Antoinegey solution

.github/workflows/python-app.yml

@@ -1,24 +1,19 @@
 # This workflow will install Python dependencies, run tests and lint with a single version of Python
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: build
+name: Assignment Validation
 
 on:
   push:
-
-  pull_request:
-
-  create:
     branches:
       - 'main'
-    tags:
-      - '**'
 
-jobs:
-  build:
+  pull_request:
 
+jobs:
+  test:
+    name: Test Code
     runs-on: ubuntu-latest
-
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python 3.8
@@ -28,15 +23,28 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install flake8 pytest pydocstyle
+        pip install pytest
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Test with pytest
+      run: pytest -v
+  lint:
+    name: Check code style
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.8
+    - name: Install dependencies
+      run: |
+        pip install flake8 pydocstyle
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=80 --statistics
-        pydocstyle
-    - name: Test with pytest
-      run: |
-        pytest
+        flake8 . --count --max-complexity=10 --max-line-length=80 --statistics
+    - name: Check doc style with pydocstyle
+      run: pydocstyle
+

README.md

@@ -1,4 +1,4 @@
-# Assignment 1 for the DataCamp course X-DataScience Master
+# Assignment 1 for the DataCamp course X-DataScience Master - numpy
 
 ## What we want you to learn by doing this assignment:
 
@@ -10,11 +10,16 @@
 
 ## How?
 
-  - For the repository by clicking on the `Fork` button on the upper right corner
-  - Clone the repository of your fork with: `git clone https://github.com/MYLOGIN/datacamp-assignment1` (replace MYLOGIN with your GitHub login)
-  - Create a branch called `myassignment` using `git checkout -b myassignment`
+  - Fork the repository by clicking on the `Fork` button on the upper right corner
+  - Clone the repository of your fork with: `git clone https://github.com/MYLOGIN/datacamp-assignment-numpy` (replace MYLOGIN with your GitHub login)
+  - Create a branch called `my_user_name` using `git checkout -b my_user_name`
   - Make the changes to complete the assignment. You have to modify the files that contain `questions` in their name. Do not modify the files that start with `test_`.
-  - Open the pull request on GitHub
+  - Check locally that your solution meet the test by running `pytest` from the root of the repo. You may need to install `pytest` using `pip` or `conda`.
+  - Check the code formating for your solution using `flake8`. You may need to install `flake8` using `pip` or `conda`.
+  - Open the pull request on GitHub:
+     - Create a commit with `git add -u` and `git commit -m "UP my solution"`
+     - Push your branch on your fork: `git push -u origin my_user_name`
+     - Go to your repo in your browser and click the `Open a PR` button.
   - Keep pushing to your branch until the continuous integration system is green.
   - When it is green notify the professors on Slack that your done.
 

numpy_questions.py

@@ -1,3 +1,20 @@
+"""Assignment - using numpy and making a PR.
+
+The goals of this assignment are:
+    * Use numpy in practice with two easy exercises.
+    * Use automated tools to validate the code (`pytest` and `flake8`)
+    * Submit a Pull-Request on github to practice `git`.
+
+The two functions below are skeleton functions. The docstrings explain what
+are the inputs, the outputs and the expected error. Fill the function to
+complete the assignment. The code should be able to pass the test that we
+wrote. To run the tests, use `pytest test_numpy_question.py` at the root of
+the repo. It should say that 2 tests ran with success.
+
+We also ask to respect the pep8 convention: https://pep8.org.
+This will be enforced with `flake8`. You can check that there is no flake8
+errors by calling `flake8` at the root of the repo.
+"""
 import numpy as np
 
 
@@ -11,22 +28,21 @@ def max_index(X):
 
     Returns
     -------
-    i : int
-        The row index of the maximum.
-
-    j : int
-        The column index of the maximum.
+    (i, j) : tuple(int)
+        The row and columnd index of the maximum.
 
     Raises
     ------
     ValueError
-        If the input is not a numpy error or
+        If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
-
     # TODO
+    if not isinstance(X, np.ndarray) or len(X.shape) != 2:
+        raise ValueError
+
+    i = np.argmax(X) // X.shape[1]
+    j = np.argmax(X) % X.shape[1]
 
     return i, j
 
@@ -37,9 +53,33 @@ def wallis_product(n_terms):
     See:
     https://en.wikipedia.org/wiki/Wallis_product
 
-    XXX : write Parameters and Returns sections as above.
+    Parameters
+    ----------
+    n_terms : int
+        Number of steps in the Wallis product. Note that `n_terms=0` will
+        consider the product to be `1`.
 
+    Returns
+    -------
+    pi : float
+        The approximation of order `n_terms` of pi using the Wallis product.
     """
     # XXX : The n_terms is an int that corresponds to the number of
     # terms in the product. For example 10000.
-    return 0.
+
+    # Check if the n_terms if correct
+    if n_terms < 0:
+        raise ValueError
+
+    # Check if n_terms is null
+    if n_terms == 0:
+        return 2.0
+
+    # Build a range of terms
+    n = np.arange(n_terms)+1
+    n_2 = n**2
+
+    # Build the intermediate products
+    product = 4 * n_2 / (4.0 * n_2 - 1)
+
+    return 2.0 * np.prod(product)

test_numpy_questions.py

@@ -16,7 +16,7 @@ def test_max_index():
 
     X = np.random.randn(100, 100)
     i, j = max_index(X)
-    assert np.all(X[i, j] <= X)
+    assert np.all(X[i, j] >= X)
 
     with pytest.raises(ValueError):
         max_index(None)
@@ -29,5 +29,11 @@ def test_max_index():
 
 
 def test_wallis_product():
+    pi_approx = wallis_product(0)
+    assert pi_approx == 2.
+
+    pi_approx = wallis_product(1)
+    assert pi_approx == 8 / 3
+
     pi_approx = wallis_product(100000)
     assert abs(pi_approx - m.pi) < 1e-4