diff --git a/ExoCore/Curriculum/Data_Repositories/MAST.ipynb b/ExoCore/Curriculum/Data_Repositories/MAST.ipynb index 85a4a6d..c09aab3 100644 --- a/ExoCore/Curriculum/Data_Repositories/MAST.ipynb +++ b/ExoCore/Curriculum/Data_Repositories/MAST.ipynb @@ -50,8 +50,10 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 1, "metadata": {}, + "outputs": [], "source": [ "from jupyterquiz import display_quiz\n", "import warnings\n", @@ -202,9 +204,250 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "var questionsuUehteaksPyT=[{\"question\": \"How can you query an object using the basic search fuctionality in MAST?\", \"type\": \"many_choice\", \"answers\": [{\"answer\": \"Search by Object Name\", \"correct\": true, \"feedback\": \"Correct. This includes standard catalog names, common names, and star catalogs with coordinates.\"}, {\"answer\": \"Search by Coordinate\", \"correct\": true, \"feedback\": \"Correct. You can search by an object's right ascension and declination, and within a certain radius of that coordinate.\"}, {\"answer\": \"Search by Constellation\", \"correct\": false, \"feedback\": \"Incorrect. Searching by this will either return no results or an object that happens to share a close common name with the constellation.\"}, {\"answer\": \"Search by _______\", \"correct\": false, \"feedback\": \"Incorrect.\"}]}, {\"question\": \"What is the minimum required information to include in a search for a list of targets?\", \"type\": \"many_choice\", \"answer_cols\": 4, \"answers\": [{\"answer\": \"Target Name\", \"correct\": true, \"feedback\": \"Correct.\"}, {\"answer\": \"Target RA and DEC\", \"correct\": true, \"feedback\": \"Correct.\"}, {\"answer\": \"Target Galactic Coordinates\", \"correct\": false, \"feedback\": \"Incorrect. While this is a valid search parameter, it is not required.\"}, {\"answer\": \"Target Mission\", \"correct\": false, \"feedback\": \"Incorrect. While this is a valid search parameter, it is not required.\"}]}, {\"question\": \"Which of these are browsing options available in MAST?\", \"type\": \"many_choice\", \"answers\": [{\"answer\": \"AstroView\", \"correct\": true, \"feedback\": \"Correct. MAST will display the position of the object in a sky viewer.\"}, {\"answer\": \"Browser Visualizations\", \"correct\": true, \"feedback\": \"Correct. Certain data products can be viewed or otherwise visualized in a browser before download.\"}, {\"answer\": \"Exporting the Results Grid\", \"correct\": true, \"feedback\": \"Correct. While not always necessary, you can export the results of your search to a CSV file, among other common formats.\"}, {\"answer\": \"XYZ\", \"correct\": false, \"feedback\": \"False. Idk why yet.\"}]}];\n\nif (typeof Question === 'undefined') {\n// Make a random ID\nfunction 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// Convert LaTeX delimiters and markdown links to HTML\nfunction jaxify(string) {\n let mystring = string;\n let count = 0, count2 = 0;\n let loc = mystring.search(/([^\\\\]|^)(\\$)/);\n let loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n while (loc >= 0 || loc2 >= 0) {\n if (loc2 >= 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, count2 % 2 ? '$1\\\\]' : '$1\\\\[');\n count2++;\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, count % 2 ? '$1\\\\)' : '$1\\\\(');\n count++;\n }\n loc = mystring.search(/([^\\\\]|^)(\\$)/);\n loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n }\n // Replace markdown links\n mystring = mystring.replace(//g, 'http$1');\n mystring = mystring.replace(/\\[(.*?)\\]\\((.*?)\\)/g, '$1');\n return mystring;\n}\n\n// Base class for question types\nclass Question {\n static registry = {};\n static register(type, cls) {\n Question.registry[type] = cls;\n }\n static create(qa, id, index, options, rootDiv) {\n const Cls = Question.registry[qa.type];\n if (!Cls) {\n console.error(`No question class registered for type \"${qa.type}\"`);\n return;\n }\n const q = new Cls(qa, id, index, options, rootDiv);\n q.render();\n }\n\n constructor(qa, id, index, options, rootDiv) {\n this.qa = qa;\n this.id = id;\n this.index = index;\n this.options = options;\n this.rootDiv = rootDiv;\n // wrapper\n this.wrapper = document.createElement('div');\n this.wrapper.id = `quizWrap${id}`;\n this.wrapper.className = 'Quiz';\n this.wrapper.dataset.qnum = index;\n this.wrapper.style.maxWidth = `${options.maxWidth}px`;\n rootDiv.appendChild(this.wrapper);\n // question container\n this.outerqDiv = document.createElement('div');\n this.outerqDiv.id = `OuterquizQn${id}${index}`;\n this.wrapper.appendChild(this.outerqDiv);\n // question text\n this.qDiv = document.createElement('div');\n this.qDiv.id = `quizQn${id}${index}`;\n if (qa.question) {\n this.qDiv.innerHTML = jaxify(qa.question);\n this.outerqDiv.appendChild(this.qDiv);\n }\n // code block\n if (qa.code) {\n const codeDiv = document.createElement('div');\n codeDiv.id = `code${id}${index}`;\n codeDiv.className = 'QuizCode';\n const pre = document.createElement('pre');\n const codeEl = document.createElement('code');\n codeEl.innerHTML = qa.code;\n pre.appendChild(codeEl);\n codeDiv.appendChild(pre);\n this.outerqDiv.appendChild(codeDiv);\n }\n // answer container\n this.aDiv = document.createElement('div');\n this.aDiv.id = `quizAns${id}${index}`;\n this.aDiv.className = 'Answer';\n this.wrapper.appendChild(this.aDiv);\n // feedback container (append after answers)\n this.fbDiv = document.createElement('div');\n this.fbDiv.id = `fb${id}`;\n this.fbDiv.className = 'Feedback';\n this.fbDiv.dataset.answeredcorrect = 0;\n }\n\n render() {\n throw new Error('render() not implemented');\n }\n\n preserveResponse(val) {\n if (!this.options.preserveResponses) return;\n const resp = document.getElementById(`responses${this.rootDiv.id}`);\n if (!resp) return;\n const arr = JSON.parse(resp.dataset.responses);\n arr[this.index] = val;\n resp.dataset.responses = JSON.stringify(arr);\n printResponses(resp);\n }\n\n typeset(container) {\n if (typeof MathJax !== 'undefined') {\n const v = MathJax.version;\n if (v[0] === '2') {\n MathJax.Hub.Queue(['Typeset', MathJax.Hub]);\n } else {\n MathJax.typeset([container]);\n }\n }\n }\n}\n\n// Choose a random subset of an array. Can also be used to shuffle the array\nfunction 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\nfunction 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/* Callback function to determine whether a selected multiple-choice\n button corresponded to a correct answer and to provide feedback\n based on the answer */\nfunction 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 //console.log(answers);\n\n // Split behavior based on multiple choice vs many choice:\n var fb = document.getElementById(\"fb\" + id);\n\n\n\n /* Multiple choice (1 answer). Allow for 0 correct\n answers as an edge case */\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.innerHTML = jaxify(label.dataset.feedback);\n } else {\n fb.innerHTML = \"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.innerHTML = jaxify(label.dataset.feedback);\n } else {\n fb.innerHTML = \"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 { /* Many choice (more than 1 correct answer) */\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.innerHTML = feedback + \" [\" + answeredcorrect + \"/\" + numcorrect + \"]\";\n } else {\n fb.innerHTML = 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\n/* Function to produce the HTML buttons for a multiple choice/\n many choice question and to update the CSS tags based on\n the question type */\nfunction make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id) {\n\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 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// Object-oriented wrapper for MC/MANY choice\nclass MCQuestion extends Question {\n constructor(qa, id, idx, opts, rootDiv) { super(qa, id, idx, opts, rootDiv); }\n render() {\n //console.log(\"options.shuffleAnswers \" + this.options.shuffleAnswers);\n const numCorrect = make_mc(\n this.qa,\n this.options.shuffleAnswers,\n this.outerqDiv,\n this.qDiv,\n this.aDiv,\n this.id\n );\n if ('answer_cols' in this.qa) {\n this.aDiv.style.gridTemplateColumns =\n 'repeat(' + this.qa.answer_cols + ', 1fr)';\n }\n this.fbDiv.dataset.numcorrect = numCorrect;\n this.wrapper.appendChild(this.fbDiv);\n }\n}\nQuestion.register('multiple_choice', MCQuestion);\nQuestion.register('many_choice', MCQuestion);\nfunction 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 submission = Number(Number(submission).toPrecision(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.innerHTML = \"Incorrect -- try again.\";\n\n var answers = JSON.parse(ths.dataset.answers);\n //console.log(answers);\n\n var defaultFB = \"Incorrect. Try again.\";\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 var value;\n if (\"precision\" in ths.dataset) {\n value = answer.value.toPrecision(ths.dataset.precision);\n } else {\n value = answer.value;\n }\n if (submission == value) {\n if (\"feedback\" in answer) {\n fb.innerHTML = jaxify(answer.feedback);\n } else {\n fb.innerHTML = jaxify(\"Correct\");\n }\n correct = answer.correct;\n //console.log(answer.correct);\n done = true;\n }\n\n // } else if (answer.type==\"range\") {\n } else if ('range' in answer) {\n console.log(answer.range);\n console.log(submission, submission >=answer.range[0], submission < answer.range[1])\n if ((submission >= answer.range[0]) && (submission < answer.range[1])) {\n fb.innerHTML = jaxify(answer.feedback);\n correct = answer.correct;\n console.log(answer.correct);\n done = true;\n }\n } else if (answer.type == \"default\") {\n if (\"feedback\" in answer) {\n defaultFB = answer.feedback;\n } \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 console.log(\"done:\", done);\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 // After correct answer, if next JupyterQuiz question exists and has a text input, scroll by current question height\n if (correct) {\n // find the current question wrapper\n var wrapper = ths.closest('.Quiz');\n if (wrapper) {\n var nextWrapper = wrapper.nextElementSibling;\n if (nextWrapper && nextWrapper.classList.contains('Quiz')) {\n var nextInput = nextWrapper.querySelector('input.Input-text');\n if (nextInput) {\n var height = wrapper.getBoundingClientRect().height;\n console.log(height);\n nextInput.focus();\n }\n }\n }\n }\n return false;\n }\n\n}\n// Object-oriented wrapper for numeric questions\nclass NumericQuestion extends Question {\n constructor(qa, id, idx, opts, rootDiv) {\n super(qa, id, idx, opts, rootDiv);\n }\n render() {\n make_numeric(this.qa, this.outerqDiv, this.qDiv, this.aDiv, this.id);\n this.wrapper.appendChild(this.fbDiv);\n }\n}\nQuestion.register('numeric', NumericQuestion);\n\nfunction 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\nfunction 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\nfunction 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.innerHTML = \"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// Override show_questions to use object-oriented Question API\nfunction show_questions(json, container) {\n // Accept container element or element ID\n if (typeof container === 'string') {\n container = document.getElementById(container);\n }\n if (!container) {\n console.error('show_questions: invalid container', container);\n return;\n }\n\n const shuffleQuestions = container.dataset.shufflequestions === 'True';\n const shuffleAnswers = container.dataset.shuffleanswers === 'True';\n const preserveResponses = container.dataset.preserveresponses === 'true';\n const maxWidth = parseInt(container.dataset.maxwidth, 10) || 0;\n let numQuestions = parseInt(container.dataset.numquestions, 10) || json.length;\n if (numQuestions > json.length) numQuestions = json.length;\n\n let questions = json;\n if (shuffleQuestions || numQuestions < json.length) {\n questions = getRandomSubarray(json, numQuestions);\n }\n\n questions.forEach((qa, index) => {\n const id = makeid(8);\n const options = {\n shuffleAnswers: shuffleAnswers,\n preserveResponses: preserveResponses,\n maxWidth: maxWidth\n };\n Question.create(qa, id, index, options, container);\n });\n\n if (preserveResponses) {\n const respDiv = document.createElement('div');\n respDiv.id = 'responses' + container.id;\n respDiv.className = 'JCResponses';\n respDiv.dataset.responses = JSON.stringify([]);\n respDiv.innerHTML = 'Select your answers and then follow the directions that will appear here.';\n container.appendChild(respDiv);\n }\n\n // Trigger MathJax typesetting if available\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 if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([container]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\n },\n svg: {\n fontCache: 'global'\n }\n };\n\n (function () {\n var script = document.createElement('script');\n script.src = 'https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js';\n script.async = true;\n document.head.appendChild(script);\n })();\n }\n }\n }, 500);\nif (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 if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([container]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\n },\n svg: {\n fontCache: 'global'\n }\n };\n\n (function () {\n var script = document.createElement('script');\n script.src = 'https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js';\n script.async = true;\n document.head.appendChild(script);\n })();\n }\n } else {\n console.log(\"MathJax not found\");\n }\n }\n }\n // if (typeof MathJax !== 'undefined') {\n // const v = MathJax.version;\n // if (v[0] === '2') {\n // MathJax.Hub.Queue(['Typeset', MathJax.Hub]);\n // } else if (v[0] === '3') {\n // MathJax.typeset([container]);\n // }\n // }\n\n // Prevent link clicks from bubbling up\n Array.from(container.getElementsByClassName('Link')).forEach(link => {\n link.addEventListener('click', e => e.stopPropagation());\n });\n}\nfunction levenshteinDistance(a, b) {\n if (a.length === 0) return b.length;\n if (b.length === 0) return a.length;\n\n const matrix = Array(b.length + 1).fill(null).map(() => Array(a.length + 1).fill(null));\n\n for (let i = 0; i <= a.length; i++) {\n matrix[0][i] = i;\n }\n\n for (let j = 0; j <= b.length; j++) {\n matrix[j][0] = j;\n }\n\n for (let j = 1; j <= b.length; j++) {\n for (let i = 1; i <= a.length; i++) {\n const cost = a[i - 1] === b[j - 1] ? 0 : 1;\n matrix[j][i] = Math.min(\n matrix[j - 1][i] + 1, // Deletion\n matrix[j][i - 1] + 1, // Insertion\n matrix[j - 1][i - 1] + cost // Substitution\n );\n }\n }\n return matrix[b.length][a.length];\n}\n// Object-oriented wrapper for string input questions\nclass StringQuestion extends Question {\n constructor(qa, id, idx, opts, rootDiv) {\n super(qa, id, idx, opts, rootDiv);\n }\n render() {\n make_string(this.qa, this.outerqDiv, this.qDiv, this.aDiv, this.id);\n this.wrapper.appendChild(this.fbDiv);\n }\n}\nQuestion.register('string', StringQuestion);\n\nfunction check_string(ths, event) {\n if (event.keyCode === 13) {\n ths.blur();\n\n var id = ths.id.split('-')[0];\n var submission = ths.value.trim();\n var fb = document.getElementById(\"fb\" + id);\n fb.style.display = \"none\";\n fb.innerHTML = \"Incorrect -- try again.\";\n\n var answers = JSON.parse(ths.dataset.answers);\n var defaultFB = \"Incorrect. Try again.\";\n var correct;\n var done = false;\n\n // Handle default answer pattern: filter out and capture default feedback\n var filteredAnswers = [];\n answers.forEach(answer => {\n if (answer.type === \"default\") {\n defaultFB = answer.feedback;\n } else {\n filteredAnswers.push(answer);\n }\n });\n answers = filteredAnswers;\n\n answers.every(answer => {\n correct = false;\n\n let match = false;\n if (answer.match_case) {\n match = submission === answer.answer;\n } else {\n match = submission.toLowerCase() === answer.answer.toLowerCase();\n }\n console.log(submission);\n console.log(answer.answer);\n console.log(match);\n\n if (match) {\n if (\"feedback\" in answer) {\n fb.innerHTML = jaxify(answer.feedback);\n } else {\n fb.innerHTML = jaxify(\"Correct\");\n }\n correct = answer.correct;\n done = true;\n } else if (answer.fuzzy_threshold) {\n var max_length = Math.max(submission.length, answer.answer.length);\n var ratio;\n if (answer.match_case) {\n ratio = 1- (levenshteinDistance(submission, answer.answer) / max_length);\n } else {\n ratio = 1- (levenshteinDistance(submission.toLowerCase(),\n answer.answer.toLowerCase()) / max_length);\n }\n if (ratio >= answer.fuzzy_threshold) {\n if (\"feedback\" in answer) {\n fb.innerHTML = jaxify(\"(Fuzzy) \" + answer.feedback);\n } else {\n fb.innerHTML = jaxify(\"Correct\");\n }\n correct = answer.correct;\n done = true;\n }\n\n }\n\n if (done) {\n return false;\n } else {\n return true;\n }\n });\n\n if ((!done) && (defaultFB != \"\")) {\n fb.innerHTML = jaxify(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 var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n var qnum = document.getElementById(\"quizWrap\" + id).dataset.qnum;\n var responses = JSON.parse(responsesContainer.dataset.responses);\n responses[qnum] = submission;\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n\n if (typeof MathJax != 'undefined') {\n var version = MathJax.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 // After correct answer, if next JupyterQuiz question exists and has a text input, scroll by current question height\n if (correct) {\n var wrapper = ths.closest('.Quiz');\n if (wrapper) {\n var nextWrapper = wrapper.nextElementSibling;\n if (nextWrapper && nextWrapper.classList.contains('Quiz')) {\n var nextInput = nextWrapper.querySelector('input.Input-text');\n if (nextInput) {\n var height = wrapper.getBoundingClientRect().height;\n nextInput.focus();\n }\n }\n }\n }\n return false;\n }\n}\n\nfunction string_keypress(evnt) {\n var charC = (evnt.which) ? evnt.which : evnt.keyCode;\n\n if (charC == 13) {\n check_string(this, evnt);\n } \n}\n\n\nfunction make_string(qa, outerqDiv, qDiv, aDiv, id) {\n outerqDiv.className = \"StringQn\";\n aDiv.style.display = 'block';\n\n var lab = document.createElement(\"label\");\n lab.className = \"InpLabel\";\n lab.innerHTML = \"Type your answer here:\";\n aDiv.append(lab);\n\n var inp = document.createElement(\"input\");\n inp.type = \"text\";\n inp.id = id + \"-0\";\n inp.className = \"Input-text\";\n inp.setAttribute('data-answers', JSON.stringify(qa.answers));\n // Apply optional input width (approx. number of characters, in em units)\n if (qa.input_width != null) {\n inp.style['min-width'] = qa.input_width + 'em';\n }\n aDiv.append(inp);\n\n inp.onkeypress = string_keypress;\n inp.onpaste = event => false;\n\n inp.addEventListener(\"focus\", function (event) {\n this.value = \"\";\n return false;\n });\n}\n/*\n * Handle asynchrony issues when re-running quizzes in Jupyter notebooks.\n * Ensures show_questions is called after the container div is in the DOM.\n */\nfunction try_show() {\n if (document.getElementById(\"uUehteaksPyT\")) {\n show_questions(questionsuUehteaksPyT, uUehteaksPyT);\n } else {\n setTimeout(try_show, 200);\n }\n};\n// Invoke immediately\n{\n try_show();\n}\n}\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display_quiz(questions[0:3])" ] @@ -231,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -249,9 +492,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "intentType obs_collection provenance_name ... obsid distance \n", + "---------- -------------- --------------- ... --------- ------------------\n", + " science TESS SPOC ... 27463641 0.0\n", + " science TESS SPOC ... 27507606 0.0\n", + " science TESS SPOC ... 62431376 0.0\n", + " science TESS SPOC ... 62870786 0.0\n", + " science TESS SPOC ... 92616930 0.0\n", + " science TESS SPOC ... 95133384 0.0\n", + " science TESS SPOC ... 212510190 0.0\n", + " science TESS SPOC ... 213002720 0.0\n", + " science TESS SPOC ... 230167024 0.0\n", + " science TESS SPOC ... 232881378 0.0\n", + " ... ... ... ... ... ...\n", + " science HLA HLA ... 25907480 0.0\n", + " science HLA HLA ... 25907481 0.0\n", + " science HLA HLA ... 25907482 0.0\n", + " science HLA HLA ... 25907483 0.0\n", + " science HLA HLA ... 26030433 0.0\n", + " science HLA HLA ... 26036087 302.97582263477204\n", + " science HLA HLA ... 26177773 302.97582263477204\n", + " science HLA HLA ... 26036086 302.97823343218556\n", + " science HLA HLA ... 26036088 302.97823343218556\n", + " science GALEX AIS ... 9747 138.5761601079376\n", + "Length = 5691 rows\n" + ] + } + ], "source": [ "##Query the exoplanet HAT-P-7b\n", "query_table = Observations.query_object('HAT-P-7b')\n", @@ -273,9 +547,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "47\n", + "intentType obs_collection provenance_name ... objID1 distance \n", + "---------- -------------- --------------- ... ---------- ------------------\n", + " science TESS SPOC ... 809433252 118.92799122073585\n", + " science TESS SPOC ... 391038631 0.0\n", + " science TESS SPOC ... 809433245 0.0\n", + " science TESS SPOC ... 644524541 631.4755130296992\n", + " science TESS SPOC ... 120248645 0.0\n", + " science TESS SPOC ... 644482746 322.86772683788007\n", + " science TESS SPOC ... 622052027 0.0\n", + " science TESS SPOC ... 120248575 118.92799122073585\n", + " science TESS SPOC ... 1009989381 287.98135262165573\n", + " science TESS SPOC ... 391012253 516.163656032227\n" + ] + } + ], "source": [ "query_table = Observations.query_criteria(objectname=\"HAT-P-7b\", \n", " obs_collection=\"TESS\",\n", @@ -304,9 +598,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "intentType obs_collection provenance_name ... obsid distance \n", + "---------- -------------- ------------------- ... --------- ------------------\n", + " science TESS SPOC ... 62280343 0.0\n", + " science TESS SPOC ... 62324728 0.0\n", + " science TESS SPOC ... 28287315 0.0\n", + " science TESS SPOC ... 242083709 0.0\n", + " science TESS SPOC ... 28220318 642.16680527556\n", + " science SDSS SDSS Legacy Imaging ... 295157333 0.0\n", + " science SDSS SDSS Legacy Imaging ... 298312111 0.0\n", + " science SDSS SDSS Legacy Imaging ... 298312988 23.98919454120706\n", + " science SDSS SDSS Legacy Imaging ... 295157286 181.91503565346613\n", + " science SDSS SDSS Legacy Imaging ... 295157743 306.78343663816844\n", + " ... ... ... ... ... ...\n", + " science HLSP TGLC ... 169264258 719.2034100964987\n", + " science HLSP TGLC ... 189235004 719.2034100964987\n", + " science HLSP TASOC ... 91060980 719.2208006533244\n", + " science HLSP GSFC-ELEANOR-LITE ... 132103160 719.2208174859751\n", + " science HLSP GSFC-ELEANOR-LITE ... 141350209 719.2208174859751\n", + " science HLSP GSFC-ELEANOR-LITE ... 132088910 719.4582485379761\n", + " science HLSP T16 ... 358687394 719.5411072597959\n", + " science HLSP TGLC ... 169264805 719.5434578730323\n", + " science GALEX AIS ... 30996 0.0\n", + " science GALEX AIS ... 30996 0.0\n", + "Length = 2337 rows\n" + ] + } + ], "source": [ "##The syntax is \"RA DEC\", in any commonly accepted RA DEC format as outlined in the \"Search by Coordinate\" section\n", "query_table = Observations.query_region(\"100.12 -20.223\")\n", @@ -323,9 +648,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3807 132 218215\n" + ] + } + ], "source": [ "query_obj_count = Observations.query_object_count('WASP-121b')\n", "query_region_count = Observations.query_region_count('200.22, 10.33')\n", @@ -344,9 +677,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Column Name Column Label ... Examples/Valid Values \n", + "-------------- ---------------- ... ----------------------------------\n", + " intentType Observation Type ... Valid values: science, calibration\n", + "obs_collection Mission ... E.g. SWIFT, PS1, HST, IUE\n", + " Column Name ...\n", + "---------------- ...\n", + " obs_id ...\n", + " obsID ...\n", + " obs_collection ...\n", + "dataproduct_type ...\n", + " description ...\n" + ] + } + ], "source": [ "#For observations\n", "meta_data = Observations.get_metadata(\"observations\")\n", @@ -379,9 +730,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:TESS/product/tess2019199201929-s0014-s0055-0000000424865181-00652_dvr.pdf to Data/mastDownload/TESS/tess2019199201929-s0014-s0055-0000000424865181/tess2019199201929-s0014-s0055-0000000424865181-00652_dvr.pdf ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:TESS/product/tess2019199201929-s0014-s0055-0000000424865181-00652_dvr.xml to Data/mastDownload/TESS/tess2019199201929-s0014-s0055-0000000424865181/tess2019199201929-s0014-s0055-0000000424865181-00652_dvr.xml ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:TESS/product/tess2019199201929-s0014-s0055-0000000424865181-00652_dvm.pdf to Data/mastDownload/TESS/tess2019199201929-s0014-s0055-0000000424865181/tess2019199201929-s0014-s0055-0000000424865181-00652_dvm.pdf ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:TESS/product/tess2019199201929-s0014-s0055-0000000424865181-01-00652_dvs.pdf to Data/mastDownload/TESS/tess2019199201929-s0014-s0055-0000000424865181/tess2019199201929-s0014-s0055-0000000424865181-01-00652_dvs.pdf ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:TESS/product/tess2019199201929-s0014-s0055-0000000424865181-00652_dvt.fits to Data/mastDownload/TESS/tess2019199201929-s0014-s0055-0000000424865181/tess2019199201929-s0014-s0055-0000000424865181-00652_dvt.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:TESS/product/tess2024003055635-s0074-0000000424865156-0269-a_fast-lc.fits to Data/mastDownload/TESS/tess2024003055635-s0074-0000000424865156-0269-a_fast/tess2024003055635-s0074-0000000424865156-0269-a_fast-lc.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:TESS/product/tess2024003055635-s0074-0000000424865156-0269-a_fast-tp.fits to Data/mastDownload/TESS/tess2024003055635-s0074-0000000424865156-0269-a_fast/tess2024003055635-s0074-0000000424865156-0269-a_fast-tp.fits ... [Done]\n", + " Local Path ...\n", + "---------------------------------------------------------------------------------------------------------------------------------------- ...\n", + " Data/mastDownload/TESS/tess2019199201929-s0014-s0055-0000000424865181/tess2019199201929-s0014-s0055-0000000424865181-00652_dvr.pdf ...\n", + " Data/mastDownload/TESS/tess2019199201929-s0014-s0055-0000000424865181/tess2019199201929-s0014-s0055-0000000424865181-00652_dvr.xml ...\n", + " Data/mastDownload/TESS/tess2019199201929-s0014-s0055-0000000424865181/tess2019199201929-s0014-s0055-0000000424865181-00652_dvm.pdf ...\n", + " Data/mastDownload/TESS/tess2019199201929-s0014-s0055-0000000424865181/tess2019199201929-s0014-s0055-0000000424865181-01-00652_dvs.pdf ...\n", + " Data/mastDownload/TESS/tess2019199201929-s0014-s0055-0000000424865181/tess2019199201929-s0014-s0055-0000000424865181-00652_dvt.fits ...\n", + "Data/mastDownload/TESS/tess2024003055635-s0074-0000000424865156-0269-a_fast/tess2024003055635-s0074-0000000424865156-0269-a_fast-lc.fits ...\n", + "Data/mastDownload/TESS/tess2024003055635-s0074-0000000424865156-0269-a_fast/tess2024003055635-s0074-0000000424865156-0269-a_fast-tp.fits ...\n" + ] + } + ], "source": [ "##Repeat our previous TESS query for HAT-P-7b\n", "query_table = Observations.query_criteria(objectname=\"HAT-P-7b\", \n", @@ -404,9 +778,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mast:TESS/product/tess2021205113501-s0041-s0041-0000000424865181-00511_dvr.pdf\n" + ] + } + ], "source": [ "#Go to the next product in our above list for HAT-P-7b\n", "data_products = Observations.get_product_list(query_table[7:8])\n", @@ -418,9 +800,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:TESS/product/tess2021205113501-s0041-s0041-0000000424865181-00511_dvr.pdf to ../Data_Repositories/Data/TESS_Data.pdf ... [Done]\n" + ] + } + ], "source": [ "## The local path needs to reference a file, not a folder, in this case\n", "import os\n", @@ -455,9 +845,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " objID PICnameDR1 sourceId ... PICidDR1 distance \n", + "-------- ---------------- ------------------- ... -------- ------------------\n", + "29307583 PIC DR1 29307583 3002281237690853504 ... 29307583 2.5791268100984066\n", + "29273768 PIC DR1 29273768 3002277080162573312 ... 29273768 3.412897833173899\n", + "29265435 PIC DR1 29265435 3002275705773086976 ... 29265435 6.003720626738184\n", + "29287541 PIC DR1 29287541 3002280035100117760 ... 29287541 7.9166526503407955\n", + "29306080 PIC DR1 29306080 3002328718551357440 ... 29306080 8.229280048747171\n", + "29301456 PIC DR1 29301456 3002327314099544192 ... 29301456 10.261798030413424\n", + "29359276 PIC DR1 29359276 3002424208563685376 ... 29359276 11.738937090205514\n", + "29235782 PIC DR1 29235782 3002268936905107072 ... 29235782 11.837705768101078\n" + ] + } + ], "source": [ "catalog_data = Catalogs.query_region(\"100.23 -10\", catalog=\"Plato\")\n", "print(catalog_data)" @@ -481,9 +888,250 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "var questionsWbFMnHqSqPdp=[{\"question\": \"What are different methods to use when querying MAST?\", \"type\": \"many_choice\", \"answers\": [{\"answer\": \"query_object\", \"correct\": true, \"feedback\": \"Correct. This includes standard catalog names, common names, and star catalogs with coordinates.\"}, {\"answer\": \"query_region\", \"correct\": true, \"feedback\": \"Correct. You can search by an object's right ascension and declination, and within a certain radius of that coordinate.\"}, {\"answer\": \"query_criteria\", \"correct\": true, \"feedback\": \"Correct. This is the most flexible search option, allowing you to specify a wide range of criteria.\"}, {\"answer\": \"query_table\", \"correct\": false, \"feedback\": \"Incorrect. query_table is not a valid method for querying MAST.\"}]}];\n\nif (typeof Question === 'undefined') {\n// Make a random ID\nfunction 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// Convert LaTeX delimiters and markdown links to HTML\nfunction jaxify(string) {\n let mystring = string;\n let count = 0, count2 = 0;\n let loc = mystring.search(/([^\\\\]|^)(\\$)/);\n let loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n while (loc >= 0 || loc2 >= 0) {\n if (loc2 >= 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, count2 % 2 ? '$1\\\\]' : '$1\\\\[');\n count2++;\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, count % 2 ? '$1\\\\)' : '$1\\\\(');\n count++;\n }\n loc = mystring.search(/([^\\\\]|^)(\\$)/);\n loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n }\n // Replace markdown links\n mystring = mystring.replace(//g, 'http$1');\n mystring = mystring.replace(/\\[(.*?)\\]\\((.*?)\\)/g, '$1');\n return mystring;\n}\n\n// Base class for question types\nclass Question {\n static registry = {};\n static register(type, cls) {\n Question.registry[type] = cls;\n }\n static create(qa, id, index, options, rootDiv) {\n const Cls = Question.registry[qa.type];\n if (!Cls) {\n console.error(`No question class registered for type \"${qa.type}\"`);\n return;\n }\n const q = new Cls(qa, id, index, options, rootDiv);\n q.render();\n }\n\n constructor(qa, id, index, options, rootDiv) {\n this.qa = qa;\n this.id = id;\n this.index = index;\n this.options = options;\n this.rootDiv = rootDiv;\n // wrapper\n this.wrapper = document.createElement('div');\n this.wrapper.id = `quizWrap${id}`;\n this.wrapper.className = 'Quiz';\n this.wrapper.dataset.qnum = index;\n this.wrapper.style.maxWidth = `${options.maxWidth}px`;\n rootDiv.appendChild(this.wrapper);\n // question container\n this.outerqDiv = document.createElement('div');\n this.outerqDiv.id = `OuterquizQn${id}${index}`;\n this.wrapper.appendChild(this.outerqDiv);\n // question text\n this.qDiv = document.createElement('div');\n this.qDiv.id = `quizQn${id}${index}`;\n if (qa.question) {\n this.qDiv.innerHTML = jaxify(qa.question);\n this.outerqDiv.appendChild(this.qDiv);\n }\n // code block\n if (qa.code) {\n const codeDiv = document.createElement('div');\n codeDiv.id = `code${id}${index}`;\n codeDiv.className = 'QuizCode';\n const pre = document.createElement('pre');\n const codeEl = document.createElement('code');\n codeEl.innerHTML = qa.code;\n pre.appendChild(codeEl);\n codeDiv.appendChild(pre);\n this.outerqDiv.appendChild(codeDiv);\n }\n // answer container\n this.aDiv = document.createElement('div');\n this.aDiv.id = `quizAns${id}${index}`;\n this.aDiv.className = 'Answer';\n this.wrapper.appendChild(this.aDiv);\n // feedback container (append after answers)\n this.fbDiv = document.createElement('div');\n this.fbDiv.id = `fb${id}`;\n this.fbDiv.className = 'Feedback';\n this.fbDiv.dataset.answeredcorrect = 0;\n }\n\n render() {\n throw new Error('render() not implemented');\n }\n\n preserveResponse(val) {\n if (!this.options.preserveResponses) return;\n const resp = document.getElementById(`responses${this.rootDiv.id}`);\n if (!resp) return;\n const arr = JSON.parse(resp.dataset.responses);\n arr[this.index] = val;\n resp.dataset.responses = JSON.stringify(arr);\n printResponses(resp);\n }\n\n typeset(container) {\n if (typeof MathJax !== 'undefined') {\n const v = MathJax.version;\n if (v[0] === '2') {\n MathJax.Hub.Queue(['Typeset', MathJax.Hub]);\n } else {\n MathJax.typeset([container]);\n }\n }\n }\n}\n\n// Choose a random subset of an array. Can also be used to shuffle the array\nfunction 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\nfunction 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/* Callback function to determine whether a selected multiple-choice\n button corresponded to a correct answer and to provide feedback\n based on the answer */\nfunction 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 //console.log(answers);\n\n // Split behavior based on multiple choice vs many choice:\n var fb = document.getElementById(\"fb\" + id);\n\n\n\n /* Multiple choice (1 answer). Allow for 0 correct\n answers as an edge case */\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.innerHTML = jaxify(label.dataset.feedback);\n } else {\n fb.innerHTML = \"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.innerHTML = jaxify(label.dataset.feedback);\n } else {\n fb.innerHTML = \"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 { /* Many choice (more than 1 correct answer) */\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.innerHTML = feedback + \" [\" + answeredcorrect + \"/\" + numcorrect + \"]\";\n } else {\n fb.innerHTML = 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\n/* Function to produce the HTML buttons for a multiple choice/\n many choice question and to update the CSS tags based on\n the question type */\nfunction make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id) {\n\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 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// Object-oriented wrapper for MC/MANY choice\nclass MCQuestion extends Question {\n constructor(qa, id, idx, opts, rootDiv) { super(qa, id, idx, opts, rootDiv); }\n render() {\n //console.log(\"options.shuffleAnswers \" + this.options.shuffleAnswers);\n const numCorrect = make_mc(\n this.qa,\n this.options.shuffleAnswers,\n this.outerqDiv,\n this.qDiv,\n this.aDiv,\n this.id\n );\n if ('answer_cols' in this.qa) {\n this.aDiv.style.gridTemplateColumns =\n 'repeat(' + this.qa.answer_cols + ', 1fr)';\n }\n this.fbDiv.dataset.numcorrect = numCorrect;\n this.wrapper.appendChild(this.fbDiv);\n }\n}\nQuestion.register('multiple_choice', MCQuestion);\nQuestion.register('many_choice', MCQuestion);\nfunction 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 submission = Number(Number(submission).toPrecision(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.innerHTML = \"Incorrect -- try again.\";\n\n var answers = JSON.parse(ths.dataset.answers);\n //console.log(answers);\n\n var defaultFB = \"Incorrect. Try again.\";\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 var value;\n if (\"precision\" in ths.dataset) {\n value = answer.value.toPrecision(ths.dataset.precision);\n } else {\n value = answer.value;\n }\n if (submission == value) {\n if (\"feedback\" in answer) {\n fb.innerHTML = jaxify(answer.feedback);\n } else {\n fb.innerHTML = jaxify(\"Correct\");\n }\n correct = answer.correct;\n //console.log(answer.correct);\n done = true;\n }\n\n // } else if (answer.type==\"range\") {\n } else if ('range' in answer) {\n console.log(answer.range);\n console.log(submission, submission >=answer.range[0], submission < answer.range[1])\n if ((submission >= answer.range[0]) && (submission < answer.range[1])) {\n fb.innerHTML = jaxify(answer.feedback);\n correct = answer.correct;\n console.log(answer.correct);\n done = true;\n }\n } else if (answer.type == \"default\") {\n if (\"feedback\" in answer) {\n defaultFB = answer.feedback;\n } \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 console.log(\"done:\", done);\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 // After correct answer, if next JupyterQuiz question exists and has a text input, scroll by current question height\n if (correct) {\n // find the current question wrapper\n var wrapper = ths.closest('.Quiz');\n if (wrapper) {\n var nextWrapper = wrapper.nextElementSibling;\n if (nextWrapper && nextWrapper.classList.contains('Quiz')) {\n var nextInput = nextWrapper.querySelector('input.Input-text');\n if (nextInput) {\n var height = wrapper.getBoundingClientRect().height;\n console.log(height);\n nextInput.focus();\n }\n }\n }\n }\n return false;\n }\n\n}\n// Object-oriented wrapper for numeric questions\nclass NumericQuestion extends Question {\n constructor(qa, id, idx, opts, rootDiv) {\n super(qa, id, idx, opts, rootDiv);\n }\n render() {\n make_numeric(this.qa, this.outerqDiv, this.qDiv, this.aDiv, this.id);\n this.wrapper.appendChild(this.fbDiv);\n }\n}\nQuestion.register('numeric', NumericQuestion);\n\nfunction 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\nfunction 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\nfunction 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.innerHTML = \"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// Override show_questions to use object-oriented Question API\nfunction show_questions(json, container) {\n // Accept container element or element ID\n if (typeof container === 'string') {\n container = document.getElementById(container);\n }\n if (!container) {\n console.error('show_questions: invalid container', container);\n return;\n }\n\n const shuffleQuestions = container.dataset.shufflequestions === 'True';\n const shuffleAnswers = container.dataset.shuffleanswers === 'True';\n const preserveResponses = container.dataset.preserveresponses === 'true';\n const maxWidth = parseInt(container.dataset.maxwidth, 10) || 0;\n let numQuestions = parseInt(container.dataset.numquestions, 10) || json.length;\n if (numQuestions > json.length) numQuestions = json.length;\n\n let questions = json;\n if (shuffleQuestions || numQuestions < json.length) {\n questions = getRandomSubarray(json, numQuestions);\n }\n\n questions.forEach((qa, index) => {\n const id = makeid(8);\n const options = {\n shuffleAnswers: shuffleAnswers,\n preserveResponses: preserveResponses,\n maxWidth: maxWidth\n };\n Question.create(qa, id, index, options, container);\n });\n\n if (preserveResponses) {\n const respDiv = document.createElement('div');\n respDiv.id = 'responses' + container.id;\n respDiv.className = 'JCResponses';\n respDiv.dataset.responses = JSON.stringify([]);\n respDiv.innerHTML = 'Select your answers and then follow the directions that will appear here.';\n container.appendChild(respDiv);\n }\n\n // Trigger MathJax typesetting if available\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 if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([container]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\n },\n svg: {\n fontCache: 'global'\n }\n };\n\n (function () {\n var script = document.createElement('script');\n script.src = 'https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js';\n script.async = true;\n document.head.appendChild(script);\n })();\n }\n }\n }, 500);\nif (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 if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([container]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\n },\n svg: {\n fontCache: 'global'\n }\n };\n\n (function () {\n var script = document.createElement('script');\n script.src = 'https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js';\n script.async = true;\n document.head.appendChild(script);\n })();\n }\n } else {\n console.log(\"MathJax not found\");\n }\n }\n }\n // if (typeof MathJax !== 'undefined') {\n // const v = MathJax.version;\n // if (v[0] === '2') {\n // MathJax.Hub.Queue(['Typeset', MathJax.Hub]);\n // } else if (v[0] === '3') {\n // MathJax.typeset([container]);\n // }\n // }\n\n // Prevent link clicks from bubbling up\n Array.from(container.getElementsByClassName('Link')).forEach(link => {\n link.addEventListener('click', e => e.stopPropagation());\n });\n}\nfunction levenshteinDistance(a, b) {\n if (a.length === 0) return b.length;\n if (b.length === 0) return a.length;\n\n const matrix = Array(b.length + 1).fill(null).map(() => Array(a.length + 1).fill(null));\n\n for (let i = 0; i <= a.length; i++) {\n matrix[0][i] = i;\n }\n\n for (let j = 0; j <= b.length; j++) {\n matrix[j][0] = j;\n }\n\n for (let j = 1; j <= b.length; j++) {\n for (let i = 1; i <= a.length; i++) {\n const cost = a[i - 1] === b[j - 1] ? 0 : 1;\n matrix[j][i] = Math.min(\n matrix[j - 1][i] + 1, // Deletion\n matrix[j][i - 1] + 1, // Insertion\n matrix[j - 1][i - 1] + cost // Substitution\n );\n }\n }\n return matrix[b.length][a.length];\n}\n// Object-oriented wrapper for string input questions\nclass StringQuestion extends Question {\n constructor(qa, id, idx, opts, rootDiv) {\n super(qa, id, idx, opts, rootDiv);\n }\n render() {\n make_string(this.qa, this.outerqDiv, this.qDiv, this.aDiv, this.id);\n this.wrapper.appendChild(this.fbDiv);\n }\n}\nQuestion.register('string', StringQuestion);\n\nfunction check_string(ths, event) {\n if (event.keyCode === 13) {\n ths.blur();\n\n var id = ths.id.split('-')[0];\n var submission = ths.value.trim();\n var fb = document.getElementById(\"fb\" + id);\n fb.style.display = \"none\";\n fb.innerHTML = \"Incorrect -- try again.\";\n\n var answers = JSON.parse(ths.dataset.answers);\n var defaultFB = \"Incorrect. Try again.\";\n var correct;\n var done = false;\n\n // Handle default answer pattern: filter out and capture default feedback\n var filteredAnswers = [];\n answers.forEach(answer => {\n if (answer.type === \"default\") {\n defaultFB = answer.feedback;\n } else {\n filteredAnswers.push(answer);\n }\n });\n answers = filteredAnswers;\n\n answers.every(answer => {\n correct = false;\n\n let match = false;\n if (answer.match_case) {\n match = submission === answer.answer;\n } else {\n match = submission.toLowerCase() === answer.answer.toLowerCase();\n }\n console.log(submission);\n console.log(answer.answer);\n console.log(match);\n\n if (match) {\n if (\"feedback\" in answer) {\n fb.innerHTML = jaxify(answer.feedback);\n } else {\n fb.innerHTML = jaxify(\"Correct\");\n }\n correct = answer.correct;\n done = true;\n } else if (answer.fuzzy_threshold) {\n var max_length = Math.max(submission.length, answer.answer.length);\n var ratio;\n if (answer.match_case) {\n ratio = 1- (levenshteinDistance(submission, answer.answer) / max_length);\n } else {\n ratio = 1- (levenshteinDistance(submission.toLowerCase(),\n answer.answer.toLowerCase()) / max_length);\n }\n if (ratio >= answer.fuzzy_threshold) {\n if (\"feedback\" in answer) {\n fb.innerHTML = jaxify(\"(Fuzzy) \" + answer.feedback);\n } else {\n fb.innerHTML = jaxify(\"Correct\");\n }\n correct = answer.correct;\n done = true;\n }\n\n }\n\n if (done) {\n return false;\n } else {\n return true;\n }\n });\n\n if ((!done) && (defaultFB != \"\")) {\n fb.innerHTML = jaxify(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 var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n var qnum = document.getElementById(\"quizWrap\" + id).dataset.qnum;\n var responses = JSON.parse(responsesContainer.dataset.responses);\n responses[qnum] = submission;\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n\n if (typeof MathJax != 'undefined') {\n var version = MathJax.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 // After correct answer, if next JupyterQuiz question exists and has a text input, scroll by current question height\n if (correct) {\n var wrapper = ths.closest('.Quiz');\n if (wrapper) {\n var nextWrapper = wrapper.nextElementSibling;\n if (nextWrapper && nextWrapper.classList.contains('Quiz')) {\n var nextInput = nextWrapper.querySelector('input.Input-text');\n if (nextInput) {\n var height = wrapper.getBoundingClientRect().height;\n nextInput.focus();\n }\n }\n }\n }\n return false;\n }\n}\n\nfunction string_keypress(evnt) {\n var charC = (evnt.which) ? evnt.which : evnt.keyCode;\n\n if (charC == 13) {\n check_string(this, evnt);\n } \n}\n\n\nfunction make_string(qa, outerqDiv, qDiv, aDiv, id) {\n outerqDiv.className = \"StringQn\";\n aDiv.style.display = 'block';\n\n var lab = document.createElement(\"label\");\n lab.className = \"InpLabel\";\n lab.innerHTML = \"Type your answer here:\";\n aDiv.append(lab);\n\n var inp = document.createElement(\"input\");\n inp.type = \"text\";\n inp.id = id + \"-0\";\n inp.className = \"Input-text\";\n inp.setAttribute('data-answers', JSON.stringify(qa.answers));\n // Apply optional input width (approx. number of characters, in em units)\n if (qa.input_width != null) {\n inp.style['min-width'] = qa.input_width + 'em';\n }\n aDiv.append(inp);\n\n inp.onkeypress = string_keypress;\n inp.onpaste = event => false;\n\n inp.addEventListener(\"focus\", function (event) {\n this.value = \"\";\n return false;\n });\n}\n/*\n * Handle asynchrony issues when re-running quizzes in Jupyter notebooks.\n * Ensures show_questions is called after the container div is in the DOM.\n */\nfunction try_show() {\n if (document.getElementById(\"WbFMnHqSqPdp\")) {\n show_questions(questionsWbFMnHqSqPdp, WbFMnHqSqPdp);\n } else {\n setTimeout(try_show, 200);\n }\n};\n// Invoke immediately\n{\n try_show();\n}\n}\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display_quiz(questions[3:4])" ] @@ -547,7 +1195,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -557,7 +1205,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "exocore", "language": "python", "name": "python3" }, @@ -571,7 +1219,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.12.12" } }, "nbformat": 4, diff --git a/ExoCore/Curriculum/Data_Structures/Pandas.ipynb b/ExoCore/Curriculum/Data_Structures/Pandas.ipynb index 6d6de1d..ddba9e7 100644 --- a/ExoCore/Curriculum/Data_Structures/Pandas.ipynb +++ b/ExoCore/Curriculum/Data_Structures/Pandas.ipynb @@ -56,9 +56,11 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 1, "id": "fdc01e15-56bf-4ebe-801f-8317c8b9f22f", "metadata": {}, + "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" @@ -86,10 +88,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "32d9fdd7-f942-4c82-9ca8-bdb192a07040", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0\n", + "1 0.1\n", + "2 0.4\n", + "3 0.7\n", + "4 10\n", + "5 100\n", + "6 Test\n", + "dtype: object \n", + "\n", + "100 \n", + " 4 10\n", + "5 100\n", + "6 Test\n", + "dtype: object \n", + "\n", + "Identical index \n", + "\n", + "1 2 100 \n", + "\n", + "Cannot use default index if integers are assigned as a custom index!\n", + "a 15\n", + "b Exo\n", + "c Planets\n", + "d 100\n", + "dtype: object \n", + "\n", + "0 15\n", + "1 Exo\n", + "2 Planets\n", + "3 100\n", + "dtype: object\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/dg/m13m7_yx7jq5n_wrnwshkrxw0000gn/T/ipykernel_28206/3117472397.py:14: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if indexed_data[0] == indexed_data['a']:\n", + "/var/folders/dg/m13m7_yx7jq5n_wrnwshkrxw0000gn/T/ipykernel_28206/3117472397.py:23: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " print(ser['a'], ser['b'], ser[2], '\\n')\n" + ] + } + ], "source": [ "\n", "#Construct the Series by calling the function and building a list inside\n", @@ -149,10 +199,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "287f84b8-55f9-4580-a620-43ca56a6194f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 10.00000\n", + "1 4.00000\n", + "2 0.44444\n", + "3 18.00000\n", + "dtype: float64 \n", + "\n", + "0 2.00000\n", + "1 18.00000\n", + "2 199.55556\n", + "3 21.00000\n", + "4 NaN\n", + "5 NaN\n", + "6 NaN\n", + "dtype: float64 \n", + "\n", + " 0 -12.00000\n", + "1 0.00000\n", + "2 -6.44444\n", + "3 10.00000\n", + "dtype: float64 \n", + "\n" + ] + } + ], "source": [ "example = pd.Series([-10, 4, -0.44444, 18])\n", "example2 = pd.Series([12, 14, 200, 3, -12, 72, np.nan])\n", @@ -172,10 +250,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "740138d5-bb2e-49d0-9d61-2df529474ef9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "min -1.200000e+01\n", + "max 2.000000e+02\n", + "mean 4.816667e+01\n", + "product -8.709120e+07\n", + "dtype: float64 \n", + "\n", + "0 10.0\n", + "1 10.0\n", + "2 10.0\n", + "3 3.0\n", + "4 0.0\n", + "5 10.0\n", + "6 NaN\n", + "dtype: float64 \n", + "\n", + " self other\n", + "0 -10.00000 2.0\n", + "2 -0.44444 6.0\n", + "3 18.00000 8.0 \n", + "\n", + "count 4.000000\n", + "mean 5.000000\n", + "std 2.581989\n", + "min 2.000000\n", + "25% 3.500000\n", + "50% 5.000000\n", + "75% 6.500000\n", + "max 8.000000\n", + "dtype: float64 \n", + "\n" + ] + } + ], "source": [ "#Can combine operations using Series.agg\n", "print(example2.agg(['min', 'max', 'mean', 'product']), '\\n')\n", @@ -192,10 +307,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "b50861ce-e01c-4979-81d4-39930f1683a9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 False\n", + "1 True\n", + "2 False\n", + "3 True\n", + "dtype: bool 0 True\n", + "1 True\n", + "2 True\n", + "3 False\n", + "dtype: bool \n", + "\n", + "0 12.0\n", + "1 14.0\n", + "2 200.0\n", + "3 3.0\n", + "4 -12.0\n", + "5 72.0\n", + "6 0.0\n", + "dtype: float64 0 True\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + "5 True\n", + "6 False\n", + "dtype: bool \n", + "\n", + "[12.0, 14.0, 200.0, 3.0, -12.0, 72.0, nan] \n", + "\n", + "2 \n", + "\n" + ] + } + ], "source": [ "#Comparisons between series, element wise. Greater than or equal to and less than or equal to, respectively\n", "print(example.ge(example3), example.le(example3), '\\n')\n", @@ -240,10 +392,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "d0c85058-377a-4b90-915f-9e6dfcda9d5e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "count 4595.000000\n", + "mean 86.544429\n", + "std 12.116125\n", + "min 0.186500\n", + "25% 86.800000\n", + "50% 88.740000\n", + "75% 89.640000\n", + "max 176.092000\n", + "dtype: float64\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Inclination Distributions')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from astroquery.ipac.nexsci.nasa_exoplanet_archive import NasaExoplanetArchive\n", "from matplotlib import pyplot as plt\n", @@ -292,10 +480,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "07bdd0ab-aef7-4741-9442-39cfa6711013", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Planet Name Radius\n", + "0 EC1 b 1.0\n", + "1 EC1 c 1.2\n", + "2 EC2 b 0.5\n", + "3 EC2 c 4.0\n", + "4 EC2 c 2.0 \n", + "\n", + "All DataFrames are equal \n", + "\n", + " a b c\n", + "0 1 2 3\n", + "1 4 5 6\n", + "2 7 8 9\n" + ] + } + ], "source": [ "#Form it in-line, using 'column': [data]\n", "data_line = pd.DataFrame({'Planet Name': ['EC1 b', 'EC1 c', 'EC2 b', 'EC2 c', 'EC2 c'], 'Radius': [1, 1.2, 0.5, 4, 2]})\n", @@ -330,10 +538,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "4586d20d-f9a3-4fe4-a2a9-26056ac3d03f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " pl_radj pl_orbper\n", + "pl_name \n", + "Kepler-1167 b 0.152556 1.003934\n", + "Kepler-1740 b 0.296478 8.172400\n", + "Kepler-1581 b 0.071371 6.283855\n", + "Kepler-644 b 0.281024 3.173917\n", + "Kepler-1752 b 0.405086 56.358501\n", + "... ... ...\n", + "KMT-2024-BLG-1005L b 1.080000 NaN\n", + "TOI-2211.01 0.139263 3.092687\n", + "TOI-2374 b 0.607549 4.313610\n", + "LTT 3780 b 0.118209 0.768379\n", + "LTT 3780 c 0.213222 12.252284\n", + "\n", + "[6080 rows x 2 columns]\n", + " pl_orbincl pl_orbper pl_radj\n", + "pl_name \n", + "HIP 41378 f 89.980 542.07975 0.82077\n", + "HD 19994 b NaN 466.20000 1.21000\n", + "HD 156279 c 74.699 4818.25358 1.11000\n", + "GJ 179 b 61.000 2288.00000 1.23000\n", + "GJ 832 b 51.000 3853.00000 1.23000\n", + "... ... ... ...\n", + "HD 188641 b 49.245 14583.81038 1.06000\n", + "HD 28185 c 57.650 17417.92079 1.08000\n", + "MOA-2011-BLG-293L b NaN 3000.00000 1.15000\n", + "OGLE-2006-BLG-109L c 64.000 4927.50000 0.99400\n", + "HD 143811 AB b 37.000 117000.00000 1.40000\n", + "\n", + "[637 rows x 3 columns]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from astroquery.ipac.nexsci.nasa_exoplanet_archive import NasaExoplanetArchive\n", "from matplotlib import pyplot as plt\n", @@ -380,10 +635,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "97327cb7-dfbd-40f2-a763-943a55afebab", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pl_orbincl NaN\n", + "pl_orbper 11.18465\n", + "pl_radj 0.09130\n", + "Name: Proxima Cen b, dtype: float64\n", + "WASP121 b is: 1.742 Jupiter radii\n", + " pl_orbincl pl_orbper pl_radj\n", + "pl_name \n", + "Kepler-1581 b 89.490 6.283855 0.071371\n", + "Kepler-1288 b 87.580 2.761224 0.099920\n", + "Kepler-595 c 89.010 12.386020 0.090017\n", + "PSR B1257+12 b 50.000 25.262000 0.030200\n", + "TRAPPIST-1 c 89.778 2.421937 0.097868\n", + "... ... ... ...\n", + "Kepler-1629 b 89.890 3.875958 0.068695\n", + "K2-72 b 89.150 5.577212 0.096351\n", + "K2-72 d 89.260 7.760178 0.090106\n", + "Kepler-119 c 88.650 4.125103 0.082000\n", + "Kepler-42 d 88.020 1.865169 0.051000\n", + "\n", + "[368 rows x 3 columns]\n", + " pl_orbincl pl_orbper pl_radj\n", + "pl_name \n", + "HAT-P-7 b 83.11 2.204740 1.510\n", + "HAT-P-22 b 86.90 3.212220 1.150\n", + "CoRoT-3 b 85.90 4.256800 1.010\n", + "NGTS-10 b NaN 0.766894 1.205\n", + "HD 33283 b NaN 18.199100 1.120\n", + "... ... ... ...\n", + "HAT-P-21 b 87.20 4.124480 1.110\n", + "WASP-19 b 79.08 0.788839 1.415\n", + "KELT-16 b 84.40 0.968995 1.415\n", + "HAT-P-8 b 87.80 3.076340 1.400\n", + "TOI-4666 b 89.60 2.908917 1.110\n", + "\n", + "[633 rows x 3 columns]\n" + ] + } + ], "source": [ "#We can use the DataFrame imported from the last block of code above\n", "#What if we want to isolate the parameters for Proxima Centauri b?\n", @@ -416,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "98c00ff1-31f9-4a1d-a46b-edf8e673bbf3", "metadata": {}, "outputs": [], @@ -431,10 +728,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "09e3af50-cf8c-4b6a-bf0c-dda48438d7a5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pl_name PSR J1719-1438 b\n", + "pl_orbincl NaN\n", + "pl_orbper 0.090706\n", + "pl_radj NaN\n", + "Name: 5957, dtype: object\n", + "The planet with the 2nd shortest orbital period is: ZTF J1828+2308 b with a period of 0.1120067 days\n", + "The planet with the longest orbital period is: COCONUTS-2 b with a period of 402000000.0 days\n" + ] + } + ], "source": [ "#First sort the DataFrame by ascending planetary orbital period\n", "exo_data.sort_values(by = ['pl_orbper'], inplace = True)\n", @@ -506,7 +817,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "f0659f52-f1b0-4dac-919c-443a4e001a19", "metadata": {}, "outputs": [], @@ -529,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "395494a4-109d-470b-943b-f9a9c3c6ddd3", "metadata": {}, "outputs": [], @@ -555,7 +866,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "760e7f5a-1976-4084-bfe5-b89b693bd882", "metadata": {}, "outputs": [], @@ -566,7 +877,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "exocore", "language": "python", "name": "python3" }, @@ -580,7 +891,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.12.12" } }, "nbformat": 4, diff --git a/ExoCore/Curriculum/Data_Structures/Scipy.ipynb b/ExoCore/Curriculum/Data_Structures/Scipy.ipynb index a099ff3..557f7d6 100644 --- a/ExoCore/Curriculum/Data_Structures/Scipy.ipynb +++ b/ExoCore/Curriculum/Data_Structures/Scipy.ipynb @@ -50,8 +50,10 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 1, "metadata": {}, + "outputs": [], "source": [ "import scipy\n", "from jupyterquiz import display_quiz\n", @@ -71,9 +73,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The value of π is: 3.141592653589793\n", + "The speed of light, c, is: 299792458.0\n", + "The mass of the neutron is: 1.67492750056e-27\n" + ] + } + ], "source": [ "## Evoke pi using scipy.constants.pi\n", "\n", @@ -99,9 +111,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "standard acceleration of gravity\n", + "The value of g is: 9.80665\n", + "The units of g are: m s^-2\n", + "The uncertainty of g is: 0.0\n", + "(3727.3794118, 'MeV', 1.2e-06)\n" + ] + } + ], "source": [ "## We want to know the standard acceleration due to gravity\n", "## Search for the string 'gravity' to see the matches\n", @@ -139,9 +163,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The value of the integral is: 6.918788129478686\n", + "The uncertainty of the integral is: 2.8384102869472277e-08\n" + ] + } + ], "source": [ "## First, define your function\n", "import numpy as np\n", @@ -167,9 +200,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The double integral is: (0.6666666666666667, 7.401486830834377e-15)\n" + ] + } + ], "source": [ "def function_2(y,x):\n", " return x*y**2\n", @@ -188,9 +229,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(752.0286072876481, 1.9993657218719818e-07)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from matplotlib import pyplot as plt\n", "def function_3(y,x):\n", @@ -219,9 +278,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5.568327996830832, 4.4619079936998625e-08)\n" + ] + }, + { + "data": { + "image/png": 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", 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      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def gaussian(x,y,z):\n", " return np.exp(-(x ** 2 + y ** 2 + z ** 2))\n", @@ -261,9 +338,250 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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This is the mass equivalent for a beta particle/electron at rest.\"}]}, {\"question\": \"What is the integral of the function below?\", \"type\": \"numeric\", \"precision\": 4, \"answers\": [{\"type\": \"value\", \"value\": 1.885, \"correct\": true, \"feedback\": \"Correct!\"}]}];\n\nif (typeof Question === 'undefined') {\n// Make a random ID\nfunction 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// Convert LaTeX delimiters and markdown links to HTML\nfunction jaxify(string) {\n let mystring = string;\n let count = 0, count2 = 0;\n let loc = mystring.search(/([^\\\\]|^)(\\$)/);\n let loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n while (loc >= 0 || loc2 >= 0) {\n if (loc2 >= 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, count2 % 2 ? '$1\\\\]' : '$1\\\\[');\n count2++;\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, count % 2 ? '$1\\\\)' : '$1\\\\(');\n count++;\n }\n loc = mystring.search(/([^\\\\]|^)(\\$)/);\n loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n }\n // Replace markdown links\n mystring = mystring.replace(//g, 'http$1');\n mystring = mystring.replace(/\\[(.*?)\\]\\((.*?)\\)/g, '$1');\n return mystring;\n}\n\n// Base class for question types\nclass Question {\n static registry = {};\n static register(type, cls) {\n Question.registry[type] = cls;\n }\n static create(qa, id, index, options, rootDiv) {\n const Cls = Question.registry[qa.type];\n if (!Cls) {\n console.error(`No question class registered for type \"${qa.type}\"`);\n return;\n }\n const q = new Cls(qa, id, index, options, rootDiv);\n q.render();\n }\n\n constructor(qa, id, index, options, rootDiv) {\n this.qa = qa;\n this.id = id;\n this.index = index;\n this.options = options;\n this.rootDiv = rootDiv;\n // wrapper\n this.wrapper = document.createElement('div');\n this.wrapper.id = `quizWrap${id}`;\n this.wrapper.className = 'Quiz';\n this.wrapper.dataset.qnum = index;\n this.wrapper.style.maxWidth = `${options.maxWidth}px`;\n rootDiv.appendChild(this.wrapper);\n // question container\n this.outerqDiv = document.createElement('div');\n this.outerqDiv.id = `OuterquizQn${id}${index}`;\n this.wrapper.appendChild(this.outerqDiv);\n // question text\n this.qDiv = document.createElement('div');\n this.qDiv.id = `quizQn${id}${index}`;\n if (qa.question) {\n this.qDiv.innerHTML = jaxify(qa.question);\n this.outerqDiv.appendChild(this.qDiv);\n }\n // code block\n if (qa.code) {\n const codeDiv = document.createElement('div');\n codeDiv.id = `code${id}${index}`;\n codeDiv.className = 'QuizCode';\n const pre = document.createElement('pre');\n const codeEl = document.createElement('code');\n codeEl.innerHTML = qa.code;\n pre.appendChild(codeEl);\n codeDiv.appendChild(pre);\n this.outerqDiv.appendChild(codeDiv);\n }\n // answer container\n this.aDiv = document.createElement('div');\n this.aDiv.id = `quizAns${id}${index}`;\n this.aDiv.className = 'Answer';\n this.wrapper.appendChild(this.aDiv);\n // feedback container (append after answers)\n this.fbDiv = document.createElement('div');\n this.fbDiv.id = `fb${id}`;\n this.fbDiv.className = 'Feedback';\n this.fbDiv.dataset.answeredcorrect = 0;\n }\n\n render() {\n throw new Error('render() not implemented');\n }\n\n preserveResponse(val) {\n if (!this.options.preserveResponses) return;\n const resp = document.getElementById(`responses${this.rootDiv.id}`);\n if (!resp) return;\n const arr = JSON.parse(resp.dataset.responses);\n arr[this.index] = val;\n resp.dataset.responses = JSON.stringify(arr);\n printResponses(resp);\n }\n\n typeset(container) {\n if (typeof MathJax !== 'undefined') {\n const v = MathJax.version;\n if (v[0] === '2') {\n MathJax.Hub.Queue(['Typeset', MathJax.Hub]);\n } else {\n MathJax.typeset([container]);\n }\n }\n }\n}\n\n// Choose a random subset of an array. Can also be used to shuffle the array\nfunction 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\nfunction 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/* Callback function to determine whether a selected multiple-choice\n button corresponded to a correct answer and to provide feedback\n based on the answer */\nfunction 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 //console.log(answers);\n\n // Split behavior based on multiple choice vs many choice:\n var fb = document.getElementById(\"fb\" + id);\n\n\n\n /* Multiple choice (1 answer). Allow for 0 correct\n answers as an edge case */\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.innerHTML = jaxify(label.dataset.feedback);\n } else {\n fb.innerHTML = \"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.innerHTML = jaxify(label.dataset.feedback);\n } else {\n fb.innerHTML = \"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 { /* Many choice (more than 1 correct answer) */\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.innerHTML = feedback + \" [\" + answeredcorrect + \"/\" + numcorrect + \"]\";\n } else {\n fb.innerHTML = 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\n/* Function to produce the HTML buttons for a multiple choice/\n many choice question and to update the CSS tags based on\n the question type */\nfunction make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id) {\n\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 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// Object-oriented wrapper for MC/MANY choice\nclass MCQuestion extends Question {\n constructor(qa, id, idx, opts, rootDiv) { super(qa, id, idx, opts, rootDiv); }\n render() {\n //console.log(\"options.shuffleAnswers \" + this.options.shuffleAnswers);\n const numCorrect = make_mc(\n this.qa,\n this.options.shuffleAnswers,\n this.outerqDiv,\n this.qDiv,\n this.aDiv,\n this.id\n );\n if ('answer_cols' in this.qa) {\n this.aDiv.style.gridTemplateColumns =\n 'repeat(' + this.qa.answer_cols + ', 1fr)';\n }\n this.fbDiv.dataset.numcorrect = numCorrect;\n this.wrapper.appendChild(this.fbDiv);\n }\n}\nQuestion.register('multiple_choice', MCQuestion);\nQuestion.register('many_choice', MCQuestion);\nfunction 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 submission = Number(Number(submission).toPrecision(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.innerHTML = \"Incorrect -- try again.\";\n\n var answers = JSON.parse(ths.dataset.answers);\n //console.log(answers);\n\n var defaultFB = \"Incorrect. Try again.\";\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 var value;\n if (\"precision\" in ths.dataset) {\n value = answer.value.toPrecision(ths.dataset.precision);\n } else {\n value = answer.value;\n }\n if (submission == value) {\n if (\"feedback\" in answer) {\n fb.innerHTML = jaxify(answer.feedback);\n } else {\n fb.innerHTML = jaxify(\"Correct\");\n }\n correct = answer.correct;\n //console.log(answer.correct);\n done = true;\n }\n\n // } else if (answer.type==\"range\") {\n } else if ('range' in answer) {\n console.log(answer.range);\n console.log(submission, submission >=answer.range[0], submission < answer.range[1])\n if ((submission >= answer.range[0]) && (submission < answer.range[1])) {\n fb.innerHTML = jaxify(answer.feedback);\n correct = answer.correct;\n console.log(answer.correct);\n done = true;\n }\n } else if (answer.type == \"default\") {\n if (\"feedback\" in answer) {\n defaultFB = answer.feedback;\n } \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 console.log(\"done:\", done);\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 // After correct answer, if next JupyterQuiz question exists and has a text input, scroll by current question height\n if (correct) {\n // find the current question wrapper\n var wrapper = ths.closest('.Quiz');\n if (wrapper) {\n var nextWrapper = wrapper.nextElementSibling;\n if (nextWrapper && nextWrapper.classList.contains('Quiz')) {\n var nextInput = nextWrapper.querySelector('input.Input-text');\n if (nextInput) {\n var height = wrapper.getBoundingClientRect().height;\n console.log(height);\n nextInput.focus();\n }\n }\n }\n }\n return false;\n }\n\n}\n// Object-oriented wrapper for numeric questions\nclass NumericQuestion extends Question {\n constructor(qa, id, idx, opts, rootDiv) {\n super(qa, id, idx, opts, rootDiv);\n }\n render() {\n make_numeric(this.qa, this.outerqDiv, this.qDiv, this.aDiv, this.id);\n this.wrapper.appendChild(this.fbDiv);\n }\n}\nQuestion.register('numeric', NumericQuestion);\n\nfunction 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\nfunction 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\nfunction 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.innerHTML = \"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// Override show_questions to use object-oriented Question API\nfunction show_questions(json, container) {\n // Accept container element or element ID\n if (typeof container === 'string') {\n container = document.getElementById(container);\n }\n if (!container) {\n console.error('show_questions: invalid container', container);\n return;\n }\n\n const shuffleQuestions = container.dataset.shufflequestions === 'True';\n const shuffleAnswers = container.dataset.shuffleanswers === 'True';\n const preserveResponses = container.dataset.preserveresponses === 'true';\n const maxWidth = parseInt(container.dataset.maxwidth, 10) || 0;\n let numQuestions = parseInt(container.dataset.numquestions, 10) || json.length;\n if (numQuestions > json.length) numQuestions = json.length;\n\n let questions = json;\n if (shuffleQuestions || numQuestions < json.length) {\n questions = getRandomSubarray(json, numQuestions);\n }\n\n questions.forEach((qa, index) => {\n const id = makeid(8);\n const options = {\n shuffleAnswers: shuffleAnswers,\n preserveResponses: preserveResponses,\n maxWidth: maxWidth\n };\n Question.create(qa, id, index, options, container);\n });\n\n if (preserveResponses) {\n const respDiv = document.createElement('div');\n respDiv.id = 'responses' + container.id;\n respDiv.className = 'JCResponses';\n respDiv.dataset.responses = JSON.stringify([]);\n respDiv.innerHTML = 'Select your answers and then follow the directions that will appear here.';\n container.appendChild(respDiv);\n }\n\n // Trigger MathJax typesetting if available\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 if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([container]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\n },\n svg: {\n fontCache: 'global'\n }\n };\n\n (function () {\n var script = document.createElement('script');\n script.src = 'https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js';\n script.async = true;\n document.head.appendChild(script);\n })();\n }\n }\n }, 500);\nif (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 if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([container]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\n },\n svg: {\n fontCache: 'global'\n }\n };\n\n (function () {\n var script = document.createElement('script');\n script.src = 'https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js';\n script.async = true;\n document.head.appendChild(script);\n })();\n }\n } else {\n console.log(\"MathJax not found\");\n }\n }\n }\n // if (typeof MathJax !== 'undefined') {\n // const v = MathJax.version;\n // if (v[0] === '2') {\n // MathJax.Hub.Queue(['Typeset', MathJax.Hub]);\n // } else if (v[0] === '3') {\n // MathJax.typeset([container]);\n // }\n // }\n\n // Prevent link clicks from bubbling up\n Array.from(container.getElementsByClassName('Link')).forEach(link => {\n link.addEventListener('click', e => e.stopPropagation());\n });\n}\nfunction levenshteinDistance(a, b) {\n if (a.length === 0) return b.length;\n if (b.length === 0) return a.length;\n\n const matrix = Array(b.length + 1).fill(null).map(() => Array(a.length + 1).fill(null));\n\n for (let i = 0; i <= a.length; i++) {\n matrix[0][i] = i;\n }\n\n for (let j = 0; j <= b.length; j++) {\n matrix[j][0] = j;\n }\n\n for (let j = 1; j <= b.length; j++) {\n for (let i = 1; i <= a.length; i++) {\n const cost = a[i - 1] === b[j - 1] ? 0 : 1;\n matrix[j][i] = Math.min(\n matrix[j - 1][i] + 1, // Deletion\n matrix[j][i - 1] + 1, // Insertion\n matrix[j - 1][i - 1] + cost // Substitution\n );\n }\n }\n return matrix[b.length][a.length];\n}\n// Object-oriented wrapper for string input questions\nclass StringQuestion extends Question {\n constructor(qa, id, idx, opts, rootDiv) {\n super(qa, id, idx, opts, rootDiv);\n }\n render() {\n make_string(this.qa, this.outerqDiv, this.qDiv, this.aDiv, this.id);\n this.wrapper.appendChild(this.fbDiv);\n }\n}\nQuestion.register('string', StringQuestion);\n\nfunction check_string(ths, event) {\n if (event.keyCode === 13) {\n ths.blur();\n\n var id = ths.id.split('-')[0];\n var submission = ths.value.trim();\n var fb = document.getElementById(\"fb\" + id);\n fb.style.display = \"none\";\n fb.innerHTML = \"Incorrect -- try again.\";\n\n var answers = JSON.parse(ths.dataset.answers);\n var defaultFB = \"Incorrect. Try again.\";\n var correct;\n var done = false;\n\n // Handle default answer pattern: filter out and capture default feedback\n var filteredAnswers = [];\n answers.forEach(answer => {\n if (answer.type === \"default\") {\n defaultFB = answer.feedback;\n } else {\n filteredAnswers.push(answer);\n }\n });\n answers = filteredAnswers;\n\n answers.every(answer => {\n correct = false;\n\n let match = false;\n if (answer.match_case) {\n match = submission === answer.answer;\n } else {\n match = submission.toLowerCase() === answer.answer.toLowerCase();\n }\n console.log(submission);\n console.log(answer.answer);\n console.log(match);\n\n if (match) {\n if (\"feedback\" in answer) {\n fb.innerHTML = jaxify(answer.feedback);\n } else {\n fb.innerHTML = jaxify(\"Correct\");\n }\n correct = answer.correct;\n done = true;\n } else if (answer.fuzzy_threshold) {\n var max_length = Math.max(submission.length, answer.answer.length);\n var ratio;\n if (answer.match_case) {\n ratio = 1- (levenshteinDistance(submission, answer.answer) / max_length);\n } else {\n ratio = 1- (levenshteinDistance(submission.toLowerCase(),\n answer.answer.toLowerCase()) / max_length);\n }\n if (ratio >= answer.fuzzy_threshold) {\n if (\"feedback\" in answer) {\n fb.innerHTML = jaxify(\"(Fuzzy) \" + answer.feedback);\n } else {\n fb.innerHTML = jaxify(\"Correct\");\n }\n correct = answer.correct;\n done = true;\n }\n\n }\n\n if (done) {\n return false;\n } else {\n return true;\n }\n });\n\n if ((!done) && (defaultFB != \"\")) {\n fb.innerHTML = jaxify(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 var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n var qnum = document.getElementById(\"quizWrap\" + id).dataset.qnum;\n var responses = JSON.parse(responsesContainer.dataset.responses);\n responses[qnum] = submission;\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n\n if (typeof MathJax != 'undefined') {\n var version = MathJax.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 // After correct answer, if next JupyterQuiz question exists and has a text input, scroll by current question height\n if (correct) {\n var wrapper = ths.closest('.Quiz');\n if (wrapper) {\n var nextWrapper = wrapper.nextElementSibling;\n if (nextWrapper && nextWrapper.classList.contains('Quiz')) {\n var nextInput = nextWrapper.querySelector('input.Input-text');\n if (nextInput) {\n var height = wrapper.getBoundingClientRect().height;\n nextInput.focus();\n }\n }\n }\n }\n return false;\n }\n}\n\nfunction string_keypress(evnt) {\n var charC = (evnt.which) ? evnt.which : evnt.keyCode;\n\n if (charC == 13) {\n check_string(this, evnt);\n } \n}\n\n\nfunction make_string(qa, outerqDiv, qDiv, aDiv, id) {\n outerqDiv.className = \"StringQn\";\n aDiv.style.display = 'block';\n\n var lab = document.createElement(\"label\");\n lab.className = \"InpLabel\";\n lab.innerHTML = \"Type your answer here:\";\n aDiv.append(lab);\n\n var inp = document.createElement(\"input\");\n inp.type = \"text\";\n inp.id = id + \"-0\";\n inp.className = \"Input-text\";\n inp.setAttribute('data-answers', JSON.stringify(qa.answers));\n // Apply optional input width (approx. number of characters, in em units)\n if (qa.input_width != null) {\n inp.style['min-width'] = qa.input_width + 'em';\n }\n aDiv.append(inp);\n\n inp.onkeypress = string_keypress;\n inp.onpaste = event => false;\n\n inp.addEventListener(\"focus\", function (event) {\n this.value = \"\";\n return false;\n });\n}\n/*\n * Handle asynchrony issues when re-running quizzes in Jupyter notebooks.\n * Ensures show_questions is called after the container div is in the DOM.\n */\nfunction try_show() {\n if (document.getElementById(\"oUPJXCYEyCaF\")) {\n show_questions(questionsoUPJXCYEyCaF, oUPJXCYEyCaF);\n } else {\n setTimeout(try_show, 200);\n }\n};\n// Invoke immediately\n{\n try_show();\n}\n}\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display_quiz([questions[0]] + [questions[1]])" ] @@ -297,7 +615,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -331,9 +649,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Flux')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.errorbar(time, flux, yerr=flux_err, ecolor='black', elinewidth=0.75, marker='o', linestyle='', markersize=4)\n", "plt.xlabel('Time (Days)')\n", @@ -354,9 +693,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.06228037 0.04038963 25.03801824 30.0228759 0.99994084]\n", + "A1 is 0.06228037238383151\n", + "A2 is 0.040389629070401535\n", + "w1 is 25.038018235710535\n", + "w2 is 30.022875904625906\n", + "b is 0.9999408410329343\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Time (days)')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "## Create the model to fit\n", "def double_sine(t, a1, a2, w1, w2, b):\n", @@ -390,9 +762,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Time (BJD - 2457000)')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import pandas as pd\n", "from matplotlib import pyplot as plt\n", @@ -418,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -435,9 +828,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Rel. Flux')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "## Reimport so that we can rerun the block as many times as we need\n", "data = pd.read_csv(\"../../Auxiliary_Files/Data/Data_Structures/Prox_Cen.csv\")\n", @@ -464,7 +878,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "exocore", "language": "python", "name": "python3" }, @@ -478,7 +892,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.12.12" } }, "nbformat": 4, diff --git a/ExoCore/ExoCore.ipynb b/ExoCore/ExoCore.ipynb index 7c94835..74f0418 100644 --- a/ExoCore/ExoCore.ipynb +++ b/ExoCore/ExoCore.ipynb @@ -100,7 +100,7 @@ { "data": { "text/markdown": [ - "5811 Confirmed Exoplanets " + "6080 Confirmed Exoplanets " ], "text/plain": [ "" @@ -367,7 +367,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "exocore", "language": "python", "name": "python3" }, @@ -381,7 +381,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.12.12" } }, "nbformat": 4, diff --git a/environment.yml b/environment.yml index d885340..a3cf711 100644 --- a/environment.yml +++ b/environment.yml @@ -1,11 +1,13 @@ name: exocore +channels: + - defaults dependencies: - python=3.12 - astropy - conda-forge::astroquery - jupyter - - numpy - - pandas + - numpy<2 + - pandas<3 - scipy - pip - pip: