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Finish Assignment 2 #2

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e4b670b
complete assignment-2, question 1
dzedze Dec 2, 2025
a5808d5
complete assignment-2, question 2
dzedze Dec 2, 2025
259e312
complete assignment-2, question 3
dzedze Dec 2, 2025
0976e48
update code, include tests for Q2 and Q3 as suggested
dzedze Dec 11, 2025
95aab21
add more tests to ensure robust patient_summary()
dzedze Dec 11, 2025
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228 changes: 210 additions & 18 deletions 02_activities/assignments/assignment_2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -72,11 +72,78 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {
"id": "n0m48JsS-nMC"
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"all_paths = [\n",
" \"../../05_src/data/assignment_2_data/inflammation_01.csv\",\n",
Expand All @@ -95,8 +162,10 @@
"\n",
"with open(all_paths[0], 'r') as f:\n",
" # YOUR CODE HERE: Use the readline() or readlines() method to read the .csv file into a variable\n",
" \n",
" # YOUR CODE HERE: Iterate through the variable using a for loop and print each row for inspection"
" file_contents = f.readlines()\n",
" # YOUR CODE HERE: Iterate through the variable using a for loop and print each row for inspection\n",
" for line in file_contents:\n",
" print(line.strip())"
]
},
{
Expand Down Expand Up @@ -145,13 +214,13 @@
" # Implement the specific operation based on the 'operation' argument\n",
" if operation == 'mean':\n",
" # YOUR CODE HERE: Calculate the mean (average) number of flare-ups for each patient\n",
"\n",
" summary_values = np.mean(data, ax)\n",
" elif operation == 'max':\n",
" # YOUR CODE HERE: Calculate the maximum number of flare-ups experienced by each patient\n",
"\n",
" summary_values = np.max(data, ax)\n",
" elif operation == 'min':\n",
" # YOUR CODE HERE: Calculate the minimum number of flare-ups experienced by each patient\n",
"\n",
" summary_values = np.min(data, ax)\n",
" else:\n",
" # If the operation is not one of the expected values, raise an error\n",
" raise ValueError(\"Invalid operation. Please choose 'mean', 'max', or 'min'.\")\n",
Expand All @@ -165,12 +234,111 @@
"metadata": {
"id": "3TYo0-1SDLrd"
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"60\n",
"60\n",
"60\n"
]
}
],
"source": [
"# Test it out on the data file we read in and make sure the size is what we expect i.e., 60\n",
"# Your output for the first file should be 60\n",
"data_min = patient_summary(all_paths[0], 'min')\n",
"print(len(data_min))"
"print(len(data_min))\n",
"\n",
"# Helper function to verify that summary calculations return\n",
"# the expected number of data points for a given path.\n",
"def len_after_calculate_on_path(path, operation):\n",
" return len(patient_summary(path, operation))\n",
"# Quick manual check for \"max\" and \"mean\" operations\n",
"# to ensure they produce output for every patient.\n",
"for operation in [\"max\", \"mean\"]:\n",
" test = len_after_calculate_on_path(all_paths[0], operation)\n",
" print(test)\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# Unit test: all operations should successfully compute values\n",
"# for all data points.\n",
"# import unittest\n",
"def test_all_operations_should_succesfully_calculate_on_every_data_point():\n",
" n_points = 60 # Expected number of loaded data points (simplified for now)\n",
" results = []\n",
" for operation in [\"max\", \"mean\", \"min\"]:\n",
" test = len_after_calculate_on_path(all_paths[0], operation)\n",
" results.append(test)\n",
" assert results[0] == results[1] == results[2] == n_points\n",
"\n",
"# Test: Compare mean, max, and min values from summary\n",
"def test_max_must_always_greater_or_equal_mean_greater_or_equal_min():\n",
" path = all_paths[0]\n",
"\n",
" max_vals = patient_summary(path, \"max\")\n",
" mean_vals = patient_summary(path, \"mean\")\n",
" min_vals = patient_summary(path, \"min\")\n",
"\n",
" # print first 5 patients for visual confirmation\n",
" for i in range(5):\n",
" print(f\"Patient {i}: max={max_vals[i]}, mean={mean_vals[i]}, min={min_vals[i]}\")\n",
" \n",
" # logical test\n",
" for max, mean, min in zip(max_vals, mean_vals, min_vals):\n",
" assert max >= mean >= min\n",
" \n",
" print(\"All values follow max >= mean >= min (as expected).\")\n",
"\n",
"# Test: patient_summary() should raise error if user input unsupported operation\n",
"def test_invalid_operation_should_raise_error():\n",
" for op in [\"sum\", \"std\", \"range\", \"hello\", \"median\"]:\n",
" try:\n",
" print(f\"Trying operation: {op}\")\n",
" patient_summary(all_paths[0], op)\n",
" except ValueError as e:\n",
" print(\" → Error:\", e)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Patient 0: max=18.0, mean=5.45, min=0.0\n",
"Patient 1: max=18.0, mean=5.425, min=0.0\n",
"Patient 2: max=19.0, mean=6.1, min=0.0\n",
"Patient 3: max=17.0, mean=5.9, min=0.0\n",
"Patient 4: max=17.0, mean=5.55, min=0.0\n",
"All values follow max >= mean >= min (as expected).\n",
"Trying operation: sum\n",
" → Error: Invalid operation. Please choose 'mean', 'max', or 'min'.\n",
"Trying operation: std\n",
" → Error: Invalid operation. Please choose 'mean', 'max', or 'min'.\n",
"Trying operation: range\n",
" → Error: Invalid operation. Please choose 'mean', 'max', or 'min'.\n",
"Trying operation: hello\n",
" → Error: Invalid operation. Please choose 'mean', 'max', or 'min'.\n",
"Trying operation: median\n",
" → Error: Invalid operation. Please choose 'mean', 'max', or 'min'.\n"
]
}
],
"source": [
"test_all_operations_should_succesfully_calculate_on_every_data_point()\n",
"test_max_must_always_greater_or_equal_mean_greater_or_equal_min()\n",
"test_invalid_operation_should_raise_error()"
]
},
{
Expand Down Expand Up @@ -228,7 +396,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {
"id": "_svDiRkdIwiT"
},
Expand All @@ -251,7 +419,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 16,
"metadata": {
"id": "LEYPM5v4JT0i"
},
Expand All @@ -261,19 +429,42 @@
"\n",
"def detect_problems(file_path):\n",
" #YOUR CODE HERE: Use patient_summary() to get the means and check_zeros() to check for zeros in the means\n",
"\n",
" return"
" means = patient_summary(file_path, \"mean\")\n",
" return check_zeros(means)"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 17,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n",
"False\n",
"False\n",
"True\n",
"False\n",
"False\n",
"False\n",
"False\n",
"True\n",
"False\n",
"False\n",
"True\n",
"False\n"
]
}
],
"source": [
"# Test out your code here\n",
"# Your output for the first file should be False\n",
"print(detect_problems(all_paths[0]))"
"print(detect_problems(all_paths[0]))\n",
"# Test all paths to see which of them have problems (detect_problems() returns True)\n",
"for path in all_paths:\n",
" print(detect_problems(path))"
]
},
{
Expand Down Expand Up @@ -314,7 +505,8 @@
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"display_name": "python-env",
"language": "python",
"name": "python3"
},
"language_info": {
Expand All @@ -327,7 +519,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.11.7"
}
},
"nbformat": 4,
Expand Down