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Kode der definerer distance to peaks variabel

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  1. Distance_to_peak-variabel_philip.ipynb +457 -0
Distance_to_peak-variabel_philip.ipynb ADDED
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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\":\"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\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"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}]}]}};\n",
58
+ " var render_items = [{\"docid\":\"df8c1d01-e046-4a7d-8d56-6a99a583e1d8\",\"roots\":{\"bf174fcc-257c-470c-b445-9731bb40e120\":\"a8ba9ec9-b41a-4436-83d4-c63fda091fbb\"},\"root_ids\":[\"bf174fcc-257c-470c-b445-9731bb40e120\"]}];\n",
59
+ " var docs = Object.values(docs_json)\n",
60
+ " if (!docs) {\n",
61
+ " return\n",
62
+ " }\n",
63
+ " const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
64
+ " async function embed_document(root) {\n",
65
+ " var Bokeh = get_bokeh(root)\n",
66
+ " await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
67
+ " for (const render_item of render_items) {\n",
68
+ " for (const root_id of render_item.root_ids) {\n",
69
+ "\tconst id_el = document.getElementById(root_id)\n",
70
+ "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
71
+ "\t const root_el = id_el.children[0]\n",
72
+ "\t root_el.id = root_el.id + '-rendered'\n",
73
+ "\t for (const child of root_el.children) {\n",
74
+ " // Ensure JupyterLab does not capture keyboard shortcuts\n",
75
+ " // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
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+ "\t child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
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+ "\t }\n",
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+ "\t}\n",
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+ " }\n",
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+ " }\n",
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+ " }\n",
82
+ " function get_bokeh(root) {\n",
83
+ " if (root.Bokeh === undefined) {\n",
84
+ " return null\n",
85
+ " } else if (root.Bokeh.version !== py_version) {\n",
86
+ " if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
87
+ "\treturn null\n",
88
+ " }\n",
89
+ " return root.Bokeh.versions.get(py_version);\n",
90
+ " } else if (root.Bokeh.version === py_version) {\n",
91
+ " return root.Bokeh\n",
92
+ " }\n",
93
+ " return null\n",
94
+ " }\n",
95
+ " function is_loaded(root) {\n",
96
+ " var Bokeh = get_bokeh(root)\n",
97
+ " return (Bokeh != null && Bokeh.Panel !== undefined)\n",
98
+ " }\n",
99
+ " if (is_loaded(root)) {\n",
100
+ " embed_document(root);\n",
101
+ " } else {\n",
102
+ " var attempts = 0;\n",
103
+ " var timer = setInterval(function(root) {\n",
104
+ " if (is_loaded(root)) {\n",
105
+ " clearInterval(timer);\n",
106
+ " embed_document(root);\n",
107
+ " } else if (document.readyState == \"complete\") {\n",
108
+ " attempts++;\n",
109
+ " if (attempts > 200) {\n",
110
+ " clearInterval(timer);\n",
111
+ "\t var Bokeh = get_bokeh(root)\n",
112
+ "\t if (Bokeh == null || Bokeh.Panel == null) {\n",
113
+ " console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
114
+ "\t } else {\n",
115
+ "\t console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
116
+ "\t embed_document(root)\n",
117
+ "\t }\n",
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+ " }\n",
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+ " }\n",
120
+ " }, 25, root)\n",
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+ " }\n",
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+ "})(window);</script>"
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+ " <img src='data:image/png;base64,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'\n",
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+ " style='height:15px; border-radius:12px; display: inline-block; float: left'></img>\n",
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+ " \n",
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+ "\n",
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+ "\n",
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+ "\n",
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+ "\n",
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+ "</div>\n"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
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+ "data": {
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+ "text/html": [
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+ "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
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+ "*[data-root-id] > * {\n",
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+ " box-sizing: border-box;\n",
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+ " font-family: var(--jp-ui-font-family);\n",
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+ " font-size: var(--jp-ui-font-size1);\n",
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+ " color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
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+ "}\n",
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+ "\n",
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+ "/* Override VSCode background color */\n",
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+ ".cell-output-ipywidget-background:has(\n",
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+ " > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
236
+ " ),\n",
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+ ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
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+ " background-color: transparent !important;\n",
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+ "}\n",
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+ "</style>"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
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+ "data": {
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249
+ "application/vnd.holoviews_load.v0+json": ""
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+ },
251
+ "metadata": {},
252
+ "output_type": "display_data"
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+ },
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+ {
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+ "data": {
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+ "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 google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = 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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 = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction 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",
257
+ "application/vnd.holoviews_load.v0+json": ""
258
+ },
259
+ "metadata": {},
260
+ "output_type": "display_data"
261
+ }
262
+ ],
263
+ "source": [
264
+ "import matplotlib.pyplot as plt\n",
265
+ "import datashader as ds\n",
266
+ "from holoviews.operation.datashader import datashade, rasterize, inspect_points\n",
267
+ "from bokeh.models import PrintfTickFormatter, FixedTicker\n",
268
+ "import holoviews as hv\n",
269
+ "hv.extension('bokeh')\n",
270
+ "import matplotlib.pyplot as plt\n",
271
+ "import rioxarray\n",
272
+ "import hvplot.xarray\n",
273
+ "import panel as pn"
274
+ ]
275
+ },
276
+ {
277
+ "cell_type": "code",
278
+ "execution_count": 3,
279
+ "id": "16e06e14",
280
+ "metadata": {},
281
+ "outputs": [
282
+ {
283
+ "name": "stdout",
284
+ "output_type": "stream",
285
+ "text": [
286
+ " THICK geometry EAST \\\n",
287
+ "0 2193.7041 b'\\x01\\x01\\x00\\x00\\x00|\\xa8\\xdb*\\xd3\\x830AP\\xf... 1.082323e+06 \n",
288
+ "1 2899.8000 b\"\\x01\\x01\\x00\\x00\\x00<\\xbd@\\xa8J\\xb1$A'&\\x0e\\... 6.780533e+05 \n",
289
+ "2 1819.1100 b'\\x01\\x01\\x00\\x00\\x00h\\x9ez<\\xb3\\x97\\x1eAZ\\xc... 5.012288e+05 \n",
290
+ "3 1124.9400 b'\\x01\\x01\\x00\\x00\\x00\\xaa\\x0c\\xcf{\\xe9r*\\xc1\\... -8.666767e+05 \n",
291
+ "4 2122.4100 b'\\x01\\x01\\x00\\x00\\x00\\x97\\x9b\\xc4h\\xc3\\xe6$\\x... -6.848977e+05 \n",
292
+ "\n",
293
+ " NORTH vx vy v ith_bm smb \\\n",
294
+ "0 1.179464e+05 1.718832 0.091488 1.721265 2225.768378 28.600311 \n",
295
+ "1 -1.755376e+06 0.595385 0.635081 0.870523 2487.617401 83.017294 \n",
296
+ "2 -8.708590e+05 -73.866877 -42.897329 85.419531 1827.670487 21.117418 \n",
297
+ "3 3.561459e+05 -3.913554 -0.195708 3.918445 1130.303771 136.581627 \n",
298
+ "4 6.913987e+04 -1.471376 7.756460 7.894785 2105.888247 108.577022 \n",
299
+ "\n",
300
+ " z s temp gridCellId \n",
301
+ "0 3890.095924 0.004726 221.131235 137 \n",
302
+ "1 2348.626277 0.000567 235.855494 28 \n",
303
+ "2 1771.083276 0.004876 239.003074 82 \n",
304
+ "3 216.134166 0.006954 246.742698 149 \n",
305
+ "4 1400.136807 0.002945 243.399524 132 \n"
306
+ ]
307
+ }
308
+ ],
309
+ "source": [
310
+ "import pandas as pd\n",
311
+ "\n",
312
+ "# Læs en Parquet-fil ind i en DataFrame\n",
313
+ "grid_train = pd.read_parquet(\"grid_300km_30M.parquet\")\n",
314
+ "\n",
315
+ "# Vis de første rækker\n",
316
+ "print(grid_train.head())"
317
+ ]
318
+ },
319
+ {
320
+ "cell_type": "code",
321
+ "execution_count": 5,
322
+ "id": "5fa24d84",
323
+ "metadata": {},
324
+ "outputs": [
325
+ {
326
+ "data": {
327
+ "image/png": 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",
328
+ "text/plain": [
329
+ "<Figure size 640x480 with 1 Axes>"
330
+ ]
331
+ },
332
+ "metadata": {},
333
+ "output_type": "display_data"
334
+ }
335
+ ],
336
+ "source": [
337
+ "import matplotlib.pyplot as plt\n",
338
+ "\n",
339
+ "plt.hist(grid_train['z'], bins=50)\n",
340
+ "plt.xlabel('z')\n",
341
+ "plt.ylabel('Antal')\n",
342
+ "plt.title('Fordeling af z-variablen')\n",
343
+ "plt.grid(True)\n",
344
+ "plt.show()"
345
+ ]
346
+ },
347
+ {
348
+ "cell_type": "code",
349
+ "execution_count": 4,
350
+ "id": "64ac63b9",
351
+ "metadata": {},
352
+ "outputs": [
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "18303e3164d840fca112071c1c51ac16",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "BokehModel(combine_events=True, render_bundle={'docs_json': {'3d01d759-610b-4eb6-a863-44901ffb7721': {'version…"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ }
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+ ],
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+ "source": [
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+ "points_bedmap_predicted = hv.Points(grid_train, ['EAST', 'NORTH'], 'THICK') # HoloViews Points object\n",
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+ "\n",
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+ "formatter = PrintfTickFormatter(format='%i')\n",
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+ "\n",
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+ "# Rasterize the points\n",
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+ "rasterized_bedmap_predicted = rasterize(points_bedmap_predicted,aggregator=ds.mean('THICK')).opts(\n",
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+ " cmap=plt.colormaps['turbo'],\n",
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+ " cnorm='eq_hist',\n",
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+ " width=700,\n",
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+ " height=700,\n",
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+ " colorbar=True,\n",
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+ " xaxis=None, # Remove x-axis\n",
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+ " yaxis=None, # Remove y-axis\n",
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+ " colorbar_opts={\n",
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+ " 'title': 'Ice Thickness measurements [m]',\n",
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+ " 'formatter': formatter, # Apply custom formatter to the colorbar,\n",
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+ " \"ticker\": FixedTicker(ticks=[500, 1000, 1500, 2000, 2500, 3000, 4500])\n",
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+ " },\n",
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+ " colorbar_position='bottom',\n",
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+ ")\n",
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+ "\n",
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+ "\n",
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+ "# Display the plot\n",
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+ "hv.output(rasterized_bedmap_predicted)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "id": "66adcf05",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import pandas as pd\n",
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+ "import numpy as np\n",
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+ "from scipy.ndimage import maximum_filter, label\n",
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+ "from scipy.spatial import cKDTree\n",
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+ "\n",
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+ "# Lav grid til 2D-array (kræver at EAST/NORTH danner regelmæssigt gitter)\n",
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+ "# Her antager vi forenklet at data kan reshapes\n",
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+ "z = grid_train['z'].values.reshape((n_rows, n_cols)) # Du skal bestemme n_rows og n_cols\n",
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+ "z_max = maximum_filter(z, size=15) # Juster 'size' alt efter opløsning\n",
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+ "\n",
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+ "# Find koordinater for lokale maxima\n",
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+ "local_maxima_mask = (z == z_max)\n",
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+ "local_max_coords = np.column_stack(np.where(local_maxima_mask)) # row, col\n",
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+ "\n",
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+ "# Konvertér til EAST/NORTH\n",
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+ "east_grid = grid_train['EAST'].values.reshape((n_rows, n_cols))\n",
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+ "north_grid = grid_train['NORTH'].values.reshape((n_rows, n_cols))\n",
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+ "mountain_points = np.column_stack([\n",
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+ " east_grid[local_max_coords[:, 0], local_max_coords[:, 1]],\n",
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+ " north_grid[local_max_coords[:, 0], local_max_coords[:, 1]]\n",
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+ "])\n",
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+ "\n",
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+ "# KDTree for bjergtoppe\n",
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+ "tree = cKDTree(mountain_points)\n",
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+ "\n",
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+ "# Beregn afstande for alle punkter i datasættet\n",
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+ "all_points = np.column_stack([grid_train['EAST'], grid_train['NORTH']])\n",
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+ "distances, _ = tree.query(all_points)\n",
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+ "\n",
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+ "# Gem i datasættet\n",
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+ "grid_train['distance_to_peak'] = distances\n"
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+ ]
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+ }
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+ ],
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+ "metadata": {
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+ "kernelspec": {
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+ "display_name": "appml",
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+ "language": "python",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.12.9"
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 5
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+ }