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notebooks: Add notebooks/API.ipynb + fix some minor issues in BBox_Fi…
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| @@ -0,0 +1,169 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "f3e4d71b-357e-45e2-aa3e-9e89b1b662ab", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%load_ext autoreload\n", | ||
| "%autoreload 2\n", | ||
| "\n", | ||
| "from __future__ import annotations" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "15c46224-c0c6-4c7c-8c4b-fd1a0580398a", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "\n", | ||
| "os.environ['USE_PYGEOS'] = '0' # remove geopandas warnings\n", | ||
| "\n", | ||
| "import holoviews as hv\n", | ||
| "import numpy as np\n", | ||
| "import numpy_indexed as npi\n", | ||
| "import pandas as pd\n", | ||
| "import xarray as xr\n", | ||
| "\n", | ||
| "hv.extension(\"bokeh\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "1779d199-055f-47f6-a296-f823cec4329c", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%%bash\n", | ||
| "pushd /tmp\n", | ||
| "wget --quiet https://github.com/ec-jrc/Thalassa/files/10867068/fort.63.zip \n", | ||
| "unzip -o fort.63.zip\n", | ||
| "popd" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "73965237-0675-49d1-ae01-e45a67bd77d4", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from thalassa import api\n", | ||
| "\n", | ||
| "filename = \"/tmp/fort.63.nc\"\n", | ||
| "ds = api.open_dataset(filename)\n", | ||
| "ds" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "d2957b88-7e35-47c4-be69-fc510b809572", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "source": [ | ||
| "## Main API\n", | ||
| "\n", | ||
| "`thalassa` supports several types of graphs, including:\n", | ||
| " \n", | ||
| "- A visualization of the variables at various timestamps and or layers.\n", | ||
| "- A visualization of the mesh\n", | ||
| "- Extraction of timeseries from specific points\n", | ||
| "\n", | ||
| "The following cell shows the basic usage:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "f63f4df8-7160-47cd-b40b-322751b60e75", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#variable, layer, timestamp = \"salt\", 40, ds.time.values[4]\n", | ||
| "#variable, layer, timestamp = \"depth\", None, None\n", | ||
| "variable, layer, timestamp = \"zeta\", None, \"max\"\n", | ||
| "\n", | ||
| "# The trimesh is the most basic object. This is what you need to create all the others graphs\n", | ||
| "# It is on this object that you specify the timestamp and/or the layer.\n", | ||
| "trimesh = api.create_trimesh(ds=ds, variable=variable, timestamp=timestamp, layer=layer)\n", | ||
| "\n", | ||
| "# The wireframe is the representation of the mesh\n", | ||
| "wireframe = api.get_wireframe(trimesh=trimesh)\n", | ||
| "\n", | ||
| "# The tiles is using the tiling service from Open Street maps\n", | ||
| "tiles = api.get_tiles() \n", | ||
| "\n", | ||
| "# The raster object is the basic Map that visualizes the variable. \n", | ||
| "# You can specify things like the colorbar limits and/or the extents\n", | ||
| "#raster = api.get_raster(trimesh, clim_min=0, clim_max=15)\n", | ||
| "raster = api.get_raster(trimesh)\n", | ||
| "\n", | ||
| "# The pointer/tap timeseries extract the timeseries of a specific node from the xr.Dataset and visualize it.\n", | ||
| "pointer_dmap = api.get_pointer_timeseries(ds=ds, variable=variable, source_raster=raster, layer=layer)\n", | ||
| "tap_dmap = api.get_tap_timeseries(ds=ds, variable=variable, source_raster=raster, layer=layer)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "c1d58fec-9a9a-45c5-b408-780b9bdd01ed", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Let's visualize!\n", | ||
| "\n", | ||
| "After you render the layout, move the mouse over the map and click on it. This will fill in the `pointer_dmap` and the `tap_dmap`." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "f42fc4d5-37ff-4195-8cb2-03986c2bcfaa", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "layout = tiles * raster.opts(width=600, cmap=\"viridis\") \n", | ||
| "layout\n", | ||
| "(pointer_dmap + tap_dmap).opts(shared_axes=False)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "thalassa2", | ||
| "language": "python", | ||
| "name": "thalassa2" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.8.16" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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