Remove notebooks (old and confusing)
They used to distract unwarranted attention.
This commit is contained in:
Jonas Hörsch
parent
56615820ed
commit
ae458abf5d
@ -1,311 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import yaml"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"with open('../config.yaml') as f:\n",
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" config = yaml.load(f)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from vresutils import shapes as vshapes\n",
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"from vresutils import plot as vplot"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import pypsa\n",
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"import numpy as np\n",
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"import xarray as xr\n",
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"import pandas as pd\n",
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"import geopandas as gpd"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib as mpl\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"n = pypsa.Network(\"../networks/base.nc\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Regions and p_nom_max"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Onshore"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"regions = gpd.read_file('../resources/regions_onshore.geojson').set_index('id')\n",
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"regions.index.name = 'name'\n",
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"regions['area'] = regions.to_crs(dict(proj='aea')).area / 1e6"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"ds = xr.open_dataset('../resources/profile_onwind.nc')\n",
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"p_nom_max = ds['p_nom_max']"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"ds['profile'].mean()"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"ds.sel(name=ds['profile'].mean('time') > 0.01)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(figsize=(12, 8))\n",
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"plt.colorbar(vplot.shapes(regions['geometry'], p_nom_max.to_pandas()/regions['area'], ax=ax))\n",
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"ax.set_aspect('equal')"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"ds['profile'].mean('time')"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(figsize=(12, 8))\n",
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"plt.colorbar(vplot.shapes(regions['geometry'], ds['profile'].mean('time').to_pandas(), ax=ax))\n",
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"ax.set_aspect('equal')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Offshore"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"regions = gpd.read_file('../resources/regions_offshore.geojson').set_index('id')\n",
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"regions.index.name = 'name'\n",
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"regions['area'] = regions.to_crs(dict(proj='aea')).area / 1e6"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"ds = xr.open_dataset('../resources/profile_offwind.nc')\n",
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"p_nom_max = ds['p_nom_max']\n",
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"\n",
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"fig, ax = plt.subplots(figsize=(12, 8))\n",
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"plt.colorbar(vplot.shapes(regions['geometry'], p_nom_max.to_pandas()/regions['area'], ax=ax))\n",
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"ax.set_aspect('equal')"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"potentials = xr.open_dataarray('../resources/potentials_onwind.nc')"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"vsha"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"vshapes.country_cover(config['countries']).bounds"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"vplot.shapes([vshapes.country_cover(config['countries'])])\n",
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"vplot.shapes([vshapes.country_cover(config['countries'], include_eez=False)], facecolors='yellow')\n",
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"vplot.shapes(vshapes.countries(config['countries']), facecolors='None')"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(figsize=(12, 8))\n",
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"potentials.plot()\n",
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"#vplot.shapes(regions['geometry'], p_nom_max.to_pandas() == 0., facecolors='None', ax=ax)\n",
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"\n",
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"ax.set_aspect('equal')"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import atlite"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"atlite.Cutout(config['renewable']['onwind']['cutout'])"
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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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"metadata": {},
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"outputs": [],
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"source": []
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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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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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": "3.5.2"
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},
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"toc": {
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"colors": {
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"hover_highlight": "#DAA520",
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"navigate_num": "#000000",
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"navigate_text": "#333333",
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"running_highlight": "#FF0000",
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"selected_highlight": "#FFD700",
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"sidebar_border": "#EEEEEE",
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"wrapper_background": "#FFFFFF"
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},
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"moveMenuLeft": true,
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"nav_menu": {
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"height": "12px",
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"width": "252px"
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},
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"navigate_menu": true,
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"threshold": 4,
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"toc_cell": false,
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"toc_section_display": "block",
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"toc_window_display": false,
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"widenNotebook": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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