diff --git a/evaluation/configs/example_rank.yaml b/evaluation/configs/example_rank.yaml
index c8644c3f24e0fd0a5600ccefd0967a98e25348ac..5152de83b5dfd0390c8ab91cef7dc849c7b6c3be 100644
--- a/evaluation/configs/example_rank.yaml
+++ b/evaluation/configs/example_rank.yaml
@@ -8,9 +8,9 @@ maps:
queries_per_rank: 10
ranks: [2, 4, 6, 8]
setups:
- - mu_s: 300 # Start and Target Soc
+ - mu_s: 40 # Start and Target Soc
mu_t: 0
- capacity: 300 # kWh
+ capacity: 40 # kWh
consumption:
type: distance
- consumption_coefficient: 0.1 # kWh/s
+ consumption_coefficient: 0.5 # kWh/km = Wh/m
diff --git a/evaluation/run.py b/evaluation/run.py
index 0fb15aefd36ad95c47707266c1b8f94cc1ea6e17..bc8ea464707feaa184cd28ff2a6a9fafdf6ea998 100644
--- a/evaluation/run.py
+++ b/evaluation/run.py
@@ -37,8 +37,9 @@ def apply_conversions(conf):
"""kWh to Wh"""
for setup in conf['setups']:
setup['capacity'] = 1000 * setup['capacity']
- setup['consumption']['consumption_coefficient'] = \
- 1000 * setup['consumption']['consumption_coefficient']
+ if setup['consumption'].get('type') == 'time':
+ setup['consumption']['consumption_coefficient'] = \
+ 1000 * setup['consumption']['consumption_coefficient']
setup['mu_s'] = 1000 * setup['mu_s']
setup['mu_t'] = 1000 * setup['mu_t']
return conf
diff --git a/evaluation/statistics.ipynb b/evaluation/statistics.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d47ff563c1714917307166292c7e2f95d51cf941
--- /dev/null
+++ b/evaluation/statistics.ipynb
@@ -0,0 +1,298 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from pathlib import Path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Paths to Benchmark Results\n",
+ "result_dir = Path('./results')\n",
+ "example_init = result_dir.joinpath('example_init/init')\n",
+ "example_init_query = result_dir.joinpath('example_init_query/queries')\n",
+ "example_init_rank = result_dir.joinpath('example_rank/ranked')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = {\n",
+ " 'init': example_init.joinpath('init.csv'),\n",
+ " 'insert': example_init.joinpath('insert.csv'),\n",
+ " 'init_charge_queries': example_init_query.joinpath('charge.csv'),\n",
+ " 'charge_ranked': example_init_rank.joinpath('charge.csv'),\n",
+ " 'classic_ranked': example_init_rank.joinpath('classic.csv')\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for key, path in data.items():\n",
+ " data[key] = pd.read_csv(path,dtype={'start_node': str, 'target_node': str})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Ranked Stats"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ranks = np.log2(data['classic_ranked']['dijkstra_rank'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure()\n",
+ "\n",
+ "for k in ['charge_ranked']:\n",
+ " data[k]['rank'] = ranks\n",
+ " _means = data[k].groupby(['rank']).mean()\n",
+ " _std = data[k].groupby(['rank']).std()\n",
+ " _x = _means.index\n",
+ " _y = _means['query_time']\n",
+ " _yerr = _std['query_time']\n",
+ " plt.errorbar(_x, _y, yerr=_yerr,fmt='o-')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Gasstation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>time_contracted_graph</th>\n",
+ " <th>time_state_graph</th>\n",
+ " <th>nodes_state_graph</th>\n",
+ " <th>edges_state_graph</th>\n",
+ " <th>nodes_contracted_graph</th>\n",
+ " <th>edges_contracted_graph</th>\n",
+ " <th>charging_stations</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.000136</td>\n",
+ " <td>0.000030</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0.323515</td>\n",
+ " <td>0.000028</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>9.928760</td>\n",
+ " <td>0.000098</td>\n",
+ " <td>6</td>\n",
+ " <td>3</td>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>9.078340</td>\n",
+ " <td>0.000190</td>\n",
+ " <td>12</td>\n",
+ " <td>9</td>\n",
+ " <td>6</td>\n",
+ " <td>3</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>31.818989</td>\n",
+ " <td>0.000110</td>\n",
+ " <td>8</td>\n",
+ " <td>2</td>\n",
+ " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>62.850130</td>\n",
+ " <td>0.000063</td>\n",
+ " <td>10</td>\n",
+ " <td>0</td>\n",
+ " <td>10</td>\n",
+ " <td>0</td>\n",
+ " <td>10</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " time_contracted_graph time_state_graph nodes_state_graph \\\n",
+ "0 0.000136 0.000030 1 \n",
+ "1 0.323515 0.000028 2 \n",
+ "2 9.928760 0.000098 6 \n",
+ "3 9.078340 0.000190 12 \n",
+ "4 31.818989 0.000110 8 \n",
+ "5 62.850130 0.000063 10 \n",
+ "\n",
+ " edges_state_graph nodes_contracted_graph edges_contracted_graph \\\n",
+ "0 0 1 0 \n",
+ "1 0 2 0 \n",
+ "2 3 4 1 \n",
+ "3 9 6 3 \n",
+ "4 2 8 1 \n",
+ "5 0 10 0 \n",
+ "\n",
+ " charging_stations \n",
+ "0 1 \n",
+ "1 2 \n",
+ "2 4 \n",
+ "3 6 \n",
+ "4 8 \n",
+ "5 10 "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data['init']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure()\n",
+ "x = data['init']['charging_stations']\n",
+ "time_contracted=data['init']['time_contracted_graph']\n",
+ "time_state_graph = data['init']['time_state_graph']\n",
+ "plt.bar(x, time_contracted)\n",
+ "plt.bar(x, time_state_graph)\n",
+ "\n",
+ "charge_query_times = data['init_charge_queries'].groupby('charging_stations').mean()\n",
+ "charge_query_times_std = data['init_charge_queries'].groupby('charging_stations').std()\n",
+ "plt.errorbar(x, charge_query_times['query_time'], yerr=charge_query_times_std['query_time'], fmt='bo')\n",
+ "\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "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": "3.7.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/evrouting/graph_tools.py b/evrouting/graph_tools.py
index bd0c073793de2cb5383ec1d071f911374f6eab59..b521e13b36dca66ddb18f38873320454181c7237 100644
--- a/evrouting/graph_tools.py
+++ b/evrouting/graph_tools.py
@@ -57,7 +57,7 @@ def sum_weights(G, path, weight: str) -> float:
def consumption_function_distance_factory(consumption: float) -> ConsumptionFunction:
"""
- :param consumption: in kWh/km
+ :param consumption: in kWh/m
"""
def c(G, u, v):