initial commit

README.md

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+# tandy-blog
+Data blog for [Tandy Center for Ocean Forecasting](https://www.bigelow.org/services/ocean-forecasting/)
+
+## General guidelines
+
+Follow this [general guide](https://quarto.org/docs/websites/website-blog.html)  New posts should follow [this guide](https://quarto.org/docs/websites/website-blog.html) which explains about making sub-directories in the `post/` directory. 
+
+### Post naming
+
+Please use the `YYYY-mm-dd_my_post_name` format.
+
+### To code or not to code?
+
+To keep things light weight please do **not** include code in your `index.qmd` unless it is pseudo-code.  If you want to have some code that will generate output to be inlcuded in you post, then please make a separate un-rendered file (like `script.R` or `script.py`).
+
+
+### An example post directory
+
+```
+<tandy-blog>/
+  posts/
+    2024-09-30_my_cool_post/
+      index.qmd   
+      script.R
+      some_small_data.csv.gz
+      some_image.png
+      another_image.png
+```
+
+
+## Categories
+
+Let's be judicious in our use of categories (tags really) to facilitate searching by keywords. 
+
+
+## Programming examples vs tutorials
+
+Tutorials probably belong elsewhere - like [here](https://github.com/BigelowLab/handytandy) - but brief examples are good for here. 

_freeze/posts/cefi-intro/index/execute-results/html.json

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+    "markdown": "---\ntitle: \"Intro to cefi\"\nformat: html\ndescription: \"Introduction to the cefi package\"\nauthor: \"Ben Tupper\"\ndate: \"2024-09-27\"\n---\n\n\nRecently we started investigating the relatively new [CEFI data portal](https://psl.noaa.gov/cefi_portal/) which looks to be a promising source of modeled environmental covariates suitable for ecological forecasting.  The services and datasets are not fully implemented yet, but we are looking ahead to when we might be able to roll these into our workflows. To that end we have created the [cefi](https://github.com/BigelowLab/cefi) R package. \n\nCEFI data is served via a [THREDDS](https://docs.unidata.ucar.edu/tds/current/userguide/index.html) and provides dynamic data access using [OPeNDAP](https://www.opendap.org/).  We have always leveraged the [ncdf4 R package](https://CRAN.R-project.org/package=ncdf4), but this time we decided to try our hand at Michael Sumner's [tidync R package](https://CRAN.R-project.org/package=tidync).  CEFI data elements are arrayed outputs from model runs (historical and forecast); to cast these as geospatial raster data we access a second \"static\" data element (also OPeNDAP). \n\nHere we show a single forecasted data product for the temperature of the sea floor in the Northwest Atlantic region in 1995.\n\n\n::: {.cell}\n\n:::\n\n\nHere we show the temperature of the bottom (C) for one month.  Then we extract the values for the complete forecast (12 months) at the location shown using the blue circle. Then we plot the forecasted temperatures at that location.  We are still learning the finer details of how to work with this data, but it looks promising.\n\n~[map](tob-map.png)\n~[timeseries](tob-timeseries.png)\n\n",
+    "supporting": [],
+    "filters": [
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_freeze/posts/post-with-code/index/execute-results/html.json

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+{
+  "hash": "642ea65437e65466944030df23b5d2fb",
+  "result": {
+    "markdown": "---\ntitle: \"Post With Code\"\nauthor: \"Harlow Malloc\"\ndate: \"2024-09-20\"\ncategories: [news, code, analysis]\nimage: \"image.jpg\"\n---\n\n\nThis is a post with executable code.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n1 + 1\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 2\n```\n:::\n:::\n",
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+    "includes": {},
+    "engineDependencies": {},
+    "preserve": {},
+    "postProcess": true
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+}

_freeze/site_libs/quarto-listing/quarto-listing.js

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+const kProgressiveAttr = "data-src";
+let categoriesLoaded = false;
+
+window.quartoListingCategory = (category) => {
+  if (categoriesLoaded) {
+    activateCategory(category);
+    setCategoryHash(category);
+  }
+};
+
+window["quarto-listing-loaded"] = () => {
+  // Process any existing hash
+  const hash = getHash();
+
+  if (hash) {
+    // If there is a category, switch to that
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+    // Paginate a specific listing
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+    }
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+
+  const listingIds = Object.keys(window["quarto-listings"]);
+  for (const listingId of listingIds) {
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+    const list = window["quarto-listings"][listingId];
+
+    // Update the handlers for pagination events
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+    // Render any visible items that need it
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+    // Whenever the list is updated, we also need to
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+  setHash(currentHash);
+}
+
+function getListingPageKey(listingId) {
+  return `${listingId}-page`;
+}
+
+function refreshPaginationHandlers(listingId) {
+  const listingEl = window.document.getElementById(listingId);
+  const paginationEls = listingEl.querySelectorAll(
+    ".pagination li.page-item:not(.disabled) .page.page-link"
+  );
+  for (const paginationEl of paginationEls) {
+    paginationEl.onclick = (sender) => {
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+      showPage(listingId, sender.target.getAttribute("data-i"));
+      return false;
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+}
+
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+        }
+        progressiveImg.removeAttribute(kProgressiveAttr);
+      }
+    }
+  }
+}
+
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+  // #name:value|name1:value1|name2:value2
+  const currentUrl = new URL(window.location);
+  const hashRaw = currentUrl.hash ? currentUrl.hash.slice(1) : undefined;
+  return parseHash(hashRaw);
+}
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+      if (vals.length === 2) {
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+}
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+}
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+function setHash(obj) {
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+  window.history.pushState(null, null, `#${hash}`);
+}
+
+function showPage(listingId, page) {
+  const list = window["quarto-listings"][listingId];
+  if (list) {
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+}
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+function activateCategory(category) {
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+  const activeEls = window.document.querySelectorAll(
+    ".quarto-listing-category .category.active"
+  );
+  for (const activeEl of activeEls) {
+    activeEl.classList.remove("active");
+  }
+
+  // Activate this category
+  const categoryEl = window.document.querySelector(
+    `.quarto-listing-category .category[data-category='${category}'`
+  );
+  if (categoryEl) {
+    categoryEl.classList.add("active");
+  }
+
+  // Filter the listings to this category
+  filterListingCategory(category);
+}
+
+function filterListingCategory(category) {
+  const listingIds = Object.keys(window["quarto-listings"]);
+  for (const listingId of listingIds) {
+    const list = window["quarto-listings"][listingId];
+    if (list) {
+      if (category === "") {
+        // resets the filter
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+            return categories.includes(category);
+          } else {
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+      }
+    }
+  }
+}

_quarto.yml

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+project:
+  type: website
+
+website:
+  title: "Tandy Center for Ocean Forecasting"
+  description: "Data science blog for the Tandy Center"
+  navbar:
+    right:
+      - about.qmd
+      - icon: github
+        href: https://github.com/bigelowlab/tandy-blog
+html:
+  theme: cosmo
+  css: styles.css