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Assignment 2 #2

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bb39b9a
Uploading assignment 1
NickFreeland Apr 20, 2026
bd3429c
Uploading assignment-2
NickFreeland Apr 27, 2026
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310 changes: 310 additions & 0 deletions 02_activities/assignments/assignment-2.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"id": "03c9367f",
"metadata": {},
"outputs": [],
"source": [
"all_paths = [\n",
" \"05_src/data/assignment_2_data/inflammation_01.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_02.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_03.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_04.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_05.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_06.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_07.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_08.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_09.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_10.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_11.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_12.csv\"\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "69f50fd3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n"
]
}
],
"source": [
"with open(all_paths[0], 'r') as f: \n",
" lines = f.readlines() # read all lines from the file\n",
" for line in lines: # loop through each line\n",
" print(line) # print the line"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "6086e1eb",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"def patient_summary(file_path, opertation):\n",
" \n",
" \"\"\"Compute summary statistics for patient data.\n",
" Parameters ----------\n",
" file_path : str\n",
" Path to the CSV file containing patient data.\n",
" operation : str\n",
" The summary operation to perform. Supported operations are: 'mean', 'max', 'min'.\n",
" Returns -------\n",
" summary_values : numpy.ndarray\n",
" An array containing the computed summary values for each patient.\n",
" \"\"\"\n",
"\n",
" data = np.loadtxt(file_path, delimiter=',')\n",
" ax = 1\n",
"\n",
" if opertation == 'mean':\n",
" summary_values = np.mean(data, axis=ax)\n",
" elif opertation == 'max':\n",
" summary_values = np.max(data, axis=ax)\n",
" elif opertation == 'min':\n",
" summary_values = np.min(data, axis=ax)\n",
" else:\n",
" raise ValueError(\"Invalid operation. Supported operations are: 'mean', 'max', 'min'.\")\n",
"\n",
" return summary_values"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "1bd5b026",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"60\n"
]
}
],
"source": [
"#testing data file path and operation\n",
"data_min = patient_summary(all_paths[0], 'min')\n",
"print(len(data_min))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "0de87f72",
"metadata": {},
"outputs": [],
"source": [
"def check_zeros(x):\n",
"\n",
" \"\"\" \n",
" Check if there are any zeros in the input array.\n",
" Parameters ----------\n",
" x : array-like\n",
" Input array to check for zeros.\n",
" Returns -------\n",
" bool\n",
" True if there are any zeros in the array, False otherwise.\n",
" \"\"\"\n",
"\n",
" import numpy as np\n",
" x = np.asarray(x) #Ensures x is an array\n",
" return np.any(x == 0)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "b19c1b65",
"metadata": {},
"outputs": [],
"source": [
"def detect_problems(file_path):\n",
"\n",
" \"\"\" Detect if there are any problems in the patient data by checking for zeros in the mean values.\n",
" Parameters ---------- \n",
" file_path : str\n",
" Path to the CSV file containing patient data.\n",
" Returns -------\n",
" bool\n",
" True if there are any problems (zeros in the mean values), False otherwise.\n",
" \"\"\"\n",
" \n",
" means = patient_summary(file_path, 'mean')\n",
" problem_found = check_zeros(means)\n",
" return problem_found"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "790936fe",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(detect_problems(all_paths[0]))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python-env",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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