{ "cells": [ { "cell_type": "markdown", "id": "fb7fec6d-8d14-4ab0-8e15-a05eb7db28da", "metadata": {}, "source": [ "# matplotlib - Advanced Layouts" ] }, { "cell_type": "markdown", "id": "62bf5cc9-b71b-45bd-a709-77d3b2bd3059", "metadata": {}, "source": [ ":::{admonition} Learning Objectives\n", "* Be able to use matplotlib's `.subplot()` to layout plots in regular grid arrangements.\n", "* Be able to use matplotlib's `.subplot2grid()` to create more advanced layouts with plots spanning more than one \"grid space.\"\n", ":::" ] }, { "cell_type": "markdown", "id": "904c3d2a-b5a7-4e71-a943-6fa34e34d40a", "metadata": {}, "source": [ ":::{important} \n", "Much of the information and many of the figures in this notebook come from: \"Python Plotting With Matplotlib (Guide)\" written by *Brad Solomon* and published on realpython.com at https://realpython.com/python-matplotlib-guide. The website [realpython.com](https://realpython.com/) is a great way to learn python and is filled with resources. \n", ":::" ] }, { "cell_type": "markdown", "id": "68d7a93a-e479-49b6-90b4-81973c4cf869", "metadata": {}, "source": [ "(3114:05:data-file-for-lesson)=\n", "## Data File for Lesson" ] }, { "cell_type": "markdown", "id": "d1bb1cf1-0e10-4798-8850-3f8565985499", "metadata": {}, "source": [ "You will need the following data file for this lesson: \n", "[cal_housing.data](https://drive.google.com/uc?id=1l4YCgMuYTx4y4ax7uUcHevYbOVlYtajx&export=download) \n", "Pace, R. Kelley, and Ronald Barry, \"Sparse Spatial Autoregressions,\" Statistics and Probability Letters, Volume 33, Number 3, May 5 1997, p. 291-297." ] }, { "cell_type": "markdown", "id": "ffa47578-a487-4c4d-82c2-8eb2165d50c1", "metadata": {}, "source": [ "A description of the data columns included in this dataset are reprinted below." ] }, { "cell_type": "markdown", "id": "1662b21d-0944-4ebe-b6f9-72a42b1337d2", "metadata": { "tags": [] }, "source": [ "```{figure} ../images/housing_data_description.png \n", ":height: 900px\n", ":name: Calif_dataset \n", " \n", "Reprinted from https://developers.google.com/machine-learning/crash-course/california-housing-data-description \n", "```" ] }, { "cell_type": "markdown", "id": "b32b3c3a-5b16-4f83-9ee7-24fb2f112d1f", "metadata": { "tags": [ "remove-cell" ] }, "source": [ "\"california" ] }, { "cell_type": "code", "execution_count": 1, "id": "8754fdbf-9b05-4488-9d79-0552d56e6cfb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousingMedianAgetotalRoomstotalBedroomspopulationhouseholdsmedianIncome ($10,000)medianHouseValue ($)
0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0
1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0
2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0
3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0
4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0
\n", "
" ], "text/plain": [ " longitude latitude housingMedianAge totalRooms totalBedrooms \\\n", "0 -122.23 37.88 41.0 880.0 129.0 \n", "1 -122.22 37.86 21.0 7099.0 1106.0 \n", "2 -122.24 37.85 52.0 1467.0 190.0 \n", "3 -122.25 37.85 52.0 1274.0 235.0 \n", "4 -122.25 37.85 52.0 1627.0 280.0 \n", "\n", " population households medianIncome ($10,000) medianHouseValue ($) \n", "0 322.0 126.0 8.3252 452600.0 \n", "1 2401.0 1138.0 8.3014 358500.0 \n", "2 496.0 177.0 7.2574 352100.0 \n", "3 558.0 219.0 5.6431 341300.0 \n", "4 565.0 259.0 3.8462 342200.0 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# column names according to the description above\n", "column_names=['longitude' ,'latitude','housingMedianAge','totalRooms','totalBedrooms','population','households','medianIncome ($10,000)','medianHouseValue ($)']\n", "url=\"https://drive.google.com/uc?id=1l4YCgMuYTx4y4ax7uUcHevYbOVlYtajx\"\n", "raw=pd.read_csv(url, names=column_names, skiprows=0, sep=',')\n", "raw.head()" ] }, { "cell_type": "markdown", "id": "339a6c58-aef7-4b29-a9cc-6d7800a11486", "metadata": {}, "source": [ "## Layout using subplot: Figure vs Axes" ] }, { "cell_type": "markdown", "id": "19319772-4d4d-4add-bb12-befc533dbbd8", "metadata": {}, "source": [ "In matplotlib, a \"**figure**\" is the outer container that might contain multiple plots for example in a row or grid or it might contain just one plot as shown below. The individual plots are referred to as \"**axes**\". " ] }, { "cell_type": "markdown", "id": "468eb587-74fa-4f44-8c91-acb2a33616a0", "metadata": {}, "source": [ "```{figure} ../images/Axes_Figures_schematic.png\n", ":height: 350px\n", ":name: Figure_Axes\n", "\n", "reprinted from https://realpython.com/python-matplotlib-guide/#the-matplotlib-object-hierarchy\n", "```\n" ] }, { "cell_type": "markdown", "id": "21c508a9-c8ff-4a02-877b-b28e6ee6c0df", "metadata": { "tags": [ "remove-cell" ] }, "source": [ "\"Figure" ] }, { "cell_type": "markdown", "id": "904595fd-d608-49aa-920f-3a96ac3d6ebe", "metadata": {}, "source": [ "If we name the Figure above \"fig\" and the Axes box as \"ax\", we can create the layout with the code: \n", "```python \n", "fig, ax = plt.subplots(nrows=1, ncols=1) \n", "``` \n", "Then we just need to fill \"ax\" for example, with a scatter plot of red circles: \n", "```python\n", "ax.scatter(x=xdata, y=ydata, marker='o', c='r')\n", "```" ] }, { "cell_type": "code", "execution_count": 2, "id": "18ca97f6-55e2-4dd0-9bbf-7c246b053e67", "metadata": { "tags": [ "remove-input" ] }, "outputs": [], "source": [ "from jupyterquiz import display_quiz\n", "\n", "# tags: remove-input\n", "# menu: View/Cell toolbar/tags or in Jupyter lab use gear icon on top right\n", "# this will remove the code below when building Jupyter-book \n", "# could also use remove-cell but this code has no output\n", "\n", "example=[{\n", " \"question\": '''How would you create two plots named \"ax1\" and \"ax2\" next to each other horizontally?''',\n", " \"type\": \"multiple_choice\",\n", " \"answers\": [\n", " {\n", " \"code\": \"fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)\",\n", " \"correct\": True,\n", " \"feedback\": \"Correct. \"\n", " \"The .subplots() function returns two outputs. The first is the figure box and the second is a list of axes. \"\n", " \"The axes names can be a 1D or 2D list depending on the layout.\"\n", " },\n", " {\n", " \"code\": \"fig, ((ax1), (ax2)) = plt.subplots(nrows=2, ncols=1)\",\n", " \"correct\": False,\n", " \"feedback\": \"Not quite. This would produce two plots vertically.\"\n", " },\n", " {\n", " \"code\": \"myfig, (axes_1, axes_2) = plt.subplots(nrows=1, ncols=2)\",\n", " \"correct\": False,\n", " \"feedback\": \"Good! Although this would work just fine since you can name the figure and axes \"\n", " \"anything you want, the problem statement asked us to name the plots ax1 and ax2 rather than axes_1 and axes_2.\"\n", " }\n", " ]\n", " }];" ] }, { "cell_type": "code", "execution_count": 3, "id": "beac7b15-e446-4740-ba70-e6d8b971ec28", "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "var questionsuUPIbOZVDkYq=[{\"question\": \"How would you create two plots named \\\"ax1\\\" and \\\"ax2\\\" next to each other horizontally?\", \"type\": \"multiple_choice\", \"answers\": [{\"code\": \"fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)\", \"correct\": true, \"feedback\": \"Correct. The .subplots() function returns two outputs. The first is the figure box and the second is a list of axes. The axes names can be a 1D or 2D list depending on the layout.\"}, {\"code\": \"fig, ((ax1), (ax2)) = plt.subplots(nrows=2, ncols=1)\", \"correct\": false, \"feedback\": \"Not quite. This would produce two plots vertically.\"}, {\"code\": \"myfig, (axes_1, axes_2) = plt.subplots(nrows=1, ncols=2)\", \"correct\": false, \"feedback\": \"Good! Although this would work just fine since you can name the figure and axes anything you want, the problem statement asked us to name the plots ax1 and ax2 rather than axes_1 and axes_2.\"}]}];\n", " // Make a random ID\n", "function makeid(length) {\n", " var result = [];\n", " var characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz';\n", " var charactersLength = characters.length;\n", " for (var i = 0; i < length; i++) {\n", " result.push(characters.charAt(Math.floor(Math.random() * charactersLength)));\n", " }\n", " return result.join('');\n", "}\n", "\n", "// Choose a random subset of an array. Can also be used to shuffle the array\n", "function getRandomSubarray(arr, size) {\n", " var shuffled = arr.slice(0), i = arr.length, temp, index;\n", " while (i--) {\n", " index = Math.floor((i + 1) * Math.random());\n", " temp = shuffled[index];\n", " shuffled[index] = shuffled[i];\n", " shuffled[i] = temp;\n", " }\n", " return shuffled.slice(0, size);\n", "}\n", "\n", "function printResponses(responsesContainer) {\n", " var responses=JSON.parse(responsesContainer.dataset.responses);\n", " var stringResponses='IMPORTANT!To preserve this answer sequence for submission, when you have finalized your answers:
  1. Copy the text in this cell below \"Answer String\"
  2. Double click on the cell directly below the Answer String, labeled \"Replace Me\"
  3. Select the whole \"Replace Me\" text
  4. Paste in your answer string and press shift-Enter.
  5. Save the notebook using the save icon or File->Save Notebook menu item



    6. Answer String:
    ';\n", " console.log(responses);\n", " responses.forEach((response, index) => {\n", " if (response) {\n", " console.log(index + ': ' + response);\n", " stringResponses+= index + ': ' + response +\"
    \";\n", " }\n", " });\n", " responsesContainer.innerHTML=stringResponses;\n", "}\n", "function check_mc() {\n", " var id = this.id.split('-')[0];\n", " //var response = this.id.split('-')[1];\n", " //console.log(response);\n", " //console.log(\"In check_mc(), id=\"+id);\n", " //console.log(event.srcElement.id) \n", " //console.log(event.srcElement.dataset.correct) \n", " //console.log(event.srcElement.dataset.feedback)\n", "\n", " var label = event.srcElement;\n", " //console.log(label, label.nodeName);\n", " var depth = 0;\n", " while ((label.nodeName != \"LABEL\") && (depth < 20)) {\n", " label = label.parentElement;\n", " console.log(depth, label);\n", " depth++;\n", " }\n", "\n", "\n", "\n", " var answers = label.parentElement.children;\n", "\n", " //console.log(answers);\n", "\n", "\n", " // Split behavior based on multiple choice vs many choice:\n", " var fb = document.getElementById(\"fb\" + id);\n", "\n", "\n", "\n", "\n", " if (fb.dataset.numcorrect == 1) {\n", " // What follows is for the saved responses stuff\n", " var outerContainer = fb.parentElement.parentElement;\n", " var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n", " if (responsesContainer) {\n", " //console.log(responsesContainer);\n", " var response = label.firstChild.innerText;\n", " if (label.querySelector(\".QuizCode\")){\n", " response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n", " }\n", " console.log(response);\n", " //console.log(document.getElementById(\"quizWrap\"+id));\n", " var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n", " console.log(\"Question \" + qnum);\n", " //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n", " var responses=JSON.parse(responsesContainer.dataset.responses);\n", " console.log(responses);\n", " responses[qnum]= response;\n", " responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n", " printResponses(responsesContainer);\n", " }\n", " // End code to preserve responses\n", " \n", " for (var i = 0; i < answers.length; i++) {\n", " var child = answers[i];\n", " //console.log(child);\n", " child.className = \"MCButton\";\n", " }\n", "\n", "\n", "\n", " if (label.dataset.correct == \"true\") {\n", " // console.log(\"Correct action\");\n", " if (\"feedback\" in label.dataset) {\n", " fb.textContent = jaxify(label.dataset.feedback);\n", " } else {\n", " fb.textContent = \"Correct!\";\n", " }\n", " label.classList.add(\"correctButton\");\n", "\n", " fb.className = \"Feedback\";\n", " fb.classList.add(\"correct\");\n", "\n", " } else {\n", " if (\"feedback\" in label.dataset) {\n", " fb.textContent = jaxify(label.dataset.feedback);\n", " } else {\n", " fb.textContent = \"Incorrect -- try again.\";\n", " }\n", " //console.log(\"Error action\");\n", " label.classList.add(\"incorrectButton\");\n", " fb.className = \"Feedback\";\n", " fb.classList.add(\"incorrect\");\n", " }\n", " }\n", " else {\n", " var reset = false;\n", " var feedback;\n", " if (label.dataset.correct == \"true\") {\n", " if (\"feedback\" in label.dataset) {\n", " feedback = jaxify(label.dataset.feedback);\n", " } else {\n", " feedback = \"Correct!\";\n", " }\n", " if (label.dataset.answered <= 0) {\n", " if (fb.dataset.answeredcorrect < 0) {\n", " fb.dataset.answeredcorrect = 1;\n", " reset = true;\n", " } else {\n", " fb.dataset.answeredcorrect++;\n", " }\n", " if (reset) {\n", " for (var i = 0; i < answers.length; i++) {\n", " var child = answers[i];\n", " child.className = \"MCButton\";\n", " child.dataset.answered = 0;\n", " }\n", " }\n", " label.classList.add(\"correctButton\");\n", " label.dataset.answered = 1;\n", " fb.className = \"Feedback\";\n", " fb.classList.add(\"correct\");\n", "\n", " }\n", " } else {\n", " if (\"feedback\" in label.dataset) {\n", " feedback = jaxify(label.dataset.feedback);\n", " } else {\n", " feedback = \"Incorrect -- try again.\";\n", " }\n", " if (fb.dataset.answeredcorrect > 0) {\n", " fb.dataset.answeredcorrect = -1;\n", " reset = true;\n", " } else {\n", " fb.dataset.answeredcorrect--;\n", " }\n", "\n", " if (reset) {\n", " for (var i = 0; i < answers.length; i++) {\n", " var child = answers[i];\n", " child.className = \"MCButton\";\n", " child.dataset.answered = 0;\n", " }\n", " }\n", " label.classList.add(\"incorrectButton\");\n", " fb.className = \"Feedback\";\n", " fb.classList.add(\"incorrect\");\n", " }\n", " // What follows is for the saved responses stuff\n", " var outerContainer = fb.parentElement.parentElement;\n", " var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n", " if (responsesContainer) {\n", " //console.log(responsesContainer);\n", " var response = label.firstChild.innerText;\n", " if (label.querySelector(\".QuizCode\")){\n", " response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n", " }\n", " console.log(response);\n", " //console.log(document.getElementById(\"quizWrap\"+id));\n", " var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n", " console.log(\"Question \" + qnum);\n", " //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n", " var responses=JSON.parse(responsesContainer.dataset.responses);\n", " if (label.dataset.correct == \"true\") {\n", " if (typeof(responses[qnum]) == \"object\"){\n", " if (!responses[qnum].includes(response))\n", " responses[qnum].push(response);\n", " } else{\n", " responses[qnum]= [ response ];\n", " }\n", " } else {\n", " responses[qnum]= response;\n", " }\n", " console.log(responses);\n", " responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n", " printResponses(responsesContainer);\n", " }\n", " // End save responses stuff\n", "\n", "\n", "\n", " var numcorrect = fb.dataset.numcorrect;\n", " var answeredcorrect = fb.dataset.answeredcorrect;\n", " if (answeredcorrect >= 0) {\n", " fb.textContent = feedback + \" [\" + answeredcorrect + \"/\" + numcorrect + \"]\";\n", " } else {\n", " fb.textContent = feedback + \" [\" + 0 + \"/\" + numcorrect + \"]\";\n", " }\n", "\n", "\n", " }\n", "\n", " if (typeof MathJax != 'undefined') {\n", " var version = MathJax.version;\n", " console.log('MathJax version', version);\n", " if (version[0] == \"2\") {\n", " MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n", " } else if (version[0] == \"3\") {\n", " MathJax.typeset([fb]);\n", " }\n", " } else {\n", " console.log('MathJax not detected');\n", " }\n", "\n", "}\n", "\n", "function make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id) {\n", " var shuffled;\n", " if (shuffle_answers == \"True\") {\n", " //console.log(shuffle_answers+\" read as true\");\n", " shuffled = getRandomSubarray(qa.answers, qa.answers.length);\n", " } else {\n", " //console.log(shuffle_answers+\" read as false\");\n", " shuffled = qa.answers;\n", " }\n", "\n", "\n", " var num_correct = 0;\n", "\n", "\n", "\n", " shuffled.forEach((item, index, ans_array) => {\n", " //console.log(answer);\n", "\n", " // Make input element\n", " var inp = document.createElement(\"input\");\n", " inp.type = \"radio\";\n", " inp.id = \"quizo\" + id + index;\n", " inp.style = \"display:none;\";\n", " aDiv.append(inp);\n", "\n", " //Make label for input element\n", " var lab = document.createElement(\"label\");\n", " lab.className = \"MCButton\";\n", " lab.id = id + '-' + index;\n", " lab.onclick = check_mc;\n", " var aSpan = document.createElement('span');\n", " aSpan.classsName = \"\";\n", " //qDiv.id=\"quizQn\"+id+index;\n", " if (\"answer\" in item) {\n", " aSpan.innerHTML = jaxify(item.answer);\n", " //aSpan.innerHTML=item.answer;\n", " }\n", " lab.append(aSpan);\n", "\n", " // Create div for code inside question\n", " var codeSpan;\n", " if (\"code\" in item) {\n", " codeSpan = document.createElement('span');\n", " codeSpan.id = \"code\" + id + index;\n", " codeSpan.className = \"QuizCode\";\n", " var codePre = document.createElement('pre');\n", " codeSpan.append(codePre);\n", " var codeCode = document.createElement('code');\n", " codePre.append(codeCode);\n", " codeCode.innerHTML = item.code;\n", " lab.append(codeSpan);\n", " //console.log(codeSpan);\n", " }\n", "\n", " //lab.textContent=item.answer;\n", "\n", " // Set the data attributes for the answer\n", " lab.setAttribute('data-correct', item.correct);\n", " if (item.correct) {\n", " num_correct++;\n", " }\n", " if (\"feedback\" in item) {\n", " lab.setAttribute('data-feedback', item.feedback);\n", " }\n", " lab.setAttribute('data-answered', 0);\n", "\n", " aDiv.append(lab);\n", "\n", " });\n", "\n", " if (num_correct > 1) {\n", " outerqDiv.className = \"ManyChoiceQn\";\n", " } else {\n", " outerqDiv.className = \"MultipleChoiceQn\";\n", " }\n", "\n", " return num_correct;\n", "\n", "}\n", "function check_numeric(ths, event) {\n", "\n", " if (event.keyCode === 13) {\n", " ths.blur();\n", "\n", " var id = ths.id.split('-')[0];\n", "\n", " var submission = ths.value;\n", " if (submission.indexOf('/') != -1) {\n", " var sub_parts = submission.split('/');\n", " //console.log(sub_parts);\n", " submission = sub_parts[0] / sub_parts[1];\n", " }\n", " //console.log(\"Reader entered\", submission);\n", "\n", " if (\"precision\" in ths.dataset) {\n", " var precision = ths.dataset.precision;\n", " // console.log(\"1:\", submission)\n", " submission = Math.round((1 * submission + Number.EPSILON) * 10 ** precision) / 10 ** precision;\n", " // console.log(\"Rounded to \", submission, \" precision=\", precision );\n", " }\n", "\n", "\n", " //console.log(\"In check_numeric(), id=\"+id);\n", " //console.log(event.srcElement.id) \n", " //console.log(event.srcElement.dataset.feedback)\n", "\n", " var fb = document.getElementById(\"fb\" + id);\n", " fb.style.display = \"none\";\n", " fb.textContent = \"Incorrect -- try again.\";\n", "\n", " var answers = JSON.parse(ths.dataset.answers);\n", " //console.log(answers);\n", "\n", " var defaultFB = \"\";\n", " var correct;\n", " var done = false;\n", " answers.every(answer => {\n", " //console.log(answer.type);\n", "\n", " correct = false;\n", " // if (answer.type==\"value\"){\n", " if ('value' in answer) {\n", " if (submission == answer.value) {\n", " fb.textContent = jaxify(answer.feedback);\n", " correct = answer.correct;\n", " //console.log(answer.correct);\n", " done = true;\n", " }\n", " // } else if (answer.type==\"range\") {\n", " } else if ('range' in answer) {\n", " //console.log(answer.range);\n", " if ((submission >= answer.range[0]) && (submission < answer.range[1])) {\n", " fb.textContent = jaxify(answer.feedback);\n", " correct = answer.correct;\n", " //console.log(answer.correct);\n", " done = true;\n", " }\n", " } else if (answer.type == \"default\") {\n", " defaultFB = answer.feedback;\n", " }\n", " if (done) {\n", " return false; // Break out of loop if this has been marked correct\n", " } else {\n", " return true; // Keep looking for case that includes this as a correct answer\n", " }\n", " });\n", "\n", " if ((!done) && (defaultFB != \"\")) {\n", " fb.innerHTML = jaxify(defaultFB);\n", " //console.log(\"Default feedback\", defaultFB);\n", " }\n", "\n", " fb.style.display = \"block\";\n", " if (correct) {\n", " ths.className = \"Input-text\";\n", " ths.classList.add(\"correctButton\");\n", " fb.className = \"Feedback\";\n", " fb.classList.add(\"correct\");\n", " } else {\n", " ths.className = \"Input-text\";\n", " ths.classList.add(\"incorrectButton\");\n", " fb.className = \"Feedback\";\n", " fb.classList.add(\"incorrect\");\n", " }\n", "\n", " // What follows is for the saved responses stuff\n", " var outerContainer = fb.parentElement.parentElement;\n", " var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n", " if (responsesContainer) {\n", " console.log(submission);\n", " var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n", " //console.log(\"Question \" + qnum);\n", " //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n", " var responses=JSON.parse(responsesContainer.dataset.responses);\n", " console.log(responses);\n", " if (submission == ths.value){\n", " responses[qnum]= submission;\n", " } else {\n", " responses[qnum]= ths.value + \"(\" + submission +\")\";\n", " }\n", " responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n", " printResponses(responsesContainer);\n", " }\n", " // End code to preserve responses\n", "\n", " if (typeof MathJax != 'undefined') {\n", " var version = MathJax.version;\n", " console.log('MathJax version', version);\n", " if (version[0] == \"2\") {\n", " MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n", " } else if (version[0] == \"3\") {\n", " MathJax.typeset([fb]);\n", " }\n", " } else {\n", " console.log('MathJax not detected');\n", " }\n", " return false;\n", " }\n", "\n", "}\n", "\n", "function isValid(el, charC) {\n", " //console.log(\"Input char: \", charC);\n", " if (charC == 46) {\n", " if (el.value.indexOf('.') === -1) {\n", " return true;\n", " } else if (el.value.indexOf('/') != -1) {\n", " var parts = el.value.split('/');\n", " if (parts[1].indexOf('.') === -1) {\n", " return true;\n", " }\n", " }\n", " else {\n", " return false;\n", " }\n", " } else if (charC == 47) {\n", " if (el.value.indexOf('/') === -1) {\n", " if ((el.value != \"\") && (el.value != \".\")) {\n", " return true;\n", " } else {\n", " return false;\n", " }\n", " } else {\n", " return false;\n", " }\n", " } else if (charC == 45) {\n", " var edex = el.value.indexOf('e');\n", " if (edex == -1) {\n", " edex = el.value.indexOf('E');\n", " }\n", "\n", " if (el.value == \"\") {\n", " return true;\n", " } else if (edex == (el.value.length - 1)) { // If just after e or E\n", " return true;\n", " } else {\n", " return false;\n", " }\n", " } else if (charC == 101) { // \"e\"\n", " if ((el.value.indexOf('e') === -1) && (el.value.indexOf('E') === -1) && (el.value.indexOf('/') == -1)) {\n", " // Prev symbol must be digit or decimal point:\n", " if (el.value.slice(-1).search(/\\d/) >= 0) {\n", " return true;\n", " } else if (el.value.slice(-1).search(/\\./) >= 0) {\n", " return true;\n", " } else {\n", " return false;\n", " }\n", " } else {\n", " return false;\n", " }\n", " } else {\n", " if (charC > 31 && (charC < 48 || charC > 57))\n", " return false;\n", " }\n", " return true;\n", "}\n", "\n", "function numeric_keypress(evnt) {\n", " var charC = (evnt.which) ? evnt.which : evnt.keyCode;\n", "\n", " if (charC == 13) {\n", " check_numeric(this, evnt);\n", " } else {\n", " return isValid(this, charC);\n", " }\n", "}\n", "\n", "\n", "\n", "\n", "\n", "function make_numeric(qa, outerqDiv, qDiv, aDiv, id) {\n", "\n", "\n", "\n", " //console.log(answer);\n", "\n", "\n", " outerqDiv.className = \"NumericQn\";\n", " aDiv.style.display = 'block';\n", "\n", " var lab = document.createElement(\"label\");\n", " lab.className = \"InpLabel\";\n", " lab.textContent = \"Type numeric answer here:\";\n", " aDiv.append(lab);\n", "\n", " var inp = document.createElement(\"input\");\n", " inp.type = \"text\";\n", " //inp.id=\"input-\"+id;\n", " inp.id = id + \"-0\";\n", " inp.className = \"Input-text\";\n", " inp.setAttribute('data-answers', JSON.stringify(qa.answers));\n", " if (\"precision\" in qa) {\n", " inp.setAttribute('data-precision', qa.precision);\n", " }\n", " aDiv.append(inp);\n", " //console.log(inp);\n", "\n", " //inp.addEventListener(\"keypress\", check_numeric);\n", " //inp.addEventListener(\"keypress\", numeric_keypress);\n", " /*\n", " inp.addEventListener(\"keypress\", function(event) {\n", " return numeric_keypress(this, event);\n", " }\n", " );\n", " */\n", " //inp.onkeypress=\"return numeric_keypress(this, event)\";\n", " inp.onkeypress = numeric_keypress;\n", " inp.onpaste = event => false;\n", "\n", " inp.addEventListener(\"focus\", function (event) {\n", " this.value = \"\";\n", " return false;\n", " }\n", " );\n", "\n", "\n", "}\n", "function jaxify(string) {\n", " var mystring = string;\n", "\n", " var count = 0;\n", " var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n", "\n", " var count2 = 0;\n", " var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n", "\n", " //console.log(loc);\n", "\n", " while ((loc >= 0) || (loc2 >= 0)) {\n", "\n", " /* Have to replace all the double $$ first with current implementation */\n", " if (loc2 >= 0) {\n", " if (count2 % 2 == 0) {\n", " mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n", " } else {\n", " mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n", " }\n", " count2++;\n", " } else {\n", " if (count % 2 == 0) {\n", " mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n", " } else {\n", " mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n", " }\n", " count++;\n", " }\n", " loc = mystring.search(/([^\\\\]|^)(\\$)/);\n", " loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n", " //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n", " }\n", "\n", " //console.log(mystring);\n", " return mystring;\n", "}\n", "\n", "\n", "function show_questions(json, mydiv) {\n", " console.log('show_questions');\n", " //var mydiv=document.getElementById(myid);\n", " var shuffle_questions = mydiv.dataset.shufflequestions;\n", " var num_questions = mydiv.dataset.numquestions;\n", " var shuffle_answers = mydiv.dataset.shuffleanswers;\n", "\n", " if (num_questions > json.length) {\n", " num_questions = json.length;\n", " }\n", "\n", " var questions;\n", " if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n", " //console.log(num_questions+\",\"+json.length);\n", " questions = getRandomSubarray(json, num_questions);\n", " } else {\n", " questions = json;\n", " }\n", "\n", " //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n", "\n", " // Iterate over questions\n", " questions.forEach((qa, index, array) => {\n", " //console.log(qa.question); \n", "\n", " var id = makeid(8);\n", " //console.log(id);\n", "\n", "\n", " // Create Div to contain question and answers\n", " var iDiv = document.createElement('div');\n", " //iDiv.id = 'quizWrap' + id + index;\n", " iDiv.id = 'quizWrap' + id;\n", " iDiv.className = 'Quiz';\n", " iDiv.setAttribute('data-qnum', index);\n", " mydiv.appendChild(iDiv);\n", " // iDiv.innerHTML=qa.question;\n", "\n", " var outerqDiv = document.createElement('div');\n", " outerqDiv.id = \"OuterquizQn\" + id + index;\n", "\n", " iDiv.append(outerqDiv);\n", "\n", " // Create div to contain question part\n", " var qDiv = document.createElement('div');\n", " qDiv.id = \"quizQn\" + id + index;\n", " //qDiv.textContent=qa.question;\n", " qDiv.innerHTML = jaxify(qa.question);\n", "\n", " outerqDiv.append(qDiv);\n", "\n", " // Create div for code inside question\n", " var codeDiv;\n", " if (\"code\" in qa) {\n", " codeDiv = document.createElement('div');\n", " codeDiv.id = \"code\" + id + index;\n", " codeDiv.className = \"QuizCode\";\n", " var codePre = document.createElement('pre');\n", " codeDiv.append(codePre);\n", " var codeCode = document.createElement('code');\n", " codePre.append(codeCode);\n", " codeCode.innerHTML = qa.code;\n", " outerqDiv.append(codeDiv);\n", " //console.log(codeDiv);\n", " }\n", "\n", "\n", " // Create div to contain answer part\n", " var aDiv = document.createElement('div');\n", " aDiv.id = \"quizAns\" + id + index;\n", " aDiv.className = 'Answer';\n", " iDiv.append(aDiv);\n", "\n", " //console.log(qa.type);\n", "\n", " var num_correct;\n", " if (qa.type == \"multiple_choice\") {\n", " num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n", " } else if (qa.type == \"many_choice\") {\n", " num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n", " } else if (qa.type == \"numeric\") {\n", " //console.log(\"numeric\");\n", " make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n", " }\n", "\n", "\n", " //Make div for feedback\n", " var fb = document.createElement(\"div\");\n", " fb.id = \"fb\" + id;\n", " //fb.style=\"font-size: 20px;text-align:center;\";\n", " fb.className = \"Feedback\";\n", " fb.setAttribute(\"data-answeredcorrect\", 0);\n", " fb.setAttribute(\"data-numcorrect\", num_correct);\n", " iDiv.append(fb);\n", "\n", "\n", " });\n", " var preserveResponses = mydiv.dataset.preserveresponses;\n", " console.log(preserveResponses);\n", " console.log(preserveResponses == \"true\");\n", " if (preserveResponses == \"true\") {\n", " console.log(preserveResponses);\n", " // Create Div to contain record of answers\n", " var iDiv = document.createElement('div');\n", " iDiv.id = 'responses' + mydiv.id;\n", " iDiv.className = 'JCResponses';\n", " // Create a place to store responses as an empty array\n", " iDiv.setAttribute('data-responses', '[]');\n", "\n", " // Dummy Text\n", " iDiv.innerHTML=\"Select your answers and then follow the directions that will appear here.\"\n", " //iDiv.className = 'Quiz';\n", " mydiv.appendChild(iDiv);\n", " }\n", "//console.log(\"At end of show_questions\");\n", " if (typeof MathJax != 'undefined') {\n", " console.log(\"MathJax version\", MathJax.version);\n", " var version = MathJax.version;\n", " setTimeout(function(){\n", " var version = MathJax.version;\n", " console.log('After sleep, MathJax version', version);\n", " if (version[0] == \"2\") {\n", " MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n", " } else if (version[0] == \"3\") {\n", " MathJax.typeset([mydiv]);\n", " }\n", " }, 500);\n", "if (typeof version == 'undefined') {\n", " } else\n", " {\n", " if (version[0] == \"2\") {\n", " MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n", " } else if (version[0] == \"3\") {\n", " MathJax.typeset([mydiv]);\n", " } else {\n", " console.log(\"MathJax not found\");\n", " }\n", " }\n", " }\n", " return false;\n", "}\n", "\n", " {\n", " show_questions(questionsuUPIbOZVDkYq, uUPIbOZVDkYq);\n", " }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_quiz(example); " ] }, { "cell_type": "markdown", "id": "2c5f06dc-0e38-4115-b0e6-6b502b8bedc0", "metadata": {}, "source": [ "We can test our layout by running the above codes without adding plots to our axes. Let's try the vertical layout example. " ] }, { "cell_type": "code", "execution_count": 5, "id": "81fd2a49-c991-4aae-b4ba-cc778ca1a6ac", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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    " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax1),(ax2)) = plt.subplots(nrows=2, ncols=1,figsize=(4, 4))" ] }, { "cell_type": "markdown", "id": "0147970b-b8b2-4986-bd9f-72ed9e10423c", "metadata": {}, "source": [ "or how about 2 rows of 3 plots horizontally... Here we need to use a 2D list of axes: " ] }, { "cell_type": "code", "execution_count": 6, "id": "eca9c234-d686-4fb8-a4bc-bb6c8cd84eee", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
    " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax1, ax2, ax3),(ax4, ax5, ax6)) = plt.subplots(nrows=2, ncols=3,figsize=(8, 6))" ] }, { "cell_type": "markdown", "id": "3c53b4da-7025-4ffc-89c4-d98e8dbd384a", "metadata": {}, "source": [ ":::{note} \n", "Did you play around with the figsize=() in the above codes? I hope you did. You can't learn by being a passive reader. You need to type out and experiment with the code as your read this textbook. \n", ":::" ] }, { "cell_type": "markdown", "id": "b667edfa-cac8-4d78-a426-7b0378b7593c", "metadata": {}, "source": [ "Now let's add plots. First we need to generate some data. " ] }, { "cell_type": "code", "execution_count": 7, "id": "e6a7ecdc-7876-4d1a-9f43-c6d2ec513cb9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 7, 9],\n", " [ 7, 11],\n", " [ 7, 10],\n", " [ 7, 11],\n", " [10, 13]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.random.randint(low=1, high=11, size=50) #generate 50 random integers between 1 and 10\n", "y = x + np.random.randint(1, 5, size=x.size) #generate 50 random integers between 1 and 4 and add to x\n", "data = np.column_stack((x, y))\n", "data[0:5] #with numpy arrays we use different notation to slice elements as compared to pandas" ] }, { "cell_type": "code", "execution_count": 8, "id": "edce679f-ea90-43a7-879a-49430a3bc13c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
    " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (axes1, axes2) = plt.subplots(nrows=1, ncols=2,figsize=(8, 4))\n", "axes1.scatter(x=x, y=y, marker='o', c='r', edgecolor='b')\n", "axes1.set_title('Scatter: x versus y')\n", "axes1.set_xlabel('x')\n", "axes1.set_ylabel('y')\n", "\n", "axes2.hist(data, bins=np.arange(data.min(), data.max()),label=('x', 'y'))\n", "axes2.legend(loc=(0.5, 0.2))\n", "axes2.set_title('Frequencies of x and y')\n", "axes2.yaxis.tick_right()\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "596f73c9-747b-42cd-bf47-7363aeb800f1", "metadata": {}, "source": [ "**So many things to tweak in the above code...** For example, it might be nice to move that legend in the second plot. Currently, it is overlapping our bars. Also, 'edgecolor' looks like an interesting option in the first plot. " ] }, { "cell_type": "markdown", "id": "8d3c9bc8-0881-4abb-b631-e191b36d6428", "metadata": {}, "source": [ "## Advanced subplot layouts with `.subplot2grid()`" ] }, { "cell_type": "markdown", "id": "c2724d08-c69e-4510-bd16-8b617e9e7553", "metadata": {}, "source": [ "If you need more advanced layout beyond simple grids of graphs, then Matplotlib’s gridspec module allows for more subplot customization. Pyplot’s **subplot2grid()** interacts with this module. " ] }, { "cell_type": "markdown", "id": "80aad316-9ca1-4cf7-a1e5-a31c39a8ed50", "metadata": {}, "source": [ "Say we want this setup: \n", "\n", ":::{image} ../images/3114_05_layout.png\n", ":height: 500px\n", ":::" ] }, { "cell_type": "markdown", "id": "cf34a32c-e459-4bb5-9da6-a8996db569ca", "metadata": { "tags": [ "remove-cell" ] }, "source": [ "\"advanced" ] }, { "cell_type": "markdown", "id": "5e2717d7-378f-48e3-9bb2-48ab1a63c43e", "metadata": {}, "source": [ "Always think of the layout in terms of a regular grid. The above layout could be obtained from a 3x2 grid where **ax1** spans 2 columns and 2 rows as shown below. \n", "\n", ":::{image} ../images/3114_05_layout_grid.png\n", ":height: 500px\n", ":::" ] }, { "cell_type": "markdown", "id": "24a55fe3-74f3-4b86-a2cf-1cd9361f3796", "metadata": { "tags": [ "remove-cell" ] }, "source": [ "\"advanced" ] }, { "cell_type": "code", "execution_count": 42, "id": "cda8e993-2ff5-42e6-917f-7fc0e38feee1", "metadata": {}, "outputs": [ { "data": { "image/png": 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    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gridsize = (3, 2) # setup our 3 x 2 grid\n", "fig = plt.figure(figsize=(12, 8)) #overall size of figure\n", "ax1 = plt.subplot2grid(gridsize, (0, 0), colspan=2, rowspan=2) # span 2 col and 2 rows\n", "ax2 = plt.subplot2grid(gridsize, (2, 0)) #3rd row, 1st col\n", "ax3 = plt.subplot2grid(gridsize, (2, 1)) #3rd row, 2nd col" ] }, { "cell_type": "markdown", "id": "4206432e-9b81-40ba-8e43-4f85f6e1d8c6", "metadata": {}, "source": [ "## Adding Data to our Layout" ] }, { "cell_type": "markdown", "id": "d5476b99-2785-4185-8428-feed4d89f9ed", "metadata": {}, "source": [ "We already [read California housing data](3114:05:data-file-for-lesson) into variable \"raw\". " ] }, { "cell_type": "code", "execution_count": 46, "id": "ea02e0ed-75bd-4852-b6ec-48f828e90b5e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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    longitudelatitudehousingMedianAgetotalRoomstotalBedroomspopulationhouseholdsmedianIncome ($10,000)medianHouseValue ($)
    0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0
    1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0
    2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0
    3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0
    4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0
    ..............................
    20635-121.0939.4825.01665.0374.0845.0330.01.560378100.0
    20636-121.2139.4918.0697.0150.0356.0114.02.556877100.0
    20637-121.2239.4317.02254.0485.01007.0433.01.700092300.0
    20638-121.3239.4318.01860.0409.0741.0349.01.867284700.0
    20639-121.2439.3716.02785.0616.01387.0530.02.388689400.0
    \n", "

    20640 rows × 9 columns

    \n", "
    " ], "text/plain": [ " longitude latitude housingMedianAge totalRooms totalBedrooms \\\n", "0 -122.23 37.88 41.0 880.0 129.0 \n", "1 -122.22 37.86 21.0 7099.0 1106.0 \n", "2 -122.24 37.85 52.0 1467.0 190.0 \n", "3 -122.25 37.85 52.0 1274.0 235.0 \n", "4 -122.25 37.85 52.0 1627.0 280.0 \n", "... ... ... ... ... ... \n", "20635 -121.09 39.48 25.0 1665.0 374.0 \n", "20636 -121.21 39.49 18.0 697.0 150.0 \n", "20637 -121.22 39.43 17.0 2254.0 485.0 \n", "20638 -121.32 39.43 18.0 1860.0 409.0 \n", "20639 -121.24 39.37 16.0 2785.0 616.0 \n", "\n", " population households medianIncome ($10,000) medianHouseValue ($) \n", "0 322.0 126.0 8.3252 452600.0 \n", "1 2401.0 1138.0 8.3014 358500.0 \n", "2 496.0 177.0 7.2574 352100.0 \n", "3 558.0 219.0 5.6431 341300.0 \n", "4 565.0 259.0 3.8462 342200.0 \n", "... ... ... ... ... \n", "20635 845.0 330.0 1.5603 78100.0 \n", "20636 356.0 114.0 2.5568 77100.0 \n", "20637 1007.0 433.0 1.7000 92300.0 \n", "20638 741.0 349.0 1.8672 84700.0 \n", "20639 1387.0 530.0 2.3886 89400.0 \n", "\n", "[20640 rows x 9 columns]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "housing=raw.copy()\n", "housing" ] }, { "cell_type": "code", "execution_count": 68, "id": "4e6c0a8b-f93b-4283-bbc5-a07d1292b0b9", "metadata": {}, "outputs": [], "source": [ "# a function to add a textbox to our bottom to plots\n", "def add_titlebox(ax, text):\n", " ax.text(.55, .8, text,\n", " horizontalalignment='center',\n", " transform=ax.transAxes,\n", " bbox=dict(facecolor='white', alpha=0.6),\n", " fontsize=10)\n", " return ax" ] }, { "cell_type": "code", "execution_count": 67, "id": "5f823564-718b-461d-9c00-a973c35b854e", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#create layout\n", "gridsize = (3, 2)\n", "fig = plt.figure(figsize=(8, 7)) #overall size of figure\n", "ax1 = plt.subplot2grid(gridsize, (0, 0), colspan=2, rowspan=2)\n", "ax2 = plt.subplot2grid(gridsize, (2, 0))\n", "ax3 = plt.subplot2grid(gridsize, (2, 1))\n", "\n", "#fill layout with plots\n", "ax1.set_title('Home value as a function of home age & area population',fontsize=14)\n", "sctr = ax1.scatter(x=housing.loc[:,\"housingMedianAge\"], y=housing.loc[:,\"population\"], c=housing.loc[:, \"medianHouseValue ($)\"], cmap='RdYlGn')\n", "plt.colorbar(sctr, ax=ax1, format='$%d')\n", "ax1.set_yscale('log')\n", "ax2.hist(housing.loc[:,\"housingMedianAge\"], bins='auto')\n", "ax3.hist(housing.loc[:,\"population\"], bins='auto', log=True)\n", "\n", "add_titlebox(ax2, 'Histogram: home age')\n", "add_titlebox(ax3, 'Histogram: area population (log scl.)')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "e27dce50-8e5d-4b6b-b5a8-e7ebf3f32060", "metadata": {}, "source": [ ":::{note}\n", "I'm sure you noticed these new options in the scatter plot coding above: \n", "\n", "```python\n", "c=housing.loc[:, \"medianHouseValue ($)\"], cmap='RdYlGn'\n", "```\n", "\n", "\"c\" is the color which can be a single value like 'r' for red or it can be a list of values that get mapped to a color scale defined by \"cmap\". Here the color map is 'RdYlGn' or Red, Yellow, Green and the c values are the mean house value for each point. \n", ":::" ] }, { "cell_type": "markdown", "id": "3358fe5b-4619-4e59-b869-bb827b5a6e0f", "metadata": { "tags": [] }, "source": [ "## Exercises" ] }, { "cell_type": "markdown", "id": "e0490985-7ff3-40e5-8e56-12abc5258844", "metadata": { "tags": [] }, "source": [ "### Problem 1" ] }, { "cell_type": "markdown", "id": "0aa05933-2971-413e-823a-f674b85d6ba7", "metadata": {}, "source": [ "Create two blank plots (axes) named \"ax1\" and \"ax2\" layed out next to each other horizontally. " ] }, { "cell_type": "markdown", "id": "49ecd457-8f45-4a8b-93ec-e856c1341966", "metadata": {}, "source": [ "### Problem 2" ] }, { "cell_type": "markdown", "id": "f676a2eb-6f43-4b6d-a7c1-8c28c3de5c3f", "metadata": {}, "source": [ "Import the stress-strain data from the excel file [Al7075_out.xlsx](https://drive.google.com/uc?export=download&id=14uBqZM8ekl1RoFgx3nwCJM7fe9N144RI). 1) Reproduce the plot below [see your previous weeks homework](3114:02:problem-1). 2) Create an additional scatter plot where the stress is plotted to a maximum strain of 0.0075. 3) Layout these two plots next to each other horizontally. 4) Add x,y axis labels and title each plot. Your title for plot 2 should be descriptive of this \"initial\" region. " ] }, { "cell_type": "markdown", "id": "b7298776-42a4-4262-83b5-959c78bfda20", "metadata": { "tags": [ "remove-cell" ] }, "source": [ "\"stress" ] }, { "cell_type": "markdown", "id": "a5166643-6e46-49c9-9637-42a5f6396d2c", "metadata": {}, "source": [ ":::{figure} ../images/hw1_stress_strain_plot.png\n", ":height: 250px\n", "Stress-Strain response for Al7075 in tension. \n", ":::" ] }, { "cell_type": "markdown", "id": "2e539421-e616-47ac-862a-3f4fe3ed17d2", "metadata": {}, "source": [ "### Problem 3" ] }, { "cell_type": "markdown", "id": "d14af95f-0238-4010-8c0f-60af7d859404", "metadata": {}, "source": [ "Use the data from the titanic given in the previous lesson, to create 6 plots in the following layout:" ] }, { "cell_type": "markdown", "id": "61586c91-fd06-4053-8318-5aebdc9bdfb2", "metadata": { "tags": [ "remove-cell" ] }, "source": [ "\"6" ] }, { "cell_type": "markdown", "id": "8039080f-9b4b-4cac-9d61-f57ab5e0ed55", "metadata": {}, "source": [ ":::{figure} ../images/3114_05_6_plot_layout.png\n", ":height: 250px\n", ":name: 6 Plot Layout \n", "6 plot layout template. \n", ":::" ] }, { "cell_type": "markdown", "id": "3521f66c-87a7-4aa0-a46c-a0556ce0b515", "metadata": {}, "source": [ "**Plot 1**: Scatter plot of 'number of passengers'(y) vs 'Age of passenger'(x). You might try grouping by age and using .count(). \n", "**Plot 2**: Histogram of the Age of people on board. You can use something like: `ax2.hist(place your age data here, bins=30)`. Play around with the bins number to see what looks best to you. \n", "**Plot 3 - 5**: Reproduce plots shown below for plots 3, 4, and 5. \n", "**Plot 6**: Pie chart of the number of females in 1st, 2nd and 3rd class that survived. Include the percentages in each pie piece. " ] }, { "cell_type": "markdown", "id": "946497cc-c14e-4350-953c-da1df4ff495d", "metadata": { "tags": [ "remove-cell" ] }, "source": [ "\"titanic\n", "\"titanic\n", "\"titanic" ] }, { "cell_type": "markdown", "id": "f0613737-2e72-4634-8c61-d88ce31f9398", "metadata": { "tags": [] }, "source": [ ":::{figure} ../images/3114_05_ticket_price_sex_pclass.png\n", ":height: 250px\n", "Reproduce for Problem 3 Plot 3 \n", "::: \n", "\n", ":::{figure} ../images/3114_05_passengers_survived_sex.png\n", ":height: 250px\n", "Reproduce for Problem 3 Plot 4 \n", "::: \n", "\n", ":::{figure} ../images/3114_05_pie_died_sex_class.png\n", ":height: 250px\n", "Reproduce for Problem 3 Plot 5 \n", "::: " ] }, { "cell_type": "code", "execution_count": null, "id": "96734c87-7562-4557-b413-b6dd843592c5", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.9" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }