Initial commit

.github/classroom/autograding.json

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+{
+  "tests": [
+    {
+      "name": "Test",
+      "setup": "sudo -H pip3 install pytest numpy",
+      "run": "pytest",
+      "input": "",
+      "output": "",
+      "comparison": "included",
+      "timeout": 10,
+      "points": null
+    }
+  ]
+}

.github/workflows/classroom.yml

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+name: GitHub Classroom Workflow
+
+on: [push]
+
+jobs:
+  build:
+    name: Autograding
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: education/autograding@v1

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+
+.vscode/
+.idea/
+.pytest_cache/
+.directory
+.DS_Store

README.md

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+# Homework 4 - Basic NumPy
+
+The deadline of this homework is on **Tuesday, 16th of May, 23:59:00 UTC+2**.
+
+In this homework you should implement three different functions in their respective files.
+
+
+### 1 Strange Pattern
+
+You come across this strange pattern.
+
+| x     |       |       | x     |       |       | x     |       |
+| ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |
+|       |       | **x** |       |       | **x** |       |       |
+|       | **x** |       |       | **x** |       |       | **x** |
+| **x** |       |       | **x** |       |       | **x** |       |
+|       |       | **x** |       |       | **x** |       |       |
+|       | **x** |       |       | **x** |       |       | **x** |
+
+Mesmerized, you decide you must write a function to generate arbitrary sizes of it. (_Write a function `strange_pattern` that takes a shape tuple `(n, m)` as input and generates a boolean (True for x's and False for blank spaces) 2D NumPy array of the given shape with this pattern_)
+
+**Hint:** Perhaps this strange symbol might help? `::`
+
+### 2 Gaussian analysis
+
+Write a function `gaussian_analysis` which takes four parameters `loc`, `scale`, `lower_bound` and `upper_bound` and returns a tuple of two values (`mean`, `std`).
+First of all the function should make sure that `loc`, `scale`, `lower_bound` and `upper_bound` are integers or floats and that `lower_bound` is smaller than `upper_bound` and should return meaningful error messages if those are not the case.
+In the function 100 samples of a gaussian distribution should be drawn in respect to the given `loc` and `scale` parameters. Check out the Numpy documentation to find out which function you could use here.
+Next, the values below the `lower_bound` and above the `upper_bound` should be filtered out. Afterwards you should calculate the `mean` and the `std`(standard deviation) of the array and return them in a tuple.
+
+
+
+### 3. Combination of arrays
+
+Write a function `combination` that takes in two numpy arrays and an optional parameter `axis` which should be 0 by default.
+Remove unnecessary dimensions of the input arrays, check whether they can be combined along the given axis and return the combined array.
+If the combination is not possible, raise a meaningful error message.
+
+Good luck!

combination.py

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+import numpy as np 
+
+# implement your function to combine two numpy arrays 
+
+def combination():
+    # delete the NotImplementedError when you write your function.
+    raise NotImplementedError
+
+
+if __name__ == "__main__":
+    # use this for your own testing!
+
+    pass

gaussian_analysis.py

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+import numpy as np
+
+# implement the function gaussian_analysis
+
+def gaussian_analysis():
+
+    # delete the NotImplementedError when you write your function.
+    raise NotImplementedError
+
+
+if __name__ == "__main__":
+    # use this for your own testing!
+
+    pass

strange_pattern.py

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+import numpy as np
+
+# implement the function strange pattern
+
+def strange_pattern():
+    # delete the NotImplementedError when you write your function.
+    raise NotImplementedError
+
+
+if __name__ == "__main__":
+    # use this for your own testing!
+
+    pass

test_combination.py

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+from hashlib import sha1
+import numpy as np
+import types
+
+try:
+    import combination as testfile
+except ModuleNotFoundError:
+    assert False, "The name of your file is suppoesed to be 'combination.py'!"
+
+def imports_of_your_file(filename):
+    """ Yields all imports in the testfile. """
+
+    for name, val in vars(testfile).items():
+        if isinstance(val, types.ModuleType):
+            # get direct imports
+            yield val.__name__
+
+        else:
+            # get from x import y imports
+            imprt = getattr(testfile, name)
+
+            if hasattr(imprt, "__module__") and not str(imprt.__module__).startswith("_") and not str(imprt.__module__) == filename:
+                yield imprt.__module__
+
+
+def test_imports(filename="combination", allowed_imports={"numpy"}):
+    """ Checks if any non-allowed imports have been done. """
+
+    assert set(imports_of_your_file(
+        filename)) <= allowed_imports, "You are not allowed to import any modules except NumPy!"
+
+
+def test_combination():
+    a = np.array([[[[1, 2], [3, 4], [5, 6]]]])
+    b = np.ones((2,2))
+
+    result = testfile.combination(a,b)
+
+    assert sha1(result).hexdigest(
+    ) == '5709b4ea8b49ac48d42ae3e0bd1b3fb0dbb3249b', 'Your function does not return the right result!'
+
+

test_gaussian_analysis.py

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+from hashlib import sha1
+
+import numpy as np
+import types
+
+try:
+    import gaussian_analysis as testfile
+except ModuleNotFoundError:
+    assert False, "The name of your file is suppoesed to be 'gaussian_analysis.py'!"
+
+def imports_of_your_file(filename):
+    """ Yields all imports in the testfile. """
+
+    for name, val in vars(testfile).items():
+        if isinstance(val, types.ModuleType):
+            # get direct imports
+            yield val.__name__
+
+        else:
+            # get from x import y imports
+            imprt = getattr(testfile, name)
+
+            if hasattr(imprt, "__module__") and not str(imprt.__module__).startswith("_") and not str(imprt.__module__) == filename:
+                yield imprt.__module__
+
+
+def test_imports(filename="gaussian_analysis", allowed_imports={"numpy"}):
+    """ Checks if any non-allowed imports have been done. """
+
+    assert set(imports_of_your_file(
+        filename)) <= allowed_imports, "You are not allowed to import any modules except NumPy!"
+
+def test_gaussian_analysis():
+    loc =  0
+    scale = 3
+    lower_bound = 1
+    upper_bound = 3
+
+    result = testfile.gaussian_analysis(loc,scale,lower_bound,upper_bound)
+    assert type(result) is tuple, 'Your function does not return the right form of result!'
+    assert result[0] < upper_bound and result[0] > lower_bound, 'Your function does not return the right result!'
+
+
+
+

test_strange_pattern.py

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+from hashlib import sha1
+
+import numpy as np
+import types
+
+try:
+    import strange_pattern as testfile
+except ModuleNotFoundError:
+    assert False, "The name of your file is suppoesed to be 'strange_pattern.py'!"
+
+def imports_of_your_file(filename):
+    """ Yields all imports in the testfile. """
+
+    for name, val in vars(testfile).items():
+        if isinstance(val, types.ModuleType):
+            # get direct imports
+            yield val.__name__
+
+        else:
+            # get from x import y imports
+            imprt = getattr(testfile, name)
+
+            if hasattr(imprt, "__module__") and not str(imprt.__module__).startswith("_") and not str(imprt.__module__) == filename:
+                yield imprt.__module__
+
+
+def test_imports(filename="strange_pattern", allowed_imports={"numpy"}):
+    """ Checks if any non-allowed imports have been done. """
+
+    assert set(imports_of_your_file(
+        filename)) <= allowed_imports, "You are not allowed to import any modules except NumPy!"
+
+
+def test_strange_pattern():
+    result = testfile.strange_pattern((10, 10))
+
+    assert type(
+        result) is np.ndarray, "Your function does not return a NumPy array!"
+    assert result.dtype is np.dtype(
+        "bool"), "Your function does not return a boolean array!"
+    assert sha1(result).hexdigest(
+    ) == '7e69ac17197f17ebccf456b6dfbbe95fe938b7d9', "Your function does not produce the correct pattern!"
+
+    result = testfile.strange_pattern((2, 2))
+
+    assert sha1(result).hexdigest(
+    ) == "3c585604e87f855973731fea83e21fab9392d2fc", "Your function does not produce the correct pattern in an edge case!"
+
+    result = testfile.strange_pattern((0, 0))
+
+    assert result.shape == (
+        0, 0), "Your function does not produce the correct pattern in an edge case!"