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\"../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", + "# import csv\n", + "\n", + "# with open(all_paths[0], 'r') as f:\n", + "# contents = csv.reader(f)\n", + "# for row in contents:\n", + "# print(row)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, "metadata": { "id": "n0m48JsS-nMC" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0\n", + 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\"../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", + "\n", + "contents = []\n", + "\n", "with open(all_paths[0], 'r') as f:\n", - " # YOUR CODE HERE: Use the readline() method to read the .csv file into 'contents'\n", - " \n", - " # YOUR CODE HERE: Iterate through 'contents' using a for loop and print each row for inspection" + " while True:\n", + " c = f.readline().strip()\n", + " if not c:\n", + " break\n", + " contents.append(c)\n", + "\n", + "for line in contents:\n", + " print(line)" ] }, { @@ -133,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "id": "82-bk4CBB1w4" }, @@ -145,16 +328,23 @@ " # 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", + " summary_values= np.empty(60)\n", + " \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", + " for i in range(60):\n", + " summary_values[i] = np.mean(data[i])\n", "\n", " elif operation == 'max':\n", " # YOUR CODE HERE: calculate the maximum number of flare-ups experienced by each patient\n", + " for i in range(60):\n", + " summary_values[i] = np.max(data[i])\n", "\n", " elif operation == 'min':\n", " # YOUR CODE HERE: calculate the minimum number of flare-ups experienced by each patient\n", + " for i in range(60):\n", + " summary_values[i] = np.min(data[i])\n", "\n", " else:\n", " # if the operation is not one of the expected values, raise an error\n", @@ -165,16 +355,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.9" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "l = ['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", + "for i in range(len(l)):\n", + " l[i] = int(str(l[i]))\n", + "l\n", + "np.mean(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": { "id": "3TYo0-1SDLrd" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([18., 18., 19., 17., 17., 18., 17., 20., 17., 18., 18., 18., 17.,\n", + " 16., 17., 18., 19., 19., 17., 19., 19., 16., 17., 15., 17., 17.,\n", + " 18., 17., 20., 17., 16., 19., 15., 15., 19., 17., 16., 17., 19.,\n", + " 16., 18., 19., 16., 19., 18., 16., 19., 15., 16., 18., 14., 20.,\n", + " 17., 15., 17., 16., 17., 19., 18., 18.])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "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))" + "data_min = patient_summary(all_paths[0], 'max')\n", + "data_min\n", + "#len(data_min)\n" ] }, { @@ -232,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "id": "_svDiRkdIwiT" }, @@ -255,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "id": "LEYPM5v4JT0i" }, @@ -265,15 +495,23 @@ "\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" + " list_of_means = patient_summary(file_path, \"mean\")\n", + " return(check_zeros(list_of_means))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "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", @@ -331,7 +569,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.9.18" } }, "nbformat": 4,