arr1, arr2, arr3)).T", | ||
| "correct": false, | ||
| "feedback": "Incorrect. hstack will stack all arrays into a single column." | ||
| }, | ||
| { | ||
| "answer": "np.vstack((arr1,arr2,arr3))", | ||
| "correct": false, | ||
| "feedback": "Incorrect. Close, but you need to transpose." | ||
| }, | ||
| { | ||
| "answer": "np.vstack((arr1,arr2,arr3)).T", | ||
| "correct": true, | ||
| "feedback": "Correct!" | ||
| }, | ||
| { | ||
| "answer": "np.array((arr1,arr2,arr3))", | ||
| "correct": false, | ||
| "feedback": "Incorrect, this creates a new array with the three arrays inside of a tuple." | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "question": "np.argmax(array) will return the highest value in the array.", | ||
| "type": "multiple_choice", | ||
| "answers": [ | ||
| { | ||
| "answer": "True", | ||
| "correct": false, | ||
| "feedback": "Incorrect. It will return the index of the highest value." | ||
| }, | ||
| { | ||
| "answer": "False", | ||
| "correct": false, | ||
| "feedback": "Correct!" | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "question": "How do you call a random normal variate with mean 10 and variance 16?", | ||
| "type": "multiple_choice", | ||
| "answers": [ | ||
| { | ||
| "answer": "np.random.normal(loc = 16, scale = 10)", | ||
| "correct": false, | ||
| "feedback": "Incorrect. `loc` specifies the mean, and scale specifies the standard deviation." | ||
| }, | ||
| { | ||
| "answer": "np.random.normal(loc = 10, scale = 4)", | ||
| "correct": false, | ||
| "feedback": "Correct!" | ||
| }, | ||
| { | ||
| "answer": "np.random.normal(loc = 10, scale = 16)", | ||
| "correct": true, | ||
| "feedback": "Incorrect. While the mean is correct, the scale is the standard deviation, not variance." | ||
| }, | ||
| { | ||
| "answer": "np.random.random(loc = 10, scale = 4)", | ||
| "correct": false, | ||
| "feedback": "Incorrect. np.random.random creates a random variate between [0, 1), and does not specify the passed arguments." | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "question": "NumPy has methods to support a quartic fit.", | ||
| "type": "multiple_choice", | ||
| "answers": [ | ||
| { | ||
| "answer": "True", | ||
| "correct": false, | ||
| "feedback": "Correct!" | ||
| }, | ||
| { | ||
| "answer": "False", | ||
| "correct": false, | ||
| "feedback": "Incorrect. This can be done using np.polynomial.polynomial.polyfit(x,y,4)" | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "question": "In the system of equation below, what is the sum of x0, x1, and x2? Use np.linalg.solve.", | ||
| "type": "numeric", | ||
| "precision": 3, | ||
| "answers": [ | ||
| { | ||
| "type": "value", | ||
| "value": "-0.125", | ||
| "correct": true, | ||
| "feedback": "Correct" | ||
| } | ||
|
|
||
| ] | ||
| } | ||
| ] |
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