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Assignment 2 #2

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106ec0b
Live coding notebook 05/06/2025
kaylielau May 7, 2025
fd0b615
Merge pull request #198 from UofT-DSI/update
kaylielau May 7, 2025
8cc81e2
Add live code 05/07/2025
kaylielau May 8, 2025
23255cd
Merge pull request #199 from UofT-DSI/update
kaylielau May 8, 2025
6114599
assignment-1
PSulf May 2, 2025
0a31d96
Completed assignment 2
PSulf May 9, 2025
432fdc2
Revert assignment_1.ipynb to original version from main
PSulf May 13, 2025
3ca772a
Finish implementation of check_zeros & detect_problems
PSulf May 13, 2025
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200 changes: 149 additions & 51 deletions 02_activities/assignments/assignment_2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -72,31 +72,109 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {
"id": "n0m48JsS-nMC"
},
"outputs": [],
"source": [
"all_paths = [\n",
" \"python/05_src/data/assignment_2_data/inflammation_01.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_02.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_03.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_04.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_05.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_06.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_07.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_08.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_09.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_10.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_11.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_12.csv\"\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",
"]\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1\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",
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"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",
"0,0,1,2,4,1,5,5,2,3,4,8,8,12,5,15,9,17,7,19,14,18,12,17,14,4,13,13,8,11,5,6,6,2,3,5,2,1,1,1\n",
"0,0,0,3,1,3,6,4,3,4,8,3,4,8,3,11,5,7,10,5,15,9,16,17,16,3,8,9,8,3,3,9,5,1,6,5,4,2,2,0\n",
"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",
"0,0,1,3,2,5,1,2,7,6,6,3,12,9,4,14,4,6,12,9,12,7,11,7,16,8,13,6,7,6,10,7,6,3,1,5,4,3,0,0\n",
"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",
"0,0,0,2,4,4,5,3,3,3,10,4,4,4,14,11,15,13,10,14,11,17,9,11,11,7,10,12,10,10,10,8,7,5,2,2,4,1,2,1\n",
"0,0,2,1,1,4,4,7,2,9,4,10,12,7,6,6,11,12,9,15,15,6,6,13,5,12,9,6,4,7,7,6,5,4,1,4,2,2,2,1\n",
"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": [
"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"
" lines = f.readlines()\n",
"\n",
"for line in lines:\n",
" print(line.strip())\n"
]
},
{
Expand Down Expand Up @@ -130,7 +208,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {
"id": "82-bk4CBB1w4"
},
Expand All @@ -139,19 +217,20 @@
"import numpy as np\n",
"\n",
"def patient_summary(file_path, operation):\n",
" data = np.loadtxt(fname=file_path, delimiter=',') # Load the data from the file\n",
" ax = 1 # This specifies that the operation should be done for each row (patient)\n",
" data = np.loadtxt(fname=file_path, delimiter=',') # Loading the data from the file\n",
" ax = 1 # calculation across columns (days)\n",
"\n",
" # Implement the specific operation based on the 'operation' argument\n",
" # checking which operation to perform\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) # average flare-ups per patient\n",
" \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) # Calculating the highest flare-ups per patient\n",
"\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, axis=ax) # Calculating the lowest flare-ups per patient\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",
Expand All @@ -161,14 +240,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {
"id": "3TYo0-1SDLrd"
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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",
"# Test it out on the data file and make sure the size is what we expect i.e., 60\n",
"# output for the first file is 60\n",
"data_min = patient_summary(all_paths[0], 'min')\n",
"print(len(data_min))"
]
Expand Down Expand Up @@ -228,30 +315,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {
"id": "_svDiRkdIwiT"
},
"outputs": [],
"source": [
"# Run this cell so you can use this helper function\n",
"\n",
"def check_zeros(x):\n",
" '''\n",
" Given an array, x, check whether any values in x equal 0.\n",
" Return True if any values found, else returns False.\n",
" '''\n",
" # np.where() checks every value in x against the condition (x == 0) and returns a tuple of indices where it was True (i.e. x was 0)\n",
" flag = np.where(x == 0)[0]\n",
"\n",
" # Checks if there are any objects in flag (i.e. not empty)\n",
" # If not empty, it found at least one zero so flag is True, and vice-versa.\n",
" return len(flag) > 0"
" \"\"\"\n",
" Given an array-like x, check whether any values in x equal 0.\n",
" Return True if any values found, else False.\n",
" \"\"\"\n",
" # np.where() returns the indices where x == 0\n",
" zero_indices = np.where(x == 0)[0]\n",
" # If zero_indices is non‑empty, at least one zero was found\n",
" return len(zero_indices) > 0"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 11,
"metadata": {
"id": "LEYPM5v4JT0i"
},
Expand All @@ -260,20 +343,34 @@
"# 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",
"\n",
" return"
" \"\"\"\n",
" Reads patient data from file_path, computes each patient's mean inflammation,\n",
" and returns True if any mean is zero (indicating a problem), else False.\n",
" \"\"\"\n",
" # get mean inflammation per patient\n",
" means = patient_summary(file_path, 'mean')\n",
" # check for zeros in those means\n",
" return check_zeros(means)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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"
]
},
{
Expand Down Expand Up @@ -314,8 +411,9 @@
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
"display_name": "Python (dsi_participant)",
"language": "python",
"name": "dsi_participant"
},
"language_info": {
"codemirror_mode": {
Expand All @@ -327,7 +425,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.9.15"
}
},
"nbformat": 4,
Expand Down
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