From 366b0db5c685d058b121cdd69d85aa4df702e1ba Mon Sep 17 00:00:00 2001 From: Peter Mackenzie-Helnwein Date: Sun, 28 Jul 2024 21:14:30 -0700 Subject: [PATCH] cleaning up lesson 5 --- .../LessonNotes/Lesson 05-File IO.ipynb | 220 ++---------------- 1 file changed, 17 insertions(+), 203 deletions(-) diff --git a/code/jupyter/LessonNotes/Lesson 05-File IO.ipynb b/code/jupyter/LessonNotes/Lesson 05-File IO.ipynb index 8892c2b..256f564 100644 --- a/code/jupyter/LessonNotes/Lesson 05-File IO.ipynb +++ b/code/jupyter/LessonNotes/Lesson 05-File IO.ipynb @@ -96,44 +96,32 @@ "\n", "INITIALIZATION:\n", "\n", - "N = 0\n", - "sum = 0\n", - "sum2 = 0\n", + " N = 0\n", + " sum = 0\n", + " sum2 = 0\n", "\n", "LOOP:\n", "\n", - " N = N + 1\n", + " N = N + 1\n", " \n", - " $\\sum z_i$: sum[i] = sum[i-1] + zi \n", + " $\\sum z_i$: sum[i] = sum[i-1] + zi \n", " \n", - " $\\sum (z_i)^2$: sum2[i] = sum2[i-1] + zi*zi\n", + " $\\sum (z_i)^2$: sum2[i] = sum2[i-1] + zi*zi\n", "\n", "AFTER LOOP:\n", "\n", - "mean = sum / N\n", + " mean = sum / N\n", "\n", - "sig2 = sum2 / N = mean**2\n", + " sig2 = sum2 / N = mean**2\n", "\n", - "sig = sqrt(sig2)" + " sig = sqrt(sig2)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.795687209703e+01,1.557629314377e+03,1.191593706587e+00,1.059354827371e+00\n", - "\n", - "1.795687209703e+01,1.557629314377e+03,1.191593706587e+00,1.059354827371e+00\n", - "1.795687209703e+01,1.557629314377e+03,1.191593706587e+00,1.059354827371e+00\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "f = open(\"exercise6_data.csv\",'r')\n", "\n", @@ -141,130 +129,10 @@ " print(line, end='')\n", " print(type(line))\n", " print(line*2)\n", - " break" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.795687209703e+01,1.557629314377e+03,1.191593706587e+00,1.059354827371e+00\n", - "['1.795687209703e+01', '1.557629314377e+03', '1.191593706587e+00', '1.059354827371e+00\\n']\n", - "[17.95687209703, 1557.629314377, 1.191593706587, 1.059354827371]\n", - "[1.79568721e+01 1.55762931e+03 1.19159371e+00 1.05935483e+00]\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "\n", - "f = open(\"exercise6_data.csv\",'r')\n", - "\n", - "for line in f:\n", - " print(line, end='')\n", - " \n", - " vars = line.split(',')\n", - " \n", - " print(vars)\n", - " #print(vars[0], float(vars[0])**2)\n", - " \n", - " vals = []\n", - " for var in vars:\n", - " vals.append(float(var))\n", - " \n", - " print(vals)\n", - " \n", - " zi = np.array(vals)\n", " \n", - " print(zi)\n", - " \n", - " break" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.79568721e+01 1.55762931e+03 1.19159371e+00 1.05935483e+00]\n", - "[21.30828208 6.28045016 4.31374321 0.67786395]\n", - "[ 8.07432437 13.76647756 5.39585814 1.71447895]\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", + " # YOUR CODE HERE\n", "\n", - "f = open(\"exercise6_data.csv\",'r')\n", - "\n", - "cnt = 0\n", - "\n", - "for line in f:\n", - " zi = np.array( [ float(x) for x in line.split(',') ] )\n", - " print(zi)\n", - " \n", - " cnt += 1\n", - " \n", - " if cnt > 2:\n", - " break" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x: mean=10.127054375946278, stdev=5.0204001371842875\n", - "y1: mean=1023.0376305650618, stdev=4289.945369140428\n", - "y2: mean=3.928646271819789, stdev=1.705542095948374\n", - "y3: mean=1.9926873807139653, stdev=2.090070459208848\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "# INITIALIZATION:\n", - "\n", - "sumZ = np.zeros(4) # [ x, y1, y2, y3 ]\n", - "sumZ2 = np.zeros(4)\n", - "N = 0\n", - "\n", - "# LOOP:\n", - "\n", - "f = open(\"exercise6_data.csv\",'r')\n", - "\n", - "for line in f:\n", - " N += 1\n", - " zi = np.array( [ float(x) for x in line.split(',') ] ) # list of values for ONE ROW\n", - " sumZ += zi \n", - " sumZ2 += zi*zi \n", - "\n", - "# AFTER LOOP:\n", - "\n", - "mean = sumZ / N\n", - "sig2 = sumZ2 / N - mean**2\n", - "sig = np.sqrt(sig2)\n", - "\n", - "# OUTPUT\n", - "\n", - "for name, m, s in zip(('x', 'y1', 'y2', 'y3'), mean, sig):\n", - " print(\"{}: mean={}, stdev={}\".format(name, m, s))" + " break" ] }, { @@ -300,57 +168,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x: mean=10.127054375946278, stdev=5.0204001371842875\n", - "y1: mean=1023.0376305650618, stdev=4289.945369140428\n", - "y2: mean=3.928646271819789, stdev=1.705542095948374\n", - "y3: mean=1.9926873807139653, stdev=2.090070459208848\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "# INITIALIZATION:\n", - "\n", - "sumZ = np.zeros(4) # [ x, y1, y2, y3 ]\n", - "sumZ2 = np.zeros(4)\n", - "N = 0\n", - "\n", - "# LOOP:\n", - "\n", - "try:\n", - " f = open(\"exercise6_data.csv\",'r')\n", - " read_ok = True\n", - "except:\n", - " print(\"File not found\")\n", - " read_ok = False\n", - " \n", - "\n", - "if read_ok:\n", - " for line in f:\n", - " N += 1\n", - " zi = np.array( [ float(x) for x in line.split(',') ] ) # list of values for ONE ROW\n", - " sumZ += zi \n", - " sumZ2 += zi**2 \n", - "\n", - " # AFTER LOOP:\n", - "\n", - " mean = sumZ / N\n", - " sig2 = sumZ2 / N - mean**2\n", - " sig = np.sqrt(sig2)\n", - "\n", - " # OUTPUT\n", - "\n", - " for name, m, s in zip(('x', 'y1', 'y2', 'y3'), mean, sig):\n", - " print(\"{}: mean={}, stdev={}\".format(name, m, s))\n" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -367,13 +188,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -511,7 +325,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.6" } }, "nbformat": 4,