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Remove notebooks (old and confusing)

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They used to distract unwarranted attention.
This commit is contained in:
Jonas Hörsch 2018-08-17 11:37:22 +02:00
parent 56615820ed
commit ae458abf5d
2 changed files with 0 additions and 2374 deletions
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311
notebooks/intermed-results.ipynb
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@ -1,311 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import yaml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('../config.yaml') as f:\n",
" config = yaml.load(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from vresutils import shapes as vshapes\n",
"from vresutils import plot as vplot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pypsa\n",
"import numpy as np\n",
"import xarray as xr\n",
"import pandas as pd\n",
"import geopandas as gpd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"n = pypsa.Network(\"../networks/base.nc\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regions and p_nom_max"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Onshore"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"regions = gpd.read_file('../resources/regions_onshore.geojson').set_index('id')\n",
"regions.index.name = 'name'\n",
"regions['area'] = regions.to_crs(dict(proj='aea')).area / 1e6"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds = xr.open_dataset('../resources/profile_onwind.nc')\n",
"p_nom_max = ds['p_nom_max']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds['profile'].mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds.sel(name=ds['profile'].mean('time') > 0.01)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, ax = plt.subplots(figsize=(12, 8))\n",
"plt.colorbar(vplot.shapes(regions['geometry'], p_nom_max.to_pandas()/regions['area'], ax=ax))\n",
"ax.set_aspect('equal')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds['profile'].mean('time')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, ax = plt.subplots(figsize=(12, 8))\n",
"plt.colorbar(vplot.shapes(regions['geometry'], ds['profile'].mean('time').to_pandas(), ax=ax))\n",
"ax.set_aspect('equal')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Offshore"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"regions = gpd.read_file('../resources/regions_offshore.geojson').set_index('id')\n",
"regions.index.name = 'name'\n",
"regions['area'] = regions.to_crs(dict(proj='aea')).area / 1e6"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds = xr.open_dataset('../resources/profile_offwind.nc')\n",
"p_nom_max = ds['p_nom_max']\n",
"\n",
"fig, ax = plt.subplots(figsize=(12, 8))\n",
"plt.colorbar(vplot.shapes(regions['geometry'], p_nom_max.to_pandas()/regions['area'], ax=ax))\n",
"ax.set_aspect('equal')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"potentials = xr.open_dataarray('../resources/potentials_onwind.nc')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"vsha"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"vshapes.country_cover(config['countries']).bounds"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"vplot.shapes([vshapes.country_cover(config['countries'])])\n",
"vplot.shapes([vshapes.country_cover(config['countries'], include_eez=False)], facecolors='yellow')\n",
"vplot.shapes(vshapes.countries(config['countries']), facecolors='None')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, ax = plt.subplots(figsize=(12, 8))\n",
"potentials.plot()\n",
"#vplot.shapes(regions['geometry'], p_nom_max.to_pandas() == 0., facecolors='None', ax=ax)\n",
"\n",
"ax.set_aspect('equal')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import atlite"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"atlite.Cutout(config['renewable']['onwind']['cutout'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"navigate_num": "#000000",
"navigate_text": "#333333",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700",
"sidebar_border": "#EEEEEE",
"wrapper_background": "#FFFFFF"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "12px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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notebooks/pypsa-eur.ipynb
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