diff --git a/Output analysis/analysis_genmix_cost_emi.ipynb b/Output analysis/analysis_genmix_cost_emi.ipynb
new file mode 100644
index 0000000..f5f7ec0
--- /dev/null
+++ b/Output analysis/analysis_genmix_cost_emi.ipynb
@@ -0,0 +1,517 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import csv\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.dates as mdates\n",
+ "import datetime\n",
+ "from pandas.plotting import register_matplotlib_converters\n",
+ "register_matplotlib_converters()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run_no = 1 #'altered'\n",
+ "rundays = 365\n",
+ "runhours = rundays*24\n",
+ "\n",
+ "hr_range = pd.date_range('1/1/2016', periods=runhours, freq='H')\n",
+ "day_range = pd.date_range('1/1/2016', periods=rundays, freq='D')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Annual generation mix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "###Observed statistics (EDC, 2016)\n",
+ "gen_obs = [2750, 2570, 411, 792, 158, 38, 0]\n",
+ "indx = ['hydro', 'coal', 'oil','viet', 'thai', 'laos', 'slack']\n",
+ "\n",
+ "df_gen_gwh = pd.DataFrame(gen_obs,columns=['obs'])\n",
+ "df_gen_gwh.index = indx\n",
+ "##df_gen_gwh"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mwh = pd.read_csv('out_camb_R'+str(run_no)+'_2016_mwh.csv',header=0)\n",
+ "hydro = pd.read_csv('out_camb_R'+str(run_no)+'_2016_hydro.csv',header=0)\n",
+ "hydro_import = pd.read_csv('out_camb_R'+str(run_no)+'_2016_hydro_import.csv',header=0) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "###Include Generator_type to the mwh data\n",
+ "df_gen_param = pd.read_csv('data_camb_genparams.csv',header=0)\n",
+ "\n",
+ "gen_name = df_gen_param['name']\n",
+ "gen_type = df_gen_param['typ']\n",
+ "\n",
+ "for x in range(len(gen_name)):\n",
+ " mwh.loc[mwh.Generator == gen_name[x], 'Type'] = gen_type[x]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " obs | \n",
+ " sim | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | hydro | \n",
+ " 2750 | \n",
+ " 2784.532011 | \n",
+ "
\n",
+ " \n",
+ " | coal | \n",
+ " 2570 | \n",
+ " 2644.672009 | \n",
+ "
\n",
+ " \n",
+ " | oil | \n",
+ " 411 | \n",
+ " 359.070243 | \n",
+ "
\n",
+ " \n",
+ " | viet | \n",
+ " 792 | \n",
+ " 796.285118 | \n",
+ "
\n",
+ " \n",
+ " | thai | \n",
+ " 158 | \n",
+ " 100.332169 | \n",
+ "
\n",
+ " \n",
+ " | laos | \n",
+ " 38 | \n",
+ " 41.278288 | \n",
+ "
\n",
+ " \n",
+ " | slack | \n",
+ " 0 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "### Plot Daily generation by resource-type\n",
+ "daily_coal = disp_daily[0]\n",
+ "daily_imprt = disp_daily[1] +disp_daily[2] + hydro_import_daily\n",
+ "daily_oil = disp_daily[3] +disp_daily[4]\n",
+ "daily_hydro = hydro_daily\n",
+ "daily_slack = disp_daily[5]\n",
+ "\n",
+ "fig, (ax1) = plt.subplots(nrows=1,ncols=1) ##,figsize=(6,4)\n",
+ "\n",
+ "ax1.stackplot(day_range,daily_hydro,daily_coal,hydro_import_daily,\n",
+ " disp_daily[1],disp_daily[2],daily_oil,daily_slack,\n",
+ " labels=['hydro','coal','import (Laos)','import (Thailand)','import (Vietnam)','oil','slack'])\n",
+ "##ax1.set_xticks(rotation=90)\n",
+ "##ax1.set_xlabel('Day')\n",
+ "ax1.set_ylabel('Electricity generation (GWh/day)')\n",
+ "#ax1.set_ylim(0,20.5)\n",
+ "ax1.legend(loc=(0.04,1.01),ncol=3)\n",
+ "\n",
+ "# Set the locator\n",
+ "locator = mdates.MonthLocator() # every month\n",
+ "# Specify the format - %b gives us Jan, Feb...\n",
+ "fmt = mdates.DateFormatter('%b')\n",
+ "X = plt.gca().xaxis\n",
+ "X.set_major_locator(locator)\n",
+ "# Specify formatter\n",
+ "X.set_major_formatter(fmt)\n",
+ "\n",
+ "##plt.savefig('fig_daily_generation_curgrid.eps', format='eps',dpi=1200,bbox_inches = \"tight\") ##"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Annual operating costs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gen_name = df_gen_param['name']\n",
+ "gen_type = df_gen_param['typ']\n",
+ "gen_maxcap = df_gen_param['maxcap']\n",
+ "gen_heat_rate = df_gen_param['heat_rate']\n",
+ "gen_var_om = df_gen_param['var_om']\n",
+ "gen_fix_om = df_gen_param['fix_om']\n",
+ "gen_st_cost = df_gen_param['st_cost']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "###Calibrated fuel_prices (from objective function)\n",
+ "fuel_typ = ['coal_st','oil_ic','oil_st','imp_viet','imp_thai','slack']\n",
+ "cal_fuel_price = [5.2,6.0,6.0,65,66,1000] ##price excluding slack\n",
+ "hydro_imp_price = 48 ##(usd/mwh)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for x in range(len(fuel_typ)):\n",
+ " df_gen_param.loc[df_gen_param.typ == fuel_typ[x], 'fu_price'] = cal_fuel_price[x]\n",
+ "\n",
+ "fu_price = df_gen_param['fu_price']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_gen_on = pd.read_csv('out_camb_R'+str(run_no)+'_2016_on.csv',header=0)\n",
+ "df_gen_switch = pd.read_csv('out_camb_R'+str(run_no)+'_2016_switch.csv',header=0)\n",
+ "\n",
+ "thermo_bygen = (mwh.groupby(['Generator','Time'])['Value'].sum())\n",
+ "on_bygen = (df_gen_on.groupby(['Generator','Time'])['Value'].sum())\n",
+ "switch_bygen = (df_gen_switch.groupby(['Generator','Time'])['Value'].sum())\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "###Annual total cost by each dispatchable unit\n",
+ "tt_cost_unit = []\n",
+ "\n",
+ "for i in range(len(gen_name)):\n",
+ " fx_cost = gen_maxcap[i]*gen_fix_om[i]*on_bygen[gen_name[i]]\n",
+ " st_cost = gen_maxcap[i]*gen_st_cost[i]*switch_bygen[gen_name[i]]\n",
+ " vr_cost = thermo_bygen[gen_name[i]]*(gen_heat_rate[i]*fu_price[i]+gen_var_om[i])\n",
+ "\n",
+ " tt_cost = fx_cost + st_cost + vr_cost\n",
+ "\n",
+ " tt_cost_unit.append(sum(tt_cost)) ##total cost by each unit\n",
+ "\n",
+ " \n",
+ "df_tt_cost_unit = pd.DataFrame(tt_cost_unit)\n",
+ "df_tt_cost_unit.index = gen_name\n",
+ "df_tt_cost_unit.columns = ['annual cost ($)']\n",
+ "##df_tt_cost_unit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.9813578126486455"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "###Annual cost for hydropower import\n",
+ "cost_hydro_import = sum(hydro_import['Value'])*hydro_imp_price/(10**6) ##million dollars\n",
+ "cost_hydro_import"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "243.2553686345892"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "###Annual total cost for entire system\n",
+ "y_syscost = sum(tt_cost_unit[:-1])/(10**6) + cost_hydro_import ##million dollars excluding slack_cost\n",
+ "\n",
+ "y_syscost"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Annual CO2 Emissions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2.7504588891024895, 0.2621212776051299, 3.0125801667076195)"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "###CO2 emission rates according to EPPO/EIA/Mittal\n",
+ "co2_coal_rate = 1.04 ##tonne/mwh\n",
+ "co2_oil_rate = 0.73 ##tonne/mwh\n",
+ "\n",
+ "ann_coal_mwh = (simcoal)*(10**3)\n",
+ "ann_oil_mwh = (simoil)*(10**3)\n",
+ "\n",
+ "ann_co2_coal = ann_coal_mwh * co2_coal_rate/(10**6) ##in megatonnes\n",
+ "ann_co2_oil = ann_oil_mwh * co2_oil_rate/(10**6) ##in megatonnes\n",
+ "\n",
+ "ann_co2_Mtonnes = ann_co2_coal + ann_co2_oil\n",
+ "\n",
+ "ann_co2_coal,ann_co2_oil,ann_co2_Mtonnes"
+ ]
+ },
+ {
+ "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": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Output analysis/analysis_srsv_nrsv.ipynb b/Output analysis/analysis_srsv_nrsv.ipynb
new file mode 100644
index 0000000..542f7e4
--- /dev/null
+++ b/Output analysis/analysis_srsv_nrsv.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline \n",
+ "import os\n",
+ "import csv\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run_no = 1 \n",
+ "rundays = 365\n",
+ "runhours = rundays*24\n",
+ "\n",
+ "hr_range = pd.date_range('1/1/2001', periods=runhours, freq='H')\n",
+ "day_range = pd.date_range('1/1/2001', periods=rundays, freq='D')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_gen = pd.read_csv('data_camb_genparams.csv',header=0)\n",
+ "df_load = pd.read_csv('data_camb_load_2016.csv',header=0)\n",
+ " \n",
+ "sys_load = (df_load.iloc[:,4:].sum(axis=1)).values\n",
+ "#reserve = (sys_load*0.15).values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gen_name = df_gen['name']\n",
+ "gen_type = df_gen['typ']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(9.010021695594252, 19.18987059712856)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "srsv = pd.read_csv('out_camb_R'+str(run_no)+'_2016_srsv.csv',header=0)\n",
+ "nrsv = pd.read_csv('out_camb_R'+str(run_no)+'_2016_nrsv.csv',header=0)\n",
+ " \n",
+ "###Include Generator_type to the srsv data\n",
+ "for x in range(len(gen_name)):\n",
+ " srsv.loc[srsv.Generator == gen_name[x], 'Type'] = gen_type[x]\n",
+ " nrsv.loc[nrsv.Generator == gen_name[x], 'Type'] = gen_type[x]\n",
+ " \n",
+ "####Reserve_GWh by Type\n",
+ "srsv_bytype = round(srsv.groupby(['Type'])['Value'].sum()/1000,1)\n",
+ "nrsv_bytype = round(nrsv.groupby(['Type'])['Value'].sum()/1000,1)\n",
+ " \n",
+ "\n",
+ "###Reserve_ratios by Time only\n",
+ "srsv_bytime = srsv.groupby(['Time'])['Value'].sum().values\n",
+ "nrsv_bytime = nrsv.groupby(['Time'])['Value'].sum().values\n",
+ "trsv_bytime = srsv_bytime +nrsv_bytime \n",
+ " \n",
+ "srsv_ratio = srsv_bytime*100/sys_load\n",
+ "nrsv_ratio = nrsv_bytime*100/sys_load\n",
+ "trsv_ratio = trsv_bytime*100/sys_load\n",
+ "\n",
+ "avg_srsv = np.mean(srsv_ratio)\n",
+ "avg_trsv = np.mean(trsv_ratio)\n",
+ "\n",
+ "\n",
+ "avg_srsv,avg_trsv"
+ ]
+ },
+ {
+ "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": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Output analysis/analysis_transmission.ipynb b/Output analysis/analysis_transmission.ipynb
new file mode 100644
index 0000000..ea5d1e2
--- /dev/null
+++ b/Output analysis/analysis_transmission.ipynb
@@ -0,0 +1,581 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import csv\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run_no = 1\n",
+ "rundays = 365\n",
+ "runhours = rundays*24\n",
+ "\n",
+ "hr_range = pd.date_range('1/1/2016', periods=runhours, freq='H')\n",
+ "day_range = pd.date_range('1/1/2016', periods=rundays, freq='D')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "line_data = pd.read_csv('data_camb_transparam.csv',header=0)\n",
+ "vlt_angle = pd.read_csv('out_camb_R'+str(run_no)+'_2016_vlt_angle.csv',header=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "32"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "####======== Lists of Nodes of the Power System ========########\n",
+ "h_nodes = ['TTYh','LRCh','ATYh','KIR1h','KIR3h','KMCh']\n",
+ "h_imports = ['Salabam']\n",
+ "##s_nodes = ['solar1','solar2']\n",
+ "##w_nodes =['wind1','wind2']\n",
+ "\n",
+ "gd_nodes = ['GS1','GS2','GS3','GS5','GS7','KPCM','KPT','SHV','SRP'] ##Thermoplant nodes with demand\n",
+ "gn_nodes = ['STH','Thai','Viet'] ##Thermoplants nodes without demand\n",
+ "\n",
+ "g_nodes = gd_nodes + gn_nodes\n",
+ "##print ('Gen_Nodes:',len(g_nodes))\n",
+ "\n",
+ "td_nodes = ['GS4','GS6','BTB','BMC','STR','TKO','KPS'] ##Transformers with demand\n",
+ "tn_nodes = ['IE','KPCG','OSM','PRST'] ##Transformers without demand\n",
+ "\n",
+ "d_nodes = gd_nodes + td_nodes\n",
+ "##print ('Demand_Nodes:',len(d_nodes))\n",
+ "\n",
+ "all_nodes = h_nodes + h_imports + gn_nodes + gd_nodes + tn_nodes + td_nodes ## + s_nodes + w_nodes\n",
+ "##print ('Total_Nodes:',len(all_nodes))\n",
+ "\n",
+ "indx = 0\n",
+ "\n",
+ "trans_data = [[] for n in range(4)]\n",
+ "trans_mwh = []\n",
+ "trans_usage = []\n",
+ "\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "\n",
+ "for x in all_nodes:\n",
+ " for y in all_nodes:\n",
+ " if (line_data.loc[(line_data.source==x) & (line_data.sink==y),'linesus'].values) > 0: ##\n",
+ " indx += 1\n",
+ " \n",
+ " sus = line_data.loc[(line_data.source==x) & (line_data.sink==y),'linesus'].values ##single value\n",
+ " cap = line_data.loc[(line_data.source==x) & (line_data.sink==y),'linemva'].values ##single value\n",
+ " \n",
+ " vlt_angle_x = vlt_angle.loc[vlt_angle['Node']==x,'Value'].values ##all hourly values \n",
+ " vlt_angle_y = vlt_angle.loc[vlt_angle['Node']==y,'Value'].values ##all hourly values \n",
+ " \n",
+ " vlt_angle_diff = vlt_angle_x - vlt_angle_y\n",
+ " \n",
+ " line_trans = vlt_angle_diff * sus\n",
+ " line_usage = abs(line_trans)*100/cap ##percentage of line_usage\n",
+ " \n",
+ " trans_data[0].append(x)\n",
+ " trans_data[1].append(y)\n",
+ " \n",
+ " trans_data[2].append(sus[0])\n",
+ " trans_data[3].append(cap[0]) \n",
+ " \n",
+ " trans_mwh.append(line_trans)\n",
+ " trans_usage.append(line_usage) \n",
+ "\n",
+ " #print (indx, x, y,sus) \n",
+ "\n",
+ "len(trans_mwh)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "### Yearly mean of trans & usage\n",
+ "trans_mean = np.mean(trans_mwh,axis=1)\n",
+ "usage_mean = np.mean(trans_usage,axis=1)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# N-1 Violations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "### Hourly n-1 violations\n",
+ "n1_hourly = np.zeros((len(trans_usage),len(trans_usage[0])))\n",
+ "\n",
+ "for i in range(len(trans_usage)):\n",
+ " for j in range(len(trans_usage[0])):\n",
+ " if trans_usage[i][j]>=75:\n",
+ " n1_hourly[i][j] += 1\n",
+ "\n",
+ "##TS of hourly n-1 violations\n",
+ "ts_n1_hourly = []\n",
+ "for i in range(len(trans_usage)):\n",
+ " ts_n1_hourly.append(pd.Series(n1_hourly[i], index=hr_range))\n",
+ "\n",
+ "##Monthly n-1 violations\n",
+ "n1_monthly = []\n",
+ "for i in range (len(trans_usage)): \n",
+ " n1_monthly.append((ts_n1_hourly[i].resample('M').sum()).values)\n",
+ "\n",
+ "##Annual n-1 violations\n",
+ "n1_yearly = []\n",
+ "for i in range (len(trans_usage)):\n",
+ " n1_yearly.append(sum(n1_hourly[i])) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " source | \n",
+ " sink | \n",
+ " linesus | \n",
+ " linemva | \n",
+ " avg_line_usage (%) | \n",
+ " n1_yearly | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " LRCh | \n",
+ " TTYh | \n",
+ " 0.071 | \n",
+ " 1722 | \n",
+ " 5.66611 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " LRCh | \n",
+ " OSM | \n",
+ " 0.065 | \n",
+ " 1208 | \n",
+ " 16.9169 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " ATYh | \n",
+ " OSM | \n",
+ " 0.17 | \n",
+ " 1722 | \n",
+ " 1.51404 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " KIR1h | \n",
+ " KIR3h | \n",
+ " 0.084 | \n",
+ " 170 | \n",
+ " 5.86261 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " KIR1h | \n",
+ " KPS | \n",
+ " 0.041 | \n",
+ " 170 | \n",
+ " 8.78221 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " KMCh | \n",
+ " KPT | \n",
+ " 0.228 | \n",
+ " 604 | \n",
+ " 8.0581 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " Salabam | \n",
+ " STR | \n",
+ " 0.305 | \n",
+ " 170 | \n",
+ " 2.56395 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " Thai | \n",
+ " IE | \n",
+ " 0.733 | \n",
+ " 604 | \n",
+ " 1.75405 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " Viet | \n",
+ " TKO | \n",
+ " 0.05 | \n",
+ " 604 | \n",
+ " 13.921 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " GS1 | \n",
+ " GS3 | \n",
+ " 0.256 | \n",
+ " 238 | \n",
+ " 10.6726 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " GS1 | \n",
+ " KPS | \n",
+ " 0.066 | \n",
+ " 85 | \n",
+ " 6.3951 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " GS2 | \n",
+ " GS3 | \n",
+ " 0.256 | \n",
+ " 238 | \n",
+ " 15.5963 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 12 | \n",
+ " GS5 | \n",
+ " GS1 | \n",
+ " 0.768 | \n",
+ " 238 | \n",
+ " 13.5787 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 13 | \n",
+ " GS7 | \n",
+ " GS4 | \n",
+ " 0.073 | \n",
+ " 476 | \n",
+ " 15.7288 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 14 | \n",
+ " KPT | \n",
+ " STH | \n",
+ " 0.032 | \n",
+ " 860 | \n",
+ " 28.8158 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 15 | \n",
+ " SHV | \n",
+ " STH | \n",
+ " 0.292 | \n",
+ " 604 | \n",
+ " 27.1085 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 16 | \n",
+ " IE | \n",
+ " BMC | \n",
+ " 0.068 | \n",
+ " 604 | \n",
+ " 1.75405 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 17 | \n",
+ " KPCG | \n",
+ " PRST | \n",
+ " 0.034 | \n",
+ " 1722 | \n",
+ " 8.33962 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 18 | \n",
+ " OSM | \n",
+ " PRST | \n",
+ " 0.021 | \n",
+ " 1722 | \n",
+ " 13.3814 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 19 | \n",
+ " PRST | \n",
+ " BTB | \n",
+ " 0.023 | \n",
+ " 1722 | \n",
+ " 5.26933 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 20 | \n",
+ " GS4 | \n",
+ " GS1 | \n",
+ " 0.083 | \n",
+ " 238 | \n",
+ " 44.5215 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 21 | \n",
+ " GS4 | \n",
+ " GS2 | \n",
+ " 0.096 | \n",
+ " 238 | \n",
+ " 49.6285 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 22 | \n",
+ " GS4 | \n",
+ " GS6 | \n",
+ " 0.07 | \n",
+ " 1722 | \n",
+ " 3.88285 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 23 | \n",
+ " GS6 | \n",
+ " GS5 | \n",
+ " 0.059 | \n",
+ " 1722 | \n",
+ " 3.96496 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 24 | \n",
+ " GS6 | \n",
+ " KPCM | \n",
+ " 0.029 | \n",
+ " 1722 | \n",
+ " 2.07626 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 25 | \n",
+ " GS6 | \n",
+ " KPCG | \n",
+ " 0.059 | \n",
+ " 1722 | \n",
+ " 8.33962 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 26 | \n",
+ " BMC | \n",
+ " SRP | \n",
+ " 0.034 | \n",
+ " 302 | \n",
+ " 15.9937 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 27 | \n",
+ " BMC | \n",
+ " BTB | \n",
+ " 0.055 | \n",
+ " 604 | \n",
+ " 8.95662 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 28 | \n",
+ " STR | \n",
+ " KPCM | \n",
+ " 0.768 | \n",
+ " 238 | \n",
+ " 1.92392 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 29 | \n",
+ " TKO | \n",
+ " KPT | \n",
+ " 0.035 | \n",
+ " 604 | \n",
+ " 46.7799 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 30 | \n",
+ " TKO | \n",
+ " GS4 | \n",
+ " 0.057 | \n",
+ " 860 | \n",
+ " 41.04 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 31 | \n",
+ " KPS | \n",
+ " GS5 | \n",
+ " 0.073 | \n",
+ " 170 | \n",
+ " 4.0116 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "