From 374825b1b441006456868fec7ce7419c7c1a29b4 Mon Sep 17 00:00:00 2001
From: ChitYanToe <chityantoeart@gmail.com>
Date: Wed, 22 Jul 2026 10:30:49 +0200
Subject: [PATCH 01/15] Create SWASH notebook

---
 docs/user_guide/examples/tutorial_swash.ipynb | 25 +++++++++++++++++++
 1 file changed, 25 insertions(+)
 create mode 100644 docs/user_guide/examples/tutorial_swash.ipynb

diff --git a/docs/user_guide/examples/tutorial_swash.ipynb b/docs/user_guide/examples/tutorial_swash.ipynb
new file mode 100644
index 000000000..764c2d1de
--- /dev/null
+++ b/docs/user_guide/examples/tutorial_swash.ipynb
@@ -0,0 +1,25 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "da6f7651",
+   "metadata": {},
+   "source": [
+    "# Tutorial for using SWASH data in Parcels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f087323",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0364fd7a1c04ade7c4a811566fefe78b44d11171 Mon Sep 17 00:00:00 2001
From: ChitYanToe <chityantoeart@gmail.com>
Date: Wed, 22 Jul 2026 15:52:44 +0200
Subject: [PATCH 02/15] swash_to_sgrid converter

Added the converter function to convert SWASH output files to sgrid data
---
 docs/user_guide/examples/tutorial_swash.ipynb | 316 +++++++++++++++++-
 src/parcels/convert.py                        | 124 +++++++
 src/parcels/tutorial.py                       |   4 +-
 3 files changed, 440 insertions(+), 4 deletions(-)

diff --git a/docs/user_guide/examples/tutorial_swash.ipynb b/docs/user_guide/examples/tutorial_swash.ipynb
index 764c2d1de..9a19f33d9 100644
--- a/docs/user_guide/examples/tutorial_swash.ipynb
+++ b/docs/user_guide/examples/tutorial_swash.ipynb
@@ -8,16 +8,328 @@
     "# Tutorial for using SWASH data in Parcels"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cf9f8ca5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "application/vnd.holoviews_load.v0+json": ""
+     },
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+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"dec8478e-adf3-47cb-a496-cb89b38f809e\":{\"version\":\"3.9.1\",\"title\":\"Bokeh Application\",\"config\":{\"type\":\"object\",\"name\":\"DocumentConfig\",\"id\":\"d4d2972a-01e0-4bed-91d8-072285877450\",\"attributes\":{\"notifications\":{\"type\":\"object\",\"name\":\"Notifications\",\"id\":\"39a8e2d5-1540-42fb-8dc4-368844b6c07f\"}}},\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"48eaae67-aca7-4ad9-8591-d742d6cc18b2\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"11fe8a9c-d3b6-4a48-936d-02f6f17119b5\",\"attributes\":{\"plot_id\":\"48eaae67-aca7-4ad9-8591-d742d6cc18b2\",\"comm_id\":\"e8a589c1822f40dd969946c3c08ee9c2\",\"client_comm_id\":\"fd22d8ab9e5d40e18a36bbcecc41447d\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"start\",\"kind\":\"Any\",\"default\":0},{\"name\":\"end\",\"kind\":\"Any\",\"default\":100},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"use_shadow_dom\",\"kind\":\"Any\",\"default\":true},{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"use_shadow_dom\",\"kind\":\"Any\",\"default\":true},{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"max_notifications\",\"kind\":\"Any\",\"default\":5},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas 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fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"holoviews.plotting.bokeh.raster.HoverModel\",\"properties\":[{\"name\":\"xy\",\"kind\":\"Any\",\"default\":null},{\"name\":\"data\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
+       "  var render_items = [{\"docid\":\"dec8478e-adf3-47cb-a496-cb89b38f809e\",\"roots\":{\"48eaae67-aca7-4ad9-8591-d742d6cc18b2\":\"dc520f81-7763-459a-881d-7270d655dec8\"},\"root_ids\":[\"48eaae67-aca7-4ad9-8591-d742d6cc18b2\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  async function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t  for (const child of root_el.children) {\n",
+       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
+       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
+       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
+       "\t  }\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(version);\n",
+       "    } else if (root.Bokeh.version === version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "48eaae67-aca7-4ad9-8591-d742d6cc18b2"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = false;\n  const version = '3.9.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = true;\n  const Bokeh = root.Bokeh;\n  const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n  const PN_RE = /^https:\\/\\/cdn\\.holoviz\\.org\\/panel\\/[^/]+\\/dist\\/panel/i;\n\n  // Set a timeout for this load but only if we are not already initializing\n  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      // Don't load bokeh if it is still initializing\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      // There is nothing to load\n      run_callbacks();\n      return null;\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error(e) {\n      const src_el = e.srcElement\n      console.error(\"failed to load \" + (src_el.href || src_el.src));\n    }\n\n    const skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    const existing_stylesheets = []\n    const links = document.getElementsByTagName('link')\n    for (let i = 0; i < links.length; i++) {\n      const link = links[i]\n      if (link.href != null) {\n        existing_stylesheets.push(link.href)\n      }\n    }\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const escaped = encodeURI(url)\n      if (existing_stylesheets.indexOf(escaped) !== -1) {\n        on_load()\n        continue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    const scripts = document.getElementsByTagName('script')\n    for (let i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n        existing_scripts.push(script.src)\n      }\n    }\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const escaped = encodeURI(url)\n      const shouldSkip = skip.includes(escaped) || existing_scripts.includes(escaped)\n      const isBokehOrPanel = BK_RE.test(escaped) || PN_RE.test(escaped)\n      const missingOrBroken = Bokeh == null || Bokeh.Panel == null || (Bokeh.version != version && !Bokeh.versions?.has(version)) || Bokeh.versions?.get(version)?.Panel == null;\n      if (shouldSkip && !(isBokehOrPanel && missingOrBroken)) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (let i = 0; i < js_modules.length; i++) {\n      const url = js_modules[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      const url = js_exports[name];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.holoviz.org/panel/1.9.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n  const js_modules = [];\n  const js_exports = {};\n  const css_urls = [];\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (let i = 0; i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false;\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true;\n      root._bokeh_onload_callbacks = [];\n      const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || (Bokeh.versions?.has(version) && Bokeh.versions.get(version)?.Panel));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n        if (Bokeh != undefined && !reloading) {\n          const NewBokeh = root.Bokeh;\n          if (Bokeh.versions === undefined) {\n            Bokeh.versions = new Map();\n          }\n          if (NewBokeh.version !== Bokeh.version) {\n            Bokeh[NewBokeh.version] = NewBokeh;\n            Bokeh.versions.set(NewBokeh.version, NewBokeh);\n          }\n          root.Bokeh = Bokeh;\n        }\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        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handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/2v/5l455kn13xq41s2x_zl1chlr0000gn/T/ipykernel_82001/1470123736.py:7: UserWarning: This is an alpha version of Parcels v4. The API is not stable and may change without deprecation warnings.\n",
+      "  import parcels\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.tri as mtri\n",
+    "import numpy as np\n",
+    "import uxarray as ux\n",
+    "import xarray as xr\n",
+    "\n",
+    "import parcels\n",
+    "import parcels.tutorial\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1a044f75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/YanTo001/Library/Caches/parcels/data-matlab/F1GRD.mat\n"
+     ]
+    }
+   ],
+   "source": [
+    "mat_files = parcels.tutorial.open_dataset(\n",
+    "    \"SWASH_data/data\", download_only=True\n",
+    ")\n",
+    "print(mat_files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "81c491ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid_file = \"/Users/YanTo001/Documents/GitHub/parcels-data/data-matlab/F1GRD.mat\"#\"/Users/YanTo001/Library/Caches/parcels/data-matlab/F1GRD.mat\"\n",
+    "result_file = \"/Users/YanTo001/Documents/GitHub/parcels-data/data-matlab/F1ALL.mat\"#\"/Users/YanTo001/Library/Caches/parcels/data-matlab/F1ALL.mat\"\n",
+    "ds = parcels.convert.swash_to_sgrid(grid_file, result_file, total_depth=8.0)\n",
+    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7fd5d3d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Output files are stored in output-quickstart.parquet\n",
+      "\n",
+      "\u001b[A\n",
+      "\u001b[A\n",
+      "\u001b[A\n",
+      "\u001b[A\n",
+      "\u001b[A\n",
+      "Integration time: 2026-06-01T00:00:15 100%|██████████| [00:00<00:00, 1151.08it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "npart = 150  # number of particles to be released\n",
+    "# release particles in a line along a meridian\n",
+    "lat = np.linspace(1, 16, npart) ## y\n",
+    "lon = np.repeat(15, npart) ## x\n",
+    "time = np.repeat(ds.time.values[0], npart)\n",
+    "layerI = 0 # at which water depth, the particles are released\n",
+    "z = np.repeat(ds.depth.values[layerI], npart)\n",
+    "pset = parcels.ParticleSet(fieldset=fieldset, pclass=parcels.Particle, t=time, x=lon, y=lat, z=z)\n",
+    "kernels = [parcels.kernels.AdvectionRK2]\n",
+    "\n",
+    "output_file = parcels.ParticleFile(\"output-quickstart.parquet\", outputdt=np.timedelta64(5, \"s\"), mode=\"w\") # \n",
+    "pset.execute(\n",
+    "    kernels,\n",
+    "    runtime=np.timedelta64(20, \"s\"),\n",
+    "    dt=np.timedelta64(5, \"s\"),\n",
+    "    output_file=output_file,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7933d20b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x339156e90>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = parcels.read_particlefile(\"output-quickstart.parquet\")\n",
+    "waterlevel = ds.isel(time=2).watlev.plot(cmap=\"magma\")\n",
+    "scatter = plt.scatter(df['x'], df['y'], c=df['t'], s=10)\n",
+    "plt.scatter(df['x'][:npart], df['y'][:npart], facecolors=\"none\", edgecolors='r', s=10) # starting positions"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "6f087323",
+   "id": "d91ddeec",
    "metadata": {},
    "source": []
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Parcels:default (3.14.6)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.6"
   }
  },
  "nbformat": 4,
diff --git a/src/parcels/convert.py b/src/parcels/convert.py
index c8cea9ce3..ea2d403de 100644
--- a/src/parcels/convert.py
+++ b/src/parcels/convert.py
@@ -567,6 +567,130 @@ def copernicusmarine_to_sgrid(
     return ds
 
 
+def swash_to_sgrid(coord_file, data_file, total_depth) -> xr.Dataset:
+    """Create an sgrid-compliant xarray.Dataset from a dataset of SWASH netcdf files.
+    """
+    import scipy.io as sio
+    import re
+    import parcels._sgrid as sgrid 
+    import pandas as pd
+    ## First load the coordinates and data files (SWASH output - matlab binary format)
+    coord = sio.loadmat(coord_file)
+    x = coord["Xp"][0, :]
+    y = coord["Yp"][:, 0]
+    bot = coord["Botlev"]
+
+    mat = sio.loadmat(data_file)
+    keys = [k for k in mat.keys() if not k.startswith("__")]
+
+    time_keys = sorted(set(
+        (int(m.group(1)), int(m.group(2)))
+        for k in keys
+        for m in [re.search(r"_(\d{6})_(\d{3})$", k)] if m
+    ))
+    times = [t[0] + t[1] / 1000 for t in time_keys]
+
+    n_layers = max(
+        int(re.search(r"Vksi_k(\d+)_", k).group(1))
+        for k in keys if re.search(r"Vksi_k(\d+)_", k)
+    )
+    n_w_layers = n_layers
+
+    nx, ny, nt = len(x), len(y), len(times)
+
+    watlev = np.full((nt, ny, nx), np.nan, dtype=np.float32)
+    vksi = np.full((nt, n_layers, ny, nx), np.nan, dtype=np.float32)
+    veta = np.full((nt, n_layers, ny, nx), np.nan, dtype=np.float32)
+    w = np.full((nt, n_w_layers, ny, nx), np.nan, dtype=np.float32)
+
+    for ti, (ts_int, ts_dec) in enumerate(time_keys):
+        ts_str = f"{ts_int:06d}_{ts_dec:03d}"
+        for k in keys:
+            if re.match(rf"Watlev_{ts_str}$", k):
+                watlev[ti, :, :] = mat[k]
+            m = re.match(rf"Vksi_k(\d+)_{ts_str}$", k)
+            if m:
+                vksi[ti, int(m.group(1)) - 1, :, :] = mat[k]
+            m = re.match(rf"Veta_k(\d+)_{ts_str}$", k)
+            if m:
+                veta[ti, int(m.group(1)) - 1, :, :] = mat[k]
+            m = re.match(rf"w(\d+)_{ts_str}$", k)
+            if m and int(m.group(1)) < n_w_layers:
+                w[ti, int(m.group(1)), :, :] = mat[k]
+
+    t0 = pd.Timestamp("2026-06-01 00:00:00")
+    time_dt = np.array([t0 + pd.to_timedelta(t, unit="s") for t in times], dtype="datetime64[ns]")
+
+    ds = xr.Dataset(
+        {
+            "watlev": (["time", "y", "x"], watlev),
+            "U": (["time", "depth", "y", "x"], vksi),
+            "V": (["time", "depth", "y", "x"], veta),
+            "W": (["time", "depth_f", "y", "x"], w),
+            "botlev": (["y", "x"], bot),
+        },
+        coords={
+            "time": time_dt,
+            "depth": np.arange(1, n_layers + 1),
+            "depth_f": np.arange(0, n_w_layers),
+            "y": y,
+            "x": x,
+        },
+    )
+    ds.attrs.update(source="SWASH version 11.01ABC", project="progWave", run="A14", Conventions="CF-1.8")
+
+    n_layers = ds.sizes["depth"]
+    n_layers_f = ds.sizes["depth_f"]
+    if n_layers_f != n_layers:
+        raise ValueError(
+            f"Expected depth_f to match depth in length (got {n_layers_f} vs {n_layers})"
+        )
+
+    depth_centers = np.array(
+        [total_depth * (i - 0.5) / n_layers for i in range(1, n_layers + 1)], dtype=np.float32
+    )
+    depth_interfaces = np.array(
+        [total_depth * i / n_layers for i in range(n_layers_f)], dtype=np.float32
+    )
+
+    # Rename x/y -> lon/lat: Parcels' from_sgrid_conventions expects these names
+    # literally, even on a flat/Cartesian mesh (units stay in meters).
+    ds = ds.rename({"x": "lon", "y": "lat"})
+
+    ds = ds.assign_coords({
+        "depth": ("depth", depth_centers),
+        "depth_f": ("depth_f", depth_interfaces),
+    })
+
+    ds["time"].attrs.update(axis="T")
+    ds["lon"].attrs.update(axis="X", units="m")
+    ds["lat"].attrs.update(axis="Y", units="m")
+    ds["depth"].attrs.update(axis="Z", units="m", positive="down")
+    ds["depth_f"].attrs.update(axis="Z", units="m", positive="down")
+
+    if "grid" in ds.cf.cf_roles:
+        raise ValueError("Dataset already has a 'grid' variable (cf_role grid_topology).")
+
+    ds["grid"] = xr.DataArray(
+        0,
+        attrs=sgrid.SGrid2DMetadata(
+            cf_role="grid_topology",
+            topology_dimension=2,
+            node_dimensions=("lon", "lat"),
+            node_coordinates=("lon", "lat"),
+            face_dimensions=(
+                sgrid.FaceNodePadding("lon", "lon", sgrid.Padding.NONE),
+                sgrid.FaceNodePadding("lat", "lat", sgrid.Padding.NONE),
+            ),
+            vertical_dimensions=(
+                sgrid.FaceNodePadding("depth", "depth_f", sgrid.Padding.LOW),
+            ),
+        ).to_attrs(),
+    )
+        
+    return ds
+
+
 # Known vertical dimension mappings by model
 _FESOM2_VERTICAL_DIMS = {"interface": "nz", "center": "nz1"}
 _ICON_VERTICAL_DIMS = {"interface": "depth_2", "center": "depth"}
diff --git a/src/parcels/tutorial.py b/src/parcels/tutorial.py
index 49a4a8b6b..99d4f3df6 100644
--- a/src/parcels/tutorial.py
+++ b/src/parcels/tutorial.py
@@ -17,7 +17,7 @@ def list_datasets() -> list[str]:
     return _list_remote_datasets(purpose="tutorial")
 
 
-def open_dataset(name: str):
+def open_dataset(name: str, download_only=False):
     """Download and open a tutorial dataset as an :class:`xarray.Dataset`.
 
     Use :func:`list_datasets` to see the available dataset names.
@@ -33,4 +33,4 @@ def open_dataset(name: str):
     xarray.Dataset
         The requested dataset.
     """
-    return _open_remote_dataset(name, purpose="tutorial")
+    return _open_remote_dataset(name, purpose="tutorial", download_only=download_only)

From 4e1a79cdbd6a408ce20dbf280fb8aeb740dc3694 Mon Sep 17 00:00:00 2001
From: ChitYanToe <chityantoeart@gmail.com>
Date: Wed, 22 Jul 2026 17:42:28 +0200
Subject: [PATCH 03/15] Fixing SWASH tutorial

1. Fixed the SWASH data files download in remote.py
2. Changed the source path of the SWASH files in the tutorial
---
 docs/user_guide/examples/tutorial_swash.ipynb | 47 +++++++++----------
 src/parcels/_datasets/remote.py               | 29 ++++++++++--
 2 files changed, 46 insertions(+), 30 deletions(-)

diff --git a/docs/user_guide/examples/tutorial_swash.ipynb b/docs/user_guide/examples/tutorial_swash.ipynb
index 9a19f33d9..bfb81a423 100644
--- a/docs/user_guide/examples/tutorial_swash.ipynb
+++ b/docs/user_guide/examples/tutorial_swash.ipynb
@@ -58,12 +58,12 @@
      "data": {
       "application/vnd.holoviews_exec.v0+json": "",
       "text/html": [
-       "<div id='48eaae67-aca7-4ad9-8591-d742d6cc18b2'>\n",
-       "  <div id=\"dc520f81-7763-459a-881d-7270d655dec8\" data-root-id=\"48eaae67-aca7-4ad9-8591-d742d6cc18b2\" style=\"display: contents;\"></div>\n",
+       "<div id='f0dc6f77-9e34-4bee-aeaa-a69cb9197888'>\n",
+       "  <div id=\"f6676d58-63b2-407c-b2de-94b50925e248\" data-root-id=\"f0dc6f77-9e34-4bee-aeaa-a69cb9197888\" style=\"display: contents;\"></div>\n",
        "</div>\n",
        "<script type=\"application/javascript\">(function(root) {\n",
-       "  var docs_json = {\"dec8478e-adf3-47cb-a496-cb89b38f809e\":{\"version\":\"3.9.1\",\"title\":\"Bokeh Application\",\"config\":{\"type\":\"object\",\"name\":\"DocumentConfig\",\"id\":\"d4d2972a-01e0-4bed-91d8-072285877450\",\"attributes\":{\"notifications\":{\"type\":\"object\",\"name\":\"Notifications\",\"id\":\"39a8e2d5-1540-42fb-8dc4-368844b6c07f\"}}},\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"48eaae67-aca7-4ad9-8591-d742d6cc18b2\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"11fe8a9c-d3b6-4a48-936d-02f6f17119b5\",\"attributes\":{\"plot_id\":\"48eaae67-aca7-4ad9-8591-d742d6cc18b2\",\"comm_id\":\"e8a589c1822f40dd969946c3c08ee9c2\",\"client_comm_id\":\"fd22d8ab9e5d40e18a36bbcecc41447d\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"start\",\"kind\":\"Any\",\"default\":0},{\"name\":\"end\",\"kind\":\"Any\",\"default\":100},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"use_shadow_dom\",\"kind\":\"Any\",\"default\":true},{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"use_shadow_dom\",\"kind\":\"Any\",\"default\":true},{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"max_notifications\",\"kind\":\"Any\",\"default\":5},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"holoviews.plotting.bokeh.raster.HoverModel\",\"properties\":[{\"name\":\"xy\",\"kind\":\"Any\",\"default\":null},{\"name\":\"data\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
-       "  var render_items = [{\"docid\":\"dec8478e-adf3-47cb-a496-cb89b38f809e\",\"roots\":{\"48eaae67-aca7-4ad9-8591-d742d6cc18b2\":\"dc520f81-7763-459a-881d-7270d655dec8\"},\"root_ids\":[\"48eaae67-aca7-4ad9-8591-d742d6cc18b2\"]}];\n",
+       "  var docs_json = {\"9ce97755-6797-40f4-86a7-112f2609f6f5\":{\"version\":\"3.9.1\",\"title\":\"Bokeh Application\",\"config\":{\"type\":\"object\",\"name\":\"DocumentConfig\",\"id\":\"26006365-f5b0-4303-ba36-c088f5526031\",\"attributes\":{\"notifications\":{\"type\":\"object\",\"name\":\"Notifications\",\"id\":\"f1d8dfc2-5608-44d2-b707-cfbfe43ed285\"}}},\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"f0dc6f77-9e34-4bee-aeaa-a69cb9197888\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"e1dd11cc-39b6-458a-bde2-a63c90fa6f9e\",\"attributes\":{\"plot_id\":\"f0dc6f77-9e34-4bee-aeaa-a69cb9197888\",\"comm_id\":\"e697199c749d40e4a0c2e9bc7070bd3c\",\"client_comm_id\":\"a67cac75a81c4d9a8648d94d22dc0821\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"start\",\"kind\":\"Any\",\"default\":0},{\"name\":\"end\",\"kind\":\"Any\",\"default\":100},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"use_shadow_dom\",\"kind\":\"Any\",\"default\":true},{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"use_shadow_dom\",\"kind\":\"Any\",\"default\":true},{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"max_notifications\",\"kind\":\"Any\",\"default\":5},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"holoviews.plotting.bokeh.raster.HoverModel\",\"properties\":[{\"name\":\"xy\",\"kind\":\"Any\",\"default\":null},{\"name\":\"data\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
+       "  var render_items = [{\"docid\":\"9ce97755-6797-40f4-86a7-112f2609f6f5\",\"roots\":{\"f0dc6f77-9e34-4bee-aeaa-a69cb9197888\":\"f6676d58-63b2-407c-b2de-94b50925e248\"},\"root_ids\":[\"f0dc6f77-9e34-4bee-aeaa-a69cb9197888\"]}];\n",
        "  var docs = Object.values(docs_json)\n",
        "  if (!docs) {\n",
        "    return\n",
@@ -132,7 +132,7 @@
      },
      "metadata": {
       "application/vnd.holoviews_exec.v0+json": {
-       "id": "48eaae67-aca7-4ad9-8591-d742d6cc18b2"
+       "id": "f0dc6f77-9e34-4bee-aeaa-a69cb9197888"
       }
      },
      "output_type": "display_data"
@@ -181,7 +181,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/2v/5l455kn13xq41s2x_zl1chlr0000gn/T/ipykernel_82001/1470123736.py:7: UserWarning: This is an alpha version of Parcels v4. The API is not stable and may change without deprecation warnings.\n",
+      "/var/folders/2v/5l455kn13xq41s2x_zl1chlr0000gn/T/ipykernel_87891/208841806.py:7: UserWarning: This is an alpha version of Parcels v4. The API is not stable and may change without deprecation warnings.\n",
       "  import parcels\n"
      ]
     }
@@ -194,7 +194,8 @@
     "import xarray as xr\n",
     "\n",
     "import parcels\n",
-    "import parcels.tutorial\n"
+    "import parcels.tutorial\n",
+    "import glob\n"
    ]
   },
   {
@@ -207,33 +208,35 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/Users/YanTo001/Library/Caches/parcels/data-matlab/F1GRD.mat\n"
+      "['/Users/YanTo001/Library/Caches/parcels/data-matlab/F1ALL.mat', '/Users/YanTo001/Library/Caches/parcels/data-matlab/F1GRD.mat']\n"
      ]
     }
    ],
    "source": [
-    "mat_files = parcels.tutorial.open_dataset(\n",
+    "grid_file = parcels.tutorial.open_dataset(\n",
     "    \"SWASH_data/data\", download_only=True\n",
     ")\n",
-    "print(mat_files)"
+    "print(grid_file)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "81c491ae",
    "metadata": {},
    "outputs": [],
    "source": [
-    "grid_file = \"/Users/YanTo001/Documents/GitHub/parcels-data/data-matlab/F1GRD.mat\"#\"/Users/YanTo001/Library/Caches/parcels/data-matlab/F1GRD.mat\"\n",
-    "result_file = \"/Users/YanTo001/Documents/GitHub/parcels-data/data-matlab/F1ALL.mat\"#\"/Users/YanTo001/Library/Caches/parcels/data-matlab/F1ALL.mat\"\n",
-    "ds = parcels.convert.swash_to_sgrid(grid_file, result_file, total_depth=8.0)\n",
+    "# grid_file = \"/Users/YanTo001/Documents/GitHub/parcels-data/data-matlab/F1GRD.mat\"#\"/Users/YanTo001/Library/Caches/parcels/data-matlab/F1GRD.mat\"\n",
+    "# result_file = \"/Users/YanTo001/Documents/GitHub/parcels-data/data-matlab/F1ALL.mat\"#\"/Users/YanTo001/Library/Caches/parcels/data-matlab/F1ALL.mat\"\n",
+    "# ds = parcels.convert.swash_to_sgrid(grid_file, result_file, total_depth=8.0)\n",
+    "ds = parcels.convert.swash_to_sgrid(grid_file[1], grid_file[0], total_depth=8.0)\n",
+    "\n",
     "fieldset = parcels.FieldSet.from_sgrid_conventions(ds)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 5,
    "id": "7fd5d3d1",
    "metadata": {},
    "outputs": [
@@ -242,13 +245,7 @@
      "output_type": "stream",
      "text": [
       "INFO: Output files are stored in output-quickstart.parquet\n",
-      "\n",
-      "\u001b[A\n",
-      "\u001b[A\n",
-      "\u001b[A\n",
-      "\u001b[A\n",
-      "\u001b[A\n",
-      "Integration time: 2026-06-01T00:00:15 100%|██████████| [00:00<00:00, 1151.08it/s]\n"
+      "Integration time: 2026-06-01T00:00:15 100%|██████████| [00:00<00:00, 909.13it/s]\n"
      ]
     }
    ],
@@ -274,17 +271,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 6,
    "id": "7933d20b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x339156e90>"
+       "<matplotlib.collections.PathCollection at 0x337210a50>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
diff --git a/src/parcels/_datasets/remote.py b/src/parcels/_datasets/remote.py
index 5fc93868a..6ac760b3c 100644
--- a/src/parcels/_datasets/remote.py
+++ b/src/parcels/_datasets/remote.py
@@ -1,5 +1,6 @@
 import abc
 import enum
+import fnmatch
 import os
 from collections.abc import Callable
 from pathlib import Path
@@ -107,6 +108,10 @@ def _get_data_home() -> Path:
         "data/SWASH_data/field_0065552.nc",
         "data/SWASH_data/field_0065557.nc",
     ]
+    + [
+        "data-matlab/F1ALL.mat",
+        "data-matlab/F1GRD.mat",
+    ]
     # + [f"data/WOA_data/woa18_decav_t{m:02d}_04.nc" for m in range(1, 13)]
     + ["data/CROCOidealized_data/CROCO_idealized.nc"]
     # These datasets are from v4 of Parcels where we're opting for Zipped zarr datasets
@@ -145,8 +150,15 @@ def __init__(self, pup: pooch.Pooch, path_relative_to_pup: str, pre_decode_cf_ca
         first, second, *_ = path_relative_to_pup.split("/")
         self.v3_dataset_name = f"{first}/{second}"  # e.g., data/my_dataset
 
-    def open_dataset(self) -> xr.Dataset:
+    def open_dataset(self, download_only=False) -> xr.Dataset:
         self.download_relevant_files()
+        if download_only: ## Chit Yan Toe
+            import glob
+            matches = sorted(glob.glob(f"{self.pup.path}/{self.path_relative_to_root}"))
+            return matches if len(matches) != 1 else matches[0]
+        # if download_only:
+        #     return f"{self.pup.path}/{self.path_relative_to_root}"
+        
         with xr.set_options(use_new_combine_kwarg_defaults=True):
             ds = xr.open_mfdataset(
                 f"{self.pup.path}/{self.path_relative_to_root}",
@@ -163,9 +175,15 @@ def open_dataset(self) -> xr.Dataset:
         ds = xr.decode_cf(ds)
         return ds
 
-    def download_relevant_files(self) -> None:
+    # def download_relevant_files(self) -> None:
+    #     for file in self.pup.registry:
+    #         if self.v3_dataset_name in file:
+    #             self.pup.fetch(file)
+    #     return
+
+    def download_relevant_files(self) -> None: # Chit Yan Toe 
         for file in self.pup.registry:
-            if self.v3_dataset_name in file:
+            if fnmatch.fnmatch(file, self.path_relative_to_root):
                 self.pup.fetch(file)
         return
 
@@ -240,6 +258,7 @@ class _Purpose(enum.Enum):
     # ("SWASH_data/data", (_V3Dataset(_ODIE,"data/SWASH_data/field_00655*.nc"), _Purpose.TUTORIAL)),
     # ("WOA_data/data", (_V3Dataset(_ODIE,"data/WOA_data/woa18_decav_t*_04.nc", _preprocess_set_cf_calendar_360_day), _Purpose.TUTORIAL)),
     ("CROCOidealized_data/data", (_V3Dataset(_ODIE,"data/CROCOidealized_data/CROCO_idealized.nc"), _Purpose.TUTORIAL)),
+    ("SWASH_data/data", (_V3Dataset(_ODIE,"data-matlab/F1*.mat"), _Purpose.TUTORIAL)),
 ] + [
     ("Benchmarks_FESOM2-baroclinic-gyre/data", (_ZarrZipDataset(_ODIE, 'data-zarr/Benchmarks_FESOM2-baroclinic-gyre/data.zip', zarr_format=2), _Purpose.TESTING)),
     ("Benchmarks_FESOM2-baroclinic-gyre/grid", (_ZarrZipDataset(_ODIE, 'data-zarr/Benchmarks_FESOM2-baroclinic-gyre/grid.zip', zarr_format=2),_Purpose.TESTING)),
@@ -275,7 +294,7 @@ def list_remote_datasets(purpose: _TPurpose | Literal["any"] = "any") -> list[st
     return [k for (k, (_, p)) in _DATASET_KEYS_AND_CONFIGS.items() if p == purpose_enum]
 
 
-def open_remote_dataset(name: str, purpose: _TPurpose | Literal["any"] = "any"):
+def open_remote_dataset(name: str, purpose: _TPurpose | Literal["any"] = "any", download_only=False):
     """Download and open a remote dataset as an :class:`xarray.Dataset`.
 
     Use :func:`list_datasets` to see the available dataset names.
@@ -300,4 +319,4 @@ def open_remote_dataset(name: str, purpose: _TPurpose | Literal["any"] = "any"):
         )
 
     dataset_config = _DATASET_KEYS_AND_CONFIGS[name][0]
-    return dataset_config.open_dataset()
+    return dataset_config.open_dataset(download_only=download_only)

From 2fc891800eb105efc9af68df505b4085e70751c6 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 23 Jul 2026 08:04:29 +0000
Subject: [PATCH 04/15] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 docs/user_guide/examples/tutorial_swash.ipynb | 275 +++---------------
 src/parcels/_datasets/remote.py               |   7 +-
 src/parcels/convert.py                        |  51 ++--
 3 files changed, 60 insertions(+), 273 deletions(-)

diff --git a/docs/user_guide/examples/tutorial_swash.ipynb b/docs/user_guide/examples/tutorial_swash.ipynb
index bfb81a423..73a2a6e0b 100644
--- a/docs/user_guide/examples/tutorial_swash.ipynb
+++ b/docs/user_guide/examples/tutorial_swash.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "da6f7651",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Tutorial for using SWASH data in Parcels"
@@ -10,183 +10,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "cf9f8ca5",
+   "execution_count": null,
+   "id": "1",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
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-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(\n",
-       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
-       "),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
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-     "metadata": {},
-     "output_type": "display_data"
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sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== 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output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = 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handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/2v/5l455kn13xq41s2x_zl1chlr0000gn/T/ipykernel_87891/208841806.py:7: UserWarning: This is an alpha version of Parcels v4. The API is not stable and may change without deprecation warnings.\n",
-      "  import parcels\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "import glob\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.tri as mtri\n",
     "import numpy as np\n",
@@ -194,35 +24,24 @@
     "import xarray as xr\n",
     "\n",
     "import parcels\n",
-    "import parcels.tutorial\n",
-    "import glob\n"
+    "import parcels.tutorial"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "1a044f75",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['/Users/YanTo001/Library/Caches/parcels/data-matlab/F1ALL.mat', '/Users/YanTo001/Library/Caches/parcels/data-matlab/F1GRD.mat']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "grid_file = parcels.tutorial.open_dataset(\n",
-    "    \"SWASH_data/data\", download_only=True\n",
-    ")\n",
-    "print(grid_file)\n"
+    "grid_file = parcels.tutorial.open_dataset(\"SWASH_data/data\", download_only=True)\n",
+    "print(grid_file)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "81c491ae",
+   "execution_count": null,
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,36 +50,31 @@
     "# ds = parcels.convert.swash_to_sgrid(grid_file, result_file, total_depth=8.0)\n",
     "ds = parcels.convert.swash_to_sgrid(grid_file[1], grid_file[0], total_depth=8.0)\n",
     "\n",
-    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds)\n"
+    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "7fd5d3d1",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Output files are stored in output-quickstart.parquet\n",
-      "Integration time: 2026-06-01T00:00:15 100%|██████████| [00:00<00:00, 909.13it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "npart = 150  # number of particles to be released\n",
     "# release particles in a line along a meridian\n",
-    "lat = np.linspace(1, 16, npart) ## y\n",
-    "lon = np.repeat(15, npart) ## x\n",
+    "lat = np.linspace(1, 16, npart)  ## y\n",
+    "lon = np.repeat(15, npart)  ## x\n",
     "time = np.repeat(ds.time.values[0], npart)\n",
-    "layerI = 0 # at which water depth, the particles are released\n",
+    "layerI = 0  # at which water depth, the particles are released\n",
     "z = np.repeat(ds.depth.values[layerI], npart)\n",
-    "pset = parcels.ParticleSet(fieldset=fieldset, pclass=parcels.Particle, t=time, x=lon, y=lat, z=z)\n",
+    "pset = parcels.ParticleSet(\n",
+    "    fieldset=fieldset, pclass=parcels.Particle, t=time, x=lon, y=lat, z=z\n",
+    ")\n",
     "kernels = [parcels.kernels.AdvectionRK2]\n",
     "\n",
-    "output_file = parcels.ParticleFile(\"output-quickstart.parquet\", outputdt=np.timedelta64(5, \"s\"), mode=\"w\") # \n",
+    "output_file = parcels.ParticleFile(\n",
+    "    \"output-quickstart.parquet\", outputdt=np.timedelta64(5, \"s\"), mode=\"w\"\n",
+    ")  #\n",
     "pset.execute(\n",
     "    kernels,\n",
     "    runtime=np.timedelta64(20, \"s\"),\n",
@@ -271,41 +85,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "7933d20b",
+   "execution_count": null,
+   "id": "5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x337210a50>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df = parcels.read_particlefile(\"output-quickstart.parquet\")\n",
     "waterlevel = ds.isel(time=2).watlev.plot(cmap=\"magma\")\n",
-    "scatter = plt.scatter(df['x'], df['y'], c=df['t'], s=10)\n",
-    "plt.scatter(df['x'][:npart], df['y'][:npart], facecolors=\"none\", edgecolors='r', s=10) # starting positions"
+    "scatter = plt.scatter(df[\"x\"], df[\"y\"], c=df[\"t\"], s=10)\n",
+    "plt.scatter(\n",
+    "    df[\"x\"][:npart], df[\"y\"][:npart], facecolors=\"none\", edgecolors=\"r\", s=10\n",
+    ")  # starting positions"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d91ddeec",
+   "id": "6",
    "metadata": {},
    "source": []
   }
diff --git a/src/parcels/_datasets/remote.py b/src/parcels/_datasets/remote.py
index 6ac760b3c..9a8b461fd 100644
--- a/src/parcels/_datasets/remote.py
+++ b/src/parcels/_datasets/remote.py
@@ -152,13 +152,14 @@ def __init__(self, pup: pooch.Pooch, path_relative_to_pup: str, pre_decode_cf_ca
 
     def open_dataset(self, download_only=False) -> xr.Dataset:
         self.download_relevant_files()
-        if download_only: ## Chit Yan Toe
+        if download_only:  ## Chit Yan Toe
             import glob
+
             matches = sorted(glob.glob(f"{self.pup.path}/{self.path_relative_to_root}"))
             return matches if len(matches) != 1 else matches[0]
         # if download_only:
         #     return f"{self.pup.path}/{self.path_relative_to_root}"
-        
+
         with xr.set_options(use_new_combine_kwarg_defaults=True):
             ds = xr.open_mfdataset(
                 f"{self.pup.path}/{self.path_relative_to_root}",
@@ -181,7 +182,7 @@ def open_dataset(self, download_only=False) -> xr.Dataset:
     #             self.pup.fetch(file)
     #     return
 
-    def download_relevant_files(self) -> None: # Chit Yan Toe 
+    def download_relevant_files(self) -> None:  # Chit Yan Toe
         for file in self.pup.registry:
             if fnmatch.fnmatch(file, self.path_relative_to_root):
                 self.pup.fetch(file)
diff --git a/src/parcels/convert.py b/src/parcels/convert.py
index ea2d403de..3703ef0b1 100644
--- a/src/parcels/convert.py
+++ b/src/parcels/convert.py
@@ -568,12 +568,14 @@ def copernicusmarine_to_sgrid(
 
 
 def swash_to_sgrid(coord_file, data_file, total_depth) -> xr.Dataset:
-    """Create an sgrid-compliant xarray.Dataset from a dataset of SWASH netcdf files.
-    """
-    import scipy.io as sio
+    """Create an sgrid-compliant xarray.Dataset from a dataset of SWASH netcdf files."""
     import re
-    import parcels._sgrid as sgrid 
+
     import pandas as pd
+    import scipy.io as sio
+
+    import parcels._sgrid as sgrid
+
     ## First load the coordinates and data files (SWASH output - matlab binary format)
     coord = sio.loadmat(coord_file)
     x = coord["Xp"][0, :]
@@ -583,17 +585,12 @@ def swash_to_sgrid(coord_file, data_file, total_depth) -> xr.Dataset:
     mat = sio.loadmat(data_file)
     keys = [k for k in mat.keys() if not k.startswith("__")]
 
-    time_keys = sorted(set(
-        (int(m.group(1)), int(m.group(2)))
-        for k in keys
-        for m in [re.search(r"_(\d{6})_(\d{3})$", k)] if m
-    ))
+    time_keys = sorted(
+        set((int(m.group(1)), int(m.group(2))) for k in keys for m in [re.search(r"_(\d{6})_(\d{3})$", k)] if m)
+    )
     times = [t[0] + t[1] / 1000 for t in time_keys]
 
-    n_layers = max(
-        int(re.search(r"Vksi_k(\d+)_", k).group(1))
-        for k in keys if re.search(r"Vksi_k(\d+)_", k)
-    )
+    n_layers = max(int(re.search(r"Vksi_k(\d+)_", k).group(1)) for k in keys if re.search(r"Vksi_k(\d+)_", k))
     n_w_layers = n_layers
 
     nx, ny, nt = len(x), len(y), len(times)
@@ -642,25 +639,21 @@ def swash_to_sgrid(coord_file, data_file, total_depth) -> xr.Dataset:
     n_layers = ds.sizes["depth"]
     n_layers_f = ds.sizes["depth_f"]
     if n_layers_f != n_layers:
-        raise ValueError(
-            f"Expected depth_f to match depth in length (got {n_layers_f} vs {n_layers})"
-        )
+        raise ValueError(f"Expected depth_f to match depth in length (got {n_layers_f} vs {n_layers})")
 
-    depth_centers = np.array(
-        [total_depth * (i - 0.5) / n_layers for i in range(1, n_layers + 1)], dtype=np.float32
-    )
-    depth_interfaces = np.array(
-        [total_depth * i / n_layers for i in range(n_layers_f)], dtype=np.float32
-    )
+    depth_centers = np.array([total_depth * (i - 0.5) / n_layers for i in range(1, n_layers + 1)], dtype=np.float32)
+    depth_interfaces = np.array([total_depth * i / n_layers for i in range(n_layers_f)], dtype=np.float32)
 
     # Rename x/y -> lon/lat: Parcels' from_sgrid_conventions expects these names
     # literally, even on a flat/Cartesian mesh (units stay in meters).
     ds = ds.rename({"x": "lon", "y": "lat"})
 
-    ds = ds.assign_coords({
-        "depth": ("depth", depth_centers),
-        "depth_f": ("depth_f", depth_interfaces),
-    })
+    ds = ds.assign_coords(
+        {
+            "depth": ("depth", depth_centers),
+            "depth_f": ("depth_f", depth_interfaces),
+        }
+    )
 
     ds["time"].attrs.update(axis="T")
     ds["lon"].attrs.update(axis="X", units="m")
@@ -682,12 +675,10 @@ def swash_to_sgrid(coord_file, data_file, total_depth) -> xr.Dataset:
                 sgrid.FaceNodePadding("lon", "lon", sgrid.Padding.NONE),
                 sgrid.FaceNodePadding("lat", "lat", sgrid.Padding.NONE),
             ),
-            vertical_dimensions=(
-                sgrid.FaceNodePadding("depth", "depth_f", sgrid.Padding.LOW),
-            ),
+            vertical_dimensions=(sgrid.FaceNodePadding("depth", "depth_f", sgrid.Padding.LOW),),
         ).to_attrs(),
     )
-        
+
     return ds
 
 

From 32e3927396105c5531cfc81295692201bbd3b5e6 Mon Sep 17 00:00:00 2001
From: Erik van Sebille <erikvansebille@gmail.com>
Date: Thu, 23 Jul 2026 11:18:39 +0200
Subject: [PATCH 05/15] Cleaning up remote.py

---
 src/parcels/_datasets/remote.py | 15 +++------------
 1 file changed, 3 insertions(+), 12 deletions(-)

diff --git a/src/parcels/_datasets/remote.py b/src/parcels/_datasets/remote.py
index 9a8b461fd..14b9a390b 100644
--- a/src/parcels/_datasets/remote.py
+++ b/src/parcels/_datasets/remote.py
@@ -1,6 +1,7 @@
 import abc
 import enum
 import fnmatch
+import glob
 import os
 from collections.abc import Callable
 from pathlib import Path
@@ -152,13 +153,9 @@ def __init__(self, pup: pooch.Pooch, path_relative_to_pup: str, pre_decode_cf_ca
 
     def open_dataset(self, download_only=False) -> xr.Dataset:
         self.download_relevant_files()
-        if download_only:  ## Chit Yan Toe
-            import glob
-
+        if download_only:
             matches = sorted(glob.glob(f"{self.pup.path}/{self.path_relative_to_root}"))
             return matches if len(matches) != 1 else matches[0]
-        # if download_only:
-        #     return f"{self.pup.path}/{self.path_relative_to_root}"
 
         with xr.set_options(use_new_combine_kwarg_defaults=True):
             ds = xr.open_mfdataset(
@@ -176,13 +173,7 @@ def open_dataset(self, download_only=False) -> xr.Dataset:
         ds = xr.decode_cf(ds)
         return ds
 
-    # def download_relevant_files(self) -> None:
-    #     for file in self.pup.registry:
-    #         if self.v3_dataset_name in file:
-    #             self.pup.fetch(file)
-    #     return
-
-    def download_relevant_files(self) -> None:  # Chit Yan Toe
+    def download_relevant_files(self) -> None:
         for file in self.pup.registry:
             if fnmatch.fnmatch(file, self.path_relative_to_root):
                 self.pup.fetch(file)

From 3328238d56b2a1f9c2dbbcd4aa3e71582f3939ec Mon Sep 17 00:00:00 2001
From: Erik van Sebille <erikvansebille@gmail.com>
Date: Thu, 23 Jul 2026 11:41:29 +0200
Subject: [PATCH 06/15] Simplifying swash tutorial code

---
 docs/user_guide/examples/tutorial_swash.ipynb | 61 ++++++++-----------
 docs/user_guide/index.md                      |  1 +
 2 files changed, 28 insertions(+), 34 deletions(-)

diff --git a/docs/user_guide/examples/tutorial_swash.ipynb b/docs/user_guide/examples/tutorial_swash.ipynb
index 73a2a6e0b..b7905ddb1 100644
--- a/docs/user_guide/examples/tutorial_swash.ipynb
+++ b/docs/user_guide/examples/tutorial_swash.ipynb
@@ -5,7 +5,7 @@
    "id": "0",
    "metadata": {},
    "source": [
-    "# Tutorial for using SWASH data in Parcels"
+    "# 🖥️ SWASH tutorial"
    ]
   },
   {
@@ -15,13 +15,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import glob\n",
-    "\n",
     "import matplotlib.pyplot as plt\n",
-    "import matplotlib.tri as mtri\n",
     "import numpy as np\n",
-    "import uxarray as ux\n",
-    "import xarray as xr\n",
     "\n",
     "import parcels\n",
     "import parcels.tutorial"
@@ -34,8 +29,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "grid_file = parcels.tutorial.open_dataset(\"SWASH_data/data\", download_only=True)\n",
-    "print(grid_file)"
+    "swash_files = parcels.tutorial.open_dataset(\"SWASH_data/data\", download_only=True)"
    ]
   },
   {
@@ -45,12 +39,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# grid_file = \"/Users/YanTo001/Documents/GitHub/parcels-data/data-matlab/F1GRD.mat\"#\"/Users/YanTo001/Library/Caches/parcels/data-matlab/F1GRD.mat\"\n",
-    "# result_file = \"/Users/YanTo001/Documents/GitHub/parcels-data/data-matlab/F1ALL.mat\"#\"/Users/YanTo001/Library/Caches/parcels/data-matlab/F1ALL.mat\"\n",
-    "# ds = parcels.convert.swash_to_sgrid(grid_file, result_file, total_depth=8.0)\n",
-    "ds = parcels.convert.swash_to_sgrid(grid_file[1], grid_file[0], total_depth=8.0)\n",
-    "\n",
-    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds)"
+    "ds = parcels.convert.swash_to_sgrid(\n",
+    "    data_file=swash_files[0], coord_file=swash_files[1], total_depth=8.0\n",
+    ")\n",
+    "fieldset = parcels.FieldSet.from_sgrid_conventions(ds)\n",
+    "fieldset.describe()"
    ]
   },
   {
@@ -60,26 +53,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "npart = 150  # number of particles to be released\n",
-    "# release particles in a line along a meridian\n",
-    "lat = np.linspace(1, 16, npart)  ## y\n",
-    "lon = np.repeat(15, npart)  ## x\n",
-    "time = np.repeat(ds.time.values[0], npart)\n",
+    "npart = 10  # number of particles to be released\n",
+    "y = np.linspace(1, 16, npart)\n",
+    "x = np.repeat(15, npart)\n",
+    "\n",
     "layerI = 0  # at which water depth, the particles are released\n",
     "z = np.repeat(ds.depth.values[layerI], npart)\n",
-    "pset = parcels.ParticleSet(\n",
-    "    fieldset=fieldset, pclass=parcels.Particle, t=time, x=lon, y=lat, z=z\n",
-    ")\n",
-    "kernels = [parcels.kernels.AdvectionRK2]\n",
+    "pset = parcels.ParticleSet(fieldset, x=x, y=y, z=z)\n",
     "\n",
     "output_file = parcels.ParticleFile(\n",
-    "    \"output-quickstart.parquet\", outputdt=np.timedelta64(5, \"s\"), mode=\"w\"\n",
-    ")  #\n",
+    "    \"output-swash.parquet\", outputdt=np.timedelta64(5, \"s\"), mode=\"w\"\n",
+    ")\n",
     "pset.execute(\n",
-    "    kernels,\n",
+    "    parcels.kernels.AdvectionRK2,\n",
     "    runtime=np.timedelta64(20, \"s\"),\n",
-    "    dt=np.timedelta64(5, \"s\"),\n",
+    "    dt=np.timedelta64(1, \"s\"),\n",
     "    output_file=output_file,\n",
+    "    verbose_progress=False,\n",
     ")"
    ]
   },
@@ -90,12 +80,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = parcels.read_particlefile(\"output-quickstart.parquet\")\n",
-    "waterlevel = ds.isel(time=2).watlev.plot(cmap=\"magma\")\n",
-    "scatter = plt.scatter(df[\"x\"], df[\"y\"], c=df[\"t\"], s=10)\n",
-    "plt.scatter(\n",
-    "    df[\"x\"][:npart], df[\"y\"][:npart], facecolors=\"none\", edgecolors=\"r\", s=10\n",
-    ")  # starting positions"
+    "df = parcels.read_particlefile(\"output-swash.parquet\")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 4))\n",
+    "waterlevel = ds.isel(time=2).watlev.plot(cmap=\"magma\", ax=ax)\n",
+    "for traj in df.partition_by(\"particle_id\"):\n",
+    "    ax.plot(traj[\"x\"][0], traj[\"y\"][0], \"wo\", markersize=5)\n",
+    "    ax.plot(traj[\"x\"], traj[\"y\"], color=\"k\")\n",
+    "ax.set_xlim([14, 16])\n",
+    "plt.show()"
    ]
   },
   {
@@ -107,7 +100,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Parcels:default (3.14.6)",
+   "display_name": "Parcels:docs (3.14.6)",
    "language": "python",
    "name": "python3"
   },
diff --git a/docs/user_guide/index.md b/docs/user_guide/index.md
index 06a578d79..703f72cb4 100644
--- a/docs/user_guide/index.md
+++ b/docs/user_guide/index.md
@@ -40,6 +40,7 @@ examples/explanation_grids.md
 examples/tutorial_nemo.ipynb
 examples/tutorial_croco_3D.ipynb
 examples/tutorial_mitgcm.ipynb
+examples/tutorial_swash.ipynb
 examples/tutorial_fesom.ipynb
 examples/tutorial_schism.ipynb
 examples/tutorial_velocityconversion.ipynb

From 106e926dd612aa25d42f70dcb07d3bf78ff88914 Mon Sep 17 00:00:00 2001
From: Erik van Sebille <erikvansebille@gmail.com>
Date: Thu, 23 Jul 2026 11:48:03 +0200
Subject: [PATCH 07/15] Add text to swash notebook

---
 docs/user_guide/examples/tutorial_swash.ipynb | 55 ++++++++++++++-----
 1 file changed, 42 insertions(+), 13 deletions(-)

diff --git a/docs/user_guide/examples/tutorial_swash.ipynb b/docs/user_guide/examples/tutorial_swash.ipynb
index b7905ddb1..a34fdf04c 100644
--- a/docs/user_guide/examples/tutorial_swash.ipynb
+++ b/docs/user_guide/examples/tutorial_swash.ipynb
@@ -8,10 +8,18 @@
     "# 🖥️ SWASH tutorial"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "This tutorial shows how to load in native Matlab files from the SWASH model and convert them to a Parcels-compatible `FieldSet`. The tutorial also shows how to run a simple particle tracking simulation using the SWASH data."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,31 +33,50 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
-    "swash_files = parcels.tutorial.open_dataset(\"SWASH_data/data\", download_only=True)"
+    "data_file, coord_file = parcels.tutorial.open_dataset(\n",
+    "    \"SWASH_data/data\", download_only=True\n",
+    ")\n",
+    "print(data_file, coord_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "Unlike the other tutorials, this tutorial uses the original SWASH output files in Matlab format. If you want to use your own SWASH output files, you should use the `GRD.mat` and `ALL.mat` files in the `parcels.convert.swash_to_sgrid()` function below."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
     "ds = parcels.convert.swash_to_sgrid(\n",
-    "    data_file=swash_files[0], coord_file=swash_files[1], total_depth=8.0\n",
+    "    data_file=data_file, coord_file=coord_file, total_depth=8.0\n",
     ")\n",
     "fieldset = parcels.FieldSet.from_sgrid_conventions(ds)\n",
     "fieldset.describe()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {},
+   "source": [
+    "Now, we can use this `FieldSet` to run a simulation (note it's very short because the dataset provided in this tutorial is only 20 seconds long)."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,10 +100,18 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {},
+   "source": [
+    "And then plot the results of the simulation, along with the water level at a given time step. The starting positions of the particles are also shown in white."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,12 +125,6 @@
     "ax.set_xlim([14, 16])\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {

From 8e31d4f289b13b2f93f96db2e34ffcc79c858a97 Mon Sep 17 00:00:00 2001
From: Erik van Sebille <erikvansebille@gmail.com>
Date: Thu, 23 Jul 2026 12:00:27 +0200
Subject: [PATCH 08/15] Fix download_only also in remote/Zarr

---
 src/parcels/_datasets/remote.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/parcels/_datasets/remote.py b/src/parcels/_datasets/remote.py
index 14b9a390b..82d768d0f 100644
--- a/src/parcels/_datasets/remote.py
+++ b/src/parcels/_datasets/remote.py
@@ -186,8 +186,10 @@ def __init__(self, pup, path_relative_to_pup, zarr_format: Literal[2, 3] = 3):
         self.path_relative_to_root = path_relative_to_pup
         self.zarr_format = zarr_format
 
-    def open_dataset(self) -> xr.Dataset:
+    def open_dataset(self, download_only=False) -> xr.Dataset:
         self.pup.fetch(self.path_relative_to_root)
+        if download_only:
+            raise ValueError("download_only is not supported for zarr datasets.")
         return xr.open_zarr(ZipStore(Path(self.pup.path) / self.path_relative_to_root), zarr_format=self.zarr_format)
 
 

From ccde7e252a85a44ac5dd130bd7c45427342b998f Mon Sep 17 00:00:00 2001
From: Erik van Sebille <erikvansebille@gmail.com>
Date: Thu, 23 Jul 2026 12:01:12 +0200
Subject: [PATCH 09/15] Adding unit test for convert_swash

---
 tests/test_convert.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/tests/test_convert.py b/tests/test_convert.py
index 6a05bc960..f17cdd81e 100644
--- a/tests/test_convert.py
+++ b/tests/test_convert.py
@@ -174,6 +174,13 @@ def test_convert_copernicusmarine_no_logs(ds, caplog):
     assert caplog.text == ""
 
 
+def test_convert_swash():
+    data_file, coord_file = parcels.tutorial.open_dataset("SWASH_data/data", download_only=True)
+
+    ds_fset = convert.swash_to_sgrid(data_file=data_file, coord_file=coord_file, total_depth=8.0)
+    FieldSet.from_sgrid_conventions(ds_fset)
+
+
 def test_convert_fesom_to_ugrid():
     grid_file = open_remote_dataset("Benchmarks_FESOM2-baroclinic-gyre/grid")
     data_files = open_remote_dataset("Benchmarks_FESOM2-baroclinic-gyre/data")

From 004cd9801e8181338c442280ae796ffd8fcb0e12 Mon Sep 17 00:00:00 2001
From: Erik van Sebille <erikvansebille@gmail.com>
Date: Thu, 23 Jul 2026 13:07:19 +0200
Subject: [PATCH 10/15] Cleaning up SWASH convert

---
 src/parcels/convert.py | 94 +++++++++++++++++-------------------------
 1 file changed, 38 insertions(+), 56 deletions(-)

diff --git a/src/parcels/convert.py b/src/parcels/convert.py
index 3703ef0b1..4bc958ed4 100644
--- a/src/parcels/convert.py
+++ b/src/parcels/convert.py
@@ -13,11 +13,13 @@
 from __future__ import annotations
 
 import enum
+import re
 import typing
 import warnings
 from typing import cast
 
 import numpy as np
+import scipy.io as sio
 import xarray as xr
 
 import parcels._sgrid as sgrid
@@ -567,16 +569,23 @@ def copernicusmarine_to_sgrid(
     return ds
 
 
-def swash_to_sgrid(coord_file, data_file, total_depth) -> xr.Dataset:
-    """Create an sgrid-compliant xarray.Dataset from a dataset of SWASH netcdf files."""
-    import re
+def swash_to_sgrid(data_file: str, coord_file: str, total_depth: float) -> xr.Dataset:
+    """Create an sgrid-compliant xarray.Dataset from a dataset of SWASH netcdf files.
 
-    import pandas as pd
-    import scipy.io as sio
-
-    import parcels._sgrid as sgrid
+    Parameters
+    ----------
+    data_file : str
+        Path to the SWASH data file (MATLAB binary format).
+    coord_file : str
+        Path to the SWASH coordinate file (MATLAB binary format).
+    total_depth : float
+        Total depth of the water column.
 
-    ## First load the coordinates and data files (SWASH output - matlab binary format)
+    Returns
+    -------
+    xarray.Dataset
+        Dataset object following SGRID conventions to be (optionally) modified and passed to a FieldSet constructor.
+    """
     coord = sio.loadmat(coord_file)
     x = coord["Xp"][0, :]
     y = coord["Yp"][:, 0]
@@ -588,17 +597,18 @@ def swash_to_sgrid(coord_file, data_file, total_depth) -> xr.Dataset:
     time_keys = sorted(
         set((int(m.group(1)), int(m.group(2))) for k in keys for m in [re.search(r"_(\d{6})_(\d{3})$", k)] if m)
     )
-    times = [t[0] + t[1] / 1000 for t in time_keys]
+    times = np.array([t[0] * 1000 + t[1] for t in time_keys]).astype("timedelta64[ms]")
 
     n_layers = max(int(re.search(r"Vksi_k(\d+)_", k).group(1)) for k in keys if re.search(r"Vksi_k(\d+)_", k))
-    n_w_layers = n_layers
-
-    nx, ny, nt = len(x), len(y), len(times)
+    n_layers_f = n_layers + 1
+    depth_centers = np.array([total_depth * (i - 0.5) / n_layers for i in range(1, n_layers + 1)], dtype=np.float32)
+    depth_interfaces = np.array([total_depth * i / n_layers for i in range(n_layers_f)], dtype=np.float32)
 
+    nt, ny, nx = len(times), len(y), len(x)
     watlev = np.full((nt, ny, nx), np.nan, dtype=np.float32)
     vksi = np.full((nt, n_layers, ny, nx), np.nan, dtype=np.float32)
     veta = np.full((nt, n_layers, ny, nx), np.nan, dtype=np.float32)
-    w = np.full((nt, n_w_layers, ny, nx), np.nan, dtype=np.float32)
+    w = np.full((nt, n_layers_f, ny, nx), np.nan, dtype=np.float32)
 
     for ti, (ts_int, ts_dec) in enumerate(time_keys):
         ts_str = f"{ts_int:06d}_{ts_dec:03d}"
@@ -612,57 +622,29 @@ def swash_to_sgrid(coord_file, data_file, total_depth) -> xr.Dataset:
             if m:
                 veta[ti, int(m.group(1)) - 1, :, :] = mat[k]
             m = re.match(rf"w(\d+)_{ts_str}$", k)
-            if m and int(m.group(1)) < n_w_layers:
+            if m and int(m.group(1)) < n_layers:
                 w[ti, int(m.group(1)), :, :] = mat[k]
 
-    t0 = pd.Timestamp("2026-06-01 00:00:00")
-    time_dt = np.array([t0 + pd.to_timedelta(t, unit="s") for t in times], dtype="datetime64[ns]")
-
     ds = xr.Dataset(
         {
-            "watlev": (["time", "y", "x"], watlev),
-            "U": (["time", "depth", "y", "x"], vksi),
-            "V": (["time", "depth", "y", "x"], veta),
-            "W": (["time", "depth_f", "y", "x"], w),
-            "botlev": (["y", "x"], bot),
+            "watlev": (["time", "lat", "lon"], watlev),
+            "U": (["time", "depth", "lat", "lon"], vksi),
+            "V": (["time", "depth", "lat", "lon"], veta),
+            "W": (["time", "depth_f", "lat", "lon"], w),
+            "botlev": (["lat", "lon"], bot),
         },
         coords={
-            "time": time_dt,
-            "depth": np.arange(1, n_layers + 1),
-            "depth_f": np.arange(0, n_w_layers),
-            "y": y,
-            "x": x,
+            "time": (["time"], times, {"axis": "T", "units": "ms"}),
+            "depth": (["depth"], depth_centers, {"axis": "Z", "units": "m", "positive": "down"}),
+            "depth_f": (["depth_f"], depth_interfaces, {"axis": "Z", "units": "m", "positive": "down"}),
+            "lat": (["lat"], y, {"axis": "Y", "units": "m"}),
+            "lon": (["lon"], x, {"axis": "X", "units": "m"}),
         },
     )
-    ds.attrs.update(source="SWASH version 11.01ABC", project="progWave", run="A14", Conventions="CF-1.8")
-
-    n_layers = ds.sizes["depth"]
-    n_layers_f = ds.sizes["depth_f"]
-    if n_layers_f != n_layers:
-        raise ValueError(f"Expected depth_f to match depth in length (got {n_layers_f} vs {n_layers})")
-
-    depth_centers = np.array([total_depth * (i - 0.5) / n_layers for i in range(1, n_layers + 1)], dtype=np.float32)
-    depth_interfaces = np.array([total_depth * i / n_layers for i in range(n_layers_f)], dtype=np.float32)
-
-    # Rename x/y -> lon/lat: Parcels' from_sgrid_conventions expects these names
-    # literally, even on a flat/Cartesian mesh (units stay in meters).
-    ds = ds.rename({"x": "lon", "y": "lat"})
-
-    ds = ds.assign_coords(
-        {
-            "depth": ("depth", depth_centers),
-            "depth_f": ("depth_f", depth_interfaces),
-        }
-    )
-
-    ds["time"].attrs.update(axis="T")
-    ds["lon"].attrs.update(axis="X", units="m")
-    ds["lat"].attrs.update(axis="Y", units="m")
-    ds["depth"].attrs.update(axis="Z", units="m", positive="down")
-    ds["depth_f"].attrs.update(axis="Z", units="m", positive="down")
-
-    if "grid" in ds.cf.cf_roles:
-        raise ValueError("Dataset already has a 'grid' variable (cf_role grid_topology).")
+    header = mat["__header__"]
+    if isinstance(header, bytes):
+        header = header.decode("utf-8")
+    ds.attrs.update(header=header, version=mat["__version__"], globals=mat["__globals__"])
 
     ds["grid"] = xr.DataArray(
         0,

From 31f918006c3054f8b46736f0f0b9062c796c203e Mon Sep 17 00:00:00 2001
From: Erik van Sebille <erikvansebille@gmail.com>
Date: Thu, 23 Jul 2026 13:20:53 +0200
Subject: [PATCH 11/15] Separating dowload_only into new get_dataset_files call

---
 docs/user_guide/examples/tutorial_swash.ipynb |  4 +-
 src/parcels/_datasets/remote.py               | 45 +++++++++++++++----
 src/parcels/tutorial.py                       | 24 +++++++++-
 tests/test_convert.py                         |  2 +-
 4 files changed, 60 insertions(+), 15 deletions(-)

diff --git a/docs/user_guide/examples/tutorial_swash.ipynb b/docs/user_guide/examples/tutorial_swash.ipynb
index a34fdf04c..9b3685060 100644
--- a/docs/user_guide/examples/tutorial_swash.ipynb
+++ b/docs/user_guide/examples/tutorial_swash.ipynb
@@ -37,9 +37,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_file, coord_file = parcels.tutorial.open_dataset(\n",
-    "    \"SWASH_data/data\", download_only=True\n",
-    ")\n",
+    "data_file, coord_file = parcels.tutorial.get_dataset_files(\"SWASH_data/data\")\n",
     "print(data_file, coord_file)"
    ]
   },
diff --git a/src/parcels/_datasets/remote.py b/src/parcels/_datasets/remote.py
index 82d768d0f..194f626ad 100644
--- a/src/parcels/_datasets/remote.py
+++ b/src/parcels/_datasets/remote.py
@@ -151,12 +151,13 @@ def __init__(self, pup: pooch.Pooch, path_relative_to_pup: str, pre_decode_cf_ca
         first, second, *_ = path_relative_to_pup.split("/")
         self.v3_dataset_name = f"{first}/{second}"  # e.g., data/my_dataset
 
-    def open_dataset(self, download_only=False) -> xr.Dataset:
+    def get_dataset_files(self) -> list[str]:
         self.download_relevant_files()
-        if download_only:
-            matches = sorted(glob.glob(f"{self.pup.path}/{self.path_relative_to_root}"))
-            return matches if len(matches) != 1 else matches[0]
+        matches = sorted(glob.glob(f"{self.pup.path}/{self.path_relative_to_root}"))
+        return matches
 
+    def open_dataset(self) -> xr.Dataset:
+        self.download_relevant_files()
         with xr.set_options(use_new_combine_kwarg_defaults=True):
             ds = xr.open_mfdataset(
                 f"{self.pup.path}/{self.path_relative_to_root}",
@@ -186,10 +187,8 @@ def __init__(self, pup, path_relative_to_pup, zarr_format: Literal[2, 3] = 3):
         self.path_relative_to_root = path_relative_to_pup
         self.zarr_format = zarr_format
 
-    def open_dataset(self, download_only=False) -> xr.Dataset:
+    def open_dataset(self) -> xr.Dataset:
         self.pup.fetch(self.path_relative_to_root)
-        if download_only:
-            raise ValueError("download_only is not supported for zarr datasets.")
         return xr.open_zarr(ZipStore(Path(self.pup.path) / self.path_relative_to_root), zarr_format=self.zarr_format)
 
 
@@ -288,7 +287,7 @@ def list_remote_datasets(purpose: _TPurpose | Literal["any"] = "any") -> list[st
     return [k for (k, (_, p)) in _DATASET_KEYS_AND_CONFIGS.items() if p == purpose_enum]
 
 
-def open_remote_dataset(name: str, purpose: _TPurpose | Literal["any"] = "any", download_only=False):
+def open_remote_dataset(name: str, purpose: _TPurpose | Literal["any"] = "any"):
     """Download and open a remote dataset as an :class:`xarray.Dataset`.
 
     Use :func:`list_datasets` to see the available dataset names.
@@ -313,4 +312,32 @@ def open_remote_dataset(name: str, purpose: _TPurpose | Literal["any"] = "any",
         )
 
     dataset_config = _DATASET_KEYS_AND_CONFIGS[name][0]
-    return dataset_config.open_dataset(download_only=download_only)
+    return dataset_config.open_dataset()
+
+
+def get_remote_dataset(name: str, purpose: _TPurpose | Literal["any"] = "any") -> list[str]:
+    """Download the files of a remote dataset.
+
+    Use :func:`list_datasets` to see the available dataset names.
+
+    Parameters
+    ----------
+    name : str
+        Name of the dataset to open. Must be one of the keys returned by
+        :func:`list_datasets`.
+    purpose : {'any', 'testing', 'tutorial'}, optional
+        Purpose filter used to populate the error message when ``name`` is not
+        found. Defaults to ``'any'``.
+
+    Returns
+    -------
+    list of str
+        The list of dataset files.
+    """
+    if name not in list_remote_datasets(purpose=purpose):
+        raise ValueError(
+            f"Dataset {name!r} not found. Available datasets are: " + ", ".join(list_remote_datasets(purpose=purpose))
+        )
+
+    dataset_config = _DATASET_KEYS_AND_CONFIGS[name][0]
+    return dataset_config.get_dataset_files()
diff --git a/src/parcels/tutorial.py b/src/parcels/tutorial.py
index 99d4f3df6..7a5466edf 100644
--- a/src/parcels/tutorial.py
+++ b/src/parcels/tutorial.py
@@ -1,3 +1,4 @@
+from parcels._datasets.remote import get_remote_dataset as _get_remote_dataset
 from parcels._datasets.remote import list_remote_datasets as _list_remote_datasets
 from parcels._datasets.remote import open_remote_dataset as _open_remote_dataset
 
@@ -17,7 +18,7 @@ def list_datasets() -> list[str]:
     return _list_remote_datasets(purpose="tutorial")
 
 
-def open_dataset(name: str, download_only=False):
+def open_dataset(name: str):
     """Download and open a tutorial dataset as an :class:`xarray.Dataset`.
 
     Use :func:`list_datasets` to see the available dataset names.
@@ -33,4 +34,23 @@ def open_dataset(name: str, download_only=False):
     xarray.Dataset
         The requested dataset.
     """
-    return _open_remote_dataset(name, purpose="tutorial", download_only=download_only)
+    return _open_remote_dataset(name, purpose="tutorial")
+
+
+def get_dataset_files(name: str):
+    """Download the files of a tutorial dataset.
+
+    Use :func:`list_datasets` to see the available dataset names.
+
+    Parameters
+    ----------
+    name : str
+        Name of the dataset to open. Must be one of the keys returned by
+        :func:`list_datasets`.
+
+    Returns
+    -------
+    list of str
+        The list of dataset files.
+    """
+    return _get_remote_dataset(name, purpose="tutorial")
diff --git a/tests/test_convert.py b/tests/test_convert.py
index f17cdd81e..8486420fb 100644
--- a/tests/test_convert.py
+++ b/tests/test_convert.py
@@ -175,7 +175,7 @@ def test_convert_copernicusmarine_no_logs(ds, caplog):
 
 
 def test_convert_swash():
-    data_file, coord_file = parcels.tutorial.open_dataset("SWASH_data/data", download_only=True)
+    data_file, coord_file = parcels.tutorial.get_dataset_files("SWASH_data/data")
 
     ds_fset = convert.swash_to_sgrid(data_file=data_file, coord_file=coord_file, total_depth=8.0)
     FieldSet.from_sgrid_conventions(ds_fset)

From fd6396142a30e329a7faf48091a0354a26b49aca Mon Sep 17 00:00:00 2001
From: Erik van Sebille <erikvansebille@gmail.com>
Date: Thu, 23 Jul 2026 15:50:18 +0200
Subject: [PATCH 12/15] Adding get_dataset_files for other classes

---
 src/parcels/_datasets/remote.py | 8 ++++++++
 src/parcels/convert.py          | 6 +++++-
 2 files changed, 13 insertions(+), 1 deletion(-)

diff --git a/src/parcels/_datasets/remote.py b/src/parcels/_datasets/remote.py
index 194f626ad..b62003621 100644
--- a/src/parcels/_datasets/remote.py
+++ b/src/parcels/_datasets/remote.py
@@ -136,6 +136,9 @@ def _get_data_home() -> Path:
 
 
 class _ParcelsDataset(abc.ABC):
+    @abc.abstractmethod
+    def get_dataset_files(self) -> list[str]: ...
+
     @abc.abstractmethod
     def open_dataset(self) -> xr.Dataset: ...
 
@@ -187,6 +190,11 @@ def __init__(self, pup, path_relative_to_pup, zarr_format: Literal[2, 3] = 3):
         self.path_relative_to_root = path_relative_to_pup
         self.zarr_format = zarr_format
 
+    def get_dataset_files(self) -> list[str]:
+        raise NotImplementedError(
+            "get_dataset_files is not supported for zipped zarr datasets. Use open_dataset instead."
+        )
+
     def open_dataset(self) -> xr.Dataset:
         self.pup.fetch(self.path_relative_to_root)
         return xr.open_zarr(ZipStore(Path(self.pup.path) / self.path_relative_to_root), zarr_format=self.zarr_format)
diff --git a/src/parcels/convert.py b/src/parcels/convert.py
index 4bc958ed4..6b8a25ba8 100644
--- a/src/parcels/convert.py
+++ b/src/parcels/convert.py
@@ -599,7 +599,11 @@ def swash_to_sgrid(data_file: str, coord_file: str, total_depth: float) -> xr.Da
     )
     times = np.array([t[0] * 1000 + t[1] for t in time_keys]).astype("timedelta64[ms]")
 
-    n_layers = max(int(re.search(r"Vksi_k(\d+)_", k).group(1)) for k in keys if re.search(r"Vksi_k(\d+)_", k))
+    layer_indices = []
+    for key in keys:
+        match = re.search(r"Vksi_k(\d+)_", key)
+        layer_indices.append(int(match.group(1)))
+    n_layers = max(layer_indices)
     n_layers_f = n_layers + 1
     depth_centers = np.array([total_depth * (i - 0.5) / n_layers for i in range(1, n_layers + 1)], dtype=np.float32)
     depth_interfaces = np.array([total_depth * i / n_layers for i in range(n_layers_f)], dtype=np.float32)

From 38dceec67a85bd5bdfd299f7404404554b67099f Mon Sep 17 00:00:00 2001
From: Erik van Sebille <erikvansebille@gmail.com>
Date: Thu, 23 Jul 2026 16:11:56 +0200
Subject: [PATCH 13/15] Using C-Grid(?) implementation of SWASH

---
 docs/user_guide/examples/tutorial_swash.ipynb |  2 +-
 src/parcels/convert.py                        | 38 ++++++++++---------
 2 files changed, 21 insertions(+), 19 deletions(-)

diff --git a/docs/user_guide/examples/tutorial_swash.ipynb b/docs/user_guide/examples/tutorial_swash.ipynb
index 9b3685060..467f89868 100644
--- a/docs/user_guide/examples/tutorial_swash.ipynb
+++ b/docs/user_guide/examples/tutorial_swash.ipynb
@@ -116,7 +116,7 @@
     "df = parcels.read_particlefile(\"output-swash.parquet\")\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(8, 4))\n",
-    "waterlevel = ds.isel(time=2).watlev.plot(cmap=\"magma\", ax=ax)\n",
+    "waterlevel = ds.isel(time=2).watlev.plot(x=\"lon\", y=\"lat\", cmap=\"magma\", ax=ax)\n",
     "for traj in df.partition_by(\"particle_id\"):\n",
     "    ax.plot(traj[\"x\"][0], traj[\"y\"][0], \"wo\", markersize=5)\n",
     "    ax.plot(traj[\"x\"], traj[\"y\"], color=\"k\")\n",
diff --git a/src/parcels/convert.py b/src/parcels/convert.py
index 6b8a25ba8..752943be2 100644
--- a/src/parcels/convert.py
+++ b/src/parcels/convert.py
@@ -587,8 +587,10 @@ def swash_to_sgrid(data_file: str, coord_file: str, total_depth: float) -> xr.Da
         Dataset object following SGRID conventions to be (optionally) modified and passed to a FieldSet constructor.
     """
     coord = sio.loadmat(coord_file)
-    x = coord["Xp"][0, :]
-    y = coord["Yp"][:, 0]
+    lon = coord["Xp"]
+    lat = coord["Yp"]
+    XG = np.arange(lon.shape[1])
+    YG = np.arange(lat.shape[0])
     bot = coord["Botlev"]
 
     mat = sio.loadmat(data_file)
@@ -599,16 +601,12 @@ def swash_to_sgrid(data_file: str, coord_file: str, total_depth: float) -> xr.Da
     )
     times = np.array([t[0] * 1000 + t[1] for t in time_keys]).astype("timedelta64[ms]")
 
-    layer_indices = []
-    for key in keys:
-        match = re.search(r"Vksi_k(\d+)_", key)
-        layer_indices.append(int(match.group(1)))
-    n_layers = max(layer_indices)
+    n_layers = len(set([k.split("_")[1] for k in keys if "Vksi" in k]))
     n_layers_f = n_layers + 1
     depth_centers = np.array([total_depth * (i - 0.5) / n_layers for i in range(1, n_layers + 1)], dtype=np.float32)
     depth_interfaces = np.array([total_depth * i / n_layers for i in range(n_layers_f)], dtype=np.float32)
 
-    nt, ny, nx = len(times), len(y), len(x)
+    nt, ny, nx = len(times), len(YG), len(XG)
     watlev = np.full((nt, ny, nx), np.nan, dtype=np.float32)
     vksi = np.full((nt, n_layers, ny, nx), np.nan, dtype=np.float32)
     veta = np.full((nt, n_layers, ny, nx), np.nan, dtype=np.float32)
@@ -631,18 +629,22 @@ def swash_to_sgrid(data_file: str, coord_file: str, total_depth: float) -> xr.Da
 
     ds = xr.Dataset(
         {
-            "watlev": (["time", "lat", "lon"], watlev),
-            "U": (["time", "depth", "lat", "lon"], vksi),
-            "V": (["time", "depth", "lat", "lon"], veta),
-            "W": (["time", "depth_f", "lat", "lon"], w),
-            "botlev": (["lat", "lon"], bot),
+            "watlev": (["time", "YG", "XG"], watlev),
+            "U": (["time", "depth", "YC", "XC"], vksi),
+            "V": (["time", "depth", "YC", "XC"], veta),
+            "W": (["time", "depth_f", "YC", "XC"], w),
+            "botlev": (["YC", "XC"], bot),
         },
         coords={
             "time": (["time"], times, {"axis": "T", "units": "ms"}),
             "depth": (["depth"], depth_centers, {"axis": "Z", "units": "m", "positive": "down"}),
             "depth_f": (["depth_f"], depth_interfaces, {"axis": "Z", "units": "m", "positive": "down"}),
-            "lat": (["lat"], y, {"axis": "Y", "units": "m"}),
-            "lon": (["lon"], x, {"axis": "X", "units": "m"}),
+            "YG": (["YG"], YG, {"axis": "Y"}),
+            "YC": (["YC"], YG - 0.5, {"axis": "Y", "c_grid_axis_shift": +0.5}),
+            "XG": (["XG"], XG, {"axis": "X"}),
+            "XC": (["XC"], XG - 0.5, {"axis": "X", "c_grid_axis_shift": +0.5}),
+            "lat": (["YG", "XG"], lat, {"axis": "Y", "units": "m"}),
+            "lon": (["YG", "XG"], lon, {"axis": "X", "units": "m"}),
         },
     )
     header = mat["__header__"]
@@ -655,11 +657,11 @@ def swash_to_sgrid(data_file: str, coord_file: str, total_depth: float) -> xr.Da
         attrs=sgrid.SGrid2DMetadata(
             cf_role="grid_topology",
             topology_dimension=2,
-            node_dimensions=("lon", "lat"),
+            node_dimensions=("XG", "YG"),
             node_coordinates=("lon", "lat"),
             face_dimensions=(
-                sgrid.FaceNodePadding("lon", "lon", sgrid.Padding.NONE),
-                sgrid.FaceNodePadding("lat", "lat", sgrid.Padding.NONE),
+                sgrid.FaceNodePadding("XC", "XG", sgrid.Padding.LOW),
+                sgrid.FaceNodePadding("YC", "YG", sgrid.Padding.LOW),
             ),
             vertical_dimensions=(sgrid.FaceNodePadding("depth", "depth_f", sgrid.Padding.LOW),),
         ).to_attrs(),

From 974c02e67a097ae4620de2d9c383e3be577f9404 Mon Sep 17 00:00:00 2001
From: Erik van Sebille <erikvansebille@gmail.com>
Date: Thu, 23 Jul 2026 16:52:43 +0200
Subject: [PATCH 14/15] Demoving total_depth - and updating C-grid definition
 in SWASH

---
 docs/user_guide/examples/tutorial_swash.ipynb |  4 +-
 src/parcels/convert.py                        | 53 +++++++++----------
 tests/test_convert.py                         |  2 +-
 3 files changed, 27 insertions(+), 32 deletions(-)

diff --git a/docs/user_guide/examples/tutorial_swash.ipynb b/docs/user_guide/examples/tutorial_swash.ipynb
index 467f89868..c23e59834 100644
--- a/docs/user_guide/examples/tutorial_swash.ipynb
+++ b/docs/user_guide/examples/tutorial_swash.ipynb
@@ -56,9 +56,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds = parcels.convert.swash_to_sgrid(\n",
-    "    data_file=data_file, coord_file=coord_file, total_depth=8.0\n",
-    ")\n",
+    "ds = parcels.convert.swash_to_sgrid(data_file=data_file, coord_file=coord_file)\n",
     "fieldset = parcels.FieldSet.from_sgrid_conventions(ds)\n",
     "fieldset.describe()"
    ]
diff --git a/src/parcels/convert.py b/src/parcels/convert.py
index 752943be2..d5b32f971 100644
--- a/src/parcels/convert.py
+++ b/src/parcels/convert.py
@@ -569,7 +569,7 @@ def copernicusmarine_to_sgrid(
     return ds
 
 
-def swash_to_sgrid(data_file: str, coord_file: str, total_depth: float) -> xr.Dataset:
+def swash_to_sgrid(data_file: str, coord_file: str) -> xr.Dataset:
     """Create an sgrid-compliant xarray.Dataset from a dataset of SWASH netcdf files.
 
     Parameters
@@ -578,8 +578,6 @@ def swash_to_sgrid(data_file: str, coord_file: str, total_depth: float) -> xr.Da
         Path to the SWASH data file (MATLAB binary format).
     coord_file : str
         Path to the SWASH coordinate file (MATLAB binary format).
-    total_depth : float
-        Total depth of the water column.
 
     Returns
     -------
@@ -589,8 +587,8 @@ def swash_to_sgrid(data_file: str, coord_file: str, total_depth: float) -> xr.Da
     coord = sio.loadmat(coord_file)
     lon = coord["Xp"]
     lat = coord["Yp"]
-    XG = np.arange(lon.shape[1])
-    YG = np.arange(lat.shape[0])
+    XC = np.arange(lon.shape[1])
+    YC = np.arange(lat.shape[0])
     bot = coord["Botlev"]
 
     mat = sio.loadmat(data_file)
@@ -601,16 +599,15 @@ def swash_to_sgrid(data_file: str, coord_file: str, total_depth: float) -> xr.Da
     )
     times = np.array([t[0] * 1000 + t[1] for t in time_keys]).astype("timedelta64[ms]")
 
-    n_layers = len(set([k.split("_")[1] for k in keys if "Vksi" in k]))
-    n_layers_f = n_layers + 1
-    depth_centers = np.array([total_depth * (i - 0.5) / n_layers for i in range(1, n_layers + 1)], dtype=np.float32)
-    depth_interfaces = np.array([total_depth * i / n_layers for i in range(n_layers_f)], dtype=np.float32)
+    nz = len(set([k.split("_")[1] for k in keys if "Vksi" in k]))
+    depth_centers = np.linspace(1.0 / (2 * nz), 1.0 - 1.0 / (2 * nz), nz)
+    depth_interfaces = np.linspace(0, 1, nz + 1)
 
-    nt, ny, nx = len(times), len(YG), len(XG)
+    nt, ny, nx = len(times), len(YC), len(XC)
     watlev = np.full((nt, ny, nx), np.nan, dtype=np.float32)
-    vksi = np.full((nt, n_layers, ny, nx), np.nan, dtype=np.float32)
-    veta = np.full((nt, n_layers, ny, nx), np.nan, dtype=np.float32)
-    w = np.full((nt, n_layers_f, ny, nx), np.nan, dtype=np.float32)
+    vksi = np.full((nt, nz, ny, nx), np.nan, dtype=np.float32)
+    veta = np.full((nt, nz, ny, nx), np.nan, dtype=np.float32)
+    w = np.full((nt, nz + 1, ny, nx), np.nan, dtype=np.float32)
 
     for ti, (ts_int, ts_dec) in enumerate(time_keys):
         ts_str = f"{ts_int:06d}_{ts_dec:03d}"
@@ -624,27 +621,27 @@ def swash_to_sgrid(data_file: str, coord_file: str, total_depth: float) -> xr.Da
             if m:
                 veta[ti, int(m.group(1)) - 1, :, :] = mat[k]
             m = re.match(rf"w(\d+)_{ts_str}$", k)
-            if m and int(m.group(1)) < n_layers:
+            if m and int(m.group(1)) < nz:
                 w[ti, int(m.group(1)), :, :] = mat[k]
 
     ds = xr.Dataset(
         {
             "watlev": (["time", "YG", "XG"], watlev),
-            "U": (["time", "depth", "YC", "XC"], vksi),
-            "V": (["time", "depth", "YC", "XC"], veta),
+            "U": (["time", "depth", "YC", "XG"], vksi),
+            "V": (["time", "depth", "YG", "XC"], veta),
             "W": (["time", "depth_f", "YC", "XC"], w),
-            "botlev": (["YC", "XC"], bot),
+            "botlev": (["YG", "XG"], bot),
         },
         coords={
             "time": (["time"], times, {"axis": "T", "units": "ms"}),
-            "depth": (["depth"], depth_centers, {"axis": "Z", "units": "m", "positive": "down"}),
-            "depth_f": (["depth_f"], depth_interfaces, {"axis": "Z", "units": "m", "positive": "down"}),
-            "YG": (["YG"], YG, {"axis": "Y"}),
-            "YC": (["YC"], YG - 0.5, {"axis": "Y", "c_grid_axis_shift": +0.5}),
-            "XG": (["XG"], XG, {"axis": "X"}),
-            "XC": (["XC"], XG - 0.5, {"axis": "X", "c_grid_axis_shift": +0.5}),
-            "lat": (["YG", "XG"], lat, {"axis": "Y", "units": "m"}),
-            "lon": (["YG", "XG"], lon, {"axis": "X", "units": "m"}),
+            "depth": (["depth"], depth_centers, {"axis": "Z", "units": "normalised", "positive": "down"}),
+            "depth_f": (["depth_f"], depth_interfaces, {"axis": "Z", "units": "normalised", "positive": "down"}),
+            "YG": (["YG"], YC + 0.5, {"axis": "Y", "c_grid_axis_shift": +0.5}),
+            "YC": (["YC"], YC, {"axis": "Y"}),
+            "XG": (["XG"], XC + 0.5, {"axis": "X", "c_grid_axis_shift": +0.5}),
+            "XC": (["XC"], XC, {"axis": "X"}),
+            "lat": (["YG", "XG"], lat, {"axis": "Y", "units": "m", "c_grid_axis_shift": +0.5}),
+            "lon": (["YG", "XG"], lon, {"axis": "X", "units": "m", "c_grid_axis_shift": +0.5}),
         },
     )
     header = mat["__header__"]
@@ -657,11 +654,11 @@ def swash_to_sgrid(data_file: str, coord_file: str, total_depth: float) -> xr.Da
         attrs=sgrid.SGrid2DMetadata(
             cf_role="grid_topology",
             topology_dimension=2,
-            node_dimensions=("XG", "YG"),
+            node_dimensions=("XC", "YC"),
             node_coordinates=("lon", "lat"),
             face_dimensions=(
-                sgrid.FaceNodePadding("XC", "XG", sgrid.Padding.LOW),
-                sgrid.FaceNodePadding("YC", "YG", sgrid.Padding.LOW),
+                sgrid.FaceNodePadding("XC", "XG", sgrid.Padding.HIGH),
+                sgrid.FaceNodePadding("YC", "YG", sgrid.Padding.HIGH),
             ),
             vertical_dimensions=(sgrid.FaceNodePadding("depth", "depth_f", sgrid.Padding.LOW),),
         ).to_attrs(),
diff --git a/tests/test_convert.py b/tests/test_convert.py
index 8486420fb..ad82a63fb 100644
--- a/tests/test_convert.py
+++ b/tests/test_convert.py
@@ -177,7 +177,7 @@ def test_convert_copernicusmarine_no_logs(ds, caplog):
 def test_convert_swash():
     data_file, coord_file = parcels.tutorial.get_dataset_files("SWASH_data/data")
 
-    ds_fset = convert.swash_to_sgrid(data_file=data_file, coord_file=coord_file, total_depth=8.0)
+    ds_fset = convert.swash_to_sgrid(data_file=data_file, coord_file=coord_file)
     FieldSet.from_sgrid_conventions(ds_fset)
 
 

From 6dab2f3b3bab1910db618f36b6385abb85fdae81 Mon Sep 17 00:00:00 2001
From: Erik van Sebille <erikvansebille@gmail.com>
Date: Thu, 23 Jul 2026 17:12:17 +0200
Subject: [PATCH 15/15] Adding warning about experimental nature of
 swash_to_sgrid

---
 src/parcels/convert.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/src/parcels/convert.py b/src/parcels/convert.py
index d5b32f971..3fe7b36ec 100644
--- a/src/parcels/convert.py
+++ b/src/parcels/convert.py
@@ -584,6 +584,13 @@ def swash_to_sgrid(data_file: str, coord_file: str) -> xr.Dataset:
     xarray.Dataset
         Dataset object following SGRID conventions to be (optionally) modified and passed to a FieldSet constructor.
     """
+    warnings.warn(
+        "The swash_to_sgrid function is experimental and may not work for all SWASH datasets. "
+        "Furthermore, we are not entirely confident that the SGrid layout for SWASH is implemented correctly. "
+        "Please report any issues to the Parcels GitHub repository.",
+        UserWarning,
+        stacklevel=2,
+    )
     coord = sio.loadmat(coord_file)
     lon = coord["Xp"]
     lat = coord["Yp"]
@@ -624,6 +631,7 @@ def swash_to_sgrid(data_file: str, coord_file: str) -> xr.Dataset:
             if m and int(m.group(1)) < nz:
                 w[ti, int(m.group(1)), :, :] = mat[k]
 
+    # TODO double-check that the C-grid definition for SWASH here is correct
     ds = xr.Dataset(
         {
             "watlev": (["time", "YG", "XG"], watlev),