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173 changes: 150 additions & 23 deletions 02_assignments/assignment_2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -90,16 +90,101 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {
"id": "n0m48JsS-nMC"
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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]
}
],
"source": [
"with open(all_paths[0], 'r') as f:\n",
"all_paths = [\n",
" \"../05_data/assignment_2_data/inflammation_01.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_02.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_03.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_04.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_05.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_06.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_07.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_08.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_09.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_10.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_11.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_12.csv\",\n",
"]\n",
"\n",
"with open(all_paths[0], 'r') as f: # Opens the first file in the list of file paths for reading\n",
" # YOUR CODE HERE: Use the readline() method to read the .csv file into 'contents'\n",
" contents = f.readlines() # reads all lines from the file into a list called \"contents\", where each line corresponds to a row in the file\n",
" \n",
" # YOUR CODE HERE: Iterate through 'contents' using a for loop and print each row for inspection"
" # YOUR CODE HERE: Iterate through 'contents' using a for loop and print each row for inspection\n",
" for row in contents: # iterate through each row in \"contents\"\n",
" print(row.strip()) # prints each row"
]
},
{
Expand Down Expand Up @@ -133,11 +218,19 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 22,
"metadata": {
"id": "82-bk4CBB1w4"
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"60\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
Expand All @@ -149,32 +242,56 @@
" # 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, axis=1) # Calculates the mean for each patient\n",
" \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, axis=1) # Calculates the maximum for each patient\n",
" \n",
" elif operation == 'min':\n",
" # YOUR CODE HERE: calculate the minimum number of flare-ups experienced by each patient\n",
"\n",
" # YOUR CODE HERE: calculate the minimum number of flare-ups experienced by each patient patient\n",
" summary_values = np.min(data, axis=1) # Calculates the minimum for each\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"
" return summary_values\n",
"\n",
"data_min = patient_summary(all_paths[0], 'min')\n",
"print(len(data_min))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 23,
"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",
"# Testing with max operation: \n",
"data_max = patient_summary(all_paths[0], 'max')\n",
"print(len(data_max))\n",
"# Testing with mean operation: \n",
"data_mean = patient_summary(all_paths[0], 'mean')\n",
"print(len(data_mean))\n",
"\n"
]
},
{
Expand Down Expand Up @@ -232,7 +349,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 24,
"metadata": {
"id": "_svDiRkdIwiT"
},
Expand All @@ -255,7 +372,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 27,
"metadata": {
"id": "LEYPM5v4JT0i"
},
Expand All @@ -265,19 +382,29 @@
"\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"
" mean_inflammation = patient_summary(file_path, 'mean')\n",
" # check if there are any zeros in mean inflammation\n",
" has_zeros = check_zeros(mean_inflammation)\n",
" return has_zeros\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 28,
"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 True\n",
"print(detect_problems(all_paths[0]))"
"# Your output for the first file should be False\n",
"print(detect_problems(all_paths[0])) # tests if there is any zero for the mean inflammation in the first file. It returns True if any mean inflammation scores of zero are found which indicates an issue."
]
},
{
Expand Down Expand Up @@ -331,7 +458,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.9.15"
}
},
"nbformat": 4,
Expand Down