diff --git a/database_views/reeem_jupyter/reeem_db_plot_Plexos.ipynb b/database_views/reeem_jupyter/reeem_db_plot_Plexos.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Header\n",
+ "\n",
+ "\n",
+ "\n",
+ "__copyright__ \t= \"© Reiner Lemoine Institut\"
\n",
+ "__license__ \t= \"GNU Affero General Public License Version 3 (AGPL-3.0)\"
\n",
+ "__url__ \t\t= \"https://www.gnu.org/licenses/agpl-3.0.en.html\"
\n",
+ "__author__ \t\t= \"Ludwig Hülk\"
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Import"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/vnd.plotly.v1+html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import sys\n",
+ "import os\n",
+ "import getpass\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from sqlalchemy import *\n",
+ "# plot\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.ticker as mticker\n",
+ "import plotly.graph_objs as go\n",
+ "import plotly.offline as pltly\n",
+ "import colorlover as cl\n",
+ "import seaborn as sns\n",
+ "# notebook\n",
+ "from IPython.display import Image\n",
+ "from IPython.core.display import HTML \n",
+ "\n",
+ "pltly.init_notebook_mode(connected=True)\n",
+ "%matplotlib inline\n",
+ "\n",
+ "version = 'v0.1.3 (jupyther)'\n",
+ "project = 'REEEM'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Database Connection\n",
+ "\n",
+ "This function creates a database connection to the **reeem_db**.
\n",
+ "The default user is **reeem_vis**, a user that has only read rights."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def reeem_session():\n",
+ " \"\"\"SQLAlchemy session object with valid connection to reeem database\"\"\"\n",
+ " \n",
+ " print('Please provide connection parameters to database:\\n' +\n",
+ " 'Hit [Enter] to take defaults')\n",
+ " host = '130.226.55.43' # input('host (default 130.226.55.43): ')\n",
+ " port = '5432' # input('port (default 5432): ')\n",
+ " database = 'reeem' # input(\"database name (default 'reeem'): \")\n",
+ " user = 'reeem_vis' # input('user (default postgres): ')\n",
+ " # password = input('password: ')\n",
+ " password = getpass.getpass(prompt='password: ',\n",
+ " stream=sys.stderr)\n",
+ " con = create_engine(\n",
+ " 'postgresql://' + '%s:%s@%s:%s/%s' % (user,\n",
+ " password,\n",
+ " host,\n",
+ " port,\n",
+ " database)).connect()\n",
+ " print('Password correct! Database connection established.')\n",
+ " return con"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Access Data\n",
+ "\n",
+ "This section establishes the database connection and asks for the **password**.
\n",
+ "The username can be changed in the corresponding function in the section **Database Connection** above.
\n",
+ "If you don't have a username or forgot your password please contact your database admins."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Anaconda3\\envs\\reeem-vis\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning:\n",
+ "\n",
+ "The `stream` parameter of `getpass.getpass` will have no effect when using ipykernel\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Please provide connection parameters to database:\n",
+ "Hit [Enter] to take defaults\n",
+ "password: ········\n",
+ "Password correct! Database connection established.\n"
+ ]
+ }
+ ],
+ "source": [
+ "con = reeem_session()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# View 0.1: Existing entries in one column"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Database Query\n",
+ "\n",
+ "This section can be used to query one specific database **column** from one database table (**FROM**).
\n",
+ "The result is saved to a pandas.DataFrame (**df_0**) and printed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Database select (SQL)\n",
+ "column = 'nid,category,indicator' # id, pathway, version, region, year, indicator, category, value, unit\n",
+ "sql = text(\"\"\"\n",
+ " SELECT {0}, count(*) AS count\n",
+ " FROM model_draft.reeem_plexos_input\n",
+ " GROUP BY {0} \n",
+ " ORDER BY {0}; \"\"\".format(column))\n",
+ "df = pd.read_sql_query(sql, con)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Database select (SQL)\n",
+ "column = 'year' # id, pathway, version, region, year, indicator, category, value, unit\n",
+ "sql = text(\"\"\"\n",
+ " SELECT {0}\n",
+ " FROM model_draft.reeem_plexos_input\n",
+ " GROUP BY {0} \n",
+ " ORDER BY {0}; \"\"\".format(column))\n",
+ "df = pd.read_sql_query(sql, con)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# View 0.3: Table metadata\n",
+ "## Database Query\n",
+ "\n",
+ "This section can be used to get the metadata from one database table.
\n",
+ "The result is printed.
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 {\"title\": \"REEEM Plexos Input\",\\n \"descript...\n",
+ "Name: obj_description, dtype: object"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Database select (SQL)\n",
+ "sql = text(\"\"\"SELECT obj_description('model_draft.reeem_plexos_input'::regclass);\"\"\")\n",
+ "df_meta = pd.read_sql_query(sql, con).loc[:,'obj_description']\n",
+ "df_meta"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# View 3: One indicator for all regions in one pathway over time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Database Query\n",
+ "\n",
+ "This section can be used to query one specific **indicator** for all regions from one database table (_table_).
\n",
+ "Indicators are identified by the **nid** (_filter 1_).
\n",
+ "Only **region** _EU28_ is excluded (_filter 2_).
\n",
+ "It is possible to select one specific **pathway** (_filter 3_) and one specific data **version** (_filter 4_).
\n",
+ "To querry additional coulmns from the database table add the names to the **SELECT** statement (_column_).
\n",
+ "The sortation is done by the **ORDER BY** (_sorting_).
\n",
+ "The result from the database is saved to a pandas.DataFrame (**df_3**) and can be printed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Database select (SQL)\n",
+ "sql = text(\"\"\"\n",
+ " SELECT pathway, version, region, year, nid, schema, category, indicator, value, unit -- column\n",
+ " FROM model_draft.reeem_plexos_input -- table\n",
+ " WHERE nid = 1 -- filter 1\n",
+ " AND pathway = 'Base' -- filter 2\n",
+ " AND version = 'DataV1' -- filter 3\n",
+ " ORDER BY pathway, version, region, year; -- sorting \"\"\")\n",
+ "df_3 = pd.read_sql_query(sql, con)\n",
+ "#df_3"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Metadata\n",
+ "\n",
+ "The important information from the above select (**df_3**) is collected in a Dictionary (**info_dict_3**)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Filename : ['2018-08-09_reeem_Plexos_allregions_Electricity Demand']\n",
+ "Category : ['demand extraction ']\n",
+ "Indicator : ['Electricity Demand']\n",
+ "Unit : ['TWh']\n",
+ "Pathway : ['Base']\n",
+ "Year : [2030]\n",
+ "Region : ['BG' 'HR' 'HU' 'RO' 'SI']\n",
+ "Y-Axis : ['Electricity Demand in TWh']\n",
+ "Title : ['demand extraction in all regions']\n",
+ "Metadata : 0 {\"title\": \"REEEM Plexos Input\",\\n \"descript...\n",
+ "Name: obj_description, dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Facts dict\n",
+ "info_dict_3 = {}\n",
+ "info_dict_3['Filename'] = ['{0}_reeem_Plexos_allregions_{1}' .format(\n",
+ " pd.to_datetime('today').strftime(\"%Y-%m-%d\"),\n",
+ " df_3.loc[:,'indicator'].unique()[0])]\n",
+ "info_dict_3['Category'] = df_3.loc[:,'category'].unique()\n",
+ "info_dict_3['Indicator'] = df_3.loc[:,'indicator'].unique()\n",
+ "info_dict_3['Unit'] = df_3.loc[:,'unit'].unique()\n",
+ "info_dict_3['Pathway'] = df_3.loc[:,'pathway'].unique()\n",
+ "info_dict_3['Year'] = df_3.loc[:,'year'].unique().tolist()\n",
+ "info_dict_3['Region'] = df_3.loc[:,'region'].unique()\n",
+ "info_dict_3['Y-Axis'] = ['{} in {}'.format(*info_dict_3['Indicator'], *info_dict_3['Unit'])]\n",
+ "info_dict_3['Title'] = ['{} in all regions'.format(*info_dict_3['Category'])]\n",
+ "info_dict_3['Metadata'] = df_meta\n",
+ "\n",
+ "# Print facts\n",
+ "for x in info_dict_3:\n",
+ " print(x,':',info_dict_3[x])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Stack data\n",
+ "\n",
+ "This pivot function reorganises the data and makes each pathway a column. The year is used as the index.
\n",
+ "The result is saved to a new dataframe (**df_3p**) and can be printed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Reshape dataframe\n",
+ "df_3p = df_3.pivot(index='region', columns='indicator', values='value')\n",
+ "#df_3p"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot\n",
+ "\n",
+ "This is a basic plot with [matplotlib](https://matplotlib.org/).
\n",
+ "The title and y-axis labels are taken from the **info_dict_3**. They can be exchanged with manual text."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0, 53.59574977950043)"
+ ]
+ },
+ "execution_count": 52,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
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+ "