{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "3e61c1aa-b033-4628-939c-81f08eeaa32c",
"metadata": {},
"source": [
"**Note**: many of the jupyter notebooks provided here have SVG figures as cell outputs, which will only be displayed by JupyterLab or Jupyter Notebook if the notebook is marked as **trusted** (In JupyterLab: Ctrl+Shift+C -> Trust Notebook)."
]
},
{
"cell_type": "markdown",
"id": "5ec7e27e-3eef-427a-ac74-f4f6ea49c935",
"metadata": {},
"source": [
"# Data Input"
]
},
{
"cell_type": "markdown",
"id": "fca2771e-61e8-4f29-a6ae-8bbce26c4762",
"metadata": {},
"source": [
"There are 3 alternatives to import wind farm layout data for use within **optiwindnet**:\n",
"- optiwindnet API\n",
"- `.yaml` file\n",
"- `.osm.pbf` file"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "fa29e2ac-26f7-4927-bebf-eeef30a5ab37",
"metadata": {},
"outputs": [],
"source": [
"from optiwindnet.svg import svgplot"
]
},
{
"cell_type": "markdown",
"id": "1bcfeda4-e9da-4305-b7ab-22c38f96f199",
"metadata": {},
"source": [
"## Option 0: Use the optiwindnet API from Python"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1d2c0acc-b5ff-4ca0-b447-786a8d9c7ad2",
"metadata": {},
"outputs": [],
"source": [
"from optiwindnet.importer import L_from_site"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5854d182-84b6-4c64-aab2-46accfbb93a8",
"metadata": {},
"outputs": [],
"source": [
"from itertools import pairwise"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f82d5c6e-408c-4173-a434-157d53347e58",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e93a9280-594e-4840-8ea6-ec2a0bd800b3",
"metadata": {},
"outputs": [],
"source": [
"# all coordinates are sequences of (x, y) pairs\n",
"# if input coordinates are in arrays X and Y, use `np.hstack((X, Y))`\n",
"border = np.array( # coordinate sequence defines the polygon, last-first segment implicit\n",
" [[1951, 200], [1951, 1383], [386, 1383], [650, 708], [624, 678],\n",
" [4, 1036], [4, 3], [1152, 3], [917, 819], [957, 854]],\n",
" dtype=float)\n",
"# 'obstacles' is an optional location attribute\n",
"obstacles = [\n",
" # - obstacles must be strictly inside the border polygon\n",
" # - undefined behavior if obstacles and border overlap\n",
" # first obstacle\n",
" np.array([[1540, 920], [1600, 940], [1600, 1150], [1400, 1200]]),\n",
" # [second obstacle] ...\n",
"]\n",
"substations = np.array([[696, 1063],], dtype=float)\n",
"turbines = np.array(\n",
" [[1940, 279], [1920, 703], [1475, 696], [1839, 1250],\n",
" [1277, 1296], [442, 1359], [737, 435], [1060, 26],\n",
" [522, 176], [87, 35], [184, 417], [71, 878]],\n",
" dtype=float\n",
")\n",
"R = substations.shape[0]\n",
"T = turbines.shape[0]\n",
"border_sizes = np.array([border.shape[0]] +\n",
" [obstacle.shape[0] for obstacle in obstacles])\n",
"B = border_sizes.sum()\n",
"obstacle_idxs = np.cumsum(border_sizes) + T\n",
"\n",
"L = L_from_site(\n",
" R=R, T=T, B=B,\n",
" border=np.arange(T, T + border.shape[0]),\n",
" obstacles=[np.arange(a, b) for a, b in pairwise(obstacle_idxs)],\n",
" name='Example Location',\n",
" handle='example',\n",
" VertexC=np.vstack((turbines, border, *obstacles, substations)),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5539b8f8-a4d6-4aa6-b1c3-faf04331f38a",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svgplot(L)"
]
},
{
"cell_type": "markdown",
"id": "ac064931-e789-467f-8378-0e64d098d46a",
"metadata": {},
"source": [
"## Option 1: YAML input file"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f9d1c872-2453-419e-9e02-57e309b29441",
"metadata": {},
"outputs": [],
"source": [
"from optiwindnet.importer import L_from_yaml"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "501eb6be-c087-46c2-85cd-11b5de328c57",
"metadata": {},
"outputs": [],
"source": [
"with open('data/example_location.yaml', 'w') as yaml_file:\n",
" yaml_file.write('''\n",
"# coordinate format can be \"planar\" or \"latlon\"\n",
"# - for \"latlon\" examples, see `optiwindnet/data/*.yaml`\n",
"# - this field is optional, default is \"latlon\"\n",
"# - coordinates are converted to floats, so floats may be used as well\n",
"COORDINATE_FORMAT: planar\n",
"\n",
"# handle is optional (if omited, one is derived from the file name)\n",
"HANDLE: example\n",
"\n",
"# extents define a polygon:\n",
"# - do not repeat the initial vertex at the end\n",
"# - line breaks are optional\n",
"EXTENTS: [\n",
" [1951, 200],\n",
" [1951, 1383],\n",
" [386, 1383],\n",
" [650, 708],\n",
" [624, 678],\n",
" [4, 1036],\n",
" [4, 3],\n",
" [1152, 3],\n",
" [917, 819],\n",
" [957, 854]\n",
"]\n",
"\n",
"# obstacles is optional and must be a list of polygons (even if 1 obstacle)\n",
"# - obstacles must be strictly inside the extents polygon\n",
"# - the parser will not complain about borders of obstacles overlapping\n",
"# borders of extents, but optiwindnet will behave unpredictably\n",
"OBSTACLES: [\n",
" [ # first obstacle\n",
" [1540, 920],\n",
" [1600, 940],\n",
" [1600, 1150],\n",
" [1400, 1200],\n",
" ],\n",
" # [second obstacle]\n",
"]\n",
"\n",
"SUBSTATIONS: [\n",
" [696, 1063],\n",
"]\n",
"\n",
"TURBINES: [\n",
" [1940, 279],\n",
" [1920, 703],\n",
" [1475, 696],\n",
" [1839, 1250],\n",
" [1277, 1296],\n",
" [442, 1359],\n",
" [737, 435],\n",
" [1060, 26],\n",
" [522, 176],\n",
" [87, 35],\n",
" [184, 417],\n",
" [71, 878],\n",
"]\n",
"''')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a42be570-4222-4faf-a247-60611d55a09a",
"metadata": {},
"outputs": [],
"source": [
"L = L_from_yaml('data/example_location.yaml')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5dc83b7b-d9be-48e0-9255-fabcef9cc7c2",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svgplot(L)"
]
},
{
"cell_type": "markdown",
"id": "ace13a16-228e-419c-b014-e7bba9da09e5",
"metadata": {},
"source": [
"## Option 2: OSM.PBF input file (aka OpenStreetMap protobuffer format)"
]
},
{
"cell_type": "markdown",
"id": "8530db3e-a4b1-4e94-b97c-714fe14f44fb",
"metadata": {},
"source": [
"The JOSM open-source map editor is recommended if using this format: . In addition, the JOSM plugin **pbf** is required to save in the `.osm.pbf` format. The plugin **opendata** is useful for importing many common GIS file formats."
]
},
{
"cell_type": "markdown",
"id": "71c381c7-9040-4c32-b01f-9d5328265a2c",
"metadata": {},
"source": [
"The OpenStreetMaps objects used for representing a windfarm location are:\n",
"- *nodes*\n",
"- *ways*\n",
"- *multipolygons* (relation between closed ways)\n",
"\n",
"Wind turbines are represented by *nodes* with the tag `power=generator`. Substations are represented either by *nodes* or by closed *ways* tagged `power=substation` or `power=transformer`. Substations based on *ways* will be reduced to the point at the centroid of the polygon defined by the *way*.\n",
"\n",
"The border of the windfarm can be a closed *way* tagged `power=plant`. If obstacles are required, then the closed *way* for the border must be combined with the closed *ways* for the obstacles in a *multipolygon* with the tag `power=plant` (in which case the *ways* themselves should not be tagged)."
]
},
{
"cell_type": "markdown",
"id": "66e8ff88-400c-4a1a-a182-19d60cf1270a",
"metadata": {},
"source": [
"See `optiwindnet/data/*.osm.pbf` for more examples."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ebff6d6c-ae49-4634-adc8-d780dbd3ae6d",
"metadata": {},
"outputs": [],
"source": [
"from optiwindnet.importer import L_from_pbf"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0baae019-4743-40cd-b7d9-bfbd49dc3137",
"metadata": {},
"outputs": [],
"source": [
"L = L_from_pbf('data/example_location.osm.pbf')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "45f77ec2-53e9-4858-8771-d6fa22cc032e",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svgplot(L)"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 5
}