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Assignment-2 #2

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26914b3
assignment-1
kgogina May 3, 2024
8cfe952
assignment-1
kgogina May 3, 2024
4534d08
Fix error
kgogina May 7, 2024
e10389c
draft
kgogina May 12, 2024
db78741
assignment-2
kgogina May 12, 2024
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89 changes: 89 additions & 0 deletions 02_assignments/assignment-1.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,89 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"def anagram_checker(word_a, word_b):\n",
" first_word = sorted(word_a.lower())\n",
" second_word = sorted(word_b.lower())\n",
"\n",
" return first_word == second_word\n",
"\n",
"print(anagram_checker(\"night\", \"Thing\"))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"def anagram_checker_case_sensitive(word_a, word_b, is_case_sensitive):\n",
" if is_case_sensitive:\n",
" first_word = sorted(word_a)\n",
" second_word = sorted(word_b)\n",
" else:\n",
" first_word = sorted(word_a.lower())\n",
" second_word = sorted(word_b.lower())\n",
"\n",
" return first_word == second_word\n",
" \n",
"print(anagram_checker_case_sensitive(\"Slient\", \"Listen\", True))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "dsi_participant",
"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": "3.9.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
245 changes: 245 additions & 0 deletions 02_assignments/assignment-2.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Path to all inflammation lists\n",
"\n",
"all_paths = [\n",
" \"C:\\Users\\kgogi\\DSI\\python\\05_data\\assignment_2_data\\inflammation_01.csv\",\n",
" \"C:\\Users\\kgogi\\DSI\\python\\05_data\\assignment_2_data\\inflammation_02.csv\",\n",
" \"C:\\Users\\kgogi\\DSI\\python\\05_data\\assignment_2_data\\inflammation_03.csv\",\n",
" \"C:\\Users\\kgogi\\DSI\\python\\05_data\\assignment_2_data\\inflammation_04.csv\",\n",
" \"C:\\Users\\kgogi\\DSI\\python\\05_data\\assignment_2_data\\inflammation_05.csv\",\n",
" \"C:\\Users\\kgogi\\DSI\\python\\05_data\\assignment_2_data\\inflammation_06.csv\",\n",
" \"C:\\Users\\kgogi\\DSI\\python\\05_data\\assignment_2_data\\inflammation_07.csv\",\n",
" \"C:\\Users\\kgogi\\DSI\\python\\05_data\\assignment_2_data\\inflammation_08.csv\",\n",
" \"C:\\Users\\kgogi\\DSI\\python\\05_data\\assignment_2_data\\inflammation_09.csv\",\n",
" \"C:\\Users\\kgogi\\DSI\\python\\05_data\\assignment_2_data\\inflammation_10.csv\",\n",
" \"C:\\Users\\kgogi\\DSI\\python\\05_data\\assignment_2_data\\inflammation_11.csv\",\n",
" \"C:\\Users\\kgogi\\DSI\\python\\05_data\\assignment_2_data\\inflammation_12.csv\",\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1\n",
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"0,1,0,0,4,3,3,5,5,4,5,8,7,10,13,3,7,13,15,18,8,15,15,16,11,14,12,4,10,10,4,3,4,5,5,3,3,2,2,1\n",
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"0,0,0,0,1,3,1,6,6,5,5,6,3,6,13,3,10,13,9,16,15,9,11,4,6,4,11,11,12,3,5,8,7,4,6,4,1,3,0,0\n",
"0,1,2,1,1,1,4,1,5,2,3,3,10,7,13,5,7,17,6,9,12,13,10,4,12,4,6,7,6,10,8,2,5,1,3,4,2,0,2,0\n",
"0,1,1,0,1,2,4,3,6,4,7,5,5,7,5,10,7,8,18,17,9,8,12,11,11,11,14,6,11,2,10,9,5,6,5,3,4,2,2,0\n",
"0,0,0,0,2,3,6,5,7,4,3,2,10,7,9,11,12,5,12,9,13,19,14,17,5,13,8,11,5,10,9,8,7,5,3,1,4,0,2,1\n",
"0,0,0,1,2,1,4,3,6,7,4,2,12,6,12,4,14,7,8,14,13,19,6,9,12,6,4,13,6,7,2,3,6,5,4,2,3,0,1,0\n",
"0,0,2,1,2,5,4,2,7,8,4,7,11,9,8,11,15,17,11,12,7,12,7,6,7,4,13,5,7,6,6,9,2,1,1,2,2,0,1,0\n",
"0,1,2,0,1,4,3,2,2,7,3,3,12,13,11,13,6,5,9,16,9,19,16,11,8,9,14,12,11,9,6,6,6,1,1,2,4,3,1,1\n",
"0,1,1,3,1,4,4,1,8,2,2,3,12,12,10,15,13,6,5,5,18,19,9,6,11,12,7,6,3,6,3,2,4,3,1,5,4,2,2,0\n",
"0,0,2,3,2,3,2,6,3,8,7,4,6,6,9,5,12,12,8,5,12,10,16,7,14,12,5,4,6,9,8,5,6,6,1,4,3,0,2,0\n",
"0,0,0,3,4,5,1,7,7,8,2,5,12,4,10,14,5,5,17,13,16,15,13,6,12,9,10,3,3,7,4,4,8,2,6,5,1,0,1,0\n",
"0,1,1,1,1,3,3,2,6,3,9,7,8,8,4,13,7,14,11,15,14,13,5,13,7,14,9,10,5,11,5,3,5,1,1,4,4,1,2,0\n",
"0,1,1,1,2,3,5,3,6,3,7,10,3,8,12,4,12,9,15,5,17,16,5,10,10,15,7,5,3,11,5,5,6,1,1,1,1,0,2,1\n",
"0,0,2,1,3,3,2,7,4,4,3,8,12,9,12,9,5,16,8,17,7,11,14,7,13,11,7,12,12,7,8,5,7,2,2,4,1,1,1,0\n",
"0,0,1,2,4,2,2,3,5,7,10,5,5,12,3,13,4,13,7,15,9,12,18,14,16,12,3,11,3,2,7,4,8,2,2,1,3,0,1,1\n",
"0,0,1,1,1,5,1,5,2,2,4,10,4,8,14,6,15,6,12,15,15,13,7,17,4,5,11,4,8,7,9,4,5,3,2,5,4,3,2,1\n",
"0,0,2,2,3,4,6,3,7,6,4,5,8,4,7,7,6,11,12,19,20,18,9,5,4,7,14,8,4,3,7,7,8,3,5,4,1,3,1,0\n",
"0,0,0,1,4,4,6,3,8,6,4,10,12,3,3,6,8,7,17,16,14,15,17,4,14,13,4,4,12,11,6,9,5,5,2,5,2,1,0,1\n",
"0,1,1,0,3,2,4,6,8,6,2,3,11,3,14,14,12,8,8,16,13,7,6,9,15,7,6,4,10,8,10,4,2,6,5,5,2,3,2,1\n",
"0,0,2,3,3,4,5,3,6,7,10,5,10,13,14,3,8,10,9,9,19,15,15,6,8,8,11,5,5,7,3,6,6,4,5,2,2,3,0,0\n",
"0,1,2,2,2,3,6,6,6,7,6,3,11,12,13,15,15,10,14,11,11,8,6,12,10,5,12,7,7,11,5,8,5,2,5,5,2,0,2,1\n",
"0,0,2,1,3,5,6,7,5,8,9,3,12,10,12,4,12,9,13,10,10,6,10,11,4,15,13,7,3,4,2,9,7,2,4,2,1,2,1,1\n",
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"0,1,2,2,2,5,5,1,4,6,3,6,5,9,6,7,4,7,16,7,16,13,9,16,12,6,7,9,10,3,6,4,5,4,6,3,4,3,2,1\n",
"0,1,1,2,3,1,5,1,2,2,5,7,6,6,5,10,6,7,17,13,15,16,17,14,4,4,10,10,10,11,9,9,5,4,4,2,1,0,1,0\n",
"0,1,0,3,2,4,1,1,5,9,10,7,12,10,9,15,12,13,13,6,19,9,10,6,13,5,13,6,7,2,5,5,2,1,1,1,1,3,0,1\n",
"0,1,1,3,1,1,5,5,3,7,2,2,3,12,4,6,8,15,16,16,15,4,14,5,13,10,7,10,6,3,2,3,6,3,3,5,4,3,2,1\n",
"0,0,0,2,2,1,3,4,5,5,6,5,5,12,13,5,7,5,11,15,18,7,9,10,14,12,11,9,10,3,2,9,6,2,2,5,3,0,0,1\n",
"0,0,1,3,3,1,2,1,8,9,2,8,10,3,8,6,10,13,11,17,19,6,4,11,6,12,7,5,5,4,4,8,2,6,6,4,2,2,0,0\n",
"0,1,1,3,4,5,2,1,3,7,9,6,10,5,8,15,11,12,15,6,12,16,6,4,14,3,12,9,6,11,5,8,5,5,6,1,2,1,2,0\n",
"0,0,1,3,1,4,3,6,7,8,5,7,11,3,6,11,6,10,6,19,18,14,6,10,7,9,8,5,8,3,10,2,5,1,5,4,2,1,0,1\n",
"0,1,1,3,3,4,4,6,3,4,9,9,7,6,8,15,12,15,6,11,6,18,5,14,15,12,9,8,3,6,10,6,8,7,2,5,4,3,1,1\n",
"0,1,2,2,4,3,1,4,8,9,5,10,10,3,4,6,7,11,16,6,14,9,11,10,10,7,10,8,8,4,5,8,4,4,5,2,4,1,1,0\n",
"0,0,2,3,4,5,4,6,2,9,7,4,9,10,8,11,16,12,15,17,19,10,18,13,15,11,8,4,7,11,6,7,6,5,1,3,1,0,0,0\n",
"0,1,1,3,1,4,6,2,8,2,10,3,11,9,13,15,5,15,6,10,10,5,14,15,12,7,4,5,11,4,6,9,5,6,1,1,2,1,2,1\n",
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"0,0,1,2,3,4,5,7,5,4,10,5,12,12,5,4,7,9,18,16,16,10,15,15,10,4,3,7,5,9,4,6,2,4,1,4,2,2,2,1\n",
"0,1,2,1,1,3,5,3,6,3,10,10,11,10,13,10,13,6,6,14,5,4,5,5,9,4,12,7,7,4,7,9,3,3,6,3,4,1,2,0\n",
"0,1,2,2,3,5,2,4,5,6,8,3,5,4,3,15,15,12,16,7,20,15,12,8,9,6,12,5,8,3,8,5,4,1,3,2,1,3,1,0\n",
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"0,1,2,1,1,4,5,4,4,5,9,7,10,3,13,13,8,9,17,16,16,15,12,13,5,12,10,9,11,9,4,5,5,2,2,5,1,0,0,1\n",
"0,0,1,3,2,3,6,4,5,7,2,4,11,11,3,8,8,16,5,13,16,5,8,8,6,9,10,10,9,3,3,5,3,5,4,5,3,3,0,1\n",
"0,1,1,2,2,5,1,7,4,2,5,5,4,6,6,4,16,11,14,16,14,14,8,17,4,14,13,7,6,3,7,7,5,6,3,4,2,2,1,1\n",
"0,1,1,1,4,1,6,4,6,3,6,5,6,4,14,13,13,9,12,19,9,10,15,10,9,10,10,7,5,6,8,6,6,4,3,5,2,1,1,1\n",
"0,0,0,1,4,5,6,3,8,7,9,10,8,6,5,12,15,5,10,5,8,13,18,17,14,9,13,4,10,11,10,8,8,6,5,5,2,0,2,0\n",
"0,0,1,0,3,2,5,4,8,2,9,3,3,10,12,9,14,11,13,8,6,18,11,9,13,11,8,5,5,2,8,5,3,5,4,1,3,1,1,0\n"
]
}
],
"source": [
"# 1. Reading and Displaying Data from the First File\n",
"\n",
"#open the first file in the list all_path\n",
"with open(all_paths [0], 'r') as f:\n",
" contents = f.readlines()\n",
"\n",
"#Display the contents\n",
"for i in contents:\n",
" print(i.strip())\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# 2. Data Summarization Function: patient_summary\n",
"import numpy as np\n",
"\n",
"def patient_summary(file_path, operation):\n",
" # load the data from the file\n",
" data = np.loadtxt(fname=file_path, delimiter=',')\n",
" ax = 1 # this specifies that the operation should be done for each row (patient)\n",
" \n",
" # 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",
" summary_values = np.mean(data, axis=ax)\n",
"\n",
" elif operation == 'max':\n",
" # YOUR CODE HERE: calculate the maximum number of flare-ups experienced by each patient\n",
" summary_values = np.max(data, axis=ax)\n",
"\n",
" elif operation == 'min':\n",
" # YOUR CODE HERE: calculate the minimum number of flare-ups experienced by each patient\n",
" summary_values = np.min(data, axis=ax)\n",
"\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",
"\n",
" return summary_values"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"60\n"
]
}
],
"source": [
"# test\n",
"data_min = patient_summary(all_paths[0], 'min')\n",
"print(len(data_min))\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# 3. Error Detection in Patient Data\n",
"\n",
"\n",
"# Define the check_zeros function\n",
"def check_zeros(x):\n",
" flag = np.where(x == 0)[0]\n",
" \n",
" return len(flag) > 0\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# Define your function `detect_problems` here\n",
"\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",
" means = patient_summary(file_path, 'mean')\n",
" has_zeros = check_zeros(means)\n",
"\n",
" return has_zeros"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n",
"True\n"
]
}
],
"source": [
"#test csv file. 03.csv contain values equals 0 and expected result True.\n",
"print(detect_problems(all_paths[0]))\n",
"print(detect_problems(all_paths[2]))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "dsi_participant",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
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