From 289c48a618988dc5b95f1bb32bc81c91bd07472b Mon Sep 17 00:00:00 2001 From: nwhitsett Date: Sun, 12 Jan 2025 23:33:37 -0500 Subject: [PATCH] merging with fork --- ExoCore/.DS_Store | Bin 6148 -> 6148 bytes ExoCore/Auxiliary_Files/Graphics/.DS_Store | Bin 8196 -> 6148 bytes .../Curriculum/Data_Repositories/MAST.ipynb | 532 ++++++++++- .../Utility_Software/Lightkurve.ipynb | 823 +++++++++++++++++- .../Module_2/MAST/Checkpoints/1.json | 205 +---- .../Module_4/Lightkurve/Checkpoints/1.json | 133 +++ .../Lightkurve/Exercise_2.ipynb | 150 ++++ .../Lightkurve/Exercise_3.ipynb | 164 ++++ 8 files changed, 1834 insertions(+), 173 deletions(-) create mode 100644 ExoCore/Exercise_Solutions/Module_4/Lightkurve/Checkpoints/1.json create mode 100644 ExoCore/Exercise_Solutions/Utility_Software/Lightkurve/Exercise_2.ipynb create mode 100644 ExoCore/Exercise_Solutions/Utility_Software/Lightkurve/Exercise_3.ipynb diff --git a/ExoCore/.DS_Store b/ExoCore/.DS_Store index 4fdd0b0fc73c7574c950d2688ffa7499a05dfe23..85e7399d50a1d4c322e70a0df07b5da19905b13f 100644 GIT binary patch delta 83 zcmZoMXfc=|#>CJ*u~2NHo}wrt0|NsP3otO`GZZnTGUzd6G89ZKRA*$Itifu#S&T!F lWn+Ub^JaDqeh#3n&4L`?nJ4p$7&02fJ)K^7A?e4l2V|m)HD1C z{sLEi3IBx?e9v~#nr`AS38BfavcDJmeb4ci#IA`*^v0u`L@gqcD2&ZZs7^5M=dxlA z+u{L*&hd$2%4k3V4OgOFgHymM@V_a*-|iOeQ$iKW@$Wa$-|~>+$Eg}dsfrNE%fthe z-Cx?^5nTscQHozk5xfG(xfE7{l6-g>>I3?KmRv_=$XucAA^KvpjA$2LP9G5&)kgbP z8*PjFI)A8FnF^zgvDQ?m3*;)7+2U%A?fqd#$Worb_)=qR+B@et@(8EisrXrAN_wQV z$mtNX8`kfw@%C@=(WK0+?%$(4P0Fg>{vjG`%?lUT<+|LEuYyB0397Ifm%Xrf!BxDv*YjG z?aX%k>AhQR_?kFp=86vQgCa{+#`9;M#BQPJ z4W8iy&jcDTKSTY#eLeQREpj_Ns{;0^$&Irna(t(NQ{dbw!0UsL!q~MqH>j5mRQd=2 zY@%5keEyTb9N%Ks;@lv5V8T#=hAQk4Ll`>ReH+)cI5%kMB<$fs*pY=jp$I)X^7}fR zMAx9JodQmQWd+vEWsCR!gVWFd%OrQ@6mSZhD+NTeAM|^elHFTZCdYfNkMbIYjpOD9 mH3gO3jMO$9D>Y1#Xumy4J2GahHNbS&ODi4#uH>D0~5q}kc|wR J<9X&V0{{pB6Vm_y literal 8196 zcmeHM&rcIU6n+C$mJkdvYML06O+2Xtfr8O^DW&-1MqI>$n!39!?do=?=?`lpBt7f@ z;J@J2#FGcldh&nqB7SdXzyjM4s4)iSB{OekXTCQ(-^{-0n-Y;Ibjou?St4?fSWitL zOA&78bU;STlk?C5c%l|Hs74iPB3ZEvSOzQumI2FvW#E5g0B1IfGvM6Utv0s|SO!id z1MDAMB-VMOds-{kflP$}&>1{df->R&g>j9}8{N}dL19xz55ho&2{D9*quu51(0QYK zT5C874JTnn7A8UwYINXS)ty9MYnxjJECUA_;Mn~VEm525B=^McCGrr>S13eVlYE+? zm#9}C#xe|I834zh+7i;twi1+xF4HnaKt%Kw*o4?hh#Hqe%LlhzSW7aeyzQ$uk3y3 zTc;!23%ad_*-f8H|O;~^1S2foEOS^W7o3fQ|N6>b50`;aAqwPEFv$ z*#B_dr+{MRbJmpy>f)M_qeTY#XV|>29Q9-mhG+FX~64YUKnl zFskQ}<@kSP@csV@n5mtDWxz5phyjr)dc^`-cYfmR@5r&Xi}V7Ch5PliRuE(=9EVon lIP~rxhA6w}%9y;-J+1Ks^3N{ " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "var questionsbckozHcdJQLd=[{\"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 // 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\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}\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 if (submission == answer.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 // } 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 return false;\n }\n\n}\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}\nfunction jaxify(string) {\n var mystring = string;\n\n var count = 0;\n var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n\n var count2 = 0;\n var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n\n //console.log(loc);\n\n while ((loc >= 0) || (loc2 >= 0)) {\n\n /* Have to replace all the double $$ first with current implementation */\n if (loc2 >= 0) {\n if (count2 % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n }\n count2++;\n } else {\n if (count % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n }\n count++;\n }\n loc = mystring.search(/([^\\\\]|^)(\\$)/);\n loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n }\n\n // repace markdown style links with actual links\n mystring = mystring.replace(/<(.*?)>/, '$1');\n mystring = mystring.replace(/\\[(.*?)\\]\\((.*?)\\)/, '$1');\n\n //console.log(mystring);\n return mystring;\n}\n\n\nfunction show_questions(json, mydiv) {\n console.log('show_questions');\n //var mydiv=document.getElementById(myid);\n var shuffle_questions = mydiv.dataset.shufflequestions;\n var num_questions = mydiv.dataset.numquestions;\n var shuffle_answers = mydiv.dataset.shuffleanswers;\n var max_width = mydiv.dataset.maxwidth;\n\n if (num_questions > json.length) {\n num_questions = json.length;\n }\n\n var questions;\n if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n //console.log(num_questions+\",\"+json.length);\n questions = getRandomSubarray(json, num_questions);\n } else {\n questions = json;\n }\n\n //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n\n // Iterate over questions\n questions.forEach((qa, index, array) => {\n //console.log(qa.question); \n\n var id = makeid(8);\n //console.log(id);\n\n\n // Create Div to contain question and answers\n var iDiv = document.createElement('div');\n //iDiv.id = 'quizWrap' + id + index;\n iDiv.id = 'quizWrap' + id;\n iDiv.className = 'Quiz';\n iDiv.setAttribute('data-qnum', index);\n iDiv.style.maxWidth =max_width+\"px\";\n mydiv.appendChild(iDiv);\n // iDiv.innerHTML=qa.question;\n \n var outerqDiv = document.createElement('div');\n outerqDiv.id = \"OuterquizQn\" + id + index;\n // Create div to contain question part\n var qDiv = document.createElement('div');\n qDiv.id = \"quizQn\" + id + index;\n \n if (qa.question) {\n iDiv.append(outerqDiv);\n\n //qDiv.textContent=qa.question;\n qDiv.innerHTML = jaxify(qa.question);\n outerqDiv.append(qDiv);\n }\n\n // Create div for code inside question\n var codeDiv;\n if (\"code\" in qa) {\n codeDiv = document.createElement('div');\n codeDiv.id = \"code\" + id + index;\n codeDiv.className = \"QuizCode\";\n var codePre = document.createElement('pre');\n codeDiv.append(codePre);\n var codeCode = document.createElement('code');\n codePre.append(codeCode);\n codeCode.innerHTML = qa.code;\n outerqDiv.append(codeDiv);\n //console.log(codeDiv);\n }\n\n\n // Create div to contain answer part\n var aDiv = document.createElement('div');\n aDiv.id = \"quizAns\" + id + index;\n aDiv.className = 'Answer';\n iDiv.append(aDiv);\n\n //console.log(qa.type);\n\n var num_correct;\n if ((qa.type == \"multiple_choice\") || (qa.type == \"many_choice\") ) {\n num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n if (\"answer_cols\" in qa) {\n //aDiv.style.gridTemplateColumns = 'auto '.repeat(qa.answer_cols);\n aDiv.style.gridTemplateColumns = 'repeat(' + qa.answer_cols + ', 1fr)';\n }\n } else if (qa.type == \"numeric\") {\n //console.log(\"numeric\");\n make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n }\n\n\n //Make div for feedback\n var fb = document.createElement(\"div\");\n fb.id = \"fb\" + id;\n //fb.style=\"font-size: 20px;text-align:center;\";\n fb.className = \"Feedback\";\n fb.setAttribute(\"data-answeredcorrect\", 0);\n fb.setAttribute(\"data-numcorrect\", num_correct);\n iDiv.append(fb);\n\n\n });\n var preserveResponses = mydiv.dataset.preserveresponses;\n console.log(preserveResponses);\n console.log(preserveResponses == \"true\");\n if (preserveResponses == \"true\") {\n console.log(preserveResponses);\n // Create Div to contain record of answers\n var iDiv = document.createElement('div');\n iDiv.id = 'responses' + mydiv.id;\n iDiv.className = 'JCResponses';\n // Create a place to store responses as an empty array\n iDiv.setAttribute('data-responses', '[]');\n\n // Dummy Text\n iDiv.innerHTML=\"Select your answers and then follow the directions that will appear here.\"\n //iDiv.className = 'Quiz';\n mydiv.appendChild(iDiv);\n }\n//console.log(\"At end of show_questions\");\n if (typeof MathJax != 'undefined') {\n console.log(\"MathJax version\", MathJax.version);\n var version = MathJax.version;\n setTimeout(function(){\n var version = MathJax.version;\n console.log('After sleep, MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([mydiv]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\n },\n svg: {\n fontCache: 'global'\n }\n };\n\n (function () {\n var script = document.createElement('script');\n script.src = 'https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js';\n script.async = true;\n document.head.appendChild(script);\n })();\n }\n }\n }, 500);\nif (typeof version == 'undefined') {\n } else\n {\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([mydiv]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\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\n // stop event propagation for the .Link class\n var links = document.getElementsByClassName('Link')\n for (var i = 0; i < links.length; i++) {\n links[i].addEventListener('click', function(e){\n e.stopPropagation();\n });\n }\n\n return false;\n}\n/* This is to handle asynchrony issues in loading Jupyter notebooks\n where the quiz has been previously run. The Javascript was generally\n being run before the div was added to the DOM. I tried to do this\n more elegantly using Mutation Observer, but I didn't get it to work.\n\n Someone more knowledgeable could make this better ;-) */\n\n function try_show() {\n if(document.getElementById(\"bckozHcdJQLd\")) {\n show_questions(questionsbckozHcdJQLd, bckozHcdJQLd); \n } else {\n setTimeout(try_show, 200);\n }\n };\n \n {\n // console.log(element);\n\n //console.log(\"bckozHcdJQLd\");\n // console.log(document.getElementById(\"bckozHcdJQLd\"));\n\n try_show();\n }\n ", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display_quiz(questions[0:3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -193,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -305,7 +581,7 @@ "source": [ "
    \n", " \n", - "**INFO**: The **query_criteria** method is extremely robust, and can suit most search applications extremely efficiently. The different parameters to refine your search can be found [here](https://mast.stsci.edu/api/v0/_c_a_o_mfields.html)" + "**INFO**: The **query_criteria** method is extremely robust, and can suit most search applications extremely efficiently. The different parameters to refine your search can be found [here](https://mast.stsci.edu/api/v0/_c_a_o_mfields.html)." ] }, { @@ -617,6 +893,254 @@ "Note that not all query methods are available for each catalog. Look [here](https://astroquery.readthedocs.io/en/latest/mast/mast_catalog.html) for more examples, and [here](https://astroquery.readthedocs.io/en/latest/api/astroquery.mast.CatalogsClass.html#astroquery.mast.CatalogsClass) for documentation on the Catalog class." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Checkpoint 2**\n", + "\n", + "Run the following code for the quiz question." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "var questionsnLxEAhaLSzZU=[{\"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 // 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\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}\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 if (submission == answer.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 // } 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 return false;\n }\n\n}\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}\nfunction jaxify(string) {\n var mystring = string;\n\n var count = 0;\n var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n\n var count2 = 0;\n var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n\n //console.log(loc);\n\n while ((loc >= 0) || (loc2 >= 0)) {\n\n /* Have to replace all the double $$ first with current implementation */\n if (loc2 >= 0) {\n if (count2 % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n }\n count2++;\n } else {\n if (count % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n }\n count++;\n }\n loc = mystring.search(/([^\\\\]|^)(\\$)/);\n loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n }\n\n // repace markdown style links with actual links\n mystring = mystring.replace(/<(.*?)>/, '$1');\n mystring = mystring.replace(/\\[(.*?)\\]\\((.*?)\\)/, '$1');\n\n //console.log(mystring);\n return mystring;\n}\n\n\nfunction show_questions(json, mydiv) {\n console.log('show_questions');\n //var mydiv=document.getElementById(myid);\n var shuffle_questions = mydiv.dataset.shufflequestions;\n var num_questions = mydiv.dataset.numquestions;\n var shuffle_answers = mydiv.dataset.shuffleanswers;\n var max_width = mydiv.dataset.maxwidth;\n\n if (num_questions > json.length) {\n num_questions = json.length;\n }\n\n var questions;\n if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n //console.log(num_questions+\",\"+json.length);\n questions = getRandomSubarray(json, num_questions);\n } else {\n questions = json;\n }\n\n //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n\n // Iterate over questions\n questions.forEach((qa, index, array) => {\n //console.log(qa.question); \n\n var id = makeid(8);\n //console.log(id);\n\n\n // Create Div to contain question and answers\n var iDiv = document.createElement('div');\n //iDiv.id = 'quizWrap' + id + index;\n iDiv.id = 'quizWrap' + id;\n iDiv.className = 'Quiz';\n iDiv.setAttribute('data-qnum', index);\n iDiv.style.maxWidth =max_width+\"px\";\n mydiv.appendChild(iDiv);\n // iDiv.innerHTML=qa.question;\n \n var outerqDiv = document.createElement('div');\n outerqDiv.id = \"OuterquizQn\" + id + index;\n // Create div to contain question part\n var qDiv = document.createElement('div');\n qDiv.id = \"quizQn\" + id + index;\n \n if (qa.question) {\n iDiv.append(outerqDiv);\n\n //qDiv.textContent=qa.question;\n qDiv.innerHTML = jaxify(qa.question);\n outerqDiv.append(qDiv);\n }\n\n // Create div for code inside question\n var codeDiv;\n if (\"code\" in qa) {\n codeDiv = document.createElement('div');\n codeDiv.id = \"code\" + id + index;\n codeDiv.className = \"QuizCode\";\n var codePre = document.createElement('pre');\n codeDiv.append(codePre);\n var codeCode = document.createElement('code');\n codePre.append(codeCode);\n codeCode.innerHTML = qa.code;\n outerqDiv.append(codeDiv);\n //console.log(codeDiv);\n }\n\n\n // Create div to contain answer part\n var aDiv = document.createElement('div');\n aDiv.id = \"quizAns\" + id + index;\n aDiv.className = 'Answer';\n iDiv.append(aDiv);\n\n //console.log(qa.type);\n\n var num_correct;\n if ((qa.type == \"multiple_choice\") || (qa.type == \"many_choice\") ) {\n num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n if (\"answer_cols\" in qa) {\n //aDiv.style.gridTemplateColumns = 'auto '.repeat(qa.answer_cols);\n aDiv.style.gridTemplateColumns = 'repeat(' + qa.answer_cols + ', 1fr)';\n }\n } else if (qa.type == \"numeric\") {\n //console.log(\"numeric\");\n make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n }\n\n\n //Make div for feedback\n var fb = document.createElement(\"div\");\n fb.id = \"fb\" + id;\n //fb.style=\"font-size: 20px;text-align:center;\";\n fb.className = \"Feedback\";\n fb.setAttribute(\"data-answeredcorrect\", 0);\n fb.setAttribute(\"data-numcorrect\", num_correct);\n iDiv.append(fb);\n\n\n });\n var preserveResponses = mydiv.dataset.preserveresponses;\n console.log(preserveResponses);\n console.log(preserveResponses == \"true\");\n if (preserveResponses == \"true\") {\n console.log(preserveResponses);\n // Create Div to contain record of answers\n var iDiv = document.createElement('div');\n iDiv.id = 'responses' + mydiv.id;\n iDiv.className = 'JCResponses';\n // Create a place to store responses as an empty array\n iDiv.setAttribute('data-responses', '[]');\n\n // Dummy Text\n iDiv.innerHTML=\"Select your answers and then follow the directions that will appear here.\"\n //iDiv.className = 'Quiz';\n mydiv.appendChild(iDiv);\n }\n//console.log(\"At end of show_questions\");\n if (typeof MathJax != 'undefined') {\n console.log(\"MathJax version\", MathJax.version);\n var version = MathJax.version;\n setTimeout(function(){\n var version = MathJax.version;\n console.log('After sleep, MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([mydiv]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\n },\n svg: {\n fontCache: 'global'\n }\n };\n\n (function () {\n var script = document.createElement('script');\n script.src = 'https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js';\n script.async = true;\n document.head.appendChild(script);\n })();\n }\n }\n }, 500);\nif (typeof version == 'undefined') {\n } else\n {\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([mydiv]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\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\n // stop event propagation for the .Link class\n var links = document.getElementsByClassName('Link')\n for (var i = 0; i < links.length; i++) {\n links[i].addEventListener('click', function(e){\n e.stopPropagation();\n });\n }\n\n return false;\n}\n/* This is to handle asynchrony issues in loading Jupyter notebooks\n where the quiz has been previously run. The Javascript was generally\n being run before the div was added to the DOM. I tried to do this\n more elegantly using Mutation Observer, but I didn't get it to work.\n\n Someone more knowledgeable could make this better ;-) */\n\n function try_show() {\n if(document.getElementById(\"nLxEAhaLSzZU\")) {\n show_questions(questionsnLxEAhaLSzZU, nLxEAhaLSzZU); \n } else {\n setTimeout(try_show, 200);\n }\n };\n \n {\n // console.log(element);\n\n //console.log(\"nLxEAhaLSzZU\");\n // console.log(document.getElementById(\"nLxEAhaLSzZU\"));\n\n try_show();\n }\n ", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display_quiz(questions[3:4])" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -686,7 +1210,7 @@ ], "metadata": { "kernelspec": { - "display_name": "exocore", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -700,7 +1224,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/ExoCore/Curriculum/Utility_Software/Lightkurve.ipynb b/ExoCore/Curriculum/Utility_Software/Lightkurve.ipynb index 44c8ce9..4484eec 100644 --- a/ExoCore/Curriculum/Utility_Software/Lightkurve.ipynb +++ b/ExoCore/Curriculum/Utility_Software/Lightkurve.ipynb @@ -30,6 +30,28 @@ "# Overview\n", "`Lightkurve` is a user-friendly Python package that is designed to make interacting with [TESS](https://science.nasa.gov/mission/tess/) and [Kepler](https://science.nasa.gov/mission/kepler/) time-series photometry easy and accessible. It is well documented, and there are pre-existing [tutorials](https://lightkurve.github.io/lightkurve/tutorials/index.html) that cover most functionality. This lesson will go over the primary use-cases, best practices, and examples, as well as some exercises for hands-on learning and improve understanding. \n", "\n", + "**Run the following code to initialize the interactive portions of this lesson:**" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "from jupyterquiz import display_quiz\n", + "import warnings\n", + "import json\n", + "warnings.filterwarnings('ignore')\n", + "import os\n", + "with open('Documents/Exoplanets/ExoCore/ExoCore-2/ExoCore/Exercise_Solutions/Module_4/Lightkurve/Checkpoints/1.json', 'r') as file:\n", + " questions = json.load(file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "# A Hands-On Introduction\n", "There are three primary objects used in `Lightkurve`:\n", "* `LightCurve` object\n", @@ -189,6 +211,270 @@ "print('Target RA: ' + str(lc.meta['RA']), 'Target DEC: ' + str(lc.meta['DEC']))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checkpoint 1\n", + "\n", + "Let's check our understanding so far! Run the codeblock below for some quiz questions." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "var questionseoQaaeFVcqKc=[{\"question\": \"What are the three primary objects used in Lightkurve?\", \"type\": \"many_choice\", \"answers\": [{\"answer\": \"Lightcurve objects\", \"correct\": true, \"feedback\": \"Correct.\"}, {\"answer\": \"TargetPixelFile objects\", \"correct\": true, \"feedback\": \"Correct.\"}, {\"answer\": \"Periodogram objects\", \"correct\": true, \"feedback\": \"Correct.\"}, {\"answer\": \"TimeSeries objects\", \"correct\": false, \"feedback\": \"Incorrect. While there are objects that contain timeseries data, they aren't referred to as TimeSeries objects.\"}]}, {\"question\": \"How do you transform a TargetPixelFile object into a LightCurve object?\", \"type\": \"many_choice\", \"answers\": [{\"answer\": \"Use the to_lightcurve method\", \"correct\": true, \"feedback\": \"Correct. You can use an aperture_mask argument to specify which pixels to include in the lightcurve.\"}, {\"answer\": \"Use the meta attribute\", \"correct\": false, \"feedback\": \"Incorrect. The meta attribute is used to store metadata about the object.\"}, {\"answer\": \"Use the plot method\", \"correct\": false, \"feedback\": \"Incorrect. The plot method is used to visualize the data.\"}, {\"answer\": \"Use the search_targetpixelfile method\", \"correct\": false, \"feedback\": \"Incorrect. This method is used to search for TargetPixelFile objects.\"}]}];\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\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}\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 if (submission == answer.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 // } 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 return false;\n }\n\n}\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}\nfunction jaxify(string) {\n var mystring = string;\n\n var count = 0;\n var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n\n var count2 = 0;\n var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n\n //console.log(loc);\n\n while ((loc >= 0) || (loc2 >= 0)) {\n\n /* Have to replace all the double $$ first with current implementation */\n if (loc2 >= 0) {\n if (count2 % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n }\n count2++;\n } else {\n if (count % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n }\n count++;\n }\n loc = mystring.search(/([^\\\\]|^)(\\$)/);\n loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n }\n\n // repace markdown style links with actual links\n mystring = mystring.replace(/<(.*?)>/, '$1');\n mystring = mystring.replace(/\\[(.*?)\\]\\((.*?)\\)/, '$1');\n\n //console.log(mystring);\n return mystring;\n}\n\n\nfunction show_questions(json, mydiv) {\n console.log('show_questions');\n //var mydiv=document.getElementById(myid);\n var shuffle_questions = mydiv.dataset.shufflequestions;\n var num_questions = mydiv.dataset.numquestions;\n var shuffle_answers = mydiv.dataset.shuffleanswers;\n var max_width = mydiv.dataset.maxwidth;\n\n if (num_questions > json.length) {\n num_questions = json.length;\n }\n\n var questions;\n if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n //console.log(num_questions+\",\"+json.length);\n questions = getRandomSubarray(json, num_questions);\n } else {\n questions = json;\n }\n\n //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n\n // Iterate over questions\n questions.forEach((qa, index, array) => {\n //console.log(qa.question); \n\n var id = makeid(8);\n //console.log(id);\n\n\n // Create Div to contain question and answers\n var iDiv = document.createElement('div');\n //iDiv.id = 'quizWrap' + id + index;\n iDiv.id = 'quizWrap' + id;\n iDiv.className = 'Quiz';\n iDiv.setAttribute('data-qnum', index);\n iDiv.style.maxWidth =max_width+\"px\";\n mydiv.appendChild(iDiv);\n // iDiv.innerHTML=qa.question;\n \n var outerqDiv = document.createElement('div');\n outerqDiv.id = \"OuterquizQn\" + id + index;\n // Create div to contain question part\n var qDiv = document.createElement('div');\n qDiv.id = \"quizQn\" + id + index;\n \n if (qa.question) {\n iDiv.append(outerqDiv);\n\n //qDiv.textContent=qa.question;\n qDiv.innerHTML = jaxify(qa.question);\n outerqDiv.append(qDiv);\n }\n\n // Create div for code inside question\n var codeDiv;\n if (\"code\" in qa) {\n codeDiv = document.createElement('div');\n codeDiv.id = \"code\" + id + index;\n codeDiv.className = \"QuizCode\";\n var codePre = document.createElement('pre');\n codeDiv.append(codePre);\n var codeCode = document.createElement('code');\n codePre.append(codeCode);\n codeCode.innerHTML = qa.code;\n outerqDiv.append(codeDiv);\n //console.log(codeDiv);\n }\n\n\n // Create div to contain answer part\n var aDiv = document.createElement('div');\n aDiv.id = \"quizAns\" + id + index;\n aDiv.className = 'Answer';\n iDiv.append(aDiv);\n\n //console.log(qa.type);\n\n var num_correct;\n if ((qa.type == \"multiple_choice\") || (qa.type == \"many_choice\") ) {\n num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n if (\"answer_cols\" in qa) {\n //aDiv.style.gridTemplateColumns = 'auto '.repeat(qa.answer_cols);\n aDiv.style.gridTemplateColumns = 'repeat(' + qa.answer_cols + ', 1fr)';\n }\n } else if (qa.type == \"numeric\") {\n //console.log(\"numeric\");\n make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n }\n\n\n //Make div for feedback\n var fb = document.createElement(\"div\");\n fb.id = \"fb\" + id;\n //fb.style=\"font-size: 20px;text-align:center;\";\n fb.className = \"Feedback\";\n fb.setAttribute(\"data-answeredcorrect\", 0);\n fb.setAttribute(\"data-numcorrect\", num_correct);\n iDiv.append(fb);\n\n\n });\n var preserveResponses = mydiv.dataset.preserveresponses;\n console.log(preserveResponses);\n console.log(preserveResponses == \"true\");\n if (preserveResponses == \"true\") {\n console.log(preserveResponses);\n // Create Div to contain record of answers\n var iDiv = document.createElement('div');\n iDiv.id = 'responses' + mydiv.id;\n iDiv.className = 'JCResponses';\n // Create a place to store responses as an empty array\n iDiv.setAttribute('data-responses', '[]');\n\n // Dummy Text\n iDiv.innerHTML=\"Select your answers and then follow the directions that will appear here.\"\n //iDiv.className = 'Quiz';\n mydiv.appendChild(iDiv);\n }\n//console.log(\"At end of show_questions\");\n if (typeof MathJax != 'undefined') {\n console.log(\"MathJax version\", MathJax.version);\n var version = MathJax.version;\n setTimeout(function(){\n var version = MathJax.version;\n console.log('After sleep, MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([mydiv]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\n },\n svg: {\n fontCache: 'global'\n }\n };\n\n (function () {\n var script = document.createElement('script');\n script.src = 'https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js';\n script.async = true;\n document.head.appendChild(script);\n })();\n }\n }\n }, 500);\nif (typeof version == 'undefined') {\n } else\n {\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([mydiv]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\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\n // stop event propagation for the .Link class\n var links = document.getElementsByClassName('Link')\n for (var i = 0; i < links.length; i++) {\n links[i].addEventListener('click', function(e){\n e.stopPropagation();\n });\n }\n\n return false;\n}\n/* This is to handle asynchrony issues in loading Jupyter notebooks\n where the quiz has been previously run. The Javascript was generally\n being run before the div was added to the DOM. I tried to do this\n more elegantly using Mutation Observer, but I didn't get it to work.\n\n Someone more knowledgeable could make this better ;-) */\n\n function try_show() {\n if(document.getElementById(\"eoQaaeFVcqKc\")) {\n show_questions(questionseoQaaeFVcqKc, eoQaaeFVcqKc); \n } else {\n setTimeout(try_show, 200);\n }\n };\n \n {\n // console.log(element);\n\n //console.log(\"eoQaaeFVcqKc\");\n // console.log(document.getElementById(\"eoQaaeFVcqKc\"));\n\n try_show();\n }\n ", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display_quiz(questions[0:2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the codeblock below, plot the **lightcurve** of the exoplanetary system HD 209458 via the **target pixel files**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Hint: Start off with downloading the TPFs with lk.search_targetpixelfile. Make sure you're looking at the right system (HD 209458)!\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -582,6 +868,272 @@ "Feel free to export your favorite graphs into any of the above file/structure formats!" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checkpoint 2\n", + "\n", + "That was a lot of information! Let's do another check to see how you use these tools! Run the codeblock below for a quick quiz question." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
        " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "var questionsGQdOBDlVVGrg=[{\"question\": \"What does the flatten method do to a LightCurve object?\", \"type\": \"many_choice\", \"answers\": [{\"answer\": \"Remove long-term trends\", \"correct\": true, \"feedback\": \"Correct, as long as the window_length argument is a reasonable value.\"}, {\"answer\": \"Remove nans\", \"correct\": false, \"feedback\": \"Incorrect. You can remove nans with the remove_nans method.\"}, {\"answer\": \"Remove outliers\", \"correct\": true, \"feedback\": \"Correct. You can adjust the sigma argument to change the threshold for what is considered an outlier.\"}, {\"answer\": \"Normalize the lightcurve\", \"correct\": true, \"feedback\": \"Correct. This sets the median value of the lightcurve to be 1, and scales all other values accordingly.\"}]}];\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\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}\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 if (submission == answer.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 // } 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 return false;\n }\n\n}\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}\nfunction jaxify(string) {\n var mystring = string;\n\n var count = 0;\n var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n\n var count2 = 0;\n var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n\n //console.log(loc);\n\n while ((loc >= 0) || (loc2 >= 0)) {\n\n /* Have to replace all the double $$ first with current implementation */\n if (loc2 >= 0) {\n if (count2 % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n }\n count2++;\n } else {\n if (count % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n }\n count++;\n }\n loc = mystring.search(/([^\\\\]|^)(\\$)/);\n loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n }\n\n // repace markdown style links with actual links\n mystring = mystring.replace(/<(.*?)>/, '$1');\n mystring = mystring.replace(/\\[(.*?)\\]\\((.*?)\\)/, '$1');\n\n //console.log(mystring);\n return mystring;\n}\n\n\nfunction show_questions(json, mydiv) {\n console.log('show_questions');\n //var mydiv=document.getElementById(myid);\n var shuffle_questions = mydiv.dataset.shufflequestions;\n var num_questions = mydiv.dataset.numquestions;\n var shuffle_answers = mydiv.dataset.shuffleanswers;\n var max_width = mydiv.dataset.maxwidth;\n\n if (num_questions > json.length) {\n num_questions = json.length;\n }\n\n var questions;\n if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n //console.log(num_questions+\",\"+json.length);\n questions = getRandomSubarray(json, num_questions);\n } else {\n questions = json;\n }\n\n //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n\n // Iterate over questions\n questions.forEach((qa, index, array) => {\n //console.log(qa.question); \n\n var id = makeid(8);\n //console.log(id);\n\n\n // Create Div to contain question and answers\n var iDiv = document.createElement('div');\n //iDiv.id = 'quizWrap' + id + index;\n iDiv.id = 'quizWrap' + id;\n iDiv.className = 'Quiz';\n iDiv.setAttribute('data-qnum', index);\n iDiv.style.maxWidth =max_width+\"px\";\n mydiv.appendChild(iDiv);\n // iDiv.innerHTML=qa.question;\n \n var outerqDiv = document.createElement('div');\n outerqDiv.id = \"OuterquizQn\" + id + index;\n // Create div to contain question part\n var qDiv = document.createElement('div');\n qDiv.id = \"quizQn\" + id + index;\n \n if (qa.question) {\n iDiv.append(outerqDiv);\n\n //qDiv.textContent=qa.question;\n qDiv.innerHTML = jaxify(qa.question);\n outerqDiv.append(qDiv);\n }\n\n // Create div for code inside question\n var codeDiv;\n if (\"code\" in qa) {\n codeDiv = document.createElement('div');\n codeDiv.id = \"code\" + id + index;\n codeDiv.className = \"QuizCode\";\n var codePre = document.createElement('pre');\n codeDiv.append(codePre);\n var codeCode = document.createElement('code');\n codePre.append(codeCode);\n codeCode.innerHTML = qa.code;\n outerqDiv.append(codeDiv);\n //console.log(codeDiv);\n }\n\n\n // Create div to contain answer part\n var aDiv = document.createElement('div');\n aDiv.id = \"quizAns\" + id + index;\n aDiv.className = 'Answer';\n iDiv.append(aDiv);\n\n //console.log(qa.type);\n\n var num_correct;\n if ((qa.type == \"multiple_choice\") || (qa.type == \"many_choice\") ) {\n num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n if (\"answer_cols\" in qa) {\n //aDiv.style.gridTemplateColumns = 'auto '.repeat(qa.answer_cols);\n aDiv.style.gridTemplateColumns = 'repeat(' + qa.answer_cols + ', 1fr)';\n }\n } else if (qa.type == \"numeric\") {\n //console.log(\"numeric\");\n make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n }\n\n\n //Make div for feedback\n var fb = document.createElement(\"div\");\n fb.id = \"fb\" + id;\n //fb.style=\"font-size: 20px;text-align:center;\";\n fb.className = \"Feedback\";\n fb.setAttribute(\"data-answeredcorrect\", 0);\n fb.setAttribute(\"data-numcorrect\", num_correct);\n iDiv.append(fb);\n\n\n });\n var preserveResponses = mydiv.dataset.preserveresponses;\n console.log(preserveResponses);\n console.log(preserveResponses == \"true\");\n if (preserveResponses == \"true\") {\n console.log(preserveResponses);\n // Create Div to contain record of answers\n var iDiv = document.createElement('div');\n iDiv.id = 'responses' + mydiv.id;\n iDiv.className = 'JCResponses';\n // Create a place to store responses as an empty array\n iDiv.setAttribute('data-responses', '[]');\n\n // Dummy Text\n iDiv.innerHTML=\"Select your answers and then follow the directions that will appear here.\"\n //iDiv.className = 'Quiz';\n mydiv.appendChild(iDiv);\n }\n//console.log(\"At end of show_questions\");\n if (typeof MathJax != 'undefined') {\n console.log(\"MathJax version\", MathJax.version);\n var version = MathJax.version;\n setTimeout(function(){\n var version = MathJax.version;\n console.log('After sleep, MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([mydiv]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\n },\n svg: {\n fontCache: 'global'\n }\n };\n\n (function () {\n var script = document.createElement('script');\n script.src = 'https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js';\n script.async = true;\n document.head.appendChild(script);\n })();\n }\n }\n }, 500);\nif (typeof version == 'undefined') {\n } else\n {\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([mydiv]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\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\n // stop event propagation for the .Link class\n var links = document.getElementsByClassName('Link')\n for (var i = 0; i < links.length; i++) {\n links[i].addEventListener('click', function(e){\n e.stopPropagation();\n });\n }\n\n return false;\n}\n/* This is to handle asynchrony issues in loading Jupyter notebooks\n where the quiz has been previously run. The Javascript was generally\n being run before the div was added to the DOM. I tried to do this\n more elegantly using Mutation Observer, but I didn't get it to work.\n\n Someone more knowledgeable could make this better ;-) */\n\n function try_show() {\n if(document.getElementById(\"GQdOBDlVVGrg\")) {\n show_questions(questionsGQdOBDlVVGrg, GQdOBDlVVGrg); \n } else {\n setTimeout(try_show, 200);\n }\n };\n \n {\n // console.log(element);\n\n //console.log(\"GQdOBDlVVGrg\");\n // console.log(document.getElementById(\"GQdOBDlVVGrg\"));\n\n try_show();\n }\n ", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display_quiz(questions[2:3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding to the codeblock below, let's plot some comparison graphs. First, plot the lightcurve as generated from the target pixel files. Then, flatten the lightcurve with an appropriate window and plot the flattened lightcurve. Remember that it should be long enough to remove longterm variations. Finally, fold the lightcurve on a period of 3.52474859 days. After folding the curve, bin the folded lightcurve to see the shape of the transit itself. Plot both the binned and unbinned versions, and play around with the `time_bin_size` parameter to compare. Remember that it uses the unit of days!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pixelfile2 = lk.search_targetpixelfile(\"HD 209458\").download_all()\n", + "lc2 = pixelfile2[0].to_lightcurve(aperture_mask='all')\n", + "lc2.plot()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1005,6 +1557,273 @@ "The model fits quite well! We notice that the baseline is hovering higher than 1.0 normalized flux in our data; this is due to how deep the transit is. This tends to ***overestimate*** the true quiescent flux from the host due to the transit biasing the median lower than it should be. This can be corrected e.g. using the flux during the *secondary eclipse*." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checkpoint 3\n", + "\n", + "We've finally gotten through the module; before moving on to the Exercises, let's have one last checkpoint. Run the following codeblock for quiz questions." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
          " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "var questionsNyDnmNxVLaYU=[{\"question\": \"What are the two kinds of periodograms available in Lightkurve?\", \"type\": \"many_choice\", \"answers\": [{\"answer\": \"Lomb-Scargle periodogram\", \"correct\": true, \"feedback\": \"Correct. This works well for the general identification of periodic signals.\"}, {\"answer\": \"Box-Least Squares periodogram\", \"correct\": true, \"feedback\": \"Correct. This is particularly useful for finding transit signals.\"}, {\"answer\": \"Savitzy-Golay periodogram\", \"correct\": false, \"feedback\": \"Incorrect. This is a detrending algorithm, not a periodogram.\"}, {\"answer\": \"Transit periodogram\", \"correct\": false, \"feedback\": \"Incorrect. This is not the name of a periodogram in Lightkurve.\"}]}, {\"question\": \"What parameters does the simplest box-least squares model require?\", \"type\": \"many_choice\", \"answers\": [{\"answer\": \"Period\", \"correct\": true, \"feedback\": \"Correct.\"}, {\"answer\": \"Duration\", \"correct\": true, \"feedback\": \"Correct.\"}, {\"answer\": \"Depth\", \"correct\": true, \"feedback\": \"Correct.\"}, {\"answer\": \"Width\", \"correct\": false, \"feedback\": \"Incorrect. This is not a parameter required by the simplest box-least squares model.\"}]}];\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\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}\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 if (submission == answer.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 // } 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 return false;\n }\n\n}\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}\nfunction jaxify(string) {\n var mystring = string;\n\n var count = 0;\n var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n\n var count2 = 0;\n var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n\n //console.log(loc);\n\n while ((loc >= 0) || (loc2 >= 0)) {\n\n /* Have to replace all the double $$ first with current implementation */\n if (loc2 >= 0) {\n if (count2 % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n }\n count2++;\n } else {\n if (count % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n }\n count++;\n }\n loc = mystring.search(/([^\\\\]|^)(\\$)/);\n loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n }\n\n // repace markdown style links with actual links\n mystring = mystring.replace(/<(.*?)>/, '$1');\n mystring = mystring.replace(/\\[(.*?)\\]\\((.*?)\\)/, '$1');\n\n //console.log(mystring);\n return mystring;\n}\n\n\nfunction show_questions(json, mydiv) {\n console.log('show_questions');\n //var mydiv=document.getElementById(myid);\n var shuffle_questions = mydiv.dataset.shufflequestions;\n var num_questions = mydiv.dataset.numquestions;\n var shuffle_answers = mydiv.dataset.shuffleanswers;\n var max_width = mydiv.dataset.maxwidth;\n\n if (num_questions > json.length) {\n num_questions = json.length;\n }\n\n var questions;\n if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n //console.log(num_questions+\",\"+json.length);\n questions = getRandomSubarray(json, num_questions);\n } else {\n questions = json;\n }\n\n //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n\n // Iterate over questions\n questions.forEach((qa, index, array) => {\n //console.log(qa.question); \n\n var id = makeid(8);\n //console.log(id);\n\n\n // Create Div to contain question and answers\n var iDiv = document.createElement('div');\n //iDiv.id = 'quizWrap' + id + index;\n iDiv.id = 'quizWrap' + id;\n iDiv.className = 'Quiz';\n iDiv.setAttribute('data-qnum', index);\n iDiv.style.maxWidth =max_width+\"px\";\n mydiv.appendChild(iDiv);\n // iDiv.innerHTML=qa.question;\n \n var outerqDiv = document.createElement('div');\n outerqDiv.id = \"OuterquizQn\" + id + index;\n // Create div to contain question part\n var qDiv = document.createElement('div');\n qDiv.id = \"quizQn\" + id + index;\n \n if (qa.question) {\n iDiv.append(outerqDiv);\n\n //qDiv.textContent=qa.question;\n qDiv.innerHTML = jaxify(qa.question);\n outerqDiv.append(qDiv);\n }\n\n // Create div for code inside question\n var codeDiv;\n if (\"code\" in qa) {\n codeDiv = document.createElement('div');\n codeDiv.id = \"code\" + id + index;\n codeDiv.className = \"QuizCode\";\n var codePre = document.createElement('pre');\n codeDiv.append(codePre);\n var codeCode = document.createElement('code');\n codePre.append(codeCode);\n codeCode.innerHTML = qa.code;\n outerqDiv.append(codeDiv);\n //console.log(codeDiv);\n }\n\n\n // Create div to contain answer part\n var aDiv = document.createElement('div');\n aDiv.id = \"quizAns\" + id + index;\n aDiv.className = 'Answer';\n iDiv.append(aDiv);\n\n //console.log(qa.type);\n\n var num_correct;\n if ((qa.type == \"multiple_choice\") || (qa.type == \"many_choice\") ) {\n num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n if (\"answer_cols\" in qa) {\n //aDiv.style.gridTemplateColumns = 'auto '.repeat(qa.answer_cols);\n aDiv.style.gridTemplateColumns = 'repeat(' + qa.answer_cols + ', 1fr)';\n }\n } else if (qa.type == \"numeric\") {\n //console.log(\"numeric\");\n make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n }\n\n\n //Make div for feedback\n var fb = document.createElement(\"div\");\n fb.id = \"fb\" + id;\n //fb.style=\"font-size: 20px;text-align:center;\";\n fb.className = \"Feedback\";\n fb.setAttribute(\"data-answeredcorrect\", 0);\n fb.setAttribute(\"data-numcorrect\", num_correct);\n iDiv.append(fb);\n\n\n });\n var preserveResponses = mydiv.dataset.preserveresponses;\n console.log(preserveResponses);\n console.log(preserveResponses == \"true\");\n if (preserveResponses == \"true\") {\n console.log(preserveResponses);\n // Create Div to contain record of answers\n var iDiv = document.createElement('div');\n iDiv.id = 'responses' + mydiv.id;\n iDiv.className = 'JCResponses';\n // Create a place to store responses as an empty array\n iDiv.setAttribute('data-responses', '[]');\n\n // Dummy Text\n iDiv.innerHTML=\"Select your answers and then follow the directions that will appear here.\"\n //iDiv.className = 'Quiz';\n mydiv.appendChild(iDiv);\n }\n//console.log(\"At end of show_questions\");\n if (typeof MathJax != 'undefined') {\n console.log(\"MathJax version\", MathJax.version);\n var version = MathJax.version;\n setTimeout(function(){\n var version = MathJax.version;\n console.log('After sleep, MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([mydiv]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\n },\n svg: {\n fontCache: 'global'\n }\n };\n\n (function () {\n var script = document.createElement('script');\n script.src = 'https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js';\n script.async = true;\n document.head.appendChild(script);\n })();\n }\n }\n }, 500);\nif (typeof version == 'undefined') {\n } else\n {\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n if (MathJax.hasOwnProperty('typeset') ) {\n MathJax.typeset([mydiv]);\n } else {\n console.log('WARNING: Trying to force load MathJax 3');\n window.MathJax = {\n tex: {\n inlineMath: [['$', '$'], ['\\\\(', '\\\\)']]\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\n // stop event propagation for the .Link class\n var links = document.getElementsByClassName('Link')\n for (var i = 0; i < links.length; i++) {\n links[i].addEventListener('click', function(e){\n e.stopPropagation();\n });\n }\n\n return false;\n}\n/* This is to handle asynchrony issues in loading Jupyter notebooks\n where the quiz has been previously run. The Javascript was generally\n being run before the div was added to the DOM. I tried to do this\n more elegantly using Mutation Observer, but I didn't get it to work.\n\n Someone more knowledgeable could make this better ;-) */\n\n function try_show() {\n if(document.getElementById(\"NyDnmNxVLaYU\")) {\n show_questions(questionsNyDnmNxVLaYU, NyDnmNxVLaYU); \n } else {\n setTimeout(try_show, 200);\n }\n };\n \n {\n // console.log(element);\n\n //console.log(\"NyDnmNxVLaYU\");\n // console.log(document.getElementById(\"NyDnmNxVLaYU\"));\n\n try_show();\n }\n ", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display_quiz(questions[3:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Building on the previous checkpoint, use the flattened lightcurve for HD 209458, generate and plot a BLS Periodogram for the lightcurve. Then, fold the lightcurve based on the period you have from the Periodogram. Compare with the folded curve using the published value of 3.52474859 days (found in [Bonomo et al., 2017](https://arxiv.org/abs/1704.00373)). Was your periodogram fine enough? How do you know? What could you change it to get a closer value?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# You don't have to run the following lines if you've already completed Checkpoint 2 - the variables are already defined!\n", + "\n", + "# pixelfile2 = lk.search_targetpixelfile(\"HD 209458\").download_all()\n", + "# lc2 = pixelfile2[0].to_lightcurve(aperture_mask='all')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1060,7 +1879,7 @@ "* Downloading and stitching together all available light curves from Kepler\n", "* Generating a periodogram of the light curve, using the `maximum_period = 100` parameter\n", "* Derive the period with the maximum power, and use this to generate a periodogram model with `periodogram.model` method\n", - "While this is not directly related to exoplanets, it can be important to model the rotation of stars in order to **remove** the rotation signal, which can otherwise confound the BLS transit model." + "* While this is not directly related to exoplanets, it can be important to model the rotation of stars in order to **remove** the rotation signal, which can otherwise confound the BLS transit model." ] }, { @@ -1075,7 +1894,7 @@ ], "metadata": { "kernelspec": { - "display_name": "exocore", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/ExoCore/Exercise_Solutions/Module_2/MAST/Checkpoints/1.json b/ExoCore/Exercise_Solutions/Module_2/MAST/Checkpoints/1.json index 55eb96d..1385d9b 100644 --- a/ExoCore/Exercise_Solutions/Module_2/MAST/Checkpoints/1.json +++ b/ExoCore/Exercise_Solutions/Module_2/MAST/Checkpoints/1.json @@ -1,235 +1,106 @@ [ { - "question": "Choose all of the following that can be included in Jupyter notebooks?", + "question": "How can you query an object using the basic search fuctionality in MAST?", "type": "many_choice", "answers": [ { - "answer": "Text and graphics output from Python", + "answer": "Search by Object Name", "correct": true, - "feedback": "Correct." - }, - { - "answer": "Typeset mathematics", - "correct": true, - "feedback": "Correct." + "feedback": "Correct. This includes standard catalog names, common names, and star catalogs with coordinates." }, { - "answer": "Python executable code", + "answer": "Search by Coordinate", "correct": true, - "feedback": "Correct." + "feedback": "Correct. You can search by an object's right ascension and declination, and within a certain radius of that coordinate." }, { - "answer": "Formatted text", - "correct": true, - "feedback": "Correct." + "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": "Live snakes via Python", + "answer": "Search by _______", "correct": false, - "feedback": "I hope not." + "feedback": "Incorrect." } ] }, { - "question": "Testing parameter to change number of colums for answers: Choose all of the following that can be included in Jupyter notebooks?", + "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": "Text and graphics output from Python", + "answer": "Target Name", "correct": true, "feedback": "Correct." }, { - "answer": "Typeset mathematics", + "answer": "Target RA and DEC", "correct": true, "feedback": "Correct." }, { - "answer": "Python executable code", - "correct": true, - "feedback": "Correct." - }, - { - "answer": "Formatted text", - "correct": true, - "feedback": "Correct." - }, - { - "answer": "Live snakes via Python", + "answer": "Target Galactic Coordinates", "correct": false, - "feedback": "I hope not." - } - ] - }, - { - "question": "Which of these are used to create formatted text in Jupyter notebooks?", - "type": "multiple_choice", - "answers": [ - { - "answer": "Wiki markup", - "correct": false, - "feedback": "False." - }, - { - "answer": "SVG", - "correct": false, - "feedback": "False." - }, - { - "answer": "Markdown", - "correct": true, - "feedback": "Correct." + "feedback": "Incorrect. While this is a valid search parameter, it is not required." }, { - "answer": "Rich Text", + "answer": "Target Mission", "correct": false, - "feedback": "False." + "feedback": "Incorrect. While this is a valid search parameter, it is not required." } ] }, { - "question": "Enter the value of $\\pi$ to 2 decimal places.", - "type": "numeric", + "question": "Which of these are browsing options available in MAST?", + "type": "many_choice", "answers": [ { - "type": "value", - "value": 3.14, + "answer": "AstroView", "correct": true, - "feedback": "Correct." + "feedback": "Correct. MAST will display the position of the object in a sky viewer." }, { - "type": "range", - "range": [ - 3.142857, - 3.142858 - ], + "answer": "Browser Visualizations", "correct": true, - "feedback": "True to 2 decimal places, but you know $\\pi$ is not really 22/7, right?" - }, - { - "type": "range", - "range": [ - -100000000, - 0 - ], - "correct": false, - "feedback": "$\\pi$ is the AREA of a circle of radius 1. Try again." + "feedback": "Correct. Certain data products can be viewed or otherwise visualized in a browser before download." }, { - "type": "default", - "feedback": "$\\pi$ is the area of a circle of radius 1. Try again." - } - ] - }, - { - "question": "Enter the value of $\\pi$ to 2 decimal places.", - "type": "numeric", - "precision": 2, - "answers": [ - { - "type": "value", - "value": 3.14, - "correct": true, - "feedback": "Correct." - }, - { - "type": "range", - "range": [ - 3.142857, - 3.142858 - ], + "answer": "Exporting the Results Grid", "correct": true, - "feedback": "True to 2 decimal places, but you know $\\pi$ is not really 22/7, right?" + "feedback": "Correct. While not always necessary, you can export the results of your search to a CSV file, among other common formats." }, { - "type": "range", - "range": [ - -100000000, - 0 - ], + "answer": "XYZ", "correct": false, - "feedback": "$\\pi$ is the AREA of a circle of radius 1. Try again." - }, - { - "type": "default", - "feedback": "$\\pi$ is the area of a circle of radius 1. Try again." + "feedback": "False. Idk why yet." } ] }, { - "question": "Determine the output of the following Python code:", - "code": "a=\"1\"\nb=\"2\"\nprint(a+b)", - "type": "multiple_choice", + "question": "What are different methods to use when querying MAST?", + "type": "many_choice", "answers": [ { - "answer": "1", - "correct": false, - "feedback": "No. When strings are operated on by +, they are concatenated." - }, - { - "answer": "2", - "correct": false, - "feedback": "No. When strings are operated on by +, they are concatenated." - }, - { - "answer": "3", - "correct": false, - "feedback": "No. When strings are operated on by +, they are concatenated." - }, - { - "answer": "12", + "answer": "query_object", "correct": true, - "feedback": "Yes. The + operator will concatenate the strings \"1\" and \"2\"." - }, - { - "answer": "error", - "correct": false, - "feedback": "No. The + operator for strings performs string concatenation." - } - ] - }, - { - "question": "The variable mylist is a Python list. Choose which code snippet will append the item 3 to mylist.", - "type": "multiple_choice", - "answers": [ - { - "code": "mylist+=3", - "correct": false - }, - { - "code": "mylist+=[3]", - "correct": true + "feedback": "Correct. This includes standard catalog names, common names, and star catalogs with coordinates." }, { - "code": "mylist+={3}", - "correct": false - } - ] - }, - { - "question": "Which of these is the ratio of a circle's circumference to its diameter?", - "type": "multiple_choice", - "answers": [ - { - "answer": "$\\pi$", + "answer": "query_region", "correct": true, - "feedback": "Correct." + "feedback": "Correct. You can search by an object's right ascension and declination, and within a certain radius of that coordinate." }, { - "answer": "$\\frac{22}{7}$", - "correct": false, - "feedback": "$\\frac{22}{7}$ is only an approximation to the true value." - }, - { - "answer": "3", - "correct": false, - "feedback": "This is a crude approximation to the true value." + "answer": "query_criteria", + "correct": true, + "feedback": "Correct. This is the most flexible search option, allowing you to specify a wide range of criteria." }, { - "answer": "$\\tau$", + "answer": "query_table", "correct": false, - "feedback": "True for the ratio of the circle's circumference to its radius, not diameter." + "feedback": "Incorrect. query_table is not a valid method for querying MAST." } ] } diff --git a/ExoCore/Exercise_Solutions/Module_4/Lightkurve/Checkpoints/1.json b/ExoCore/Exercise_Solutions/Module_4/Lightkurve/Checkpoints/1.json new file mode 100644 index 0000000..0c026eb --- /dev/null +++ b/ExoCore/Exercise_Solutions/Module_4/Lightkurve/Checkpoints/1.json @@ -0,0 +1,133 @@ +[ + { + "question": "What are the three primary objects used in Lightkurve?", + "type": "many_choice", + "answers": [ + { + "answer": "Lightcurve objects", + "correct": true, + "feedback": "Correct." + }, + { + "answer": "TargetPixelFile objects", + "correct": true, + "feedback": "Correct." + }, + { + "answer": "Periodogram objects", + "correct": true, + "feedback": "Correct." + }, + { + "answer": "TimeSeries objects", + "correct": false, + "feedback": "Incorrect. While there are objects that contain timeseries data, they aren't referred to as TimeSeries objects." + } + ] + }, + { + "question": "How do you transform a TargetPixelFile object into a LightCurve object?", + "type": "many_choice", + + "answers": [ + { + "answer": "Use the to_lightcurve method", + "correct": true, + "feedback": "Correct. You can use an aperture_mask argument to specify which pixels to include in the lightcurve." + }, + { + "answer": "Use the meta attribute", + "correct": false, + "feedback": "Incorrect. The meta attribute is used to store metadata about the object." + }, + { + "answer": "Use the plot method", + "correct": false, + "feedback": "Incorrect. The plot method is used to visualize the data." + }, + { + "answer": "Use the search_targetpixelfile method", + "correct": false, + "feedback": "Incorrect. This method is used to search for TargetPixelFile objects." + } + ] + }, + { + "question": "What does the flatten method do to a LightCurve object?", + "type": "many_choice", + "answers": [ + { + "answer": "Remove long-term trends", + "correct": true, + "feedback": "Correct, as long as the window_length argument is a reasonable value." + }, + { + "answer": "Remove nans", + "correct": false, + "feedback": "Incorrect. You can remove nans with the remove_nans method." + }, + { + "answer": "Remove outliers", + "correct": true, + "feedback": "Correct. You can adjust the sigma argument to change the threshold for what is considered an outlier." + }, + { + "answer": "Normalize the lightcurve", + "correct": true, + "feedback": "Correct. This sets the median value of the lightcurve to be 1, and scales all other values accordingly." + } + ] + }, + { + "question": "What are the two kinds of periodograms available in Lightkurve?", + "type": "many_choice", + "answers": [ + { + "answer": "Lomb-Scargle periodogram", + "correct": true, + "feedback": "Correct. This works well for the general identification of periodic signals." + }, + { + "answer": "Box-Least Squares periodogram", + "correct": true, + "feedback": "Correct. This is particularly useful for finding transit signals." + }, + { + "answer": "Savitzy-Golay periodogram", + "correct": false, + "feedback": "Incorrect. This is a detrending algorithm, not a periodogram." + }, + { + "answer": "Transit periodogram", + "correct": false, + "feedback": "Incorrect. This is not the name of a periodogram in Lightkurve." + } + ] + }, + { + "question": "What parameters does the simplest box-least squares model require?", + "type": "many_choice", + "answers": [ + { + "answer": "Period", + "correct": true, + "feedback": "Correct." + }, + { + "answer": "Duration", + "correct": true, + "feedback": "Correct." + }, + { + "answer": "Depth", + "correct": true, + "feedback": "Correct." + }, + { + "answer": "Width", + "correct": false, + "feedback": "Incorrect. This is not a parameter required by the simplest box-least squares model." + } + ] + } +] \ No newline at end of file diff --git a/ExoCore/Exercise_Solutions/Utility_Software/Lightkurve/Exercise_2.ipynb b/ExoCore/Exercise_Solutions/Utility_Software/Lightkurve/Exercise_2.ipynb new file mode 100644 index 0000000..36b754d --- /dev/null +++ b/ExoCore/Exercise_Solutions/Utility_Software/Lightkurve/Exercise_2.ipynb @@ -0,0 +1,150 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exploring Transit Timing Variations Using the River Plot\n", + "\n", + "### Import `lightkurve`, `astropy`, and retrieve relevant data" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "import lightkurve as lk\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import astropy as ap\n", + "import astropy.units as u\n", + "\n", + "# Find and download all data for KIC 6185476 from Kepler with a long exptime\n", + "data = lk.search_lightcurve(\"KIC 6185476\", author=\"Kepler\", exptime=1800).download_all()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clean Data: Detrend, Normalize, and remove NaNs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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A8CiS9DMCyx+/XITrRZXiGpPRRIMZF8gn++8MYTIqFJCn0iN/5DiplNtkTE7KiAk5PjcmRD9LNzsB+zL66mooAmsCRHrwXbZ8F7zj7fU+SAEgvB73dQ40OhCugoY4Ichlki12Yu0OkxxHGYvlhGyUOJ6P02Pvnxva1RcVISA6ilbw/QBSAAiPIpNx3IegTptwL3KZjLsb2UxaCWng+Tydfuoyf290xcVQRlIOAH/A5QpARQVzZAKCkBL39Kk+NNGxnLQ5msB5bMDygvst88Fcasx5AGQaClMO8FEM3GmS4+q2vMW8SDYw9rfyuYe6oiIEREV7WgzCDYhSAGbNmoWioiKH5U6dOoVx48aJaYIgXIrbu1s/myi6HW/fkueqRz5zA9niqUmonBUgy3siZzn9Ac31654WgTc6cxIwJ3FmzDM4eT3BC1EKwKFDhzBu3DgcPnyY8bxer8eaNWswZcoU5OXlOSUg4R140xxFfL/iig6JOjl2vODefNQUKHVHHyfPe+HPq7/+avrk07joZ3bu3vss2pC+EX1FBXSFhZLUZVQAZGYCxLlAQr9DsYhSAEaMGIHi4mLMmjULS5YsQXV1tfnctWvX8Oabb+K7775DREQEpk2bJpmwhDzxtKJOAzHhUTTlbmhEXu+45W/OHbbwclU05CqXtyGbHQovfZw3P/0MOaPHSFKXvmYHQKFQeM3kOn/lSk+L4JUEirloxIgR6NSpE9LT0/HLL7/g+PHjmDRpEs6dO4evvvoKlZWV6Nq1K1577TVESbGV5GdMnDgRSqW1bpaWloa0tDSXtvv40v349oUeLm3D+5G6Q/RQB2s1aXMgg5cMAj6LTOZGgHwmvHKQg03x4TOZdWcUIG+AfEqcw6DRwFBVJUlduqJiOxMgg14PhdJ3YsZU5+ZClZzsU99JDKIUAABo2bIlFi1ahCVLlmDHjh2YOHEiACA4OBivvPIKHnzwQcmE9DfS09OhVqvd3u7Bi/lub1MqhA4conYNPL3VwQdvkJE3Qp6R5yeEhOuQ08RQTrIQ0lF96RL/wp7qbtywGKMrKoIy0loBON26DVqe/NfNE2bX/c7O93sAjX74HiGtW1sdz/m//0OD5ctd1q7ccOpphoSEoFevXlCr1eYJVadOnXDfffc5uJIgjHhiviq6SVoJ54+z98qVLwY9R8IP8CsnYJ9a+PAsOlMeAFvceY9t+2iuPltCucr27ZesLm9AtAKg0WjwxRdfYObMmaisrMTgwYORmJiIAwcO4PXXX8e5c+eklJOQOZ60w/fb6dwvb0pTj19OiOU2YfDHZ+CdSGF+5C4TJgUUXuEjJRsFha8YNeU0YoKcyPx56MvKoAwL87QYroOURTOiFICLFy9iwoQJ2LJlC+rVq4e5c+fi+eefxyeffIJ+/frhypUrmDx5Mr799luv6HwI5/Cbn5PcOo6DS+2P8f29+fLv0vI5XdpT+7de535ZfAymiRr18dbIZjIL9ygacvDHkAyBX+Vcr94SNSyfdwYGQ41/i6edgF3UNvVXZkQpAJMmTUJOTg7uv/9+LFy4EC1btgRgNAl69dVXMWXKFKjVaqxduxZvvfWWpAITBCExclNsxMDWqa+0cJx/L9b4f1/4vhKx8dxGp66X02RXCBeLLkJv0HtaDIJwCoNGU/OHd0xqKzIyoLlxg7NM9qhR0JeVuUki/0aUAhASEoK33noLr7/+OkJDQ+3Od+/eHZ999hk6duyI06dPOy0kIS9y88tx4orjRHB88JJ+qxa5C1zK3bmKwlMTZrnfa3fhwtswbe80vPjHi65rwEU4u+swaOMgXC657FQdCiiQX5mP3t9YrwLzWRGnKECu49YXX6Dwhx9d384Shh1YN5MzdmztB6n6aRf297mvvorC77/nLFO+/wD0paXgvSsiti+ghSBxCsCnn36Ku+66i7NMTEwMZs6cidGjR4sSjJAvX+6+gDc3HDd/9vQ0zX0/Yy9IBPbds8KvkeVE29VPleE7z28GFGS7uF2xuO5+7L2yl3dZ0+TWF8w+pJggl2vKkV8pPnpapbYSd63jHkvF4o3P6JFNj4i8svZZlv61E+WHDkkiDysG4OYnn0hboQi017gzDJcd+Buaa9f4SyHXhFuyHKO8H1FhQOvUqcO77MCBA8U0gfLycnzzzTe4cOECLly4gOLiYowYMQIjR47kdX1FRQXWrFmDPXv2oKSkBCkpKRg6dCh69erl8rKZmZmYMmUKo1zz5883m0z5G6bORerEQe7rGiRuSVsF/CqRI68rEdP5GgxOrrA4aLOyGFCpgQDRkYztKcsDqorZz5feBMLrSteeDLlRdgNHbhzBw40f9rQonJj6kAptBQDP+CGwTbCFKBYavQYl1SVSicQoizsStTmLScZrZfwnq64RhE8ZD95Prug4DL+By+PGIe6FsVbHbn72X9R99RXG6vVlZVCGhzstJuEdSDh6SktJSQl+++03pKamonv37ti2bZug6+fMmYOsrCw8++yzSE5Oxs6dOzF//nzo9Xrce++9bin7zDPPoF27dlbHGjZsKOh7eAN8u8MBn+7BK/c1xcPtEl0qj7zgmJhUlwHZ/Fdf/Q6ugXZxd6D3f4DOph0PBxNAncZ5eT5qCsyQxvTNDpmscB28fhBT9kyRnQJgO9k2Tfi7ru0KdaD7c6a4i3kH5yE00N7MVgjkpO0HCFBKbn32GasCoCssQkBkZO0BT707EilZBo0G2vwCqOrFS1KfryFKAZg6daqg8rNnzxbcRnx8PNatWweFQoGioiJBCsDhw4dx7NgxvPHGG+jd22if2b59e+Tl5WHFihXo2bMnAgICXFoWAJKSkvx2tZ+JrBuluF1WbXXMCxanjNz4F6gu5V/+ylHgN2G/E5ez/kmgqma10WtuPAvltwFtJf/yei3/sv9uNJpSMU32q8uA498CXUZZHHTHvaRJnD9yoegCwlTGkIy/XvwVDzV6SND1QsyAblXcQlxonKD6fQ1DhYA+RUK0eTdFXSe1mZeuqLA2C7CnxwgJ2i/e+huuTp6MVqdPSSCQ7yHKByAzM9PhvxMnTpj/LwaFQvzW5f79+xEaGop77rnH6njfvn2Rn5+Ps2fPurysryO36YgzfcXec7ccFyrMEVZp8RUgZ584gVzF6Z+AizsZTsjtadbAtPq0tBfwr4sd/G78y36u4BLw03ibgy6+fzLW1bzRzlwqnPEhEHPfbpaLmySyMW7HOKvP930rfQLPjec2YtflXZLX6yoKvv7a9Y0w9Gu5Y8a4vl0e6IuLmZOAiYB1/ibWpFQMXKGfPa3gyABROwBffvkl43G9Xo9bt27h6NGj2LJlC9LS0vDww+7fSs7OzkZKSorVajwApKamms+3atXKpWUBYMmSJfjwww8RHByMli1b4oknnkCbNm0cyl9eXs7re6pUKqhUKl5lpUShUODUtWL8+I9zUTSkZkvGVUSFqtCrObedtm3H9OSXf+PS3DSW0rbUdESnfwGKcoFuLzgoz9HJCO3UCnOA6AbCrpGqbUf8+QEQkQBoKiSqkOW+5Z0CyngobLYUXHJKGk5+mwoMX+tkJd4xkWaa9LIN9GcLzmJ79na81PElyduVs+LhDTb3APBn7p+S1MNlYjRt7zQAQOazmZK0RdhguvVO+1wZ0RUVQWlpAmRB2e7dUHfvzl80hvfCk5GptDdtFGgyjROnAMTHs9tTJSQkoG3btmjfvj1mzJiBFi1acJZ3BSUlJUhISLA7HhERYT7vyrJqtRqDBg1C27ZtERkZiWvXruGHH37AlClTMH36dNxxxx2c8o8aNYrzvAkhTtGuYOEfWcIvcsGPzlTlyn2X0CguzKECIAnnfgeuHOGhAEjIJ+1cZ4PuLDvnAq0H15oYuRtH79Vii4FL6nfw9E/S1gcY76NOA6hjpa9bAoqqat/Dn87/hFl3z7Irc+LWCSzOWCyJAmDLR4c/svosZ4WACb4TIWcnTGKu3391P3ok9XCqXZ9Gov7j2syZAIAbH86XpD4p0BUWIZDFXj73hReRumGDmyWSBn1VFbJ69kJwixa08m+By5yAO3bsiKZNm2LDhg3o0cO/OpMmTZqgSZMm5s9t2rRB9+7d8dprr2HFihUOFYAVK1ZArXbs2OaJ1X82+HSJ1Tr2xDuPL92PHo3rYEK/5k7LsjnjKgZ1SHK6Htmy9W2jLbpU2A5olw9LUakEdbgStkHARYPDjChhytsfM4BrGcDoP4S1U10GVJe7LFqRaaK97PgyZBVmISooClqDAP8KiTidX5tfhm3Ffc3JNehUrxPa1HG86+oNOKPkCNmVGPv7WF4r9sfyjqFjfEfRMrkUL1jdLVy3HgCQv3y5+Eok/p664mIEN68dg6W+jYLeYb7vLI9yWgGhUMVyZdIbSPpwHhQ2FiJyRpQPAF/i4uKQkyPQdloCIiIirFbuTZiOmVbsXVnWlvDwcNx55524dOkSqqqqOOVXq9W8/slFAeDzOy2q0HAWPngxH0dzCiSRZ9y6fySph5ka+SXpGUXWceYX4DzD9r2lbfz6J8XVDQD7PxN/rVSUsdg766qBIytd27YnVohs3ye9Vpjjsond6cCK/tLI5AJyi3Mlr5PNBGVxxmIcvHaQ/UKRj9kyg7C7zX0KKwvxT54r+zf+PP3r054WwTPIKQwoC9WXLqFk+3ZB1wAmJ+AaEyAnvyfrb0Pq+8fj+xm0DH2pxHIU//wzDDoOnwMZ4jIFoKqqCllZWR6ZpKampuLy5cvQ2TyM7Gxjkh/LUJyuKsuEq+Lgewoh01e93nUrMpszrtr9ln8/eQN/nsljvUb8/N3yQo7nKKUpzIEl/Mp991zt364wS3EnR/8H1jfshrjAAryRy+qhSQ4h4hh04hQHEYgJL/nwj67xCRNlKiPyMU/Zw5zjxVKWck05blVw+6rouBwULTCPG1BgR+4OPPPrM/wEhfeZRnFRuGEDDNXVzCdtHr9Bo8Htr5xYWfcWWH6Dxb9tw7V3pllNcvNXrXJYna6oqDYKkIsp3b0H+gqePmNS9sly6d9lgCgFIC8vj/VfTk4O9u/fj3fffRe3bt1C586dpZbZId27d0dFRQX27bOOwrJ9+3bExsaiucUWl6vK2lJaWopDhw6hcePGCAoKcubruZUTV4qg4TDdkRum3/aaA9n45mDtamOlRmd2Whanftle5aAT2Sih3fPW/9T+nb0P0Lg6VJ1IBdWjHasLtpbdiZ1Mjj67Sgyetuk8yn32j2d3klyZYAsA6yPZeXkn/nfyf3jql6c4L++4uiNyShzskMvwVfUU196ZBm0Bv11iXUkx8uZ71rbeoNFAk1e7CFX4/Q8eEKK2X6y+lA3NDaM82oICaG/ZK6j6omLrPAAulCl3zBhUnuIIzynBeGJepKBJPyOifABGjx7tcAAwGAxITk7m7dDKxOHDh1FVVYWKGi0xJycHe/caEyd17twZISEhyMzMxDvvvIPhw4djxIgRAIAuXbqgY8eOWLx4McrLy5GYmIhdu3bh6NGjmDRpklUUH1eUnT9/PurWrYtmzZohMjISV69exY8//ojCwkKMHz9e9P3wBAM+3YMtr96DdikSrQrw+CH2ni8uOgXXG3mtqBITvslA31b1RNXN3CCf0Vni1OorauKAR0kUDcgoiPVHpybINdf+8gbQfx4QWKPsVkhj3uU0Qu/5bBG+JLvmA70mC7vm50nC23EBUiaNulnBbMY1bsc4LOqzSFSdBoNB0A6qq1fAmXYetudsR7OYZtDy2ImptMhlcanoEupH1EeA0sKG2DLRqxtX8z889CHevNMLspQ7QF9ZiYpjxxAmIHqNiQuPPIrGG1lCDnP8TvI++QTRjz2GnNFjoMnJMcegv2bKnyTgN3Z9zhzEjR2LwDiW/AwcdelMylLN76Vo82YU1jjxXn/vPeiLitFg+VfW1Wm1UHAtUDL89AwaDa5MnIiUTz9lv858uXR2/VJQ/s8/CIiMRLCFz6YYbi5y/N3liCgFoE2bNqydcGBgIGJiYtCuXTv06tXLqdXuzz//HHkWGvTevXvNCsCXX36JkJAQAMbwo7YD15QpU7B69WqsXbsWJSUlSElJweTJk9GrVy+7dqQum5qaij179mDr1q2oqKhAREQEWrdujYkTJ3LuEsiVEV8cwImZD9odd5CFXDCmOrJv8wuDanc9HPcb7WZsQ0SwmNdeZisJUvaPm14BOj0FlN4ErvBwAL56DLh8COjKEbv68HLg/neNCsDt88Cn3I7vDtk5HwiNdq4OXtg8X40IZ+sd79cqAKd/4XdNlrBM596M0PCTD//wMFb2X8lZRtTk2NnfkCR9Xm0lAzcOxNcPf412dduxlhdq6iQ2itDqk6t9QgGoPHECOc+NEpUIqur0aceFGLi9ZClC2rSBRgL/x4JVqxGVlmanAOgr2XeBr06diqAG9ubIBr0eBo3RF0+hUMKZF9ig10OhNBqQ6KuqUfK7wGAFfJBQCWBb2Lgxew5CWrdG4nszUbp7j+j6by1eLPpaTyJKAfjggw+kloORr776ymGZdu3aYcuWLXbHQ0NDMXbsWIwdO9ZhHVKXHTZsGIYNG+awLm+htEpCm2KRP+p2M35D5gx7JcSrOSGzkGoX/gR+GAO0HWp/rjy/NiTlhT+BvQu5FQBL1jLUBxjDXAZw+QhZvCsXdxrzDPDlj5lAONduD8fgJ8XAo9cDSiWwfoTzdQnBSQVVTqvrJnJLcvHyHy+znlcoFKjScQdWcDdME44VJ1bguTbPWR1bfXK11We7+y/RHMiV8df5vAez9s/CtB7TXCaD1BT+uJG7gM3z1VdVQRkcLEnbBq0WmuvXEZSSwlqm5PffWc8Vff8D6k6YYHdcAYteT6mEgckvz/J7KayusG7jhx8QPXRobTEmnOlHHfRj2oICBMbEADBGLhKFjXzmHZqa+pVhYVB6kbm2GFwaBYjwbUy/n28PX8aFm6VO18c1kJRUCldCdmXdNOcqkGz4Y+vUZsUD+wRuA259S7wc7t6I+LARz4IMgmkZHPfK84FZLNvabHVlfsdR1MY5O2c/cO2Yg/otMJsoKWrr+mcN/+tt2TbVcRlB8Hjg2iqjcmbztu+9shcVWqkStBlRQIHs4mz8e5sja7IDDlw7AI1Ow6vsmYIznOc//edTXCsVGOpPihV8jkpsJ/PpR9KtIggBwI7cHc4LwYGh5j9P8+3ZbznP81dQmMuxKa5VFy4wHjcYDJzmbtob1xmP60pKalbfra890+kOlP8jTXQmzeXLyF/5v9oDTir1efMtcmaY6lIqjIsUAqk4avyOumL35nuxvQVZPe4y/627fVuSyrUW1iaXhg5DwWru/l+bn+9cuzKAFABCEoo5Jui5+eXoNIt9xUJK8kqMK4EnrhShvFqH745whx3s8cF2njXbhP+07ZF0Va7NNutO3LEzoWOJ5sGGq02v5qXaH7skfksYxVfEX2um5jvznRvdPmfMHWAzOXnxjxdxJp97Am3LLxeYTZcm7bT2VdDo+U3gmRizbQwuFl8Ufb0ttrIB0vo02KEATt4+KegSMZNxpmtW/es4ogvfdsftGCdYJrlicnI1fV1zKEwbsp96GkXffy+4/iuvjweYQj3q9TCwmOVorlsrEyYzHC4MLKHC7Z6iwYDCjRtRtHGjwzotF68UCqWdAmCorobCQdTGyhMM+SFcYa/PUueVSW8IrMa6nqozZ2wLMF6nLy9nDh1qQdZdd7OeK921i5+AHoaXCdC6detEN6BQKDB8+HDR1xOeYcORyxjamX0LEuA/J/v2sMUk3IUDsgLArrNGx8PbZfwmmNeKRETUyVgP3MqCR0N0WDZddgsIc7SabsPlQw4qtUTIM2Oow7KT1esApU2ilJUDgPaPA3fwD2/oELb3zHT87FbxdbgDrkF1bgNg/AkgxHXROs4VnmM8frHIOGF35aqy3qBHSXUJooKj7FbMJcULIuzYroybJjSrTq7CM234/V4cra4fus7UF3gnFx95BEGNG5t/u4Yq5nGg+sIFaG8LX8HVmybmAvoGrY0CkHVPTzTdtZPzGkN1rQLApMRenVzrn3Htrbftzt/8+GPO+qsvX7Y7pisuhjLKUZ9i/S7lpX+MmJFGM8fqS5cQlJoKALj4xBOIfZrH+8l1Hxle2+Kff0bygo/sTwjBhQ7Gpppzx74gyu/E3fBWABQKhajVFFIAvJM3vsuwUwDm/HIKj3ep77I2hbxeJ64UYf/52xjTq7HL5GHl50lAdSmQZOPYyvcLrH0ceJJ7S1wQJdeEKwDfPcu/bGGNM9vZ34CkTvyvY7of+z4F7hlvfezmaaD0Bns9VSWAqIkgS0fv6DmJHSD0eosY/E4OMlwyVhYZzX1cgCttxfmy58oevLL9FWQ+m4kOqzpYnTty4wi6JHSRpJ3N5zfjlY6vAADKNeICD7DBpiCZjl8utZ+AuQJPPM/0w+mCr6nU8VyIYfg6ladP49Z/a5ww9XqrMuUHDpj/NhgMqDxxAqHt2pkOMDZhm2tAX1GBvI8/RsIU7vwPgnCwC6C3VFwMQPnhw1B36WLnWKy5erX2g6N+y+J8xZEjCLUJ0S4oB0BNXbeXLUP4vfcCAC49+RSa7zXumlZmHIe2P0syR0/gxHqFQafjnd3XAK9YVzDDSwF4/fXXXS0H4WEWbTfayq/cy74lv2zXBbRNFh4O1PIHMW3TvxjSOQXqIFH+5wCMib9uFFVi0fYsKwVAUL4k0T2C0OsYuoOs30S2zSKGq1eqf3zB+P+vHwceXQr8MQMIiTYeO/sb0NyRc7bFPagsrPm/AMetj9sYJ72ccNyDGWJD2Aq8r4e+AI5/I7KtGhZ1AvIvAJ0twifzFcONOxZCJpa7Lu/CJ0c/wQ+D7OOgMy0qcfkFXCi6YKcAiJ3kLslYYlYAun3dTVQdXHDJlVvCbZp45MYRdK7HnkNHyh2YUk0pcortI9a8tuM1fNrH6NeUeTMTm89vxtTujn1bVvy7QlD7/97+F8N/ql0kzC7ORsNI+yg2bFQcP252iuWKjmPQaHBp2OPGlVmOyXLxNmtzVV1hIQpWrTYqAJwr1sx1Mr3jjn6qluZEBo0GOc8+i1anT1nb80uMrqgYAVHRtQcUsBfU4jsaTCZErBvHtdcKmUSzVCbuKp594tm772E9d7pNW69YzRcDr1nY/fff72o5CAsmTpwIpdLaPSMtLQ1paWkua/OTP84CAGZsqbVpfeBj+23Kcev+wXN3pQqr3KZj1GgNAINzvaMFjNIqLcKDAzFu3T+Y/GAL62tZeqHLBRX2dbtaRU9vbfHBpgO6XmNDOStewgYlisEqZuX768eBGQ4m50zV/jmbfxsOJ/9MeMB8p6IAKL7quBwX+TVOiw4Tgwn7fqfzTyMoIAg/ZP2Ad7q/I1q8Wmn4vyu3K24jqyDL6lgZR3hVOTitOks1m48Lz6/23NbnkPmssa+wnMQIVXaqddXIK89zeF3GzQy7Y3/l/mX++0zBGaw/s56XAiAUy1wIAPDzhZ/xckf2qE9cGKqqUH3hIkLatOFTWlQbAMvE0uLYldcs/CoYip7v25e7fgsTIL6KfdWZM6jKynJckAVdUaGgJGBZPY2hz/lEDjvdpi2a/33A/oSUoT41Goc+DGw47UjspYhfhiVcRnp6OtRqtafFwNkbtZF9Vh/INv9t+s3m5DveNl+687x5d8GEAQYUlWsQpbb+sTrq59b9nWNe8WfqgN2UK5WjpRqZ2BxAj6wEttTspjkKW5jpwBHXUgSpVn6drofH9eY2av6v03DY6zspDuPgwsP8hyt7ZJmDbW3TNXwHtv9Kv/LM9H7+evFXqFVqfHPmG8EKgFavRbWuGmqVdH3Saztek6wuE1Kau+y6vAuV2ko8kPqAqOsLqwqhMzA4i0oFz9/GqXxhK5dFVdbK9rG8Y+gY35FXUjOpEBJZifH6qirHg4HDeozntTdvouK4heOrg+tKdkgT1UnP4rvgiKpLl9hPOggxqysqQkC0g91SizpMk+ZLw/mFOjYwOU/zbIsPp9u157VSbx/hyUE4VIEw+VfIFV5RgHbs2IFTXCmbCZ9n2sYToq47fd0+XNjhSwXo8J7ziY9KqrR4ZvlBp+vhhbkzsomTXGixlX94OXcdeQJ+Q98/70gg/nUBtXb8rsay0+bTgS/tDZTfcm6yL8ScSCy6mgnQqsHO1aO3GQRvciQbMhiAapaVcoEDlAEG1sg+ljBNor8/+z0e/+lxzuv2XBEWMYnL8VQOOwCbz2/Gd2c5ws6CW+HYeG4j8srz7I6L+W6WidMybzFEYWFBaOQnAHb5Fi4UGXekZv/NvmMndaQlwY7fIleRi3/+GRUn7MPY3ly0CKYOqfCHH3GFwQRam8ewEKBQoGDt13aH8z78ULBs7FGAHNkOCTdRMqEvLnbsAyDkXtvIUvKbANNXi3ZOd7rDzi/DGSpPWfS5XN/H4lzeR7WmV6datmK9RF9aivN9+zklnzvhpQB88skn2LaNecI2Z84c/PTTT5IKRfg2n/3JHGFEyDhiKmuK+uNyzGmK91sf/6Ste9q3w8DyNwufsGcXNSPFdizjQ7Sod/9nxozDJgokCAM5t76wl4e3s5xFnbYmK2vZEv05kOO9WO7ztUIY/zcnCagqAn63TaLE0I6D77X3qjGLeoW2AldLr+J2hf22N9MEo1xbjvxK64gpv1761eqzpbkInzqZyC3Jxb6r+zjL2JqLOKrflcqEuxQVy3a+OcPfx0RIcrQpe4wOrkzOuJYT/H1X9+FCIXNsfUdsOrcJ607XRhRs9792zO8gx2/5+uw5/Bpz9GgUClRlnWNMqMWZwbdGtoLVq9nL2FBx7BjvsuZmqmqfw5WJEwEApXv3omyn68JL6gqLoHRkAmTe4RRef8n2mt0RlmdT8tdfdm1p8/NhqKhwGJJTDBcHP8I6bugKC62UjttfOk5K6404nQfgwIEDOH/+vBSyEB6EKSkgGxodvxWaNzfY25VKhZCht/sc+1jQRRUa5PIwYbLj7K8WEy0BvaDtyq+UeCJcZWUh8N1zxr+3mqJj2NwPJrn0WuCmxU6IM9FXzvzK3IalGY+ryGJaEFEYTZpMf9tiksnkPJfPpfywyM/lO2JrXsXCihMr8MbON/DZsc84y0mNo0g7v178FVP3TOWcAM4/PF9qsWSN5S7D+tPrzX+7c5fEUimbfWA2Np7fyFq2z7d9WM/tvrIbO3KsTWSqdFWCTLdKthrD9+pZVshNlO3ebfWZbQJ5e+lS3m07RhoTNIPBYGUCpKtJOHXjfR5+U068FrpiGydgsO9EsKEtKDD/ra9icMiuGTv1psm1xW/98osv2RU3P0eL73Vt2rucMhT+8CNPabnRl1kv+vAyYXJhiFFXQInACOiEzP4BrDnAvkJSqdFBX1Pft4fdZwvH9bu7XlxpNcgoYIxo1PPDP9kvkpqyPODvJcKvc9Z05/Z5YAH7lqVwLG70rRrfjgP/Nf6/MBs4zzexmrCmGE9eYHl+rpj88zUzquCILf5VjT35ezHG/x9bK0wGbXWt7wjXdyy6wnneZJvO7Efj/ABmu/L897W/Abgm0g7g3lCXJ26JM4WUAneEDmW6lwVVBQwlmblZ4fyOLB/l5kyHjua/+fgFaG/dqq3fJlQoF47i6VtjkKzvYZp4V190brdUX8zdh9n6ACgUClwYMFBQG5YZem8t+tT6pFMLM7XXFX7HbZp3jTFcq43pLtPfNdxa9gVjvVL5d8gJUgAISXng41348R/uLKimn2KlRmfVHxhg4L274Ew/y5W1WHaYTHd+mmhzwsoLmP36jHVACY/INFIMXLeYTbuY4TECc4pUc/LSbubTbJMCh/H3WM5LEbr1shB/FaYsOJYTQBs5b1rYe+s1QBF7mMllx5cJsiXng6Uy0WWNdZhOkx25kDq4sF1FdoZjecesPn+e8Tln+RE/83N4ZOOrTA+ZEti8TsdvHud96YGrDNFbRGI7uWea7PN9D3SlZcxRbxh+++fuvQ/6UmNQC34mOSz9hxtWeEt+2yZ45d2ME/24vrQUyrAwu+O60tpgIHxt5jnhewst63PjLvfNdJY8FhY7AJUna6MlavNrF30K1ghc1PEwpAAQkq6fXS+qRFm1Fr/9a/S0v3iLPdzfPfN24PdT1gmgLCMGVWqst9wqLD47uwWu0Qm83tmOf/cC564/zDFx4PoquwSYTHCapDiB7b2TsjMvZ1hxL7gE5P7NXP7XycZdET5kWqw0/TyJxwUCv5eU9+G/XaWri4GS6hJM+ovPPXAOPqv5n/7zqcMyTGh0GlRqK/HfY/81H/vlorVj9OJji3nXJ2bnYXGG4/qZ/BycZcWJFea/q3RVePKXJ3lfu+n8JusDIl9btvtVrbd28EwKT+JVX/FPP+HC4EcYGhKm/FcKCXDC+ZuVZiS9+cknDk2cJIHRepJhB2jdOosCHPU5stNXGKMqwVBrXqOvsH7Xi218MmzPS4JlLoPKShR+5yDiHgMXHxti/rtsX61f4K3//pepuGwhBYDAh78JjxbhiF8yrwEAjuUW2p3LqDl2q7Qa5VW1nYbBABRX1CYBajltK7Jv1yoQWovdAdvBxBNm8PKB5ctX8N+6BwD8+b74tgAOJcnm+IU/nVeo7CZJFnIVmcymGNo4vBy4xfG+S7nCt/pRJyuwvdeOZONwhmVKRuRgJldSXYJN52onf7ar5bZk3MzAv7fso6pwrehaylCmKbNyQmVb6T9XKGSnqZZPj32K8X+Ox5IMEaZ4bsQydCcf8xY22O77xqyNrNc4ai+nJAc6g86sQBy/eRwaPXdWW0cyjtk2xupY4ygB2d31elx7x8ZBXuBgUCwkOo2LMSXXMlRVSR5dyXHjNlF7dggzkdUVFnIXqHm3DFqN2VQod/RoAEDewoUAbPInALg+YwajbI7gSghnVa6igvVc/ooVrOd8Bd55AE6ePImFNQ9JyDmFQoFx48YxniPkwaZj3CY7rsTR+Paf749j9fNG2+FFO85h4gMtuC9wJ3wGZ5d14jzqLch2XMbE3PriRXGE7X3a8zHQxtnJMQceUwZtbEvPOzJVceUulLCJo0angSpAZZ4AKhQKnCs8h3f28ssZYIABi48tRpjKaEJgmfjLkaKx9eJWtKnTxpxB1lT+9T/twy86Q0l1CQqrCu2OXy+zjQsOKzmkwDa+vrM4kq3Pt30QGcQc0UUPgWE2ba836M0mRE/+8iQ+78ttNiVHLBeQqs6clazeihPO+YiYnV71eqtswJ5Ak8tgRmjRP4jl9hJ7x+uyvdzRv4SOo2c6drLOCcCaa4a7XivzJx+EtwJw7do1XLt2TfA5UgDkjydXzy3bVijsO5YDF/JRpa0dsFLf+tl4nc0A6Kg/mv3LSe4CgpCBpz8f52Auh1Rbqnl2dGKiGeWLCxtIcLxnjsKtCuR/J/+HLvW62IX7tGqSY9JpipnfPq49AGFmLKfzT/M2+2DCPJkT+fXnH7I3k7OcIP6Q9QO0ei0eb8GdC4GLd/ZwK1JMCsJfuX+JVhxuVtxEVHAU4zlnnaa3XNgi/mKRY41ldBmxlWX16i2gtH3d196djoqjR+2O68tro1s5crR12KqmdjdFc0XMwpyQcMgWV/HMBF+04XsRMjnGoLXfRVJI6QMg8vqb6RZO4Ax1XBg0GNFPiO8XPA0vBeB1hkQYBMFEdY2ZDt8h5ss9tXbnBgOwct8lXtexRX9k47d/b7CfFE1Ng3lcyZxcGP7T3AZL5+a0CQoDQrb7Lx8Cmj8orP5fJlt84NFpH/qS4aD4WNVuxeGgxGUC5OBagSt0C48uRGhgKFrEtKgRTdyAefyWcWWYb+QYg8GACq31Nry7E4Fty7YP6Wopw4LDC9Chbgc7BaCgkr+JncbA/bv55Ogndse4siW7yzzkaql9AAFbpUSMSZUpJCzfZ20ZXcYRerZVW8swjg5+H9XZ9gssRZs2MZQEztzRmbdsQtBXCvcDuDJ+gri2ysoYHYAB51b7LeHKYVB1ktsP48qb/3GucZHfweBgwavq7FkYOMyI5A4vBeD+++93tRwEIYiF2xmiP7idmsFrMUd4Q2fi3POl+DKwZq7r2wGAHVyxqG062VObBSgANffy4DJh8pjMnOTgBGKpHGU72NJ2BNOA5fDesN+DYzeP2Zd24T17Zfsrte04mOSdKzwnyClVLIx+EBz3wLRSXlzNvKr77217fwcmntv6HEICQ3iVlYJ+G9gzkf6Q9QPjpN6E7e7A0uOO4+Rn3BSQ76WmelNIWI3OwcqvGyj9097WvehHEbHkJcj1Yvk+Gpji6EtIxeEjqDx7FiHNm0NfVOQ4C7AlHM+oOluA6SlPKo4csTtWuns3wnv2lLwtVti+s5fF/reEtwkQ4T4mTpwIpdLaPzstLQ1paWkeksg9ODsdcdsckOv3vpslhJgrKb4GnPvDPW2V5bGfs4v2w1WRJ/0nXISlvDoHqeuLrzkeOK7bhOrcb5G4S4J742hibpl4ik951nYcyHqr4pb1AQke+49ZIiZwLuLIjSO4O+luSeu8XWmfQdcEm0/DkM1DcLbgrNlMy9Uw7gwYjBmfTWzIEh6BpRaxEy8FtHkc/ZhICiU2jxEdClQAmstXENK8uTEHAIsCwPj75fhNV1+2zldxe8VKwXLxWVUv/OEHTgVAV1zssB7NZWG5Na5Ns83IDu8bpywgBUCGpKenQ61We1oMp3D3Ks7urFvo0jBG9PUGg4G/zFpTx8xQ/pZ0DmV+hbMJzyRDqvfWQT3pLYF7bHM7WHBys4P6JVAAOAau/Vf348RtaZJeOfpdlWud2yUr0ZTYHXt3nzFbqMFgwIazG5DWOI1RDi7ZDlyTLv793qt7eZVbd3odNp939OzFcbbAcd90rYzZl08MttGaTLsLq0+uNh/T6p3JySL2N+CaCZulL4BoLH6TbgkFWnMvjAoAs9O4sxRv/VXklcL74oL135j/Nuh0uD7zPQAid3QAwCLyoFjzKjlDYUAJlFV7UWIsDio04rdgBSnxLojT7ZNUFrKfs514Febwj89vVY/oky7AwPK3CLh2Wnhh/O46DrMEu/juFjBN/sWaDGUXcZsEuNoHYOb+mayr4kwcuVFrbnCp6JJTMvGd4Jomx1JO/tnCpZr8NJhgipIkFtv35ULRBZy4fQJlmtrQzutOr7O9TLa4Y0Xe8jVzS3s16IqKoWTZAWBUkgUs8FVliQvbq70pvA80hw21If9/q0TJ4CjrsLdDCgCBEokz41Zr9Q6zATPi5Lh/o9h9HSYAr7b9kwQ7+12b+3H6J/51KRTAjlniZdFaPPtjpmyMIl+onfPEXSd4guzEC8/U1p5P7A4JjdHuip0720RPtlRobBQAF22p8633ua3Pmf8euHGgU21aTna5qHZkMuYDnCk4gzJNmWRKjq7EfueHHy7qtyV+b/nGsneOmoUCoT4AArCMbMRExXFmhZRPgq6Kf/n54ViiyZHLbrPnIQWAkJzLBZ7yihffAXuvFZ8HsUuoJfGEFgbmTL9M5Ox3XIYPm18Vd53OVol2tXLIcL9OWAyYYqNecDnFukA5uFJ6xU5BcGUUIPsEgsxt2ZYrqRY72eQH126Mt+LqaE43F3jA38pNKEJCYKhyh1JoaQLEXwG4MecD/k04UABKdjjKl8LOzU+Y808B8GrbfHdBCgAhGw5eYp7s8Z123CoV32E6Xh3089V+qTlh4zB3cScY77Gl4ysjNdcwrqCKeGZX7KNN8GJWHZumebS952PHZdyIrSmOLa5YmV941H4A1xucS1RlafZk6ywrdlKacTNDUG4DQvxzdDZPgS+gCA52qwmQvphdAcj7aIFL23a0QyCWUicUCyFUnRNhuioTSAEgJEfqicLFW/y20Z3Bu9cKZCJ9FUO4xDL2aCV23LTJp2AwACU8bbflYEJhKX/ZTde2tddi4lwpXZZZdzvvMyF2km7KTttxdUfzscslxigfCiic/m4rTqwQfE1Osf+aG2zP2e5pEdxK6c6dzldSM3Yqg4JgqHanD4DrTIAckf/VcpfUe+0dhog9LqBo40a3tOMKSAEgJMcZZ1wm/jojfcg2W6TTWTw/gZIV8xvzL1tkE5KtuszCnt8BTkUTkYgzv7ivrcMWg+aO9yWrVg6rr1IuIOy8bJyU6Qw6xnrZlIKtl7baHVv570rB7Tsb4cibaPe/dm5px6BzQ3JFD1F96RIAowmQmERgYtEVFiEg0jVRgAj5wisMaJ6TMXPj4+Odup7wLr49LCy2riPW/i2HVTSmSYnnJ0s+TRWPlW2uVV0ZrGa7BcZIP+K++9G8o6znXGHTzeSk7KwJEBOPbHoEQ5oNcUrBEbODMHrbaNHtESzopX8/5MLNTz4BACiCglyeCMwSg1YLRVCQ9UGFQtTKmLsy45b8uhVhvdyYCMwH4aUAjB492qnt000sKbQJgg/XilzfEW7OuIqhnVOcr+jSbufrEIyfTHSZKLjkaQk8j8F7V0SZ/A5c7Txq1ZaACY4cdkcI/0ARHOSmPABcQohTAG4tFZjN3RlkYv3KhKG62l6pkhm8FIA2bdrYKQAajQZnzhijgISHh6Nu3boAgJs3b6K0tBQA0KJFC6hUKinlJQiXcLVQolWLolzHZQhmmHwICMccWWl/rKa/dudkWgxMCoAzOwCOwmk6cz9IAfANSv90j3OoMyiDgt0UBUh6DFoZmGPKgNwXX0KD5V95WgxOeCkAH3xgHfKpvLwcU6ZMQWpqKkaNGoVOnTpZnf/nn3+wcuVKaDQazJw5UzppCcJFOF7oYBj8c6XLFOoUv73taQk8D9MDPCou+YvXsPNDoPebbmsuv5JnSFYBSK0AZBVmsZ5z2sGZ5v9uwdWO6Le/+NKl9UuBIjgYBnfkAeAa+EQ+B5Mfg78jPk+F+xDlBLx69Wrk5eVhzpw5dpN/AOjUqRPef/995OXlYdUqHx+ECZ9A7iulhCP88Pn9OdthEb9bteZ4DTactU8sdLOCf7Qmv7uXMqX6srQ+ZnLEnSZABo0GisAA+xNKcTFi3OUDAICUcicR9YT379+P9u3bIzw8nLVMREQE2rdvjwMHZLJKShAcUM4QgpAHroy3/+9t68yhQnYbiqvJRE0OXH7xJU+L4HIUAYFuywOgKy6GkiEEqFfMrbXe6/8kB3iZANlSUlKCKh4vZ3V1NUq8YBtEbkycOBFKG+07LS0NaWlpHpLI93FFkiPCnXjFcEXwYP7h+aKvpZ0836c6O9vTIricsj17oL7zTre0xZYDwBt+SWX79nlaBK9GlAKQmJiI48ePIzc3F/Xr12csk5ubi4yMDCQlJTkloD+Snp4OtVrtaTEIwnvY/Kr4a6u8eJGCNdGafypEQhX5Ij6hZgnCzShUKujdlAjMmAOAIQkYOfM6RWVmpqdFcIgoE6ABAwZAo9HgrbfewoYNG3D9+nVotVpotVpcv34dGzZswJQpU6DVamnVmvAKHPsA++eEyi+4wh77XvbcZnd6JRzDlffAm6AdTN9CERQEg5sSgemKWbIAi/QBILwHUTsA/fv3x5UrV7Bp0yasXr0aq1evtitjMBgwePBg9O/f32khCcLV0Pjpx6wa5GkJxKNhyTTrpwqrP5kAaXQaqAJ8L8w2OVsbdwDc5QOgLypCQDSDAkD4PKIUAAB4/vnncffdd+OXX37BqVOnkJ9vDBEXExOD1q1bo3///mjdurVkghKEK3E4cSANgZAj1SwKAOHzXC69jEZRjTwtBuECjOORG8YcgwG6oiIEN21qf85PFxH8CdEKAAC0bNkSLVu2lEoWgvAYDuf3TsQmJwiXoXFjyD1CVuy6vMusAPjTzodf4KbHWXE8EwqVijEKECkAvg8ZeREEePS37sxSqyPnK4InbCZAfoorQ4jKDWcSphEyR++eZ1v4zTesUYAI38epHYCioiL89ddfyMrKQnFxMTp06IAhQ4YAALKzs3H9+nV06NABISEhkghLEK5CVjsAeo372iK8G1YFwD9X747dPOZpEdyGr676k2ID6PLzocuXPvM2Y1tsTsCEzyNaAdi1axc+++wzVFVVwWAwQKFQoE6dOubzV69exdy5c/H666+jT58+kghLEK7C4WCqd2PCkYJL7muL8G5un+c87W/Jqw5eO+hpEdyGr06UhWRnJpzDYDBAX1oGJUdSV8J3EWUC9O+//2LBggVQqVR4/vnnkZ6ebheG7M4774Rarcb+/fslEZQgXIqcdgCOfe2+tgjvpuQa8/Ea+91id5quyYBT+ac8LYJH8KUwoBQFyI0YDEDNAq4t9BR8H1E7AN999x0CAwPx/vvvo1Ej5igEgYGBSElJQU5OjlMCEoQ7cDh8Gty4A+CjK3uEC6gu87QEsiI1MhXHbx33tBhuwVd3AAiCcA+idgDOnDmDFi1asE7+TcTFxaGgoECUYAThThyuoLnTBIgg+EJRgKy4M+FOT4vgNkgBIAjCGUQpANXV1YiIiHBYrrycIlQQ3oHDHXR3hkTzoe18wsVouHcAmLb2fZm7k+/2tAhuw9JvyVcdggkXwxVtyM/6Dn9ElAlQ3bp1cenSJc4yOp0Oly5dQmJiopgm/JqJEydCaZOGOy0tDWlpaR6SyPdxOHwmdXKxAIbaDpdW9tyHtyfSciC/L9mG88GvEmP516MlXIC+rAzKsDDmk6QA+DyiFIA777wTmzdvxs8//8w6Kd24cSMKCwvx0EMPOSWgP5Keng61Wu1pMQgrXNwZ6nVAgOnnSCO72/jmKU9L4BwOTID8bQfAn9C50y/JjdA7614oBKj/IkoBGDp0KHbv3o1ly5bh9OnT6N69OwCgsLAQBw8exIEDB7B9+3bExcVh4MCBkgpMEK7g7qZ1HBdyJQYdzD9HP1u19SgPzgEWd/O0FOJxZAJEsTx8Fl81+6F31r0EREV6WgTCQ4hSAKKiojBr1izMmzcPO3fuxK5duwAAR48exdGjR2EwGJCSkoIpU6YgjG17iSBkhMd9ACydjMkEyH3Et/S0BM7h7SZMhGgszbt8SRnwpe/iDdAOgP8iOhFY/fr1sWjRIvz999/IyMjAjRs3oNfrERcXh44dO+Kuu+5CQECAlLIShO9itZ1PAyDBE8oa7bf46kSZdgDci5IUAL9FtAIAAEqlEj169ECPHj2kkocgPILHrW5oB4CQEtMLTXMpn8VXHbxJAXAvAZGkAPgrosKAnjhxAidPnnRY7sqVKzhx4oSYJgjCrTgeSl08KFlmbPXRgZ0gCNdwrZQlIzRBOCAgmkUBIGdsn0fUDsCUKVOgUChw55134o033kBISAhjue+++w5//vknNm3a5JSQBOHzXNoLdGxg/PviTs/KQhCE7LE0ATpy44gHJSG8mYBIFidgUgB8HtEmQAqFAgcPHsTkyZMxbdo0xMfHSykXysvL8c033+DChQu4cOECiouLMWLECIwcOZLX9RUVFVizZg327NmDkpISpKSkYOjQoejVq5dbygqpkyBw4S+g4wjj31WlHhWFIAj5Y6kAUOhMQizkBOy/iFYAevfujfDwcGzZsgWTJk3Cf/7zH7Rt21YywUpKSvDbb78hNTUV3bt3x7Zt2wRdP2fOHGRlZeHZZ59FcnIydu7cifnz50Ov1+Pee+91eVkhdRKex6E9rasH2LKbtcnAyAeAIEThT/bjln2WL31vX3VulivkBOy/iFYAlEolxowZg4YNG2LJkiWYNm0aXnjhBfTv318SweLj47Fu3TooFAoUFRUJUgAOHz6MY8eO4Y033kDv3r0BAO3bt0deXh5WrFiBnj17miMUuaKskDoJL8HVdvnxrYC8U0C91q5th/AxFKCoUQTtABBiUQYFeVoEwkOIcgK25IEHHsCsWbMQFhaGzz//HEuXLoVe7/wKpkKhEN2p7d+/H6Ghobjnnnusjvft2xf5+fk4e/asS8sKqZOQBx6fQjW+z2gGRBBCCOLOs+JLK8OENb4aBYh2AAjCPTitAABAmzZtkJ6ejgYNGuCXX37B9OnTUVrqOTvm7OxspKSk2K2yp6amms+7sqyQOpkoLy/n9U+joRjgUuHxRGANewDZe13bBuF7qNQsJ4wvNCkAvosetQtt9JwJyaFdJZ/HqTwAlsTHx2P+/PlIT0/HgQMHMHHiRMTGxkpVvSBKSkqQkJBgdzwiIsJ83pVlhdTJxKhRozjPmxDiFE3InKAwQK8FtNWeloTwJlShnpaA8BC+ugNAyAMyLvR9JFMAACAkJARTpkzBmjVr8O233+LGjRtSVu83rFixAmo128peLSqVyg3SEG4jpQtw+RCo2yV4QwqA30KmMgRBOIMoBaBPnz5o3ZrdWfGpp55Cw4YNsWrVKo+sUkRERDCuspuOmVbiXVVWSJ1MqNVqXgoAISUyGEwb9wHObgWlbyV4w2oCZIScQ30XnUX2cHrOBEEIRZQCMH78eIdlevbsiZ49e4qp3mlSU1Oxa9cu6HQ6Kzt8k+19w4YNXVpWSJ2Et+CGATapI/DXB65vh/AdHDgBk5mI71KhrfC0CC6B3lmCcA+SOAHLje7du6OiogL79u2zOr59+3bExsaiefPmLi0rpE5CHshizFEGGCd0VcWeloTwFsgEyAp/Wgkv15ab/yYnYEJq9OXljgsRXg2vHYATJ04AAJo3b46goCDzZ76ITRB2+PBhVFVVoaLCuNKRk5ODvXuNkVI6d+6MkJAQZGZm4p133sHw4cMxYoQxk2qXLl3QsWNHLF68GOXl5UhMTMSuXbtw9OhRTJo0yWpV3hVlhdRJyAM5zP8BAI16Aic3eloKwltgUwBqNFp/mhD7G2WaMk+L4BLIt8GNkB+hX8NLAZgyZQoUCgUWL16M5ORk82e+bNq0SZRwn3/+OfLy8syf9+7da1YAvvzyS4SEhAAA9Hq93bbhlClTsHr1aqxduxYlJSVISUnB5MmT0atXL7t2XFFWSJ2EN+CmQanxfe5ph/ANHPgAEL5LqaY21LYv7QD40neRO9GPPuppEQgPwksBuO+++6BQKMyOqabPruarr75yWKZdu3bYsmWL3fHQ0FCMHTsWY8eOdViHK8oKqZPwPLIwAQKA2MaeloDwJsgEyG8p11iYAPnQTk+AgnbI3UaAT1qBEzzhpQBMmDCB8zNBEBLhQwM54QYcRQGi1VSfxXIHwJfwJWWGIOQMqX8EITfaPe5pCQhvgUyA/BZLHwBfUvQClZKmJyIIggVSAAgCMnM8S+niaQkIb4FMgPwWqzCgvjP/h1JB0xK3IRvbV8IT8FK1d+zY4VQjffr0cep6gvArqFMm+MK2A5B/AYio515ZCLfiq/HylbQu6T7I3Mqv4aUAfPLJJ6Ls8gwGAxQKBSkAhOzx0bGU8HVUIczHaWD3eWS1aykhTaKbeFoEgvALeCkAw4cPJ8ccwqfxzaGU8HlYI6Z4R38dHBCMKl2VZPX5ki28EHzpe9cLo50rt0ErX34NLwVg5MiRrpaDsGDixIlQKq23QdPS0pCWluYhiQiCkCcsA3jNgo03TAzjQuNwq+KWJHX56qo4E75qAkQQvkDYPfd4WgSHkLu9DElPTzfnXCBkAg22hDfhJY6UBoMB9dT1JFMA/Al/UnYIwttQ33mnp0VwiHeMEgThYmg1jfBKWN9b+a/8m/CGXQo5ckf8HdAb9ADoHhIEIRyndgDy8vJw6NAhXL16FRUVFYyTKIVCgXHjxjnTDEEQhOt4dCnw4wuelkJaFDb/lzFSrmT7kyJfL6weKrWVUKvU5KNHEIRgRCsA69atwzfffGPV4Zr+NnVGpihApAAQBCFbfDHxUCDlB/B11IFqlGnKoKZkcAQhP7xAKRc18u3evRvr1q1DfHw8Hn/8cezduxfHjh3DzJkzcf36dezZsweZmZkYPHgwunbtKrXMBCE5frRwSPgULC9uvdbuFUMkUq9c+5NdfJgqDGWaMtRFXU+LQriAoIYNUZ2d7dpG/Ofn4naUYfJXzEUpAD///DMCAwMxZ84cxMfH4+TJkwCATp06AQAeeughbNy4EStXrkT37t2lk5YgCILwGfzJZEdqwlRhKNeWe1oMwkUEt2zpBgWAfn+uQhkc7GkRHCLKCfjSpUto1aoV4uPjAVib/Jh45JFHkJycjG+//VYCMQnCn6BOmeCJlw/gCoVC0lV7f3KGNZkAEYRovMBMhXAdohQAjUaD6Oho82eVSgUAKCuz7owaNWqErKws8dIRBEEQPotS4nCl/mQCpFapUa6hHQCCIMQhqveNjY1FYWGh+XOdOnUAANk221W3bt2CXq8XLx1BuAmHEwetdNlKHUOrMm7Fq1fRuWWX+4q43OWTM5YmQHQfCYIQiigFoGHDhrh8+bL5c7t27WAwGPD111+joqICgNFR+OTJk2jQoIE0khKEC3E4B7x+3C1yEIQ/QeErxUMmQARBOIMoJ+CuXbvi4MGDOHbsGDp27IjWrVujXbt2yMzMxMiRIxEaGoqysjIoFAo88cQTUstMEO6nSR9PS0Bw0WsysGu+uGtpEuoxaOVaPGGqMOSW5AKg+0gQhHBE7QDcd999WLx4MZo0aWI+NnXqVDz44IMIDw9HRUUF6tevjwkTJqBz586SCUv4NqGqAI+17XAHILaRW+QgRBLgRMQFbzYBciC73FfYFQoFRQISiVqlRpmWdgB8lciHH/a0CIQTeEO/JmoHQKVSISUlxeqYWq3GK6+8gldeeUUSwfyZiRMnQqm01s3S0tKQlpbmIYkIlxISZXNA/h0Hb9yVZTdQBiHXer0J7PrQvW3K4Xs7geROwF4w6EqFPzsBx738Mm4tXuxpMVxK5IMP4IqnhSB8Gh9Mgen9pKenQ62WfxIJqVEHBaBCo/O0GB5A3qu0TiFkQvbcz8BKkUquSgaZb3tOcr8C0OwBztNynxArxW1CEwDCAsPMCkCA0nO7p4QX44L+QRESAkNlpeT1EtJDvS8hG3o391xGS97dYKPe3Od7viG8cTszDR9WCLgIChd/bWCIdHKIReUJGRTAkxs80K40yN1ESc5YmgDFq+M9LI2bofdGtiR/nM54XJWc7GZJCEeI3gHIzs7Gjz/+iBMnTqCgoABarZa17KZNm8Q2Q/gTCuDhdgn4JfM6Z7HX72+GszdK8OsJ7nJC4L1SGtsIuLiT/XyQmJ0bHx7M3DVQy0EB8BTN+rGekvsEOyggyNMieC3qQPmZACWEJeB6mXT9sonQLp1RcfhI7QGZ72z5M4oA2o3yFkTtAGRmZmLixInYsWMHbt68idDQUMTFxbH+Iwi+8Ilm8Vqfpkhrn+gGadyExHbQ3osTg3ogTSS9kcX3+7Ydt1iOPHXEYZkAZYDsTLxcFY1IGcJs4qcIdd70r+74152uw2sJcN/Yo7l2zW1tEfwQtQOwcuVKaDQaPP7443jsscf80l6d8ByBAUrJF4B4V2dquFEv4OIuaRq3W6WV16DuFK0Hu8kJ2I93AEzcPx3YPtPTUggiOjha0vp8JROw2J2RqOAoqAPVuFbm45Otmj5TVa8eqi9dcqqqkDZtJRDIO4l78UXpK2XbARCQFDb+jUnI+2gBZ5moIY+h6PsfhEgmmqZ/7sC5+/ogMD4e2rw8fhfJTDlnQpT6d+nSJbRo0QJPPfUUTf4J6WD4vUQEM+uoUv+0VAECV65YJ5wiVsDkvAPQ41XnrpfSOTe6Ifu5qPpOVOyhjtoZnwczNbK/sBuISbU7S/Hha/lu4HeeFsGlvNzhZewZvgfbhm5ze9vv3fUemkY3RbAz4Xi5sF0kkXByFdQoVbK6vA2lBDsotoT16OF0HaEdOzosExhbx+l2hBLWq6fb23QlomYe4eHhiImJkVoWgrDjrYdbWn1++6GWLCX58dnITozHB7ZPElYR6wAkYmBSyjgYl7tXMaZeZ1eIuOzZvTEKyuP/k66uxPbS1eWjtIx1ru+QA8+0fsbTIrDy4+Afee1c/D70d+GVs/z0w++7T1A1AdHRwtv2c4KbNRVUXqFUIqStk7sqPHyXFKHidn0bfv218ItqxkG5+1QJRZQCcOeddyIrKwsajUZqeQg/xgDYdfS2K5hJ0Y5XLJI5ygxgmegH8raFNNj8XwLG7HDu+qQ7av+WPGOxmxUAVajRlAUA7rCd7DB0vg+87zpZ2g51Xd0A0OR++2Pj/nFtmz4Ok028L0XIiQiK8LQIvFn78FrG4wlhCVafhzQbIrqN+EkThV3gYxM49yDPe1Zn9GhR1wXG+05/4CyiFICnn34agYGBWLhwIcrL5RWFgPA8ceHSbQOz2fR63PnNwN+e0SERCTYHajrcB+fwuz4orPbv1HskEcmMO++zqa3oGnOelK7W55kG75hG1teKgmWA84RplkqASWXfmUBorOtkcQMKhcLldvtCzaDqqesxHo8Nkee99nhfiNqV0RYxLczH+OYmCA10Yw4PUgCEI+aeOftO8mhTGSQ28IMI2Xz0vRE1wkVFRWHBggW4ePEinn/+eUybNg2ffPIJFi5caPdv0aJFUstMuJiezZyL3NQ+xTazLT+YBrLoUJlFd2ls2nJ2ZYdQcx/sFAMJmXSGZ0GbZ9JrMnfxlgNEicNIK5u6pJiQM+6QsAwIXf7P+fYA4JWD/MtGJADvWDiZjf2LvWzrQUCApfmYtBPBwU0GS1ofE3GhcU5PYKd1n8Z5Xqpt+1c62me5n9JtiiR1iyUoIAhVuiqXK1Gf9vmUV7k5PS0WLTyvl3ATKGPTSxej7taNf2GpJr8umERHPvyw8IvcpDBH9GMPzywXRI2o5eXlmDdvHi5fvoyysjJkZGRgx44d2L59O+M/wneY/GALh2XYBvTm9YQ7PQYohXcajeuGOS4klraPua5uZ2g1SFh5vlFG7CbdDp7HXa85rpPVjKHmvQkMNf4LtfEzqsthw813cLHdVeAiNAZIdaHTF5vMgU7uoIlJRsfAPSn3YPsw+fffcaHcCxZiHaH5rPgHKIT5nix/cLkoWUzYfpcwVRjKNGVO1SkFLnc2l2jyWO+t/zDU6T2ru0GNG0tST8T9NQshfO6rR1a/+bUZ1ETE/XBCARCyWBHoBX6yolTg5cuXIzMzEw0bNsSDDz6IhIQEBAe7yPuf8AiX5qYh9a2f7Y5HhqocXsv2E1E66Eic0cvPz3kYTab8AgAY07MxdmfdcqI2Hog1AWo1EDi1hWdhBdB/LrD1LcdF2SaNHUYCGSKcngCgzzRjGM8DFrHaJRkMHDzp5v2BN87aH398FfCek2YYQuQ3lW33OJD5rXPtioZLXpYM0n3esTgi/nkFKAJ428/3SumFXZfFhcX1ZOjOMe3GYP/V/Thx+4TDss1jmjvd3p0Jd/IuO7rdaKz6dxXUKjUKqwoZy4SpwlCuZTfDraeuhxvlN4SKacWbd77p1PVsbHlkCwZuHOiSutkIbl77DI3mZ0BgnPujyYglad5cXBr2uNP1GHQCxi8xfT7DNcrQUOj5moy7UueoMU2r/+WXyBXpRxB+770o/esvCYXyDKJ2AP7++2/ExcVh/vz5GDBgALp06YJ27dqx/iOEMXHiRLz88stW/37+2X4y7irqx7LbIatErMibeKZHquhrbbFVxC13CtyzYOGGSYtCwW+1ok4TjpNOyBkUxrBT4Iabq1QCIZEMJyRom8mMKIyHydvkC/bHOo/i16a7bbQl+gEIiZpjuTKW+Wwma7n0e9OdksmhHAzv+6y7ZzGWfaPLG3is2WOoH8kcQtZ2tU+MM/GotjzfEQYigyIRHBDMqcSFBoZyZgP++THnxw1Xr+7zUQAdyRDz9NMiGja2qwyRYQ4RV5sn6XX8y0r0+CMHDHA+OpAAUv77GeNxVXJNIBAnvldgXd9IcCtKAaiqqkLz5s0RIscfjg+Qnp6OxYsXW/1LS0tzW/uzH2H/kQ7pnGL1+cKcWhu8ATXZedl+VyO7NeBsd3DHJN6/SY8n/HHLpE4BXhP4+94x7hQA9nJ1EjEwcookoNeswxI+ju3exXIpMg7atqzzvnfYyzG9YU36AI8usz8eoKptN4xhlXDgJxztuAEXarpKhRL1I5zJrcAfV/6WAxQBrIrMs22eRUpEit1PzBRpRxLfAYFfrV0c94KZrUxWJkA24poUCD5sG+Jc7gBXh0e0fUeUYTZmnm62UgmsW9c1FdfcR4WSeWoW3KyZJM2YdwB4+FUpasooI4y/i8AEkb5pAUr758bapkKcUsezbsC5e6lqwD2X8RZEKQCNGzdGHt9saITXwdWZq2zCZSotVt4/G3mHbXEz/3d3I842H2hdD31aMkffcERYELcd7sAOzKE/2yWLc1Y2InBkHy0i1CfvQdXAvoodmVj7t6UDrDu2SUyT8m4v2Z6wL5vaEwiN5q6Pr8xCrGZMdHjC/lisCPvSl/8Wfg0XbN+5zztAmOMVadvfcmRQpNUKvZjY+N0SBDgQAk4lh3Lk4OuIcBHJ1gIZ8nLcnXy3U3Lwxc7G2MErz2UCtHfEXt7tRgWz94VyjH0e+2xNiOAa2dR32I89AXHMfWL9LxiUfYGwrS5b0uLIYUF1tjieAVX9GqWbJZuuMiQEwS1rf7PqO+9EvKVfA1/0OigjIxEQzt9fTpViXPxTsGX6tYThnVEICeJgmqS3cOxzKBYVz3CgDZZ/BWVNwlvTbyEoOdllcrkTUQrA8OHDcf78eezfv19qeQjCipYJERjT0155ELIAHxfO7PC65OnOwoRJ7iJOAMBo1mJJ0748LpLA9jKqAdDpKePfUuxaNOpl/P+Ek8zn6zJ02C7M4smoTHR5nr04x0RHEgIZ3jVXTKB6vgEECc/C7uxqe4uYFvjywS95l08IS7Ca1FoqA1ymQia61OvisAyXechdiXc5nsBK9Hj4mvs4cloWojCpVWpJnIC5QnGyOT6OaTfG/LflM4gMYjLfY4ePiZFtGYWNiYyCpzVCcNOmCO8pgWM/n8ksw3sXZtF2SHvr5H3KoCAE1a+PoKZNkDRvLmu1jTf+aPVZTHIzg07H3/RJQH/dYAWHk7vAflAREGD3nJkwTc5dRdhddyEgqnbcCGrYUFz0IRkiSgEIDAzEgAED8OGHH2LhwoX466+/kJmZiRMnTjD+I4jH7hCnMafGhWFqWmu74yGq2lUI2+7Jtr9i67+SogSasI3hEREl2MHgFxJt/L+KR+xrKSaOAYHAQFMoXo6OnG824oQaE4UoludpFbmHR7bkN7L4tSuE4AijI7E5ZKsFJlOexvdy1zH4v7V/C1FYpDYNUzp2uudCLSSvgAVsk7KXO74sqJ7fh/6OpPDaHbh7ku9B/9T+VmUCFYF4qNFDgmVsU6cN5/nuid3xYe8PBddrQgEFlvVbZv67nroeZzhMR+Y7XHwz4BvWc0Oa1ybKsosCFBjG6QNgS5/6zIkCHSlJTErAuDvGATAqAh3qdrC/hqeyySuSkoC+UKEyme4xnKuZUCqCgxHahX0BSOFoUslHHoZ7FpnmeOIY3KQpIh94wHH9ThAYH+9wdb32PvK794Hx8QiIdRCkoaau0E6dHJar+9qrqP/lF7zaJsQhytNkypQpRg96gwHbt2/Hjh3c5g2bNm0SJRzhO7TlaW7Dd7u5f5sEjOjaAOsO5oiSZ8gdKY4LcWHbuYfGAhX5xtjxv3CEYUxbAPyzmmcjPAc9R5NOPgl5VGFAVRFT5ezXPPYF8MMY9vNsODNJrtcWuGG5qGC6R7Ze4UGAjiNTeauBwIW/2M8HClQOh3xl/bnlAOD0TzWimRKcNQAKBb6v8a2A5/8AvuKzY2QP22rspM6TsODIAtbrhO4UcJX/T9f/4KU/jGZgSoXSLputAQZWOZnMcUxEB0cLkpEPL3d8GVdLr+J0/mkAQI+kHuZzAcoA3Fv/XtZrHa1kmxygmcq1rlO7yGF7/vU7XseXmbW7LoGKQGgNWgBGBe96+XXOdi2x9etICEvA9bLa6++IvwNH847yrg8Anm79NGJCxIc87BjfEf87+T/R14uh6R+/QxkejjOdak2HAqKjoSssBODY+yqwjnGi22zPbugKCnBhoMAwzB4mZtgwxAwbxq8wzzG52a6dtR+Y+njLevgk+goL4+0z4CwtT2TidFv/C1gjagfgvvvuw3333Yc+ffqgT58+5s9s/wjv5OvRwmx9nUHoYrdSqcC0Aa3wWh97R1PbugIZIhf1ah4HhUKB07P6253jh00HZ1oRZ5tsmyYypnCdfCaYkpuOKFj+ZsHRRN2UhZeN9sPZKnbcNm947DJYwte8wjKsKp/n0G6o9WeheRnYUCgAtYQZaGtuy3Ntn0N8KLsNLJvZB9sk96lWT4kWKTggGCqWnY6YkBjcEc/uWyQFlt/ppQ4vMTo/M33v59o8h4QwZofI6T2mA6hVjCZ3mYy7kxz7EVgqUv0a9sPcnvamIJEWu4xmEyAHP6luicx9+cf3fozk8NrdvP89JHwibqv8CfUZkDrKUPLCTxiPN/7lF/PfgXXr1q5w19B0+x+821AlGn2rAuPibJLxWcBoB8/9XWOfexYxwxn8kdgwGFwXkM4ka83/nY58wyOqXdzLtv5iFtTs3kQ/IeD+sNBg5Qpr0WrqDqxn9ENstts+pLEqKcn4zlg8wzpjRCyAyQhROwATJkyQWg5ChtzVVNwP3vQTZ8slABizDTPF6h9yRzJCApX47shlh+2ogwIRreZOaPVwuwQ82b0hvtxzEVE8chjwov9coOQ6kM3hZPd/24DlFtu4dVuJaEgBRDkRiYWXn4GJmqcWkwoUXLI4XHN86Apgg8CQhveyOKdZDgJ8TY8cYqPcGAzME/cWDzOUt+GB2UALB1G3plxzII4CGPkd8LXFKtvDHwFf843hbbEG6aqIU07Ou9rUaYN/b/8LAHar+kL47/3/hUqpwn+6/gft/mdchRvTbgy+yDRu///vof+Zj/Old0pvvNOdKxqU80zqMgkZNzN4lR3QZABvcyzTJDElIsUYpQjWq/6WE2a+JkCf9vkUXdd2tZust41ri61Dtjq83hWRmlxRZ1Djxojow2zmFNzYZsGCh6IS9/JLuLX4cylE44Uk/glSYep3au5T/IQJuLhrt1V/pKpfH5rcXH71Wd5uhj4toE4dxDz9tPF+MzwbVXw8NFevWl8rcpEsrHt3zvNMUZ6a7tiOGx/Otz7IEq0JKonmGi5G1A7A66+/jrlz2Z1UCEIs97aIx0PtRIYZYyAuPBhBgcbXPGN67YTcqTlV95eAWJvBxFF9th1VJIdPRLMHaq9pxDIgjN3JfDwg2Oj4CxjNkSxp8yi7PCZGrGc+zuTbwLfztYtQZHGzpFzdtoNBPrYO25LgCMflTA64r9pE+rC8J81t7HhDBXzXaMswczX3q8Fd/K9nYFLnSXbHuiYwZ0ZmW6m0nLTx9QdwlD0zKjjKbnJssi8XiqmtuNA4xtX5QU3sd2bqhDIngpIiDCpXdB0h/PPMP+a/LZ8B30zAXE6+TDSNZgnhywDbCn49dT30a9hPULvsjdREX2HIhKtUq2sdQS3eW2d3FupOmIC4lzhWpG1ofviQ40J8zDFFEvFQfzTatFH6ik230dQnivR/UARaTIod1mF9PjAhAXpNtfmzK6MDOYRB9paZxwHA7APRquaz3BGlAFy5cgUBfEJBET5JqIr72Yvpdl0ZVt8lQewEC2wjRa/J7EXNib04JLeaOFvI0nUM8IJJObC5vsXDQDKL45vBALQdagwtyVZGLHdPAEbYODk6WmH3JEJWldjeg2aWk3+e74qlA7WlDCE1k8i2j/GXy4KfHv3JQhJrWcJU4m1sXZ0g6o0ubwieuDqCSeaJnScylk0MS2Q87ojhLWpN30a1EbZr1qpOKzzWjP052ypmapWaMxOw3fU8n5lJKWoZ21KwEvOfO407f3XVdTG/l3HFdGTLkaJlscQyGouJZgf2Q32nMcNy4ozpguusFchaHmVoCBQqFZofPsyoeNjJFm4TcpbJCbj/g6j/Za0/R/SwoXZlxKJQKBHigomxqN85w3cPsoydL3D8jOhzH5purwnCoVCgsaWiI6WprFjlpmbFP2m++KADnkDU/ntSUhJKSkqkloXwEoJVSlRorDMJ3tEg2vx3v9YJ+PPMTdH1Cwu6wl1YAddEYayt3YWaS1JH4dcEBlvbr9ty31RgzWNGp99uLwF/W2xvD/2K4QKDzf9ZaDcMyPyO+ZxSaW8n2+Q+4Nzv3HUKwoHTGZ/yUlKT06BcF4CC6zehrwwFrt8CKkOBJn2B838AubnGzyaiWxiPAUBZoPGc6XN1JHCrpLb8ZQYTuZtF1tfUcKPsBlTFKpTcLEFAcQBya84rChUovVmKKS2mYP4h661thUJhLqcqVuGZ1s9g1clVyL+ej1xFLlTFKty+fhuqYuPAV327Gs2VzXGx8CJyc3PNx3Nzc3H7hrFcj6QeyM3NRcWtCqiKVeb6LbG8rk9kH1y7cs3qeIPIBsgpNjpSa0I1UBWrkH8933z+2pVrUBWrUHm70lx/mabM3F5VfpVVG0ztlt4shapYharbVebvUh1abXU/YkNjkZubi5sFN83XmuR4JvkZbLu0DapiFa5euWr+f1mQcaVeUaSAqtLaRMDUTiNDI9yXeB/rvSnJK4GySAlVlfH7aPVaFN4oRF5UHspullnJaPu3qliF8lvlZnkB42JeTEwM1DZRb0yTvvVp66FUKPFn7p928piwVSoHNhmIKXumWB3rltgNX5/+mvH6JtFN8GSrJ7H21FpsGswSMKTmtxwQHY1wG79CZVCtGail6YZUJkYB4WFo/PNPON3KPhqdUBQqFQLjpUsi5sokesroKOhu3rLoRxn6U5bx104uoYMwQ3nL58xHhlCbMKvuQI45M7gQpQD069cPK1asQG5uLurXd36blJA3W8f3RP9Pdps/M/3efni51sEtWu1Z+7ekaOtVw4TIEOyaLLUzuoCOV83kS8Hj+ogEoKKARwMsnQ5jZ1TTbkAg8NDcWgUgwtJcgqMTY7Njbv0IuwIAAAkdjDbwv7wBGPRG2YQkhuFD035GpUKhAMLqckcBYmPgIqMy4yR6vR7fffcdDmTfDXz5DVByN7BqC1B6N5CvADR3A+npQKGFY+jtYCDbGCkGxU0BfaqxDAAUdgN+OglU1JQ3Hbekuhwov9vunFavRbPyZjh++jhSNClIP2M8H18Wj7PHzuKC8gKaVRqzYoYGhqJCW2Fs4ryxXLPSZsi+kI1mpc2w/ex27A3ci2alzfDb2d/QFV1RUFmAtRfWogEaILA0EOkX0tGstBnCVGFIv5COSm0lmlU2Q+nJUqTvSUdhVSGaaZoh/YL9d2hWapTD9pzpeF11XQSXGxVcTYAGzXTN8MfZP8zyrzy3Es3Km+HGqRtI32GsQ2/Qo1mZsb3CykI009q3YdluSXUJmlU3Q/HJYqTvNn6X6oBqpO+vvR8BigCk/5uOal01mlUYr91xdgeaVRrbKdOUoVlVM/O9+OL8F1DWvO/1yuohzmDdJ5jK7T2zF/+o/gETvdEb/57+FwmaBMQb4s3yl1aUYsPODajSVSE9qFZG03nT381KmyH7VDZaaFuY78tHFz4CAHTv3p2xSwpwocmKaZKYGpWKt7q+hbWn1vLbruVa9BEyAeMoW//LLxFUP8WiKHvZoJRkzsRgitBQGCoqkDBzpsO62AhMSID2Oku0Jxdtnzf5+Wec7dqN8z6F3XM3CtczhLC1ECmiXz+UbLcJoS1hbg6mb9/kjz/s7PibbP0V5/tzhxuu+9pruDZ1KkdjLL5lXoooBWDgwIHIycnBlClTMGTIEHTt2hV169aFykscHwhh2Gb/dUR4cCA+eaIjAODO1Bgk20zIv3+pB/Zk3WZ0AhYKU2fapG641XmFQoEGdSROFmLqCAw8VshTHCczEo6VRxWPMuDuuGx9Gsz12lzDNiGwrbujTVSY8LpG86Rf3jDes45PAc2Fx37npNuLtbsKD38EwAAcW8P/+pAoY54DPjka2KhRkL777jscOHAAAwc/hsYd7kJAcTYQnQoUXgKCIoHqYqNj+M1TtdeG1gHCayLz3D4H6DW1zuNlN4GgcOP1luUsqSoGiq/YOZxr9BpkF2cjKSwJZZoy1FUbB8ZLRZcQGRyJoIAgcyjIpLAkXC27CqDWDvxc4Tk0jW4KrV5rDsuZX5mPyKBIaPVaXC69bFf2XOE5RAVHoW5oXZRpynCt7BrCVGFIDEvEzfKbKKouYrQzP1d4zqpt2+MNIhogp8S4A6BWqVGuKUdiWCKulRl3ChpGNkR2cTaigqLM31Nv0ONC0QU0jW6KvPI8FFcX27Vh2W5BZQHyK/PRKKoRlAolzhWeg1qlRlJYkrlsoCIQqVGpqNRW4nKpcTfGJEfT6KYoqi7CzfKb5nvRKKqROd79paJLZodeE6Zy9dT1OB2qCyoLUFhVCJ1BZ5b/Wtk1xAbHokxbhtiQWPN3Nikcls8kOjgaZZoy6A166Aw6NIpohAsXLmDLli1Wpj4tYlrgwLUDrHLwxZ0rok3/+tMcxaXOmDEotEmYJQT1HZ14l1UEBSHi/vs5ChjvgaJmHFVGRiGoYUNEDRiAwPh4FH63wWEbCTOm4/KL/P0R4l59Fbc+c5ytmIuAyBq/L3M0oJoTFgpHUM0CsG1UHcsy4b17oSIzE0p1KON5Pljt9DtwJk7+5BMEpdj72AWlpjLWbWWSNeQxVP77ryDZrPAy5UCUAjB48GAAxoeyYsUKrFixgrM85QEQxsSJE6G0cUJMS0tDWppn7KaDBCoASoUCj3Qy/gC/e9HecbFzw1gcvsS+su2O31DzeuKjltQiwMSE6Us9sxlYJZP40b1ZIvbEpALjM4GbZxxUYPP9ur/IUdYAqELYk4lxwRU1yFIEttB8XPwn2/45qUKNOQUA4JElwEbb72XznCMSUK4LME7+Bw5En5YxQN0GwM1yIC4ZCCkzJoOrDAaS6gMBFkpwksWkI6gY0FUbywAAav5/tQyIa8GcBbiiAFBbXmOkSleFioIKtIiztg8uDytHTEgMggOCoS/RAwCSI5OhLdZCqVCifh1jPcWhxagfZ11n/Rp5yjXlqC6qNp83lS0OLUZSeBJiQmJQUl0CXbEOkUGRqB9ZHwGlAQiuDLar03Q9ALtzpuPJ0cmoLKwEAIQHhaO0uhTJkckILA9ElbYKqXVSoSg2Ju0yORbr9DqUqktRP87YdkhliF0bIXVCcKviFurXqY+Q8hAEVQahYWxDc9vhQeGoH1n7HQOVgagfW9/8/QEgKSIJuhId6sfVR1hlGBSlCvO9SIlNMStP5WHl0OqtFQDLexZtShbIgEk2nV5nlt9QbEDd0Loo0ZQgXm2vGFo+kzqhdYzPw6CDTq9DalwqUmsmRtlrs1FebvQneOPON5BdnM0qhyVMNuLDmlvvonVP7I51aesw4ucRvOrkblDB+LcqoXYX02qyKTNU9eLR5LfayEtXXNBG3VdfsVMAOHcRBBI9bChKttWacKq72YSZtelHm/zyMxQhISj8/gde9XMqjrZDq82hyP4P8mqDjYR3p3EXsJTNRgFRsJkpyRRRCkBcnJPxYAlO0tPT7Wwy3UFYUADKqnV2x+PCrW3KvUzJZaR1krB09fbU/PCb9wcK+A2UdjTuzaOQEzeb6UGxZZZl3KVQGOuIbmCtADBNUJx5KcLrcZ9P6gRcrTGLeOp7YH6T2nON+NxDnjB9hyFf1e56dBzBoADYU6AxDgKNGzcGwoOAAI6d0TpNjav9osLEuoaQwBAEW+RLaBLdhLUsV6Iu2+RQUtsrm1bU1YFqNI5qjFO3T0GpUKJRFNNuFjfeZrsrBZbKQuPGjRGoDERBQe3CzKf322c9fq7Nc/gr9y9cKr5kPsb0XN/t8a7VZ7VKjbZxbR3KxMvhVCqTF4ZnHvHAAyjZto31ksC6daG9KdK/zRXvmItdmQDYy13zmbNpm2ektJ3PuDLqhytxIHfoHa7NWSI1ohSAr75ichYkvJ19b9+PDjPtO7/QIG470Ngwa61XTmOpy2QxmQBFpRjNLjg7BmeE4JHoiq3tWIvIFT0nGcNbpvYE3jjnhDwA3qpReCxXrG2/o5AOPrUnUHqDu8yLe4z/tw0pGlKjyAVb7uhI+NBDhCuK+pr/BwQEAJFGsxEktAO0VfaFg2rM1VQCMw+7EIVCYTURC+FIWhcUIO2KV53QOrhdcZtX2YigCBRVFSFAGQC9Qc9ajs/kPjgg2KlcBkKIDo7GrQrnzR+lJCAgAAoooNcz30dXOJuKii7DI5mUUzh4VxJnv4/csS+Iq5tDbjbzFIENSFAHA5y3pMa8yfa+OfuMOHcAbPIAODvIOyOr7Q6AnCY/PJDYC4/wZqJCVbg0V7iZ0dFpEsV69joUQM83gCHLa81ETNh2KlJ0DAkCU5XHNav9+/53jdGBlEqjPT4XNRFsrGHaTbBQDF3d8Vl+94c+BJ79CYi1WJmOSrG/hhOJ5A2OYHHytkGyhGfCUSlVnDHt1YFqq2ywroLPhI8tsy7bcRNc0cCUPJzNI4IizEm3XE29MOYdr6YxTa2y/Poyvw/9HQ0jGwq/0NWTf0f9mEWf11IOsd55dmNM4VNFY/MMLDMss5Wxw6ETsMLmo0wm1uQEzEx5eTkUCgVCQ+Vre0fIl7oRHKErnUCy7L9MKBRAZCKARKMph8HefArN+9sfe/nv2pVfIby4Bzj3B7BmCFyU3cDIo8uA+Y0hbEVJgMOxHQIH9W41K3DjjvIrH1bX6ERrSVJHoI8EmWIjk4A3zztfjwtRKpScE0tVgArRAdG4UspsjXzlyhUsW7YM//77L0JCQtCrVy88++yzCA4W/pt9++23oVAp8OS4J83HKisrMXPmTFy8eBGzZs0CYozlQkJCMH26Ma57ndA6uF52Hbm5ufhm/Tc4f+o8ykrKEBISgnZt22HgoIEISOTeqdTpdPhty2/YtWMXCm4VICoyCl27dsUrr7xiLrN7925s/2s7ss5mobiwGKNGjUKLXvax1fNv5mP+V/ORmZmJsvIyxNWLwyOPPIIGnWpjnZ8/fR5vzXvL7tqePXsi7f/sF1qCA4Jx69YtvPTSS6isrMSaNWsQVTNxu3HjBkaPHs34vQIDA7H227VWx7Kzs7Fq1SqcOHECWp0WDeo3QL/H+6FOe+akZ2JRB6oRpOS/C5QamQqAXaFzdW4JW8LuuUfYBZbuBxIGPYl/kyMvDIDg1FREDWHJD8FDKYp5ciQKf9woQrLaZ8I8CTfYZ1i2kUndpYv9cQcyB9vkXTDYrvoTkuCUAnDkyBFs2bIFp06dQmWl0TErODgYbdq0wYABA9C5s8QJhQiXcPidvli6k/8kRqqfX2RIIIortcic8QDUQfav4hfP8Iuew9aXpLVLxKv38c9o6TR8TQjiW4pvo147oN8sm4MSr4o5EwVHjjTqBVzcZa0ExDTiTsbmSoLDgcpCz7QtkNLSUkydOhXx8fF4++23UVRUhC+//BIlJSWYNMk+s7BQqqqqMGvWLFy4cAGzZs1Cs2bN8O8t5igcp46dwrrP16FOQh30e6QfWjVqhUt5l3D15FVMe2caJs+bDHBsxixatAhHjh7BfQPvQ3xSPKIN0cjKyrIqs3fvXtzMu4nWnVrjwJ+1UXAsE6ZpNBosm78MwYHBeGbUMyhVliLj7wwsXrQYT73yFNr0b2NV59Dnh6JLiy5mn4XIyEgUgDkIwldffYWQkBDzeGoiNjYW8+fX5mq4WHQRjaIaYcaMGWjaqqmVCdTFixfx1ltvoUuXLpg8eTIul12G9qYWsSp+magzn820P2iet1n3NX8/+TevOk1seXSLoPJ28DUB4jFJVCgUaPDlFzYHRcrFBw6ZIvr04bw0KDUVSbNnWx1Td+niMA9OSPv2qDzu5E4Fi9zct8ool7p7d1HmTYKcaT3qT+Clvgw1iFYAvvzyS2zZssX8ApqcVsvLy3HkyBEcPXoUAwcOZF21IORDXHgwpqbxT3KSHBOKgnIRMdZZiAhhXknp17p2q/yzkZ3w6tf28bG5OqEH2yYg0EEEI9GLCUI6Ha5GZhQJazeiHnD3OKDQPlGQR3FqVUbiUddOFpvPfWfa+Ay4mbC6QBFDIi8ZsnXrVpSWlmLhwoXm1WilUokFCxbg8ccfdyoPTHV1Nd5//31kZWXhvffeQ/PmzVnLFhQU4Jtl36BZ82YYMW4EAlWBaB7THM00zRD7YCwe7P8gSoNLWa8/e+Isdu7ciXfmvoPQOKOC2yauDXr27GlV7s0338Ttytu4XXnbSgGIUNW+L1cuXsGtG7cwe/ZsNG3VFBeLLqJZm2a4dvEajh88jif6P2FVZ0JyAlq0aGHlMF1wy14ByMjIQEZGBoYNG4bly5dbnVOpVGjZsnbhQHdLB801DcrKytDt7m7QWew+Ll682Dz5B4DQW6FoE2dUSkzhXplwJiu0qwnr3h2lO3YASgV4WS6LnBQqeJiLBdSVRxAUZUQEdMXFFkfs+9GQ1q2kUwAE9PGOFBNCHojyAdi9ezc2b96MyMhIjB07FuvWrcP69evN/1544QVERUVhy5Yt2L17t+MKCa+iYR3xAwUfHwOmvmNA+yTUjQhGeAh/nbURDzmDAwOQNVtMPHrbGPm2toEe6ACnF7q/TTMCJvFCnS3DnM2cafMs7hnPHZnHWZ6WIuwxy/2MSuHO9Cwxhw8fRseOHc2TfwC4++67oVKpcPjwYdH1ajVazJ49G2fOnMHMmTOtJrdMbNu2DZUVlRg7ZiwCVcY+QBWgMse9b9GiBcIi2H/vh3cdRvv27ZGUkoSggCCEsuxy2YZfZkKnM062bSO1qdVqRmfZ1MhU1mhJJidqrVaLpUuXYuTIkYiI4Pf72LlzJ9RqNTp17gS9QQ8FjNmbT58+jYEDB/KqwxLLPABsmMxAVjzIHfpbLGy23rHPPA0ASHr/fSTNmc1YRiIBHBZpznNO4645cFADkxLuKidg63viyuzDfFCG2PR/7jYJctBc0+1/uEcOCRClAPz8889QqVSYO3cuBgwYgLCw2o5XrVYjLS0NH3zwAQIDA/ELk4MI4d1w/P53Tr4X3Rrx22oW2o3s/U8f3NvcejIoRVckNNGZsWEXOwO9kcV93rJtU2hPl3aEjpy6bD+z2GP3eNUm6zCPup8QkMzLlrB4wN2OlabIP3xQKCBIeQqry56MTRUGREmbmf3y5ctISbF2jlWpVEhISEBubu0u1Ndff42BAwci/1Y+AKB5TO1qfoDNu6DVarHmv2tw8uRJTJ8+Ha1aOQ6BmpmZiTp16hhDq4og50IOkpOT8c3/vsGUF6Zg8v9NxsyZM3FdRFz01GapSKmfgtWrV+PG9RuoLK/EpYOXcOHCBXS7t5td+VmzZmHw4MF47rnnsHz5clRV1UaDigmJgVKhxObNm6FUKvHQQ/wWI3RaHfbt24fu3bsjODjYbAJ05owxXG9ZWRnGjRuHwYMHY+4bc7Fli5OmNzZ0SeBnnim1TX9AdDQCoqMlrdPVWOYnYCLigQecql99550Iu8s+3w4Axq41qKFA52sROwDsuSlr66g3zeiDFf344zzqM1YYGB8PZYSw/tzhjo3E/mqqZNcHVJAKUSZAly5dQvv27ZHM8UWTk5PRvn17nDp1irWMIyoqKrBmzRrs2bMHJSUlSElJwdChQ9GrVy+H1549exZr1qzB6dOnYTAY0KxZMzz11FNo3dre1EXqspmZmZgyZQqjXPPnz3e42uXN8N0dMP3mmHYE2H6PQYHME3XP+AQxZMllQqyjKVOmV6vmazqid/LcuiLMi8nnAbVAZ0Ouh+jM95ucBeyqsZ1ufK/4elxJUkdp6gkMAgKlNU8oLS21WuAxER4ejtJSdpMblcUOi1qlRriF0/upTOOYMHHiRLRp08buWiZu377tVP6ZkqISbN++HYkpiXj65acRGxiLVatWYebMmfjss8+MIVt5EhAYgGkzpyF9XjrGvTwOgNER9/XXX0fnbrV+b6GhoRgyZAjatGmDoKAgHD9+HD/++CNyc3Mx9JWh5nJFBUVYv349pk6dyluOM5lnUFJSgt69e0OpUJoVAFMc/wULFuDRRx/F6NGj8cvOX7Bs2TJERESg5Z3ixp6k8CTcnXy3qGs9gthBwdF1AlecUr/fgOBmzXCmfQewjRcpixYKq9SEgJColiY5iR98IK49U7MClLqkD+exCQSl2tivxE9+A4XffuuUTFw0XLkSF9IGsBfwY3MlUQqARqNBSIjjuNUhISHQaMTbis+ZMwdZWVl49tlnkZycjJ07d2L+/PnQ6/W49957Wa87e/Ys3nrrLTRv3hwTJkwAAHz//fd45513MGfOHKsJuKvKAsAzzzyDdu2sQzc2FKp9yxFywoc5SRYb0Q2BTk8JD90pFFdO/gV1jBb3wjZWv+RtORKF5bk8I4Vpjp9gGe2Ex2Rq5MiRGDlyJKsDryWNGjXCrfxbWLduHTp16oRoHiu6BoPBuVCABkCv1+OVN16BUq1E0+imSEhIwIQJE7Bv3z47XwAuNNUafLzwY+h0Okx6cxKKDEXIO5OHTxd9iqkRU83BL5IbJqNf59oQyR06dEBsbCyWLFmCbhe6oX5j427Nz9/8jI4dO6JDhw68ZTi2/xiio6PRoUMHFGuKodUbszeb4vj369cPw4YZs/EGJAVAU6jBt99+i3fvfJerWlZa1WmFJX2XYMHhBaKu54tkOwYW/UnkoIEo3sxjB0SKmPI21YXyVHBFNyCovOkPgX2tAkh4byZUSUmoM3YsSnfuBGBcWbdL8FWDsiYapCreZiHLyX5elZRkFdLUtk9ImDnTqfod4XQ/JDNEKQCJiYk4ceIEKisrWRWByspKnDhxAomJiaIEO3z4MI4dO4Y33ngDvXsbs322b98eeXl5WLFiBXr27Mm6WrJ27VqEhYVhxowZZvk6dOiAMWPGYPny5fjwww9dXhYAkpKSfHq136+54xmglcWqgm3HFpUMDP6vdO29tE+6ukTh0vAYLqy7Bhmv8kz9MRM3iq2jvqCq1BhWNuSQqDrrRYZg9qPClM+o4Ci7xF5sK/2lpaV2pkF8qVOnDgaNGoQv532J6dOnY86cOYy7DJbExcXh8mXxjtOhYaFITkxGRGQEyrRlAICmTZsiLCwMOTk5guo6tOsQzp09h5UrV0KlVuFi0UUM7jUYeXl5WLlyJWf0u3vuuQdLlizBlUtX0KhpI5w7ew6ZhzKxYMEC8302mQhVVFQgODjYboytqKjAqYxT6P9gfwQEBECpNe4AKBVKs/9A+/btra5p3749Dh8+DK1WK+i7yh4ek7HkDz/kpwAATndF6h7dUb7/gOOCXoRCoUBMjZlO/MQJKDtg/H6NvvuONVqPIjAQUY+xhC2F0Y9AGR6GwPh4QYpMw9WrAIt5n62zccwTPMyJCDOiFIB77rkHX3/9NT744AO89NJLSLCxcbt27RqWLFmC4uJiDBjAsfXCwf79+xEaGop7bOL09u3bFx999BHOnj3Lajt66tQpdOnSxarjVKvVaNOmDfbv34/8/HzExsa6tCzhPkx9QLRahUIJoxNxEqQ2/nMX9Vy4kiQFsl4VkbNsYJ6o3zgJ6Kpssi27FqZEWCkpKXYTb41Gg+vXr6NfP4EJAC0eQ2L9RLz77rt49913MWvWLMycOZMzr0C7du2QkZGBS5cuASJSaNRNZHckt3X8VSqUdn4LluRdzUNsnVhERUVBb9CjUZQxDnrjxo3xzz/2kcrYMMCA61evQ6fTYfz48Xbnx4wZg549e+LNN9+0Or5//35oqjXmhTFLEyC2qEwGgwFKpdKnVi+FUGf089JU5OD2NVi6FKfb2+/khPXuJco2PO61V3Hr0894yeIw8o75tLB3QMminCu5cj7xWHCJ6NsX4b16wVBd7ViImuqYci8oQkIQPWAACr/5xmE19b/8kvVc2F09eMphcf9kvLDEB1EKwKOPPoq///4b//zzD1588UU0b94c8fHxUCgUuHHjBs6ePQu9Xo+mTZvikUceESVYdnY2UlJS7Fb5U2tiymZnZ7MqABqNBiqGF8V07NKlS+aJuqvKAsCSJUvw4YcfIjg4GC1btsQTTzzBy+a1vLzcYRlTu0zy+BOWXdkv43rirrk7AABtkyMREyaze8MjxBz/uuQ2kDvjSOXdnagv06VLF3zzzTcoLi5GZKTR+W7//v3QaDTo0oWfI6gZm8fcunVrvPXWW5g9ezbmzZvHaQP/wAMP4Mcff8QXX3yB4eOGQ2njuH/2zFmUhZSx5gFo1aEV/tj4B0pKSqAMNV6blZWFsrIy85hiIiYkBtHB0axfI7pONPJv56OwsBDR0dFQq9Tm+uJtTR5s2LVrFwAgpVEKFFDg7q53o35ifaudlyNHjuD777/H1KlTkZRk71C+c+dO1ImvgxYtjAnKAhQBZgWgZcuWCA8Px7Fjx6x2IjIyMlC/fn3j/WXIVygXJDMBsukf4994g+dl0vWrlnPDBkuXiqqj7iuvsCoACksfAAu51T261/7dpTMKv/nG5nvx72/VPboDgSKjxXPcSgUUUCgUUAQHQ8dHAeBCqUCd5/+PVQEIiIxEUCOjkh5+D7sfSwOb0LsA0PLUSedkkzminmxwcDDmzJmDVatW4ffff8fp06dx+vRp8/mgoCD069cPzzzzjKhskQBQUlJit7MAwLzFWVJSwnptgwYNcObMGej1evPqjk6nw9mzZ+2udUVZtVqNQYMGoW3btoiMjMS1a9fwww8/YMqUKZg+fTruuOMOzu8+atQozvMmRowYgZEjR/IqKzdcoTgnRdeuSPz0Gn+bXkmw7GDfzWcuM505+Y/sEBP1gW9ZtnKSDLxcdUj4wrUdIl1dHiRQGQilA6W0f//++Omnn/D+++9j+PDhKCwsxFdffYV7773XarXZFAraUTIuW7p06YLx48djwYIFWLhwodm3ypaYmBhMnDgRc+fOxdI5S9H9vu4wNDWgpKQEhw4dwo4dO/DGXPZJXtd7u+LQn4fw6bxPcf/A+3FVdRWrVq1CkyZN0K1bbeSenJwcq+hG2dnZqD5UjeLYYvToZlwh7NSjE3b9sgszZszA0KFDERYWhv379+PQoUN4+eWXzdeuX7oeLRu1RJMmTaBSqXD8+HFs2rQJ3bp1Q0oj425L3Tp1UbeO9e7EjRs3AACtWrWyCr8KAEVFRcjIyEDvh3ubjykVSnMeAJVKheHDh2PlypUIDw9H8+bNsXXXVhw+fJg1MIUQ4tXxaBDZwHFBr0XhVVFczFgMqA1X1IZoVXezj0olFFulKOaJxxFQhzvIgyo52Wjew4KSJdStQoAzvhUcE4rAunXR5Fdx0Sh9fcdMdCKw0NBQvPDCC3j22Wdx/vx55OcbJz2xsbFo0qQJLydhVzFgwAAsWrQIS5YswRNPPAG9Xo9169YhLy8PgPWWryvKNmnSBE2aNDFf16ZNG3Tv3h2vvfYaVqxY4VABWLFihV2MaSbcsfq//237DIVS/SR86qdl2QGxhWn0RSKSgEY9IehpMnXWrtxKlboT7+taRzN30SiqkcMV1/DwcMyePRtLly7FnDlzEBwcjF69euG5556zKmcwGKDX67ljhLM01bt3b5SWlmLJkiUIDw/H3Y8xr9J17doVH3/8MdasX4Nfv/8VG0o2ICwsDC1btsQ777yD+g3YQ6CGhIZg9uzZ+PTzT7H689UIDAhE586dMXr0aKs+fs+ePVi3bp35844dO4AdQN26dc0KQFRsFN6d9S42rNuApUuXorKyEklJSXjttdeszKLqJdfD3r17sXHjRmg0GtSrVw+PP/44hg4dirNFZ9nvEwd79uyBTqdDx+4dzceUCiX0qM0EPHjwYCgUCmzevBnr169HTN0YTJgwAT169OBMBMaHp1o9hadaPeVUHZx4elBQKBBYr57jcjKE92RVSF/LUDR6iOMFkKSP5jMeV6WkAAcPWh+0kDsgMhItjjDkF/H0e8GCtyc8E60AmAgJCeEdyk0IERERjKv8pmNcyVL69euHoqIifPvtt/j1118BGLdGH330UXz//fdWZjquKmtLeHg47rzzTvz666+oqqri3BlRq9W8FAB3kBjFYedHuB5WW2QX9oimTo1P56YMAKIbCJhkM5WTae/u4zha/TeRnJyM9957j7MMryhABuCDmhCEWr21M2paWhrS0owhgf+99a+5nC0NGzbE6FdHo6iqCM1j2TMHM5GYmIhx/xmHkuoSNItpxvk9LPn31r9oGt3U6lhqo1RMnz6ds737BtyHV557hfU8m7LUt29f9O3bl/Gc6T5Z3mdLHwATgwYNwqBBg8zymzIBm2gU2Yg1ORkXYlZEP7n3E17ldgzbgbhQeWTZ9RnMXbl7J6oKloR6ie/Pgr6kBPqKCtZr2XwOuBuUfgxp8sfvjpsNlJmZsUCcVgBcRWpqKnbt2gWdTmdlF5qdnQ3AcTjNoUOHYvDgwbh69SpCQ0MRHx+Pzz77DCEhIWjatKlbytpi+hH6+raSs8SGBaN+rOsUj8PvMA+usiQkEnibIfpJZBLwupMp3qVC0ODCVFaqwYmlnrB4oA73b5OwwA1zBTGTT4IZy/CffAl2U+4QhUKB+xvez6tsXbWzGb+dxBQG1EtWdeu8+CIUHPb5TX7bisD4ugjvU7OLb5p2ePD7KZRKcZN1FpHNcykXfKcgpihnNglAgxo0QPODf0vetrvg1Qt///33TjUyhMeWkS3du3fHb7/9Zhejefv27YiNjUXz5o5Xf1QqlVlRyMvLw549e/DAAw8wrr67qqyJ0tJSHDp0CI0bN0YQS+gsb+KOBtGYN6S944IcsP1kO9aPxu437U2PpCIuXGaJsxwRzLDbpVAAMXLIKaGw+b/YalyoFHd6Cuj4pOvqJwgRSOXwqoCC2/TKR+CyKZcMCRUAhdK1C31BDRsiILw2JJbtKr8p42/9xf9F3kKRycYAuH6H1rsXRANqgiO0OJ7hYUmEw0sB+N///sd71ZpplVuMAtClSxd07NgRixcvRnl5ORITE7Fr1y4cPXoUkyZNMu8KZGZm4p133sHw4cMxYsQIAMZdgn379qFp06ZQqVS4ePEiNmzYgMTERDz1lLX9oivKzp8/H3Xr1kWzZs0QGRmJq1ev4scff0RhYSFjuDdvQ6FQQB0UiGb12M2wHNchjSwPt0tEiwTxckiH7w/AnDhlAuQkrziIlS9xgh/5RWDyEui2WSHVTrC/7Cg327XT9Y0owKoAKEJCEMDTPKXF0SOsSbJcgqveAW/YDXG3iCz3WumFC7u8FIDhw4cL6mQKCgqwY8cOVFdXO9U5TZkyBatXr8batWtRUlKClJQUTJ48Gb169bIqp9frrbTfwMBAZGRkYMuWLaioqEDdunXx0EMPYejQoXbOya4om5qaij179mDr1q2oqKhAREQEWrdujYkTJ/LauSD4kxAVgoQozzmcW+EnAzEzTn53ZwaauvSb8gq8YC4hKRw/ifCgcOlCXhJuocWhg4xx6K2oGQPcM/kX+f4I7WulHte8Qalgw5tlZ4CXAsA31GRxcTG+//57/PXXX6iurkZISAgefvhh0cKFhoZi7NixGDt2LGuZdu3aYcsW6yx/ycnJmDt3Lq82XFF22LBh5jTsvsLiJ+/Ay2uPeloMQq4IGiRc1Yn62IRK7R0OkaGBFCjA03AlLiMEoGA3p3I4+fdFXDXhtRgvlEE87qtsJ95ylYsfknhilZSU4IcffsDPP/+MqqoqBAUF4dFHH8Vjjz1mF8eY8E66pMZ4WgTC7fDp3CRaTfLr3RMWpEwc50IaRzfmV5AescvgG9HJ1+C0MNALn5wZk2s5IZBcsZhAC4oIZOP06goUQUFImDkTRT/84NJ2JMEN98OdOKUAlJaWmif+FRUVCAoKwqBBgzB06FCa+Psw7ZOjUFyp8bQY8iImFQiO9LQUnsHZDlG2qzuEJwgPCndcSCRSOczKzXzHXxUANpShoZyhJtmRXxSg5E8+cVimztixUKi4p3Nye2ctiX58GKKHPCb4OmnCm8rrebsTUQpAaWkpNm7ciJ9++gkVFRUIDAzEoEGDMGTIEMTE0EqxrzOmF88VP3/i/nc9LYGHcTIRmIwHJ8K9NIx0EN3KG14VN88pSAGwJuqxx6Du0QMXBw0WdqEMV3cj+z/IeNxS1LBuXXnXJ0dnVYVCAXCENOW+GLJT2rwFQXe8rKwMGzduNDvBBgYGYsCAARg6dChN/AnCn5HhwEn4KDTW20EKgDUBEREI4EgWyoiX5QEQQ/PDh4Qn2pJx3+4vEbBcBS8FoLy8HJs2bcKmTZtQXl4OlUqFhx9+GMOGDePMfksQhBciqlOVQ0fsuwO3u5GzuYAvIPX9JQVAInx8NdkybwAvZHAv6r4+DmH33MNewGkRnfktenc/yUsBeP7551FeXo7AwECkpaXRxN/FTJw4EUqbVNqmFPAE4T4UjH8yF3UmD4DnBxlByGBQdBemHCZnzpxBdnY2UlJS8N///ld0fW+//TZCQkIwffp087HKykrMnDkTFy9exKxZs9CsWTPGcgCQk5ODNevX4OSJkygrKYNarUbr1q0xePBgtG3blrNtnU6HrVu2Yuf2nSi4VYDIyEh07doVr7zyirnM7t27sXv3bpw5cwb5+fkYNWoUWvRqYVdXXl4evvv6O5w4cQLl5eVISkrCo48+invvvddc5vyp8/jPvP/YXduzZ0888eITjErArVu38NJLL6GyshJr1qwx+9LduHEDo0ePZvxegYGB+HzN51bHsrOzsWrVKpw4cQJanRYN6jfA2LFjEdPAz3bqAwRGR/KmHQB3rX5L3oyw+xv30kuOC7ltJ8DWCdhL3hUWeCkAZWVlUCgU0Ol02LZtG7Zt28a7AYVCgQ0bNogW0B9JT0+H2p1JRHjgkhVB7/7t+AE8H5DBAKdGCckiK3j3aowcycnJweHDh9G8eXMYDAbo9XpJ66+qqsKsWbNw4cIF8+SfjYMHD2LevHmol1QPDz72IDo17YSSkhIcOHAAU6dOxRdffIF4jmyxixYtwpF/jqDvwL7o3LIzCgsLkZWVZVVm7969uHHjBrp27YqtW7cy1qPRaDBn5hwolUqMHj0aERER2LVrFxYsWICgoCDcddddVuVff/11pKSkmD9HRkZCA+YgCl999RVCQkJQWVlpdTw2Nhbz5883f75YdBGNohphxowZaNeundUOwMWLF/HWW2+hS5cumDx5MnQGHXKzc1FVVcV6b3yVVv+e8LQIXo3LMkxL3VV7i9ImM3j7ABgMBpcMAARBeAGs/auYnpw6a4fIRJfp2rUrunfvDgD4+OOPce7cOcnqrq6uxvvvv4+srCy89957nEkSCwoKkJ6ejhYtWuDlN19Gub4czWON5Xv06IGHHnoIwcHBrNefPXEWO3fuxPQPpyMsLsx8bc+ePa3Kvfnmm+bdVzYF4MrFK7h27Rpmz56N9u3bAwA6duyI06dPY/fu3XYKQMOGDe0Um+zibLt6MzIykJGRgWHDhmH58uVW51QqFVq2bGn+rLulg+aaBmVlZejduzeUCqU5IsrixYvNk38T3e7sBgC4Xnad5Q4RgHHB0mWTXm/EQHb2lhgMBtn0zVLASwHYvHmzq+Ug/BEf+iH5LzWDJd9BwhcGE1/4DjyxNUWUCo1Gg9mzZ+PMmTOYOXOm1eSWiW3btqGsrAxjxoyBSqUCbBazHV1/eNdhtG/fHskpySisLGQtx+f76nQ6ALDbpQ0LCxMdllCr1WLp0qUYOXKkXQZ6Nnbu3Am1Wo2uXbuiTF8GrUGL3NxcnD59Gs8//7woOQgn8wD4Ud/gO/ivwkeeQwRBOIdCAee1ORo4vZmvv/4aAwcORHVRtcOyWq0Wc+bMwcmTJzF9+nS0atXK4TWZmZmoU6cOGjVqJOpVybmQg+TkZHy98mtMfXEqhgwZgpkzZ+L6deEr4qnNUpFSPwWrV6/G9evXUVZWhq1bt+LcuXN46KGH7MrPnDkTgwcPxnPPPYfly5ejqqrKzqRy8+bNUCqVjNczodPqsG/fPnTv3h1BQUFmE6AzZ84AMJrtjhs3DoMHD8bzzz+PLVu2AADiQuMch1klvANSNsxIkw+AV0PuacdNSJIJmCAIP0fIWGTXifpWp+rtOGMCkRSe5LDMsWPHABiDHbRp04ZXvbdv30ZcXJxouUqKSrB9+3YkpiTiyZeeRJwqDqtWrcLMmTPx2WefIUCAs2hAYADenfkuFsxbgDFjxgAwOuKOHz8eHTp0MJcLUYdgyJAhaNOmDYKCgnD8+HH8+OOPyM3NxagJo6y+2/r16zF16lTecpzJPIOSkhL07t0bQG0UoIKCAgDAggUL8Oijj2L06NH4+++/sWzZMkRERFg5KRPWKADvcgJ2G6Ro+CqkABAEIQFODhL+vJr10wSg+Jr1seoSQKkCAvmZg9gRmQgM+Nh52XgycuRIjBw5klfZRo0aobCwEOvWrUOnTp0QHR3t8BqDwVBriyxmfmYA9Ho9xr05DtogLVrEtkBCQgImTJiAffv22fkCcKGp1iB9YTp0Oh2mTJkCtVqNgwcPYuHChQgPD0fnzp0BAMkNk9Hvjn7m6zp06IDY2FgsWbIE956/F42aNAIALF++HB07drRSHhxxbP8xREdHm68xKQAmH71+/fph2LBhAID27dvj+vXr+Pbbb0kBcIRCAZCfYy1yV4ZqFDa3+SnYtiP3++MAUgAIgmBGSOfmzxN4Z2GaqOedBIIjgagU+3NeTp06dTBhwgS8/fbbmD59OubMmYMwB8mJ4uLicPnyZdFthoaFIjkxGRGRESioNK6SN23aFGFhYcjJyRFU16Fdh3Du7DmsXLnSHKazQ4cOyMvLw8qVK80KABP33HMPlixZgtyLuWjUpBFOnz6Nffv24aOPPkJpaSkAmKP1VFRUIDg42M4noKKiAqcyTqH/g/3NOwahgaEIUgYhoib5lck52UT79u1x+PBhaLVaBIrNuOoCdj6x09MiWONNOwCemvT6MwaDT+VIIR8AgiDciAs7T58aqHzpu9jTqFEjvPvuu7hy5QpmzZrlMERlu3btcPv2bVy6dEnUrambWJf1nFBH57yreYitE2ue/Jto3Lgxrl27xnIVM5cvX4ZWq8X48eMxYsQIjBgxAkuWLAEAjBkzBosWLbK7Zv/+/dBUa8zmP4BxB0AVoEL9+vUZ2zEYDFAqlbKL6BIbQvmE3IJYncYFypCk9vo1dTlXp7x+E+6EFACCIKxRquyPubKPlGpA8JaVO5mjUqoQoBCYQEkErVu3xltvvYUzZ85g3rx55ug6TDzwwAMICwvDF198Aa1Wa3f+zJkzKCoqYr2+VYdWuHjxIkqKS8zHsrKyUFZWhtTUVEFyR9eJRv7tfBQWFlodz8rK4sxDAAC7du0CADRsbHTE7dy5M+bMmWP1b8iQIQCAqVOnYvjw4XZ17Ny5E3Xi66BFC/sEZS1btkR4eLjZz8JERkYG6tevL8jXwS9RAD7rkyRW+XOF0ihlne5Uag1ubs/FyGcvkCAIeRAQCARHOS5nhSAvYIbPvtOpejspESnmbe7KykocOXIEAHDz5k2Ul5dj7969AIC2bduaV8HXrVuH9evXO0zGZUuXLl0wfvx4LFiwAAsXLsSECRMYV6ljYmIwceJEzJ07F/OmzUPPvj1R3agaJSUlOHToEHbs2IFly5axttP13q449OchLJy3EL3TeuNG0A2sWrUKTZo0Qbdu3czlcnJykJuba/6cnZ0N7REtSmNK0a2rsVynHp2w65ddmDFjBoYOHYqwsDDs378fhw4dwssvv2y+dv3S9WiZ2hJNmjSBSqXC8ePHsWnTJnTr1s2sAMTExCAmxjo7740bNwAArVq1sttlKCoqQkZGBno/3BtMqFQqDB8+HCtXrkR4eDiaN2+OgwcP4vDhw5gyZQrr/SGMKBQK90WU8QKS5s+HIohhQcin8N/nTQoA4Tn893fnezibB8CHVlW8HcusskVFRZg7d67VedPnOXPmoF27dgBgThIpZvLUu3dvlJaWYsmSJQgPD8fYsWMZy3Xt2hUff/wxNmzYgJ82/IS1RWsRFhaGli1bYtq0aZyKR0hoCGbPno3PlnyGr5d8jcCAQHTu3BmjR4+2MgHas2cP1q1bZ/68Y8cOYAcQHx9vVgCiYqMwfdZ0fLfuOyxduhSVlZVISkrCa6+9hn79ap1+6yXXw969e7Fx40ZoNBrUq1cPjz/+OIYOHYqrFVdF2RLv2bMHOp0OHbt3ZC0zePBgKBQKbN68GevXrzc7O/fo0UNwe/6Hk3kA3Ikb+syglGSXt0F4DlIACI/gS440vgnPUfCpHwB1LFB8VXxT9bsB2krx17sbP1ohrFevnjmGPBd8owB98MEHjMfT0tKQlpbmsFzDhg0xadIkh+0wkZiYiPH/GY/blbfRMpY5cRjf75HaKBXTp0/nLHPfgPvwyrOvMJ+sYL+ub9++6Nu3L+M5033699a/nG0PGjQIgwYN4ixD2ECLEIQjDAar98Tbd4tIAZAhEydOtHNMsx0g3Q2lR/dRJp4C0h0nYmJ9/HWbG/9fIsz50Yq7XhN/LUEIIEAZABWTj4tAHC1gUH/ppSiVzin4pES4Hy+fhHsSUgBkSHp6ul2aeY/jJb+xvq3qeVoE7yKSLXGT0IHMjwY+GuS9juaxRkU1JjgG0cHRnhUGtAMqW7wpDKgQfPE7AdQXOwkpAIRP8eWzXTwtgvdxzwT3tueqsciXBgNlIOCGSDz+gmnVX6FQ0OSbYEcB75ksC+3v5NI/uuL2eskjkxukABCEv9N3hvN18B5cZDIIyZ3YJnSrCILwSbxWCbdRDuWWV0MopAAQHoFsZL0Mh/2cd3eEbkEVCsTV+EzUZXZCNSMwORXhXRjgWxlFfQWFr5oA+TosP6VGP3zvgrZ853dLCgDBC5d0ib7zOyIEdYouGmDlPnArlEBQmPFvVahnZSGcw8HrHq4KhzKMlDivQ6HwmsUpwcOn3PtHZ2D5aiGtW7u2WS+/p6QAELzw8vecEIWQh+5kHgCC8CGCA4MRHBjMep5W/2WIQgGvygMgBOp3JcLgU/eSligIgrBHcBAg3+kUCYIgCPmjjIyEqh5F/hMLKQAEL7xlW5TwEDGNgKd/9LQUBEEQ4vFVHwBf/E4AYoYPR8P16xwXlAiDweBTpstkAkTwwiX9h2/2Sf5JYBDQpI9nZaBdCIIgnMGbFACvDQMq3f1VKJVQBAVJVp+/QQoA4RHIBtbFzCjytATseMsAS2DPnj3466+/cP78eZSUlCAxMREPPfQQ+vfvb5etnA9vv/02QkJCMH36dPOxyspKzJw5ExcvXsSsWbPQrFkzxnIAkJOTgw0bNuD48eMoKiqCWq1G69atMXjwYLRt25azbZ1Oh02bNmHbtm24ceMGIiMj0bVrV7zyyivmMrt378bu3btx5swZ5OfnY9SoUXjsscfs6srLy8O3a7/FiRMnUF5ejqSkJDz66KO49957zWUyMzMxZcoUu2t79uyJ4S8OZ5Tx1q1beOmll1BZWYk1a9YgKioKAHDjxg2MHj2a8ZrAwED8+KP17lt2djZWrVqFEydOQK/XIyUlBWPHjkWrVjyyfvsz3pQHwJuRizLi55ACQPCCukQ/Q2EzuYtIBOp3k6JiCerwIEoVEOg/EXw2btyIunXrYtSoUYiOjsbx48exbNkyXL9+Hf/3f//ndP1VVVWYNWsWLly4YJ78s3Hw4EHMmzcPKSkpePLJJ5GYmIiSkhIcOHAAU6dOxRdffIH4+HjW6xctWoRjx47hiSeeQP369VFYWIisrCyrMnv37sWNGzfQtWtXbN26lbEejUaD2TNmQ6lUYvTo0YiIiMCuXbuwYMECBAUF4a677rIq//rrryMlJcX8OTIyElpoGev+6quvEBISgsrKSqvjsbGxmD9/vvnzxaKLaBTVCDNmzEC7du2syl68eBFvvfUWunTpgsmTJyMgIADnz59HVVUV670hCEIEXq4skgJAEIQ9L+0DQqJrP9drAzy/zWPiyIZ3b3laArcybdo08yo0ALRv3x6VlZX4+eef8fTTT0OlUomuu7q6Gu+//z6ysrLw3nvvoXnz5qxlCwoKkJ6ejhYtWmDmzJlW7fbo0QMPPfQQgoPZo+4cPXoUO3fuxKJFi9CgQQPz8Z49e1qVe/PNN807G2wKwJWLV3Dt2jXMnj0b7du3BwB07NgRp0+fxu7du+0UgIYNG9opNjnFOXb1ZmRkICMjA8OGDcPy5cutzqlUKrRsWZs7oiVaIjMzE2VlZejdu7dV2cWLF5sn/yY6derE+F0IG3zZBMinceKZecvzdgHkBEzwwtvj3RICiUwCgtQuqpzeJW/BcvJvonHjxqiurkZJSYnoejUaDWbPno0zZ85g5syZVpNbJrZt24aysjKMGTOGUelo2bIlo6wmfv/9d7Rv395q8s8EH7MmnU4HAFCrrX8fYWFhovtJrVaLpUuXYuTIkYiIiOB1zc6dO6FWq9G1a1fzsdzcXJw+fRoDBw4UJYdfo1BA4UV5AAgPYPCtBH60AyBDJk6caDcQpaWlIS0tzUMS+bWSTEgJrVp5PSdPnkRERITVhPvrr7/GunXr8OWXX6Keg7B8Wq0Wc+bMwcmTJzFjxgxedumZmZmoU6cOGjVqJErmM2fOoFu3bli2bBm2b98OrVaL9u3b44UXXkBCQoKgulKbpSKlfgpWr16Nl156CREREdi9ezfOnTuHp556yq78zJkzUVJSgpiYGPTq1QtPPvmkXZnNmzdDqVTioYcewp9//ulQBq1Wi3379qF79+4IsnCCPHPmDACgrKwM48aNQ3Z2NuLi4vDII4+QUuAIgwE+mweABnB2/HhMIgVAhqSnp9utLhEE4YDIZE9L4PNkZWXhjz/+wIgRIxAQECCqjmPHjgEwLnS0adOG1zW3b99GXFycqPYAownR9u3b0aBBA0yePBmVlZVYtWoVZs6cic8++0zQdwkIDMCM92Zg/tz5GDNmDACjI+748ePRoUMHczm1Wo0hQ4agTZs2CAoKwvHjx/Hjjz8iNzcXoyaMsvpu69evx9SpU3nLceTIEZSUlNiZ/xQUFAAAFixYgEcffRSjR4/G33//jWXLliEiIsLKSZnwM3x6ouum7+ZjehQpAARBuI8OI4DUe1xTd90WrqnXxczaPwt55XmS1hmvjse0HtMkrbOgoAAffPABmjdvjiFDhlidGzlyJEaOHMmrnkaNGqGwsBDr1q1Dp06dEB0d7fAag8EAhZMTGL1eb+XTkJCQgAkTJmDfvn12vgBcaKo1WLBwAXQ6HaZMmQK1Wo2DBw9i4cKFCA8PR+fOnQEATZo0QZMmTczXdejQAbGxsViyZAnuO38fUpukAgCWL1+Ojh07WikPjvjrr78QHR1td41erwcA9OvXD8OGDQNg9Nu4fv06vv32W1IAuDAYAKXSe1bLfXpCLxQ3PjMfuu+kABAE4T5iGxn/8SU4Ulj9d48XVl4GSD1RdwVlZWWYMWMGgoOD8c477yAwUPzQUadOHUyYMAFvv/02pk+fjjlz5iAsLIzzmri4OFy+fFl0m+Hh4ahXr56V2VLTpk0RFhaGnBx7h1wuDu06hKyzWVi5cqW5vg4dOiAvLw8rV640KwBM3HPPPViyZAlyLuYgtUkqTp8+jX379uGjjz5CaWkpAJij9VRUVCA4OBghISFWdVRUVODQoUN44IEH7HYMTP4DJudkE+3bt8fhw4eh1WqdenY+jwJAjRJFuAhvUbD44OVfhZyACYKQL2/nCivfb6Zr5PBjqqurMWvWLBQWFmLmzJmIjBSolDHQqFEjvPvuu7hy5QpmzZrlMERlu3btcPv2bVy6dElUe5ZhOG0Rms8g72oeYuvE2jkdN27cGNeuXRNU1+XLl6HVajF+/HiMGDECI0aMwJIlSwAAY8aMwaJFi+yu2b9/P6qqquzMfwCgfv36jO0YDAYolUqnd1F8HWVICJTh4Z4Ww/fx1vfQYPBe2RkgBYDghdRK+0PtErBoJIWmIwg5o9PpMG/ePFy6dAkzZszgjLMvlNatW+Ott97CmTNnMG/ePHN0HSYeeOABhIWF4YsvvoBGo7E7f+bMGRQVsSe/u/POO3Hx4kWrMllZWSgrK0NqaqoguaPrRCP/dj4KCwutjmdlZTm8P7t27QIANGzcEADQuXNnzJkzx+qfybxq6tSpGD7cPmHYzp07kZiYiBYt7E3eWrZsifDwcLOfhYmMjAzUr19ftN+Gz6NUAgoFYp9+Gqnr13laGsKdCJ3cWJb3cl2A9gIJj5ASo0ZKDDk6E4Sc+fzzz3Hw4EGMGjUKVVVVOH36tPlcgwYNzMEK1q1bh/Xr1ztMxmVLly5dMH78eCxYsAALFy7EhAkTGFepY2JiMHHiRMydOxdvvvkm0tLSkJCQgJKSEhw6dAg7duzAsmXLWNvp378/fvrpJ7z33nt4/PHHUVVVhVWrVqFJkybo1q02wV1OTg5yc2t3nbKzs7F3714EBwejS5cuAIBOPTph1y+7MGPGDAwdOhRhYWHYv38/Dh06hJdfftl87YIFC5CYmIgmTZpApVLh+PHj2LRpE7p162ZWAGJiYhATE2Ml640bNwAArVq1sttlKCoqQkZGBoYOHcr4PVUqFYYPH46VK1ciPDwczZs3x8GDB3H48GHGrMSEkVYn/zX/HWARVUkwSiWS0xdIIBFBuB5SAAheUGxkgvA//vnnHwDAihUr7M7NmTPHnIXWYDBAr9eLioPfu3dvlJaWYsmSJQgPD8fYsWMZy3Xt2hUff/wxNmzYgNWrV6OoqAhhYWFo2bIlpk2bxql4qNVqzJ49G8uWLcNHH30EpVKJzp07Y/To0VYmQHv27MG6dbUrwDt27MCOHTsQHx+Pr776CgCQkpCCWbNnYd3adVi6dCkqKyuRlJSE1157Df369TNf26BBA/z111/YuHEjNBoN6tWrh8cffxxDhw7F1YqrouKJ79mzBzqdjtH8x8TgwYOhUCiwefNmrF+/3uzs3KNHD8HtEcJQKBSIfPhhT4thR9Qjg6G2yBdBOIEPmQCRAkDwwpf8dgiC4Idp0usIvlGAPvjgA8bjtnlO2Mo1bNgQkyZN4iWTLYmJiZg+fTpnGT7fo0FkAyASDusaNmyYORKPHRVgtcfv27cv+vbty3iObz6YQYMGYdCgQQ7LEd5JzJNPCpqIBjdpgmCLiFSESHxsIkQKAEEQBEG4ESnCmhL+S8K0dzwtgrxwZmIu9HdoWd7L9QFyAiZ44eXvOUEQhGxQKBQIUJBDLuGH+Moqug8o8LQDQBAEQRBuJCU8hXYACP/FW999HwsDSgoAwQsxzn0EQRCEPQFKWv0nCMlwZlLux3MbUgBkyMSJE+2S0/B1/nIV/vsTIQiCIAhCtrhtEu9bMyFSAGRIenq6Ob42QRAEQRCELxDSpo1PmdF4M6QAELzw410ygiCcJCQgBEoFxZwgCH+n7muveloE6fDyiREpAARPvPtFJwjCc9QLq+dpEQiC8FECIiMR3KKFexozbV74wC4GLckQBEEQBEEQXklg3bpovGmjp8XwOmgHgOBFZKgKjeuGeVoMgiDcyNGjR/Htt9/i/9u787Coyv7x428QkF1xQVRAFDWXRFNCMtdcyzVDUUrLR7M9l1aXUjSpHk2/ll8zfVwKFUtNzXpEckXBBVzYBIREwFBQkEU2geH3h9+Zn+MMMCOMYHxe1+V1yTn3uc8999wHzuece0lNTaWgoICmTZvi6enJ5MmTsbLS//fBvHnzMDc3V1tFt6ioCF9fX5KSkli6dCkdOnTQmg4gJSWFXbt2ERkZSU5ODpaWlnTp0oWxY8fy5JNPVnrusrIy9u3bR1BQEOnp6dja2uLh4cE777yjSnPixAlOnDhBfHw8WVlZTJs2jfHjx2vklZ6ezk8//UR0dDQFBQW0atWKF198kYEDB6rSREVFMX/+fI1j+/Xrx8cff6y1jLdu3eKtt96iqKiIrVu30qhRI9X5ZsyYofUYExMT9uzZo7YtOTlZVT6FQoGjoyMzZ86kc+fOldaREPWOHk/y/2mzIUoAIHRib2POkQ8G1nYxhBCPUF5eHp07d2bs2LFYW1uTnJzM9u3bSU5OZunSpdXOv7i4mKVLl3LlyhXVzX9Fzp49y9dff42joyMvv/wyLVu2JC8vj9OnT7NgwQI2bNiAvb19hcd/++23XLx4EW9vb5ycnMjOziYhIUEtTUhICOnp6Xh4eBAYGKg1n7t37/L5559jZGTEjBkzsLGxITg4mG+++QYzMzP69Omjln7WrFk4Ojqqfra1ta2wjBs3bsTc3JyioiK17U2aNGH58uUa6RcvXky3bt3UtiUlJfHpp5/i7u7ORx99RIMGDfjrr78oLi6u8LxC1Ft63tQb8fh3/VGSAEAIIYRWAwYMYMCAAaqfu3XrhqmpKWvWrCEzM5OmTZs+dN53797liy++ICEhgSVLltCxY8cK096+fZuVK1fyxBNP4Ovri6mpqWrfM888w/PPP0/Dhg0rPP78+fMcP36cb7/9FmdnZ9X2fv36qaX7+OOPVVMwVxQAJCQkkJaWxrJly3BzcwOgR48exMXFceLECY0AoE2bNpUGNkoRERFEREQwYcIENm3apLbP1NSUTp06qW2LiooiPz9f7fsBWLt2rermX+mpp56q8vxCiCr8s14A1O0AoLCwkK1bt3Ly5Eny8vJwdHTEy8uL/v37V3ns5cuX2bp1K3FxcZSXl9OhQwdeeeUVunTp8kjSVqfsQghRV9nY2AD3utQ8rJKSEpYtW0Z8fDy+vr4aN7cPCgoKIj8/n9dff13t5l+pquP//PNP3Nzc1G7+tXlw/RVtlJ/7wamaraysHrqLQGlpKT/88AM+Pj6Ym5vrdMzx48extLTEw8NDtS01NZW4uDimT5/+UOUQQlTh/7oMtft9Pyb2j/fkBnV6ELCfnx+HDx9m0qRJLF68mA4dOrB8+XKOHTtW6XGXL1/m008/5e7du8yZM4e5c+dy9+5dFi5cSFxc3CNJ+7BlF0KIuqasrIy7d++SmJjIjh078PDwUOtus337dkaPHk16enqVeZWWluLn58elS5dYtGiRTv3So6KiaNq0KW3btn2o8sfHx9O6dWvWr1+Pt7c3L730Er6+vty4cUPvvLp06YKTkxP+/v7cuHGD/Px8AgMDSUxM5Pnnn9dI7+vry9ixY3nttdfYtGmT1q44v/32G8bGxlqP16a0tJTQ0FA8PT0xMzNT+5wA+fn5vP/++4wdO5bp06ezf/9+vT+nEKJiDdu1o4H14z0uss6+AQgPD+fixYt8+OGHqlecbm5uZGRksHnzZvr160eDBtqXU9+2bRtWVlYsXrxY9TSle/fuvP7662zatIl///vfBk1bnbILIURdM336dDIzMwHo2bOnWvcSfV28eBG4t+J5165ddTomMzOTZs2aPfQ5b9++zeHDh3F2duajjz6iqKiIn376CV9fX9asWaPX72MTExP8/Pz44osveP3111XbZs+eTffu3VXpLC0teemll+jatStmZmZERkayZ88eUlNT1QY3Z2ZmsmPHDhYsWKBzOc6dO0deXp5G95/bt28D8M033/Diiy8yY8YMzpw5w/r167GxsVEbpCyEqN/qbABw6tQpLCws6Nu3r9r2IUOGsGLFCi5fvlzhk6PY2Fjc3d3VXqVaWlrStWtXTp06RVZWFk2aNDFY2uqUXQhRv1xfvJjS9IwazdOkhT0tFy+usfwWLVpEUVERKSkp7Nixg6VLl7JkyRLVDauPjw8+Pj465dW2bVuys7MJCAjgqaeeonHjxlUeU15ejlE1591WKBR89tlnqpl1HBwcmDNnDqGhoRpjASpTXFzMV199RVlZGfPnz8fS0pKzZ8+yevVqrK2t6dWrFwCurq64urqqjuvevTtNmjRh3bp1XL58WTXmYdOmTfTo0UMteKjKsWPHaNy4scYxCoUCgKFDhzJhwgTg3sOnGzdu8Msvv/yjAoB2v8tbDSGqo84GAMnJyTg6Omo8EXFxcVHtr+gmuqSkRGs/UeW2q1evqm7qDZG2OmUHKCgoqHDfg+fVVh4hxOOjJm/UDUXZ9aZz5864uroyZ84cTp8+zbPPPqt3Xk2bNmXOnDnMmzePRYsW4efnV+WUos2aNePatWsPVXYAa2trWrRoobr5B2jfvj1WVlakpKToldeff/5JfHw8W7ZsUeXXvXt3MjIy2LJliyoA0KZv376sW7eOxMREOnbsSFxcHKGhoaxYsYI7d+4AqLoIFRYW0rBhQ40xAYWFhYSFhTFs2DCNvzHK8RnKwclKbm5uhIeHU1paiolJnf2zr5eG7dvXdhFEfSPTgD4aeXl5ODg4aGxX/oLLy8ur8FhnZ2fi4+NRKBSqQV1lZWVcvnxZ41hDpK1O2QGmTZtW6X6lyZMn6/zUTQghakLbtm0xNjYmLS2tWnl8/vnnfP755yxduhRfX99KZ/Hp1q0bERERXL16VfUgRR+Ojo6UlJRo3afLwN/7paSk0LRpU7VgAqBdu3ZcuHBBr7yuXbtGaWkps2fP1tj3+uuva10z4NSpUxQXF2t0/wFwcnLSep7y8nKMjY2r/RZFiH8Ss7ZtaWDXRL+D/kHXUJ0NAKpj1KhRfPvtt6xbtw5vb28UCgUBAQFkZNx7zX7/L3xDpa2OzZs3a8wwoY08/RdCPGpxcXEoFAqtDzn00aVLFz799FOWLVvG119/XWkf+GHDhrFnzx42bNjA4sWLNX73xcfH4+DgoHFTrvT000+zbds2cnJyVGkSEhLIz8/XO6Cwt7cnMzOT7Oxste5LCQkJla5DABAcHAygmha0V69e+Pn5qaU5d+4cu3fvZsGCBbRq1Uojj+PHj9OyZUueeOIJjX2dOnXC2tqaixcvqr2JiIiIwMnJScaeCXEf1wP/1e8AeQPwaNjY2Gh9Uq7cpnyars3QoUPJycnhl19+4cCBA8C9X4wvvvgiu3fvVnXpMVTa6pQd7o0r0CUAEEIIQ/Lz86N9+/a4uLjQsGFDkpKS2L17Ny4uLnh6eqrSBQQEsGPHjioX43qQu7s7s2fP5ptvvmH16tXMmTNH61NqOzs75s6dy1dffcXHH3/MyJEjcXBwIC8vj7CwMI4cOcL69esrPM+IESP4/fffWbJkCRMnTqS4uJiffvoJV1dXevfurUqXkpJCamqq6ufk5GRCQkJo2LAh7u7uAAwcOJBdu3axePFivLy8sLKy4tSpU4SFhfH222+rjv3mm29o2bIlrq6umJqaEhkZyb59++jdu7cqALCzs8POzk6trMqZlDp37qwR0OTk5BAREYGXl5fWz2lqasqkSZPYsmUL1tbWdOzYkbNnzxIeHq51VWIhhJ7kDYDhubi4EBwcTFlZmdpTi+TkZODe4iqV8fLyYuzYsaSlpWFhYYG9vT1r1qzB3Nyc9g/0HazptNUtuxBC1AUdOnTg5MmT7Nq1i/Lycuzt7Rk+fDjjx49XewpfXl6OQqF4qHnwBwwYwJ07d1i3bh3W1tbMnDlTazoPDw9WrVrFrl278Pf3JycnBysrKzp16sRnn31WaeBhaWnJsmXLWL9+PStWrMDY2JhevXoxY8YMtTe3J0+eJCAgQPXzkSNHOHLkCPb29mzcuBG4Nx7Bz88Pf39/fvjhB4qKimjVqhXvvfceQ4cOVR3r7OzMsWPH2Lt3LyUlJbRo0YKJEydWePOui5MnT1JWVqa1+4/S2LFjMTIy4rfffmPHjh2qwc7PPPPMQ59XCPEPVF5HhYWFlY8aNao8ODhYbfvnn39ePnXq1PLS0lK98ktPTy/39vYuX79+vcHTPmzZ8/Pzy0eNGlWen59f5XmFEHVTSkpK+ezZs8tTUlJquyhC6ETarBBVuzZnbvnVqa/WdjEqpO89ZJ19A+Du7k6PHj1Yu3YtBQUFtGzZkuDgYM6fP88HH3ygerIeFRXFwoULmTRpEpMnTwbuPWkPDQ2lffv2mJqakpSUxK5du2jZsiWvvPKK2nkMkVbXsgshhBBCiMeEdAF6NObPn4+/vz/btm0jLy8PR0dHPvroI/r376+W7sFXzyYmJkRERLB//34KCwtp3rw5zz//PF5eXhpTqhkqra5lF0IIIYQQdZt5166U5ebWdjFqjFF5+T9sWPNjrKCgAG9vb37++WcZBCzEYyo1NZWVK1cyd+7cCqdlFKIukTYrxONP33vImpm3UgghhBBCCPFYkABACCGEEEKIekQCAFGnlJSUsH379gpX7RSGJfVfffevEv4wFAoFmZmZKBSKmiyW0FF9rH9lW62pxSyrQ34H1S6p/9r1KOu/9q92Ie5TUlJCQECA/PKpJVL/1adc2OnKlSsPdXx5eTlZWVkPNae+qL76WP/KtvrgomS1QX4H1S6p/9r1KOu/Ts8CJIQQjxtLS0s8PT3Zv38/AO3atdNr6l+FQkF6ejpmZmZ14olsfVOf6r+srIwrV66wf/9+PD09ZfIJIeoRCQDqoLlz52r84Rk5ciQjR46spRIJIfQxYcIEAFUQoA+FQsGtW7do1qzZP/4GtC6qj/Xv6emparNCiPpBAoA6aOXKlfIkRojHmLGxMd7e3owePZrbt2/r1Z+8qKiIuXPnsnjxYo31RYTh1af6NzY2xs7OTv7eCFEfGWpJYqG/qpZx/v3332v0fDWZX03lpe9S1lWpD3VWk3nVdP2Xl9fNz1mX86rL10B9yEvqv3bzqy/1X9P51Yf6r+n86mJe1al/fY+tH+83/yH++OOPOptfTZetptSXOqur9Q9193PW1bxqWl39nHU1r5pWVz9nXf7dWJPqS53Vh/qv6fzqal6PigQAQgghhBBC1CMSAAghhBBCCFGPSAAghBBCCCFEPSKzANUh5f+38ExBQYHW/QqFosJ9D6Mm86upvJR51LVyGSK/uphXTdc/1M3PWZfzqsvXQH3IS+q/dvOrL/Vf0/nVh/qv6fzqYl7VqX/lMeU6LmJoVK5rSmFwt27dYtq0abVdDCGEEEII8RjavHkzzZo1qzKdBAB1iEKhICsrCwsLC4yMjGq7OEIIIYQQ4jFQXl5OYWEhTZo00WkRQwkAhBBCCCGEqEdkELAQQgghhBD1iAQAQgghhBBC1CMSAAghhBBCCFGPyDSgwmAKCgr4+eefuXLlCleuXCE3N5fJkyfj4+Ojli4qKor58+drzWP58uV06tRJbVthYSFbt27l5MmT5OXl4ejoiJeXF/379zfYZ3kcRUREcOzYMWJjY7l16xZWVlZ06NCBSZMm0b59e7W0+tSp1L9udK1/af+GceXKFfz9/bl69Sq5ubmYmZnRunVrRo4cyaBBg9TSSvs3DF2/A7kGHo2DBw+yZs0azM3N2blzp9o+uQYMr6L6r632LwGAMJi8vDwOHjyIi4sLnp6eBAUFVZp+6tSpdOvWTW1bmzZtNNL5+fmRkJDAq6++SuvWrTl+/DjLly9HoVAwcODAmvwIj7UDBw6Ql5fHmDFjcHJyIjc3lz179vDhhx/i6+tL9+7dVWn1qVOpf93oU/8g7b+m5efn06xZM/r370/Tpk0pKiri+PHjrFy5koyMDLy9vVVppf0bhj7fAcg1YEiZmZls3ryZJk2aaJ1jXq4Bw6qq/uHRt38JAITB2NvbExAQgJGRETk5OVUGAK1atdKIdB8UHh7OxYsX+fDDDxkwYAAAbm5uZGRksHnzZvr160eDBg1q7DM8zt58800aN26stq1nz57MnDmTnTt3qm5A9alTqX/d6Vr/StL+a1a3bt00/ph6eHiQnp5OYGCg6uZT2r/h6PodKMk1YDj/+7//S9euXbG2tiY0NFRtn1wDhldZ/Ss96vYvYwCEwRgZGdX4eganTp3CwsKCvn37qm0fMmQIWVlZXL58uUbP9zh78OYTwMLCAmdnZ27duqXapk+dSv3rTtf614fUf/XZ2Nio/YGU9v/oPfgd6EO+A/0dPXqU6Oho3nrrLa375RowrKrqXx81Wf8SAIg6Y926dYwdO5aJEyfy+eefExMTo5EmOTkZR0dHjT8eLi4uqv2iYvn5+fz11184OzurtulTp1L/1aOt/pWk/RuGQqGgrKyMnJwc/vjjDy5cuMBLL72k2i/t3/Cq+g6U5BqoednZ2WzYsIFXX321wtVh5RowHF3qX+lRt3/pAiRqnaWlJWPGjOHJJ5/E1taW69ev8+uvvzJ//nwWLVpEz549VWnz8vJwcHDQyMPGxka1X1Rs3bp1FBUVMXHiRNU2fepU6r96tNW/tH/D+v777wkMDATAxMSEmTNn8vzzz6v2S/s3vKq+A7kGDOf777/H0dGRF154ocI0cg0Yji71X1vtXwIAUetcXV1xdXVV/dy1a1c8PT1577332Lx5s1rjFw9v69atHDt2jDfeeENjFiBheBXVv7R/w5owYQLDhg0jJyeHs2fP8sMPP1BUVMT48eNru2j1RlXfgVwDhhESEsLZs2dZvXp1jXfHFVXTtf5rq/1LACDqJGtra55++mkOHDhAcXExDRs2BO5FudoiXOU2ZRQs1AUEBPDzzz8zZcoURo0apbZPnzqV+n84ldW/NtL+a469vT329vYAuLu7A/DTTz8xePBgGjVqJO3/EajqO9BGroHqKSwsZN26dYwaNYomTZpw584dAEpLSwG4c+cOJiYmmJubyzVgAPrUvzaPov3LGABRZ5WXlwOoRc4uLi5cu3aNsrIytbTKfm/apsyq7wICAti+fTs+Pj5qXU+U9KlTqX/9VVX/FZH2bxgdO3akrKyMGzduANL+a8OD30FF5Bp4eLm5uWRnZ7N3714mT56s+hccHExRURGTJ09mxYoVgFwDhqBP/VfE0O1fAgBRJ925c4ewsDDatWuHmZmZarunpyeFhYUa02gdPnyYJk2a0LFjx0dd1Dptx44dbN++HW9vbyZPnqw1jT51KvWvH13qXxtp/4YTGRmJsbGxqh+ttP9H78HvQBu5BqrHzs4OPz8/jX89e/bEzMwMPz8/pkyZAsg1YAj61L82j6L9SxcgYVDh4eEUFxdTWFgIQEpKCiEhIQD06tULc3Nzli9fTvPmzenQoQO2trakpaWxZ88esrOzmT17tlp+7u7u9OjRg7Vr11JQUEDLli0JDg7m/PnzfPDBBzL/8H327NnDtm3b6NmzJ+7u7sTFxantV843rE+dSv3rTtf6l/ZvGGvWrMHCwoKOHTvSuHFjcnNzCQkJ4cSJE4wfP17V9UTav+Ho+h3INVDzzMzMNNZgADh06BDGxsZq++QaqHn61H9ttX+jcuU7BiEMYPr06WRkZGjd95///IcWLVqwc+dOTp48SXp6OoWFhdjY2NClSxe8vLy0RrOFhYX4+/urLYM9YcIEWYb8AfPmzSM6OrrC/fv371f9X586lfrXja71L+3fMA4dOsShQ4dITU0lPz8fc3Nz2rZty7Bhwxg0aJBaWmn/hqHrdyDXwKOzatUqQkND2blzp9p2uQYeDW31X1vtXwIAIYQQQggh6hEZAyCEEEIIIUQ9IgGAEEIIIYQQ9YgEAEIIIYQQQtQjEgAIIYQQQghRj0gAIIQQQgghRD0iAYAQQgghhBD1iAQAQgghhBBC1COyErAQwuBGjx6tV3p7e3s2btyoWkxLuWjcP9mdO3cIDw8nLCyMpKQkMjIyMDY2xsnJiQEDBvDCCy9gYlL1r+ySkhJmzZpFamoqpqam/PrrrxppDh06xOrVqyvMo1+/fnz88cdVnuvIkSOsWrUKgKlTpzJhwgSt6c6dO8dvv/1GQkICBQUFWFtb06FDB8aNG0f37t21lu/8+fMkJSWRnZ1NUVERtra2dOrUiXHjxtG5c2eNY6Kjozl69CiJiYlkZWVx584d1cJTQ4cO1Vj8qyoPttn58+fzzDPPqH5etWoVR44cUUtjZGSEpaUlLi4uDB06lOeeew4jIyONz7Z69Wqee+455syZo3He1NRUFixYwO3bt3nhhRd48803MTIyUl0Lfn5+aquIPrjgnLGxMRYWFtja2uLi4kL37t0ZOHAgVlZWen3+qjwu1+akSZPIz89X/Txr1iyGDBlSiyUSom6QAEAIYXDPPfecxrbY2FiuX79O27Ztadu2rdo+W1vbR1W0OmPPnj388ssvGBsb065dOzw8PMjJySE2NpbLly8TEhKCr68v5ubmleazc+dOrl27ptM5tdU9wBNPPFHlsTk5OWzcuBEjIyMqW09y7969qnSdO3emadOm3Lhxg/DwcMLDw3n77bd5/vnn1Y75448/SEpKok2bNnTp0gVTU1P+/vtvQkNDOXXqFO+88w7Dhw9XO+bMmTMEBQXRunVr2rVrh7W1NZmZmcTExBAVFcWFCxeYO3euTvWiZG5uTp8+fQBo3ry51jSdO3emZcuWAJSVlXHjxg1iYmKIiYkhNjaWd999V+fzpaSksGDBArKzsxk1ahRvvPGGzsf27NmTxo0bA/dWCr116xZhYWGcOnWKH3/8kZkzZ9bLG9/+/ftTXFxMUlISSUlJtV0cIeoMCQCEEAan7UnnqlWruH79Op6envj4+FR4XHFxMU2bNjV0EWudubk5EyZMYOTIkWqfNy0tjYULF3Lp0iV++eUXpk6dWmEeqamp7Ny5k2HDhnHw4MEqz1lZ3VflP//5D0VFRQwYMIBjx45pTZOTk8OPP/6IiYkJX3zxBV27dlXtCwkJ4euvv2bjxo0MHDgQCwsL1b4333wTJycnLC0t1fI7c+YMX375JevXr6dPnz7Y2Nio9g0dOpRx48ZptJW0tDTmzZvH0aNHGTBgAL169dL5M9ra2mptu/cbNmyYxo11eHg4S5Ys4eDBg4wYMYL27dtXea7k5GQWLlxIdnY2Y8aM4fXXX9e5nABeXl5qbwYA8vPz2bdvHz///DOrV6+mrKxMI3D6p3v77bcB2L59uwQAQtxHxgAIIeose3t7nJycdOr68ribMGECU6dO1biBbdWqFa+++ioAx48fr/D48vJy1qxZg7W1Na+99pohi8qFCxc4duwYEydOVD391iY+Pp7S0lLc3NzUbv4Bnn32WVxcXCguLiY1NVVt3xNPPKFx8w/Qu3dvunXrxt27d4mPj1fb5+zsrDVQbNWqFS+88AIAkZGROn/G6nB3d6dLly4AxMTEVJk+OTlZ9eR/3Lhxet/8V8TKygofHx9mz54NwPr167l9+3aN5C2EeLz98/+qCiEeWxX1Mx49ejT29vasX7+enTt3cuTIETIzM7G3t+ell15SPZGNiIjg559/JjExEWNjYzw8PJgxY4bWLkYlJSUcOHCAo0eP8vfff6NQKHB2dmbEiBEMHTpUoy/3o6TsppOVlVVhmsDAQC5dusTcuXOxtrY2WFmKi4tZu3YtTk5OjB8/np07d1aY1tTUVKc89SmvsfG951b6BIUPc0x1KbvjlJWVVZru6tWrLFy4kJycHF566SWDBG+DBg1StY+goCC8vb11PjYwMJDff/+dtLQ0bGxs6NOnD6+88kqF6cPCwggNDSUuLo7MzEwUCgUtW7akX79+vPjii2pt4tdff2Xz5s2q4Feb+fPnExUVxVdffaUKIm/evMmuXbuIiIjg5s2bmJmZYWdnR5cuXRg3bhyOjo46fz4h6isJAIQQj62vv/6aixcv0qlTJxwcHIiOjlYNbrWwsGD58uW0bduWHj16cPnyZY4ePUp6ejpfffWV2g19UVERixcvJiYmBltbWzp37oyxsTFxcXF89913JCQk8M4779TWx+TGjRsA2NnZad2flZXFjz/+iJubm16DXRMTE9m0aRMFBQXY2dnh5uam0Y3kQdu3b+fGjRv4+flVeYPfoUMHrKysiIyMJCYmRu0tQGhoKFevXqVz5860atVKp/JGREQQGRmJjY0NHTt21OmYmzdvEhgYCNzrJ/8olJWVceXKFQCcnJwqTJeUlMTChQvJzc2t9Ca4JvTv359Lly4RGRmpcwCwceNG9u7di6mpKW5ubjRs2JBjx45x6dKlCr/7b7/9luLiYpydnWnTpg2FhYVcvnwZf39/IiIiWLJkCQ0aNABg8ODBbN26lcOHD/Pyyy+rtiulpaURHR2No6Ojqu3cunWL2bNnk5ubi4uLCx4eHpSUlJCRkUFQUBCdOnWSAEAIHUgAIIR4LGVkZGBhYcHatWtp1qwZcK+Lx4IFC/D396ekpISPPvqIZ599FoCCggI++ugjLl26RFRUFG5ubqq8Nm3aRExMDIMGDeKtt95S9UfPyclh6dKlBAYG4uHhwdNPP/3oPyiwf/9+4F4XGG3WrVvH3bt3Vf2ddRUWFkZYWJjq5x07dvDkk0/y8ccfaw02rly5wr59+xgyZEiVgQLce7L/3nvvsWLFCubNm6caBJyenk5CQgI9e/ZUdU/R5tChQ0RFRVFSUsL169dJTEzE0tKSDz/8UGsXIYC4uDgOHDiAQqEgKyuLS5cuoVAoeOWVVzS6IdW00tJSbty4wc6dO7l+/Trt2rWrMOj4+++/WbBgAXl5eXh7e1f6VL0mKN8i6TpAPDY2lr1792JjY8OXX35JmzZtAMjNzWXBggUaXbCU3n77bZ566im1weoFBQWsWLGCsLAwjh8/rpoUoFGjRvTp04fjx48TFhaGp6enWl5BQUGUl5erjVsICgoiNzeX6dOnM27cOLX0GRkZVb5xEULcIwGAEOKxNXPmTNXNP4Cbmxuurq789ddfPPfcc6qbfwBLS0uGDx/Ohg0biI6OVgUA2dnZ/Pnnn7Ro0YL33ntP7clmo0aNeOedd3j//fcJDAyslQDgwIEDXLx4ESsrK7y8vDT2nz59mlOnTjF58mRat26tU55NmjTBx8eH3r174+DgQHFxMQkJCWzevJno6GiWLFnCihUr1J7IlpWV8d1332Fpacm0adN0Lv+zzz6LtbU1//73v7l06ZJqe+PGjXFzc1MbyPugS5cuqU21aW1tzbvvvlvpk/zr16+rHWNsbIyPjw/jx4/Xucz6WL16tcaUqkZGRowcOZJXXnlF46m2kvIGukOHDga/+Yf/P7PWnTt3dEp/4MABAMaNG6e6+VfmM23aNBYtWqT1uPunSlWytLRkxowZhIWFcfr0abVZwUaMGMHx48cJCgpSCwDKyso4cuQIJiYmam+1srOzAdQCeCV7e3udPpsQQgIAIcRjysTEhCeffFJju4ODA3/99Rc9evTQ2KccsHp/X/ro6GhKS0vp2bOn1m4Nbdu2xcLCgoSEhJorvI6ioqJYv349RkZGzJo1S2OQa0FBAT/88AOtWrWqcA5+bXr27Kl2E21paYmHhwfdunVjzpw5JCYmcuLECQYOHKhKs3//fhITE5k1a5Ze07Tu2bOHLVu20Lt3b3x8fHBwcODGjRts27aNzZs3Ex8fz7x587Qe+/777/P+++9TWFjI33//ze7du/nqq68YPnx4hdNrDho0iEGDBqm6hRw5coQdO3YQFhbG4sWLa3x8xP3TgALcvn2bxMREgoKCsLCwYMqUKaoxCPdr164daWlpJCQksGXLFoMP3K5sqlZtlMFa3759Nfb17NkTGxsb8vLytB6blpZGeHg4169fp6ioCIVCodp3/fp1tbRPPvkkTk5OnDt3jszMTFUbP3v2LLdv36Zfv340atRIlV45o9K6detUb3UqCrKEEBWTAEAI8Viys7PTemPVsGFDAK0zwii7JZSUlKi2ZWRkAPeeeCqfempz9+7dKsuUmprKrl27NLZ7eXlV2hdcm6SkJJYtW0ZpaSkzZ87U+mTV39+fW7du8cUXX+g84LYyFhYWjB49mnXr1nHhwgVVAJCRkcG2bdvo2rUrgwcP1jm/qKgoNm3ahKurK59++qnq+3JxceHTTz9l7ty5hIaGcv78+Uqf6ltYWNC+fXs++eQTSkpKOHjwIE899ZTaG54HmZqa0rp1a6ZMmYKNjQ0bN25k27Ztes2trwtt04AWFBSwfPlydu3ahYWFBRMnTtQ4zsXFhWnTprFkyRJ2796NhYWFXoNz9ZWbmwtQ6RuX+2VlZWFkZFTh+gfNmzfXCADKy8vZtGkT+/btqzDgKCgo0Ng2YsQINmzYwJ9//smkSZMAVNPYPjht6eDBg7lw4QInT55kwYIFNGzYkPbt29OrVy+GDh2qGnwthKicBABCiMdSVbPy6Dprj7LPcLt27XBxcalWmbKzszVWhwUYMmSIXgHA9evXWbRoEfn5+fj4+FS4kvLZs2cxMzNjx44d7NixQ2N/aWmp6un6e++9p9NgW21vSSIjIykqKiInJ4f58+erpVcGUAcPHuT8+fN06dKFKVOmAHD06FHgXreQB4O1Bg0a0KdPH65cuUJ0dLTOA3QHDhzImTNnOHPmTKUBwP0GDRrExo0bOX36dI0HANpYWlry2muvER4ezt69e7UGAAA9evTgk08+4csvv2Tr1q1YWlrqvWq2rpRz4OsbiOrjxIkT7N27l2bNmjFjxgw6depEo0aNMDExoaSkpMJuWM899xw//vgjf/75J97e3ty6dYsLFy7g4OCg0dWnQYMGfPLJJ3h5eXHmzBkiIyOJj48nJiaGXbt24evrS6dOnQz2GYX4p5AAQAhRrynHEHTr1o0ZM2ZUK69u3bqpBuw+rMzMTD777DNu377NmDFjmDx5cqXp7969S3R0tNZ95eXlqn1FRUU6nT8/Px9AbWEupWvXrlU4iDQ9PZ309HSsrKxU227dugVQ4YBd5Tkq6kqijbL7UU5Ojs7HWFtbY2xsrHoK/igop63Ny8sjJydHrRvL/Xr37s3s2bNZtWoVGzZswMLCwiAr9gYHBwPa+85rY2dnR0ZGBjdv3tQaON68eVNj26lTpwB466238PDwUNunnMlKG2tra/r168fhw4e5cOEC8fHxKBQKhg0bVmEg7+rqiqurKz4+PhQUFBAQEMDevXvZsGED33zzjU6fUYj6TAIAIUS95ubmhrGxMWFhYUybNq1W+xPfuXOHRYsWkZ6ezpAhQ6oMSDZu3FjhvtGjR2Nqasqvv/6qVxlCQkKAezdYSkOGDKnwpnT79u0EBAQwdepUjXEIypmEEhMTtR6rHFehz+BNZUBT2QJkD1LOBOTg4KDzMdWVnp4O3HsTpeyWVpGBAwdSXFzMmjVr+O6777CwsND57YYujh49SmxsLA0bNmTYsGE6HdOlSxcyMjIICQnR+F4vXLigNWhTDjDW1m3o5MmTlZ5vxIgRHD58mMDAQBITE2nQoIHO3c0sLS2ZOnUq+/bt4+rVqzodI0R9JysBCyHqtaZNmzJ48GDS0tJYuXKl1ifLsbGxhIeHG7QcyrUIkpOT6du3L++++67BFh/77bffKCwsVNtWWlpKQEAAISEhmJmZ1chTaOWsLseOHePs2bNq+06fPk1wcDDGxsZq4xtSUlI4ePAgxcXFaunLy8sJDg5m9+7dGBkZqc0kA/cCEW2r3CYkJPDdd98BGOTJujYFBQVs3rwZuDfI9f4pMSsyfPhwpk+fjkKhYMWKFZw7d67a5cjPzycgIID/+Z//AeDNN9+s8E3Eg0aMGAHA3r171VZqzsvLY8uWLVqPUc5CFRgYqDYGICYmhj179lR6vk6dOtG2bVtOnTrFzZs3efrpp2nSpIlGuiNHjpCcnKyx/dy5c5SXl1c4ZkEIoU7eAAgh6r2ZM2eSnp5OcHAwYWFhtGvXjiZNmnD79m2uX79OZmYmY8aMwd3d3WBl8Pf3Jz4+HmNjYxo0aMC3336rNd2cOXOqfa4NGzbw448/4uzsTPPmzSkpKeHKlStkZWVhZmbGBx98oHUQtb48PT159tlnCQkJYenSpbRv354WLVqQnp6ueiswZcoUtYWbcnJyWLNmDZs2baJ9+/bY2dmRn59PSkoKGRkZGBsb869//UtjIbCAgAB27tyJq6sr9vb2lJaWkp6erlqQq2/fvowZM6ban+lBQUFBREVFqX7Ozs4mISGBvLw8bG1tefPNN3XOa9y4caruLH5+fvj6+mrMdFVRULhr1y4OHToE3AsmMzMz+euvvygtLcXS0pI33nhDI2iqTNeuXRkzZgy//fYbs2bNonv37jRs2JDIyEiaN2/OE088obEWwOjRozl8+DD//e9/iYqKom3btmRmZnLp0iXGjRtXZRAwYsQIvv/+e0Bz8K9SaGgoq1atomXLlrRp0wYzMzMyMjJU144hF1MT4p9EAgAhRL1nbm7OkiVLOHz4MEePHuXq1avEx8fTuHFjHBwcGDNmDP379zdoGZTdJxQKBcePH68wXU0EAJMmTSIuLo5r166RkpIC3HsTMmLECMaOHVtjK6kaGRnxySefcOjQIQ4fPszVq1dJSkrCysoKd3d3Ro0aRa9evdSOcXZ25uWXXyYqKoq0tDRiY2MxMjKiWbNmDB06lBdeeEE1FeT93njjDSIjI0lKSiI5OZnS0lIaNWpE7969GTx4sNZZlGpCbGwssbGxqp/NzMxo0aIFgwcPZvz48RWu3lwRHx8fCgsL2bt3L0uWLGHZsmV06NBBNXNVRW8Tzp8/D9xb98DCwgIbGxuefvppunfvzqBBgyoch1GZGTNm4OjoyO+//87FixextbWlb9++TJ06lWXLlmmkb926NStXrmTLli1cvnyZM2fO0Lp1a9555x2GDx9eZQDQvXt34N64nKeeekprmnHjxtGsWTNiY2OJiYmhuLiYpk2b0r9/f1588UW1rmtCiIoZles7ObAQQgjxDzd69Gjs7e0rHWfxKE2ZMoXs7Gy2bt2qczeex80vv/yCv78/kydPxsfHp0bzVo5VmTVr1iPrCiZEXSZvAIQQQggtcnNzWbVqFXAvIND25uFROHHiBNnZ2Tg5Of1jb/4LCgr4448/MDExqbD7z8NYu3YtxcXFqmlQhRD3SAAghBBCaFFUVKRa18HT0/ORBwAbN24kMTFRtSpvVVPCPo4OHTpEVFQUMTExZGVlMXbs2BoZf6IUHBysmtpWCPH/SRcgIYQQog6aPn06OTk5uLi4MH78ePr06VPbRapxq1at4siRIzRq1Ih+/frxr3/9q0ZWtRZCVE4CACGEEEIIIeoRWQdACCGEEEKIekQCACGEEEIIIeoRCQCEEEIIIYSoRyQAEEIIIYQQoh6RAEAIIYQQQoh6RAIAIYQQQggh6hEJAIQQQgghhKhHJAAQQgghhBCiHpEAQAghhBBCiHrk/wHUO48cwqbDigAAAABJRU5ErkJggg==", + "text/plain": [ + "
            " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Normalize and remove outliers from each lightcurve individually before appending\n", + "for index, light_curves in enumerate(data):\n", + " # It may take some trial and error to find the right window size\n", + " data[index] = data[index].flatten(window_length=401).remove_nans()\n", + " data[index] = data[index].remove_outliers()\n", + "\n", + "# Stitch and plot the lightcurves\n", + "lc = data.stitch()\n", + "lc.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use NEA data to create the Phase Folded Light Curve" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
            " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "folded_data = lc.fold(period=17.68899400, epoch_time=0)\n", + "folded_data.scatter()\n", + "plt.show()\n", + "# Hmmm, even though we know the period is 17.68899400 days, \n", + "# the folded lightcurve doesn't look right. \n", + "# What if we try a different kind of plot?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the river plot for the system" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
            " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lc.plot_river(period=17.68899400)\n", + "plt.show()\n", + "\n", + "# Interesting! That dark band on the right side is the transit of the planet.\n", + "# You can see how it meanders back and forth through the phase of the transit,\n", + "# which is why the folded lightcurve didn't look right. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ExoCore/Exercise_Solutions/Utility_Software/Lightkurve/Exercise_3.ipynb b/ExoCore/Exercise_Solutions/Utility_Software/Lightkurve/Exercise_3.ipynb new file mode 100644 index 0000000..a9ff6bf --- /dev/null +++ b/ExoCore/Exercise_Solutions/Utility_Software/Lightkurve/Exercise_3.ipynb @@ -0,0 +1,164 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Periodograms to Model Stellar Rotation\n", + "\n", + "### Import `lightkurve`, `astropy`, and retrieve relevant data" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "import lightkurve as lk\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import astropy as ap\n", + "import astropy.units as u\n", + "\n", + "# Find and download all data for KIC 2157356 from Kepler\n", + "data = lk.search_lightcurve(\"KIC 2157356\", author=\"Kepler\").download_all()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clean Data: Detrend, Normalize, and remove NaNs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
            " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Normalize and remove outliers from each lightcurve individually before appending\n", + "for index, light_curves in enumerate(data):\n", + " # It may take some trial and error to find the right window size\n", + " data[index] = data[index].normalize()\n", + " data[index] = data[index].remove_outliers()\n", + "\n", + "# Stitch and plot the lightcurves. We can see a huge variance in the lightcurves, +/- 0.2!\n", + "lc = data.stitch()\n", + "lc.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use a periodogram to find the highest power peak" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
            " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "periodogram = lc.to_periodogram(oversample_factor=100, maximum_period=100)\n", + "periodogram.plot(view='period')\n", + "plt.xscale('log')\n", + "plt.show()\n", + "\n", + "# We can see a pretty clear spike!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate the periodogram model" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max period: 13.896599694142502 d\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
            " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Find that spike in period\n", + "max_period = periodogram.period_at_max_power\n", + "print(f\"Max period: {max_period}\")\n", + "# Create a model of the lightcurve using the frequency at the max power\n", + "lc_model = periodogram.model(time=lc.time, frequency=periodogram.frequency_at_max_power)\n", + "# Plot the model over the lightcurve\n", + "\n", + "ax = lc.plot()\n", + "lc_model.plot(ax=ax, ls='--', c='red')\n", + "\n", + "plt.show()\n", + "# This model fits the lightcurve pretty well! \n", + "# Can you remove the signal from the lightcurve, and reveal any other signals?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}