From 977b40057332742b557e07529242102e80ff9f08 Mon Sep 17 00:00:00 2001
From: Mathias Berg Rosendal <77012503+Mathias157@users.noreply.github.com>
Date: Mon, 30 Sep 2024 18:30:52 +0200
Subject: [PATCH 01/19] Improvements (#4)

* Added offshore wind

* Updated DAG plot

* Merge remote-tracking branch 'origin/clustering' into improvements
---
 src/Analysis/dot_source.txt    |  30 ++++---
 src/Analysis/snakemake_dag.pdf | Bin 11449 -> 15182 bytes
 src/Modules/offshore_wind.py   | 159 +++++++++++++++++++++++++++++++++
 src/assumptions.yaml           |  13 ++-
 src/snakefile                  |  17 +++-
 5 files changed, 196 insertions(+), 23 deletions(-)
 create mode 100644 src/Modules/offshore_wind.py

diff --git a/src/Analysis/dot_source.txt b/src/Analysis/dot_source.txt
index 8caa644..772167b 100644
--- a/src/Analysis/dot_source.txt
+++ b/src/Analysis/dot_source.txt
@@ -2,20 +2,21 @@ digraph snakemake_dag {
     graph[bgcolor=white, margin=0];
     node[shape=box, style=rounded, fontname=sans,                 fontsize=10, penwidth=2];
     edge[penwidth=2, color=grey];
-	0[label = "all", color = "0.58 0.6 0.85", style="rounded,dashed"];
-	1[label = "exo_electricity_demand", color = "0.62 0.6 0.85", style="rounded,dashed"];
-	2[label = "create_conversion_dictionaries", color = "0.31 0.6 0.85", style="rounded,dashed"];
-	3[label = "format_energinet_data", color = "0.40 0.6 0.85", style="rounded,dashed"];
-	4[label = "transport_road_demand", color = "0.53 0.6 0.85", style="rounded,dashed"];
-	5[label = "format_dkstat_transport_data", color = "0.36 0.6 0.85", style="rounded,dashed"];
-	6[label = "exo_heat_demand", color = "0.00 0.6 0.85", style="rounded,dashed"];
-	7[label = "format_vpdk21_data", color = "0.49 0.6 0.85", style="rounded,dashed"];
-	8[label = "format_dkstat_industry_data", color = "0.04 0.6 0.85", style="rounded,dashed"];
-	9[label = "format_balmorel_data", color = "0.09 0.6 0.85", style="rounded,dashed"];
-	10[label = "geographic_sets", color = "0.13 0.6 0.85", style="rounded,dashed"];
-	11[label = "grids", color = "0.18 0.6 0.85", style="rounded,dashed"];
-	12[label = "investment_options", color = "0.22 0.6 0.85", style="rounded,dashed"];
-	13[label = "transport_h2_demand", color = "0.27 0.6 0.85", style="rounded,dashed"];
+	0[label = "all", color = "0.25 0.6 0.85", style="rounded,dashed"];
+	1[label = "exo_electricity_demand", color = "0.29 0.6 0.85", style="rounded,dashed"];
+	2[label = "create_conversion_dictionaries", color = "0.17 0.6 0.85", style="rounded,dashed"];
+	3[label = "format_energinet_data", color = "0.50 0.6 0.85", style="rounded,dashed"];
+	4[label = "transport_road_demand", color = "0.00 0.6 0.85", style="rounded,dashed"];
+	5[label = "format_dkstat_transport_data", color = "0.08 0.6 0.85", style="rounded,dashed"];
+	6[label = "exo_heat_demand", color = "0.42 0.6 0.85", style="rounded,dashed"];
+	7[label = "format_vpdk21_data", color = "0.38 0.6 0.85", style="rounded,dashed"];
+	8[label = "format_dkstat_industry_data", color = "0.54 0.6 0.85", style="rounded,dashed"];
+	9[label = "format_balmorel_data", color = "0.58 0.6 0.85", style="rounded,dashed"];
+	10[label = "geographic_sets", color = "0.62 0.6 0.85", style="rounded,dashed"];
+	11[label = "grids", color = "0.04 0.6 0.85", style="rounded,dashed"];
+	12[label = "investment_options", color = "0.12 0.6 0.85", style="rounded,dashed"];
+	13[label = "transport_h2_demand", color = "0.21 0.6 0.85", style="rounded,dashed"];
+	14[label = "offshore_wind", color = "0.33 0.6 0.85", style="rounded,dashed"];
 	1 -> 0
 	4 -> 0
 	6 -> 0
@@ -24,6 +25,7 @@ digraph snakemake_dag {
 	11 -> 0
 	12 -> 0
 	13 -> 0
+	14 -> 0
 	2 -> 1
 	3 -> 1
 	5 -> 4
diff --git a/src/Analysis/snakemake_dag.pdf b/src/Analysis/snakemake_dag.pdf
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diff --git a/src/Modules/offshore_wind.py b/src/Modules/offshore_wind.py
new file mode 100644
index 0000000..a9f65a6
--- /dev/null
+++ b/src/Modules/offshore_wind.py
@@ -0,0 +1,159 @@
+"""
+TITLE
+
+Loads profiles generated with "createVRE.py"
+
+Created on 27.09.2024
+@author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+"""
+#%% ------------------------------- ###
+###        0. Script Settings       ###
+### ------------------------------- ###
+
+import pandas as pd
+from geofiles import prepared_geofiles
+from pybalmorel import IncFile
+from Submodules.municipal_template import DataContainer
+import xarray as xr
+import click
+import geopandas as gpd
+import matplotlib.pyplot as plt
+
+#%% ------------------------------- ###
+###        1. Offshore Wind         ###
+### ------------------------------- ###
+
+def load_profiles(choice: str = 'dkmunicipalities_names', plot: bool = False):
+    # Load files
+    profiles = xr.load_dataset('Output/VRE/2012_offshore_wind.nc')
+    ind, geo, c = prepared_geofiles(choice)
+    offshore_geo = gpd.read_file('Data/Shapefiles/Offshore/OffshoreRegions.gpkg')
+
+    if plot:
+        fig, ax = plt.subplots()
+        geo.plot(ax=ax)
+        offshore_geo.plot(ax=ax,color='r')
+        plt.show()
+    
+    return profiles, geo, offshore_geo
+
+def create_geo_sets(profiles: xr.Dataset):
+    
+    # Get names
+    profiles = profiles.to_dataframe().reset_index()
+    areas = pd.Series(profiles['Name'].unique())  
+
+    # CCCRRRAAA and AAA
+    f = IncFile(name='OFFSHORE_CCCRRRAAA', path='Output',
+                prefix="SET CCCRRRAAA 'All geographical entities (CCC + RRR + AAA)'\n/\n",
+                body="\n".join(areas),
+                suffix='\n/;')
+    f.save()
+    f.name = 'OFFSHORE_AAA'
+    f.prefix = "SET AAA 'Offshore areas'\n/\n"
+    f.save()
+    
+    # RRRAAA
+    f = IncFile(name='OFFSHORE_RRRAAA', path='Output',
+                prefix="SET RRRAAA(RRR,AAA) 'Areas in regions'\n/\n",
+                body="\n".join([area.split('_OFF')[0] + ' . ' + area for area in areas]),
+                suffix='\n/;')
+    f.body = f.body.replace('Nordsoeen . Nordsoeen', 'Esbjerg . Nordsoeen')
+    f.save()
+    
+def create_profiles(profiles: xr.Dataset, year: int = 2012):
+    
+    # Get 00:00 first monday to 23:00 last sunday (hardcoded for 2012)
+    profiles = profiles.to_dataframe().reset_index().pivot_table(index='time', columns='Name', values='specific generation')
+    t0 = 24
+    t1 = 24
+    
+    # To check
+    # time = pd.Series(profiles.index).iloc[t0:-t1].dt.isocalendar()
+    # print('\nCheck if first time is week 1, day 1, and last time is week 52, day 7\n', time)
+    assert year == 2012, 'Change hardcoding to obtain correct 1st monday to last sunday in timeseries'
+    
+    # Select profile 
+    profiles = profiles.iloc[t0:-t1] 
+    
+    S = ['S0%d'%i for i in range(1, 10)] + ['S%d'%i for i in range(10, 53)]
+    T = ['T00%d'%i for i in range(1, 10)] + ['T0%d'%i for i in range(10, 100)] + ['T%d'%i for i in range(100, 169)]
+    
+    profiles.index = ['%s . %s'%(s, t) for s in S for t in T]
+    
+    # Make WND_VAR_T
+    f = IncFile(name='OFFSHORE_WND_VAR_T', path='Output',
+                prefix="TABLE WND_VAR_T1(SSS,TTT,AAA) 'Variation of the wind generation'\n",
+                body=profiles,
+                suffix="\n".join([
+                    "",   
+                    ";",                 
+                    "WND_VAR_T(AAA,SSS,TTT) = WND_VAR_T1(SSS,TTT,AAA);",
+                    "WND_VAR_T1(SSS,TTT,AAA) = 0;"
+                ]))
+    profiles.columns.name = ''
+    f.save()
+    
+    # Make WNDFLH
+    f = IncFile(name='OFFSHORE_WNDFLH', path='Output',
+                prefix="PARAMETER WNDFLH(AAA)  'Full load hours for wind power'\n/",
+                suffix="\n/\n;")
+    f.body = (
+        profiles
+        .sum()
+        .div(profiles.max())
+        .T
+        .to_string()
+    )
+    f.save()
+    
+def create_investment_options(profiles: xr.Dataset):
+    
+    # AGKN
+    areas = profiles.coords['Name'].data
+    options = [
+        "DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2020",
+        "DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2030",
+        "DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2040",
+        "DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2050"
+    ]
+    
+    f = IncFile(name='OFFSHORE_AGKN', path='Output',
+                prefix='', suffix='')
+    f.body = "\n".join(["AGKN('%s','%s') = YES;"%(area, option) for area in areas for option in options])
+    f.save()
+    
+def distribute_offshore_potential(total_potential: float, geofile: gpd.GeoDataFrame):
+    
+    # Calculate area as potential distribution
+    temp = geofile.copy().to_crs('EPSG:4093') # To geocentric (meters)
+    geofile['Values'] = temp.geometry.area / temp.geometry.area.sum() * total_potential
+    geofile['TECH_GROUP'] = 'WINDTURBINE_OFFSHORE'
+    
+    print(geofile.drop(columns='geometry').pivot_table(index=['Name', 'TECH_GROUP']))
+    
+    # SUBTECHGROUPKPOT
+    f = IncFile(name='OFFSHORE_SUBTECHGROUPKPOT', path='Output',
+                prefix="TABLE SUBTECHGROUPKPOT(CCCRRRAAA, TECH_GROUP, SUBTECH_GROUP)  'Subtechnology group capacity restriction by geography (MW)'\n",
+                suffix='\n;')
+    f.body = geofile.drop(columns='geometry').pivot_table(index=['Name', 'TECH_GROUP'], values='Values', aggfunc='sum')
+    f.body.columns.name = 'RG1'
+    f.body.index.names = ['', '']
+    f.save()
+    
+#%% ------------------------------- ###
+###            2. Main              ###
+### ------------------------------- ###
+
+@click.command()
+@click.option('--weather-year', type=int, required=True, help='The weather year chosen for wind profiles')
+@click.option('--total-offshore-wind-potential', type=int, required=True, help='The weather year chosen for wind profiles')
+def main(weather_year: int, total_offshore_wind_potential: float):
+    profiles, geo, offshore_geo = load_profiles(plot=False)
+    create_geo_sets(profiles)
+    create_profiles(profiles, weather_year)
+    create_investment_options(profiles)
+    distribute_offshore_potential(total_offshore_wind_potential, offshore_geo)
+
+if __name__ == '__main__':
+    main()
diff --git a/src/assumptions.yaml b/src/assumptions.yaml
index b691645..6c28e26 100644
--- a/src/assumptions.yaml
+++ b/src/assumptions.yaml
@@ -6,13 +6,6 @@ balmorel_input:
 grid_assumptions:
   electricity:
     investment_cost: 3.1 # €/MW/m DEA high bound
-    # XE_cost: 2.511 # €/MW/m DEA low bound
-    # XE_cost: 2.175 # €/MW/m actual transmission data (from US though): Maxwell Brown, Wesley Cole, Kelly Eurek, Jonathon Becker, David Bielen, Ilya Chernyakhovskiy, Stuart Cohen, Allister Frazier, Pieter Gagnon, Nathaniel Gates, et al. Regional energy deployment system (reeds) model documentation: Version 2019. Technical report, National Renewable Energy Lab.(NREL), Golden, CO (United States), 2020.
-    XE_substation_cost: 76000 # €/MW very high bound
-    # XE_substation_cost: 4476 # €/MW low bound
-    # XE_substation_cost: 0 # No cost
-    XE_FOM: 21.7/1e3 # €/MW/m/year
-    Xsub: 99 # Amount of substations (assumed similar to today ~ one pr. municipality)
     lifetime: 40 # Lifetime of grid elements
     transmission_loss: 3.318383e-08 # fraction of loss pr. m, From Balmorel DK1-DK2 line
     transmission_cost: 0.0001 # €/MWh
@@ -28,4 +21,8 @@ grid_assumptions:
 
 fuel_assumptions:
   h2_demand_per_shipfuel_mwh: 0.412  # Assuming TG-MeOH
-  h2_demand_per_jetfuel_mwh:  0.614  # Assuming TG-FT-H2
\ No newline at end of file
+  h2_demand_per_jetfuel_mwh:  0.614  # Assuming TG-FT-H2
+
+timeseries:
+  weather_year: 2012
+  total_offshore_wind_potential: 40000 # MW
\ No newline at end of file
diff --git a/src/snakefile b/src/snakefile
index ee411e0..793b1d8 100644
--- a/src/snakefile
+++ b/src/snakefile
@@ -16,7 +16,8 @@ rule all:
         f"{out_path}CCC.inc",
         f"{out_path}XINVCOST.inc",
         f"{out_path}AGKN.inc",
-        f"{out_path}TRANSPORT_TRANSDEMAND_Y.inc"]
+        f"{out_path}TRANSPORT_TRANSDEMAND_Y.inc",
+        f'{out_path}OFFSHORE_WND_VAR_T.inc']
 
 rule create_conversion_dictionaries:
     output:
@@ -208,3 +209,17 @@ rule grids:
         ]
     script:
         f"{modules_path}grids.py"
+
+# 7. Other
+rule offshore_wind:
+    input:
+        f'{out_path}VRE/2012_offshore_wind.nc'
+    params:
+        weather_year=config['timeseries']['weather_year'],
+        total_offshore_wind_potential=config['timeseries']['total_offshore_wind_potential']
+    output:
+        f'{out_path}OFFSHORE_WND_VAR_T.inc'
+    shell:
+        """
+        python {modules_path}offshore_wind.py --weather-year={params.weather_year} --total-offshore-wind-potential={params.total_offshore_wind_potential}
+        """
\ No newline at end of file

From e132b2eb2f7b6735966381c52d0a58c72c1dd7ea Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Mon, 30 Sep 2024 18:53:28 +0200
Subject: [PATCH 02/19] Cleaned up

---
 README.md                                     |  11 +-
 src/ElectricVehicles.ipynb                    | 142 --------
 src/Modules/createDH.py                       | 331 ------------------
 src/Modules/createHYDROGEN.py                 |  83 -----
 src/Modules/createINDUSTRY.py                 | 286 ---------------
 .../existing_powerplants.py}                  |   0
 .../technology_data.py}                       |  11 -
 src/Modules/{createVRE.py => vre_profiles.py} |   0
 src/README.md                                 |  19 -
 src/create_Demands.py                         | 274 ---------------
 src/dot_source.txt                            |  36 --
 src/main.py                                   | 187 ----------
 12 files changed, 9 insertions(+), 1371 deletions(-)
 delete mode 100644 src/ElectricVehicles.ipynb
 delete mode 100644 src/Modules/createDH.py
 delete mode 100644 src/Modules/createHYDROGEN.py
 delete mode 100644 src/Modules/createINDUSTRY.py
 rename src/{create_GKFX_DK.py => Modules/existing_powerplants.py} (100%)
 rename src/{create_TechData.py => Modules/technology_data.py} (94%)
 rename src/Modules/{createVRE.py => vre_profiles.py} (100%)
 delete mode 100644 src/README.md
 delete mode 100644 src/create_Demands.py
 delete mode 100644 src/dot_source.txt
 delete mode 100644 src/main.py

diff --git a/README.md b/README.md
index 7908082..9163f29 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,8 @@
 # Balmorel Pre-Processing
 
-These scripts process raw data into Balmorel input. An application presented at the EGU24 conference is illustrated in the poster below (check Zenodo link below for a high-res pdf). For more info, read README in the src folder.
+These scripts process raw data into Balmorel input for a 2050 greenfield model of the Danish energy system at municipal spatial resolution (99 nodes). 
+
+An application presented at the EGU24 conference is illustrated in the poster below (check Zenodo link below for a high-res pdf). For more info, read README in the src folder.
 ![Application example](https://github.com/Mathias157/balmorel-preprocessing/blob/master/Raw%20Data%20Processing/Conference%20Poster%20for%20Analysis%20of%20Spatial%20Resolutions%20for%20Modelling%20Sector-Coupled%20Energy%20Systems.png)
 
 Data can be downloaded in the Zenodo link below and should be placed in src/
@@ -13,4 +15,9 @@ The necessary python packages can be installed in a virtual environment by follo
 
 ```` 
 conda env create -f environment.yaml
-````
\ No newline at end of file
+````
+
+## Get Started
+
+The processing is initiated through a snakemake command in a command-line interface in the src directory.
+A plot of the processes can be found [here](src/Analysis/snakemake_dag.pdf). Note that vre_profiles.py and existing_powerplants is currently not part of the snakemake process, but the output from vre_profiles.py is used in the offshore_wind process. 
\ No newline at end of file
diff --git a/src/ElectricVehicles.ipynb b/src/ElectricVehicles.ipynb
deleted file mode 100644
index 3e9659a..0000000
--- a/src/ElectricVehicles.ipynb
+++ /dev/null
@@ -1,142 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Bus__count',\n",
-       " 'European_countries_car_ownership.csv',\n",
-       " 'KFZ__count',\n",
-       " 'Lfw__count',\n",
-       " 'Lkw__count',\n",
-       " 'LoA__count',\n",
-       " 'Lzg__count',\n",
-       " 'Pkw__count',\n",
-       " 'PmA__count',\n",
-       " 'Sat__count',\n",
-       " 'traffic.tex']"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import os \n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from pybalmorel import IncFile\n",
-    "\n",
-    "path = r'C:\\Users\\mathi\\gitRepos\\balmorel-preprocessing\\Raw Data Processing\\Data\\PyPSA-Eur-Sec Data\\emobility'\n",
-    "\n",
-    "os.listdir(path)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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b3vAGjI8bFsKWLVtw55134q677sJ///d/44c//CHe/OY3Y/fu3bj//vvxjW98A5/73Ofw8MNGRiIWi+E73/kOnnnmGdxyyy2477778OlPf7rmWPL5PK699lr86Ec/wgMPPICdO3fik5/8JPv6t771Ldx888246aab8OCDD2J8fBy/+MUvfK+BJR7+V+CG0+dvdQUlL4uPBRYcDmgqsP3B9h5TK1GcJlkfAOhcCKx+Hfl4rq0BVV66Fts/j29UF900NB38bCOAy7xE1UZtQaLdBxA4ynngaw5ecbPw93uBVKfvlznyyCPx5JNP4sEHH8QjjzyCkZERpNNpAMC1116LX/7yl7jtttvwN3/zNwAAVVVx0003obu7G0cffTTOPPNMvPDCC7jjjjsQi8VwxBFH4Bvf+AZ+97vf4dRTTwUAfPzjH2c/b9WqVfjKV76CD37wg/je977HPl8ul3HDDTfgsMMOAwBcccUV+NKXvsS+/u1vfxtXX3013vGOdwAAbrjhBtx9992+f39LPPEj0v/i6Z8B6z/SvJ8jK2ipdHYBcOgZwNjLxDo66i1tPayWgaouqS4glTXKiKf2tu+YgoamATPD5OOuRfbP7TsEgAKUpgmx7Rxs+uHNZ7BqI5p50RvVRX1e2oO5p7zMAWiaBkVRsHnzZszMzGDBggXo6upi/7Zt24YtW7aw569atQrd3d3s/0NDQzj66KMR42aiDA0NYWTE6JHx29/+Fm94wxuwbNkydHd34+KLL8bY2Bjy+Tx7TjabZcQFAJYsWcJeY3JyEvv27WNkCAASiQROPvnkYBeDx+xB8jjX7rRFQZWX7ACw6nTy8a45WCpsBUpe6Cad7iGPxTmUeSlMANUi+djJNkp2AD3LyMeRddR0NFQbUdsomizdFsw95SWZJQpIu352AHjuueewevVqzMzMYMmSJdi4cWPDc/r6+owfm6ztBUErmOo/R6uYtm/fjre85S340Ic+hK9+9asYGBjAgw8+iPe///0olUrIZrOWr9vWTAtVHrb/gQzli8XbdyztAK+8xPS3bmGibYfTcjDyoisSHTp5mUuBXWoZdfQScuKEgdXA1G5gbAuw4pTmHts8hqZp3Gyj+j4vkfLSDsw98qIogVg37cJ9992Hp556CldeeSWWL1+O/fv3I5FIYNWqVYH9jMceewyqquJb3/oWU2d++tOfunqN3t5eLFmyBA8//DBe+9rXAgAqlQrL5QSOShEo58jHxUliHy09IfifIzOY8rLAmKhcnLF+/lwD3dg7F5JHqrwUJttzPM2AaFiXgiovfOfhCIGjUFZZrCjq8yIH5h55CRGKxSL279+ParWK4eFh3HXXXdiwYQPe8pa34JJLLkEsFsP69evx9re/Hd/85jexdu1a7N27F7fffjvOP/98zxbN4YcfjnK5jH/5l3/Beeedhz/84Q+44YYbXL/Oxz72MXz961/HmjVrcOSRR+K6667DxMSEp2NyRL6ux8m2388v8qJWDdssu4BkuwCgNI/ICx3K2KWTl45e8jiXbCMW1hUkL5l+8kjPjQhNAVVdACCTrO3zUorIS1sQZV7aiLvuugtLlizBqlWrcO655+J3v/sdvvOd7+BXv/oV4vE4FEXBHXfcgde+9rW47LLLsHbtWvzVX/0VduzYgaEhwYubCY477jhcd911+MY3voFjjjkGP/7xj7FhwwbXr/OJT3wCF198Md73vvdh/fr16O7uxvnnn+/5uGxR36Bt+++b83NkxewEAP3WL9MPpPSMU6UAVOdJT6BcnfIyJ20jl8pLRF5aAtrjJZuKI6YPZIz6vLQXkfLSJtx8882sl4sduru78Z3vfAff+c53TL/+xS9+EV/84hcbXrse9bmZK6+8EldeeWXN5y6++GL28aWXXopLL7205utvf/vbazIviUQC3/72t/Htb3/b8ffwDXpxjiUBtQzseIhs2vF5cgpTyyjdS+bapLuMr5WmjU1sLqM+85LWlZdybu6cC7TSqNuhTJoiIi8tgdHjxTjHItuovYiUlwjhALWNlh5P7ILSNLBvs+23zClQ5Sk7QB4TaULkgPmTe5mpqzaiygswd6wjZhs5lElTROSlJcjr5KUzbRQJGNVGEXlpByLyEiEcYJv3IHDIaeTj7Q+073haDT6sS0HVl/mSe6HKC93Y40mjwm+uhHZZYDdSXmRCjtlGhvJi9HmJbKN2ICIvEcIBqrxk+o0eJ9vmUe7FjLzQ3Esp1/rjaQfqMy/A3Ov14ll5mWjK4UQgYMpLKlJeZEFEXsKCwiQw/AxpkT4fwdsmq3XysvNPwHyZwM03qKOgyst8OCcqRUNd4cnLXAvt0sCucOaljzxG5KWpYMpLOsq8yIKIvIQFhQmgWjI2sfkGKotn+o2ZLuWc0fslYNzyx+14y7/8Hgemi015fdcwVV7mkW1Ey6RjCaCjz/j8XFJeKkXjPHdbbVSaBqrl5hxXBHPlJao2aivmDHmZ89OMaTlscSb0Q9g8/a3ytMfJAMk5KPqp2yTL5McP78DTe6bw8DZJyGK+LrALcMrLfCAvnGXEjb0wlJc5kHmhllEsKV491tELgJTuRupL85ArmWReoj4vbUXoyUs8TphwqVRq85E0GbSjqlomCkyIQecn1Y8fsAW1jTIDehfl5m3cmqZhz8FZAMDUrCQ9VPjRABS0k/R8Ul7qhw/SRnVzwTbiG9Qpitj3xOLGGkSh3aYhXzSpNorGA7QVoW+MkEgkkM1mceDAASSTyZphhHMKxRKg6orF9HhT+3oUy1VM5Evo70whlQhufpCmacjn8xgZGUFfXx8jnkLgA7sA2biLU0QuDxgT+TK705ouSCLF2wV250PmhY0GqAuyziXbiOVdXDagzPQTWzkiL02DmfISZV7ai9CTF0VRsGTJEmzbtg07duxo9+E0D5P7AE1/k6Rma+2DgHEwX0KuWEVPRwI9GRfqiCD6+vqweLFgIJGivs8Jy3sEbxvt1lUXAJiSjbxkTGyjeaG80B4vC2s/P6dsI71BnWjehSLTDxzcFpGXJsK22ijKvLQFoScvAJBKpbBmzZr2WkelWaICiJY4ukG1DHz/AuP/PcuBS34Z/M/R8Y3/fBQvDc/g7ccvxd+9YXWgr51MJt0pLgDJ+LDArr55NzHvsWcizz6WxzayCezOi8wL7fFSR15ol905QV5clklTRL1emg7TaqN4lHlpJ+YEeQGAWCyGjg6BEfLNwk/eSUp3P7YZ6F0W7GtPTwAzuwAoxAuf2QWUxoGepcH+HACVqoo/bp9GqaJi34za3jWlKE4Dqk4iGpSX4Ddu6ZSXaoXYAsD8bVJnqbzMoeGMbhvUUbBy6Yi8NAv2yktEXtqBORoQaQMOvEDCtAeeC/61+bvuxevIxzv+GPzPAbBtNMfuJKaLkqgO1DJKZIBkhnycal6PkxryMisBeaHEBajNOrEmdfOAvFhlXuZSn5dIeZEWtn1eoiZ1bUFEXoJCWd/w6AUoSNBKi+wCrjX+g8H/HADP7TfIgOuwamEKKOWdn+cW9WFdgFMdmpF54WyjggQEjpLXjr7a4YPzqlSaVhvV20ZzMbDrVnmJyEuzEfV5kQ8ReQkCmmY0S2sGeclzZaKrdPLSJOXluX3GJjDtZuOuloHrTwFuOC34rrf1YV2gdbaRDMqLWXddYH41qaMbe70qMSeVFw+BXSAiL01E1OdFPkTkJQhUS0YlUFPIC7d5r1xPPh59wZiyGyCe58jLjBvbKD8GTO8DxrcaZCMo5LnuuhRNao3P93gBJMm8mIV1gaYrL5OzZYxMF5ry2q5QKRmZl/qcV3qOVBtpmr9qIyAiL02EeZ+XqFS6nYjISxDgrYtcM22jQUJg+g4h/x/fEviPem4fbxu5IC9lY8PH9L4AjwgtVV6mZis1WR9X1UaVEnDnZ4CX7gn0mCzJC8u8NKfPy/nX/wGvv/Z+5NqdfaKqSzzVuAZzJbA7e9BoPhllXqSDeZ8Xsn1WVA2VakRgWo2IvASBMpfzoHdPQYJuXrS7KL3bDHjjPpgrYf+Ucac944a8VLg79KmgyUtdmTTQtD4vu/UyaXphmi1XxWXhnX8EHr4BuOcLgR6TNXnRO+w2QXnJFSvYOprDTLGCvROzzt/QTEztJY/dixs7z1LbqFIg5DGsoIptRx+QSLv73oi8NB0s85JurDYCgFJEXlqOiLwEAV51aIKVwzIvdPNibeGD3bif20/uXgc6U+TlqyoKZcEwWo3ysjfQ47IN7Aa8cdO8y5qhLvY54eAyPZaJXYEek+nvDzS1VHqEG0h5MN9m64ySlx6TFgSUyAPhVl/qb1DcICIvTUWpoqJcJd3NazIv8VjNcyK0FhF5CQKttI2AplXaPK9bRieuNDZJYeuIV15ov4qg0ELbiJKXQwY60aWXRQpXHNE1KE0HGyA1m2sEGGtQLQWuOoxwCtzBfJsVDWpDdi9p/FosbqxDmHMv/Owut6DkpTBpzECLEBio6gIAWa7aKBGPIR4jSmCUe2k9IvISBHjVIT8W/Gj6+onCTbILaKXRK5b2sI1bOLTLr8FUs5SX5pMXGtZd3p9BT4dOXkQrjmrWYE9wB8UmKtfdlae7jY8DXocDM4byMtFu8sKUF4umjB1zoMuu2dRwUXT06R9o4V4DSUHzLqlEDMl47ZbJyqWjXi8tR0RegkC5TgGhSklQ4EulgaZt3M/rPV6OWtKDbn3jFrZMKsZm15LAbtNsI5J5Wd6fYXOdhCuOanI/AZIXmiHqrtu840kgrucjAj4XRqZ48iKJbWSmvABzo9eLVa5JBImUcU2IrKPAwSqNUo1jTaJeL+1DRF6CQH1jtiBDu5rGXdjqyUtwtlGlquKFYUpeug3lRdgyaWK1UQuVF2obLevPoKdDJy+iFUc8eZkMkLzQ9ewx2bybROKkyryw399KeZkDvV5mLXJNomC5l4lADieCAbNKI4oUIy+R8tJqROQlCJTrqjFyAYZ2C5PcXJ/6wG5wG9b2MTIWoDMVx4r+LFNehPMe5WZWG02Qx5rW+M3ZtPdMUNsoi56MS/WpZg0Css7KBWNjM1MemkTi+P4u0ttGc0J50RUTr9Pio/lGTYNZjxeKqNdL+xCRlyBQbxsFqbxQ1SXVBST1IYlNIC+7xsmmvWqwE7GYgm5ddRDOvPDKS340uABptQIUdR/fzDYqzRB1KgBMFcqY1PMty/o45UXYNuIzL7sDOSamOsTT5nflNPcScLO+A9MebaNqGdi6MThVUNPsA7sAp7yEOO9B3+deArtAVHHURNgpL5Ft1D5E5CUI1CsvQXbZNfPCm2Ab5VgfA/IG7XKbeSnXdWKdCajiiL8Ys2AijDWAVttnxwdoWLc/m0RnOmFkXkRto2YoL7Ryq2dJY48ToHnKyxRvG7kgopt/AvzH24CNXw/mQPJjRvM2S/JCA7shVl7Mcl1uEJGXpsGsxwtFNFm6fYjISxBoyLwESF74oYwUTSiVzrO7C/IG7WHkxYPyAgRnHdGLekdv7VDCZBaAvpkHZB3tPmhYRoCxBp4Cu0FlXqadwqrNyrzwtpEL5WVS73Gz/8lgDoSSwM6FJJhqhjlhG1mUw4uClUtPBHI4EQywidJmmZd4NN+oXYjISxCgd/6KzsyD7PVSX2kENKVUelYnL536G9R9qXSd8hJUaNcsrAsAsVjg9tkertIIAKe8eKk2Clh5sSIvTVBeShW1JqQ7MetCeaFrcHBHMAfjZBkBcyywGykvssFsojRFlHlpHyLyEgQoeenVO4C2zDYKbsOiyktGf4PSzIt4qXSTyItdFUbA6zChk5R+vcOwkXnx0OumNB1MBsMprMqCy8FlXka5Hi8AqTbSRHNFlMRO7gqmYZrT7w9wwxkn/P+8dkBVDdIR2UbSgSkvaZPMC7WNRDuRRwgMEXkJAtS+6V9FHpttGzUh80LvLrKMvLi0jepzP0EpD3YX9YAtk1n9ApRN6tZZxmWTunoCF8QaMOVhsfnXmzAigJZJ9+rKU6misrVxBLUP1UowvW6cerwARhYqrLZRYcKYSh8pL9LBXnmJMi/tQkReggDduCl5CdQ20pUHM9uoCZkXqrxQ28j1eAB68Q1qRICVbQQErrwU6tbAdbVRPYELIvciahsFaCHS0QCrFmSZpy/c64W3Dw9u938w0zZzjSjCbhtRwpHqts71OCEiL00DJe6ZZCN5Sem2UZR5aT0i8lKH/ZMFXPiDh3DnUy5sD2obUfIyezC4UuH6oYxAU0qlWWA3SUiL+1JpfdMaOJQ8BmUb0bLzzoWNXwuYvNCLVAdTXtw2qau1WwJVHix7nASvwlHlZWF3B3qzZA2Ee73wwe0gyMuUTYM+irAHdllY12ODOiAiL00EJSa0IR2PSHlpHyLyUofbn9qHh7eN47//7GIyMCUvPcuBmO6LBtWorn4oI1BrG6nBvGlm68oBXY8HoHfcA6vJY9DkpXuo8WuB20ZkLekdVrfraiN946YqgV/yUtPjxMI2YudCcJkXSl4W9aTRz8iLF+UlgNBumwO7uWIFlWqTNya/PV6AiLw0EWLkJcq8tBoReanD1gNkI5wtCd5tA0apdKoT6FxEPg6qUZ1ZYJdu2tAaS5S9/piGwK7HUmmqvEztC6Z5nJ1tErTyYmEb5UtVlEU2MLpxLziMPPolL4UJQ9GyLJWmTeqCU+EO6GXSi7rT6MsSG0O410slYNuIrqFQYHcysIaFADCZL2P9hntxyU2PBPaapvDb4wWoJS8BrkEEoKi/91NxM/ISVRu1CxF5qcPWA0R+p5u5EKjykswAXbq9EZTyQskLn3lJZBB0j5P6Pi+uZxvRjbtfV17KuWBkfEoCu5qvvBTqvG1K4ABBEscInE5e/GZeqGWS6SfnlhmaUHlGu+su6u5AX8at8sKR6Qmfykspb1Rs2ZEX2qROLTeGpn3gpZFpTBUqeHTHQfFqKy/w2+MFMMiLWgm8YeF8h6G8mGVeoj4v7UJEXuqwdZQqLx7IS6rT2GSDUF7KBeNCxF/YmtDjhCbqM/WZl1IFqipw4aYbd3bA2EyCCO1OU9vIxDZpcuYlEY+xCgOhiiOaeWHKi89qI6cGdUBTmtQx26g7jX5deRHPvASovFDLKNlpqCtmSHWBkfkArSNK4koVVTz75QV+e7wAhNzSCePRcMZAEdlGciIiLxxmihUM623RhUtDAcM2SmY42yiAiiOqusQSBiGgCLjiqF55oaqDphmjA2xBlZdEh7HZ+t28y7PGXCMz5aXJthHAhXZFci/lOuVlao8/Cd+p0gggFSpAsKXSUzSwm0ZfJ/n9xauNOOUld8Df+cnCyhajEShisaaEdvnJ2mMzTRxOyaxhH+QFAFKkM3RQ4zIiEFBiYkpeWJ+XSHlpNSLywmH7qHGhdWcb6RfsZCfQpZOXIGwjvtKo/uIdcK8XStZoYDediCEZJz/TlWXCkxe/oV26eSc6Gskb0HTbCODKpUUqjip1mZfSjL/NdEogrMrWIJjArqpqrEndop40+jI+Mi+Av9CuSI8XiiYMZ+SHU47lijbP9Am7dgBukAy+hUIETnmJMi9SISIvHLYcMDZBd7aRfrFIZQ3yEoRtZFZpRBG4bUQ3bqK4KIrCddl1obwkO4x8gl/ywuddWjCU0KyfA2tU56S8aFptrxuaQfCTe5kWKBPmz4MAchnj+RIqqgZFAQa7jGqjSVHlha5BQs/o+Mm9iPR4oWDDGZtDXkabqbz47a5LESkvTUFJD+ymTZSXKPPSPkTkhQMN6wLkhBUqkayUSEgO0AO7lLwEoLzYVVoEvHHni7UddgF+vpFI3oMqLxkjn+J3OCOzTZzKhAMmL6lG5cWxZJxXHBJprlzah3UmUiZM10CtNPaZ8QBqGQ1kU0jGY+6rjSiJXbiWPPrJvVDiZ0feKJoRXJ5plW0UQLURoA8rReOg2Ai+EGVe5IQn8nL99ddj1apV6OjowKmnnopHHrEvJfz2t7+NI444AplMBitWrMCVV16JQiG4qoCgsHW0Vm4Vyr3wdznJgEulJ3eTx97ljV8LsDmZpmnIl2szLwDf58Sl8hKUbWRXaQQE3+fFLvPiZBvx5CWZ4cjLbu8HJGKb0E0bCORcoNOkF3aT8Gefmz4vGle6v/Ao8uiHvIy9TB5phsgOyQ7yWD8g1AdqbKOZZtpGAfR5AQzyUo5soyARNamTE67Jy6233oqrrroK11xzDR5//HEcd9xxOOecczAyYh5Q/a//+i989rOfxTXXXIPnnnsOP/zhD3Hrrbfi7//+730ffNDYeqB2ExSyjviJ0vEkl3kJILA7oTfKMyMvAQZ2ixWVOQ4ZM+XFibyoKlDVL+6JjNENl16UvaKFyouqauwCVJt5EbSN6KZJzwOqlvlSXvTf3055iCcMiyaARnWsTLqHkAFWbSRSbVUtGzN6mPLiwzYafYk8Dq51fi5dg4D6HgH1mZcmKS+aFkyfF4CzjYJbgwhR5kVWuCYv1113HT7wgQ/gsssuw9FHH40bbrgB2WwWN910k+nz//jHP+K0007DRRddhFWrVuGNb3wj3v3udzuqNa2GpmnYVqe8CIV26YUi1UlyGTSfUpgkF3M/mNTJS9/Kxq9R8hJAUJP/PbMpo7eJcOalyt2VJjuC6/bppLwESOAKnOxbm3mhyouTbURD2/om6pfAVSsGAe626XECBKpA8WXSALgOuyXnknmeOCw8kjx6VV5KOUO1Glzj/Hy67gEpL3xwGWictB0YSjmgqhMjP31eAM42ipSXIEEzL3bKS5R5aT1ckZdSqYTHHnsMZ511lvECsRjOOussPPTQQ6bf8+pXvxqPPfYYIytbt27FHXfcgb/4i7+w/DnFYhFTU1M1/5qN/VMF5EtVxGMKk8qFbCN6oaAXjkwfWM8J6mV7hZ1tFGC1Ee3xkk7EEI8ZwViqOjhmXvg7vUTGuIP0S16clJcAu8vyKhsfzDOGMzoQOL5UHDDWwCt5mRkmKoYSr21QaIYAFagDdeSFzjZSNQESy4iDAgweQT6c2OEtSEwto+wCMUUiGazyMjFbRoUja03LvNDzI542riFeQcl8FNgNFMUo8yIlXJGX0dFRVKtVDA3V3gkPDQ1h/37zhmQXXXQRvvSlL+E1r3kNkskkDjvsMJxxxhm2ttGGDRvQ29vL/q1YscLNYXoCDeuuHMjWtIV3BN9dFwBicf8bF0CsGBrYbTp5acy7AECX6IgAmveIJYiNQZWX/Li/ChimvAjM9fFZaWM0qIshxhM4Wm3kqLzUkxf9LtorgeWJW6yxs2cNAlReaEiVZl7SiTg7LyZmHTZwvly+bwUAhbw/vLQNcGMZ0Z8JBGaZ8JYR4LJUemov8Ph/iKlAvGVk18tGBFFgtymwtY2SUealXWh6tdHGjRvxta99Dd/73vfw+OOP4+c//zluv/12fPnLX7b8nquvvhqTk5Ps365dLoYkegTNuxw62Mku1q4yL/SuBzCsI9qnxQtyI0ROVmIWc32CK5U2yEui5vPC843KXKURwLUqL/sjV2wDdwjsaqrvTcusxwvAKy+C5CVZr7x4JS8uepywRnUBWIhFOqDTOBf6WcWRYO4n2aFXXOl2l5fcy+iL5FHEMgI42yhY8pLQiawr5eW+rwK//jvg6Z85PzeoHi8AtwaRbRQUNE2zLZVmmZeoSV3LkXB+ioHBwUHE43EMD9dW0gwPD2PxYvO7489//vO4+OKLcfnllwMAjj32WORyOfzN3/wN/t//+3+IxUxOiHQa6XTazaH5Bq00OnRhJwvnidlGdcoLYNx153yQF2oZdS8lAdB6BGgVsNEA9cpLWjDzwlQH/W+WzALxFCFfswe5QZIuUC0b5M9KeUlyhLGUMwKLHjBbagzrAi6qjRoIHLXOPJKX8W3k0Ux1q0eAyotZr5veTBJ7Jmady6UrdWvQt5Koh5M7gRWvdHcgjLy4VF4Cmm10YIa8zmELu/DC8DTG8yVUVa3GVrUEzSqNveT83KB6vACcbRQFdoNCRdWYqGtmG7E+L82ePB6hAa6Ul1QqhZNOOgn33nsv+5yqqrj33nuxfv160+/J5/MNBCUeJxfGpg47cwlqG60e7GLKS16oLT4NanIbZye1DHzYRpM2lUZAoKXSsxa2kaG8iN5x65uWovjfvOl4hVjCOsgYi3FdRf2pDsw2slgD98oLdw54Oc8PPE8eFx3l/NwAiexsuZHE9XcaoV1blOvWgIaWvZD4UT3zskBUeWmObbRmqAuKQv6E4r1u9GOgNyB2CGo0ABDZRk0AH8S1zby4GScTIRC4to2uuuoq3Hjjjbjlllvw3HPP4UMf+hByuRwuu+wyAMAll1yCq6++mj3/vPPOw/e//3385Cc/wbZt23DPPffg85//PM477zxGYmQAHch46EK3thHtrmtmG/kgL3Zl0vzPa2LmpZsFdp2UFy7rQOG34mhGt4w6FxGSYoWAVAczxQHgxwO4zbwMGJ/3EqAceY480qodOwSovBRMet30seGMomugk1ivozJU1VAtRG0jViodkPKik5clvR3MNhO2jlyRlwBtI6a8RLZRUKghL1GptFRwZRsBwIUXXogDBw7gC1/4Avbv34/jjz8ed911Fwvx7ty5s0Zp+dznPgdFUfC5z30Oe/bswcKFC3Heeefhq1/9anC/hU8UK1XsPkguOIcu7ERGz34IBXabbRs5kZcANiyqMHnPvNTdcQP+yQubJm2Rd6FIdQEY9q06zLKp2uYELleq2tsG9dVGqS7DOsuP15JbJ6gqcOAF8rGI8pIMbtOiJeMdvPKSFRzOWK8+UeXF7ZDSyV3kteIpoO8Qse9pkvKysDuNBZ0pjOdKeqO6budvdkNegurxAoRXecmP62ptv/G5mQPA1t8BR/yFN9s5IFA7KKaQKfP1oMpLRdXEbcUIgcA1eQGAK664AldccYXp1zZu3Fj7AxIJXHPNNbjmmmu8/KiWYCJfhqaRE3RhVxoZPUHuqsNujW0UQGCXXvj6LCqtAs28NN5tA3yfF8EeJwmewPkMrFLlxSrvQhGQAmU2GgAA0twmXq6qiFtV/rA+L/omSq2zmf1EgbP6O5phchchIrEkMHCo8/PpzwxgPADrMsz93nQ4o7NtVHceeLWNaKXRwGGkek0E9P0XWOaFIy9dKbw0AoyKNqqj14SpvaRfj93vwEYD+OzxAnCB3RCRl1IeuP4UQK0Cf303aW5YmARu/guSe+pdAfzFtcAR57bn8GzKpOs/X6qoDdePCM1DNNsItRdsRVGYAuGq2ognL0HYRizz4kReArSN6lQHY7aRF+Wljzw2W3lhvV58Zl70wG5H3RrwUrGtNFyus0wAY0Nym/uheZfBNeZh7Xokgqu0MSNxwiMCrJQXt92mmWV0uPj3sFLpYG2jhV0dWNBFgujCIwLoOmhVg4RbIajRAECgVnLLsOcxYivOjgM//ksyD+1nlxuB7cldwH9fCPz6o205vKJNmTRQW4EU9XppLSLyAl55SOiP8ZrP24J12OXJi34hyjUxsBvghcopsOvYoK0+6wAEl3lxVF6CUaCsMi/JuCEDl+0qCpj6xFXJeVWf3ORdAE558b9xF7h+NxT9osMZy3XZJ6+ZF7eVRkDgTep422iw023mhVM+nKyjZthGYao22vUn4+OJncD3TgVe+g05hy67EzjtY6RdxOO3AGNbWn54hvJirqgk4kZjzyj30lpE5AX83SZZDrqBueuwy2Ua/NpGxRlj07ckL/qmXc6RjIQPMOUlXZd50UulSxXV/q6i/o4b4KqN/CovDuQloKorqz4viqKwuy7bFuDUskkGYJ25qTQCAmvQVq6qKFdJZVSNbeRWeUnUKS9uJ6y7bVDH/8wAlJdSRWX5nkXdaUN5EW1Ux/8dnMgLtdSCsI3YbKMQ2Ua79DExp/wtWYPCJPn/264HDnk1cPaXjIqziZ0tPzy7Hi8URsVRRF5aiYi8gFMekmTzNqqNREqlzQK7nG3kpUyWdtZN9wIdvebP4UNsPoOas2U9sFtvG3UYZMZ2OGN91gEITnlxIi8BBZfNJkpTUPXFlrzUqw5Abbm0G7hVXgLqcVLgyDpvn9E1KTiR+XJd7oeSl3LOHbl026AOCDTvQUlKMq6gN5PEgi6ivIyKKC9q1ZhVBBgKqhkqJWPyupU97AbJkNlGqgrseph8fPy7gYt+CixeB7zxq8Cx7zSeR/NiIgHogOGUeeG/Vqq20TaqVnx3GQ8bPAV25xrqe3z4t430TUutkDsJmv8QhZNlBJANS4mR7rKlnJH98IBc0XzjjscUdKbiyJWqmClW2B1oA+qb1AHBVRtZDWWkCKi7rDEeoJG8pBIx5EpVB9uortcNYKhPbsiLqhqbt6jyElB3WboGilJ7p8mUJ6dGXPX2YbqbzOypFol1JFJxVZg0xkKI9ngBAm1SRy2jwa40YjEFCzpdZF7q/wZ2G+7UHvL+TXQYFpsfhE15GX2B/L2TWWDoGJLv+uDvG59Hr4N2RLBJoORFRHkpNFN52bqRXAvNrgnlWeAHryN//8vvs28tMYcwP35LB7BSYX3j8mYbceQl2WHYOl5Cu049XgCyw9Cf4VN1sBoPAAjON2J33AFVG6lVYwMTtY2a1OcFAJJxgfkl9aXSgLfA7sQOsvnEU0D/arHvYRu3v2qjItegTuHm7KREJ+fWKy+KwuVeBC1U2pyuazHQ0SP2PUCg4wH4vAsADOrKy5hItZEb8kJtkN4V/ucaAbUVV2oIwqNUdVl2kn0wnZGXNigvuppip7w0vdfL2BbgR+cDt7yVKCz12HIfIYJ7n/A2RyykiMgLuLyDrjy4a1Jn0mEX8G4ZAM49XigCmm/EbCMTy4Ru5raWQX3WAfCnvOTHSKUGFNKkzg4BVV0ZzdmsW4ALKS815MWD8sIqjda6KBMOJqxqReCEfn/APLhN81+ivV68WEZAU5SXhbrSaFQbCZCX+r+BLXnRZz71C/aycQJ/DQqD+rJTJy8rTrV/Xu9K8thG5cWq2ghowWTp3X8mCl1uBNj5x8avP/sr4+M2ELx2ISIvaMw7uGpSxzrsWpAXL43qnHq8UAS0cVt12AUM1UEo75G0yLy49WLp79+50HkDD4zAWSsvQsqDWWjZy2Rpt3kXwLDrfIZV6fvAqlzc8c7SbA06XVYceak0AjgCV/Dt/dcrLzTzMlOsiOd+KOw2XKq89K30dJwNSGYAKObHISOo8rLyVfbPozdxE60nL0U3mZdmKS/7nzI+fv722q9VisALdxr/n2x9qLldiMgLgHzdxsWUF6EmdRbKi5+KI6a8OJGXgBq02dhGQoPHTJUXXXVQy+6JBe0uu/AI5+cG3KTONPMSp8qDzaZoFlr2Yp2xSiM35CVY5YUvkwYECSxg3uvGba8Xt2MBKHji7FN94RvUAUB3OsHOAUfriJ4HaT1oX5i07kEUNHlRFK7LruSh3ZkDwLhe+rz8ZPvn0pu4qT2+KyvdQiSwaygvTTq2fZuNj5+/vZacb70fKE4Z/4+Ul/mFQp3ywDIvrsYD1CsvPhrVUfbsaBtR5cVfWDVnMVUaELyrMFNekhkS1gTcW0cjz5JHkcBqQHkPu2ojoWoC23JxL8qLYFiX/5l+lRerLsNubSN+Dbpcdtkd9UheeMLkU3WoV14URWHqi2Nol/7szkGgo498PLnH/LkHddtIdASCCMIS2t2tl0gvPKp2LIAZupeQ4oRqqeWZDnrTZm8bNTHzomm1ysvkLmD/k8b/qWWk6MfXBnWqXYjIC7gmdTSw62qqtElgF+Aa1blUXsqzxsXO6Y4scOXFxjZyq7zws0pckxd9Axea6xOM6mDV5wXglQcb5cU086LbRuW82LwZteq+0gjglBd/BK5gMhoAMMibqgEVu/PATH1yM9+oWjEakbm1jeIJMoEc8K28jNRlXgBw5MVJeeFuZpyCpkx5CZC80PeD7PONdurN6Vac4vzceJIQGKDluRch5SXZxMnSk7uAwgQ5t9e8kXyOWkfVMvCC/vFRb9WfHykv8wr1loEn26g+89LpUXnZ8zgJq3YtNt6wVgioQZtd5kXorttMeQH8N2kTUR9o3sOv8mKXeREhcGbVRuluY0MVUV/4gYT9q0QOm4B12PVJ4EyGMgIGeQMESazXzMvEDmIzJjJAj4PqaIaAxiSM6urKYDdHXvRy6VEn5YVfA7sS30rR6PESlG0EBDqks6nY+wR5FCEvgGGhS0hehFsJeAFVXRYeCbziHeRjSl62/57cGGYHgXUXks9FmZf5hXrlgW5g5apmv2lXK0ZDqqBsIxZiO9W5fDKAsKqmaWzjNs28uOkuy2/cgDflpTBlXKBEch8BbVj1vX54JCmBs10DEwKnKO5Cu2N6mfDAoYDVAEgz0HWvlnyVyNL5TlbKC+ChUR8l8SLkhVpGCw731qsioDEJtO9RF9dxeoFouTRP5O2Ul8ndADRy3aBrFASYbSR5YJeeD6LN+dpULi3U50V/vzSlw+4+3SJavA5Yew6gxIHhp4GnbgPu/yb52lFvMW52IttofqHe6+c9f1v1hfeVrUql3dpGlLw4lQ8CgfR5KZRVlv8yU17EKm1MNi3AG3mhqkv3UmcvHOCUF7+VNuYbN+BDeQHcNaqjlskCFwMJ63+mj3WwCi0nYgrj0a7tQzfzjbyWSVMERGQL5UYlclB0OCO9JiQcyAstk+5bGUyPF4qwBHbpNUG0gWebKo7EMi9NDOxS5WXxsUTJPuTV5P8/ez+w8yGi7B7/XmN9ChO+h9SGBRF5QWPmJcUN27IN7dILlRKr7S4LeKs20jSOvDiUDwKBlErnuFyPbd7DttLGxC4AOPLiJrBKw7qiQwm5ElkfsMu8pBLkXBAbzFi3Bm4a1VHlZcFhzs/lwas9PkK7vuc7mdmHNPOSHzdvsMXDa5k0BQsueycvmqaxrBu/Dr0Z0kTtoNN8J/ZeyHBWhxl5CbjSiIKqsTIHdjUNmJ0gH4vcoABtGxHgrtqoCZkXGs5dso48Hn8ReUx1Aye+D3j/PcCKV5KGjnSUzDzJvUTjAdCovCiKgkwyjplixb7XC99dt/7uyUuPj9GXyB1JooMwbScEENg1envEEIs13gG6U17qMi+MvEyIH9AILRU+Wuz5AfQ44a0z02ojV4MZ68mLi9wPIy8ulZdYHIglSV7Ej/JiV3EVj6FYUcV63TSElhUAGlGfum3GPXitNKIIoFFdqapC1Xk6byFSNcq5XNwssGuiFjSLvIQhsFueJSMjAHHy0qbMS1v7vOTHjd936BjyeNy7gaUnkPOmftxG7wpSmj+5213gP6SIlBeYV9tkRLrsWvV4AQzyUpoR31jpePhlJwGJlPPzGXnxLhPajQYAREulHZQXV03aXJRJA1yljffmZOWqhqq+Y5n1eXGsuNI080oboDXkBQhEgXKa7wQI9rrhlZdYnLNQHSqOvPZ4oUj6z3sUSsbfmFdeUqJ312aB3am9jVmkZpRJA+EI7BYmyKMSN9RjJ7QrsMtsI+sMWtNKpall1HeIYa8pCrk2ms0Jo2vUhunb7UBEXmB+0TYqjuxm+uh3N/WVRgCR8GL6vA5R68hN3gUIxDZic51M7rYBPu8hMh7AYuN2k3lx2+eEESatdpqvC/C5Jk8ddqsl8vNrjkeH6JiIStHw872QF6ZAed+4A+syXG+dieRecmPGGnn5/YFAArt5/f2ejCs1VVZp0btrXnnpWkw2aLUMTO+vfV7TbCOaeZFYeWF5l37xvA8lgrMHfc8xc4O22kZ83kUEbZwB1Q5E5AXmHWbpBdzWNipbNKgD6ipNBCuORGd9UARQKm3X4wVwe8ftM7CbGzPuzkW66wKBhFVp1iMeU5CMN15Mk6zDrsXGxRMGr4Hd8W0ANCDdY+RE3CDhX3kxZnxZz3eyJLGaZj5ZG+DmG9mQF6q69K4Qmz5thgACu1YjEoRDmbz6FE8AA/pwTZrnoWiabRSCaiOWd+kT/56OHqNr8ZRF078mwE2fl8CnSrO8y3Fiz+9rjzrVLkTkBeZ3nEK2kVV3XQo3FUe5MeMCLtr7IIDMS47lHCxsI6e8h1old5aATeZF0DY6oKsufYcYxMwJ8RSMeS7eNu5ZLrCtmNwJOt51sx4zin48HEQDu3xY10v1SQBhVZFGfZabN99np57AifR68VtpBASivMyaVBoBbpSXuuA2nVFFR17Q58zoSoybfj4iSIXANuKVFzegm3MLK45EyEu3XlI/UxBoauoGrpWX9oSa24WIvMBcfRBqVMeUl4z51ztdKC+0XfbgEYbd4gRWKu0n86LbRiYbFiCwafGbZYPy4tI2ctNZl0JRuKCmt43bLusBCMz24Xu8NAS3BZUXP3kXIJCwKn0fpG3KxS0VOH7tG5QXgflGrMeLD/ISoPJi1etGXHnRb2iogkhbAADG5pLqcr+BOyEZItuIjk8QhV0AukmgmZe0Tal0j16JNlVwqERz9YPzxjkjqrz0tp7ctRPznryoqma6eWWSApOlWebFQuZ206iO5V0EVRf+57bENrLauLnN0q7Pi0iY1m1Yl4LdcXvrsjtrY5cAvGVi8TtY9XgBOOvQgcD5JS8BBnY9ZV7oGihx0s6dh8h8I7+VRkCgykujbSRabVQXWjZTXprV44X/uTKXStPArlvi1gZlQUR56ekg5/t0kMrL8NOAphLV0qnTOgVVpqb3OrclmAOY9+SFv5MyqzayL5UO0Dba/iB5XLne+bkUAdxts2qjtMdqI/qzY8nGrrD04qRWxNQht2XSFAl/lonVTB8KV8pLPYSVF48N6ijYGvjZuAUa9XlZA5H5Rn57vACBKi/1ZF682qievFDl5TmDwDer0ggIbN5ZU+G2QR1FG5QX+ve2tY06yLUzUOVl7ybyuPR4cYLbuYjY1ppKCMwcx7wnL/zwxZpqI/3jgpBtZEFeWFBxv/nXKWYPAnseIx8feob9c3kkA7hYU4/fYuMWvuM227RSWWNTdbKONM278uJzsrSd4gAIqE9MeUk3fo1aZ+WcPbHw2qCOwqd1BnAkzqbTsqc1cMq8FCaBg9vIx6JBbTMEkPtxmqztWnkZXAtAIec/vYk5uJ08Bh3WBUIW2HWrvLS+yy5TXkRso9kAycu+TeRxyfHi3xOLAT3LyMfzIPcy78kLvVilE0ZXXUBwsrRdqTRgbMJ7N9sfxLbfE7Y8eATQu0zouAEYG5ZWJRNGPSBX1LuJWpZKkzWx7HFi1VmWQrTiaHo/kZOVmPvcQ5MzL8LqU31gGSAl84r+ulah3cKkkQcZ8Ehekv6VF6vBjIDIGlj0uQG4zIsFednxEDn/Bw4Fuhe7OuYasIqr4DMvwr08+PEAACExNJRLMwzUIl58jOfjtASbbRQG5cUleaGVW5TotwAiTeqo8jJdqEDz2GuqAbzy4gZtGqPQDkTkxeJu051tZBHYXa7nV0aeIQMHrbDlPvJ42JmOx1uDmrbw3i7YdhOlARd33PVhXQrRiiNaaTRwmPVrWcFv5sVGcQAMAueY+zE7bkUxrCMr+5BaRl1DpCTUC5q4cQPGnWfRy3nQxZEX1eT7t/+ePK463dXxNoApkf5t1PrqO+EuqmZKJMu9PE+uA7sfJf93o7KKgjapkzmwSzMvbgO7C48CoBCib2dBBgjWpE4g81Lh8pO+UJ7lwrrHu/tequbNg3LpiLxYWAZCtpHdHTcA9CwhJ5OmGraQGbb+jjwe9nqhY2YIoMeJY2A37nDHaXfHDYhXHHmpNKLwmXUwG8THwwjsOlRcWa0BvRuidkE9/OZdAN8EDuAVqMbLQtKP8tK9hHy+WmrsdwIA2x4gj6tf6/qYaw/Sv3VmXA9q18B9nxdOjWW5lxeAHX8gSunAoU2yjUIQ2PWqvKSyhq06/Eywx2QBkcBuNhVnqn0god39T5NzpHMh0LPU3fe2qRNxOzDvyUvej/JCO7ratfKn6svuP5t/fXwr2dRiSeCQ00QO2QBfJuxVeSmb32lS0KZtzpkXC7Ukq1+gnELLXvMuADdZ2l/mxXuptE3eAzC6BfPlsjz85l2AQMKqBYH5Tp4UuHgSWH4y+XjnH2u/lh83+lmseo3rY65Bwr/yYtXrhiewttYAC+xy68ArL1vvJx+vfp3nY7RFKgTKi5cmdRQ0zN9q8mKTeVEUxQjtBpF74fMubqvRaOxgsnWN/NqFeU9erJQXMfKin6j1jcl40G651Oeux5bfGc8TbczGw2fF0aye6en0XCrtoDrQigor1YGCVRp5IC9Jf5bJbMm6ygbgRyQ42UYWa0AnZFN1qR5+y6QBjsB5Ow/KVZX1cPFUKm01GoDikFeTxx0P1X5+xx8AaCTY6ifvAgQzHsDCNkpzd9626ovZfCe+18vWjeTjZlhGABfYnYOZF8AYUEhvdpoM1ufFRnkBDOsokIojr3kXwKhwpdbcHEZEXixsE/p/W9uIKi+25EVXXnb92dzvZ3mXM0QOtxE+K45yRYe8h3C1kcWmNXAoeRzfZn0QqmqoEm7LpAHfk6Wdqo2SwpU2FmvA33mbgV6I/TRo83ke8Oe5aWDXSYFzInC0BcDOOvKyLaC8CxBMqbSD8gI4Deg0qUCk5d+5A3q2S/FvkVmBBnbViucQf1OhqiSgDrjPvADA0CvI4/DTgR2SHURsI4Avlw7ANvJSaUTRoY9QoGs8hxGRF4tZJkJN6hh5SVo/Z+gYciErTgKjL9R+rVoxLt6Husy7UPhsTpZneQ9z2yjtlPdwqjaiFQLjW60PYnIXmb4dSxpkxw18zvWxs0sAo7umc48TB/Iy+lJj86jZg4YiQ60VL/CrwOlroCjmd5nOwW2H82DFKUAsQf7W/NRbGtZdHQB5CUB5McrFa9eAtw2KVjNsqmWSbwNq1yHdBfRy+ZYl68S7aLtFkmuYKWOvl+Ik2BBTL7bRkH5zM/J8SxqxiZIXprz4tY3Ks8b1wIvyEpGX+QOrWSbZoGyjeAJYdhL5eNcjtV/b/Qh5M3f0eTtRAd93m7MOU6Ud8x5OqgMlIwe3kTlIZqBv1sG19kTQCj4tEysCS2EoLx467AIkRJfsJDOg6knczocBaMQyotOXvcAniS1yDerM5js5tsd3Ul5SnUabc2od5UYN1UkS5cXKNlIURSC4zeVM6ns/8f1rmmUZAeT9Q0vzZQzt0rxLstM6I2aHvlXke6tFYHxLkEfWAFXVUFHJe94u8wLUlkv7wvAzJKybHTR6trhBRF7mD6zKQ43BjDYno4htBHC5F468FKeB//0Y+XjtOY3daUXh827TKrBMIZx5sdq0epYTRaVaAqYsuj7SMmkveRf+Z/tUHRwzL17zHrFYbadVHjTA6qazshl8dth1XgO9Pb5X6wzgrCP9d6aqy6KjjYaOfhDgeACzdWAVR1ZWMiVNZiMSWkVeFEXu0K7X7roUsZihvjTZOuLPdUflJaj5RnufII9uOuvyoOSlUvAVXA8DIvJiUW1DL162dftMeXFQC+pDu5oG/OojpGy0eynwxq+4Pm4G38oL+f06HaZKO04Tttq04gmgXw/tWllHfsqk+Z/td+N2Up+8BnYBwzqiwWQKqkLQQKtX+G3U56g+OWVeHGwjoDG0++yvyWMQqgvAvRe8b9pC5MXLgE7694+ngBWv8nx8QpA5tOsnrEvBci/NrTji/87itpFP5WXP4+Rx6Qnevj/VDUA/94o2vcXmAOY9eclbKC9itpGg8kKzDGMvAX/+IXDnZ4Bnf0UUiXf9h0+7wN/dZs63beSgvABcaNeKvPgokwYCqLgSGw/guAZ2GzetOOJDu+VZ405rpc8NzWeHXbseL4DPUmkKqryMvgA88I/AMz8HoADHvMPLITcigCZ1dn2PHIcz2r0XDn0dsTuOvcC6I3dQoK8vo/LitUEdj0WUvDS34oj/O4vbRj6VFxpopze8bhGLGY0u57h1ZH67PY9g1aDMsI0CIC/ZAdL6f/QF4ParjM+/6evAile6PuYa+OjzomkaCnrWweqOO+1oGwnYBXzupR5qFTigNy7zbBsFE1a1HA8gahvZKi8mvV72PEZyMF2Lgf7Vro65AT477DqpT46zfZxK5gHyPlh4FLHO7tPVxtd/zj9xo+BL5jXNk+xudy445n7smhX2rQQ+u4OMv2g2aGhXysyLT9sIaJnywrrrxmOmOTAehm3kQ3mZHtavkQqw3Me+0NFLiEtEXuY26Oyi+ot2Vq82qqgaylWVKRA1EKk2ojjnq0R1gQZAAQ5/A3Dy+30cuQ4feQ/+Ipy2uuPWL9iqBlSqKhL16+BXeRnfRsJ3iQwJ43mBz0Z9juMBhCttbAKIVHkZfYnYjfEkZxmt9+Zv8/DZYddpsrbvknmKQ9YbuZ9XvAM4/ROuj9USNR2ni+7HTMD+XHAkcE7vBS9hdC+QucuunwZ1FDTzMrmTbNA05xEwRCuNgICUl11/Io9Dr/C3Piy0O+H9NUKAeU9eZrkqCx78xStfqqI3Y0deHJQXAFhzNvkXNNjG7f5CxZOXjoS9bQSQO5EG8uJGeTHr9UIto4VHEMnTC3xOlbbqqkpBuwxXVA2qqiEWqyMaLPdjQ+B6lpM74nKOkLiFR3BhXZ95F/5ne+3zYjOUERDJ/QgoLwCw5o3AozcBi48F3vZd/6St5iC5n12Z9UZebEZFGMqLQ2DXjsi3AjLbRkFkXjL95P00tZtYR4f4DLtbwA15CaRUeqeeifRqGVFQS44SxTmKeZ95mbVQXpJxhc2rsLSO3JCXZsGHz0+rJmKKsUHXo6Y5l9kdp8gFm1oi41uJnM+DhXU9NKejCGiqtJPqAFhs3naDGSlqKo70HhW0+iyIi69f68ypy7CjbVSsPQ4rrD0X+Ou7yb9Up/1z3aKmTDj4/JOwddZu8sJsIwkDu0FkXoCWVByJjAag6MkE0KSO5l38Vh7Ok3LpiLxY3GkpisKGM+atyqVFq42aCR9hVaq8pBPmvT0AIBFT2M2x7cZtZ5n0rSRefzkPzAzXfo2Wy3rtcwP4nyrN8h7mb4d69akBToMZKWimZ+R5YPgp0pgv3euPuFEEVHHlmPvxOpySQlFIxiVo4kLhY1SEpmnBZF7aTV6kVl4myKMf5QUAlulFEM/80t/r2KBUJeeCG+XFs21UygH7NpOPV/pVXiLyMi+QtykRZaFdq3JpqZQX9xfrgkOFCaA357ILrIpsWomUMe2Uz73MTgA7dOvEj6UWUObFaeMGgLLZGogQOMAol332V8Btet5pxSnee/zw8Kk+OVpnorONPFg1gcIHiStwnXO9VRuZjAZoB1iptPdmfU1DUOTlhPcSlW3Hg2QKcxNQ9GQbeVRe9jxGmtP1LDOulV4RkZf5AbvSSMeKI5EOu82GD+WFXqzTFnkXClvyImoXmOVeXv4tecMOHuFtLACFj8yLqmrsImW1ccdiijFd29Y2crjjZr1eniHdQTP9wGs/5fqYTUF/drVk3cnYBk6h5bRjqbSg8tJs+FBeZh3nOzllXgTyX61ASmLbKIhqI4BMTz76reTjR37g77UsUGLKtHhgd7ZctX6P2GGnHtZd+Sr/ObCIvMwP2OUdMsw2clJe2mgb+VBeihVn5QXgq21M2uOzNXBQHcwqjl68mzweca7jsdrCx4ZV4DYiq40bMKyjcsVkDUQ3rRWvBLqGgM6FwNlfAj7+lH+JmKK+0sYlHG2jsCgvPjJgdA1SiRjLu/GgFXnWthFVXiQhcAHbRsVKFZt2TUCrz625Ac28+FVeAOCUvyWPT/4UyI/7f706eKk2AjyOCKDkJYgGhjRPFJGXuQ07yyAbBtuoFcqL3cbF1CeHwrV68lKtAC/9hny81id5YbONPGzaHDG1qrgCuDWompwLokHNTD9w5bPAJ14ATvsYkO52fbyWqCEv3jdu/4362rxx+7AQneZ8OXabZpkXWWyjYJWXz//yabz9+j/g3udGvL8IVV78BnYBolIsXkfO98dv8f96deD7vDghEY+hUz9vXFccqVUjvB9Ez6NIeZkfsCuNpJOWTW0jtWpMkA1p5kVUeTHKZE3WQZTA1ZOXXQ+Tu7BMP7D8FNFDNoePMmH6908nYo0l0ByMTsN2yovAoLl4IpiMi+nr6gTSR/7JKbRcshpOKZr7aTb82EYOFVdUeZFefWrCbKOZYgW/3kxmk70wPO3tRSpFQ50KQnlRFOBUXX358w8DnzLtRnkBgG4W2nV5HCPPAaVp0tqfNuDzg4i8zA/Yef0ddrYR3bSB8CsvFhdrCuOu2842ciIvtFx6GymXfvEu8v/Dz3ZWbZzgY6p0wcEuobCttqk6zHdqFViX3eDXwTgHHPIe7bZMfAR2RYdTOttGsigvwZGXe57dz64XnnuZsL4jCpDuCeS4cMw7iYozuQvY+3gwr6nDTeYF4MulXa7PxE7yuHBtMDc2EXmZ+yhXVTbynHbU5WF43BKTlwCUF6c3p+3Grep3GU65n/5VABSgOAncdhmpuAH8512A2i7DLv34gkWTwnpYdtlVVW4N2ngeANx8Iy+qg89SaZHBjK2AD+XFqts2haPyIjLjqhVIBT8e4FebjInwE3mv5IUL63ptSFmPZAdpeAgAY1uCeU0dzDZyrby4XJ8gGvfxiMjL3AevqHSYyOVpuyqbKneChrTPi9NcIwr7zIug8pLMACf/Nfn4mV8AEzuIzXHYG1wdsyl4q8Jl7kWkXBywqbhSJTkPAE558R7Y9ZR5UavGeRBi5cWpXDwt3GG33cpLsIHdsZkifv/SKPv/pFflJciwLg+q6prNTvMBN03qAKBHD+26LpcOel0i8jL3QS9W8ZhieoIKbdqxZLAtzt0igD4vwsqL0zo44S3XAR/6I3DMXwJQgFec779kEqgNibokcaKh5WTColS6hsRKorx4KhN26LAbN2ZcVdU6dYtf83arDgGUSjvNuPI1Yb0VYJmXYAK7dzy1r+ZvPjFbsnm2DYIM6/Lo5yzpAOGmzwvAD2eURHmpFn1NWJcd85q85LlW4GYdZo0KEx+KQ7MRQIddUeXFtH+B2y7DQ68A3nkT8Pd7gLffIHystognjWm9LtdBuFzcisDJYh8ChgLlRXUQHE4JmKwBr/S0e+P2obzkHYZTUoJrmXmZo+MBqGV0+ppBAMCk10ZsQTWoq0ezlRdh28jjiICgSV2qy7gezmH1ZV6TF8dpwiK2UbutAh99LYSVF6FSaZcbd6rTf1CXQlE8kzjR0HLSqkkb/f2VWHOqiNwg4V91cArsAibnAVUcYsn2r4GvaqNIeanH7oN5PLrjIBQFeO+rDgEATOZ9Ki9BqK08qPJycHugL2uUSoud056HMwZN6mIxIxAdkZe5idmyHtBzuGCb3mlJp7x4CeyKKS+0u2yxfuPWNCPzIcs6uCRxwqFlq43LjW3WbCSbl/dIcGXkDUqkaIfhViAAMu8/8yILefGfefnTVtL87aSV/ThiiPQl8px5Keol1kFVGlFQ5SV3wPgZAaBlpdLNIHUs9zIR3GtKhvlNXvS+DpZNqcJgG/loCy8cVtXl8oa5PrKElgHfyotwqbQVeWn3eQD4KpWedTgXFEWxfj/I0uMF8KU+5Z1GJNhdDwB5GvWlushjZdbTqAgeE7rKsqw/g14905EreWyBX5rRjy/goZwdvUBmgHwcoPrilrx4LpUOOvMCzIvQ7rwmL7Q00lNLdFlsIx+dVfmp0nawLJOtyXu02z7zSl7cKS+WtlG7f3/A8xoAfJM663PBsvpOdEREK+CnXFxUeSmHxDYCfFtHNL/R05FkgVTAo/pCS7cpuQoS/avIY4ChXdd9XrzaRs2oworIy9yGXXddQLDKpt133PyF0q1lIqy8hCGs6m3TErfOLCxEWWwzwPMalKsqm1tl1+/GcrK0TATOx3gAJ9vIVokF5GlSl0iTicuAf/Kib8Q9mQTiMYWVA3vq9UKPJWjlBWhKaNfNeADACOx6to2CrMKKbKO5jVnH6oIQkJdY3MhbuJTKCyzv4aS86GXCDRs39yaNBRS+9QqPk6XFrTOL4ZSynAeAD+vMfpoyRcoytCzRGiS9W2fOtpFebWSlvMgyHkBRAgvtUguEqgq9WfLoSXlhtlETyF0TyqXd20YeSqVVtTlVWPNgOOP8Ji+ifR3M7rQqEkyUpvAYUiyyvIdXy4TbtNrZ6wbwHFwWJXBJS8tEItXBY88f+j5QFHuJ3DLALhN58TWY0cf1QFU58tJm5QXgyMuMr5ehDdfoxtyXIX/jSS+9Xpjy0gTbqAnKi+s+L16Ul+IUAP2GqCmB3Yi8zEmIdhU1728i0QXbY3looWJfHksRik0r6U15ESVwaUcCJwF58ag+FbkGdWb9jiis7UMJCVwTJmsbmRezyeISNeoDAhsR0KC8ZPwoLzTz0gTbqBnKi0vbqIcbD6DWN3K0ArWMktlgA+8ReZnbcO7zYtOUSsZNy6Py4hRIS1oGdvULWLstI8DzZGnWpM7JOrO665bpPPBIYp16vLCXD4Nt5Gcwo4ONbKu88OdduwO7QHC2EZd5AQzbyFfmpRnKFFVeJnfXVkH6AB1C6tY2UjUgVxJUX5o1MiEiL3MbvppSeW3O1gz4VF5Ep0o3lkrLtGnRydJuMy+0SZ0TgbPI/ch0HnjcuJ02bQrH4HaICRzgbCPbZl6owhFPt79RH2BYMz5tI2qBdAeivNDMSxNso67F5PzXqsaUZp9waxulEzF2nRC2jpo1MiEiL3MbeT/D6KTauL1tWgVB5cW6VFqijdvjplUUtc50FU7qNfCY+3Hq8UKRtjoPKhK9F3w0qRO1kRuaNQLyhHUpglZe9DxHXyYA5aUZtlEsZpRLB9TrxW1gV1EUo1xaNLTbjB4vQERerHD99ddj1apV6OjowKmnnopHHnnE9vkTExP4yEc+giVLliCdTmPt2rW44447PB1wkKDzXBxLpW0zL+G92xTeuMOQdfA414c1qRMczGitPkmwBh477DopDhTOyosE5MXPiATRJnUVFZpWl2mQpUyagh6HD/JSVTVMF2sDu1R5cd3LBOD6vDSBvAAceQkm98L6vAhmXgAP5dKs0qjPxZEJYB6QF9dhhVtvvRVXXXUVbrjhBpx66qn49re/jXPOOQcvvPACFi1a1PD8UqmEs88+G4sWLcJtt92GZcuWYceOHejr6wvi+H3BaRBbaGyjVikvodi0PI4HEB3MKLXy4tE+FLSNmHUmde7HR+bFqe8TP9+pqtZWqMnSoI6C2UbeycsMtwHTTbmPZl7ckhdVba7yAgQe2mWBXUHlBTBI3qSoMtWseU8ReWnEddddhw984AO47LLLAAA33HADbr/9dtx000347Gc/2/D8m266CePj4/jjH/+IZJL8YVetWuXvqAOCccdpvgyh6PMCcFK5u8qComBQMxRhVZZ58UrgPE7WlnG2kcvcj2hg11mBk+C9kODeC5rmqoSfKi9W68CT/GLFgry0ezQARQC2EbU+OpIx9rt6zrxUZsFKgptFXli59PZAXs6tbQQAnfpeIhzYbZZtRMlQYdL1+yAscGUblUolPPbYYzjrrLOMF4jFcNZZZ+Ghhx4y/Z5f//rXWL9+PT7ykY9gaGgIxxxzDL72ta+hWrWeuVEsFjE1NVXzrxnwVV1AL9gJGS7YHpuTCXaXtdy0aJM6GciLxxJZNh5AVHmR2TbyWHElPN9J38CkVuCY8qG5InFVVWMBTcvrAWcfNIR2pVNe/JOXSZZ3Mc7tXr3Py4TbydJsSKTSPIIXtPLigbxQ1Y7uLY6g1UbNCuxWS57aBoQBrsjL6OgoqtUqhoaGaj4/NDSE/fv3m37P1q1bcdttt6FareKOO+7A5z//eXzrW9/CV77yFcufs2HDBvT29rJ/K1ascHOYwpgT4wEAT83JNE0Tnt1hXSot0Rp4zLyw8QCiTeoaOuzKpDp4VZ9ES6X13I/M8534u3oXSiTfZThrocTaDqdkmRfZyIv3aiPW4yXDkxeqvLhsgU+PI5kl4dpmoE/fJ6b2BPJyjLy4yLxk01R5ESQvzeiuCxDbUInV/ow5hqY36FBVFYsWLcK//uu/Ih6P46STTsKePXvwj//4j7jmmmtMv+fqq6/GVVddxf4/NTXVFAJz69++Crlilfm59QhdtZGLTYvvXSOqvMjd38Of8iI+36nuoiTTbCOPHXZZs0IHEmtpo8p0HsTipFy5WiSqQ3ZA6NtmOfJiR+bT8RhKFbWxUR2brD13qo1Yd13u+tjHxgOUoGmabVPDGjQ77wIYakNxOhCrpOgh85JNUuWlzbaRopD1mD1IrKOeJcG+vgRwRV4GBwcRj8cxPDxc8/nh4WEsXrzY9HuWLFmCZDKJeNzYII866ijs378fpVIJqVTjBS+dTiOdbv6E2mwqYXmXBdTaRg1vVJnuNj1sWgXBizVgN01YpjXwOtdHLPNiNGizmm0kwRp4tQ8dsh4UjtPFZSAvANkgZ4uulBcj7xJDLGa96aWTMUwXbdYgyC6pfhBg5sVMeSlXNcyWq7bXzxq0gryke8ijViU/L+29nwyvTLsiL2nyHsq7Vl76XBydIHjyMgfhSr9LpVI46aSTcO+997LPqaqKe++9F+vXrzf9ntNOOw0vv/wyVNV4s7/44otYsmSJKXGRCfXVBTWQ6YLtQ3mJxxS2MVvBepqwRGFVjxu3US7uVXWQUHnxnH3yOl1cIhILeLJMDAvZfjOmBK4h8yJTrxsgkPEAUyaZl2wqzuxDV71eyk2ca0SR6jSskuK0r5fib1LScXtSz4NGEMTJS5OUF2DOVxy5Nh+vuuoq3Hjjjbjlllvw3HPP4UMf+hByuRyrPrrkkktw9dVXs+d/6EMfwvj4OD72sY/hxRdfxO23346vfe1r+MhHPhLcb9Ek8F6n1EFNVh7qXnlxUl0AfpqwVd5DgjXwOSJBtDW+1OeB55J5d+MBpCbyANfjxL3y4lQuTrtRS78GAXTYnSrQHi8GoVMUxVvFEVNemtgHR1GAdDf5uOivyIP/+7oL7JK1yrsdDxB0YBeY8+TFdeblwgsvxIEDB/CFL3wB+/fvx/HHH4+77rqLhXh37tyJGBfIWrFiBe6++25ceeWVWLduHZYtW4aPfexj+MxnPhPcb9Ek2JMXie64PeQ9ioKVRoDdYEaZ1sB9d9mqqrGLlGOvG8vcj4QErlokfTUEg5Gi5MWxSZ0MlXeAsUG6UB3yJXcKXIPyUtUrm6RZgyAyL43KC0Cso9GZkjvlpRW2EQCke8lmXfBJXipeyQt5DwkFdssF4xxtqvIyEfxrSwBPgd0rrrgCV1xxhenXNm7c2PC59evX409/+pOXH9VWxGIKEjEFFW6TY5DpTsuH8uIU0gRswqoyroGL8tiSi9AylcpDMVkbIERW8C5XuFlhGKwzwFODtoKobcRycPWBXXoezO3MCwD0ZVMAch6VlyaTl44eYBL+lRfOVo/bZKDq4apUmpIKJWbkdYJEmgaYm9NqpN2Y17ONRBCKYXQelBdjIKGA8uJoF8gwVdo7gQOCUF4k2Lj5/hmuzoU5Ftj10Brfaa4RhbXyItsaBFAqzaqNGpUXgFQcCaPUgswLEJxt5KFMGjDIb64oYBvRsG5Hb3PKxzt0QjRHbaOIvDggFHebHlqis7b4LpQX68yLBGuQcK+80BLhRExBwuEi5TweQAISG08AMZ1IuiEvDs3ZKJxL5iVYA8CfbSQ638my2kiC9wIQsPJSe3PS5yfz0uzZT1TB8Gsb6cqaG8sI4JSX+lJ6MzQzrAsEthayIiIvDmDVBTLbBR5m2nhRXqqqhqrKERgZe5y4WAPRsC7AbdwynweApy67rpUXmYk8wKkO7pWXrKPyQr7eWG2kk2bZbCO1YlhaLmGVeenxMlmaKkCtsI0A39VGRQ9l0gAf2HVBXpoR1gW4tYjIy7xEKOb6+FBeRDIvSb5knN+4ZFoD1l3WvfLiRn2yPA9kKBcHPHXZLbps1Cc1kQc8lQkXHCZKs5emNzMyXw+AWpLg0TqaLtROlKYwGtW5KZWmE6Xnum2kl0qL2EY08xIpL54QkRcHOGdeJLhg+1BehFQHvuqKv2jLdMfN5340zf65OtwoL3yTOo1/fZlsI8CQ5V0QWdFzwVgD2Tdu95mXvGCjPjoDq6HDrmxN6uJJQwXyaB0ZykutbUQzL64mS7eiVBoIzjYSHJ1SD0ZeZLCNIuVlfsN5IJ8EG3eTMy+00gawUF5kUB34TUNQdRAdygjYNCyU6TwAuLyHe8vEe5M6ydbAj20kqLw0KHCy2UaAr9xLVdUwXbRXXqZkrTYC2mcb6bON8kU35KXP1c8QRqS8zG84d1aVYeNurvKiKIr5RVumNUi6r7QpsLsrd+pTTXBZptwP4KnSxmhY6DOsKssaeAjsis64MpQXia8HFD7Iy0zBsD3qZ7/1esq8tKraiJIXfxU2VInrTLurpMxyTQwr9e+TejRrKCNFpLzMb4Tigu1BeXHTYRewuOuWyTaKJYzW4ILrIJr1AFAzQsF8DSTZtDxsWMEFdmVbA/cddp3HA1h12KVN6mRUXtxnXmilUUcy1kBqezPk/S5ln5d0MMoL7ZDrpMTVg842AgSso2YHdmmfl0h5mZ8IhVTOKy+ieQ8XHXYBizJZmdZAUVz3u2EzfQSUF75ZlbRrAHhr0OZytpH1cEpJ1sBDj5NZQQJnrbxItgaAr/lGkxaVRgCvvHjo89LsUumOYKwS2iG3U3TwpI5UPMauE47WUbMDu3QtStOAKjhrKUSIyIsDQtHfo6azqli1TdGt8mJ21y1bUNNlpY2bzAsQkjVwGVZVVWN6rnflRbKN24NtJJz7YdcDqw67kqwB4Ms2suquCxiZl+lipbZ1gh1YqXQ4qo1otRCvpIhAURRmHTnON2pVnxfAtxIlIyLy4oBQKS+AcO7FrfKSTJi0x5eJwAGupyoXXSgvgBFcljb3A3B322IbFv/3FJ1tZD3jSpI18DCYsSDaYddSeZHRNvI+nNHortuoPFDlRdOA6YKgdcRKpcNhG3lVXgCD8Dj2eml2YDfZYQTI52DuJSIvDkjpG5vU5CWeBBT9oiuY9xANKFKkzMpkZQurupyq7CbzAlicCzKdB4DrSht+RIJTzx/nUmlJ1oBu2i4qrkTJSyiUWAoP4W0KO+UlGY+xdZouCE5PblWpdFC2kUflBXDRqK7ZgV0gsPWQERF5cYDzxUqCC7aiuO4wW3RRaQNYbdySXbDZiACXtpHoGsTNMi8SnQeA67AqbdSXjDuPSLCuvJONvHifbeQ0HoB2pJa+wy7gzzayybwARjM/oTb4qtrCJnX6Zl2ZNd6bHkAtny4vyktKwDbSNGPmUDOGMlIwJSoiL/MOobCNANeqg3vlRbdMpFYd3JGXomBQlcK+4koSAudyw2Il8yLl4paVd5KtgQfbiFYbOdpGljczc8w2Yt11zTdvuk5C05P57FHTbaNu42Mf1lFOD9tmXZZKAzx5sVmbShHQ9K+nm0jo5vBwxoi8OMD0blNVycwQQKKN253y4lp1MNu4ZCMvLidLG4Fd0cyLTWBXhkZ9gOvyWLr5+JpxJdt5wFfZCFbfifY9YpmXSn2HXckIHOCpZJwiUOWFEmklZrxHm4V40iCvPjZsqpp0uiyVBgRtI/7mItlEQjeHG9VF5MUBppu2ysmRslys3OY9qG3kUnUwtUxi7u9OmgKXk6WNwK5L5SUMtpFgpQ21jYR63XDrxM4DGYk8u7vXXBNZ4cxLvRI712wjm8wLwCkvQuRFJ9LJTmJxNxsBVBwFo7zY2EZ0TRIZMg2+WZjDjeoi8uIA2/JYQJ4LNrNM3F2shfu8mE3Xlm3jTnhbg0CUF1lIrMuQppvzgO8yzM4DKYk8V30nuA6zbquNrGzkhCTvBcBfkzpWbeRAXkRso1Y1qKMIoOLIj/KSEbGNWta0jzaqi2yjeQfT8tCqxBdswTtNI7ArdgrYb9ySXLBT7rIO1Cpw22W4pkmbdGvgrkmdMZzS44yrGiIvieoQi3EDKp3XQdM0LrDr1OclBNWHFIEoLxaZl5SHzEuryEsAFTY5wY7LZuh0Yxu1bNZTpLzMO5iHNPULlRIHYu6ZeVPgckSAa+XFzjaShcC5tEyKFW/qE2tQpqpG6E6WTct1YFdfA4HsEz/jip0HMhJ5wFVot1hRWTTGk/KiaRx5kYTAAc2tNvJiG7VMeQnCNtKrjfzYRkUB26hV1VdR5mX+wXwgoYR3WS6HM3pVHeRWXtxVV7gZTglwBK6i73QyWiYuy4QLbglc/XkgI5EHXBFZvuzZU5fhGvVJkvMA8EVepgvmE6UpqPJScBPYDZFtFEifF7u1iZQX34jIiwOMizV3IsqW9QBcKy+uZxuZXbRVSZUXl6qD2xEJxaqZZSLLGrhr0FZwYRsBJqFl2QgshYu8B1UPEjGlZgCnGUyrjfjzQMZSaRfN+igM5cVceehICuQ6KErtso285Tw0TTOmSvvp82KrvLR4UGWkvMw/2CsOkmxYgGvlxfVsozDZRqJ5D9cjEqjyYmaZSLJ5e7WNvJJYGWf6AK5sI9GwLmC0FqhpUleRMMAPeFZeqqqG6aKD8iK1beRPbShVVVT0VgCelBeR8QAlXRWKlBfPiMiLA0JjG7VIeSnKvA4uqyu8jkhoUB2UmDyWCd201UrtpmoBt9YZnXHVqLxIQmApXAxnpKFTp+66ABfgN3sfyGqduSQvM1zL/24L5SXrJrDLJkqHwzbip0FnBd8XPISa1DHlJcq8eEVEXhxgqrxUJLxgu1BeqqrGNh/PWYeakKIs5MVlpY3rEQn6eIB65UWW3x+ovZMTIHGeCVx95kWmNQBcnQuiE6WB2qaVGk35ythdF6idc6Wq9s/lMF0k53UqEbN8b0idefFZbZTTy6TTiZjjyAwzZJK02kgC26hDL5WOlJf5B9uAnkwXbBfKC/+7eC6VVrk3piwkzq1tFJTyItN5EE8axyOwDm6qjQCTGVeyWYcULvrdFF3YRinu/cLOA1mtM75Zn6CdDAAzumXUbVNp4yrz0upSaVZt5E15oQ3qvFQaAUCnkG3UYvISKS/zD+Z9XiS8WLlQXvi7Jc+ZFz7vIV1rfNFKG3fKS7KBvEi6cbuotHGfeakbTinjewHgbCNx5UUs82LSqE/WNaAEDnA1IoDaRl0WlhHgNfPSZIuEwmfmhSovXvIugKht1OJS6dI0oAr8rUKEiLw4wL4lvESblosGbbQ8NhFzniRM0TDjScYuwy5LpV0rL1ZrIAt5o3CxDp6rjaTfuMXn+sx67DJsrIGktlEsxq2DeJddGta1yrsAxgbtzjbK2j8vKPi0jWjmxUulEeBytlGrArvAnLOOIvLigFD0NwFcbVhFlyFNwEZ1AOQhcS6nCRfcVhtZNWiT6TwAXK2D5z4v0qtP7gO7GYHArqIojWqsrLYR4Cm0S3u82NkmHXN4PABTXjyMBuC/Tyjz0syJ0gAh1LRx4hyzjiLy4oDQZF5c+Lx0wxK1jACexNGQIqc6tGLYmghcXKjLVZVNRvbcqE/aShvxdXA736lhxpWM7wXAVeWZ29xPOiyhZcB100KAs43S1ue10PweilZV1lAw8uKtzwuba+Qx88JP3Nasppq30kqbo+XSEXlxQDo0tlFzlRfrsKqEa1B2rq7gM0xuy8XZbCNVUuWFZV5EyIu7ydqN6pOkG7cL24iugYjyApg0qpNxKCOFSysVAGb0aiM724hmXlzZRskW20bFacCKPNiATZT2qLxQu0nTjHOrAa1Uo+ZouXREXhwQGtuIyo8iygu72xb/8yfZOtC5ProkKhV54S4EDnaBn9DyXFRevI8HkJDIA+5sI9+N+vTMi0zXAwoPttGMgG2UTbkJ7LbaNtJVaE31NBrBt/LCnUc5K+uolWsSKS/zE7bkRaY7LXqHVRRQXlxW2QAh6e+RzADQLSyHixbdtFOJGBRB2yvZYJlIrryIkBf9dxGptAHCFNgVt0vcVBsBhsXWaJ1JFtgFXA8rBcQCux1uqo3KLbaNklnSMBDwtGHP+AzsxmKKUY1lZatFyotvROTFAXTTrqgaVJXmPSTctOjdRhMakwGGOsEsExk3LUURlslZh2EPuZ9Gy0Qy1cFDjxP31pnka+CmSR0L7Pps1CfTzQyFy67TgGCptKfMS4uUF0XxVXGU9zGUkYIqU9bKS5R58YuIvDjAtCmVjBs3v2k75D2M/iZeAruy2wViqoNbuwQAknqPE/nXQHzjdt1h1/I8kOi9ALiyjWh2RTiwa5V5kW0NANddpwGj2siuSR1VFkoVI/huiVaXSgO+GtXlfAxlpHCcb9RS5YU2qvMWYJYVEXlxgD15kWjT4kvuHIKabu+2AZtSael6nIiSF53AeVKfJCaxQEvyHg2TtWVbAzd9XlyUSgMhy7xQFU7ATqagHXbtlBc+zGob2lWrQEXv+t0q2wgwNmwPFUd5n6XSAJDVRwSY2kaVkvG+iTIvnhGRFweYN6WS8G4zmSUDAgHHCxXrb+Im8xKWrIMgeXF7tw3YddiVdQ1a2aRONhLrvsOu29ByQ+ZFtiZ1QG3ljSBo5sWuVJpXbW1zL/z7sFW2EeDLNqLVRl4Du4BBhHNFE9uIf1+2YljlHB0REJEXByiKEo6wqpu8h4dqo8ZSacktE0f1yb3yEp6N271t5HZEQkOjPtk2bheh5VlaKj3XQssA0NFHHgsTwt8yUyB/U7tqI0URCKUCxvrHEq1dHx+2URDKC51vZErs6JrEU63JSfkclyArIvIigNBtWg5v2KIn5aU+7yHpBVuwKZcX5cWawEm2BoIddjVNM84Fzxu3pJYJvaOtFBxnuhS82kayD2YEuLtucftkRqDaCKhtxmYJ1uOls7XNLH1s2Lmic6m4E+hkaari1CBkU7ZlRUReBGDdEl2yi1VaTHnx0uclFa+fJizpBdtl5sVV7scq8xLS3E9toz6vM65kVeC4cKhTz5+K39CypLONAG/kpSBIXoSUF/1GqpWWEeDJLqOggd2sj8CuMVnazDZqU8fhKLA7/xAK2wgQ7vXi9m4bMCkTZk3qvL/BmwJB68ywSzxYZ9Jv3GIddvmgpVvlReqSeQBIdMDo+WNPXujm6119klSJBYBMH3mcnRB6elXV2ObtpDwIKS/0WkRtnFaB/jwfpdKdAZRKm1YbtXrKdhTYnb9oDOhJumkJ9nrxtHEn6vrdyLppuVQdvBE4yTdul+pTIqawLIsTGhv1SboGLjJgrpvU1ZMXZhuFX3mZ4QKmdtVGgKDyQpWPVpMXD4oTRRDKC7WNzMlLmwZVRrbR/ENoAnqCITVvU6UNv7pUVeVdA5d9XjyFlul5IPtsoyb0uglNcBsQLhmn6yCaeWmsOpM09wNwgV135CUVjzmGuDMiXXZLbVJeMv3k0UVQmcIYDxBAYNfWNmpV5oWWjUfkZd7BeiihZBcrUcvEx1RpgJIXSTctwTJh1ufFTam0ZfZJsjVICpIXl1kPgCfyIWjQJhhcZraR4LlgaR/K2GGXkpfSNFC16PbKQaS7LgWzjWyVF33DTLewxwtgkJf8uKtvK1VUpqz6Ul5Yh10JbCOqvJRmHMPrYUJEXgQQGo87LZZ5MVQH93fcgL4Osm5agmXCRS8bN7dpaVr4rTO66bgicPWTtWVdA0Ao+6NpmjHfSbTaqN5GZk3qZLSNeoyPBe68RSZKUwgpL8w26rF+TjNAycvsQVffxgdsO/2USqdsmtS1q9oImFPqS0ReBBAa20hQecmz9tfib05FUWrb48+RDrteLBOAZH+kJbHULlHLRh7DBG4b1AEhqjYChM6FclVj7e1dB3Zl7rhNEU8aSpyAhTItMFGaQkx5abNt5JK8UNssnYghIZgDM/3xdrONWk1eEmk9wI45VXEUkRcBsAt2VXKpPC3W5yXvMZBWM5RP1k1LlLz4tc4qEq8B37XTRnUwbKM52GkZELKNeNXAd5M6GUulAVcVRzMuepwITZam16JWjgYAgMwAeZw9CGgOs5c4sBs7Hz1eAEN5sa82amXH4bnXZTciLwJIhqZEVqzaKOexFLDmoi3rpiVqG/kMLZdlDi0nUsYx2ayDlxlX1pO1JVsDQMg2ohZqPKbU/H1tX7a+y7DMthHgqvJmWrDHC2CUA4vZRm1SXtSyq4na9Nrop7su//32tlELCZ2P6itZEZEXAYSmz4tg5sWz8sJ7/bJK5exuO3jlJRGPIUZbh9QoL5KdB4CQ6uDFNmoslZaUyANC54IR1o1BEewAGyrrDHC1cc24sY3cNKlrNXlJZgwy6cI6ygcwURpwso3aqbxE5GVewXIQm2wXK+HMizflpaZElDWpk2zjFu3z4kF5Aeo2b5k3LYFzgZVK+xqRICmRB7hzwYbAVdyVSQMh67ALuJpvNC0wUZqCrpntVOl2KS+K4in3wpQXH2XSgGSBXWBOzjeKyIsAQjcewEF5YVNTPSovZZmVF9HhlB6qjYA620Tqjdu5x4mnPi9hqbwDhGwjt911AZPrwRyyjYzRAM5/T7pmprkOinaRFwDIcrkXQeQFuws7QWiqdGQb+UJEXgQQmpAiy7xYB3ZVVTM6irr0dWvuumXdtNw2qXOhOgAWayBbxRUgtA4FL12GraZKy/ZeAFwFdkXDuoAx5ysU1hngjrwUnSdKU4iVSrdho6bwoLzMBJR5EZoqHdlGvhCRFwFYz7SR7IItoLzwbyavyovUeQ96QXAoEzbGA3hUXiqa3JuWG9vIU5M6yYk8IDRh3G13XSCEtpGHaiM3gV0pbSPAU6M6Zqn7zLxk9fEA5apmnCcUEXkJBBF5EUBjQE/SC7bAhkUDZIriYeOOh8Ay4S8IAec9AN4yqMq7BoDLwK77jduYcRUCAmdbbaSvgZvcj+VsIwnPA8BTtZGrUmlZbSNG2txkXvRiBp+ZF54MN6xPO0YmRORlfsK6KZVkFyv6ZqgULFuB57m8i2h1BUVttZGkm1Y8aWQPbO+49fEALgmcUTavyTvbCBCzjXwoL4D+fqhIPNdHYA1Y5sWN8hKm0DLQNPKSsZucDACq2r7ZRkBtrxdBBKW8pBIxVnqfL9ddiyPlJRBE5EUA1raRZBs37ytb5F6o8uLF063pdyNrh11AaNOiF1w6/VX4pWsyL5KeBwAX2BUgL67GA4RkQCdgVNnY2CVG5sWPdSa5beSi2siwjZzPaZp5sbSNyjkAeoO4dtpGAnYZRRATpdmPT9LQbr3yEpGXIBCRFwGExufnm5NZ5F78dJCsUaBkXQNAyC7IuegkyiMZhoorQKhZn5+p0oDk2SeA27ysMw8FT4FdK9tIwvMA8FhtFECTOmoZxRJGe/pWQuDvX4+8xwaeZqDX2BrbqFohyjjQ4mqjPvLoYcq2rIjIiwBqNm2ZB/IBjrkXPx0kQ1EqDTgqL1Wu4srtRSptWnEl43kgYhu5Dy0rilK7ecu8BgLVJoGUSrM1kFV5cVNtFOB4AL7SyKVFHQi89HkJaDwAwNtqnG3E31BFyosvRORFADXKi8qdiDJu3A4VR0Z3XffkJW1qmUi4aTlUmfBdL91epJIJchGWer4TINRdlnUZdt2oT1+DSlXu3A/r8zFB8hcm8D3fSa0Cmr55y2obCVYbqapmkBcXTeosMy/tmihN4aHPS1DjAQCL+Ub0/RhLtPY9E5GX+QnT3h6AnBdsh14vXkcDAHWZF1XijdtBdaAXqERMcTUeADDOhaLsqkOTbCOA27xLReOTMp4HVCqHBhTNL9qzJUJqPJdK11wPJFwDwNi4qkWgXLB8Gk/q3fR5KVVUNpm7Bu0aDUDhR3kJIvNiRu74vEsr1Sh+MKMFkQ8bIvIiAOuLlYSblqPy4t3TNeRyTXLbSMw660y7r7hK1pSLSzoiARDssEvLhD32uqkhLxKuQSJlnAsWvT68Nakzbma0CkcGZLWNUt0A9PPc5s6bVhol42KknlcnTEO7THlpQ4M6oLbPi+Bk6XxA4wEAY31q5hu1o7suYJAXaLZNTMOEiLwIIG3WnA0KEPN/ggcOx43bu/ISiiZ1gKPyMlP03gLcfLK2jAROvFTadadl2uulLDl5ARwrTjwFdvly8SJPXiQ8DwAgFhOyDfhKIxFSzxMc09xLO3u8AHWTpe07blMENZiRf42awC7LAbUw7wIAyQ6DXM8R68gTebn++uuxatUqdHR04NRTT8Ujjzwi9H0/+clPoCgK3v72t3v5sW2DZZVNO0JoThBVXnyXSstsmYjZRp7UJ7NGfVKWizs3LCx6GA8AGOdBhSkvkhJ5wNE6MAK74pdCftNm6pOs1wMKRl4mLJ/ipscLQMLbtpOl2zkaACC5L5eTpXM+lOl6mE6WbkeZNMUcy724Ji+33norrrrqKlxzzTV4/PHHcdxxx+Gcc87ByMiI7fdt374dn/zkJ3H66ad7Pth2gc4ykX7TBhwzL0YHST+l0lXOMgnfxj3D2UauX5paJuWyEdSU8VwQ6rDrsctwvE55kXnjdiiX9RTY5crFKyVdeZHVMqJwoby4USQzduXSdIJxu5QXl5OlNU1jpeJBVBvRG8QaYtcu2wiIyMt1112HD3zgA7jssstw9NFH44YbbkA2m8VNN91k+T3VahXvec978A//8A849NBDfR1wO1BjFcje06GJykualUprkqsOYsqLF9uIqQ5lyYOaIt1lPXTYBYzzoFKSuLsuhaDy4sY6i8UUJGKErFWobZSQeA0Ao+LIjrxQ5UWg0oi9rK3y0uZqI8BVr5fpYgUVPXjcn/X/98zYVRu1VXmZav3PbgJcXbVKpRIee+wxnHXWWcYLxGI466yz8NBDD1l+35e+9CUsWrQI73//+4V+TrFYxNTUVM2/dqK2Lb7syotD5sVHtZH5VGkJ14Ft3OaqA7ONfOR+qpWy8UkZ14BuGEXr947faqMqGw0gIXmjcCiX9ZJ5AXgFLgQEDjA2LhsFgk6U7g5KeWGjAdpkGwGulJeDOXJNy6birt8TZsia9Xlh5CVSXvzCFXkZHR1FtVrF0NBQzeeHhoawf/9+0+958MEH8cMf/hA33nij8M/ZsGEDent72b8VK1a4OczAYd4SXtKLlZPy4qOPAe3vUZJ5thHAkRcr28h7IyqqvFRlV17opl2c4kLmBjRN8z3faU4oLz7JS6U0Sz4h8xoAQhvXdNOUlzbZRoCrXi9jOnkZ6Azmb5l1KpVuNWzOgfO/9we8esO9eGKneFl5u9HUaqPp6WlcfPHFuPHGGzE4OCj8fVdffTUmJyfZv127djXxKJ0RmgoTgFNeHPq8eMp76Nkf2ZWXpKht5L1cXKWqgxKXM6za0QtWHmty4aZhXcCH8lKW+Byg4MtlTUDJi9tGfakwETiAaw/vTF5ERgNQZOy67MpAXlxMlj4YOHkh61gz20jSzMu+iQL2ThaQiIWnANnVDjY4OIh4PI7h4eGazw8PD2Px4sUNz9+yZQu2b9+O8847j31O1RvkJBIJvPDCCzjssMMavi+dTiOdlicAlzYlL5JerOiFogmZl9rOohIrUI6l0j4Cu7r6pJYlJ7GxONm4Z8eB/BjQtajmy8UyR148BnY12fNfgKPyQtUnr8pLtSz5UEYKgdk2RmBX/O+ZMQulUlDy0q5qI8CRvPIY18lLEHkXwKhYmi3LX200XSDXczeqW7vhimalUimcdNJJuPfee9nnVFXFvffei/Xr1zc8/8gjj8RTTz2FTZs2sX9vfetbceaZZ2LTpk1tt4NEYV4qLekFu4mZF9YWvlwGNH3zk3EdhEulvWdeNNlJLGBI5iYX7hmdxKbisZq+JSJINmReJF6DjEPmxUNgF+DJC602kngNALFqo6YpLzIEdiccn0rJy4KAlBe6NvLYRvrfoe4cqKoa2xfc/O3bDddHetVVV+F973sfTj75ZJxyyin49re/jVwuh8suuwwAcMkll2DZsmXYsGEDOjo6cMwxx9R8f19fHwA0fF5m0DvNqqqhWp5FHACSmbYekyUEMy9e+hiw/hayt0R3JHDeq43ouaCGIqy6ABh7mSgvdaB3Wl4uVmmmvIRgDRyqTTxnXuqzT7KTF5FqIz+l0rJmXhzIK4/xvK68BERe6M1RvsY2ompUO5WXiZpP0787MMfJy4UXXogDBw7gC1/4Avbv34/jjz8ed911Fwvx7ty5E7EQ+WYi4O9Mq0WdvLRjxLsIWJ+XJlQbsbyH5CMSBDvsegrssjWQ2DajYBfuxo3bS76Bgp0HYai0sbGNylWVlca6LRdvWAPZS6VZtdGE5VOmPZAX28nSIa02CirzwmYbmdpG7ci89JHHOgJLyUsqHkPapYXcTniiWVdccQWuuOIK069t3LjR9ntvvvlmLz+yrUjyTamKeaSAECgvVoFd78pLsj7rAIS8z4v3Drua7PYhwNlGjcqLl54eFGzjlr3yDmicLM3dWPHzeFyHlqkCVw5Lk7o+8mgb2HWvxmVtm9TJoLyI93kZb1a1Ea+8FNtI6CysQz8qbDsxtySSJoFmPQCgSksjZQ3o8ZZJ3TCyUkVFuUo+l036sUxog7pEzWYgDegalHOmE1SDyLxIH9wGbDMvU/SC5SKcSdFAYmUmcPxk6Tq5nG64igL308UZgQvBeQAIZV4m8+Sc6HMRWLUslVarxlDQlAzkRcA2Cjqwa9qkTr5qIy8l8jJAwp1HPiiKMWXVIC+SKy9qBagUa77EX2DcBhQBPqyq33HLqLoAtX5yZbbhy/6qjfS3TEXiDsMUGWvyEoRtZJAXiTdufrJ03QZWKBmVRm6ni4fWNipMWk5Ynpil5CWAaiNe+W2nbcT3eXGYLH1QJ29B20az5SpU3Z40lJc2EDoL8uIlqC0DIvIiCCorV2nX1qSsmRfuQlGXe8n5qDABQqQ6JDNgPU5MgstBjAfQVImb9FFkF5BHm8yLJ9soXnceyKpCUlhUnHgN6wJ8uTjN/ci+Bn3kUaua5uFUVcOEHljty4if05aZF0pe4un2nh/0b18tGUqQBcZmyN8yKPLCd/Bm68MCu21UXopTNYq0HxW2nYjIiyBooE9lyouk5CUWN4by1eVeaN4l63FiamjyHopi2w495yOwywhcGAK7dqXSeiv4ng73f8NGEivpeUBhYR3MehyPAJipT5KvQaLDOFfN+nwUK6DiQI8L8mJZKi1DWBfQJ0vrv7dNr5dyVcWUTuiDIi8dyRibV8qsIxn6vGhqDYFlVWaR8jI3YSgvdBCbpOQFsCwVZpu2h0ojgN+0QrBxd+odnfOjNZ8uVVTSrwdAl6deN2QNFJmb9FFQ5cW0VNqHbRQP0XkAWIY2Cx4HUwL8eyEkTepqCP1Ew5dp3iWTdDfXh4ZSC1bKSzvDuoA+Wdq5XHpC//0VBeh1Qd7sf7TC9XqpEKuZEv52kDoLAuvnWtBORORFELQLqVbWlRdZq40Ay14v1DbyMtcIMC7YSigsE5285GrJS47raeCl4ipUqkOTS6VDsQaApfKSY3O+PPS6oe+FMOR+KOh7Yma44UsTs7pl5CLvAhg3dfmGzIs+ELTd5AUQCu3yYd14zF3+yQ5ZPrTL30y2I8TME9ga8uJ+IKcMiMiLIOjdGSMvMt9pWSgvtGTPy1wjgFMdZB7KSGGhvFCJNJ2IIRH3cMetf08sFMoLXyZcu7mwzIsHn7uRxEq8BoDlcD66Bj0Z7+pTKDotU/TpHc0ndzd8iSoPblUH0+GDgHHj1M5KIwqblgEUBnkJ9ppWsz5UjUp0APE2EQU+96LDCOxKfD03QUReBEEHt2kVahtJrLxYyMNMefE47p1uWklFVy9kvmBTyyRXe8Hy010X4DbuMKgO9I4TWsO54Ke3Q7I+sCvzeQBYzrehQUU/uR8lLKFlAOhdTh4nGwfdeqk04p9Pw74MsthGgHEjkztg+ZSD+WB7vFAY5KXS3rwLhanyEtlGcxpUHlVoUypZq40AoHMheax7s9K7Iy92CWDcbaZAyYvEG7eF8uKnxwtg9PwJhXUWTwJp/WJVd9cZhG0UCgUOsLQNpmbnUWgZAHqtlZdJVmnkbvOmm/1YrgSNL0WWirzoQ0lnRiyfMhZwgzqKGuWlnT1eKMzISxTYndvoYFUmIQjsUvJSt3HnfYwGAAzykoAuEct8x22RefEzGgAIs2VSqzpQ+8xPYDcU1hlgSV782Ub6zYxKyUsIlJe+leRxwkR50W2j/k53JGxBJ/m9SxWVjR4hn5Ck2ggwJqrnrMlL0KMBKNh8o1JFDkJnl3mJbKO5Caa8VMNAXsxlUj9DGQEgFlOQiClIIgS2kYPy4mU0AMBt3FoI1gCw9Pv9XLBoWDWmhYW8mGdepnysASWxsVDaRjsbvkRto16XyksmFWcVNeMznHUkU2CX3szNWNtGQXfXpaiZLC2r8kJV2CiwOzdBA7sxWhopc7URIy/1eQ9/ygtALtqMvMQkPtktMi9+uusCxqaV0EJgnQFcubShvGia5ss2SjYoL5KvgUWp9NSsrrz4sc7CsgaAYRtN7W0IcNPMh9vMC8BbR1xHb6oyyBDYFVBegp5rRFEz36idc40oTMiLHxW2nYjIiyCo8hJntpHEd1pZC+WFDmX0WCoNkIt2KgyB3aZlXkIUWgZMy6WLFWOaspfgMlMdQm4bscCuh74edA3iYbKNuheTGw61Akzvr/kSm2vkYS0WdJG/PyUAANrbBr8eLPPShsAus42qkgV2J9inpqNqo7kNSl5iKm1KJbPyYp55yfkslQbI5h2qzEt+rGamCbONfDbqY6FlmWcbAaa2Ed20FcVbw8JU2Gyj+snSOnwFdvXgNiNwMt/MUMTiQM9S8nFdxZHXaiOgNrTLIEO+g6KLFjCMWM43GtMtr/6gyQu1jcoVbjSAXJkXPxPm24mIvAiCBnYTlLxIXW1kHlYNRHmJc7aRzFI5XQO1UnOX4Tewm4gpUBSEYw0A0xEBRo+XBGIeGnLRiquEGpJKG4vJ0kFUXMXDZBsBQK8e2q2rOKKlzm4zL4BBXmqUF5kCu1R5qRQaRqZQUOVlQbOUF2lsoz7yqJOXQrnKOo5HttEcBe3zwshLGAK7xamaydI085LxkXlJJ2LhKJVOpI07HC734jewqygKkjUETnLVwWSyNL3T8qI4AEZgNx6W0LLFZGlftpFebRTXQmQbAUZod6I2tDvpQ3lZYEZeZArsprLG39+k14umaU0L7EpbKq33faIEHvA+NqZdiMiLIDrCRF46+owwLae+zAaUeQnNxt1Jw6rGGvjNvABAOh5DMgzWGWA6WZpXXrzA2LhDYhsBDZOlNU0zAru+Mi8hU15Yl13DNtI0jZVKe7ONCHEbmzGxjWQI7AJcxVFjaDdfqqJYIepDS5rUtVN5qXsfsKGM6USgYxFagYi8CIJUG2lI0TstmauNFIXLfPAbdzCZlzRCsmmxZn3GGvitNgKAZCIk1hlgmnnx010XCGHFFdAQ2i1yAzr9VBsxAheGzAtg2qguV6qyALfbJnUAr7xw1UZ5XeGi51+7YVNxRFWXdCLmee6bFWpmGzFC10byUnc98HstaCci8iKIjkTc2LQB+S9WJr1egqo26lB0AtfO1LwIzAicz/EAgJ77CUu1kUmptN924CzzEhYFDmgol6aWUcxraJk2bGTqk+TXAwpmGxnKC827pBIxTxO2GzIv1TJQ1AOh9PxrN2yUF77SSFGCVR8oGZqVxTaif49yDijPGmHdkPV4ASLyIoyOZBxpcLKozNVGgGmvl0D6vMRj6KDrILN1Bhi2UY3y4i+wCwDJhBIe5YVv0KZX2hjtwL0dO5txFSbywvr+EDJPLSOvoWVjDcJmG9HA7i5WeTPBlUl72bwHuuqqjViuiJti3G4w5aUx8zLWpLwLYJCXXKkiR2A33WNECvLjmArpXCMgIi/C6EhydokSk/9iZdLrxW+HXYBYJhmEoFEfUFsurSMXwBoQAkftAskJHJWJtSq7Gw7KNgoNgQOMEuGpveTBR1gXMELLzDqTXYmloMpLaYZVXrHRAB4374bALn2/ZfpJebYMsJlvREcD0H41QYLeKEqjvCgKp8aOsmuB1xuZdiIiL4LoSMYNuySRISeBzKjr9aKqGvLlgJQXug7JrK9DbDpMSsZzRf8yaZJXn2QncIm0cbHUrSPftlGsnryEYOPurQ2q+unxApgpLyFQnwByvlJSr1tHE7N6mbSHsC5g2Eb5UhWFctUgL7JYRgDX66VReWlWpRFQp7yw8vE2h5i5m7qwdtcFIvIijI4kf7cdgot1Z61MXqhUWX8mP6G0dCKGDNu4ZVcdGtWnIAK76UQMGYWqT5ITOKChXHrG5yyTWExBMq6Eo9MyRe8y8qgHVad8DGUEjMxLMizl4jzqKo4mfHTXBciNAF2PsVzJyFfJEtYFbJWXZo0GAOpKpalt1O6sINf7iQ0njcjL3EU6wWVeZL/bBrhKG3IXlOcmvtJhYV6QjCucbST5xl03IkDTtMCUl9AQOKBhsvR00f8U2VQ8RPYhwA0l3APA/yRdorxoSCshuqGhqKs48tPjBSC9j1hod6YkqfJCyctww5eaNRoAqLWNNBlsI4CzjcZqSqXDhoi8CKIjGQ9PUBVoUB3ytEw6FfcUUKSoqTaSfdOqG85YKKvQK0J9KS+pRAwdYSFwgEl5pH+pOFWjwEl+HgBAj05eZoaBSokbyuidvLBeP0C4lBdKXvRGdRNsKKP336FmOCPtKZSRSXmxto2Gp8h7uSmZFz1bp6hlKHQCebu7DnPkxS+Jbyci8iKIjmQMHUpIQppAQ+aFlgj7ybsAdOPmsj8yg1deNI3dZQDGzBEvSMZjyISFwAEN5dJ+m9QBQDKmIBsm66xzUM/maMD0Xi6w6z20zDI/QLjIi4Vt1OvRNgLqhjPKaBtR5aWcN+wbHVsPkP+vHgzezqHXmSwKxiclUl6molLpuQ++VFoLBXmpDauyHi8+qmwA0l01NHfcVH2qloDitFFpFID6FBrrDOAyL/WNqbxvVl0JbuNOhWANFKUm9+I7sBuP1bVOCK9t5GcoI0VNrxcZbaNUl3GzxTWqK5Sr2DmeBwAcvih4UpGIx5BKxNCFWfKJeLr91Xm8bRSVSs99ENuIvMnVMJGX0gxQnjW66/pUXpIJJTxh1VTWOMb8aCBhXaCu143sBA6ouVgBwdhGPXG+YWMI1gCoyb34XYNUPIasTmC1eFqekmAR1DWqm2SB3SBsI0mVF0UxKo5mDOto+1gOqkbOg4VdzSGg2VQcnYquvLTbMgIi22i+oYOzS9RYCCTidA8Q00/I3Ggg3XUBMtcnHaqwqtGsL4iwLgCk4xrStNJGdgIHAD1LyKN+p01JnFfVAQC649x7IR6Suzaae5nc5bvPSyymoDumK7FhOAd49OgKVO4AUC2zwKof5WWB7IFdwKg44pSXLSNk3tDhi7oC765LkU3GDeWl3ZYRYJDKXFQqPS+QiMeQ1S9W1XgINm1Fqcm90M6yGZ/kJXRBTW44Y64UjPKSVTi7IAxr0HcIeZzYiUpVZZVnXT4uWF0xsvmH4r1AQRWHqT2+bSMA6EnoBC4syhNFdoF+Y6MBM8PMNvKTeWHDGXnbSKbALsBVHBnk5eURknc5bGHzSEU2neCUFwkGVdYoLxF5mRfojJOLfiUWEn+ba48/PEXePIu6/W02yViISqUBTnkZ5UYD+CNwnTHeMgnB5k1bwk/sxEzBIF5+LliMvIRp42a20W7ffV4AwzpTEyF4H/CIxYDuxQAAbWqvYRsFknnhqo2kU14aK4626GHdZuRdKLKpuBHYlUF5YYUMhm0UBXbnOLr0i1UlDLYRUDNVec8EkS2X9fnbbDviKuKKXm8cBtWBqzgKyjbK6lVn5Vha/k7LANm0lRhQLSI3RtrjdyRjSMa9v/27YoTAVmIhIG8UXGCXXrR9WWdUiQ0TgaPoJlZi6eBuNl3bT4dZWm00OZMHCpINZaRol/KSiqOLkRcJhtlSRUwts0nXUeZljqOTblpKSJQXrtfLnoM6een3d6FlYV0gHEHNrKE+5QIK7NI1KIdl444nWd6jNLodANCV9nexotZZJVS2Ea2y2eO7zwtgkJdKGN4H9dBzUIVx0rQvGVd8dd6myks1R6eXK0Cmz88RBo+6zIuqatg62grlJYFORc+8yBDY5QoZ+hVKXiLlZU4jGyMXvHJobCMj82IoL/4kbpp3qSIEwymBGok0qGqjTp28lJQQbdy6dVQd3wbAfztwap2FhsABRlC1OIl4mVy0/dhGXXFdfYqHkLzoykv5IFHiejMpX4FVGthNlCbIJzJ98lVg1VUb7ZmYRaGsIhWPYYXPmzo71CovEmReAHZTN4BpJOMKGzQaJoTviNuIrH7BLikh2LQBtnFrASovtLtuUekIh2XCZV6Cso1o1VkpLCQWAPppaJeUx/q908oy9SlEG3e6C+joAwAsUYhC4Odc6KTqU5jWgEInL6o+ZdtP3gUgClY8pmAAhBRKZxkBQNcQedSVF5p3WTWYRcKHheqEbCqOrEyl0gCrOOpXptHdkWxapVUzEZEXF8jotlEJIdm0dPJSnjqAWX2i9JJen5kXjaoOYcv9HMDoTDDTY2lguRQW+xBgyktiirSE91NpBID1OAkVgQNYaHeZMoqudMLXptVFCVwYlZeepQCA2Mx+AN6HMlLEYgr6syn0K3r3WtkqjQBuOCNRXlqRdwF020imwC7AyOUCTIcyrAtE5MUVOnTlpYhwbdyVaXKnMdiVRoePtviAoToUw0LgaEhzYid2jJGeDocs8Ged0T43xVCRF6K8pGd05cVn5oXah6GyzgBGXpYoY77Vp84wqk8UerVRMq+TF5/KC0CsI5qhkFJ56daVl9I0kB/HlgNGj5dmgthGtM+LBIFdgP19iPISkZc5jwzbuENiG3GWCeDfMgKAlEbuIAphIXD9q8jj7DjGxsgd18qBgMhLWAgcwGyjbJ4ENP1esDqUEKpPAMu9LFXGfIV1AcM6K4Up90PRTZSXTIHc2PT66K5LMdCZ4mwjCZWXdLdxPdj/JLa0THmJy9XnBTAyLxF5mR+g4wEKWkjIi24bJQukadTyPv/kJa2RjTs05CXdzRSorjzpMLvSp/JCrbNCmMiLbht1FfcjBtW3bUQVuELYNm5deVmqjPkK6wKGfVgM2xoATHlJVfPoxCwW9/o/lwe6UuijtpGM5AUAlhxHHvdtbkmPF4DYRlJ12AUM5QXTvisP24WIvLhASr9gz4Zl09IDakm1gB7MYKnPHi8AkNI37tCsAQD0rwYArFRG0J9N+r7jpmsQGvsQIAHNWBJxrYolGPPd16FDV+BCpT4BBnmBf+WFkZewWWcACY6mewAAi5VxLOn1f2OzoDOFAZltI4CRl9KuJ0g3YACHLmyulUMCu3qLCWkCu4by4rfysF2IyIsLULtgNizKSyrLQmorlANYFoDyklLJpjWrhWjj1qXiQ5Rh35YRYFhns2EiL7E427iXK6O+L1hppj6FaA2AQDMvoSYvAKs4GlIOBnJtWNiVRj+1jWQM7AKMvFT3bgYALOvL+B5W64TujiQ6ZVVeIttofiClWyazakjIC8CyDiuUA1jW73/jTuprkA8TeRmgysswVi7wf5eVUnX1SQuZ6kDPhdiI7wtWOozWGcAyL0uUMfR2+Ayv0/xXWMmL3qhuCAexJABVdmF3Wu7ALgAsJuSlY2obOjHbdNUFIGHoLkmrjQYwHcruukBEXlyB2gV5NURMVVcdVirDgdxdJUOpvBDycogygpUDwa1BqAgcwHIvy5UDvn1uQ30KG3lZChUK0koFQ/FpXy+VDusa6Kh0Elt5sXIQS4NQXro55UVW8tK1EOhZBgUajlJ24NhlvU3/kX3ZpFwddoEa5WVhdzjP34i8uECCqQ7hYarlHrJhrVRGAiEviTBu3LryckhsGIcM+L/TSuqbVj5slkmfocL5vdNOhfE8AIB4Erl4HwBgUWzK10sx8qKFU3mZSpIg+/LEhO/8D0CGvvbLHtgFmHV0TGw71i3va/qP68umWJ8XTbZSacxgqDtEN+McIvLiAkndLshVw/PHHk8RmXx1fNR3dQUAxKv6GqhJaJrm+/VaAl15WYIxHNLnfw0SVX3jVsO1cZe6DeVltU/7zFCfwnfXNhkjd9sLMOnrdVj+K2zl4jrGYmQDW5n0tw4UCzvj6FNI7xS1Q17yUl50LABKXlqgvKQ0pBXS3XtWkWQCuU4uY4qGxamiw5PlREReXCChk5eZaniUl30xEtg9JH4gkBbQiSqRPwtIoVwNB3mpZhcir6URVzSsSow7f4MD6HmQC5nqsBfkTntl7AD6O/0de4LlfsK1BgBwUOkDAPRp/pSXVIgJHAAMa/0AgCWxg4G83oJ4jn18UJNkkzbBjtQaAMBx8e2+O46LIEvzLgAOVuR4v2ixBKb0v9HixEybj8YbIvLiAnGV2EbTIVJetlcJeVmsjgBq1ffrxatUKk+jVFV9v14rsHeygJ0aWYeF5b2+X4+uQS5MwW0AL1fInfYQxoFKyddrJVVCYmdCSF7GNFIi3KP627RTdA3UcJKXneU+AMCg5p/QA0CyOAEAmNA6cSDv/1rTLDxWItPFD8VuKJWCw7P9QykRclDUkpgoynHDN5EvY1wjDfMGlIi8zG1UK4hpRPqbCRF5ebnQhZIWRwIVYMr/xh2rGmXCpUo4yMvO8TwjL7GJ7b5fj6pPYSMvL0xnUNCSiEEDpnb7eq0EI3DhIy8jKrlod1UmfL0OzX+FTYGj2FIg69BTHgPUAN7LedIMc1zrxsiUvFbEwwfSOKD1IA4VGH62+T+wSMjBDDowmS83/+cJYHi6gIMgf/9UMRjlrdWIyIsoKrPsw6mqZKPebbB7ooTdmj6c8OB2368Xqxi2UZjIyw5Nn2syvs3368X0u7XpENmHALBtLM+dCzt8vVY8pAQOAPZVyEU7W/ahOFTLiNObmZDaRi/lsqhqCmKoArkD/l9QJy8T6MKBaXnJy+Y9k3hWXUX+s29T839gidhpOa0DByUhLyNTRaa80L9b2BCRF1FUjDfjdDk8ysveiYKxYU3427AAAGU+8xIO8rJjjCMvBwMgL1VqF4Trjnv7aA57Nb2EdXq/r9eK6wRuqhquNZjMl7FXJy+Zso87zpKR75gJIYEDgN1TJYxCD6xO+1dlkSdkcFzrxoEZOcnLdKGMraM5PK2tIp9oCXkh5eM5ZDAx68+uDQrDU4WIvMwb6Jt2UUugUJHDtxTBnolZZpkEobzQdZjV0iiGRHnZNZ7HrgDXgKpPOTWFqhqec2HbaM7YrGaGvb+QpjHlJWwEbsvoDMu8JGZHvb9QOU8etDhmQ6TEUmiahr0Ts9iv6VVBPsksAE556ZZWeXl6zxQ0DdibOZJ8Yu+m5v9Q3TbKoQMTsigv00WMgbwPAlHd2oCIvIiiQme5pFAoyxtG41Gpqtg/VWgKeQmTbbRjPIcd/Br4LPFWKuFTnyZnyxjLlXBAo+RlxPuLcSHHqZBZZ1tGDPLi66JdIuRlFunQVN3xmMiXUSirGNErjoLIw1HyclDrwoik5OXJ3RMAAG3J8eQTI88C5SaHdou68qJ1YCIvh/IyMlUI5lrQRkTkRRT6BbuAFAoh2bT3TRZQVTXsU6hlst3/i1LlJSQbt6Zp2DGWxx5tITQlRu6Y/agOAKtQmEV41Kfto8TmmE2RSePI+bhg6Rs3EK7wOgBsOZDDKL3jnDngnciWyXrmkQ4Nieexd5K8jycS+vkwvc//i+oEaL82gAPTza/i8YIn95CeNstXrwWyg4BaAYafbu4PpUFmdMuTeZku4oDWR/7j83rYLkTkRRQ6Oy9qydAoL7sOkk2m0EVKA/2GNAEwuTwspdKTs2VMFyooIwGthwzl8x3aZdZZOAgcAGwfI5ttvFtXoPxcsPRzoKglUaj67x3USrw8MoMxesdZma3JrriCTuDyWhrFkJwDPPZO6DdjHfr5MBUAeZnYCQDYrS2U1jaiystxK/qBpSeQT+55vLk/NE/syXGtRxrbaHiqgAPoI/+JlJc5Ds4qKJSroeguu3ucHHNsAekwi9yI94s1BadAheGOc8cY2WQWdacR08cE+ArtVitQqvqAzhDddW89QP7umYGl5BN+Llic+haW359i64EZ5JFGNa43J/NqHenKS5jOAR77dOWl3KWfDz5L5wGwgoDd2kIpbaPJ2TJ26dfEY5b2AstOJF/Y+0Rzf3COKC9jWjcmpQnsFjnbKIC8UxsQkRdRVOgU3SRUDaHwuanysmBwEdDRRz7pV33R77rDQl52jpPjPWRBlg2p9GWfcSXzYcq8bNNto+5BMi7CH3kJZ96jVFGxYzwPQIHWqVfg5TyGdqnygjRKlXAosTyo8hLr01XZSZ/kpZRnRHCXNojpQkU6hZq+BxZ1p9GbTQJLKXlpkfKCHilsI03TcIC3jQqTzc/9NAEReREFrTbSh/EVQnDB2qVv3Cv6s0A/GcrnO/fCVRuFwTai5GXlQCfQo99l+qmsKBvkpYhkKAgcYNhGC5fom9Wsjy67zDpMoVRVQ6FCAsDO8RyqqobOVNywzzwrL+GyT+uxd4Kcx+lBMu8Kk7v9BdkndwEAtHQPCglSgiubdbRDfw+sGtTnelHb6MALLFTbFOSobdQthW00kS+jVFUxiU5ocb1a0E8Grk2IyIsomF1Cqitku6sww66D5AK1YiBjqA5+er2oVYBZJuFQXugFa+VAFujSg8sB5D1mkQaghGLj0jQN23TbaPnSZYCil/b63bhBmrOFYQ0A4OURsgaHLeqCwpQXj2tQooHdjlC8D+pBbaOehYcAUMj1zU+/Dz3vovStxKJuYsnJZh1R5WXVAn3uUvcQ0LMMgAbs29y8H6wrL2NaDybypbaTffp36cumoLBrYkRe5i70O+6yPkG2WJb/glWrvKwin/SjvJRrLZMwXLRrbKPuxeSTASgvBbpxh2ANxnIlTBcrUBRg5YIuoIuqDh4vWCzzEp41AIAtB0i/jcMWdgGdtOrKH4ELbbWRbhstXtBrkHpdPfEEelPUdwgWdpPzQjblhVbcMeUFMNSXZuZecrTaqAcVVUOu1N4b3+Ep8rcf6u4wrgUhrDiKyIso9MxLRdFtI8mVl0K5yhj2ioEs0BeAbVRnmYQh77FTD+yuCFh5KSnh2bjpHefS3gw6knHuguWTvOgzfcKSezHISycQUOZlVgsfeamqGvbrG9jSvg6gV6/C85N70ZUX9K3Ewi6dvEjWZXebfi1YvYAjLzS026yKo0qRddidiZOA7MFce0O7lLws6kkHc01sEzyRl+uvvx6rVq1CR0cHTj31VDzyyCOWz73xxhtx+umno7+/H/39/TjrrLNsny8t9KBmOUbemAXJlZfdumXUmYqjP5vU5VH46+egr0FJSUNDTHq7oFipYp/+Rq1RXmZ8TNimJfM6eQnDxk3Jy6EL9Yt2p8+7Ld0yKYaIwAGkxwtAlRdKXrwSOK7Pi+Tvg3ocmC6iqmqIxxRi8QRBXmghQN9KaZUXaiEfssBMeWkSeaHkWIkjlukDQKqe2gl6U7uoRnmZB7bRrbfeiquuugrXXHMNHn/8cRx33HE455xzMDJi/stv3LgR7373u/G73/0ODz30EFasWIE3vvGN2LNnj++Dbyn0TatCyYvkgV1aabRiIAtFUTjLxI/qUGudyb5p7T44C00Dsqk4FnSm9E1bAbSqd3+/bBA4AChV5T4PAN7r1y/afn1uap0pJNsg+3kAkNzP1hFdeVnEkxevmRfDNipXNaghGhPx4jBRApb3ZxCPKYErLzTzIlOjuol8iYVlVw1mjS9Q8nJwO5vNFCjodSa7AP2daf1Y2kxeqG0035SX6667Dh/4wAdw2WWX4eijj8YNN9yAbDaLm266yfT5P/7xj/HhD38Yxx9/PI488kj827/9G1RVxb333uv74FsKPbBbjZE3puy20W4967G8n4bTdPKS86M66PNcdAIne3dZo9JIJ3DxhJF18Jp7obZRjG7c8m9a1OtfTb1+37ZRnXUWAuXhwHQR08UKYoquwvm1jbhqIwDIlSpBHGZLQBu1rVveRz7RS8ul/WReONtIQuWFEvihnjSyKa4rdKYfGDiUfNyM3Ise1kXnIHozpNjjYJtHBBjKS3r+KC+lUgmPPfYYzjrrLOMFYjGcddZZeOihh4ReI5/Po1wuY2BgwPI5xWIRU1NTNf/aDp28qPFw2Ea00mjlgE5eOhcCSgzQVB8hRV150Rt8yZ55oXmXQxZwd1pdnHXkBfoaUAUuDBv3Nkvy4vFui54HsfAoLy/reZeVA1mkE/EAlBeypvS9cDDX/hJYUWzaRVrkH7dcb1LmV3kp5YxNWlLysoNdCzobv0jnHO1/KvgfnOOUlyzJiE202TZigd2ejvmjvIyOjqJarWJoaKjm80NDQ9i/X+xO9jOf+QyWLl1aQ4DqsWHDBvT29rJ/K1ascHOYzYF+wTbIi9zKC6s0GsiQT8TiRtbBa+6Fbdzh2LToBYsROICURwLeu0oy9UkncJKvgapqrMdLA3nxWWlTDomFCtTlXQCDvOTHvCmR+hrEUmRN2303LQpN07BZV16OX9FHPumXvEzoik1HL5DpI3f0kKtUmhF4M/KyUJ8wPfpi8D+YU176skR5mWhzYJcpL7xt5CdO0Ca0tNro61//On7yk5/gF7/4BTo6Oiyfd/XVV2NycpL927XLh5wZFPRqIzURDtuIZV76+Y3bZ+5FJy+0tbrs5IXZRvwFiyovnm2jOgInufKyf6qAQllFIqZgeb9OZP3ebekbt5Yk51a7A4gi2MLnXQAgu4A8aiowe9D9C+qZl1g6XORl/1QBB6aLiMcUvGIpVV70m8OZYXadcwXOMgLAlJfRmaI0WaDt9Q3qeCxcSx6bQV6oLZkdJF190V7lRdM0jEzxgV3uWhCSZpMUrsjL4OAg4vE4hodrL3rDw8NYvHix7fdee+21+PrXv47f/OY3WLdune1z0+k0enp6av61HXqljUaVF8k3bjrDY8WAGXnxp7xUw2IbjXMN6ih8WyZk06rqJDZXlDvrQPMuKweySMT1t3un38wLOQ/iuuowEYKNu6ZMGiD5p4xuXXtRoPRqo0SaroH8BA4ANuuW0dqhbmRSerPC7ACQ0IntlIdCCq7HCwAs6DJK6GUhttt1FXYVbyFTDOrk5cCLwW/gnPJCbaN2El3aXRegyot+LagWyZiAEMEVeUmlUjjppJNqwrY0fLt+/XrL7/vmN7+JL3/5y7jrrrtw8sknez/adoLOfkiSN3lRYuVlcrbMLhrsbhvgSoU9btyVWvIic2BX0zSjQZ0pgfOnvMRS5DVl37S2mjXmohes4lRN7x5h6AQurm/c4yHIe2ytt40Af7kXXXlJZkgr/LAoL2yqMs27AIDis+JowiiTBoB0Is4sElmsI9MGdRQLDid5wOJk8NkPprwsQJ8e2J1s4zXD6K6bJNmvZAZI0wGN4QrturaNrrrqKtx444245ZZb8Nxzz+FDH/oQcrkcLrvsMgDAJZdcgquvvpo9/xvf+AY+//nP46abbsKqVauwf/9+7N+/HzMzM8H9Fq2AHthFCGwjmndZ0JlCZ5pL1ncFo7woOoGbLsirOhyYLqJQVhFTgGU8gfNrmejnQVwnL7JvWg2VRgDJJugKoqcLFt24OyiBk3sN8qUK9uizfAIjLzqBS2fJ68kwcE8ENO9yHM27UPgiL7W2EQAs7iHXyb2THshxwDiYK7GbuUPMlJdE2uhAHrR1REulOwfRJ0Fgl46FGOrmYhsh7bLrmrxceOGFuPbaa/GFL3wBxx9/PDZt2oS77rqLhXh37tyJffuMzfH73/8+SqUS3vnOd2LJkiXs37XXXhvcb9EK6JuWwsiLvKrDbj3vsnyg7o3qO/NC77jJ647l5LirMsMOncAt7csgGedOc9/KC3ndREiyDg2VRgC50/YT2tVJbFhUB6q6LOhMob8zZXyBls3PeFFeyGt2ZMkayE7gABLefnI3sQbW8coLEDh5oVYtvZFqJ2jepaFMmgezjl4I9ofzyku2/aXS9HpQW4EZzooji7+kPa644gpcccUVpl/buHFjzf+3b9/u5UfIByqvp8hdvNzKi5534RUHIIDMCyFwbOOW2C4wLZMGGgNqiuLuhdnGLf8aABbkBSDkZXKXtwuWTuA66BpIrjrUzDTi4YvAkTXIdvUAmJR+DQBg21gO04UKOpIxrB3qrv2in14vjLwcwj5FyQt9H7YTLKxrVmlEMbgWePGuJigvRmC3Xx+n0U7byOi2zb0XQtrrJZptJApdeYkldeVF4vJQvrtuDfxmXvQLdkrftMYlvtvcwTWoqwFdg0rBW0CN2gUZ+ZWXSlVluZ9G8uLjbksncJlOXXlpc+mnE4xKo7o18GobqVV2PejsIsUEsq8BYORdXrG0t1aNBLwrL8UZwxrpM1parNRvGnZKoLxsG6VhXRvysvAI8hgkealWgNkJ8jFfKj1bbttkaapCslEhQGiVl4i8iIKRF6q8yGsb7bLcuJeQx5lhj70tyKbFNu5c+8e7W8FYg7oLlt+AGt24dbtgXOJNa/fBWVRUDelEjGUQGOjG7cUy0dcgS8mL5KpDQ48XCjZZ2mWX3bKxIXd368P2JCaxFJtZc7q+xi96JS9UdenoI1kqHfTGSQbyssOuTJqCrzgKCrMHAejXx8wA67BbVTVMt6lKcauuQh5qFuCPlJc5inJtUFNm28hSdfDbZVevNqI+f6WNb0In0AtWwxoA/hrVsY2bbIQT+fbdRTlhG9ecLhars8d8KS/kdTu7ieoge97D0jbyOpyxRDdkBT3dNPMiN4ED+LBub+MXefLi5nw2ybsAnG00nm/7+4P+/VcPmlwLKCh5md4LFALq6E4to0w/EE+gIxlHJknK0yfaYDfPlqrYO0n2sRrbyK8i3yZE5EUU+sYdT8utvFSqKvOZG6yCmi673jfuZDqLrN4jQla5nJVJm1UX+Okqqd91d3aRTatUVZEryUlktx2w8fr9VBjo50G3bpnIrD5VVY2VizeQF6pETrnMgOnkDcksG7Ynu/JSVTU8t49syscsMyEvdOp8Oe+uaZ8FeVnen4GiAPlSFWNtPD/KVRUv7ifk5aglNv3CMn3GdWHspWB+ONegjsKwjlq/JjTv0ptJol8/DgDzp9po3oKGVfXAblHSzMueCRurADBUBx/kBckMa7jUzguTFaYLZYzOkONaaUZe2J2G9zVIdXQhnSBvH1kJHBsLsNCGvLhV4DSNEbjeXrIZFCsqZiUlcHsOzqJUUZFKxGpL5gEjpDq9F6i6uBOmyksqy94H+VJV2msCQMh8oayiIxkzJ7PJDuPGxk1ot65BHUU6EccS/frTTuvo5ZEZlKoqutOJ2m7jZgjaOuIa1FGwcuk2KHVGWLeTDKqliDIvcxjFadKBEICSJV05ZbWNto7aWAUAl3vxsnHrF6FklnXRlHHjpjONBrtS6OlINj6hyz+BU7iNS9a7btt5Ll4vWLTfEYBstgvJODnHZF2DLZzHH2+wzhaRvk2a6q6zLPc+6O5IgL6szNbR87rqsnaou3EdKKh1RNUUEdDn9h/S8KUVEpRLP7OX/N5HLe0xvx7yoORlNKByaa5MmqKvjZOljbxLfdWdfi3IjZKQcUgQkRcRUFk51Y1kltxtymobsU6SVsl6Xxu30WWYbtwyWgbbRNfAh2WCZIb1DJE1sMrIi53y4jakVzI2IiXVye4kZScvDZYRoHeW1dUXNxu23uMFKXKDIPsaAMDz+6cBAEcu7rZ+ElMenhd/YQvbCJCjXPqZvSSkfLSdZUTBKo4Cso24BnUUA/o1gyrDrQSvvNQgu4BkIaEZalEIEJEXEUzvJY89S6VoNGQH2w0LMJQXT+RFvwglMuxNKOM6bLNrBQ74a1THyEuW+cYyqk+FcpV1lTXPvOhrUM4D+XHxF6bnQDwNxOLcGshJ4BpmGtWjzwN54ZQXANKvAQA8v58oEEcsttnEFx1FHkeeE3/hutEAPFZKUHH0rK68vGKpAHkZXEMeg2pUZ6K8rNJDw1QFaSW2UPJimoWk1YfhsY4i8iICqrz0LMGQ7uOOTBXbnqI3g2VTMoqAMy8yzrUxbYnPw5fyQjcuQ3mRUX0iVR5AdzqBwa5U4xNSWWMd6AYkAu4cACC9dbZlRA/rLjJRXgBj03WlvBiZF0D+NQAM5eUoO+Vl0dHkUZS8FKaMcG/vioYvt7vXi6ZpeHYfJS8mIeV6DOrKy8Ft7jJQVsg3BnapArilxeRF0zRso1VXZkR+weHAgjVARd5zuB6eOuzOO1DlpXspGSMOUmVyMF9mCoQscCYvPjIvFcM2Gugkd5vjEo4I4EuETeF1TEK1DKj6RS3Rwe64ZSwVfpk1ZuuqDefx6F9FCNzB7cDSE8ReuEF1oAFE+dYAcLCNAI68uAipsmojcn7JbhvlihVGII6wJS+68jL6ItnEEg7XNhrszfQDHY3KRrszL7vGZzFdqCAVj+FwK/LKo2cpkOoGStPA2MvGenhFrjGwa5CXnL/XdomxXAlThQoUxUKJveyOlh5PEIiUFxFwyksqEcMCnbDsnyzYfFPrwVsFjqqDH9somcGAXiIqs/LimHkpTrqbqsw/N5nFAFWfJNy0Xhomm7btRZsOozu4XfyFy3WqQye1UeU7Dw7mSqwazvL9QKtkfCkvlMTKtwYA8OLwNDQNWNidxoKutPUTe5cD6R5ArZDN2wk2eRfAsI32TRXaUolF8y5rhrqQSghsdYoCLDqSfOzGOrMCzbxwthFVAA9MF9mwyFaA3tQu68ugQ+81E3ZE5EUEdBaQrlos0q2j4Wm5yMsuzipYYKUIMeVlxH2XXc4yGOiUM/szkS+xjXSVVVOqjl42HdyVdcTIiwIk0lIHdl8aITbBmmaRF9026pM4uL11lBC4pb0dtdPVeXgJ7HJ9XgAY54GEawAAL4iEdQF986a5l2edX9hkphGPBZ0pZFNxaBopWW81DMtIIO9C4SX3YwWTwG5XOsFaWLTSOtrKGvXZdBkOGSLyIgJaRtmzFACZTgoAI1NykZetXFjX0iroXAhAAbSqu7bomlYXVpXzgk3vMGwnyCqKQeImvZXIQlGkXQPAsI3WDAVNXoxzAABTn2S0jaj6ZJl3AQzVYGqPeJkoU16obSSv+gQIVhpRuNm8HZQXRVHaGtp9Zq+LvAsFy/0IkDc7aBqnvAzWfInO2KIzt1oBy0aNIUZEXkQwVau8UOa8f1KuvIdj3gUA4gmuTNaFdVQpgs3pSHSwrI9slsl2p7wLxcCh5FFEHqfgMj8ApFVeKlWVXazWLLLZsOgdsw/yIvPGTTftI+onKPPoGgLiKULmabbNCSHL/dDOukfaVRpRuAnt0vPGQnkB2pt7obaRN+XFJ3nJjxH7DaixjQDgcJ1AvNxS5UXwuhgiROTFCdWKMftEV15ktY0csx4UXnIv3DA6vtJmIl9GpSpPzxs6QdbxTUrLIt2QF8sSWbk2rV16V9mOZAzL+jLWT6TKy+RuF6pDnWUicViVbtq2beFjMffN2VifF/nXQNM0vDCskzhXyosb28hceQHaVy49OlPE8FQRigIcKdLjhYKSt/FtNT2NXGP/U+SxfzXpXsyBKoG0Eq4VsOzxEmJE5MUJM8OkA6di1MJT5WVYssDuVtETlM10EbzTBAzVIZYE4kn0ZZKgztREC4NnTnBsUEex4HDyOLZF/MUtyoTH83JN135J36wOW9hl31W0ewlRHdSKeIfZ+jWQNPukaZphlyxx2LTdVhwxEkvOsX6J1afhqSIm8mXEY4pYxQ3dvA9uN0iaFVyQlx0tblRH+7usWtCJLqu8kxk6F+pKieav0+7+J8njknUNX6LWTat6vcyWquzGNrKN5hNYWHcxaeYDI/MSWuWFWiZuOknW2QWJeIyNeJdJedju1KCOYsFh5NHNELaGjZuQl1JFxaxE4yJeonkXp80qFnNvHdUFdpllIlnV2f6pAiZnBTdtt71e6quNJG7YSJvTrR7sFKsy6RzUb9I0+2ZthUmgMEE+llB5eWqP3lnXjWUE6KFll/1uzLBPJy+Lj234Ej0fd4znUao0X7V+fOdBVFQNS3s7sKTXZN5dSBGRFydQdYKqFQBrVDc8JU/mZaZYwcg0OR7HjZuWA7ppA07DZ1w/hwHJKk00TWPkpaGLZD0W6LbR+DZxy6Ru4+5MxZGKk7eQLGsAGEHANXZZDwq3oV1K4FJUdSDnwHSxgrJE9uHz+6j61Il0wmHT7nVJXhr6vBASPzlbRlWVR4EDXIZ1KURCq1Slyi4A0tbkkF6Lto3mWnp+/GkruV6dfEi/+28OIvdClZfFxzW+fHcaXekEqqqGHWPNt47oWpx66ALrQo4QIiIvTpg2erxQUPIyOlOU5oJNN+3BrhRTRCyx0AN5ofbKwGr2KdnuOMdyJUwXSSMmGhS0RM8yUi6tloFJl1kHnbwoimKMuJfIMqDKi5BN4Jq80BER5D3Qw9mHspwHAPDcfhchVaociJ4Heb2rrE7k+zLkfaBpwJREFioAPLWbKBC2uZ96iCgPApYRABwykEVPRwLFisoIZbNRqqh4dDv5G60/bIHDs03gt1y6lDNUbRPbSFEUNq6iFeXSD28l4z9edehA039WKxGRFycw5WUp+9SCzhQSMQWaRgiMDBDOegAGeZnaQ1p8i4AGW2lWBMZd95gkqgMlcEt7BRoxxWLAgG4djQqGdukFu8c4FwYkGxGgqhork24KeaFroCuR8ZjCyLJMBI5ulI55F8CdbaRWDatRD32nEjGWq5CJwGmahkd3kI3rJDcKhIjyYDPTiEcspuCEleRnP7HroPgx+MCTuycwW65ioDOFtXbVdlbwaxsNPwNAAzoXGd2860BDuy83uVx6tlTFpl0TAIBTV3sgchIjIi9OMFFeYjEFi7r13Isk1hF9EwiVwmX6DBtMdAiZCXlZIFlzrq0ipeI8BmloV5C8jL6of99a9inZKk32TMxitlxFMq7gECf1CXBPXugaLDTWYEDCfjc06yGkONDhjJO7nRs3HtxOwuuJjpoSYRlLxvdMzGJ4qohETMFxy/vEvzFA5QUATlhJfvYTOyfEj8EHHtpCbJJXHTpgH1i3An9zNzvh/vttwroUrRoT8MTOgyhVVSzu6cAhCwSuByFCRF6cYKK8AEa5tCwjAmhATbinAR3/LmodUduIV16Y6iDHBdtxIGM9FrglL/odN83LgKu2kWTjpiT20MEuJOICb2835KVaBsa3ko85Aifbxl2sVNmmcJSIbdS9BIglSNWVU/sA+n4ZXMsC/ICcvV4e20GUjlcs7UEm5aIl/KIjSXXl9D7rajyH7ro8mPKyszXKy0N6xmP9oR6VhkwfsZUBd9Y6BQvripCX5iovf9pGlLdTDx2YU3kXICIvzmDKSy15oeXSIxJUHGmahid1b3vdij6xb1qoS8Mib05VBcYbyYtsIwLohcAxsEzByItAxZGqGiTHVHmRY+NmlpFdZ10e/frmMztOKkjsML6VbPCpLuPiDvnUp5eGZ1BVNfRlk6wy0BaxuHivF6pG1A3tk7Fh4eM6eTnRbWg13Q2sPp18/NyvzZ8jaBsBwPG66rN9LN90e7VYqTLS5invQuEntCugvBzOer3MNLXNAg3rvsorkZMYEXmxg6ZxQxlryQu9KMqgvOyfKmB0poh4TMHRosE8N8rL9D4S1Iwlai5W/ZJVG1ECJ6w+UQVFpNfL9F5jDfqNu03pNm6RmUY80t1G+/KDO+yfSy3GwTUAdxcn21RlvsJG+G5TdMYRfb/Q948OGRsWPqpv4icf4iGoefTbyOOzJuSlWgHGt5OPBchLbzbJAqqbmpx7eWLnBIoVFYNdaX89Tah1NuySvFTLxvfYKC+HLMgiGVeQK1Wbpr4UynzeZW6FdYGIvNijOGWURXKl0gAw1CtPufTmXWTTXjvULT4xlN1ZCJAXqrr0rwLiRiWTTGHV4akC9k0WEFOAY5cJzjKhvV6m9jg35KJZj4FDa9agX6I1AFxWGlGIWke0addg7cZNFThZArvPu2mHT8H63Wyzfx4jL3XKi2QELlessA7DJx7S5/4FjnwLAAXY+3gjoXvpN0BpmvSD4ZRYOxjW0YT7Y3EBQ2nwaZN4nXE0+iJQLQKpbtJd1wLJeAyvXUOanv7PY7u9HqUtntg5gVJFxaLu9JwaC0ARkRc7UNWlo5c1pKIY6pbHNnpqzwQA4LjlLgaQ0TvHqd3OFUcmYV1ALvJC7zDWDnVbTxCuR3YAyOh3JE7qy2ijZQQYd9wybNylisrNsXFRZSFKXg40hnUB+SZLU+VFWIUEgKXHk8cdf7B+jlo1ck+0V5IO2XI/m3dNQNWAZX0ZLOm1GRFhha5FwCGnkY+f+9/arz3+H+TxuHfXEHk7tCq0S8O6viwjABh6BXnc/xSxjEXBN6eL2W+v73olUft+9tjuprTceHibYRnNtbwLMJ/Ji6YBv/0i8N1TrCcLT5uHdQGj14sMthG1S451Q14y/UCXXsZHVQUrmIR1AYO8yHC3ScnL8aKZHwrRGUd0jerWQCbl5ak9kyiUVQx0ptxJ5j6VF9nCqrTSSKhMmuLQM8njzj9Zz7SxqDQC5FsDahm5KpGux9FvJY/P/sr43NRe4KW7yccnvk/4pU7UlZdNuyaa1sgvV6wwcuQ5rEux6Cjydy5OGcqzCATyLhSvP3IRBrvSGJ0p4d7nRjweqDk0TcMdT5Gbb99ETlLMX/KiKMCOP5IL8ot3mT9nqrFMmmJxLy2Vbi950TSNVRqtW9bn7pvp3aNTPwOmvBxW82m6cedLVRTa3B5/k37Rck1eRCuOTMqkAbk2rUf0yoJTVrmUzGnjQbvzQFUN1cEq7yGB6rBnYhajMyXEFIeJ2vVYcBjQsxyoloCdfzR/jkWlEWC0wX9676QUc64eC4K8HHUeedz1sHEt3PRjMuvtkNOMVgMCWDvUjWwqjplipSm9TVRVwyf/ZzNKVRUrBjL+bZJ40mjtv/cJ8e/b+RB5tMm7UCTjMbzzJBIU/+mjgnO1BPHojoN4cXgGmWQcb17XuH/NBcxf8gIAR7yJPL5wp/nXbZQXWio9VahgttS+jXvX+Cwm8mWk4jGxqbE8RDvtWthG3ekEEnofhXaqL1VVw5O7JwAAx+vytDDYjCMH8sIqjdbUfHqAG87YblCZ+BS34TxqD+x62LqvxdQeLrC8quZLMnVa/v2LBwCQjIWr8mBFAQ47g3y85Xfmz7GoNALImqfiMewan2UNI9sFVdXw+M4AyEvPUmD5KeTjp35KCOzjPyL/P/ESVy8V53rNNKNk+trfvIA7n96PVDyG6951fDA2ydITyeOex8Wev/cJ8i+WBA4/S+hb3nUyIS8bXxjBvslZL0dpih//iYTv33rcUvR0iFl7YcM8Jy9/QR633Q8UTe4GbJSX7nQCWf3i2E715Uk973LUkm6kEi7/nCLkpVo27ISBWuVFURRh26SqarjuNy/gLf/ye7zmG/dh3RfvxiU3PcLUAj/YcmAGuVIV2VTc3d02IKa8FGeMqcsNthG5MBTKqhCJrVTVpqhUVVVjLdFdk5eB1URN0KrAlvvMn0Mto4HDGnIOg11Ehdw7MYtipb0K3AMvEfJy+ppB999MraOt95t/3aLSCAA60wm8cjUhCvfrBKpdeHFkGtOFCrKpuLvskxlo1dE9XwC+v56USKd7gaPe6vqlaO7l3ueDs0jGcyV8976X8L2NxNrZ8I5j8cpVAVXWLNPJi6jy8vC/ksdXvB3oHhL6lkMXduGU1QNQNeAnjwSjvozNFHHHU6Rf0Xtf5dyHJ6yY3+RlcC2pHqmWgK0md1v7nyKPtIySg6Io3IBGZ/JSrqp4aXgaL+wn/0amCoHIy57yLhSMvNh02Z3YSXp7JLMNFVeA0WV3bMaavFRVDZ/6n834zn0v4+k9U9h9cBZThQoeePEA3vWDh3DhDx7Ci8Pe555Qy+jYZb2Iu+2oSddg+FlzAgsYxCY7SEK+HLrSCUYadx20n5x7/4sH8KoN9+HIz9+FIz53J077+n341m9eQK4oOBjSBs/tm8JMsYLudMLdHBuKNW8kjy/9xvzrFmFdgAzBXNSdRqGssjkqViiUq7j7mf144MUD2D8ZzHuAoqpqePClUQDAa9cudP8Ch55BHoefAmZMNliLSiOK1+k/c+ML7SUvdz1NNq5TVg+INSq0wysvB054L1ET6O+/7oKGAgYRnH/CMigKcM+zw2zmklds2jWBi3/4MF751d/i2t+Qc/MjZx6Gv9RtmECw9ATyuG+z8/DW3Cjw9M/Ix6f8rasf81d6cPef730Jn75ts+/83G2P7UapqmLd8l5v+0JIML/Ji6IAay2so/FtwO5HACVmXNjrwHq92JAXTdNw19P78PpvbcTZ//QAzvk2+XfK1+7FCV++B+/6wUO45Y/bPd+NU7vEdd4FMDIvk7us7QK6cQ8cZpqep97yo9vNN61KVcVVP92Enz+xB/GYgi+97RX4+Ydfjf+94jV49ykrkYwreHjbON723T/g5497Kxl8goZ13VpGACGw/auAyqx19olmPQYbN25FUVgPBavQnaZpuOH+Lbjs3x9hs7CKFRV7JmbxL/e9jDOv3YjbHtvtayN/WFewTl7V757AAcDac8jjS/eYV1dYhHUBMi7j9UcuAgDcZ3FXraoafrVpD97wrfvxtz96DJfc9AheteFenPSV3+Kf7nkRkwEMNNy8ewJThQp6OhJYJ1ouz6Nz0Mg51KsvNpVGFGccQdbgT1vHGt7PD740ijOv3Yj33fQI/mzxXgkCmqbhV5uI3f224xvtbtdIdgBvux648hngjL8nisvpn/D0UmuGuvH240lzw2/dIziWpA6qquEH92/BO7//R/z+pVFUVQ3HLOvBF887Gp84u/Hc9IUFa0jJc2XWOP+t8NjNpER66QnA8pNd/Zi3Hb8M7zmV9Mv56aO78fpvbcStf94J1UOwWVU1/NcjpLSdvuZcxfwmL4CRe3nxrtq5Jk/9D3lc/TpT2whwrjganirgohsfxgf/83HsGp9FNhXHYFcKCzpTiCmkvPaRbeO45tfP4LXf/B3+7fdbXeVnVFXD03tIZcW6FR4u1pl+YzOidw31sAjrUrzxFUQevesZ87bqX7n9Ofxq014kYgquv+gEXLJ+FU5c2Y9jl/diwzuOxf2fOhOnrxnEbLmKq366GVf//CnXGaLNOnk5wW1YFyAE9hXvIB8/8wvz57BBfOYBxTceTdbgnmfN1+Dvf/E0vn7n81A14MKTV+Dxz5+N33/6TFx/0Yk4ZEEWI9NFfPJ/NuPjt27yTGIfYXkXj5UFK15FLtT5UXOZnCkv5hvEG44ia3Dv88MNJGy2VMVf3fgnfOwnm7BnYhYLu9M4bGEn4jEF47kS/vnel3D6N+7D9za+jIqPktHfv0hUl9MOH/SuODDrqE6Jtak0olizqAtLejtQrKiMTKqqhn/+7Uu4+KaHsW00h/tfPIALbngIf/WvDzWlOdmTuyexbTSHjmQMbzzafCigJ3QPAWd8BrjwRw0NO93gY29Yg3hMwcYXDlje8FhhulDGX9/yZ2y483lUVA1vXrcEGz95Bv7v707Hpaet9jbHyA6xmFFCb5d7qVaAR28iH5/ytzUNHEUQjyn46vnH4mcfWo8jF3djIl/GZ372FC74wUOs9YEo/uuRndgxlkd3RwLnHRcAeZUYEXlZ+Sqgow/IjwG7/0w+p2nAk7eSj9ddaPmttIup2d1muariwz9+HA9tHUMqEcPfvf5w/Pn/nYVHP3c2Hvv82Xj2S+fi//7uNfjCW47G0t4OjEwX8ZXbn8Pp37wPP7h/i5CV8OSeScwUK+hIxnC4126SJ+nljo/+O/m962ER1qV4/ZFDSMQUvDg803AxfmTbOG7+43YAwL+8+wSce0wjCVzal8HNl52Cj71hDRQF+O9HduLN3/k9Cxw6YbZUxQu65XT8Co/hxGP+kjy+dI95i3yLSiOKs3Ty8sSuiYa+P7/evBf//chOxGMKvvz2Y/D1vzwWA50prBjI4s3rluA3V74Wnz73CCRiCn61aS8uuOEh18E9TdOMSiOvnTQTKeAwfeOmpbA8mPJivganHb4AqQQJrPLVJJqm4e9/8RQe2TaOzlQcn3zjWjzwqTNx7yfOwLNfOgfXX3Qi1izqwlShgm/e9QL+6l//hL0T3oKLNO/iyTKioGuw5Xe17webSiMKRVE462gE+VIFl//Ho/in374ITSeuVG3809ZxvPP7fww8vPrLTSSbdfbRi8X7HbUQqwY7WUj1H+9+QVhtnMiX8N5/exgbXziAdCKGr51/LL777hPER4F4BbWO7HIvL9xOMnHZQeCYd3j+UScdMoD/+7vX4HNvPgrZVByP7TiIt/zLg/jK/z2LGX0/eGH/NG58YCtufGAr/vNPO3DX0/vZ1/77kZ343C+fBgC8/zWrkU3J9/cPEhF5iSeBNWeTj6l1tPcJsmknMsBRb7H81necuBwxhUj2L9VlNv7pnhfx2I6D6E4ncMdHT8cn3nhEzcWkIxnHMct68devWY2NnzoTG95xLFYMZDA6U8KGO5/H67+10dEX/sH9JKR27isWe7/TPO7dQDxNfP49jzV+3YG89GaSePXhJBxJvXaAZBs++zPS8+CvXrkCbzrWulwvHlNw5dlr8aO/PhWLezqwdTSHd37/j/iXe19yvLg9tWcSVVXDUE8ai/Wux64x9AqyKVWLwPN3NH7dZCAjjyW9Gaxb3gtNq7WO9k8W8LlfkNzUFWcejotfdUhDFUQ6EceHzzgcP3r/qejPJvHUnkn8xT//Hj9zYSO9PDKDg/kyOpIx8e7CZqDW0Yt15CU3Rsg90FBtRZFNJfBqvZ/Eb7k1+NGfduAXumX4w0tfiStev4ZVAaUTpIzzro+/FtdecBy60wk8uuMg3vTPv8ddT+9zdeiTs2XW68dTWJdi5Xryfpjea1wPqmXg4R+Qj2nzMgtQ8nLvcyO4+IeP4L7nR9CRjOHaC47DN965DhvecSw2fupMHLe8FwfzZVx048PY+EIwAdZKVcX/bibr9vYgLKMm4e9evwapeAwPbxvHJTc9gj9tHYOqatg/WcDmXRPYMZarado2Ml3Au298GJt3T6I/m8RtH3w1Ljp1ZWsarzHyYqO8PPlT8njCe4GEwCwtGyTiMVx++qG49xOvw5uOWYyqquHfHtyGs6+7H2/659/jnG8/gK/e8Ry+esdz+Nwvn8YH//MxnPTle/Def3sYV/+cXGsuffUqfOwN5u/TuYSIvACGdfTML0iFET0Zj3wzmf1igaV9GZyly+X/+SdjLszvXzqA7+vE4ut/uc6xVXsqEcO7T1mJ+z5xBv7xneuwciCL4akiLvzXh/A7iwzByyPTzKr58Jni/RYakB0w7hYe/ffar03uAXbrhMYkqEnxpmOIPH03Zx19596XsHU0h0XdaVz9F+YBx3q8Zs0g7v74a3H+CcugasC37nkR191j30DvR/q6e7ZLACLzUvXlmZ/Xfk2tWpZJ8zCso2EARHH41G2bMVWoYN3yXlzxevu/0frDFuDXV7wGr1jag4P5Mj7xP5vx3h8+jF3j9iFgwJgce+LKfvcVZzwO10n8vk2105VpBVLvCiBlfaf7BpZ7IWvw5+3j+NL/kvbqV7/pSMvhcPGYgneetBy3f/R0rFvei8nZMj74n4/jo//9hPCsoIe2kPzDoQs7sbzffZiUIZkhmxAA3PbXpGndXVeTisRkJ/Dqv7P99tPWDCIeU7BzPI/HdhxET0cCP778VayfB0C63v7XB17F7NLLb3mUdYb1gz9uGcPoTBH92aQ/9anJWNqXwafPPQLxmILfvzSKv/rXP2Ht5+7Eqzbci7dd/we87h834ojP3YkTv3wP1n7uTpzy1Xvx3L4pDHalcevfrm9tCJVWHO1/GqiYjIIpTBHFFgCOfWdgP3ZJbwbff+9J+PdLX4kVAxnsmyzguX1TSMZJvuz8E5bhjUcPYfVgJ4oVFQ++TCzTy1+zGtecd/Sc7Khbj4i8AKQmv6OPlAFefwqw6b/I520sIwpaivbzx/cgV6xg22gOV966GZoGvPuUla4aBCXjMVxw8grc/tHX4PQ1g8iXqnj/LX/GrX9uHBb3vd9tgaaRTXPtkM9yyJMuI4//v707D2vqTvcA/j0BEnYQkH0RcMGyqagU9ypVrHWptlrLrdq6VMWt1tbajtXpzFRGn6t3ptfR9o5arR21WqvV1nFAARURFaGOGwMUQQuIgiwakUDe+8chwZRVSIgh7+d5eMRzTuIvP9+cvPmtV77THLj7z5Xi3k5ezwNufZt8+IvPuUAiiP3tt+/LcTrrLr449QsA4A+TgmBn0fp1BuwszbBpWh988rK4t8jnJ7Pxl/jGd32+fLsMR34ugCAAC4Y3Pian1VTjXnJOAvIn+uKT/yKOdTC3b3KsAwCMDhQTuDPZ91BZpcD645k4nXUPMlNx3QmzVrSMeTlY4lDMYHwQ1QsyUwmSs0swaXOyukWhMYpaJXbWdc0Nau9KmjYu9d8049fWDVLNBn5cLh5r4eb8Ql3ykpZ3H5sTshH991RxbEKwG2YPaXqfFxVvR0scmD8IC0f4QyKIXW4vbkpSzyBqjmp6smq/mHYZ+2cxkat5BOyaCFz4PwACMPnLFltebM3NEFa3mqzqw7axtVasZKbYNnMAXgp2RY2SEPOPS/i1jd1lKqouo3Ehbq2KN32aM9QPiStG4L+e94bUVIIaJcFEIsDFVgZzMwmUJE6Drq4RW2D8u1rh23eeb/+97mnZ+4hbiCgVwJ2rDc9n/iS22Dr1BFyCtP7PvxDgjH8tG44/vRKE2MnBuPBxJLbPGoBN0/rgyxn9cfK94Ti6eAgWj+yOP0wMxMfjehtF4gIAnbtTrLXM7YC3fgJ+WFzfdWLpVN//3Ywh3Z3QzdESN0vk2BT3Hxz+uQD3HjxGgKsN1ox/rk3FsTE3w/ZZA/DRwX9jf9ptfHjw35CaSvBKX/HbW36JHId/FmcUtPSNvlW8BgLOgUDxVSB9NzBoEZD5T3FPE8EEeHljs/t0OFnLMKCbA1JzS7Hq4L+RklOC2roBdWMC2zZo8O0hvqhVEv7003Vsiv8PZGYSzH8iQSEixB4TxyFM6uOB51q7k3RTuvYEXILF7rNLu4Ahy8QpkgmfiefHfAaYNP126eFsDR9HS+SVyDFpczJy7ooLlf1uXO+n2iTRzESChSO646UgN8T84xKuFlTg9S9T8L/T+6nH1jxpV0oesosfwMFKijcjuj3NK27c0PeAb2cAP+8RN6ssyRaXSPd6Hnjh42Yf6tnFEgGuNrhRVIkNx8UxMi/06oo/vxrS6huq1FSCD6ICMCbQFSv2/4ys4geYueM81ox/DjOaeH1Xfi3HgbrN7VSzntrFxAyYugv4epK4cB8ARK5ptgv5SR++FIC95/MR80J3+Dg23VIlNZXgv1/rg7ySs7haUIF5uy7iwPxBT7e4Xp30/Ps4elnVZeTx1I/XBy8HS/xxUjA+iApAZVUNXGxkMDWRgIhw98FjlD6shrXMFLYWZrCRmernQ1kQxIQ+54Q4JlLVEqNypa6lNnDyUw/UbS0LqQmiwxv/4iQIAoI87BDUnu5iA/Vsp+cdySUQmB0HRMWKU2dHfNiqTcckEkEdWH8/k4u7lWLisntOeOt3eG6EmYkE618NwcwIHxABK/Zfxj+vFCEtrxS/O3wFtUrC0B5OCKlbtbJdBAHoX9f68q+Pgd2vAj+9L/49IqbFb5sAEFXXdXQ66x5qlIRJfdyxcWpou4o1d5gfPogSZ7fEHruBby/UL+KU9J+7OJtTAqmJBMtfbLpL66mEvi7+Gb8G+G4ucHCe+I2r93igzxvNPlQQBHXXUc7dh5CaiuMc2ppQdHOywr53IjCsZ1dUKZSY9/VF9Qe0yr0Hj/E/8WK32vtjej1VC1eTeo8HXtsJmEiB6z+Iu+pauwBTd7bq/aDqRpWaSrB2/HPYPmsArNswcDTUyx5HFg/B5H4eqFUSPjl8FasPXWkwG0leXYMle9KhqCWMCXRp33iXJ0ktgTf2id2Jwz8EBi9r9UP7eXfB+ldDm01cVCykJvjizTA4WElxtaAC77ZhxtmtUjnm7rqI6holRgU4t29VXT2wNTeDh72FetyeIAhwtjFHgKstPLtYwtbcTL+tCX7DxT+T/wI8fmJso7xUTGqAdg3UZW0j0LOwEUcLKioqYGdnh/LyctjatvMbtg7cf1iN8HUnUF0jLgy06+2B6p1220upJLx/4DK++80aKIIA7JsX0fbZJb+lqAKOLKkb71MXEnZeQExqs+McVArKHmHo+gTUKgnLInvUzR7Szg0n9tgNbE3KgUQQV9CUV9die3IubpU+wpwhvvjdy21r4WqgtgZI+KN4k6K6D0lrF2BBCmDVcpfM5dtlmLg5GS425tj6ZtjT77PUCEWtUt0CJwhA7ORgTBsgrt/w4XeXsffCLQR52OJwzJC2re/SlJwEYG+02CQ+8wjgM6hVDyuXK/D1uZsYHeiqlSZ+cY2cX7D++A0QiS2dm9/oB7u6/ZRUdeBqa45jS4eqV3w2NCk5JXhzm9jNFuxhh61vhsHDvuXdoCurFHh1Swoy71QiwNUGBxYMalOyyJqheAT87XlxuvzzMUBUXWvspV1ia71zILCwif2wmM4+vzl50ZLv0m4j/dZ9fBAVoPW9JGpqlVi8Jx3HrhTBRmaKUb2dMbW/l3qWj1aV/gKk/A24eRp4aQPgO6zVDz2ddRcSQcBgLZeLiPDBgcvY/5uWB0crKeKXD9f+B9btNODQAuB+LjB9T6v3KQHEgdQutuaw0WIMKJWEtUeuYldK/X4lmUWV6iniB+ZHoL+2lkR/UmWRuJ+Rg5/2n/sp/etqEZbty4C8uhZ+TlaY0McdZ7NLcP5mKQQB+GZOOAb56+D90IHOZt/Doj3pKH1YDQcrKZaM7I5xIe7oatNwBkuVohZ7z+dja9IvKKqoQlcbGQ7HDIZ7KxIe1gZZ8cA3U8RFS+clAm6hwK5J4npAI1cDw1bou4TPLE5envHkRdeUSsL1ogp0d7aGzLTt3VGGqqZWiaV7M3D8ahH6d+uCkQHOmBDq0fbp0S1RKoHH5eJCfs8AIsIfjl7H9uRc9TFBAN4Z5o8Pxza+4mtnc62gAnN2XkDBbxaFfO/FnljcSaaG3r4vx/zdaerFJyWCuMmki60MdhZmqFIocfu+HFnFD1BWt4u3m505vnyzf6deCv6ZsH+WOCPVzkvcLkW19tGS9GciwX9WcfJi5MkLEz/AlQTtdo8YECLC9uSbyL33ABF+Tojwd4SDgXaTtNXdysdY+4M462NwdycM6e4Eb8d2TI1+BlUpavGP1Hz88HNBszPNPOwtsPAFf7wa5mmUX2g6XEWhOBv18ROr3vYeD0zbrb8yGQBOXjh5YYwZmfwSOdJv3Uf5IwXK5ApITSXwsLeAZxcLBHnYPfNTojud2xeBvGTA+TlxHyxrF53NMuosdPX5zSO7GGPsGeXtaNnpWpYMmmf/p954kekGp+2MMcYYMyicvDDGGGPMoHDywhhjjDGDwskLY4wxxgwKJy+MMcYYMyicvDDGGGPMoHDywhhjjDGDwskLY4wxxgwKJy+MMcYYMyicvDDGGGPMoHDywhhjjDGDwskLY4wxxgwKJy+MMcYYMygGsas0EQEQt9ZmjDHGmGFQfW6rPse1xSCSl8rKSgCAl5eXnkvCGGOMsadVWVkJOzs7rT2fQNpOh3RAqVSioKAANjY2EARBa89bUVEBLy8v3Lp1C7a2tlp7XkPF9VGP60IT10c9rgtNXB/1uC40qerj2rVr6NWrFyQS7Y1UMYiWF4lEAk9PT509v62tLQfaE7g+6nFdaOL6qMd1oYnrox7XhSYPDw+tJi4AD9hljDHGmIHh5IUxxhhjBsWokxeZTIY1a9ZAJpPpuyjPBK6PelwXmrg+6nFdaOL6qMd1oUmX9WEQA3YZY4wxxlSMuuWFMcYYY4aHkxfGGGOMGRROXhhjjDFmUDh5YYwxxphBMerkZfPmzejWrRvMzc0RHh6O8+fP67tIOrdu3ToMGDAANjY2cHZ2xqRJk5CZmalxzYgRIyAIgsbP/Pnz9VRi3Vq7dm2D1xoQEKA+X1VVhZiYGDg6OsLa2hpTpkzBnTt39Fhi3enWrVuDuhAEATExMQA6f1ycOnUK48ePh7u7OwRBwKFDhzTOExE++eQTuLm5wcLCApGRkcjKytK4prS0FNHR0bC1tYW9vT1mz56NBw8edOCr0I7m6kKhUGDlypUIDg6GlZUV3N3dMWPGDBQUFGg8R2PxFBsb28GvRDtaio1Zs2Y1eK1RUVEa1xhDbABo9B4iCAI2bNigvkYbsWG0ycu+ffuwfPlyrFmzBpcuXUJoaCjGjBmD4uJifRdNp5KSkhATE4Nz584hLi4OCoUCo0ePxsOHDzWumzt3LgoLC9U/69ev11OJdS8wMFDjtZ45c0Z97t1338WRI0ewf/9+JCUloaCgAJMnT9ZjaXXnwoULGvUQFxcHAHjttdfU13TmuHj48CFCQ0OxefPmRs+vX78ef/3rX7F161akpqbCysoKY8aMQVVVlfqa6OhoXL16FXFxcTh69ChOnTqFefPmddRL0Jrm6kIul+PSpUtYvXo1Ll26hIMHDyIzMxMTJkxocO2nn36qES+LFy/uiOJrXUuxAQBRUVEar3XPnj0a540hNgBo1EFhYSG2b98OQRAwZcoUjevaHRtkpAYOHEgxMTHqv9fW1pK7uzutW7dOj6XqeMXFxQSAkpKS1MeGDx9OS5cu1V+hOtCaNWsoNDS00XNlZWVkZmZG+/fvVx+7fv06AaCUlJQOKqH+LF26lPz9/UmpVBKRccUFAPr+++/Vf1cqleTq6kobNmxQHysrKyOZTEZ79uwhIqJr164RALpw4YL6mmPHjpEgCPTrr792WNm17bd10Zjz588TAMrLy1Mf8/HxoU2bNum2cHrQWH3MnDmTJk6c2ORjjDk2Jk6cSCNHjtQ4po3YMMqWl+rqaqSlpSEyMlJ9TCKRIDIyEikpKXosWccrLy8HADg4OGgc/+abb+Dk5ISgoCCsWrUKcrlcH8XrEFlZWXB3d4efnx+io6ORn58PAEhLS4NCodCIk4CAAHh7e3f6OKmursbu3bvx9ttva2yGakxx8aTc3FwUFRVpxIKdnR3Cw8PVsZCSkgJ7e3v0799ffU1kZCQkEglSU1M7vMwdqby8HIIgwN7eXuN4bGwsHB0d0bdvX2zYsAE1NTX6KWAHSExMhLOzM3r16oUFCxagpKREfc5YY+POnTv48ccfMXv27Abn2hsbBrExo7bdu3cPtbW1cHFx0Tju4uKCGzdu6KlUHU+pVGLZsmUYPHgwgoKC1MffeOMN+Pj4wN3dHZcvX8bKlSuRmZmJgwcP6rG0uhEeHo6vvvoKvXr1QmFhIX7/+99j6NChuHLlCoqKiiCVShvckF1cXFBUVKSfAneQQ4cOoaysDLNmzVIfM6a4+C3V/3dj9wzVuaKiIjg7O2ucNzU1hYODQ6eOl6qqKqxcuRLTp0/X2IxwyZIl6NevHxwcHHD27FmsWrUKhYWF2Lhxox5LqxtRUVGYPHkyfH19kZOTg48++ghjx45FSkoKTExMjDY2du7cCRsbmwZd7dqIDaNMXpgoJiYGV65c0RjjAUCjHzY4OBhubm4YNWoUcnJy4O/v39HF1KmxY8eqfw8JCUF4eDh8fHzw7bffwsLCQo8l069t27Zh7NixcHd3Vx8zprhgraNQKDB16lQQEbZs2aJxbvny5erfQ0JCIJVK8c4772DdunWdbvn8119/Xf17cHAwQkJC4O/vj8TERIwaNUqPJdOv7du3Izo6Gubm5hrHtREbRtlt5OTkBBMTkwazRu7cuQNXV1c9lapjLVq0CEePHkVCQgI8PT2bvTY8PBwAkJ2d3RFF0yt7e3v07NkT2dnZcHV1RXV1NcrKyjSu6exxkpeXh/j4eMyZM6fZ64wpLlT/383dM1xdXRsM+K+pqUFpaWmnjBdV4pKXl4e4uDiNVpfGhIeHo6amBjdv3uyYAuqRn58fnJyc1O8NY4sNADh9+jQyMzNbvI8AbYsNo0xepFIpwsLCcOLECfUxpVKJEydOICIiQo8l0z0iwqJFi/D999/j5MmT8PX1bfExGRkZAAA3Nzcdl07/Hjx4gJycHLi5uSEsLAxmZmYacZKZmYn8/PxOHSc7duyAs7Mzxo0b1+x1xhQXvr6+cHV11YiFiooKpKamqmMhIiICZWVlSEtLU19z8uRJKJVKdaLXWagSl6ysLMTHx8PR0bHFx2RkZEAikTToPumMbt++jZKSEvV7w5hiQ2Xbtm0ICwtDaGhoi9e2KTbaNdzXgO3du5dkMhl99dVXdO3aNZo3bx7Z29tTUVGRvoumUwsWLCA7OztKTEykwsJC9Y9cLiciouzsbPr000/p4sWLlJubS4cPHyY/Pz8aNmyYnkuuG++99x4lJiZSbm4uJScnU2RkJDk5OVFxcTEREc2fP5+8vb3p5MmTdPHiRYqIiKCIiAg9l1p3amtrydvbm1auXKlx3BjiorKyktLT0yk9PZ0A0MaNGyk9PV09gyY2Npbs7e3p8OHDdPnyZZo4cSL5+vrSo0eP1M8RFRVFffv2pdTUVDpz5gz16NGDpk+frq+X1GbN1UV1dTVNmDCBPD09KSMjQ+M+8vjxYyIiOnv2LG3atIkyMjIoJyeHdu/eTV27dqUZM2bo+ZW1TXP1UVlZSStWrKCUlBTKzc2l+Ph46tevH/Xo0YOqqqrUz2EMsaFSXl5OlpaWtGXLlgaP11ZsGG3yQkT0+eefk7e3N0mlUho4cCCdO3dO30XSOQCN/uzYsYOIiPLz82nYsGHk4OBAMpmMunfvTu+//z6Vl5frt+A6Mm3aNHJzcyOpVEoeHh40bdo0ys7OVp9/9OgRLVy4kLp06UKWlpb0yiuvUGFhoR5LrFvHjx8nAJSZmalx3BjiIiEhodH3xsyZM4lInC69evVqcnFxIZlMRqNGjWpQTyUlJTR9+nSytrYmW1tbeuutt6iyslIPr6Z9mquL3NzcJu8jCQkJRESUlpZG4eHhZGdnR+bm5tS7d2/67LPPND7MDUlz9SGXy2n06NHUtWtXMjMzIx8fH5o7d26DL8LGEBsqX3zxBVlYWFBZWVmDx2srNgQiota30zDGGGOM6ZdRjnlhjDHGmOHi5IUxxhhjBoWTF8YYY4wZFE5eGGOMMWZQOHlhjDHGmEHh5IUxxhhjBoWTF8YYY4wZFE5eGGOMMWZQOHlhjDHGmEHh5IUxxhhjBoWTF8YYY4wZFE5eGGOMMWZQ/h/Nu+wGRowgGAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# KFZ lader til at være alle køretøjer, og den som PyPSA bruger \n",
-    "\n",
-    "all_profiles = pd.DataFrame()\n",
-    "for file in os.listdir(path):\n",
-    "    if '__count' in file:\n",
-    "        f = pd.read_csv(os.path.join(path, file), skiprows=2)\n",
-    "        f = f.pivot_table(index=['day', 'hour'],\n",
-    "                            values='count')\n",
-    "        f = f / np.max(f)\n",
-    "        \n",
-    "        if len(all_profiles) == 0:\n",
-    "            all_profiles = pd.concat((all_profiles, f.reset_index()))\n",
-    "            all_profiles.columns = ['day', 'hour', file]\n",
-    "        else:\n",
-    "            all_profiles[file] = f.loc[:, 'count'].values \n",
-    "        # f.plot(title=file)\n",
-    "        \n",
-    "# all_profiles.pivot_table(index=['day', 'hour']).plot()\n",
-    "inv_demand = 1/all_profiles['KFZ__count'] \n",
-    "inv_demand = inv_demand / np.max(inv_demand)\n",
-    "ax = inv_demand.plot(label='Inverse', legend=True)\n",
-    "(all_profiles['KFZ__count']).plot(ax=ax, label='Demand', legend=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "FLEXDEM_MAXCONS = IncFile(name='FLEXDEM_MAXLIMIT',\n",
-    "                          path='base/data',\n",
-    "                          suffix=\"\\n;\\nFLEXMAXLIMIT(FLEXUSER, RRR, SSS, TTT) = FLEXMAXLIMIT1('S01',TTT,FLEXUSER);\\nFLEXMAXLIMIT1(SSS,TTT,FLEXUSER)=0;\")\n",
-    "\n",
-    "\n",
-    "FLEXDEM_MAXCONS.prefix = \"\"\"* Normalised, inverse of mobility demand as a maximum charging constraint on flexible EV demand\n",
-    "* Data from KFZ mobility demand in Brown, T., D. Schlachtberger, A. Kies, S. Schramm, and M. Greiner. ‘Synergies of Sector Coupling and Transmission Reinforcement in a Cost-Optimised, Highly Renewable European Energy System’. Energy 160 (October 2018): 720–39. https://doi.org/10.1016/j.energy.2018.06.222.\n",
-    "TABLE FLEXMAXLIMIT1(SSS, TTT, FLEXUSER) 'Maximum flexible consumption wrt. season and term (%)'                          \n",
-    "\"\"\"\n",
-    "\n",
-    "# Make correct index\n",
-    "ind = pd.MultiIndex.from_product((['S01'], ['T00%d'%i for i in range(1, 10)] + ['T0%d'%i for i in range(10, 100)] + ['T%d'%i for i in range(100, 169)]))\n",
-    "ind.names = ['S', 'T']\n",
-    "body = pd.DataFrame(data=inv_demand.values, index=ind, columns=['ELECTRIC_VEHICLES']).reset_index()\n",
-    "\n",
-    "# Insert to incfile and prepare\n",
-    "FLEXDEM_MAXCONS.body = body\n",
-    "FLEXDEM_MAXCONS.body_prepare(index=['S', 'T'],\n",
-    "                             columns=None,\n",
-    "                             values='ELECTRIC_VEHICLES')\n",
-    "FLEXDEM_MAXCONS.save()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python (.BAF-Env)",
-   "language": "python",
-   "name": ".baf-env"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/src/Modules/createDH.py b/src/Modules/createDH.py
deleted file mode 100644
index fc27113..0000000
--- a/src/Modules/createDH.py
+++ /dev/null
@@ -1,331 +0,0 @@
-"""
-Creating Danish heat demand for Balmorel 
-
-Futuregas dataset default. Can be used to get temporal profiles
-
-Aalborg dataset, can be used to get individual and district heat gross supply:
-https://vbn.aau.dk/da/datasets/kommunepakker-varmeplan-danmark-2021
-- Note that heat demand should be combined wrt. the s1-s5 scenarios (sums to 53.95 TWh without heat efficiency investments)
-
-Python package requirements:
-- xlrd 
-
-Created on 11.03.2024
-
-@author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
-"""
-#%% ------------------------------- ###
-###        0. Script Settings       ###
-### ------------------------------- ###
-
-from typing import Union
-import matplotlib
-import matplotlib.pyplot as plt
-import pandas as pd
-import geopandas as gpd
-from format_vpdk21 import VPDK21 
-from format_dkstat import DKSTAT 
-from Submodules.municipal_template import DataContainer
-import xarray as xr
-import numpy as np
-import os
-try:
-    import cmcrameri
-    cmap = cmcrameri.cm.cmaps['roma_r']
-    cmap = cmcrameri.cm.cmaps['vik']
-    colors = [cmap(i) for i in range(256)]
-except ModuleNotFoundError:
-    print('cmrameri package not installed, using default colourmaps')
-    cmap = matplotlib.colormaps['viridis']
-    colors = [cmap(i) for i in range(256)]
-
-style = 'report'
-
-if style == 'report':
-    plt.style.use('default')
-    fc = 'white'
-elif style == 'ppt':
-    plt.style.use('dark_background')
-    fc = 'none'
-    
-#%% ------------------------------- ###
-###    1. FutureGas District Heat   ###
-### ------------------------------- ###
-
-class DistrictHeat:
-    """Class for district heating data
-    
-    Existing datasets:
-        - Denmark_Futuregas
-
-    Args:
-        dataset (str, optional): _description_. Defaults to 'DK'.
-    """
-    
-    def __init__(self, dataset: str = 'Denmark_Futuregas') -> None:
-        if dataset.lower() == 'denmark_futuregas': 
-            DH_shapes = 'Data/Shapefiles/Balmorel Areas/Balmorel_areas.shp'
-            name_col = 'BalmorelAr'
-            # Load data
-            self.DH = pd.read_parquet('Data/Timeseries/DKMUNI36_DH.gzip')
-            self.DHT = pd.read_parquet('Data/Timeseries/DKMUNI36_DH_VAR_T.gzip')
-            self.geo = gpd.read_file(DH_shapes)
-            self.geo.index = self.geo[name_col]
-            self.geo = self.geo.to_crs('EPSG:4326')
-            
-            # Delete CA_Vestfrb with no data in DH
-            self.geo = self.geo.drop(index='DK_CA_Vestfrb')
-            
-            # Aggregate to the 36 DH areas:
-            i = 0
-            for dh_area in ['DK_SA_E_BG',
-                            'DK_SA_E_NG_CHP',
-                            'DK_SA_E_NG_HO',
-                            'DK_SA_E_ST_CHP',
-                            'DK_SA_E_ST_HO',
-                            'DK_SA_E_WO_HO']:
-                if i == 0:
-                    temp = self.geo.loc[dh_area].copy()
-                    temp.crs = 'EPSG:4326'
-                else:
-                    temp.geometry = temp.geometry.union(self.geo.loc[dh_area].geometry) 
-                self.geo = self.geo.drop(index=dh_area)
-                i += 1 
-            self.geo.loc['DK_E_Rural'] = temp
-            i = 0
-            for dh_area in ['DK_SA_W_BG',
-                            'DK_SA_W_EB',
-                            'DK_SA_W_NG_CHP',
-                            'DK_SA_W_NG_HO',
-                            'DK_SA_W_ST_HO',
-                            'DK_SA_W_WO_CHP',
-                            'DK_SA_W_WO_HO']:
-                if i == 0:
-                    temp = self.geo.loc[dh_area].copy()
-                else:
-                    temp.geometry = temp.geometry.union(self.geo.loc[dh_area].geometry) 
-                self.geo = self.geo.drop(index=dh_area)
-                i += 1 
-            self.geo.loc['DK_W_Rural'] = temp
-            self.geo.crs = 'EPSG:4326'
-  
-        else: 
-            print("Dataset doesn't exist - this is an empty object\n")
-            print("Available datasets:\n- Denmark_Futuregas (default)")
-
-    def assign_DH(self, areas: gpd.GeoDataFrame, df_intercepts: pd.DataFrame,
-                  value_col: str = 'Value') -> None:
-        """DH data must have A and Y sets, where A matches the areas index\n
-        Ends up with format:\n
-            Y1\tY2\tY3\t--\tYN\n
-        A1\n
-        A2\n 
-        |\n
-        AM
-        """
-        # DH table for IncFile
-        df_DH = pd.DataFrame(index=areas.index) 
-        
-        # Use df_intercepts to assign DH
-        temp = self.DH.pivot_table(index='A', columns='Y')
-        
-        # Check the dataframe structure
-        if type(temp.columns) == pd.Index:
-            col_ind = "year"
-        elif type(temp.columns) == pd.MultiIndex:
-            col_ind = "(value_col, year)"
-        else:
-            print('Wrong column format!')
-        
-        for agg_area in areas.index:
-            for year in temp.columns.get_level_values(1):
-                df_DH.loc[agg_area, year] = (temp.loc[:,eval(col_ind)] * df_intercepts[agg_area]).sum()
-
-        self.dfDH = df_DH
-        
-        # Finalise index
-        self.dfDH.index = 'RESH . ' + pd.Series(self.dfDH.index) + '_A'
-        self.dfDH.index.name = '' 
-        
-    def assign_DHT(self, areas: gpd.GeoDataFrame, df_intercepts: pd.DataFrame,
-                  value_col: str = 'Value', agg_func: str = 'sum') -> None:
-        """DHT data must have S.T and A sets, where A matches the areas index\n
-        Ends up with format:\n
-        \t\t\tA1\tA2\tA3\t--\tAN\n
-        S01\t.\tT001\n
-        S01\t.\tT002\n
-        |\n
-        S52\t.\tT168\n
-        """
-        
-        # Pivot DHT
-        temp = self.DHT.pivot_table(index=['S', 'T'], columns='A')
-
-        # DHT table for IncFile
-        df_DHT = pd.DataFrame(index=temp.index, columns=areas.index,
-                              data=0) 
-        
-        # Check the dataframe structure
-        if type(temp.columns) == pd.Index:
-            col_ind = "dh_area"
-        elif type(temp.columns) == pd.MultiIndex:
-            col_ind = "(value_col, dh_area)"
-        else:
-            print('Wrong column format!')
-        
-        if agg_func.lower() == 'sum':
-            for agg_area in areas.index:
-                for dh_area in [dh_area0 for dh_area0 in df_intercepts[agg_area].index if df_intercepts.loc[dh_area0, agg_area] > 1e-10]:
-                    df_DHT[agg_area] += temp.loc[(slice(None),slice(None)),eval(col_ind)] * df_intercepts.loc[dh_area, agg_area]
-                    print()
-
-        self.dfDHT = df_DHT
-        
-        # Finalise index
-        self.dfDHT.index = pd.Series(self.dfDHT.index.get_level_values(0)) + ' . ' + pd.Series(self.dfDHT.index.get_level_values(1))
-        self.dfDHT.index.name = ''
-        self.dfDHT.columns.name = ''
-        self.dfDHT.columns = pd.Series(self.dfDHT.columns) + '_A'
-
-
-    def assign_DH_profile():
-        ...
-    
-    def join_geo_with(self, df: pd.DataFrame, kwargs: dict = {'how' : 'inner'}) -> None:
-        # Join df to pandas dataframe
-        self.geo = self.geo.join(df, **kwargs)
-        
-    def plot_original_data(self, year: str, areas: gpd.GeoDataFrame, 
-                  plot_density: bool = False, fc: str = 'white',
-                  area_fc: Union[str, list] = [.85, .85, .85],
-                  ax: Union[matplotlib.axes._axes.Axes, str] = '') -> tuple[matplotlib.figure.Figure, 
-                                                       matplotlib.axes._axes.Axes]:        
-        if ax == '':
-            # Create plot if no ax was inserted
-            fig, ax = plt.subplots(facecolor=fc)
-        areas.plot(ax=ax, facecolor=area_fc, linewidth=.3)
-        df = self.geo.copy()
-        df = df.join(self.DH[self.DH.Y == year].pivot_table(index='A'), how='inner')
-        df = df.to_crs('EPSG:4328')
-        if plot_density:
-            df['Value'] = df['Value'] / df.area # Heat density (MWh/m^2)
-            leg = 'MWh$\cdot$m$^{-2}$'
-        else:
-            df['Value'] = df['Value'] / 1e6 # TWh 
-            leg = 'TWh'
-        df = df.to_crs('EPSG:4326')
-        df.plot(ax=ax, column ='Value', legend=True, cmap=cmap)
-        ax.set_title('Original Data - %s'%leg)
-        ax.set_ylabel('Latitude')
-        ax.set_xlabel('Longitude')
-        
-        return fig, ax
-    
-    def plot_aggregated_data(self, year: str, areas: gpd.GeoDataFrame, 
-                  plot_density: bool = False) -> tuple[matplotlib.figure.Figure, 
-                                                       matplotlib.axes._axes.Axes]:
-                      
-        if hasattr(self, 'dfDH'):
-            fig, ax = plt.subplots(facecolor=fc)
-            df = areas.copy()
-            df2 = self.dfDH.copy()
-            df2.index = df2.index.str.replace('RESH . ', '').str.replace('_A', '')
-            df = df.join(df2, how='inner')
-            df = df.to_crs('EPSG:4328')
-            if plot_density:
-                df[year] = df[year] / df.area # Heat density (MWh/m^2)
-                leg = 'MWh$\cdot$m$^{-2}$'
-            else:
-                df[year] = df[year] / 1e6 # TWh
-                leg = 'TWh'
-            df2 = areas.copy()
-            df2[year] = df[year]
-            df2.plot(ax=ax, column =year, legend=True, cmap=cmap)
-            ax.set_title('Aggregated Data - %s'%leg)
-            ax.set_ylabel('Latitude')
-            ax.set_xlabel('Longitude')
-            
-            return fig, ax
-        
-        else:
-            print('Aggregation have not been made.')    
-
-def find_value(df: pd.DataFrame, element: any, 
-                func: str = 'max', ind: int = 0) -> any:
-    val_idx = eval("df[element].%s()"%func) == df[element]
-    temp = df.index[val_idx]
-    l = len(temp)
-    if l > 1:
-        print('%d %s values! Picked index %d'%(l, func, ind))
-    return temp[ind]
-    
-
-#%% ------------------------------- ###
-###     2. VPDK21 District Heat     ###
-### ------------------------------- ###
-
-class DistrictHeatAAU:
-    def __init__(self) -> None:
-        f1 = VPDK21()
-        f2 = DKSTAT()
-        self.f1 = f1
-        self.f2 = f2
-        
-    def combine_data(self, plot: bool = False):
-        data = DataContainer()
-        data.muni = data.muni.merge(self.f1.DH)
-        data.muni = data.muni.merge(self.f1.IND)
-        data.muni = data.muni.merge(self.f2.IND)
-
-
-        ## Get industry demands per municipality, per heat type 
-        data.muni.heat_demand_mwh.loc[{'user': 'industry_phl'}] = data.muni.heat_demand_normalised.sel(year=2019, user='industry_phl') * data.muni.energy_demand_type_mwh.sel(year=2018, user='other') / data.muni.energy_demand_type_mwh.sel(year=2018).sum() * data.muni.energy_demand_mun_mwh.sel(year=2018)
-        data.muni.heat_demand_mwh.loc[{'user': 'industry_phm'}] = data.muni.heat_demand_normalised.sel(year=2019, user='industry_phm') * data.muni.energy_demand_type_mwh.sel(year=2018, user='other') / data.muni.energy_demand_type_mwh.sel(year=2018).sum() * data.muni.energy_demand_mun_mwh.sel(year=2018)
-        data.muni.heat_demand_mwh.loc[{'user': 'industry_phh'}] = data.muni.heat_demand_normalised.sel(year=2019, user='industry_phh') * data.muni.energy_demand_type_mwh.sel(year=2018, user='other') / data.muni.energy_demand_type_mwh.sel(year=2018).sum() * data.muni.energy_demand_mun_mwh.sel(year=2018)
-
-        ## Drop year 2018, user electricity, other and district heat from industry data
-        data.muni = data.muni.drop_vars(['energy_demand_mun_mwh',
-                        'energy_demand_type_mwh',
-                        'heat_demand_normalised'])
-        data.muni = data.muni.sel(year=[2019], user=['district_heating', 'individual',
-                                    'industry_phl', 'industry_phm', 'industry_phh'])
-
-        self.data = data.muni
-
-        if plot:
-            # Plot it
-            temp = data.muni.heat_demand_mwh.sel(year=2019, user='district_heating').data
-            temp += data.muni.heat_demand_mwh.sel(year=2019, user='individual').data
-            temp += data.muni.heat_demand_mwh.sel(year=2019, user='industry_phl').data.astype(float)
-            temp += data.muni.heat_demand_mwh.sel(year=2019, user='industry_phm').data.astype(float)
-            temp += data.muni.heat_demand_mwh.sel(year=2019, user='industry_phh').data.astype(float)
-
-            fig, ax = plt.subplots()
-            data.get_polygons().plot(ax=ax,
-                                    column=temp,
-                                    cmap=cmap,
-                                    vmin=0,
-                                    vmax=6e6,
-                                    legend=True).set_title('sum')
-            fig.savefig(f'Output/Figures/Heat/total_heatdemand.png',
-                        transparent=True,
-                        bbox_inches='tight')
-            
-            for user in ['district_heating', 'individual', 'industry_phl',
-                        'industry_phm', 'industry_phh']:
-                fig, ax = plt.subplots()
-                a = data.get_polygons().plot(ax=ax,
-                                            cmap=cmap,
-                                            vmin=0,
-                                            vmax=6e6,
-                                            column=data.muni.heat_demand_mwh.sel(year=2019, user=user).data,
-                                            legend=True).set_title(user)
-                fig.savefig(f'Output/Figures/Heat/{user}_heatdemand.png',
-                            transparent=True,
-                            bbox_inches='tight')
-
-
-if __name__ == '__main__':
-    X = DistrictHeatAAU()
-    X.combine_data()
- 
diff --git a/src/Modules/createHYDROGEN.py b/src/Modules/createHYDROGEN.py
deleted file mode 100644
index 13ba1ba..0000000
--- a/src/Modules/createHYDROGEN.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""
-Created on 17.03.2024
-
-@author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
-"""
-#%% ------------------------------- ###
-###        0. Script Settings       ###
-### ------------------------------- ###
-
-import matplotlib.pyplot as plt
-import pandas as pd
-import geopandas as gpd
-from shapely import Point
-import numpy as np
-from pybalmorel.functions import IncFile, read_lines
-
-style = 'report'
-
-if style == 'report':
-    plt.style.use('default')
-    fc = 'white'
-elif style == 'ppt':
-    plt.style.use('dark_background')
-    fc = 'none'
-
-#%% ------------------------------- ###
-###        1. 
-### ------------------------------- ###
-
-# From Kountouris et al 2024
-XH2LOSS = 2.5e-08   # MWh/Mwh
-XH2COST = 1e-6      # €/Mwh
-XH2INVCOST = 0.55    # €/MW - new onshore pipe 
-# XH2INVCOST = 0.9    # €/MW - new offshore pipe
-# XH2INVCOST = 0.15   # €/MW - repurposed onshore pipe
-# XH2INVCOST = 0.2    # €/MW - repurposed offshore pipe
-
-
-class Hydrogen:
-    
-    def __init__(self, choice: str) -> None:
-        
-        # Read pre-calculated distances
-        self.grid = pd.read_parquet('Data/Shapefiles/Distances/%s_Distances.gzip'%choice.upper()) 
-
-        # Gas storage DK1
-        self.gasstorage = gpd.GeoDataFrame({'geometry' : Point(9.416895889014997, 56.64039606410427)},
-                                       index=['Lille Torup'], crs='EPSG:4326')
-        
-
-    def create_hydrogen_data(self, areas: gpd.GeoDataFrame, the_index: str) -> dict:
-        
-        incfiles = {}
-        incfilenames = ['HYDROGEN_XH2INVCOST',
-                        'HYDROGEN_XH2LOSS',
-                        'HYDROGEN_AGKN'] 
-        
-        for name in incfilenames:
-            incfiles[name] = IncFile(name=name,
-                                    path='Output',
-                                    body=pd.DataFrame(),
-                                    prefix=read_lines(name+'_prefix.inc',
-                                                      file_path='Data/IncFilePreSuf'),
-                                    suffix=read_lines(name+'_suffix.inc',
-                                                      file_path='Data/IncFilePreSuf'))
-
-        # Make investment and loss 
-        incfiles['HYDROGEN_XH2INVCOST'].body = self.grid[self.grid.Y == '2050']
-        incfiles['HYDROGEN_XH2LOSS'].body = self.grid[self.grid.Y == '2050']
-        incfiles['HYDROGEN_XH2INVCOST'].body.loc[:, 'Value'] = incfiles['HYDROGEN_XH2INVCOST'].body['Value']*XH2INVCOST
-        incfiles['HYDROGEN_XH2LOSS'].body.loc[:, 'Value']    = incfiles['HYDROGEN_XH2LOSS'].body['Value']*XH2LOSS 
-        incfiles['HYDROGEN_XH2INVCOST'].body.loc[:, 'Y'] = incfiles['HYDROGEN_XH2INVCOST'].body['Y'].str.replace('2050', '2030')
-        incfiles['HYDROGEN_XH2LOSS'].body.loc[:, 'Y']    = incfiles['HYDROGEN_XH2LOSS'].body['Y'].str.replace('2050', '2030') 
-
-        # Find salt-cavern investment option
-        saltcavern = areas[areas.geometry.contains(self.gasstorage.geometry[0])][the_index].iloc[0]
-        incfiles['HYDROGEN_AGKN'].body = "AGKN('%s_A', 'GNR_H2S_H2-CAVERN_Y-2030') = YES;\n"%saltcavern
-        incfiles['HYDROGEN_AGKN'].body = "AGKN('%s_A', 'GNR_H2S_H2-CAVERN_Y-2040') = YES;\n"%saltcavern
-        incfiles['HYDROGEN_AGKN'].body = "AGKN('%s_A', 'GNR_H2S_H2-CAVERN_Y-2050') = YES;\n"%saltcavern
-        
-        return incfiles
-
-
diff --git a/src/Modules/createINDUSTRY.py b/src/Modules/createINDUSTRY.py
deleted file mode 100644
index 6493272..0000000
--- a/src/Modules/createINDUSTRY.py
+++ /dev/null
@@ -1,286 +0,0 @@
-"""
-Created on 14.03.2024
-
-@author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
-
-Script for creating data for industry sector
-Sources:
-    - Balmorel Data 2024
-    - Manz, Pia, Tobias Fleiter, and Wolfgang Eichhammer. “The Effect of Low-Carbon Processes on Industrial Excess Heat Potentials for District Heating in the EU: A GIS-Based Analysis.” Smart Energy 10 (May 1, 2023): 100103. https://doi.org/10.1016/j.segy.2023.100103.
-
-"""
-#%% ------------------------------- ###
-###        0. Script Settings       ###
-### ------------------------------- ###
-
-import os 
-import matplotlib
-import matplotlib.pyplot as plt
-import pandas as pd
-import numpy as np
-import geopandas as gpd
-from pybalmorel import IncFile
-from pybalmorel.functions import read_lines
-from Modules.geofiles import prepared_geofiles
-from shapely.geometry import Point, Polygon, LineString
-from typing import Union
-try:
-    import cmcrameri
-    cmap = cmcrameri.cm.cmaps['roma_r']
-    colors = [cmap(i) for i in range(256)]
-except ModuleNotFoundError:
-    print('cmrameri package not installed, using default colourmaps')
-    cmap = matplotlib.colormaps['viridis']
-    colors = [cmap(i) for i in range(256)]
-
-style = 'report'
-
-if style == 'report':
-    plt.style.use('default')
-    fc = 'white'
-elif style == 'ppt':
-    plt.style.use('dark_background')
-    fc = 'none'
-
-#%% ------------------------------- ###
-###        1. Industry Data         ###
-### ------------------------------- ###
-
-### 1.0 Assumptions
-XHLOSS = 0.1        # MWh/Mwh
-XHCOST = 0.001      # €/Mwh
-XHINVCOST = 396000  # €/MW
-
-
-class Industry:
-    """Class for industry addon data
-    
-    Existing datasets:
-        - Denmark
-
-    Args:
-        dataset (str, optional): _description_. Defaults to 'DK'.
-    """
-    
-    def __init__(self, dataset: str = 'EUBalmorel') -> None:
-        if dataset.lower() == 'eubalmorel': 
-            choice = 'Balmorel2022'
-            the_index, self.geo, c = prepared_geofiles(choice)
-            
-            # Load data
-            datasets = pd.Series([ds0 for ds0 in os.listdir('Data/BalmorelData') if 'INDUSTRY' in ds0])
-
-            for ds in datasets:
-                setattr(self, ds.replace('INDUSTRY_', '').replace('.gzip', ''), pd.read_parquet('Data/BalmorelData/' + ds))
-            
-        else:
-            print("Dataset doesn't exist - this is an empty object\n")
-            print("Available datasets:\n- EUBalmorel (default)")
-
-        # Load high resolution dataset
-        self.PS = pd.read_csv('Data/Gas, Transport and Industry Data/Industrial_Database.csv',
-                              sep=';', encoding='UTF-8')
-        
-        # Delete sources with no coordinates (you could look the cities up with google maps API)
-        self.PS = self.PS[self.PS.Latitude.isna() == False]
-        self.PS.index = np.arange(len(self.PS)) # Fix index
-        
-        # Fill NaN's with zeros
-        self.PS['Emissions_ETS_2014'] = self.PS['Emissions_ETS_2014'].fillna(0)
-        
-        self.PS['geometry'] = gpd.GeoSeries([Point(xy) for xy in zip(self.PS.Longtitude, self.PS.Latitude)])
-        self.PS = gpd.GeoDataFrame(self.PS)
-        self.PS.geometry.crs = 'EPSG:4326'
-        self.geoindex = the_index
-        
-
-    def assign_original_region(self):
-        self.PS['R'] = ''  
-        for area in self.geo.index:
-            # Find containing region
-            idx = self.PS.within(self.geo.geometry[area])
-            self.PS.loc[idx, 'R'] = area # Should only have one value
-
-        # If it's not contained within a region, use smallest distance to find remaining
-        for PS in self.PS[self.PS.R == ''].index:
-            temp = self.geo.copy().to_crs('EPSG:4328')
-            dist = temp.distance(self.PS.geometry[PS])
-
-            try:
-                self.PS.loc[PS, 'R'] = dist[dist == dist.min()].index[0] # Should only have one value
-            except IndexError:
-                print('Datapoint %d probably had invalid geometry'%PS)
-        
-    def assign_emission_fractions(self):
-        # Use EPRTR emissions for CH
-        self.PS.loc[self.PS.R == 'CH', 'Emissions_ETS_2014'] = self.PS[self.PS.R == 'CH']['Emissions_EPRTR_2014']
-        # RS only has one source with no data
-        self.PS.loc[self.PS.R == 'RS', 'Emissions_ETS_2014'] = 0
-
-        # Total emissions in a region
-        emisum = self.PS.pivot_table(index='R', values=['Emissions_ETS_2014'], fill_value=0, aggfunc='sum')
-
-        # The fraction of emissions at point sources
-        for R in self.PS.R.unique():
-            idx = self.PS.R == R
-            self.PS.loc[idx, 'EmiFrac'] = self.PS.loc[idx, 'Emissions_ETS_2014'] / emisum.loc[R, 'Emissions_ETS_2014']  
-
-
-    def apply_ef_to_original_data(self, incfiles: dict, original_area: str, 
-                  new_area: str, frac_sum: str, include_new_area: bool = False) -> None:
-        """Assign industry heat demand from original area to new area"""
-
-        for attr in ['DH', 'DH_VAR_T', 'GKFX', 'DE']:
-            # Find original Balmorel data
-            if attr != 'DE':
-                temp = getattr(self, attr)[getattr(self, attr).A.str.find(original_area) != -1].copy()
-            else:
-                temp = getattr(self, attr)[getattr(self, attr).R == original_area].copy()
-            
-            # Apply sum to Balmorel data
-            temp.loc[:, 'Value'] = temp['Value'] * frac_sum
-            
-            # Replace original area name with new area name
-            if attr != 'DE':
-                temp.loc[:, 'A'] = temp['A'].str.replace(original_area, new_area)
-            else:
-                temp.loc[:, 'R'] = temp['R'].str.replace(original_area, new_area)
-            
-            if include_new_area:
-                temp.loc[:, 'new_area'] = new_area # If wanting to plot it
-            
-            # Concatenate to body in incfile 
-            incfiles['INDUSTRY_' + attr].body_concat(temp) 
-    
-    def create_industry_data(self, areas: gpd.GeoDataFrame,
-                             save_new_area_to_df: bool = False,
-                             plot_point_sources: bool = False,
-                             ax: matplotlib.axes.Axes = '') -> dict:
-        """Prepares industry data .inc files  
-        """
-        
-        # Placeholder for incfiles
-        incfiles = {}
-        incfilenames = ['INDUSTRY_AGKN', 'INDUSTRY_DE', 'INDUSTRY_DH',
-                        'INDUSTRY_DH_VAR_T', 'INDUSTRY_GKFX', 'INDUSTRY_CCCRRRAAA',
-                        'INDUSTRY_RRRAAA', 'INDUSTRY_AAA', 'INDUSTRY_DISLOSS_E_AG'] 
-        for name in incfilenames:
-            incfiles[name] = IncFile(name=name,
-                                    path='Output',
-                                    body=pd.DataFrame(),
-                                    prefix=read_lines(name+'_prefix.inc',
-                                                      file_path='Data/IncFilePreSuf'),
-                                    suffix=read_lines(name+'_suffix.inc',
-                                                      file_path='Data/IncFilePreSuf'))
-        incfiles['INDUSTRY_DISLOSS_E_AG'].body = ''
-            
-        for new_area in areas.index:
-            idx = self.PS.within(areas.geometry[new_area])
-            
-            try:
-                if plot_point_sources & (ax != ''):
-                    self.PS[idx].plot(ax=ax, zorder=5)
-                
-                # Get original areas (can be more, if the chosen spatial resolution is coarser than the original)
-                original_areas = self.PS.R[idx].unique()
-
-                # Go through original areas
-                for original_area in original_areas:    
-                    
-                    # Sum fraction of emissions in the new area
-                    frac_sum = self.PS.loc[idx & (self.PS.R == original_area), 'EmiFrac'].sum()
-                    
-                    # Disaggregate DH, DH_VAR_T, DE, GKFX
-                    self.apply_ef_to_original_data(incfiles, original_area, new_area, frac_sum, save_new_area_to_df)
-                    
-                    # Disaggregate AGKN
-                    temp = self.AGKN[self.AGKN.A.str.find(original_area) != -1].copy()
-                    temp['A'] = temp['A'].str.replace(original_area, new_area)
-                    
-                    incfiles['INDUSTRY_AGKN'].body_concat(temp) # perhaps make a IncFile.body.concat function..
-            
-                    # Create technology specific distribution loss
-                    for a in temp['A'].unique():
-                        incfiles['INDUSTRY_DISLOSS_E_AG'].body += f"DISLOSS_E_AG_IND('{a}',G)$((GDATA(G,'GDTYPE') EQ GETOH OR  GDATA(G,'GDTYPE') EQ GESTO OR GDATA(G,'GDTYPE') EQ GESTOS)  AND (SUM(Y,GKFX(Y,'{a}',G)) OR AGKN('{a}',G)))=SUM(IR$RRRAAA(IR,'{a}'),DISLOSS_E(IR));\n"
-
-            except ValueError:
-                print('No industry in %s'%new_area)
-                
-        return incfiles
-     
-    def plot_original_data(self, emission_scale: float = 5e5, 
-                           bounds: list[float, float, float, float] = [-12, 30, 33, 73],
-                           fc: str = 'white',
-                            area_fc: Union[str, list] = [.85, .85, .85],
-                            ax: Union[matplotlib.axes._axes.Axes, str] = '') -> tuple[matplotlib.figure.Figure, 
-                                                                                                matplotlib.axes._axes.Axes]:        
-        if ax == '':
-            # Create plot if no ax was inserted
-            fig, ax = plt.subplots(facecolor=fc)
-        self.geo.plot(ax=ax, facecolor=area_fc)
-        self.PS.plot(ax=ax, marker='o', color=[.3, .3, .3],
-                    markersize=self.PS['Emissions_ETS_2014']/emission_scale)
-        ax.set_title('Original Data')
-        ax.set_xlabel('Longitude')
-        ax.set_ylabel('Latitude')
-        ax.set_xlim([bounds[0], bounds[1]])
-        ax.set_ylim([bounds[2], bounds[3]])
-        fig.savefig('Output/Figures/IND_original.png', bbox_inches='tight')
-        fig.savefig('Output/Figures/IND_original.pdf', bbox_inches='tight')
-        
-        return fig, ax
-    
-    def plot_aggregated_data(self, incfiles: dict, 
-                             areas: gpd.GeoDataFrame,
-                             indicator: str = 'DH') -> tuple[matplotlib.figure.Figure, 
-                                                            matplotlib.axes._axes.Axes]:
-    
-        # Generate industry data for a specific spatial resolution
-
-        # Plot
-        try:
-            fig, ax = plt.subplots()
-            
-            if indicator == 'DH':
-                temp = incfiles['INDUSTRY_DH'].body.pivot_table(index=['Y', 'new_area'], values=['Value'], aggfunc='sum').loc['2050']
-                areas['new_col'] = temp.Value / 1e6
-                ax.set_title('Industry Heat Demand (TWh)')
-            elif indicator == 'DE':        
-                temp = incfiles['INDUSTRY_DE'].body.pivot_table(index=['Y', 'new_area'], values=['Value'], aggfunc='sum').loc['2050']
-                areas['new_col'] = temp.Value / 1e6
-                ax.set_title('Industry Electricity Demand (TWh)')
-            elif indicator == 'GKFX':
-                temp = incfiles['INDUSTRY_GKFX'].body.pivot_table(index=['Y', 'new_area'], values=['Value'], aggfunc='sum').loc['2020']
-                areas['new_col'] = temp.Value 
-                ax.set_title('Industry Generation Capacity (MW)')
-            
-            areas.plot(ax=ax, zorder=2, column='new_col', legend=True, cmap=cmap)
-            areas.plot(ax=ax, zorder=1, facecolor=cmap(0))
-        
-        except KeyError:
-            print('New area column not created in .inc file')
-            print('save_new_area_to_df was probably set to False in create_industry_data function')
-
-        return fig, ax
-    
-
-
-# Need to make incfiles:
-# INDUSTRY_AGKN
-# INDUSTRY_CCCRRRAAA
-# INDUSTRY_RRRAAA
-# INDUSTRY_AAA
-# INDUSTRY_GKFX
-# INDUSTRY_DH
-# INDUSTRY_DH_VAR_T
-# INDUSTRY_DE
-# INDUSTRY_DE_VAR_T
-
-# Later:
-# INDUSTRY_XHMAXK
-# INDUSTRY_XHKFX
-# INDUSTRY_XHLOSS
-# INDUSTRY_XHCOST
-# INDUSTRY_XHINVCOST
-
-
diff --git a/src/create_GKFX_DK.py b/src/Modules/existing_powerplants.py
similarity index 100%
rename from src/create_GKFX_DK.py
rename to src/Modules/existing_powerplants.py
diff --git a/src/create_TechData.py b/src/Modules/technology_data.py
similarity index 94%
rename from src/create_TechData.py
rename to src/Modules/technology_data.py
index 7f37720..5f7e596 100644
--- a/src/create_TechData.py
+++ b/src/Modules/technology_data.py
@@ -262,16 +262,5 @@ def get_value(search_series, search_strings, y0, y1, den, y0_dif):
     f.write('\n'.join(GDATA.index))
     # f.write('\n/;')  
 
-#%% ------------------------------------- ###
-###            4. Create AGKN             ###
-### ------------------------------------- ###
 
-### 4.1 Hack, all techs can be invested everywhere
-with open('./Output/AGKN.inc', 'w') as f:
-    # f.write("SET GGG  'All generation technologies'\n")
-    for A in areas[the_index]:
-        for G in GDATA.index:
-            f.write("AGKN('%s_A', '%s') = YES;\n"%(A, G))
-    # f.write('/\n')
-    # f.write('\n/;')  
 
diff --git a/src/Modules/createVRE.py b/src/Modules/vre_profiles.py
similarity index 100%
rename from src/Modules/createVRE.py
rename to src/Modules/vre_profiles.py
diff --git a/src/README.md b/src/README.md
deleted file mode 100644
index 95cf88d..0000000
--- a/src/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
-# Creating .inc Files for Balmorel
-
-Last updates: 
-
-- main in 2024
-- Modules/* in 2024
-- create_Demands in 2022
-- create_GKFX_DK in 2022
-- create_Grids in 2022
-- create_SetFiles in 2022
-- create_TechData in 2022
-
-Run all python scripts in this folder to process data for a Danish Balmorel case study at various spatial resolution (main.py, create_XXXX...)
-
-main.py is a new and more systematic way of processing data, using classes and function instead of the long, hard-to-read create_XXXX python files. It is the plan to migrate everything into this structure in the future, but main.py currently only process data for industry, district heating and hydrogen.
-
-A further description of how each script works is found within each python file
-
-NB: No data is provided in this repo, download data from https://zenodo.org/records/10960910
diff --git a/src/create_Demands.py b/src/create_Demands.py
deleted file mode 100644
index 6a575fa..0000000
--- a/src/create_Demands.py
+++ /dev/null
@@ -1,274 +0,0 @@
-#%%
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Dec 22 17:17:25 2022
-
-@author: Mathias Berg Rosendal, Research Assistant DTU Management
-
-Use NUTS3 data for population density, which can be downloaded here:
-https://ec.europa.eu/eurostat/web/products-datasets/product?code=demo_r_d3dens
-    
-And demand data from:
-
-
-This is used to create demand files for Balmorel.
-
-
-Works with the environment.yaml distributed in XXX
-"""
-
-import matplotlib.pyplot as plt
-from matplotlib import rc
-import pandas as pd
-import geopandas as gpd
-import cartopy.crs as ccrs
-from shapely.geometry import MultiPolygon
-from pyproj import Proj
-import numpy as np
-import matplotlib.pyplot as plt
-from Modules.geofiles import prepared_geofiles
-from Modules.createDH import DistrictHeat
-from pyproj import Proj
-import datetime as dt
-from pybalmorel.functions import IncFile
-
-
-style = 'report'
-
-if style == 'report':
-    plt.style.use('default')
-    fc = 'white'
-elif style == 'ppt':
-    plt.style.use('dark_background')
-    fc = 'none'
-
-#%% ----------------------------- ###
-###         0. ASSUMPTIONS        ###
-### ----------------------------- ###
-
-### 0.1 Limit area
-# If you type 'All', then no filter is applied
-Afilt = 'All'
-
-
-### 0.2 Population Year
-Y = '2019'
-
-
-### 0.3 Load geodata
-# What areas to load?
-choice = 'DK Municipalities'
-# choice = 'NUTS1'
-# choice = 'NUTS2'
-# choice = 'NUTS3'
-# choice = 'NordpoolReal'
-
-### 0.4 Input parameters
-DE = pd.DataFrame({'MWh' : 37000000}, index=['DK']) ### DUMMY DATA only for DK
-DH = pd.DataFrame({'MWh' : 50000000}, index=['DK']) ### DUMMY DATA only for DK
-Esector = 'RESE'
-Hsector = 'RESH'
-Dprof = pd.read_excel(r'.\Data\Timeseries\DummyProfiles.xlsx')
-profYear = 2017 # Year for profiles
-
-Dprof = pd.read_parquet('Data/Timeseries/DKMUNI36_DH.gzip')
-### Assumptions
-# 2.3 ToDo: Only one country at a time right now
-# 2.3 ToDo: Should include yearly change
-# 3.1 ToDo: Include different sectors
-# 3.2 ToDo: Need to include data on profiles
-#           Right now, DK1 large and DK el demand profiles are used for all
-
-
-#%% ----------------------------- ###
-###        1. Read files          ###
-### ----------------------------- ###a
-
-### 1.1 Read NUTS3 data
-pop = pd.read_csv(r'.\Data\demo_r_d3dens.tsv', sep='\t')
-N3 = gpd.read_file(r'.\Data\Shapefiles\NUTS_RG_01M_2021_4326\NUTS_RG_01M_2021_4326.shp')
-
-## Make more readable
-pop.columns = ['NUTS_ID'] + list(pd.Series(pop.columns).str.replace(' ',''))[1:]
-pop['NUTS_ID'] = pop['NUTS_ID'].str.replace('PER_KM2,','')
-
-## Apply filter
-# Only NUTS3
-pop = pop[pop.NUTS_ID.str.len() == 5]
-N3 = N3[N3.NUTS_ID.str.len() == 5]
-# Customised filters
-if Afilt.lower() != 'all':
-    N3 = N3[N3.NUTS_ID.str.find(Afilt) != -1]
-    pop = pop[pop.NUTS_ID.str.find(Afilt) != -1]
-# Year
-pop = pop[['NUTS_ID', Y]]
-# Convert to Numbers
-pop[Y] = pop[Y].str.replace(':','0')
-pop[Y] = pop[Y].astype(float)
-   
-## Merge
-N3 = pd.concat((N3.set_index('NUTS_ID'), pop.set_index('NUTS_ID')), axis=1, join='inner')
-
-## Change projections to a geocentric one
-N3 = N3.to_crs(4328)
-
-## Factor in area and normalise
-N3[Y] = N3[Y]*N3.geometry.area
-N3[Y] = N3[Y] / N3[Y].sum() ### HAS TO BE DONE PR COUNTRY
-
-## Plot density heatmap
-# fig, ax = newplot(fc=fc)
-fig, ax = plt.subplots(facecolor=fc)
-N3.plot(ax=ax, column=Y, cmap='coolwarm', legend=True, zorder=2)
-ax.set_title('Fraction of population - ' + Y)
-
-
-### 1.2 Read demand data
-### DEMAND data should be on country scale, and used to iterate through in the next section
-
-## Plot yearly demand
-
-
-### 1.3 Read geodata
-the_index, areas, country_code = prepared_geofiles(choice)
-if 'nuts' in choice.lower():
-    areas = areas[(areas[the_index].str.find('DK') != -1)]
-areas.plot()
-areas = areas[(areas[country_code] == 'DK') | (areas[country_code] == 'DE')] # Testing DK and DE
-
-## Change projections to a geocentric one
-if choice.lower().replace(' ', '') == 'nordpoolreal':
-    areas.crs = 4326
-areas = areas.to_crs(4328)
-
-
-
-
-#%% ----------------------------- ###
-###     2. Calculate Demands      ###
-### ----------------------------- ###
-
-
-### 2.2 Calculate intersecting area of areas with N3
-### HAS TO BE NORMALISED PR COUNTRY !!!! Only works with one country at a time right now
-### idea, use demand: for country in DE...
-for i,row in areas.iterrows():
-    # Intersecting Areas 
-    w = N3.geometry.intersection(areas.geometry[i]).area
-    # Normalise with N3 total area to get area fraction of country
-    w = w / N3.area
-    # Final weight is factoring population fraction on area fractions
-    w = w * N3[Y]
-    
-    # Population Fraction X Intersecting Fractions
-    areas.loc[i, 'DE'] = (w * DE.loc['DK', 'MWh']).sum() ### NEED key
-    areas.loc[i, 'DH'] = (w * DH.loc['DK', 'MWh']).sum() ### NEED key for country
-
-areas.plot(column='DE', cmap='coolwarm', legend=True, zorder=2)
-
-### 2.3 Adjust to actual demands
-print('DE input: %0.2f'%DE.loc['DK', 'MWh'], 'DE sum after intersect: %0.2f'%areas.DE.sum())
-print('DH input: %0.2f'%DH.loc['DK', 'MWh'], 'DH sum after intersect: %0.2f'%areas.DH.sum())
-
-# Linearly adjust all to fit actual input
-areas.loc[:, 'DE'] = areas.loc[:, 'DE'] * DE.loc['DK', 'MWh'] / areas.DE.sum()
-areas.loc[:, 'DH'] = areas.loc[:, 'DH'] * DH.loc['DK', 'MWh'] / areas.DH.sum()
-
-
-### 2.3 MAKE CALCULATIONS ON YEARLY PROJECTION
-
-
-#%% ----------------------------- ###
-###           3. Save             ###
-### ----------------------------- ###
-
-inc = {} # Incfile container
-
-### 3.1 Save DE and DH
-DEtable = pd.DataFrame({'2050' : areas['DE'].values}, index=areas.index + ' . %s'%Esector)
-DEtable.index.name = ''
-DHtable = pd.DataFrame({'2050' : areas['DH'].values}, index='%s . '%Hsector + areas.index + '_A')
-DHtable.index.name = ''
-
-inc['DE'] = IncFile(name='DE', path='./Output',
-                    prefix="TABLE DE1(RRR, DEUSER, YYY)        'Annual electricity consumption (MWh)'\n",
-                    body=DEtable.to_string(header=True, index=True),
-                    suffix="\n;\nDE(YYY,RRR,DEUSER) = DE1(RRR,DEUSER,YYY);\nDE1(RRR,DEUSER,YYY)=0;")
-
-with open('./Output/DH.inc', 'w') as f:
-    f.write("PARAMETER DH(YYY,AAA,DHUSER)  'Annual brutto heat consumption';\n")
-    f.write("TABLE DH1(DHUSER, AAA, YYY)\n")
-    dfAsString = DHtable.to_string(header=True, index=True)
-    f.write(dfAsString)
-    f.write('\n;')
-    f.write("\nDH(YYY,AAA,DHUSER) = DH1(DHUSER, AAA, YYY);\nDH1(DHUSER, AAA, YYY)=0;")
-
-
-### 3.2 Save DE and DH profiles - HACK
-
-#%% Convert data
-# Get correct timeseries index for Balmorel
-t = Dprof['Time'].dt.isocalendar()
-t.index = Dprof['Time']
-t['hour'] = t.index.hour
-
-# Filter away first week, from last year 
-idx = t.index.year == t['year'] 
-idx = idx & (t.index.year == profYear)
-t = t[idx]
-DT = Dprof[idx]
-
-# Fix summer-period switch
-t.index = np.array([t.index[0]+dt.timedelta(hours=i) for i in range(8736)])
-t = t.index.isocalendar()
-
-# Make seasons
-t['S'] = t['week'].astype(str)
-idx = t['S'].str.len() == 1
-t.loc[idx, 'S'] = '0' + t.loc[idx, 'S']
-t['S'] = 'S' + t['S']
-
-# Make terms
-try:
-    t['T'] = np.array([i for i in range(1, 169)]*52)
-except ValueError:
-    print("\nWARNING!\nYou didn't load 8736 hours of data! Select a bit of the next year, in cutout (T parameter in beginning).")
-    print("The current profile will be %d too short (%d hours in total)\n"%(8736-len(t), len(t)))
-    
-    array = np.array([i for i in range(1, 169)]*52)
-    t['T'] = array[:len(t)]
-
-    
-t['T'] = t['T'].astype(str)
-idx = t['T'].str.len() == 1
-t.loc[idx, 'T'] = '00' + t.loc[idx, 'T']
-idx = t['T'].str.len() == 2
-t.loc[idx, 'T'] = '0' + t.loc[idx, 'T']
-t['T'] = 'T' + t['T']
-
-
-# Create new index - ASSUMING DK1 LARGE HEAT and DK EL PROFILE
-DT.index = t['S'] + ' . ' + t['T']
-
-DET = pd.DataFrame(data={A : DT['El demand'] for A in areas.index}, index=DT.index)
-DHT = pd.DataFrame(data={A : DT['DK1_Large'] for A in areas.index}, index=DT.index)
-DHT.columns = DHT.columns + '_A'
-
-#%%
-with open('./Output/DE_VAR_T.inc', 'w') as f:
-    f.write("PARAMETER DE_VAR_T(RRR,DEUSER,SSS,TTT) 'Variation in electricity demand';\n")
-    f.write("TABLE DE_VAR_T1(SSS,TTT,RRR)\n")
-    dftostring = DET.to_string(header=True, index=True)
-    f.write(dftostring)
-    f.write("\n;\n")
-    f.write("DE_VAR_T(RRR,'RESE',SSS,TTT) = DE_VAR_T1(SSS,TTT,RRR);\n")
-    f.write("DE_VAR_T1(SSS,TTT,RRR)=0;\n")
-
-with open('./Output/DH_VAR_T.inc', 'w') as f:
-    f.write("PARAMETER DH_VAR_T(AAA,DHUSER,SSS,TTT) 'Variation in heat demand';\n")
-    f.write("TABLE DH_VAR_T1(SSS,TTT,AAA)\n")
-    dftostring = DHT.to_string(header=True, index=True)
-    f.write(dftostring)
-    f.write("\n;\n")    
-    f.write("DH_VAR_T(AAA,'RESH',SSS,TTT) = DH_VAR_T1(SSS,TTT,AAA);\n")
-    f.write("DH_VAR_T1(SSS,TTT,AAA)=0;\n")
diff --git a/src/dot_source.txt b/src/dot_source.txt
deleted file mode 100644
index 4207ab0..0000000
--- a/src/dot_source.txt
+++ /dev/null
@@ -1,36 +0,0 @@
-digraph snakemake_dag {
-    graph[bgcolor=white, margin=0];
-    node[shape=box, style=rounded, fontname=sans,                 fontsize=10, penwidth=2];
-    edge[penwidth=2, color=grey];
-	0[label = "all", color = "0.10 0.6 0.85", style="rounded"];
-	1[label = "exo_electricity_demand", color = "0.05 0.6 0.85", style="rounded,dashed"];
-	2[label = "create_conversion_dictionaries", color = "0.14 0.6 0.85", style="rounded,dashed"];
-	3[label = "format_energinet_data", color = "0.52 0.6 0.85", style="rounded,dashed"];
-	4[label = "transport_demand", color = "0.29 0.6 0.85", style="rounded"];
-	5[label = "format_dkstat_transport_data", color = "0.00 0.6 0.85", style="rounded,dashed"];
-	6[label = "exo_heat_demand", color = "0.62 0.6 0.85", style="rounded,dashed"];
-	7[label = "format_vpdk21_data", color = "0.24 0.6 0.85", style="rounded,dashed"];
-	8[label = "format_dkstat_industry_data", color = "0.38 0.6 0.85", style="rounded,dashed"];
-	9[label = "format_balmorel_data", color = "0.57 0.6 0.85", style="rounded,dashed"];
-	10[label = "geographic_sets", color = "0.19 0.6 0.85", style="rounded,dashed"];
-	11[label = "grids", color = "0.33 0.6 0.85", style="rounded,dashed"];
-	12[label = "investment_options", color = "0.43 0.6 0.85", style="rounded,dashed"];
-	1 -> 0
-	4 -> 0
-	6 -> 0
-	9 -> 0
-	10 -> 0
-	11 -> 0
-	12 -> 0
-	2 -> 1
-	3 -> 1
-	5 -> 4
-	2 -> 6
-	7 -> 6
-	8 -> 6
-	3 -> 6
-	6 -> 10
-	9 -> 11
-	2 -> 11
-	6 -> 12
-}            
diff --git a/src/main.py b/src/main.py
deleted file mode 100644
index f9ccaa9..0000000
--- a/src/main.py
+++ /dev/null
@@ -1,187 +0,0 @@
-"""
-Created on 11.03.2024
-
-@author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
-"""
-#%% ------------------------------- ###
-###        0. Script Settings       ###
-### ------------------------------- ###
-
-import matplotlib.pyplot as plt
-import pandas as pd
-from pybalmorel.utils import read_lines
-from pybalmorel import IncFile
-from Modules.createDH import DistrictHeat
-from Modules.createINDUSTRY import Industry
-from Modules.createHYDROGEN import Hydrogen
-from Modules.geofiles import prepared_geofiles, calculate_intersects, assign_area_to_region
-from runpy import run_module
-
-style = 'report'
-
-if style == 'report':
-    plt.style.use('default')
-    fc = 'white'
-elif style == 'ppt':
-    plt.style.use('dark_background')
-    fc = 'none'
-
-# Go to section X. for the execution of the functions below
-
-#%% ------------------------------- ###
-###     1. District Heating Data    ###
-### ------------------------------- ###
-
-def generate_districtheat_files(areas):
-    ### 1.0 Aggregate district heating data (only danish dataset for now)
-    DKareas = areas[areas[the_index].str.find('DK') != -1]
-    DH = DistrictHeat('Denmark_Futuregas')
-    DH.dfint = calculate_intersects(DKareas, DH.geo) # Find intersects between district heat areas and chosen areas
-    DH.assign_DH(DKareas, DH.dfint)
-    DH.assign_DHT(DKareas, DH.dfint)
-
-
-    ### 1.1 Check that the aggregation got all data:
-    # Annual DH
-    # print('\nOriginal data, annual DH:')
-    # print(DH.DH[DH.DH.A.str.find('DK') != -1].pivot_table(index='A', columns='Y').sum() / 1e6)
-    # print('\nNew data, annual DH:')
-    # print(DH.dfDH.sum() / 1e6)
-
-
-    ## Plot original vs aggregated data
-    year = '2050'
-    DH.plot_original_data(year, DKareas, plot_density=True)
-    DH.plot_aggregated_data(year, DKareas, False)
-
-
-    ### 1.2 Save .inc files
-    incfiles = {}
-    bodies = {'DH' : DH.dfDH,
-              'DH_VAR_T' : DH.dfDHT}
-    for file in ['DH', 'DH_VAR_T']:
-        incfiles[file] = IncFile(prefix=read_lines(file+'_prefix.inc', 
-                                                file_path='Data/IncFilePreSuf'),
-                                body=bodies[file],
-                                suffix=read_lines(file+'_suffix.inc',
-                                                file_path='Data/IncFilePreSuf'),
-                                path='Output',
-                                name=file)
-
-    for file in incfiles.keys():
-        incfiles[file].save()
-
-    return incfiles, DH
-
-#%% ------------------------------- ###
-###          2. VRE Data            ###
-### ------------------------------- ###
-
-# Run createVRE.py - takes a very long time!
-#run_module('Modules.createVRE')
-
-#%% ------------------------------- ###
-###          3. Industry            ###
-### ------------------------------- ###
-
-def generate_industry_files(areas):
-    IND = Industry()
-
-    ### 1.2 Assign Original Region
-    IND.assign_original_region()
-
-    # Assign fraction of emissions in region
-    IND.assign_emission_fractions() 
-
-    incfiles = IND.create_industry_data(areas, True)
-    
-    # Plot
-    IND.plot_aggregated_data(incfiles, areas, 'DH')
-    
-    # Prepare inc-files
-    ind_areas = incfiles['INDUSTRY_DH'].body['A'].unique()
-    incfiles['INDUSTRY_CCCRRRAAA'].body = '\n'.join(list(ind_areas))
-    incfiles['INDUSTRY_RRRAAA'].body = '\n'.join([row['R'] + ' . ' + row['A'] for i,row in assign_area_to_region(pd.Series(ind_areas), A_suffix='', 
-                                                                                                                 method='splitstring', splitstring='_IND').iterrows()])
-    incfiles['INDUSTRY_AAA'].body = '\n'.join(list(ind_areas))
-
-    # GKFX requires long names
-    pd.set_option('display.max_colwidth', None)
-    incfiles['INDUSTRY_GKFX'].body_prepare(['A', 'G'], ['Y'])
-    pd.set_option('display.max_colwidth', 30)
-    incfiles['INDUSTRY_DH'].body_prepare(['DHUSER', 'A'], ['Y'])
-    incfiles['INDUSTRY_DH_VAR_T'].body_prepare(['S', 'T'], ['A', 'DHUSER'])
-    incfiles['INDUSTRY_DE'].body_prepare(['DEUSER', 'R'], ['Y'])
-    incfiles['INDUSTRY_AGKN'].body = '\n'.join([f"AGKN('{row['A']}', '{row['G']}') = YES;" for i,row in incfiles['INDUSTRY_AGKN'].body.iterrows()])
-    
-    # Make dedicated industry area set
-    incfiles['INDUSTRY_INDUSTRY_AAA'] = IncFile(prefix=incfiles['INDUSTRY_AAA'].prefix.replace('SET AAA', 'SET INDUSTRY_AAA'),
-                                                body=incfiles['INDUSTRY_AAA'].body,
-                                                suffix=incfiles['INDUSTRY_AAA'].suffix,
-                                                name='INDUSTRY_INDUSTRY_AAA',
-                                                path='Output')
-    
-    # Make investment option for LT areas that become infeasible otherwise
-    for A in pd.Series(incfiles['INDUSTRY_AAA'].body.split('\n')).unique():
-        # Add a woodchip investment option in LT areas
-        if 'IND-LT' in A:
-            incfiles['INDUSTRY_AGKN'].body += f"AGKN('{A}', 'GNR_IND-DF_WOODCHI_E-100_MS-10-MW_Y-2020') = YES;\n"
-    
-    for file in ['INDUSTRY_GKFX', 'INDUSTRY_DH', 'INDUSTRY_DH_VAR_T',
-                 'INDUSTRY_DE', 'INDUSTRY_AGKN', 'INDUSTRY_CCCRRRAAA',
-                 'INDUSTRY_RRRAAA', 'INDUSTRY_AAA', 'INDUSTRY_INDUSTRY_AAA',
-                 'INDUSTRY_DISLOSS_E_AG']: 
-        incfiles[file].save()
-
-    return incfiles, IND
-
-# Plot data
-# IND.plot_original_data()
-# IND.plot_aggregated_data(incfiles, areas, 'GKFX')
-
-#%% ------------------------------- ###
-###          4. Hydrogen            ###
-### ------------------------------- ###
-
-def generate_hydrogen_files(areas, choice, the_index):
-    
-    H2 = Hydrogen(choice)
-    
-    incfiles = H2.create_hydrogen_data(areas, the_index)
-    
-    # Prepare grid files
-    incfiles['HYDROGEN_XH2INVCOST'].body_prepare(['Y', 'RE'], ['RI'])
-    incfiles['HYDROGEN_XH2LOSS'].body_prepare(['RE'], ['RI'])
-    
-    for file in incfiles.keys():
-        incfiles[file].save()
-    
-    return incfiles, H2
-
-#%% ------------------------------- ###
-###        5. Generate Files        ###
-### ------------------------------- ###
-
-if __name__ == '__main__':
-
-    ### 5.1 Load the desired spatial resolution
-    choice = 'DKMunicipalities'
-    the_index, areas, c = prepared_geofiles(choice)
-    areas = areas[areas[the_index].str.find('DK') != -1]
-    hierarchical = False # Need to code something that can use another geofile as R-set (and improve the assign_area_to_region function to be able to find geometries related to A within geometries related to R)
-
-
-    ### 5.2 Generate Files
-    DHinc, DH = generate_districtheat_files(areas)
-    
-    INDinc, IND = generate_industry_files(areas) # Note: No possiblity to meet demand in LT areas! (only storage investments allowed)
-        
-    H2inc, H2 = generate_hydrogen_files(areas, choice, the_index)
-        
-    ### 5.3 If hierarchical approach, change RRRAAA sets here
-    if hierarchical:
-        # Change RRRAAA sets
-        # the_index2, areas2, c2 = prepared_geofiles(choice2)
-        # RRRAAA
-        INDinc['INDUSTRY_RRRAAA']
-        
\ No newline at end of file

From 03bc3b9b051a2964cd9e091ef4b3d221d6cd2958 Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Mon, 30 Sep 2024 19:06:20 +0200
Subject: [PATCH 03/19] Made clustering script work again

---
 src/Modules/clustering.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/src/Modules/clustering.py b/src/Modules/clustering.py
index faffd1c..85a1e47 100644
--- a/src/Modules/clustering.py
+++ b/src/Modules/clustering.py
@@ -19,7 +19,7 @@
 from geofiles import prepared_geofiles
 from scipy.sparse import csr_matrix
 from Submodules.municipal_template import DataContainer
-from createDH import DistrictHeatAAU
+from exo_heat_demand import DistrictHeatAAU
 from sklearn.cluster import KMeans, AgglomerativeClustering
 from sklearn.preprocessing import StandardScaler
 from sklearn.neighbors import kneighbors_graph
@@ -162,6 +162,7 @@ def collect_clusterdata(plot_cf: bool = False):
 
 
 def cluster(con: DataContainer,
+            data_list: list,
             n_clusters: int,
             use_connectivity: bool = True,
             manual_corrections: list = [
@@ -176,14 +177,7 @@ def cluster(con: DataContainer,
             data_remark: str = 'all combined + xy coords'):
     
     # Stack data for clustering
-    X = np.vstack((
-        con.muni.coords['lat'].data,
-        con.muni.coords['lon'].data,
-        con.muni.electricity_demand_mwh.sum(dim=['year', 'user']),
-        con.muni.heat_demand_mwh.sum(dim=['year', 'user']),
-        con.muni.wind_cf.data,
-        con.muni.solar_cf.data,
-    )).T
+    X = np.vstack([eval(data_evaluation) for data_evaluation in data_list]).T
 
     # K-Means Clustering
     # est = KMeans(n_clusters=n_clusters)
@@ -244,6 +238,7 @@ def cluster(con: DataContainer,
         # ax.set_title('%d clusters, %s linkage, %s'%(n_clusters, name, connection_remark))    
         
         ax.axes.axis('off')
+        plt.show()
         
         # fig.savefig(r'C:\Users\mberos\Danmarks Tekniske Universitet\PhD in Transmission and Sector Coupling - Dokumenter\Deliverables\Spatial Resolution\Investigations\240912 - Initial Clustering Method Tests'+'/'+plot_title.replace('data: ', '_').replace('\n', '').replace(' clusters', 'N').replace(' ', '_').replace(',', '') + '.png',
         #             transparent=True,
@@ -273,4 +268,12 @@ def cluster(con: DataContainer,
 
 if __name__ == '__main__':
     collected = collect_clusterdata()
-    fig, ax, clustering = cluster(collected, 20)
+    fig, ax, clustering = cluster(collected, [
+        "collected.muni.coords['lat'].data",
+        "collected.muni.coords['lon'].data",
+        "collected.muni.electricity_demand_mwh.sum(dim=['year', 'user'])",
+        "collected.muni.heat_demand_mwh.sum(dim=['year', 'user'])",
+        'collected.muni.wind_cf.data',
+        'collected.muni.solar_cf.data'
+    ],
+    6)

From 258b92be8a9b80666ce3950c13f7d8c2b022c964 Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Mon, 30 Sep 2024 21:14:33 +0200
Subject: [PATCH 04/19] Prepared the second workflow

---
 .gitignore                                    |   9 ++--
 README.md                                     |  39 +++++++++++++-
 src/.workflow/clustering                      |  26 +++++++++
 src/{snakefile => .workflow/preprocessing}    |   0
 src/Analysis/clustering_dag.pdf               | Bin 0 -> 6126 bytes
 src/Analysis/dot_source.txt                   |  41 --------------
 src/Analysis/plot_dag.py                      |  28 ++++++----
 ...nakemake_dag.pdf => preprocessing_dag.pdf} | Bin 15182 -> 15183 bytes
 src/Modules/clustering.py                     |  51 +++++++++++-------
 src/clustering.bat                            |   6 +++
 src/preprocessing.bat                         |   6 +++
 11 files changed, 134 insertions(+), 72 deletions(-)
 create mode 100644 src/.workflow/clustering
 rename src/{snakefile => .workflow/preprocessing} (100%)
 create mode 100644 src/Analysis/clustering_dag.pdf
 delete mode 100644 src/Analysis/dot_source.txt
 rename src/Analysis/{snakemake_dag.pdf => preprocessing_dag.pdf} (74%)
 create mode 100644 src/clustering.bat
 create mode 100644 src/preprocessing.bat

diff --git a/.gitignore b/.gitignore
index 71ad7fd..03b6004 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,9 +7,12 @@ __pycache__
 .new_env/*
 prebalm
 *.pyc
-snakemake_dag
-snakemake_dag.*
-!snakemake_dag.pdf
+
+clustering_dot_source.txt
+clustering_dag
+preprocessing_dot_source.txt
+preprocessing_dag
+
 gurobi.log
 
 exo_elec_dem_conversion_dictionaries.pkl
diff --git a/README.md b/README.md
index 9163f29..556a9ef 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,44 @@ The necessary python packages can be installed in a virtual environment by follo
 conda env create -f environment.yaml
 ````
 
+This requires [anaconda](https://www.anaconda.com/download?utm_source=anacondadoc&utm_medium=documentation&utm_campaign=download&utm_content=topnavalldocs) (or the more lightweight [miniconda](https://docs.anaconda.com/miniconda/#miniconda-latest-installer-links)) installs an environment with the following packages:
+````
+name: spatialstudy
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - Cartopy=0.23.0
+  - geopandas=1.0.1
+  - matplotlib=3.9.2
+  - ipywidgets=8.1.3
+  - nbformat=5.9.2
+  - numpy=1.26.2
+  - openpyxl=3.1.2
+  - pandas=2.1.4
+  - plotly=5.16.1
+  - pyarrow=15.0.1
+  - pyproj=3.6.1
+  - scipy=1.11.2
+  - shapely=2.0.2
+  - atlite=0.2.12
+  - xarray=2024.2.0
+  - xlrd=2.0.1
+  - graphviz
+  - python-graphviz
+  - click
+  - snakemake-minimal
+  - pip
+  - pip:
+    - gamsapi[transfer]==45.7.0
+    - pybalmorel==0.3.10
+````
+
 ## Get Started
 
-The processing is initiated through a snakemake command in a command-line interface in the src directory.
+The processing is initiated through a snakemake command in a command-line interface in the src directory. If using windows, the pre-processing can be run by calling `preprocessing` and the clustering through `clustering`. If on other systems, do either of the following commands:
+```
+snakemake -s workflow/preprocessing
+snakemake -s workflow/clustering
+```
 A plot of the processes can be found [here](src/Analysis/snakemake_dag.pdf). Note that vre_profiles.py and existing_powerplants is currently not part of the snakemake process, but the output from vre_profiles.py is used in the offshore_wind process. 
\ No newline at end of file
diff --git a/src/.workflow/clustering b/src/.workflow/clustering
new file mode 100644
index 0000000..2c99a61
--- /dev/null
+++ b/src/.workflow/clustering
@@ -0,0 +1,26 @@
+# Load the config file at the top
+configfile: "assumptions.yaml"
+
+out_path     = "Output/"
+data_path    = "Data/"
+modules_path = "Modules/"
+submod_path  = "Modules/Submodules/"
+balmorel_path = config['balmorel_input']['model_path']
+scenario = config['balmorel_input']['scenario']
+balmorel_sc_folder = f"{balmorel_path}/{scenario}/model/"
+
+# 1. General Purpose
+rule all:
+    input:
+        [f"{out_path}WND_VAR_T.inc",
+        f"{balmorel_sc_folder}{scenario}_input_data.gdx"]
+
+
+rule clustering:
+    output:
+        [f"{balmorel_sc_folder}{scenario}_input_data.gdx",
+        f"{out_path}WND_VAR_T.inc"]
+    script:
+        """
+        python {modules_path}clustering.py --model-path={balmorel_sc_folder} --scenario={scenario}
+        """
diff --git a/src/snakefile b/src/.workflow/preprocessing
similarity index 100%
rename from src/snakefile
rename to src/.workflow/preprocessing
diff --git a/src/Analysis/clustering_dag.pdf b/src/Analysis/clustering_dag.pdf
new file mode 100644
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diff --git a/src/Analysis/dot_source.txt b/src/Analysis/dot_source.txt
deleted file mode 100644
index 772167b..0000000
--- a/src/Analysis/dot_source.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-digraph snakemake_dag {
-    graph[bgcolor=white, margin=0];
-    node[shape=box, style=rounded, fontname=sans,                 fontsize=10, penwidth=2];
-    edge[penwidth=2, color=grey];
-	0[label = "all", color = "0.25 0.6 0.85", style="rounded,dashed"];
-	1[label = "exo_electricity_demand", color = "0.29 0.6 0.85", style="rounded,dashed"];
-	2[label = "create_conversion_dictionaries", color = "0.17 0.6 0.85", style="rounded,dashed"];
-	3[label = "format_energinet_data", color = "0.50 0.6 0.85", style="rounded,dashed"];
-	4[label = "transport_road_demand", color = "0.00 0.6 0.85", style="rounded,dashed"];
-	5[label = "format_dkstat_transport_data", color = "0.08 0.6 0.85", style="rounded,dashed"];
-	6[label = "exo_heat_demand", color = "0.42 0.6 0.85", style="rounded,dashed"];
-	7[label = "format_vpdk21_data", color = "0.38 0.6 0.85", style="rounded,dashed"];
-	8[label = "format_dkstat_industry_data", color = "0.54 0.6 0.85", style="rounded,dashed"];
-	9[label = "format_balmorel_data", color = "0.58 0.6 0.85", style="rounded,dashed"];
-	10[label = "geographic_sets", color = "0.62 0.6 0.85", style="rounded,dashed"];
-	11[label = "grids", color = "0.04 0.6 0.85", style="rounded,dashed"];
-	12[label = "investment_options", color = "0.12 0.6 0.85", style="rounded,dashed"];
-	13[label = "transport_h2_demand", color = "0.21 0.6 0.85", style="rounded,dashed"];
-	14[label = "offshore_wind", color = "0.33 0.6 0.85", style="rounded,dashed"];
-	1 -> 0
-	4 -> 0
-	6 -> 0
-	9 -> 0
-	10 -> 0
-	11 -> 0
-	12 -> 0
-	13 -> 0
-	14 -> 0
-	2 -> 1
-	3 -> 1
-	5 -> 4
-	2 -> 6
-	7 -> 6
-	8 -> 6
-	3 -> 6
-	6 -> 10
-	9 -> 11
-	2 -> 11
-	6 -> 12
-	5 -> 13
-}            
diff --git a/src/Analysis/plot_dag.py b/src/Analysis/plot_dag.py
index 71f29b0..af6f889 100644
--- a/src/Analysis/plot_dag.py
+++ b/src/Analysis/plot_dag.py
@@ -3,17 +3,27 @@
 https://graphviz.org/download/
 """
 
+import click
 from graphviz import Source
 
-# Do snakemake --dag > analysis/dot_source.txt to within the dot_source below
-with open('Analysis/dot_source.txt', 'r') as f:
-    dot_source = f.read()
+@click.command()
+@click.option('--workflow', type=str, required=True, help="Which workflow to plot")
+@click.option('--view', type=bool, required=False, help="View the plotted DAG?")
+def main(workflow: str, view: bool = False):
+    # Do snakemake --dag > analysis/dot_source.txt to within the dot_source below
+    with open('Analysis/%s_dot_source.txt'%workflow, 'r') as f:
+        dot_source = f.read()
 
-# Create a Source object from the DOT source
-graph = Source(dot_source)
+    # Create a Source object from the DOT source
+    graph = Source(dot_source)
 
-# Render the diagram as a PNG file
-graph.render('Analysis/snakemake_dag', format='svg', cleanup=False)
+    # Render the diagram as a PNG file
+    graph.render('Analysis/%s_dag'%workflow, format='pdf', cleanup=False)
 
-# Optionally view the diagram (depends on your environment)
-graph.view()
+    if view:
+        # Optionally view the diagram (depends on your environment)
+        graph.view()
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/src/Analysis/snakemake_dag.pdf b/src/Analysis/preprocessing_dag.pdf
similarity index 74%
rename from src/Analysis/snakemake_dag.pdf
rename to src/Analysis/preprocessing_dag.pdf
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diff --git a/src/Modules/clustering.py b/src/Modules/clustering.py
index 85a1e47..223e1b7 100644
--- a/src/Modules/clustering.py
+++ b/src/Modules/clustering.py
@@ -13,6 +13,8 @@
 import matplotlib
 import matplotlib.pyplot as plt
 import matplotlib.colors as mcol
+from pybalmorel import Balmorel
+import click
 import pandas as pd
 import numpy as np
 import xarray as xr
@@ -38,14 +40,6 @@ def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
         cmap(np.linspace(minval, maxval, n)))
     return new_cmap
     
-style = 'ppt'
-
-if style == 'report':
-    plt.style.use('default')
-    fc = 'white'
-elif style == 'ppt':
-    plt.style.use('dark_background')
-    fc = 'none'
 
 #%% ------------------------------- ###
 ###        1. 
@@ -266,14 +260,35 @@ def cluster(con: DataContainer,
     
     return fig, ax, clustering
 
+@click.command()
+@click.option('--model-path', type=str, required=True, help='Balmorel model path')
+@click.option('--scenario', type=str, required=True, help='Balmorel scenario')
+@click.option('--plot-style', type=str, required=False, help='Style of the plot. Options are "report" (bright background) or "ppt" (dark background)')
+def main(model_path: str, scenario: str, plot_style: str = 'report'):
+
+    if plot_style == 'report':
+        plt.style.use('default')
+        fc = 'white'
+    elif plot_style == 'ppt':
+        plt.style.use('dark_background')
+        fc = 'none'
+
+    # Collect Balmorel input data from scenario
+    model = Balmorel(model_path)
+    model.load_incfiles(scenario)
+
+    # Old collection and clustering    
+    # collected = collect_clusterdata()
+    # fig, ax, clustering = cluster(collected, [
+    #     "collected.muni.coords['lat'].data",
+    #     "collected.muni.coords['lon'].data",
+    #     "collected.muni.electricity_demand_mwh.sum(dim=['year', 'user'])",
+    #     "collected.muni.heat_demand_mwh.sum(dim=['year', 'user'])",
+    #     'collected.muni.wind_cf.data',
+    #     'collected.muni.solar_cf.data'
+    # ],
+    # 6)
+
+
 if __name__ == '__main__':
-    collected = collect_clusterdata()
-    fig, ax, clustering = cluster(collected, [
-        "collected.muni.coords['lat'].data",
-        "collected.muni.coords['lon'].data",
-        "collected.muni.electricity_demand_mwh.sum(dim=['year', 'user'])",
-        "collected.muni.heat_demand_mwh.sum(dim=['year', 'user'])",
-        'collected.muni.wind_cf.data',
-        'collected.muni.solar_cf.data'
-    ],
-    6)
+    main()
\ No newline at end of file
diff --git a/src/clustering.bat b/src/clustering.bat
new file mode 100644
index 0000000..785acc4
--- /dev/null
+++ b/src/clustering.bat
@@ -0,0 +1,6 @@
+@REM Run workflow
+snakemake -s .workflow/clustering
+
+@REM Make DAG plot
+snakemake -s .workflow/clustering --dag > analysis/clustering_dot_source.txt
+python analysis/plot_dag.py --workflow=clustering --view=false
\ No newline at end of file
diff --git a/src/preprocessing.bat b/src/preprocessing.bat
new file mode 100644
index 0000000..167dc3a
--- /dev/null
+++ b/src/preprocessing.bat
@@ -0,0 +1,6 @@
+@REM Run workflow
+snakemake -s .workflow/preprocessing
+
+@REM Make DAG plot
+snakemake -s .workflow/preprocessing --dag > analysis/preprocessing_dot_source.txt
+python analysis/plot_dag.py --workflow=preprocessing --view=false
\ No newline at end of file

From 4cf15e54588b4f2f7f3b30fe19a9a45f3377d77f Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Mon, 30 Sep 2024 21:14:58 +0200
Subject: [PATCH 05/19] Corrected link to DAG pdf

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 556a9ef..a178db2 100644
--- a/README.md
+++ b/README.md
@@ -57,4 +57,4 @@ The processing is initiated through a snakemake command in a command-line interf
 snakemake -s workflow/preprocessing
 snakemake -s workflow/clustering
 ```
-A plot of the processes can be found [here](src/Analysis/snakemake_dag.pdf). Note that vre_profiles.py and existing_powerplants is currently not part of the snakemake process, but the output from vre_profiles.py is used in the offshore_wind process. 
\ No newline at end of file
+A plot of the processes can be found [here](src/Analysis/preprocessing_dag.pdf). Note that vre_profiles.py and existing_powerplants is currently not part of the snakemake process, but the output from vre_profiles.py is used in the offshore_wind process. 
\ No newline at end of file

From 4ab46994e2b2892192ed5e7dbdd162609eea14f0 Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Mon, 30 Sep 2024 21:48:05 +0200
Subject: [PATCH 06/19] Prepared structure in clustering snakefile

---
 src/Analysis/clustering_dag.pdf    | Bin 6126 -> 6909 bytes
 src/Analysis/preprocessing_dag.pdf | Bin 15183 -> 15182 bytes
 src/assumptions.yaml               |   2 +-
 src/{.workflow => }/clustering     |  21 ++++++++++++++++-----
 src/clustering.bat                 |   4 ++--
 src/{.workflow => }/preprocessing  |   6 +++---
 src/preprocessing.bat              |   4 ++--
 7 files changed, 24 insertions(+), 13 deletions(-)
 rename src/{.workflow => }/clustering (52%)
 rename src/{.workflow => }/preprocessing (95%)

diff --git a/src/Analysis/clustering_dag.pdf b/src/Analysis/clustering_dag.pdf
index 94617d1ba9643a230ead707cdfd280b99059879f..11a7172260b5573dcc6512c027425e01b47dec01 100644
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diff --git a/src/Analysis/preprocessing_dag.pdf b/src/Analysis/preprocessing_dag.pdf
index b4e71603c8d56bb261ecb996dff55a463439f51f..5794b0ed5766b6cb487016c28084f79fe2746861 100644
GIT binary patch
delta 3587
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diff --git a/src/assumptions.yaml b/src/assumptions.yaml
index 6c28e26..ccc05f3 100644
--- a/src/assumptions.yaml
+++ b/src/assumptions.yaml
@@ -1,6 +1,6 @@
 balmorel_input:
   model_path: "../../Balmorel"
-  scenario: "muni"
+  scenario: "new_muni"
   load_again: False
   
 grid_assumptions:
diff --git a/src/.workflow/clustering b/src/clustering
similarity index 52%
rename from src/.workflow/clustering
rename to src/clustering
index 2c99a61..9ed9186 100644
--- a/src/.workflow/clustering
+++ b/src/clustering
@@ -13,14 +13,25 @@ balmorel_sc_folder = f"{balmorel_path}/{scenario}/model/"
 rule all:
     input:
         [f"{out_path}WND_VAR_T.inc",
-        f"{balmorel_sc_folder}{scenario}_input_data.gdx"]
+        ]
 
+rule collect_balmorel_input:
+    output:
+        f"{balmorel_sc_folder}{scenario}_input_data.gdx"
+    run:
+        from pybalmorel import Balmorel
+        model = Balmorel(balmorel_path)
+        model.load_incfiles(scenario)
 
 rule clustering:
+    input:
+        [
+            f"{balmorel_sc_folder}{scenario}_input_data.gdx",
+            f"{modules_path}clustering.py"
+        ]
     output:
-        [f"{balmorel_sc_folder}{scenario}_input_data.gdx",
-        f"{out_path}WND_VAR_T.inc"]
-    script:
+        [f"{out_path}WND_VAR_T.inc"]
+    shell:
         """
-        python {modules_path}clustering.py --model-path={balmorel_sc_folder} --scenario={scenario}
+        python {modules_path}clustering.py --model-path={balmorel_path} --scenario={scenario}
         """
diff --git a/src/clustering.bat b/src/clustering.bat
index 785acc4..d3d0533 100644
--- a/src/clustering.bat
+++ b/src/clustering.bat
@@ -1,6 +1,6 @@
 @REM Run workflow
-snakemake -s .workflow/clustering
+snakemake -s clustering
 
 @REM Make DAG plot
-snakemake -s .workflow/clustering --dag > analysis/clustering_dot_source.txt
+snakemake -s clustering --dag > analysis/clustering_dot_source.txt
 python analysis/plot_dag.py --workflow=clustering --view=false
\ No newline at end of file
diff --git a/src/.workflow/preprocessing b/src/preprocessing
similarity index 95%
rename from src/.workflow/preprocessing
rename to src/preprocessing
index 793b1d8..91e692d 100644
--- a/src/.workflow/preprocessing
+++ b/src/preprocessing
@@ -1,10 +1,10 @@
 # Load the config file at the top
 configfile: "assumptions.yaml"
 
-out_path     = "Output/"
-data_path    = "Data/"
+out_path = "Output/"
+data_path = "Data/"
 modules_path = "Modules/"
-submod_path  = "Modules/Submodules/"
+submod_path = "Modules/Submodules/"
 
 # 1. General Purpose
 rule all:
diff --git a/src/preprocessing.bat b/src/preprocessing.bat
index 167dc3a..fbf1e7f 100644
--- a/src/preprocessing.bat
+++ b/src/preprocessing.bat
@@ -1,6 +1,6 @@
 @REM Run workflow
-snakemake -s .workflow/preprocessing
+snakemake -s preprocessing
 
 @REM Make DAG plot
-snakemake -s .workflow/preprocessing --dag > analysis/preprocessing_dot_source.txt
+snakemake -s preprocessing --dag > analysis/preprocessing_dot_source.txt
 python analysis/plot_dag.py --workflow=preprocessing --view=false
\ No newline at end of file

From 7056343049532dbf5fe0a4cd01d2ae74b6eff0cf Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Mon, 30 Sep 2024 23:15:22 +0200
Subject: [PATCH 07/19] Added data path to some hardcoded spots

---
 src/preprocessing | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/preprocessing b/src/preprocessing
index 91e692d..7bfa443 100644
--- a/src/preprocessing
+++ b/src/preprocessing
@@ -68,7 +68,7 @@ rule format_vpdk21_data:
 
 rule format_dkstat_industry_data:
     input:
-        [r"Data\Danmarks Statistik\Industriforbrug Type.xlsx",
+        [f"{data_path}Danmarks Statistik/Industriforbrug Type.xlsx",
         f"{modules_path}format_dkstat.py"]
     output:
         f"{data_path}Danmarks Statistik/industry_demand.nc"
@@ -121,7 +121,7 @@ rule exo_heat_demand:
 # 4. Transport Demand
 rule format_dkstat_transport_data:
     input:
-        [r"Data\Danmarks Statistik\Transportforbrug Type.xlsx",
+        [f"{data_path}Danmarks Statistik/Transportforbrug Type.xlsx",
         f"{modules_path}format_dkstat.py"]
     output:
         f"{data_path}Danmarks Statistik/transport_demand.csv"

From a91118da0a2774849372660ebc9d8948b89fbc4e Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Mon, 30 Sep 2024 23:18:06 +0200
Subject: [PATCH 08/19] Made .bat scripts accept more arguments for the
 snakemake executions

---
 src/clustering.bat    | 4 ++--
 src/preprocessing.bat | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/clustering.bat b/src/clustering.bat
index d3d0533..71a438d 100644
--- a/src/clustering.bat
+++ b/src/clustering.bat
@@ -1,5 +1,5 @@
-@REM Run workflow
-snakemake -s clustering
+@REM Run workflow with additional arguments
+snakemake -s clustering %*
 
 @REM Make DAG plot
 snakemake -s clustering --dag > analysis/clustering_dot_source.txt
diff --git a/src/preprocessing.bat b/src/preprocessing.bat
index fbf1e7f..6c1a7c9 100644
--- a/src/preprocessing.bat
+++ b/src/preprocessing.bat
@@ -1,5 +1,5 @@
-@REM Run workflow
-snakemake -s preprocessing
+@REM Run workflow with additional arguments
+snakemake -s preprocessing %*
 
 @REM Make DAG plot
 snakemake -s preprocessing --dag > analysis/preprocessing_dot_source.txt

From 9c24ae4298fd48dbbc09d20a80ceb665bad8250c Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Tue, 1 Oct 2024 10:37:29 +0200
Subject: [PATCH 09/19] Fixed some errors

---
 src/Modules/investment_options.py | 166 +++++++++++++++++++++++++++---
 src/Modules/offshore_wind.py      |  10 +-
 src/preprocessing                 |   5 +-
 3 files changed, 162 insertions(+), 19 deletions(-)

diff --git a/src/Modules/investment_options.py b/src/Modules/investment_options.py
index 1a02f19..d173333 100644
--- a/src/Modules/investment_options.py
+++ b/src/Modules/investment_options.py
@@ -33,25 +33,37 @@ def save_symbol_from_all_endofmodel(symbol: str,
     f.to_parquet('Data/BalmorelData/AGKN_fromKountouris2024.gzip')
 
 def base_AGKN(AGKN: pd.DataFrame, print_options: bool = False):
-    # 1. Special options
+    # Options not to be included
     # This was copy pasted from the outcommented print statements below
-    wind_off_G = [
-        "DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2020",
+    other_vre = [
+        "GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2020",
+        "GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2030",
+        "GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2040",
+        "GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2050",
+        "GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2020",
+        "GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2030",
+        "GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2040",
+        "GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2050",
         "DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2020",
-        "DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2020",
         "DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2020",
-        "DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2030",
         "DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2030",
-        "DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2030",
         "DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2030",
-        "DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2040",
         "DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2040",
-        "DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2040",
         "DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2040",
-        "DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2050",
         "DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2050",
-        "DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2050",
         "DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2050",
+        "DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2020",
+        "DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2020",
+        "DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2030",
+        "DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2030",
+        "DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2040",
+        "DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2040",
+        "DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2050",
+        "DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2050",
+        "GNR_PV_SUN_LS-8-MW_RG2_Y-2020",       
+        "GNR_PV_SUN_LS-8-MW_RG2_Y-2030",
+        "GNR_PV_SUN_LS-8-MW_RG2_Y-2040",    
+        "GNR_PV_SUN_LS-8-MW_RG2_Y-2050",
     ]
     
     solar_heating = [
@@ -61,7 +73,6 @@ def base_AGKN(AGKN: pd.DataFrame, print_options: bool = False):
         'GNR_SH_SUN_LS_Y-2050',
     ]
     
-    # This was copy pasted from the outcommented print statements below
     hydrogen_options = [
         'GNR_ELYS_ELEC_AEC_Y-2020',
         'GNR_ELYS_ELEC_AEC_Y-2030',
@@ -86,13 +97,144 @@ def base_AGKN(AGKN: pd.DataFrame, print_options: bool = False):
         'GNR_STEAM-REFORMING-CCS_E-70_Y-2020',
     ]
     
+    # Small scale investments
+    small_scale = [
+        "GNR_CC_NGAS_BP_E-51_SS-10-MW_Y-2020",
+        "GNR_CC_NGAS_BP_E-53_SS-10-MW_Y-2030",
+        "GNR_CC_NGAS_BP_E-54_SS-10-MW_Y-2040",
+        "GNR_CC_NGAS_BP_E-55_SS-10-MW_Y-2050",
+        "GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020",
+        "GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030",
+        "GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040",
+        "GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050",
+        "GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2020",
+        "GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2030",
+        "GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2040",
+        "GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2050",
+        "GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2020",
+        "GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2030",
+        "GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2040",
+        "GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2050",
+        "GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2020",
+        "GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2030",
+        "GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2040",
+        "GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2050",
+    ]
+    
+    medium_scale = [
+        "GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2020",
+        "GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2030",
+        "GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2040",
+        "GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2050",
+        "GNR_BO_NGAS_E-105_MS-5-MW_Y-2020",
+        "GNR_BO_NGAS_E-106_MS-5-MW_Y-2030",
+        "GNR_BO_NGAS_E-106_MS-5-MW_Y-2040",
+        "GNR_BO_NGAS_E-106_MS-5-MW_Y-2050",
+        "GNR_ST_MSW_BP_E-23_MS-80-MW-FEED_Y-2020",
+        "GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2030",
+        "GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2040",
+        "GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2050",
+        "GNR_ST_NGAS_BP_E-7_MS-15-MW_Y-2020",
+        "GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2020",
+        "GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2030",
+        "GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2040",
+        "GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2050",
+        "GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2020",
+        "GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2030",
+        "GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2040",
+        "GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2050",
+        "GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2020",
+        "GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2030",
+        "GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2040",
+        "GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2050",
+    ]
+        
+    large_scale = [
+        "GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2030",
+        "GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2040",
+        "GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2050",
+        "GNR_ST_MSW_CND_E-23_LS-220-MW-FEED_Y-2020",
+        "GNR_ST_MSW_CND_E-24_LS-220-MW-FEED_Y-2030",
+        "GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2040",
+        "GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2050",
+        "GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2020",
+        "GNR_ST_MSW_BP_E-23_LS-220-MW-FEED_Y-2020",
+        "GNR_ST_MSW_BP_E-24_LS-220-MW-FEED_Y-2030",
+        "GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2040",
+        "GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2050",
+        "GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2020",
+        "GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2030",
+        "GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2040",
+        "GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2050",
+        "GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2020",
+        "GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2030",
+        "GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2040",
+        "GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2050",
+        "GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2020",
+        "GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2030",
+        "GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2040",
+        "GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2050",
+        "GNR_ST_NGAS_CND_E-47_LS-400-MW_Y-2020",
+        "GNR_ST_NGASCCS_CND_E-47_LS-400-MW_Y-2020",
+        "GNR_GT_NGAS_CND_E-42_LS-40-MW_Y-2020",
+        "GNR_GT_NGAS_CND_E-43_LS-40-MW_Y-2030",
+        "GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2040",
+        "GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2050",
+        "GNR_CC_NGAS_CND_E-59_LS-100-MW_Y-2020",
+        "GNR_CC_NGAS_CND_E-61_LS-100-MW_Y-2030",
+        "GNR_CC_NGAS_CND_E-62_LS-100-MW_Y-2040",
+        "GNR_CC_NGAS_CND_E-63_LS-100-MW_Y-2050",
+        "GNR_CC_NGASCCS_CND_E-59_LS-100-MW_Y-2020",
+        "GNR_CC_NGASCCS_CND_E-61_LS-100-MW_Y-2030",
+        "GNR_CC_NGASCCS_CND_E-62_LS-100-MW_Y-2040",
+        "GNR_CC_NGASCCS_CND_E-63_LS-100-MW_Y-2050",
+        "GNR_ST_NGAS_EXT_E-47_LS-400-MW_Y-2020",
+        "GNR_ST_NGASCCS_EXT_E-47_LS-400-MW_Y-2020",
+        "GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2020",
+        "GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2030",
+        "GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2040",
+        "GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2050",
+        "GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2020",
+        "GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2030",
+        "GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2040",
+        "GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2050",
+        "GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2020",
+        "GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2030",
+        "GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2040",
+        "GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2050",
+        "GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020",
+        "GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030",
+        "GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2040",
+        "GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050",
+        "GNR_BO_STRW_E-102_LS-6-MW_Y-2020",
+        "GNR_BO_STRW_E-102_LS-6-MW_Y-2030",
+        "GNR_BO_STRW_E-102_LS-6-MW_Y-2040",
+        "GNR_BO_STRW_E-102_LS-6-MW_Y-2050",
+        "GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2020",
+        "GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2030",
+        "GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2040",
+        "GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2050",
+        "GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2020",
+        "GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2030",
+        "GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2040",
+        "GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2050",
+        "GNR_CC_NGAS_EXT_E-59_LS-100-MW_Y-2020",
+        "GNR_CC_NGAS_EXT_E-61_LS-100-MW_Y-2030",
+        "GNR_CC_NGAS_EXT_E-62_LS-100-MW_Y-2040",
+        "GNR_CC_NGAS_EXT_E-63_LS-100-MW_Y-2050",
+        "GNR_CC_NGASCCS_EXT_E-59_LS-100-MW_Y-2020",
+        "GNR_CC_NGASCCS_EXT_E-61_LS-100-MW_Y-2030",
+        "GNR_CC_NGASCCS_EXT_E-62_LS-100-MW_Y-2040",
+        "GNR_CC_NGASCCS_EXT_E-63_LS-100-MW_Y-2050",
+    ]
+    
     # 2. Base and Hydrogen investments
     temp = AGKN.query(
         "~A.str.contains('IDVU') and ~A.str.contains('IND')"
     )
     
     # Get base investment options and remove elements in wind_off_G from base_options (add hydrogen when the error arrives)
-    base_options = [option for option in temp.G.unique() if option not in wind_off_G and option not in hydrogen_options and option not in solar_heating]
+    base_options = [option for option in temp.G.unique() if option not in other_vre + hydrogen_options + solar_heating + medium_scale + large_scale]
     
     # Use this to inspect offshore and hydrogen investment options
     if print_options:
diff --git a/src/Modules/offshore_wind.py b/src/Modules/offshore_wind.py
index a9f65a6..7208e57 100644
--- a/src/Modules/offshore_wind.py
+++ b/src/Modules/offshore_wind.py
@@ -88,7 +88,7 @@ def create_profiles(profiles: xr.Dataset, year: int = 2012):
                 suffix="\n".join([
                     "",   
                     ";",                 
-                    "WND_VAR_T(AAA,SSS,TTT) = WND_VAR_T1(SSS,TTT,AAA);",
+                    "WND_VAR_T(AAA,SSS,TTT)$WND_VAR_T1(SSS,TTT,AAA) = WND_VAR_T1(SSS,TTT,AAA);",
                     "WND_VAR_T1(SSS,TTT,AAA) = 0;"
                 ]))
     profiles.columns.name = ''
@@ -129,16 +129,14 @@ def distribute_offshore_potential(total_potential: float, geofile: gpd.GeoDataFr
     temp = geofile.copy().to_crs('EPSG:4093') # To geocentric (meters)
     geofile['Values'] = temp.geometry.area / temp.geometry.area.sum() * total_potential
     geofile['TECH_GROUP'] = 'WINDTURBINE_OFFSHORE'
-    
-    print(geofile.drop(columns='geometry').pivot_table(index=['Name', 'TECH_GROUP']))
+    geofile['SUBTECH_GROUP'] = 'RG1'
     
     # SUBTECHGROUPKPOT
     f = IncFile(name='OFFSHORE_SUBTECHGROUPKPOT', path='Output',
                 prefix="TABLE SUBTECHGROUPKPOT(CCCRRRAAA, TECH_GROUP, SUBTECH_GROUP)  'Subtechnology group capacity restriction by geography (MW)'\n",
                 suffix='\n;')
-    f.body = geofile.drop(columns='geometry').pivot_table(index=['Name', 'TECH_GROUP'], values='Values', aggfunc='sum')
-    f.body.columns.name = 'RG1'
-    f.body.index.names = ['', '']
+    f.body = geofile.drop(columns='geometry')
+    f.body_prepare(index=['Name', 'TECH_GROUP'], columns='SUBTECH_GROUP', values='Values')
     f.save()
     
 #%% ------------------------------- ###
diff --git a/src/preprocessing b/src/preprocessing
index 7bfa443..7fd6245 100644
--- a/src/preprocessing
+++ b/src/preprocessing
@@ -213,7 +213,10 @@ rule grids:
 # 7. Other
 rule offshore_wind:
     input:
-        f'{out_path}VRE/2012_offshore_wind.nc'
+        [
+            f'{out_path}VRE/2012_offshore_wind.nc',
+            f"{modules_path}offshore_wind.py"
+        ]
     params:
         weather_year=config['timeseries']['weather_year'],
         total_offshore_wind_potential=config['timeseries']['total_offshore_wind_potential']

From 46e043b25cefa1d033f8aef2fe1419542cb25224 Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Tue, 1 Oct 2024 11:17:54 +0200
Subject: [PATCH 10/19] Made last fixes to offshore

---
 src/Modules/grids.py         | 4 ++++
 src/Modules/offshore_wind.py | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/Modules/grids.py b/src/Modules/grids.py
index 1b473db..684537d 100644
--- a/src/Modules/grids.py
+++ b/src/Modules/grids.py
@@ -202,6 +202,10 @@ def create_tech_specific_distribution_loss(wind_offshore_loss: dict,
                         f"DISLOSS_E_AG(IA,G)$(GDATA(G,'GDSUBTECHGROUP') EQ RG3_OFF4 AND (AGKN(IA,G) OR SUM(Y, GKFX(Y,IA,G))))={wind_offshore_loss['RG3']};",
                         f"DISLOSS_E_AG(IA,G)$(GDATA(G,'GDSUBTECHGROUP') EQ RG3_OFF5 AND (AGKN(IA,G) OR SUM(Y, GKFX(Y,IA,G))))={wind_offshore_loss['RG3']};",
                     ]))
+    
+    # Add larger loss for Nordsøen
+    f.body += f"\nDISLOSS_E_AG('Nordsoeen', G)$(GDATA(G,'GDSUBTECHGROUP') EQ RG1) = {wind_offshore_loss['RG3']};"
+    
     f.save()    
 
     
diff --git a/src/Modules/offshore_wind.py b/src/Modules/offshore_wind.py
index 7208e57..cfc908d 100644
--- a/src/Modules/offshore_wind.py
+++ b/src/Modules/offshore_wind.py
@@ -129,7 +129,7 @@ def distribute_offshore_potential(total_potential: float, geofile: gpd.GeoDataFr
     temp = geofile.copy().to_crs('EPSG:4093') # To geocentric (meters)
     geofile['Values'] = temp.geometry.area / temp.geometry.area.sum() * total_potential
     geofile['TECH_GROUP'] = 'WINDTURBINE_OFFSHORE'
-    geofile['SUBTECH_GROUP'] = 'RG1'
+    geofile['SUBTECH_GROUP'] = 'RG1_OFF1'
     
     # SUBTECHGROUPKPOT
     f = IncFile(name='OFFSHORE_SUBTECHGROUPKPOT', path='Output',

From 6d06794b40762cdeee030f810051a5f643e0c8f8 Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Tue, 1 Oct 2024 13:14:21 +0200
Subject: [PATCH 11/19] First effort to make vre profile generation 'pure'
 script

---
 src/Modules/vre_profiles.py | 989 ++++++++++++++++++------------------
 1 file changed, 491 insertions(+), 498 deletions(-)

diff --git a/src/Modules/vre_profiles.py b/src/Modules/vre_profiles.py
index 263b09b..2fab6cf 100644
--- a/src/Modules/vre_profiles.py
+++ b/src/Modules/vre_profiles.py
@@ -19,29 +19,18 @@
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib import rc
-from scipy.optimize import curve_fit
 import atlite
 import geopandas as gpd
 import cartopy.crs as ccrs
-from shapely.geometry import MultiPolygon
-from pyproj import Proj, Transformer
+from pyproj import Proj
 from rasterio.plot import show
 import xarray as xr
 from atlite.gis import shape_availability, ExclusionContainer
-import os
 from Modules.geofiles import prepared_geofiles
 import logging
+import click
 logging.basicConfig(level=logging.INFO)
 
-style = 'report'
-
-if style == 'report':
-    plt.style.use('default')
-    fc = 'white'
-elif style == 'ppt':
-    plt.style.use('dark_background')
-    fc = 'none'
 
 ### Function fo load duration curve analysis
 def doLDC(file, cols, idx, r, c, title=''):
@@ -92,530 +81,534 @@ def doLDC(file, cols, idx, r, c, title=''):
 ###         0. Assumptions          ###
 ### ------------------------------- ###
 
-### 0.1 Capacity pr. km for PV and Wind
-cap_per_sqkm_pv = 1.7 # MW/km2
-cap_per_sqkm_wind = 0.67 # MW/km2 According to NREL: 2 MW / 1.5 acres (0.00607028 km2)
-
-
-### 0.2 Choice of technologies
-panel = atlite.solarpanels.CSi # Possible to choose crystalline Si (CSi) or advanced cadmium-tellurium (CdTe)
-wind_turbine = atlite.windturbines.Vestas_V66_1750kW
-# wind_turbine = atlite.windturbines.Enercon_E82_3000kW
-# wind_turbine = atlite.windturbines.Bonus_B1000_1000kW
-
-# Offshore wind
-# wind_turbine = atlite.windturbines.NREL_ReferenceTurbine_5MW_offshore
-
-### 0.3 What areas to load?
-# choice = 'DK Municipalities'
-# choice = 'NUTS1'
-# choice = 'NUTS2'
-# choice = 'NUTS3'
-# choice = 'NordpoolReal'
-choice = 'Balmorel2024'
-# choice = 'BalmorelVREAreas'
-
-## Cutouts
-# path = "Nicolas_2015_full.nc"
-path = r"M:\Project\RQII\DK+Surrounding2012.nc"
-# cutout_bounds_x = (11.015880, 13.078094) # Longitude
-# cutout_bounds_y = (43.239006, 45.207804) # Latitude
-# cutout_bounds_x = (11.7, 12.2) # Longitude
-# cutout_bounds_y = (43.85, 44.4) # Latitude
-cutout_bounds_x = (3, 33) # Longitude
-cutout_bounds_y = (47, 73) # Latitude
-
-
-### 0.4 What time to load?
-# T = "2011-01-01"
-T = '2012'
-
-### 0.5 Overwrite cutout or load previous?
-OverW = False
-
-### 0.6 Read Geodata
-areas = gpd.read_file('Data/Shapefiles/2024_balmorelmapwithoffshoremunidk.gpkg')
-areas = areas[(areas.NAME_0 == 'Germany') | (areas.NAME_0 == 'Norway') |\
-    (areas.NAME_0 == 'Sweden') | ((areas.Type == 'Offshore') & ((areas.Country == 'DE') |\
-        (areas.Country == 'DK') | (areas.Country == 'NO') | (areas.Country == 'SE')))
-]
-areas.loc[areas.Type == 'Offshore', 'id'] = areas.loc[areas.Type == 'Offshore', 'Region']
-
-# areas.loc[areas.Type != 'Offshore', 'id'] = areas.loc[areas.Type != 'Offshore', 'Region']
-# areas = gpd.read_file('Data/Shapefiles/2024 BalmorelHighResolutionMapWithOffshore.gpkg')
-# areas.loc[areas.Type == 'Offshore', 'id'] = areas.loc[areas.Type == 'Offshore', 'Region']
-# areas.index = areas.id
-# the_index='id'
-# areas = areas[(areas.NAME_0 == 'Denmark') | (areas.NAME_0 == 'Germany') |\
-#               (areas.NAME_0 == 'Sweden') | (areas.NAME_0 == 'Norway') |\
-                #   ((areas.Type == 'Offshore') & (areas.id.str.find('DK') != -1)) |\
-                #   ((areas.Type == 'Offshore') & (areas.id.str.find('DE') != -1)) |\
-                #   ((areas.Type == 'Offshore') & (areas.id.str.find('NO') != -1)) |\
-                #   ((areas.Type == 'Offshore') & (areas.id.str.find('SE') != -1))]
-
-# MUNI DK with offshore filtering
-# areas = areas[(areas.Country == 'DK') & (areas.Type == 'Offshore')] # From BalmorelHighResolution
-the_index, areas2, country_code = prepared_geofiles('DKMunicipalities')
-areas2['id'] = areas2['NAME_2']
-areas2['muni_id'] = areas2['GID_2']
-areas = pd.concat((areas, areas2[['id', 'muni_id', 'geometry']]))
-# areas = areas.loc[['DK1', 'DK2']] # Testing DK and DE
-areas.geometry = areas['geometry']
-areas.index = areas.id
-the_index='id'
-# areas.loc[:,'GID_2'] = areas.GID_2.str.replace('.', '_')
-
-# Read homemade offshore potentials for DK
-OFFWNDPOT = gpd.read_file(r'.\Data\RandomOffWindPot\DK.gpkg')
-
-# Plot
-fig, ax = plt.subplots()
-areas.plot(ax=ax)
-
-# ax.set_xlim(cutout_bounds_x)      
-# ax.set_ylim(cutout_bounds_y)
-ax.set_title(choice)
-# OFFWNDPOT.plot(ax=ax)
-
-# Filtering Italy
-# if choice == 'Nordpool':
-#     areas = areas[areas.index == 'IT_NORD']
-
-
-
-### ASSUMPTIONS
-# 3.3 A quick fix for SOLH_VAR_T and SOLHFLH
-# 3.4 Capacity is based on RG1 VRE technology - all others regions are set to 0 potential
-# 3.4 Offshore wind potential is a hack right now - manual inputted GW
-
-#%% ------------------------------- ###
-### 1. Load Geodata and Pre-process ###
-### ------------------------------- ###
-
-## Projections
-
-UTM32 = Proj(proj='utm', zone=32, ellps='WGS84', preserve_units=False)
-GM = Proj('EPSG:900913', preserve_units=False)
-# transformer = Transformer.from_crs('EPSG:900913', 'EPSG:4326')
-# out = Transformer(GM, UTM32, (11, 13), (43, 45), always_xy=True) 
-
-    
-
-### 1.2 Visualise current areas
-# Set projection
-crs = ccrs.UTM(32)
-# Make compatible with geopandas
-# projection = crs.proj4_init # doesn't work, so actually cartopy is useless - continuing only with geopandas
-
-# Make figure
-fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={"projection": crs},
-                       dpi=200)
-
-
-# Add areas
-ax.add_geometries(areas.geometry, crs = crs,
-                  facecolor=[.9, .9,.9], edgecolor='grey',
-                  linewidth=.2)
-
-ax.set_xlim(cutout_bounds_x)
-ax.set_ylim(cutout_bounds_y)
-# ax.set_xlim(7.5,16)      
-# ax.set_ylim(54.4,58)  
-
-
-### 1.3 Load Nordpool regions
-# NP = pd.read_csv(project_dir/'geo_files/coordinates_RRR.csv')
-# NP = NP.loc[df_unique['Type'] == 'region', ]
-
-#%% ------------------------------- ###
-###   2. Calculate RE Potentials    ###
-### ------------------------------- ###
+@click.commands()
+@click.option('--cutout-path', type=str, required=True, help="The path of a cutout .nc file")
+@click.option('--weather-year', type=int, required=True, help="The weather year")
+@click.option('--overwrite-cutout', type=str, required=False, help="Overwrite an existing cutout?")
+def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
+    ### 0.1 Capacity pr. km for PV and Wind
+    cap_per_sqkm_pv = 1.7 # MW/km2
+    cap_per_sqkm_wind = 0.67 # MW/km2 According to NREL: 2 MW / 1.5 acres (0.00607028 km2)
+
+
+    ### 0.2 Choice of technologies
+    panel = atlite.solarpanels.CSi # Possible to choose crystalline Si (CSi) or advanced cadmium-tellurium (CdTe)
+    wind_turbine = atlite.windturbines.Vestas_V66_1750kW
+    # wind_turbine = atlite.windturbines.Enercon_E82_3000kW
+    # wind_turbine = atlite.windturbines.Bonus_B1000_1000kW
+
+    # Offshore wind
+    # wind_turbine = atlite.windturbines.NREL_ReferenceTurbine_5MW_offshore
+
+    ### 0.3 What areas to load?
+    # choice = 'DK Municipalities'
+    # choice = 'NUTS1'
+    # choice = 'NUTS2'
+    # choice = 'NUTS3'
+    # choice = 'NordpoolReal'
+    choice = 'Balmorel2024'
+    # choice = 'BalmorelVREAreas'
+
+    ## Cutouts
+    # path = "Nicolas_2015_full.nc"
+    # cutout_bounds_x = (11.015880, 13.078094) # Longitude
+    # cutout_bounds_y = (43.239006, 45.207804) # Latitude
+    # cutout_bounds_x = (11.7, 12.2) # Longitude
+    # cutout_bounds_y = (43.85, 44.4) # Latitude
+    cutout_bounds_x = (3, 33) # Longitude
+    cutout_bounds_y = (47, 73) # Latitude
+    # DK + Nordsøen
+    cutout_bounds_x = (2.2, 17.4) # Longitude
+    cutout_bounds_y = (53.7, 59) # Latitude
+
+
+    ### 0.4 What time to load?
+    # T = "2011-01-01"
+
+
+    ### 0.6 Read Geodata
+    areas = gpd.read_file('Data/Shapefiles/2024_balmorelmapwithoffshoremunidk.gpkg')
+    areas = areas[(areas.NAME_0 == 'Germany') | (areas.NAME_0 == 'Norway') |\
+        (areas.NAME_0 == 'Sweden') | ((areas.Type == 'Offshore') & ((areas.Country == 'DE') |\
+            (areas.Country == 'DK') | (areas.Country == 'NO') | (areas.Country == 'SE')))
+    ]
+    areas.loc[areas.Type == 'Offshore', 'id'] = areas.loc[areas.Type == 'Offshore', 'Region']
+
+    # areas.loc[areas.Type != 'Offshore', 'id'] = areas.loc[areas.Type != 'Offshore', 'Region']
+    # areas = gpd.read_file('Data/Shapefiles/2024 BalmorelHighResolutionMapWithOffshore.gpkg')
+    # areas.loc[areas.Type == 'Offshore', 'id'] = areas.loc[areas.Type == 'Offshore', 'Region']
+    # areas.index = areas.id
+    # the_index='id'
+    # areas = areas[(areas.NAME_0 == 'Denmark') | (areas.NAME_0 == 'Germany') |\
+    #               (areas.NAME_0 == 'Sweden') | (areas.NAME_0 == 'Norway') |\
+                    #   ((areas.Type == 'Offshore') & (areas.id.str.find('DK') != -1)) |\
+                    #   ((areas.Type == 'Offshore') & (areas.id.str.find('DE') != -1)) |\
+                    #   ((areas.Type == 'Offshore') & (areas.id.str.find('NO') != -1)) |\
+                    #   ((areas.Type == 'Offshore') & (areas.id.str.find('SE') != -1))]
+
+    # MUNI DK with offshore filtering
+    # areas = areas[(areas.Country == 'DK') & (areas.Type == 'Offshore')] # From BalmorelHighResolution
+    the_index, areas2, country_code = prepared_geofiles('DKMunicipalities')
+    areas2['id'] = areas2['NAME_2']
+    areas2['muni_id'] = areas2['GID_2']
+    areas = pd.concat((areas, areas2[['id', 'muni_id', 'geometry']]))
+    # areas = areas.loc[['DK1', 'DK2']] # Testing DK and DE
+    areas.geometry = areas['geometry']
+    areas.index = areas.id
+    the_index='id'
+    # areas.loc[:,'GID_2'] = areas.GID_2.str.replace('.', '_')
+
+    # Read homemade offshore potentials for DK
+    OFFWNDPOT = gpd.read_file(r'.\Data\RandomOffWindPot\DK.gpkg')
+
+    # Plot
+    fig, ax = plt.subplots()
+    areas.plot(ax=ax)
 
-### 2.1 Load Cutout
-cutout = atlite.Cutout(path=path,
-                       module="era5",
-                       x=slice(cutout_bounds_x[0], cutout_bounds_x[1]),
-                       y=slice(cutout_bounds_y[0], cutout_bounds_y[1]),
-                       time=T
-                       )
-cutout.prepare(overwrite=OverW)
+    # ax.set_xlim(cutout_bounds_x)      
+    # ax.set_ylim(cutout_bounds_y)
+    ax.set_title(choice)
+    # OFFWNDPOT.plot(ax=ax)
 
+    # Filtering Italy
+    # if choice == 'Nordpool':
+    #     areas = areas[areas.index == 'IT_NORD']
 
 
-#%% 2.2 Load Map for RE Spatial Availabilities
-# CORINE = 'corine.tif'
-CORINE = r'Data\CORINE\u2018_clc2018_v2020_20u1_raster100m\u2018_clc2018_v2020_20u1_raster100m\DATA\U2018_CLC2018_V2020_20u1.tif'
-excluder = ExclusionContainer()
-excluder.add_raster(CORINE, codes=range(20))
 
-# Convert crs to CORINE map
-A = areas.geometry.to_crs(excluder.crs)
-A.index = getattr(areas, the_index)
+    ### ASSUMPTIONS
+    # 3.3 A quick fix for SOLH_VAR_T and SOLHFLH
+    # 3.4 Capacity is based on RG1 VRE technology - all others regions are set to 0 potential
+    # 3.4 Offshore wind potential is a hack right now - manual inputted GW
 
+    #%% ------------------------------- ###
+    ### 1. Load Geodata and Pre-process ###
+    ### ------------------------------- ###
 
+    ## Projections
 
+    UTM32 = Proj(proj='utm', zone=32, ellps='WGS84', preserve_units=False)
+    GM = Proj('EPSG:900913', preserve_units=False)
+    # transformer = Transformer.from_crs('EPSG:900913', 'EPSG:4326')
+    # out = Transformer(GM, UTM32, (11, 13), (43, 45), always_xy=True) 
 
-### 2.3 Calculate eligible shares
-masked, transform = shape_availability(A, excluder)
-# eligible_share = masked.sum() * excluder.res**2 / A.loc[[3]].geometry.item().area # Only eligible share for bornholm 3
-# Eligible share of all of A
-Aall = gpd.GeoDataFrame({'geometry' : [A.geometry.cascaded_union]})
-eligible_share = masked.sum() * excluder.res**2 / Aall.geometry.item().area # Only eligible share for bornholm 3
+        
 
+    ### 1.2 Visualise current areas
+    # Set projection
+    crs = ccrs.UTM(32)
+    # Make compatible with geopandas
+    # projection = crs.proj4_init # doesn't work, so actually cartopy is useless - continuing only with geopandas
 
+    # Make figure
+    fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={"projection": crs},
+                        dpi=200)
 
 
-### 2.4 Plot figures of availabilities (green is available land)
-# Simple plot
-# fig, ax = plt.subplots()
-# ax = show(masked, transform=transform, cmap='Greens', ax=ax)
-# A.plot(ax=ax, edgecolor='k', color='None')
-# ax.set_title(f'Eligible area (green) {eligible_share * 100:2.2f}%')
+    # Add areas
+    ax.add_geometries(areas.geometry, crs = crs,
+                    facecolor=[.9, .9,.9], edgecolor='grey',
+                    linewidth=.2)
 
-### Plot that shows the discrete rectangles used
-fig, ax = plt.subplots()
-ax = show(masked, transform=transform, cmap='Greens', ax=ax)
-A.plot(ax=ax, edgecolor='k', color='None')
-# cutout.grid.plot(edgecolor='grey', color='None', ax=ax, ls=':')
-cutout.grid.to_crs(excluder.crs).plot(edgecolor='grey', color='None', ax=ax, ls=':')
-ax.set_title(f'Eligible area (green) {eligible_share * 100:2.2f}%')
+    ax.set_xlim(cutout_bounds_x)
+    ax.set_ylim(cutout_bounds_y)
+    # ax.set_xlim(7.5,16)      
+    # ax.set_ylim(54.4,58)  
 
-# ax.set_xlim(4.5e6-0.1e6, 4.5e6+0.1e6)
-# ax.set_ylim(2.46-0.1e6, 2.4e6+0.1e6)
 
-### 2.5 Calculate Availability Matrix for all Regions
-# Amat.index = ['Denmark']
-A = A.geometry.set_crs(excluder.crs)
-Amat = cutout.availabilitymatrix(A, excluder)
+    ### 1.3 Load Nordpool regions
+    # NP = pd.read_csv(project_dir/'geo_files/coordinates_RRR.csv')
+    # NP = NP.loc[df_unique['Type'] == 'region', ]
 
-### Plot first region availability  
-fig, ax = plt.subplots()
-Amat.sel({the_index :A.index[0]}).plot(ax=ax) # Amat gives fractional availabilities in each weather cell
-A.plot(ax=ax, edgecolor='k', color='None')
-cutout.grid.plot(ax=ax, color='None', edgecolor='grey', ls=':')
-# ax.set_xlim(7.5,16)      
-# ax.set_ylim(54.4,58) 
+    #%% ------------------------------- ###
+    ###   2. Calculate RE Potentials    ###
+    ### ------------------------------- ###
 
-### Calculate areas in weather cells in sqkm
-area = cutout.grid.set_index(['y', 'x']).to_crs(3035).area / 1e6 # 3035 is CRS of CORINE map
-area = xr.DataArray(area, dims=('spatial'))
+    ### 2.1 Load Cutout
+    cutout = atlite.Cutout(path=cutout_path,
+                        module="era5",
+                        x=slice(cutout_bounds_x[0], cutout_bounds_x[1]),
+                        y=slice(cutout_bounds_y[0], cutout_bounds_y[1]),
+                        time=weather_year
+                        )
+    cutout.prepare(overwrite=overwrite_cutout)
 
 
 
+    #%% 2.2 Load Map for RE Spatial Availabilities
+    # CORINE = 'corine.tif'
+    CORINE = r'Data\CORINE\u2018_clc2018_v2020_20u1_raster100m\u2018_clc2018_v2020_20u1_raster100m\DATA\U2018_CLC2018_V2020_20u1.tif'
+    excluder = ExclusionContainer()
+    excluder.add_raster(CORINE, codes=range(20))
 
-### 2.6 Calculate PV Potential
-capacity_matrix = Amat.stack(spatial=['y', 'x']) * area * cap_per_sqkm_pv # Converts fraction of weather cells to
-cutout.prepare()
+    # Convert crs to CORINE map
+    A = areas.geometry.to_crs(excluder.crs)
+    A.index = getattr(areas, the_index)
 
-# Sum of matrix is total potential...?
 
-# Get production
-# pv = cutout.pv(matrix=capacity_matrix, panel=panel,
-#                 orientation='latitude_optimal', index=A.index)
-pv = cutout.pv(matrix=capacity_matrix, panel=panel,
-                orientation={'slope': 30, 'azimuth': 180.}, index=A.index)
-ax = pv.to_pandas().div(1e3).plot(ylabel='Solar Power [GW]', ls='--', figsize=(15, 4))
-ax.legend(ncol=8, loc='center', bbox_to_anchor=(.5, 1.5))
 
-# Getting a specific profile
-pv.loc[:, getattr(pv, the_index).values[0]]
 
+    ### 2.3 Calculate eligible shares
+    masked, transform = shape_availability(A, excluder)
+    # eligible_share = masked.sum() * excluder.res**2 / A.loc[[3]].geometry.item().area # Only eligible share for bornholm 3
+    # Eligible share of all of A
+    Aall = gpd.GeoDataFrame({'geometry' : [A.geometry.cascaded_union]})
+    eligible_share = masked.sum() * excluder.res**2 / Aall.geometry.item().area # Only eligible share for bornholm 3
 
 
 
-### 2.7 Calculate Wind Turbine Potential
-capacity_matrix = Amat.stack(spatial=['y', 'x']) * area * cap_per_sqkm_wind
-cutout.prepare()
 
-# Get production
-wind = cutout.wind(matrix=capacity_matrix, turbine=wind_turbine,
-                index=A.index)
-ax = wind.to_pandas().div(1e3).plot(ylabel='Wind Power [GW]', ls='--', figsize=(15, 4)) 
-ax.legend(ncol=8, loc='center', bbox_to_anchor=(.5, 1.5))
+    ### 2.4 Plot figures of availabilities (green is available land)
+    # Simple plot
+    # fig, ax = plt.subplots()
+    # ax = show(masked, transform=transform, cmap='Greens', ax=ax)
+    # A.plot(ax=ax, edgecolor='k', color='None')
+    # ax.set_title(f'Eligible area (green) {eligible_share * 100:2.2f}%')
 
-# Getting a specific profile
-wind.loc[:, getattr(wind, the_index).values[0]]
+    ### Plot that shows the discrete rectangles used
+    fig, ax = plt.subplots()
+    ax = show(masked, transform=transform, cmap='Greens', ax=ax)
+    A.plot(ax=ax, edgecolor='k', color='None')
+    # cutout.grid.plot(edgecolor='grey', color='None', ax=ax, ls=':')
+    cutout.grid.to_crs(excluder.crs).plot(edgecolor='grey', color='None', ax=ax, ls=':')
+    ax.set_title(f'Eligible area (green) {eligible_share * 100:2.2f}%')
 
-#%%
-# Plot data (done with mix municipality and 2024 balmorelhighres )
-# a2 = areas[areas.Type != 'Offshore']
-# a2 = areas
-# winddata = wind.to_pandas()
-
-# N_stop = 24
-# n = 0
-# for i,row in winddata.iterrows():
-#     if n > N_stop:
-#         break
-#     fig, ax = plt.subplots()
-#     a2['Wind'] = row.values/ winddata.max()
-#     a2.plot(column='Wind', ax=ax)
-#     ax.set_title(i) 
-#     ax.axes.axis('off')
-#     fig.savefig(('Output/Figures/VRE_time/wind_%s.png'%i).replace(':','').replace(' ','-'))
-#     plt.close(fig)
-#     n += 1
-# Make gif = C:\Users\mberos\Danmarks Tekniske Universitet\PhD in Transmission and Sector Coupling - Dokumenter\Documents\Social\Friday Bar\createGIF.py
-
-#%% ------------------------------- ###
-###     3. Create Balmorel Input    ###
-### ------------------------------- ###
+    # ax.set_xlim(4.5e6-0.1e6, 4.5e6+0.1e6)
+    # ax.set_ylim(2.46-0.1e6, 2.4e6+0.1e6)
 
+    ### 2.5 Calculate Availability Matrix for all Regions
+    # Amat.index = ['Denmark']
+    A = A.geometry.set_crs(excluder.crs)
+    Amat = cutout.availabilitymatrix(A, excluder)
 
-### 3.1 Convert data
-# .to_pandas() can be used to store profiles from wind or pv
-W = wind.to_pandas()
-S = pv.to_pandas()
-
-# Get correct timeseries index for Balmorel
-t = W.index.isocalendar()
-t['hour'] = t.index.hour
-
-# Filter away first week, from last year 
-idx = t.index.year == t['year'] 
-t = t[idx]
-W = W[idx]
-S = S[idx]
-
-# Make seasons
-t['S'] = t['week'].astype(str)
-idx = t['S'].str.len() == 1
-t.loc[idx, 'S'] = '0' + t.loc[idx, 'S']
-t['S'] = 'S' + t['S']
-
-# Make terms
-try:
-    t['T'] = np.array([i for i in range(1, 169)]*52)
-except ValueError:
-    print("\nWARNING!\nYou didn't load 8736 hours of data! Select a bit of the next year, in cutout (T parameter in beginning).")
-    print("The current profile will be %d too short (%d hours in total)\n"%(8736-len(t), len(t)))
-    
-    array = np.array([i for i in range(1, 169)]*52)
-    t['T'] = array[:len(t)]
-    
-t['T'] = t['T'].astype(str)
-idx = t['T'].str.len() == 1
-t.loc[idx, 'T'] = '00' + t['T']
-idx = t['T'].str.len() == 2
-t.loc[idx, 'T'] = '0' + t['T']
-t['T'] = 'T' + t['T']
- 
-
-# Create new index
-W.index = t['S'] + ' . ' + t['T']
-S.index = t['S'] + ' . ' + t['T']
-
-# Clean up areas
-W.columns = W.columns.str.replace('.', '_')
-W.columns.name = ''
-W.columns = W.columns + '_A'
-S.columns = S.columns.str.replace('.', '_')
-S.columns.name = ''
-S.columns = S.columns + '_A'
-
-# Clean up values
-# W.iloc[:,:] = W.iloc[:,:].astype(str)
-# S.iloc[:,:] = S.iloc[:,:].astype(str)
-
-
-
-
-### 3.2 Variation Profiles
-# Format of SOLE_VAR_T and WND_VAR_T
-# TABLE WND/SOLE_VAR_T1(SSS,TTT,AAA)               "Variation of the wind/solar generation"    
-#               A1     A2     ...
-# S01.T001      val    val
-# ...
-# S52.T168      val    val
-# ;
-#
-
-## Saving directly to .inc files:
-# Wind
-# f = open('WND_VAR_T.inc', 'w')
-with open('./Output/WND_VAR_T.inc', 'w') as f:
-    f.write('TABLE WND_VAR_T1(SSS,TTT,AAA)            "Variation of the wind generation"\n')
-    # f.write('+') # If adding to another WND_VAR_T
-    dfAsString = W.to_string(header=True, index=True)
-    f.write(dfAsString)
-    f.write('\n;')
-    f.write('\nWND_VAR_T(AAA,SSS,TTT) = WND_VAR_T1(SSS,TTT,AAA);')
-    f.write('\nWND_VAR_T1(SSS,TTT,AAA) = 0;')
-
-# Solar
-# f = open('SOLE_VAR_T.inc', 'w')
-with open('./Output/SOLE_VAR_T.inc', 'w') as f:
-    f.write('TABLE SOLE_VAR_T1(SSS,TTT,AAA)            "Variation of the solar generation"\n')
-    dfAsString = S.to_string(header=True, index=True)
-    f.write(dfAsString)
-    f.write('\n;\n')
-    f.write('SOLE_VAR_T(AAA,SSS,TTT) = SOLE_VAR_T1(SSS,TTT,AAA);\n')
-    f.write('SOLE_VAR_T1(SSS,TTT,AAA) = 0;\n')
-
-
-
-
-### 3.3 Full load hours
-# Format of SOLEFLH and WNDFLH
-# TABLE WND/SOLEFLH(AAA)               "Full load hours for wind/solar power" 
-# /   
-# A1        val
-# A2        val
-# ...
-# An        val
-# /;
-#
-
-# Calculating full load hours by sum of normalised timeseries
-FLH_W = W.sum() / W.max() * (8736/len(t))
-FLH_S = S.sum() / S.max() * (8736/len(t))
-with open('./Output/SOLEFLH.inc', 'w') as f:
-    f.write('Parameter SOLEFLH(AAA)            "Full load hours for solar power (hours)"\n')
-    f.write('/')
-    dfAsString = FLH_S.to_string(header=True, index=True)
-    f.write(dfAsString)
-    f.write('\n/\n;')
-    
-with open('./Output/WNDFLH.inc', 'w') as f:
-    f.write('Parameter WNDFLH(AAA)            "Full load hours for wind power (hours)"\n')
-    f.write('/')
-    dfAsString = FLH_W.to_string(header=True, index=True)
-    f.write(dfAsString)
-    f.write('\n/\n;')
-    
-# Quick fix for solar heating profiles
-FLH_SH = FLH_S / 5
-with open('./Output/SOLHFLH.inc', 'w') as f:
-    f.write('Parameter SOLHFLH(AAA)            "Full load hours for solar heat (hours)"\n')
-    f.write('/')
-    dfAsString = FLH_SH.to_string(header=True, index=True)
-    f.write(dfAsString)
-    f.write('\n/\n;')
-    
-    
-with open('./Output/SOLH_VAR_T.inc', 'w') as f:
-    f.write('TABLE SOLH_VAR_T1(SSS,TTT,AAA)            "Variation of the solar generation"\n')
-    dfAsString = S.to_string(header=True, index=True)
-    f.write(dfAsString)
-    f.write('\n;\n')
-    f.write('SOLH_VAR_T(AAA,SSS,TTT) = SOLH_VAR_T1(SSS,TTT,AAA);\n')
-    f.write('SOLH_VAR_T1(SSS,TTT,AAA) = 0;\n')
-
-### X.X CALCULATE POTENTIALS
-exc_points = gpd.read_file(r'.\Data\Shapefiles\BalmorelVRE\BalmGrid-Urb-GLWD123-WDPA012-MTabove1km.gpkg')
-VREareas = gpd.read_file(r'.\Data\Shapefiles\BalmorelVRE\BalmorelVREAreas.gpkg')
-exc_points = exc_points.set_crs(VREareas.crs) # Set CRS
-exc_points_bounds = exc_points.bounds
-#%%
-for i,row in areas.iloc[10:13].iterrows(): # West-germany and DK in NordpoolReal
-    print(row['RRR'])
-    
+    ### Plot first region availability  
     fig, ax = plt.subplots()
-    geo_ser = VREareas.intersection(row.geometry)
-    geo_ser_df = gpd.GeoDataFrame({'geometry' : geo_ser.geometry})
-    geo_ser.plot(ax=ax)
-    xlims = ax.get_xlim()
-    ylims = ax.get_ylim()
-    
-    ## Narrow points down
-    idx = exc_points_bounds.minx > xlims[0]
-    idx = idx & (exc_points_bounds.maxx < xlims[1])
-    idx = idx & (exc_points_bounds.miny > ylims[0])
-    idx = idx & (exc_points_bounds.maxy < ylims[1])
-    temp_points = exc_points[idx]
-    
-    ## Get points inside polygon intersection
-    temp_points = gpd.sjoin(temp_points, geo_ser_df)
-    
-    temp_points.plot(ax=ax, markersize=0.5, color='r')
-    
-    ## Get capacity
-    available_space = temp_points.shape[0] * 30 * 30 # km2
-    
-    print('Potential of PV installation: %0.0f MW'%(cap_per_sqkm_pv*available_space)) 
-    print('Potential of Wind installation: %0.0f MW'%(cap_per_sqkm_wind*available_space)) 
-    
+    Amat.sel({the_index :A.index[0]}).plot(ax=ax) # Amat gives fractional availabilities in each weather cell
+    A.plot(ax=ax, edgecolor='k', color='None')
+    cutout.grid.plot(ax=ax, color='None', edgecolor='grey', ls=':')
+    # ax.set_xlim(7.5,16)      
+    # ax.set_ylim(54.4,58) 
+
+    ### Calculate areas in weather cells in sqkm
+    area = cutout.grid.set_index(['y', 'x']).to_crs(3035).area / 1e6 # 3035 is CRS of CORINE map
+    area = xr.DataArray(area, dims=('spatial'))
+
+
+
+
+    ### 2.6 Calculate PV Potential
+    capacity_matrix = Amat.stack(spatial=['y', 'x']) * area * cap_per_sqkm_pv # Converts fraction of weather cells to
+    cutout.prepare()
+
+    # Sum of matrix is total potential...?
+
+    # Get production
+    # pv = cutout.pv(matrix=capacity_matrix, panel=panel,
+    #                 orientation='latitude_optimal', index=A.index)
+    pv = cutout.pv(matrix=capacity_matrix, panel=panel,
+                    orientation={'slope': 30, 'azimuth': 180.}, index=A.index)
+    ax = pv.to_pandas().div(1e3).plot(ylabel='Solar Power [GW]', ls='--', figsize=(15, 4))
+    ax.legend(ncol=8, loc='center', bbox_to_anchor=(.5, 1.5))
+
+    # Getting a specific profile
+    pv.loc[:, getattr(pv, the_index).values[0]]
+
+
+    ### 2.7 Calculate Wind Turbine Potential
+    capacity_matrix = Amat.stack(spatial=['y', 'x']) * area * cap_per_sqkm_wind
+    cutout.prepare()
+
+    # Get production
+    wind = cutout.wind(matrix=capacity_matrix, turbine=wind_turbine,
+                    index=A.index)
+    ax = wind.to_pandas().div(1e3).plot(ylabel='Wind Power [GW]', ls='--', figsize=(15, 4)) 
+    ax.legend(ncol=8, loc='center', bbox_to_anchor=(.5, 1.5))
+
+    # Getting a specific profile
+    wind.loc[:, getattr(wind, the_index).values[0]]
+
+    #%%
+    # Plot data (done with mix municipality and 2024 balmorelhighres )
+    # a2 = areas[areas.Type != 'Offshore']
+    # a2 = areas
+    # winddata = wind.to_pandas()
+
+    # N_stop = 24
+    # n = 0
+    # for i,row in winddata.iterrows():
+    #     if n > N_stop:
+    #         break
+    #     fig, ax = plt.subplots()
+    #     a2['Wind'] = row.values/ winddata.max()
+    #     a2.plot(column='Wind', ax=ax)
+    #     ax.set_title(i) 
+    #     ax.axes.axis('off')
+    #     fig.savefig(('Output/Figures/VRE_time/wind_%s.png'%i).replace(':','').replace(' ','-'))
+    #     plt.close(fig)
+    #     n += 1
+    # Make gif = C:\Users\mberos\Danmarks Tekniske Universitet\PhD in Transmission and Sector Coupling - Dokumenter\Documents\Social\Friday Bar\createGIF.py
+
+    #%% ------------------------------- ###
+    ###     3. Create Balmorel Input    ###
+    ### ------------------------------- ###
+
+
+    ### 3.1 Convert data
+    # .to_pandas() can be used to store profiles from wind or pv
+    W = wind.to_pandas()
+    S = pv.to_pandas()
+
+    # Get correct timeseries index for Balmorel
+    t = W.index.isocalendar()
+    t['hour'] = t.index.hour
+
+    # Filter away first week, from last year 
+    idx = t.index.year == t['year'] 
+    t = t[idx]
+    W = W[idx]
+    S = S[idx]
+
+    # Make seasons
+    t['S'] = t['week'].astype(str)
+    idx = t['S'].str.len() == 1
+    t.loc[idx, 'S'] = '0' + t.loc[idx, 'S']
+    t['S'] = 'S' + t['S']
+
+    # Make terms
+    try:
+        t['T'] = np.array([i for i in range(1, 169)]*52)
+    except ValueError:
+        print("\nWARNING!\nYou didn't load 8736 hours of data! Select a bit of the next year, in cutout (T parameter in beginning).")
+        print("The current profile will be %d too short (%d hours in total)\n"%(8736-len(t), len(t)))
+        
+        array = np.array([i for i in range(1, 169)]*52)
+        t['T'] = array[:len(t)]
+        
+    t['T'] = t['T'].astype(str)
+    idx = t['T'].str.len() == 1
+    t.loc[idx, 'T'] = '00' + t['T']
+    idx = t['T'].str.len() == 2
+    t.loc[idx, 'T'] = '0' + t['T']
+    t['T'] = 'T' + t['T']
     
-    ### OFFSHORE IF STATEMENT! OR AFTERWARDS?
 
-    # print(VREareas[VREareas.intersects(row.geometry)].Region)
-    # l = gpd.GeoSeries(areas.loc[R].geometry).plot(ax=ax)
-    # ax.set_title(R)
-    
-## Each marker is 30x30 km
-
-
-#%% 3.4 Potentials
-# Format of SOLEFLH and WNDFLH
-# TABLE WND/SOLEFLH(AAA)               "Full load hours for wind/solar power" 
-# /   
-# A1        val
-# A2        val
-# ...
-# An        val
-# /;
-#
-
-# Calculating capacity by maximum power output from atlite series
-CAP_W = W.max()
-CAP_S = S.max()
-
-CAP_OFFW = pd.DataFrame({'WINDTURBINE_OFFSHORE.RG1_OFF1' : [0]*len(CAP_W)}, index = CAP_W.index)
-### For offshore wind - HACK! Manually created points
-for i,row in OFFWNDPOT.iterrows():
-    
-    # Assign point to nearest polygon
-    dist = {r[0] : r[1].geometry.distance(row.geometry) for r in areas.iterrows()}
-    
-    # Get index of closest region
-    ind = min(dist, key=dist.get)
-    
-    # Accumulate capacity to closest region and make new offshore area
-    # try:
-    #     CAP_OFFW.loc[ind+'_A_OFF'] = CAP_OFFW.loc[ind+'_A_OFF'] + row['Pot_GW']
-    # except KeyError:
-    #     CAP_OFFW.loc[ind+'_A_OFF'] = row['Pot_GW']
-    # Accumulate capacity to closest region
-    CAP_OFFW.loc[ind+'_A'] = CAP_OFFW.loc[ind+'_A'] + row['Pot_GW']
-         
-    
-    # print(ind)
-    # if choice == 'NUTS3':
-    #     pp.loc[i, 'area'] = areas.loc[ind, 'NUTS_ID']
-    # elif choice == 'DK Municipalities':
-    #     pp.loc[i, 'area'] = areas.loc[ind, 'GID_2']
-
-# Make format
-CAP = pd.DataFrame({'SOLARPV.RG1' : np.hstack((CAP_S.values, np.zeros(len(CAP_OFFW) - len(CAP_S)))),
-                    'WINDTURBINE_ONSHORE.RG1' : np.hstack((CAP_W.values, np.zeros(len(CAP_OFFW) - len(CAP_S)))),
-                    'WINDTURBINE_OFFSHORE.RG1_OFF1' : CAP_OFFW.values[:,0]*1e3},  # In MW
-                   index=CAP_OFFW.index)
-
-with open('./Output/SUBTECHGROUPKPOT.inc', 'w') as f:
-    f.write("TABLE SUBTECHGROUPKPOT(CCCRRRAAA,TECH_GROUP,SUBTECH_GROUP)       'SubTechnology group capacity restriction by geography (MW)'\n")
-    dfAsString = CAP.to_string(header=True, index=True)
-    f.write(dfAsString)
-    f.write('\n;')
-    # These lines make sure that the RG2-3 OFF2-5 regions are not available (all assigned to RG1!)
-    f.write("\nSUBTECHGROUPKPOT(AAA,'SOLARPV',SUBTECH_GROUP)$(SUBTECHGROUPKPOT(AAA,'SOLARPV',SUBTECH_GROUP) = 0) = EPS;")
-    f.write("\nSUBTECHGROUPKPOT(AAA,'WINDTURBINE_ONSHORE',SUBTECH_GROUP)$(SUBTECHGROUPKPOT(AAA,'WINDTURBINE_ONSHORE',SUBTECH_GROUP) = 0) = EPS;")
-    f.write("\nSUBTECHGROUPKPOT(AAA,'WINDTURBINE_OFFSHORE',SUBTECH_GROUP)$(SUBTECHGROUPKPOT(AAA,'WINDTURBINE_OFFSHORE',SUBTECH_GROUP) = 0) = EPS;")
+    # Create new index
+    W.index = t['S'] + ' . ' + t['T']
+    S.index = t['S'] + ' . ' + t['T']
+
+    # Clean up areas
+    W.columns = W.columns.str.replace('.', '_')
+    W.columns.name = ''
+    W.columns = W.columns + '_A'
+    S.columns = S.columns.str.replace('.', '_')
+    S.columns.name = ''
+    S.columns = S.columns + '_A'
+
+    # Clean up values
+    # W.iloc[:,:] = W.iloc[:,:].astype(str)
+    # S.iloc[:,:] = S.iloc[:,:].astype(str)
+
+
+
+
+    ### 3.2 Variation Profiles
+    # Format of SOLE_VAR_T and WND_VAR_T
+    # TABLE WND/SOLE_VAR_T1(SSS,TTT,AAA)               "Variation of the wind/solar generation"    
+    #               A1     A2     ...
+    # S01.T001      val    val
+    # ...
+    # S52.T168      val    val
+    # ;
+    #
+
+    ## Saving directly to .inc files:
+    # Wind
+    # f = open('WND_VAR_T.inc', 'w')
+    with open('./Output/WND_VAR_T.inc', 'w') as f:
+        f.write('TABLE WND_VAR_T1(SSS,TTT,AAA)            "Variation of the wind generation"\n')
+        # f.write('+') # If adding to another WND_VAR_T
+        dfAsString = W.to_string(header=True, index=True)
+        f.write(dfAsString)
+        f.write('\n;')
+        f.write('\nWND_VAR_T(AAA,SSS,TTT) = WND_VAR_T1(SSS,TTT,AAA);')
+        f.write('\nWND_VAR_T1(SSS,TTT,AAA) = 0;')
+
+    # Solar
+    # f = open('SOLE_VAR_T.inc', 'w')
+    with open('./Output/SOLE_VAR_T.inc', 'w') as f:
+        f.write('TABLE SOLE_VAR_T1(SSS,TTT,AAA)            "Variation of the solar generation"\n')
+        dfAsString = S.to_string(header=True, index=True)
+        f.write(dfAsString)
+        f.write('\n;\n')
+        f.write('SOLE_VAR_T(AAA,SSS,TTT) = SOLE_VAR_T1(SSS,TTT,AAA);\n')
+        f.write('SOLE_VAR_T1(SSS,TTT,AAA) = 0;\n')
+
+
+
+
+    ### 3.3 Full load hours
+    # Format of SOLEFLH and WNDFLH
+    # TABLE WND/SOLEFLH(AAA)               "Full load hours for wind/solar power" 
+    # /   
+    # A1        val
+    # A2        val
+    # ...
+    # An        val
+    # /;
+    #
+
+    # Calculating full load hours by sum of normalised timeseries
+    FLH_W = W.sum() / W.max() * (8736/len(t))
+    FLH_S = S.sum() / S.max() * (8736/len(t))
+    with open('./Output/SOLEFLH.inc', 'w') as f:
+        f.write('Parameter SOLEFLH(AAA)            "Full load hours for solar power (hours)"\n')
+        f.write('/')
+        dfAsString = FLH_S.to_string(header=True, index=True)
+        f.write(dfAsString)
+        f.write('\n/\n;')
+        
+    with open('./Output/WNDFLH.inc', 'w') as f:
+        f.write('Parameter WNDFLH(AAA)            "Full load hours for wind power (hours)"\n')
+        f.write('/')
+        dfAsString = FLH_W.to_string(header=True, index=True)
+        f.write(dfAsString)
+        f.write('\n/\n;')
+        
+    # Quick fix for solar heating profiles
+    FLH_SH = FLH_S / 5
+    with open('./Output/SOLHFLH.inc', 'w') as f:
+        f.write('Parameter SOLHFLH(AAA)            "Full load hours for solar heat (hours)"\n')
+        f.write('/')
+        dfAsString = FLH_SH.to_string(header=True, index=True)
+        f.write(dfAsString)
+        f.write('\n/\n;')
+        
+        
+    with open('./Output/SOLH_VAR_T.inc', 'w') as f:
+        f.write('TABLE SOLH_VAR_T1(SSS,TTT,AAA)            "Variation of the solar generation"\n')
+        dfAsString = S.to_string(header=True, index=True)
+        f.write(dfAsString)
+        f.write('\n;\n')
+        f.write('SOLH_VAR_T(AAA,SSS,TTT) = SOLH_VAR_T1(SSS,TTT,AAA);\n')
+        f.write('SOLH_VAR_T1(SSS,TTT,AAA) = 0;\n')
+
+    ### X.X CALCULATE POTENTIALS
+    exc_points = gpd.read_file(r'.\Data\Shapefiles\BalmorelVRE\BalmGrid-Urb-GLWD123-WDPA012-MTabove1km.gpkg')
+    VREareas = gpd.read_file(r'.\Data\Shapefiles\BalmorelVRE\BalmorelVREAreas.gpkg')
+    exc_points = exc_points.set_crs(VREareas.crs) # Set CRS
+    exc_points_bounds = exc_points.bounds
+    #%%
+    for i,row in areas.iloc[10:13].iterrows(): # West-germany and DK in NordpoolReal
+        print(row['RRR'])
+        
+        fig, ax = plt.subplots()
+        geo_ser = VREareas.intersection(row.geometry)
+        geo_ser_df = gpd.GeoDataFrame({'geometry' : geo_ser.geometry})
+        geo_ser.plot(ax=ax)
+        xlims = ax.get_xlim()
+        ylims = ax.get_ylim()
+        
+        ## Narrow points down
+        idx = exc_points_bounds.minx > xlims[0]
+        idx = idx & (exc_points_bounds.maxx < xlims[1])
+        idx = idx & (exc_points_bounds.miny > ylims[0])
+        idx = idx & (exc_points_bounds.maxy < ylims[1])
+        temp_points = exc_points[idx]
+        
+        ## Get points inside polygon intersection
+        temp_points = gpd.sjoin(temp_points, geo_ser_df)
+        
+        temp_points.plot(ax=ax, markersize=0.5, color='r')
+        
+        ## Get capacity
+        available_space = temp_points.shape[0] * 30 * 30 # km2
+        
+        print('Potential of PV installation: %0.0f MW'%(cap_per_sqkm_pv*available_space)) 
+        print('Potential of Wind installation: %0.0f MW'%(cap_per_sqkm_wind*available_space)) 
+        
+        
+        ### OFFSHORE IF STATEMENT! OR AFTERWARDS?
 
-    
- #%% ------------------------------- ###
-###          4. Analysis            ###
-### ------------------------------- ###
+        # print(VREareas[VREareas.intersects(row.geometry)].Region)
+        # l = gpd.GeoSeries(areas.loc[R].geometry).plot(ax=ax)
+        # ax.set_title(R)
+        
+    ## Each marker is 30x30 km
+
+
+    #%% 3.4 Potentials
+    # Format of SOLEFLH and WNDFLH
+    # TABLE WND/SOLEFLH(AAA)               "Full load hours for wind/solar power" 
+    # /   
+    # A1        val
+    # A2        val
+    # ...
+    # An        val
+    # /;
+    #
+
+    # Calculating capacity by maximum power output from atlite series
+    CAP_W = W.max()
+    CAP_S = S.max()
+
+    CAP_OFFW = pd.DataFrame({'WINDTURBINE_OFFSHORE.RG1_OFF1' : [0]*len(CAP_W)}, index = CAP_W.index)
+    ### For offshore wind - HACK! Manually created points
+    for i,row in OFFWNDPOT.iterrows():
+        
+        # Assign point to nearest polygon
+        dist = {r[0] : r[1].geometry.distance(row.geometry) for r in areas.iterrows()}
+        
+        # Get index of closest region
+        ind = min(dist, key=dist.get)
+        
+        # Accumulate capacity to closest region and make new offshore area
+        # try:
+        #     CAP_OFFW.loc[ind+'_A_OFF'] = CAP_OFFW.loc[ind+'_A_OFF'] + row['Pot_GW']
+        # except KeyError:
+        #     CAP_OFFW.loc[ind+'_A_OFF'] = row['Pot_GW']
+        # Accumulate capacity to closest region
+        CAP_OFFW.loc[ind+'_A'] = CAP_OFFW.loc[ind+'_A'] + row['Pot_GW']
+            
+        
+        # print(ind)
+        # if choice == 'NUTS3':
+        #     pp.loc[i, 'area'] = areas.loc[ind, 'NUTS_ID']
+        # elif choice == 'DK Municipalities':
+        #     pp.loc[i, 'area'] = areas.loc[ind, 'GID_2']
+
+    # Make format
+    CAP = pd.DataFrame({'SOLARPV.RG1' : np.hstack((CAP_S.values, np.zeros(len(CAP_OFFW) - len(CAP_S)))),
+                        'WINDTURBINE_ONSHORE.RG1' : np.hstack((CAP_W.values, np.zeros(len(CAP_OFFW) - len(CAP_S)))),
+                        'WINDTURBINE_OFFSHORE.RG1_OFF1' : CAP_OFFW.values[:,0]*1e3},  # In MW
+                    index=CAP_OFFW.index)
+
+    with open('./Output/SUBTECHGROUPKPOT.inc', 'w') as f:
+        f.write("TABLE SUBTECHGROUPKPOT(CCCRRRAAA,TECH_GROUP,SUBTECH_GROUP)       'SubTechnology group capacity restriction by geography (MW)'\n")
+        dfAsString = CAP.to_string(header=True, index=True)
+        f.write(dfAsString)
+        f.write('\n;')
+        # These lines make sure that the RG2-3 OFF2-5 regions are not available (all assigned to RG1!)
+        f.write("\nSUBTECHGROUPKPOT(AAA,'SOLARPV',SUBTECH_GROUP)$(SUBTECHGROUPKPOT(AAA,'SOLARPV',SUBTECH_GROUP) = 0) = EPS;")
+        f.write("\nSUBTECHGROUPKPOT(AAA,'WINDTURBINE_ONSHORE',SUBTECH_GROUP)$(SUBTECHGROUPKPOT(AAA,'WINDTURBINE_ONSHORE',SUBTECH_GROUP) = 0) = EPS;")
+        f.write("\nSUBTECHGROUPKPOT(AAA,'WINDTURBINE_OFFSHORE',SUBTECH_GROUP)$(SUBTECHGROUPKPOT(AAA,'WINDTURBINE_OFFSHORE',SUBTECH_GROUP) = 0) = EPS;")
+
+        
+    #%% ------------------------------- ###
+    ###          4. Analysis            ###
+    ### ------------------------------- ###
 
-### 4.1 Look at representative periods
-# The weeks to compare to full resolution
-# per = ['S01', 'S14', 'S27', 'S40']
-# # per = ['S27']I
+    ### 4.1 Look at representative periods
+    # The weeks to compare to full resolution
+    # per = ['S01', 'S14', 'S27', 'S40']
+    # # per = ['S27']I
 
-# idx = W.index.str.find(per[0]) != -1 
-# for s in per[1:]:
-#     idx = idx | (W.index.str.find(s) != -1)
+    # idx = W.index.str.find(per[0]) != -1 
+    # for s in per[1:]:
+    #     idx = idx | (W.index.str.find(s) != -1)
 
-# for i in range(9):
-#     doLDC(W, W.columns[i*11:(i+1)*11], idx, 3, 4, title='Wind LDC [%]')
-#     doLDC(S, S.columns[i*11:(i+1)*11], idx, 3, 4, title='Solar LDC [%]')
+    # for i in range(9):
+    #     doLDC(W, W.columns[i*11:(i+1)*11], idx, 3, 4, title='Wind LDC [%]')
+    #     doLDC(S, S.columns[i*11:(i+1)*11], idx, 3, 4, title='Solar LDC [%]')
 
+if __name__ == '__main__':
+    main()
\ No newline at end of file

From cc89231c413b91f997df58c437b3f90aa5578d9b Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Tue, 1 Oct 2024 13:29:15 +0200
Subject: [PATCH 12/19] Adjusting bounds

---
 src/Modules/vre_profiles.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/Modules/vre_profiles.py b/src/Modules/vre_profiles.py
index 2fab6cf..d23c06e 100644
--- a/src/Modules/vre_profiles.py
+++ b/src/Modules/vre_profiles.py
@@ -118,8 +118,8 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
     cutout_bounds_x = (3, 33) # Longitude
     cutout_bounds_y = (47, 73) # Latitude
     # DK + Nordsøen
-    cutout_bounds_x = (2.2, 17.4) # Longitude
-    cutout_bounds_y = (53.7, 59) # Latitude
+    cutout_bounds_x = (6.37, 17) # Longitude
+    cutout_bounds_y = (53.7, 58.5) # Latitude
 
 
     ### 0.4 What time to load?
@@ -158,6 +158,8 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
     the_index='id'
     # areas.loc[:,'GID_2'] = areas.GID_2.str.replace('.', '_')
 
+    areas = gpd.read_file('Data/Shapefiles/Offshore/OffshoreRegions.gpkg')
+
     # Read homemade offshore potentials for DK
     OFFWNDPOT = gpd.read_file(r'.\Data\RandomOffWindPot\DK.gpkg')
 

From 58044b620606a7230b0a70468b4499b54b891993 Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Tue, 1 Oct 2024 13:36:19 +0200
Subject: [PATCH 13/19] Updated requirements and stores vre profiles to .nc

---
 environment.yaml            | 2 +-
 src/Modules/vre_profiles.py | 5 +++++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/environment.yaml b/environment.yaml
index 068ed66..e2c3941 100644
--- a/environment.yaml
+++ b/environment.yaml
@@ -26,4 +26,4 @@ dependencies:
   - pip
   - pip:
     - gamsapi[transfer]==45.7.0
-    - pybalmorel==0.3.10
\ No newline at end of file
+    - pybalmorel==0.3.11
\ No newline at end of file
diff --git a/src/Modules/vre_profiles.py b/src/Modules/vre_profiles.py
index d23c06e..7d20d59 100644
--- a/src/Modules/vre_profiles.py
+++ b/src/Modules/vre_profiles.py
@@ -315,6 +315,8 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
     # Getting a specific profile
     pv.loc[:, getattr(pv, the_index).values[0]]
 
+    # Save profile
+    pv.to_netcdf('pv_%s'%cutout_path)
 
     ### 2.7 Calculate Wind Turbine Potential
     capacity_matrix = Amat.stack(spatial=['y', 'x']) * area * cap_per_sqkm_wind
@@ -329,6 +331,9 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
     # Getting a specific profile
     wind.loc[:, getattr(wind, the_index).values[0]]
 
+    # Save profile
+    wind.to_netcdf('wind_%s'%cutout_path)
+
     #%%
     # Plot data (done with mix municipality and 2024 balmorelhighres )
     # a2 = areas[areas.Type != 'Offshore']

From b40832df0cd87c8bea33a63d33340a0ff0ead640 Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Tue, 1 Oct 2024 14:35:16 +0200
Subject: [PATCH 14/19] Making paths work on HPC

---
 .gitignore                  |  1 +
 src/Modules/geofiles.py     | 18 ++++++++--------
 src/Modules/vre_profiles.py | 42 ++++++++++++++++++-------------------
 3 files changed, 31 insertions(+), 30 deletions(-)

diff --git a/.gitignore b/.gitignore
index 03b6004..7857155 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
+Logs
 src/Data/*
 src/Output/*
 !src/Data/IncFilePreSuf/README.md
diff --git a/src/Modules/geofiles.py b/src/Modules/geofiles.py
index 58c36df..7b3b6c3 100644
--- a/src/Modules/geofiles.py
+++ b/src/Modules/geofiles.py
@@ -75,7 +75,7 @@ def prepared_geofiles(choice: str, plot: bool = False) -> tuple[str, gpd.GeoData
     if choice.replace(' ','').lower() == 'dkmunicipalities':
     # Filter away unnescescary columns
     # areas = areas[['NAME_1', 'NAME_2', 'geometry']]
-        areas = gpd.read_file(r'.\Data\Shapefiles\Denmark\Adm\gadm36_DNK_2.shp')
+        areas = gpd.read_file('Data/Shapefiles/Denmark/Adm/gadm36_DNK_2.shp')
         # # Aggregate hovedstaden - MODIFY TO USE NUTS3 AREAS FOR CAPITAL REGION
         # idx = (areas.NAME_1 == 'Hovedstaden') & (areas.NAME_2 != 'Bornholm') & (areas.NAME_2 != 'Christiansø')
         # hovedstaden = MultiPolygon(areas[idx].geometry.cascaded_union)
@@ -101,7 +101,7 @@ def prepared_geofiles(choice: str, plot: bool = False) -> tuple[str, gpd.GeoData
     if choice.replace(' ','').lower() == 'dkmunicipalities_names':
         # Filter away unnescescary columns
         # areas = areas[['NAME_1', 'NAME_2', 'geometry']]
-        areas = gpd.read_file(r'.\Data\Shapefiles\Denmark\Adm\gadm36_DNK_2.shp')
+        areas = gpd.read_file('Data/Shapefiles/Denmark/Adm/gadm36_DNK_2.shp')
         # # Aggregate hovedstaden - MODIFY TO USE NUTS3 AREAS FOR CAPITAL REGION
         # idx = (areas.NAME_1 == 'Hovedstaden') & (areas.NAME_2 != 'Bornholm') & (areas.NAME_2 != 'Christiansø')
         # hovedstaden = MultiPolygon(areas[idx].geometry.cascaded_union)
@@ -137,7 +137,7 @@ def prepared_geofiles(choice: str, plot: bool = False) -> tuple[str, gpd.GeoData
         
     # NUTS3 (Also contains NUTS2, and NUTS1)
     elif choice.replace(' ','').lower() == 'nuts1':
-        areas = gpd.read_file(r'.\Data\Shapefiles\NUTS_RG_01M_2021_4326\NUTS_RG_01M_2021_4326.shp')
+        areas = gpd.read_file(r'Data\Shapefiles\NUTS_RG_01M_2021_4326\NUTS_RG_01M_2021_4326.shp')
         areas = areas[(areas.LEVL_CODE == 1)] 
         
         # The index for next file
@@ -147,7 +147,7 @@ def prepared_geofiles(choice: str, plot: bool = False) -> tuple[str, gpd.GeoData
         # areas = areas[areas.NUTS_ID.str.find('DK') != -1]
     
     elif choice.replace(' ','').lower() == 'nuts2':
-        areas = gpd.read_file(r'.\Data\Shapefiles\NUTS_RG_01M_2021_4326\NUTS_RG_01M_2021_4326.shp')
+        areas = gpd.read_file(r'Data\Shapefiles\NUTS_RG_01M_2021_4326\NUTS_RG_01M_2021_4326.shp')
         areas = areas[(areas.LEVL_CODE == 2)] 
         
         # The index for next file
@@ -157,7 +157,7 @@ def prepared_geofiles(choice: str, plot: bool = False) -> tuple[str, gpd.GeoData
         # areas = areas[areas.NUTS_ID.str.find('DK') != -1]
         
     elif choice.replace(' ','').lower() == 'nuts3':
-        areas = gpd.read_file(r'.\Data\Shapefiles\NUTS_RG_01M_2021_4326\NUTS_RG_01M_2021_4326.shp')
+        areas = gpd.read_file(r'Data\Shapefiles\NUTS_RG_01M_2021_4326\NUTS_RG_01M_2021_4326.shp')
         areas = areas[(areas.LEVL_CODE == 3)]
         
         # The index for next file
@@ -168,7 +168,7 @@ def prepared_geofiles(choice: str, plot: bool = False) -> tuple[str, gpd.GeoData
         
     # Nordpool market regions
     elif choice.replace(' ','').lower() == 'nordpool':
-        p = r'.\Data\Shapefiles\NordpoolRegions\geojson'
+        p = r'Data\Shapefiles\NordpoolRegions\geojson'
         
         areas = gpd.GeoDataFrame()
         for file in os.listdir(p):
@@ -179,7 +179,7 @@ def prepared_geofiles(choice: str, plot: bool = False) -> tuple[str, gpd.GeoData
         # areas = areas[(areas.zoneName == 'DK_1') | (areas.zoneName == 'DK_2')]
     
     elif choice.replace(' ','').lower() == 'nordpoolreal':
-        p = r'.\Data\Shapefiles\NordpoolRegions\geojson_real'
+        p = r'Data\Shapefiles\NordpoolRegions\geojson_real'
         
         i = 0
         areas = gpd.GeoDataFrame({'RRR' : []})
@@ -205,11 +205,11 @@ def prepared_geofiles(choice: str, plot: bool = False) -> tuple[str, gpd.GeoData
         areas = areas[~areas.id.isnull()]
     
     elif choice.replace(' ','').lower() == 'balmorelvreareas':
-        areas = gpd.read_file(r'.\Data\Shapefiles\BalmorelVRE\BalmorelVREAreas.gpkg')
+        areas = gpd.read_file(r'Data\Shapefiles\BalmorelVRE\BalmorelVREAreas.gpkg')
         area_names = 'Region' 
         country_code = 'Country'
     elif choice.replace(' ','').lower() == 'antbalm':
-        areas = gpd.read_file(r'.\Data\Shapefiles\240112 AntBalmMap.gpkg')
+        areas = gpd.read_file(r'Data\Shapefiles\240112 AntBalmMap.gpkg')
         areas.loc[(areas.ISO_A3 == 'FIN'), 'id'] = 'FIN'
         areas.loc[(areas.ISO_A3 == 'DZA'), 'id'] = 'DZA'
         areas.loc[(areas.ISO_A3 == 'EGY'), 'id'] = 'EGY'
diff --git a/src/Modules/vre_profiles.py b/src/Modules/vre_profiles.py
index 7d20d59..343d30a 100644
--- a/src/Modules/vre_profiles.py
+++ b/src/Modules/vre_profiles.py
@@ -26,7 +26,7 @@
 from rasterio.plot import show
 import xarray as xr
 from atlite.gis import shape_availability, ExclusionContainer
-from Modules.geofiles import prepared_geofiles
+from geofiles import prepared_geofiles
 import logging
 import click
 logging.basicConfig(level=logging.INFO)
@@ -77,11 +77,11 @@ def doLDC(file, cols, idx, r, c, title=''):
     
     return fig, axes
 
-#%% ------------------------------- ###
+### ------------------------------- ###
 ###         0. Assumptions          ###
 ### ------------------------------- ###
 
-@click.commands()
+@click.command()
 @click.option('--cutout-path', type=str, required=True, help="The path of a cutout .nc file")
 @click.option('--weather-year', type=int, required=True, help="The weather year")
 @click.option('--overwrite-cutout', type=str, required=False, help="Overwrite an existing cutout?")
@@ -123,7 +123,7 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
 
 
     ### 0.4 What time to load?
-    # T = "2011-01-01"
+    T = "2011-01-01"
 
 
     ### 0.6 Read Geodata
@@ -148,7 +148,7 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
 
     # MUNI DK with offshore filtering
     # areas = areas[(areas.Country == 'DK') & (areas.Type == 'Offshore')] # From BalmorelHighResolution
-    the_index, areas2, country_code = prepared_geofiles('DKMunicipalities')
+    the_index, areas2, country_code = prepared_geofiles('DKMunicipalities_names')
     areas2['id'] = areas2['NAME_2']
     areas2['muni_id'] = areas2['GID_2']
     areas = pd.concat((areas, areas2[['id', 'muni_id', 'geometry']]))
@@ -161,7 +161,7 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
     areas = gpd.read_file('Data/Shapefiles/Offshore/OffshoreRegions.gpkg')
 
     # Read homemade offshore potentials for DK
-    OFFWNDPOT = gpd.read_file(r'.\Data\RandomOffWindPot\DK.gpkg')
+    OFFWNDPOT = gpd.read_file('Data/RandomOffWindPot/DK.gpkg')
 
     # Plot
     fig, ax = plt.subplots()
@@ -183,7 +183,7 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
     # 3.4 Capacity is based on RG1 VRE technology - all others regions are set to 0 potential
     # 3.4 Offshore wind potential is a hack right now - manual inputted GW
 
-    #%% ------------------------------- ###
+    ### ------------------------------- ###
     ### 1. Load Geodata and Pre-process ###
     ### ------------------------------- ###
 
@@ -222,7 +222,7 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
     # NP = pd.read_csv(project_dir/'geo_files/coordinates_RRR.csv')
     # NP = NP.loc[df_unique['Type'] == 'region', ]
 
-    #%% ------------------------------- ###
+    ### ------------------------------- ###
     ###   2. Calculate RE Potentials    ###
     ### ------------------------------- ###
 
@@ -231,15 +231,15 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
                         module="era5",
                         x=slice(cutout_bounds_x[0], cutout_bounds_x[1]),
                         y=slice(cutout_bounds_y[0], cutout_bounds_y[1]),
-                        time=weather_year
+                        time=str(weather_year)
                         )
     cutout.prepare(overwrite=overwrite_cutout)
 
 
 
-    #%% 2.2 Load Map for RE Spatial Availabilities
+    ### 2.2 Load Map for RE Spatial Availabilities
     # CORINE = 'corine.tif'
-    CORINE = r'Data\CORINE\u2018_clc2018_v2020_20u1_raster100m\u2018_clc2018_v2020_20u1_raster100m\DATA\U2018_CLC2018_V2020_20u1.tif'
+    CORINE = 'Data/CORINE/u2018_clc2018_v2020_20u1_raster100m/u2018_clc2018_v2020_20u1_raster100m/DATA/U2018_CLC2018_V2020_20u1.tif'
     excluder = ExclusionContainer()
     excluder.add_raster(CORINE, codes=range(20))
 
@@ -316,7 +316,7 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
     pv.loc[:, getattr(pv, the_index).values[0]]
 
     # Save profile
-    pv.to_netcdf('pv_%s'%cutout_path)
+    pv.to_netcdf('pv_%s'%cutout_path.split('/')[-1])
 
     ### 2.7 Calculate Wind Turbine Potential
     capacity_matrix = Amat.stack(spatial=['y', 'x']) * area * cap_per_sqkm_wind
@@ -332,9 +332,9 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
     wind.loc[:, getattr(wind, the_index).values[0]]
 
     # Save profile
-    wind.to_netcdf('wind_%s'%cutout_path)
+    wind.to_netcdf('wind_%s'%cutout_path.split('/')[-1])
 
-    #%%
+    ###
     # Plot data (done with mix municipality and 2024 balmorelhighres )
     # a2 = areas[areas.Type != 'Offshore']
     # a2 = areas
@@ -353,9 +353,9 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
     #     fig.savefig(('Output/Figures/VRE_time/wind_%s.png'%i).replace(':','').replace(' ','-'))
     #     plt.close(fig)
     #     n += 1
-    # Make gif = C:\Users\mberos\Danmarks Tekniske Universitet\PhD in Transmission and Sector Coupling - Dokumenter\Documents\Social\Friday Bar\createGIF.py
+    # Make gif = C:/Users/mberos/Danmarks Tekniske Universitet/PhD in Transmission and Sector Coupling - Dokumenter/Documents/Social/Friday Bar/createGIF.py
 
-    #%% ------------------------------- ###
+    ### ------------------------------- ###
     ###     3. Create Balmorel Input    ###
     ### ------------------------------- ###
 
@@ -500,11 +500,11 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
         f.write('SOLH_VAR_T1(SSS,TTT,AAA) = 0;\n')
 
     ### X.X CALCULATE POTENTIALS
-    exc_points = gpd.read_file(r'.\Data\Shapefiles\BalmorelVRE\BalmGrid-Urb-GLWD123-WDPA012-MTabove1km.gpkg')
-    VREareas = gpd.read_file(r'.\Data\Shapefiles\BalmorelVRE\BalmorelVREAreas.gpkg')
+    exc_points = gpd.read_file('Data/Shapefiles/BalmorelVRE/BalmGrid-Urb-GLWD123-WDPA012-MTabove1km.gpkg')
+    VREareas = gpd.read_file('Data/Shapefiles/BalmorelVRE/BalmorelVREAreas.gpkg')
     exc_points = exc_points.set_crs(VREareas.crs) # Set CRS
     exc_points_bounds = exc_points.bounds
-    #%%
+    
     for i,row in areas.iloc[10:13].iterrows(): # West-germany and DK in NordpoolReal
         print(row['RRR'])
         
@@ -543,7 +543,7 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
     ## Each marker is 30x30 km
 
 
-    #%% 3.4 Potentials
+    # 3.4 Potentials
     # Format of SOLEFLH and WNDFLH
     # TABLE WND/SOLEFLH(AAA)               "Full load hours for wind/solar power" 
     # /   
@@ -600,7 +600,7 @@ def main(cutout_path: str, weather_year: int, overwrite_cutout: bool = False):
         f.write("\nSUBTECHGROUPKPOT(AAA,'WINDTURBINE_OFFSHORE',SUBTECH_GROUP)$(SUBTECHGROUPKPOT(AAA,'WINDTURBINE_OFFSHORE',SUBTECH_GROUP) = 0) = EPS;")
 
         
-    #%% ------------------------------- ###
+    ### ------------------------------- ###
     ###          4. Analysis            ###
     ### ------------------------------- ###
 

From 85d047918efd70d989cdd296734df569fe5d2aec Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Tue, 1 Oct 2024 18:26:11 +0200
Subject: [PATCH 15/19] Finished clustering in the pure coding format

---
 src/Modules/clustering.py           | 233 ++++++++++++++++++----------
 src/Modules/format_balmorel_data.py |   8 +-
 src/assumptions.yaml                |   2 +-
 src/clustering                      |   8 +-
 src/clustering.yaml                 |   9 ++
 5 files changed, 167 insertions(+), 93 deletions(-)
 create mode 100644 src/clustering.yaml

diff --git a/src/Modules/clustering.py b/src/Modules/clustering.py
index 223e1b7..652bd79 100644
--- a/src/Modules/clustering.py
+++ b/src/Modules/clustering.py
@@ -14,10 +14,14 @@
 import matplotlib.pyplot as plt
 import matplotlib.colors as mcol
 from pybalmorel import Balmorel
+from pybalmorel.utils import symbol_to_df
+from Submodules.utils import convert_names
 import click
 import pandas as pd
 import numpy as np
 import xarray as xr
+import gams
+import geopandas as gpd
 from geofiles import prepared_geofiles
 from scipy.sparse import csr_matrix
 from Submodules.municipal_template import DataContainer
@@ -155,117 +159,179 @@ def collect_clusterdata(plot_cf: bool = False):
     return con
 
 
-def cluster(con: DataContainer,
-            data_list: list,
+def apply_filters(df: pd.DataFrame, value_name: str, aggfunc: str = 'sum'):
+    if 'A' in df.columns:
+        df['R'] = df.A.str.split('_', expand=True)[0]
+        
+    df = df.pivot_table(index='R', values='Value', aggfunc=aggfunc)
+    df.columns = [value_name]
+    
+    return df.query('R != "Nordsoeen"')
+
+columns = {'DE' : ['Y', 'R', 'DEUSER', 'Value'],
+           'DH' : ['Y', 'A', 'DHUSER', 'Value'],
+           'WNDFLH' : ['A', 'Value'],
+           'SOLEFLH' : ['A', 'Value']}
+
+def gather_data(db: gams.GamsDatabase,  
+                cluster_params: list,
+                aggfuncs: list):
+    
+    for i in range(len(cluster_params)):
+        try:
+            df = symbol_to_df(db, cluster_params[i], columns[cluster_params[i]])
+        except KeyError:
+            print('Column names not found for %s'%cluster_params[i])
+            df = symbol_to_df(db, cluster_params[i])        
+            
+        if i == 0:
+            collected_data = apply_filters(df, cluster_params[i], aggfunc=aggfuncs[i])
+            continue
+            
+        collected_data = collected_data.join(
+            apply_filters(df, cluster_params[i], aggfunc=aggfuncs[i]),
+            how='outer'
+        )
+        
+    return collected_data.to_xarray().rename({'R':'IRRRE'})
+
+def cluster(collected_data: pd.DataFrame,
             n_clusters: int,
             use_connectivity: bool = True,
             manual_corrections: list = [
-                ['Bornholm', 'Christiansø', 1],
-                ['Bornholm', 'Dragør', 1],
-                ['Esbjerg', 'Fanø', 1],
-                ['Rødovre', 'Frederiksberg', 1],
+                ['Bornholm', 'Christiansoe', 1],
+                ['Bornholm', 'Dragoer', 1],
+                ['Esbjerg', 'Fanoe', 1],
+                ['Roedovre', 'Frederiksberg', 1],
                 ['Slagelse', 'Nyborg', 0],
-                ['Samsø', 'Kalundborg', 0]
+                ['Samsoe', 'Kalundborg', 0]
             ],
+            linkage: str = 'Ward',
             connection_remark: str = 'connec. included + artifical',
             data_remark: str = 'all combined + xy coords'):
-    
-    # Stack data for clustering
-    X = np.vstack([eval(data_evaluation) for data_evaluation in data_list]).T
-
-    # K-Means Clustering
-    # est = KMeans(n_clusters=n_clusters)
-    # est.fit(X)
-
-    # Agglomorative clustering
-    linkage = 'Ward'
-
-    X = StandardScaler().fit_transform(X) # Normalise dataset
-    ## Make higher weighting of coordinates..?
-    # X[:,0] = X[:,0]*10000
-    # X[:,1] = X[:,1]*10000
 
+    # collected_data = collected_data.drop_sel(IRRRE='Christiansoe')
 
     # Connectivity
     if use_connectivity:
         ## Use connectivity from Balmorel (Submodules/get_grid.py)
-        connectivity = xr.load_dataset(r'Data\Power Grid\municipal_connectivity.nc')
+        connectivity = xr.load_dataset('Data/BalmorelData/municipal_connectivity.nc')
+        connectivity_old, connectivity = convert_names('Modules/Submodules/exo_grid_conversion_dictionaries.pkl', connectivity, 'connection')
 
         ## Manual Corrections
         for manual_connection in manual_corrections:
             connectivity.connection.loc[manual_connection[0], manual_connection[1]] = manual_connection[2]
             connectivity.connection.loc[manual_connection[1], manual_connection[0]] = manual_connection[2]
             
-        knn_graph = connectivity.connection.data # get numpy array
+        
+        ## Make symmetric index, so the indices fit
+        collected_data = collected_data.assign_coords(IRRRI=collected_data.coords['IRRRE'].data)
+        
+        ## Combine with data
+        # connectivity = connectivity.drop_sel(IRRRE='Christiansoe', IRRRI='Christiansoe')
+        X = collected_data.merge(connectivity)
+        
+        ## Make symmetric connectivity graph 
+        knn_graph = X.connection.data # get numpy array
         knn_graph = csr_matrix(knn_graph) # make dense format
+        
+        ## Drop the connection variable again
+        X = X.drop_vars('connection')
     else:
         knn_graph = None # don't apply connectivity constraints
+        X = collected_data
 
+    # Prepare data for clustering
+    Y = np.vstack([X.get(variable).data for variable in X.data_vars]).T
+    Y = np.nan_to_num(Y)
+    Y = StandardScaler().fit_transform(Y) # Normalise dataset
+    
+    ## Make higher weighting of certain coordinates..?
+    # X[:,0] = X[:,0]*10000
+    # X[:,1] = X[:,1]*10000
+    
     # Perform Clustering
     agg = AgglomerativeClustering(n_clusters=n_clusters, linkage=linkage.lower(),
                                 connectivity=knn_graph)
-    agg.fit(X)
+    agg.fit(Y)
     
+    # Merge labels to xarray
+    X['cluster_groups'] = (['IRRRE'], agg.labels_)
     
     # Plot the different clustering techniques
-    clustering = con.get_polygons()
-    for name, labelling in [(linkage, agg.labels_)]:
-        
-        # Set labels
-        clustering['cluster_group'] = labelling
+    ## Get geofiles
+    the_index, geofiles, c = prepared_geofiles('DKmunicipalities_names')
+    geofiles.index.name = 'IRRRE'
+    X = X.merge(geofiles['geometry'].to_xarray())
+    
 
-        # Plot clustering
-        fig, ax = plt.subplots()
-        clustering.plot(column='cluster_group', 
-                  ax=ax, 
-                #   legend=True,
-                  cmap=truncate_colormap(cmap, 0.2, 1))
-        
-        if knn_graph == None:
-            connection_remark = 'no connectivity'
-
-        plot_title = '%s, %d clusters, %s\ndata: %s'%(name, 
-                                                    n_clusters,
-                                                    connection_remark,
-                                                    data_remark) 
-        ax.set_title(plot_title)
-        # ax.set_title('%d clusters, %s linkage, %s'%(n_clusters, name, connection_remark))    
-        
-        ax.axes.axis('off')
-        plt.show()
-        
-        # fig.savefig(r'C:\Users\mberos\Danmarks Tekniske Universitet\PhD in Transmission and Sector Coupling - Dokumenter\Deliverables\Spatial Resolution\Investigations\240912 - Initial Clustering Method Tests'+'/'+plot_title.replace('data: ', '_').replace('\n', '').replace(' clusters', 'N').replace(' ', '_').replace(',', '') + '.png',
-        #             transparent=True,
-        #             bbox_inches='tight')
-        
-        ### Label municipalities
-        # clustering.reset_index().apply(lambda x: ax.annotate(text=x['municipality'], xy=(x.geometry.centroid.x, x.geometry.centroid.y), ha='center'), axis=1)
-        
-        ### Look at specific coordinates    
-        ## København region - Frederiksberg have 0 in both wind and solar cf, which may drive the clustering weirdly 
-        # ax.set_xlim([12.3, 12.8])
-        # ax.set_ylim([55.5, 55.8])
-        ## Nordjylland region - Nær Læsø
-        # ax.set_xlim([10, 11.3])
-        # ax.set_ylim([57.0, 57.5])
-        ## Vestjylland region - Nær Fanø
-        # ax.set_xlim([8.2, 9])
-        # ax.set_ylim([55.2, 55.7])
-        ## Storebælt
-        # ax.set_xlim([10.3, 11.6])
-        # ax.set_ylim([55.0, 55.6])
-        ## Samsø
-        # ax.set_xlim([10, 11.5])
-        # ax.set_ylim([55.4, 56.1])
+    # Plot clustering
+    fig, ax = plt.subplots()
+    
+    clustering = gpd.GeoDataFrame({'cluster_group' : X.cluster_groups.data},
+                            geometry=X.geometry.data)
+    
+    clustering.plot(ax=ax, column='cluster_group',
+                    cmap=truncate_colormap(cmap, 0.2, 1))
+            
+    if knn_graph == None:
+        connection_remark = 'no connectivity'
+
+    plot_title = '%s, %d clusters, %s\ndata: %s'%(linkage, 
+                                                n_clusters,
+                                                connection_remark,
+                                                data_remark) 
+    ax.set_title(plot_title)
+    # ax.set_title('%d clusters, %s linkage, %s'%(n_clusters, name, connection_remark))    
+    
+    ax.axes.axis('off')
+    plt.show()
+    
+    # fig.savefig(r'C:\Users\mberos\Danmarks Tekniske Universitet\PhD in Transmission and Sector Coupling - Dokumenter\Deliverables\Spatial Resolution\Investigations\240912 - Initial Clustering Method Tests'+'/'+plot_title.replace('data: ', '_').replace('\n', '').replace(' clusters', 'N').replace(' ', '_').replace(',', '') + '.png',
+    #             transparent=True,
+    #             bbox_inches='tight')
+    
+    ### Label municipalities
+    # clustering.reset_index().apply(lambda x: ax.annotate(text=x['municipality'], xy=(x.geometry.centroid.x, x.geometry.centroid.y), ha='center'), axis=1)
+    
+    ### Look at specific coordinates    
+    ## København region - Frederiksberg have 0 in both wind and solar cf, which may drive the clustering weirdly 
+    # ax.set_xlim([12.3, 12.8])
+    # ax.set_ylim([55.5, 55.8])
+    ## Nordjylland region - Nær Læsø
+    # ax.set_xlim([10, 11.3])
+    # ax.set_ylim([57.0, 57.5])
+    ## Vestjylland region - Nær Fanø
+    # ax.set_xlim([8.2, 9])
+    # ax.set_ylim([55.2, 55.7])
+    ## Storebælt
+    # ax.set_xlim([10.3, 11.6])
+    # ax.set_ylim([55.0, 55.6])
+    ## Samsø
+    # ax.set_xlim([10, 11.5])
+    # ax.set_ylim([55.4, 56.1])
     
     return fig, ax, clustering
 
 @click.command()
 @click.option('--model-path', type=str, required=True, help='Balmorel model path')
 @click.option('--scenario', type=str, required=True, help='Balmorel scenario')
+@click.option('--cluster-params', type=str, required=True, help='Comma-separated list of Balmorel input data to cluster (use the symbol names, e.g. DE for annual electricity demand)')
+@click.option('--aggregation-functions', type=str, required=True, help='Comma-separated list of aggregation functions used for clustering (E.g. sum for annual electricity demand and mean for wind full-load hours)')
+@click.option('--cluster-size', type=int, required=True, help='How many clusters?')
 @click.option('--plot-style', type=str, required=False, help='Style of the plot. Options are "report" (bright background) or "ppt" (dark background)')
-def main(model_path: str, scenario: str, plot_style: str = 'report'):
-
+def main(model_path: str, 
+         scenario: str, 
+         cluster_params: str,
+         aggregation_functions: str,
+         cluster_size: int,
+         plot_style: str = 'report'):
+
+    # Convert comma-separated string to list
+    cluster_params_list = cluster_params.replace(' ', '').split(',')
+    aggfuncs = aggregation_functions.replace(' ', '').split(',')
+
+    # Plot styles
     if plot_style == 'report':
         plt.style.use('default')
         fc = 'white'
@@ -277,17 +343,12 @@ def main(model_path: str, scenario: str, plot_style: str = 'report'):
     model = Balmorel(model_path)
     model.load_incfiles(scenario)
 
-    # Old collection and clustering    
-    # collected = collect_clusterdata()
-    # fig, ax, clustering = cluster(collected, [
-    #     "collected.muni.coords['lat'].data",
-    #     "collected.muni.coords['lon'].data",
-    #     "collected.muni.electricity_demand_mwh.sum(dim=['year', 'user'])",
-    #     "collected.muni.heat_demand_mwh.sum(dim=['year', 'user'])",
-    #     'collected.muni.wind_cf.data',
-    #     'collected.muni.solar_cf.data'
-    # ],
-    # 6)
+    # Get parameters for clustering    
+    collected = gather_data(model.input_data[scenario], 
+                            cluster_params_list, aggfuncs)
+    
+    # Do clustering
+    fig, ax, clustering = cluster(collected, cluster_size)
 
 
 if __name__ == '__main__':
diff --git a/src/Modules/format_balmorel_data.py b/src/Modules/format_balmorel_data.py
index ed90be5..d0bb0fc 100644
--- a/src/Modules/format_balmorel_data.py
+++ b/src/Modules/format_balmorel_data.py
@@ -45,16 +45,16 @@ def store_balmorel_input(symbol: str,
         f = pd.read_parquet('Data/BalmorelData/%s.gzip'%symbol)
     else:
         # Check Balmorel input has been loaded
-        if not('muni_input_data.gdx' in os.listdir(os.path.join(balm.path, 'muni', 'model'))) or load_again == True:      
+        if not('%s_input_data.gdx'%scenario in os.listdir(os.path.join(balm.path, scenario, 'model'))) or load_again == True:      
             print('\nLoading results into %s_input_data.gdx...\n'%scenario)
             balm.load_incfiles(scenario)
         else:
             print('\n%s_input_data.gdx already loaded!'%scenario)
-            print('Loading %s_input_data.gdx...\n'%(os.path.join(balm.path, 'muni', 'model', scenario)))
+            print('Loading %s_input_data.gdx...\n'%(os.path.join(balm.path, scenario, 'model', scenario)))
             
             # Load the input
             ws = GamsWorkspace()
-            balm.input_data[scenario] = ws.add_database_from_gdx(os.path.join(balm.path, 'muni', 'model', '%s_input_data.gdx'%scenario))
+            balm.input_data[scenario] = ws.add_database_from_gdx(os.path.join(balm.path, scenario, 'model', '%s_input_data.gdx'%scenario))
 
         # Get symbol
         f = symbol_to_df(balm.input_data[scenario], symbol, columns)
@@ -101,7 +101,7 @@ def get_grid(balmorel_model_path: str,
         XINVCOST = store_balmorel_input('XINVCOST',
                             ['Y', 'RE', 'RI', 'connection'],
                             balmorel_model_path, scenario, load_again,
-                            lambda x: x.query("Y == '2050' and (RE.str.contains('DK_') and RI.str.contains('DK_'))"),
+                            lambda x: x.query("Y == '2050' and (RE.str.contains('DK_') and RI.str.contains('DK_'))"), # Hardcoded for old database
                             False)
 
         ## Convert names and get 0/1 2D array
diff --git a/src/assumptions.yaml b/src/assumptions.yaml
index ccc05f3..6c28e26 100644
--- a/src/assumptions.yaml
+++ b/src/assumptions.yaml
@@ -1,6 +1,6 @@
 balmorel_input:
   model_path: "../../Balmorel"
-  scenario: "new_muni"
+  scenario: "muni"
   load_again: False
   
 grid_assumptions:
diff --git a/src/clustering b/src/clustering
index 9ed9186..67d464a 100644
--- a/src/clustering
+++ b/src/clustering
@@ -1,5 +1,5 @@
 # Load the config file at the top
-configfile: "assumptions.yaml"
+configfile: "clustering.yaml"
 
 out_path     = "Output/"
 data_path    = "Data/"
@@ -29,9 +29,13 @@ rule clustering:
             f"{balmorel_sc_folder}{scenario}_input_data.gdx",
             f"{modules_path}clustering.py"
         ]
+    params:
+        cluster_params=config['clustering']['data_for_clustering'],
+        aggregation_functions=config['clustering']['aggregation_functions'],
+        cluster_size=config['clustering']['cluster_size']
     output:
         [f"{out_path}WND_VAR_T.inc"]
     shell:
         """
-        python {modules_path}clustering.py --model-path={balmorel_path} --scenario={scenario}
+        python {modules_path}clustering.py --model-path={balmorel_path} --scenario={scenario} --cluster-params "{params.cluster_params}" --aggregation-functions="{params.aggregation_functions}" --cluster-size={params.cluster_size}
         """
diff --git a/src/clustering.yaml b/src/clustering.yaml
new file mode 100644
index 0000000..54cecec
--- /dev/null
+++ b/src/clustering.yaml
@@ -0,0 +1,9 @@
+balmorel_input:  
+  model_path: "../../Balmorel"
+  scenario: "new_muni"
+  load_again: False
+
+clustering:
+  data_for_clustering: "DE, DH, WNDFLH, SOLEFLH"
+  aggregation_functions: "sum, sum, mean, mean"
+  cluster_size: 6
\ No newline at end of file

From 264146bc3ff2bfa8faa5a9123cef078c3dafdf5d Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Tue, 1 Oct 2024 19:54:40 +0200
Subject: [PATCH 16/19] Saved figure instead of doing plt.show, fixed unicode
 error

---
 src/Modules/clustering.py   | 5 ++---
 src/Modules/vre_profiles.py | 2 +-
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/Modules/clustering.py b/src/Modules/clustering.py
index 652bd79..2d16120 100644
--- a/src/Modules/clustering.py
+++ b/src/Modules/clustering.py
@@ -285,7 +285,6 @@ def cluster(collected_data: pd.DataFrame,
     # ax.set_title('%d clusters, %s linkage, %s'%(n_clusters, name, connection_remark))    
     
     ax.axes.axis('off')
-    plt.show()
     
     # fig.savefig(r'C:\Users\mberos\Danmarks Tekniske Universitet\PhD in Transmission and Sector Coupling - Dokumenter\Deliverables\Spatial Resolution\Investigations\240912 - Initial Clustering Method Tests'+'/'+plot_title.replace('data: ', '_').replace('\n', '').replace(' clusters', 'N').replace(' ', '_').replace(',', '') + '.png',
     #             transparent=True,
@@ -348,8 +347,8 @@ def main(model_path: str,
                             cluster_params_list, aggfuncs)
     
     # Do clustering
-    fig, ax, clustering = cluster(collected, cluster_size)
-
+    fig, ax, clustering = cluster(collected, cluster_size, connection_remark='', data_remark=cluster_params)
+    fig.savefig('Output/Figures/Clustering/clustering.pdf', transparent=True, bbox_inches='tight')
 
 if __name__ == '__main__':
     main()
\ No newline at end of file
diff --git a/src/Modules/vre_profiles.py b/src/Modules/vre_profiles.py
index 343d30a..0485382 100644
--- a/src/Modules/vre_profiles.py
+++ b/src/Modules/vre_profiles.py
@@ -10,7 +10,7 @@
 REQUIRED:
     Using ERA5 requires an API-key from Copernicus
     1) Sign up at https://cds.climate.copernicus.eu/#!/home
-    2) Create a file called .cdsapirc in your home drive (typically C:\Users\USERNAME)
+    2) Create a file called .cdsapirc in your home drive (typically C:/Users/USERNAME)
         Copy paste the UID and API key that you got into .cdsapirc in the following format:
         url: https://cds.climate.copernicus.eu/api/v2
         key: UID:API  

From d01151a6d54d1dad10ee4283d07ae98536aa0340 Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Tue, 1 Oct 2024 20:09:00 +0200
Subject: [PATCH 17/19] Made clustering output

---
 .gitignore                      |   1 +
 src/Analysis/clustering_dag.pdf | Bin 6909 -> 7427 bytes
 src/Modules/clustering.py       |   7 +++++--
 src/clustering                  |  12 +++++++++---
 4 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/.gitignore b/.gitignore
index 7857155..451e5c9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,6 +9,7 @@ __pycache__
 prebalm
 *.pyc
 
+clusteroutput
 clustering_dot_source.txt
 clustering_dag
 preprocessing_dot_source.txt
diff --git a/src/Analysis/clustering_dag.pdf b/src/Analysis/clustering_dag.pdf
index 11a7172260b5573dcc6512c027425e01b47dec01..724997ea85241e3b42b38a4ac3ca913e53b64007 100644
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diff --git a/src/Modules/clustering.py b/src/Modules/clustering.py
index 2d16120..5dfd7fe 100644
--- a/src/Modules/clustering.py
+++ b/src/Modules/clustering.py
@@ -269,7 +269,9 @@ def cluster(collected_data: pd.DataFrame,
     fig, ax = plt.subplots()
     
     clustering = gpd.GeoDataFrame({'cluster_group' : X.cluster_groups.data},
-                            geometry=X.geometry.data)
+                                  index=X.coords['IRRRE'].data,
+                            geometry=X.geometry.data,
+                            crs='EPSG:4326')
     
     clustering.plot(ax=ax, column='cluster_group',
                     cmap=truncate_colormap(cmap, 0.2, 1))
@@ -348,7 +350,8 @@ def main(model_path: str,
     
     # Do clustering
     fig, ax, clustering = cluster(collected, cluster_size, connection_remark='', data_remark=cluster_params)
-    fig.savefig('Output/Figures/Clustering/clustering.pdf', transparent=True, bbox_inches='tight')
+    fig.savefig('ClusterOutput/Figures/clustering.pdf', transparent=True, bbox_inches='tight')
+    clustering.to_file('ClusterOutput/clustering.gpkg')
 
 if __name__ == '__main__':
     main()
\ No newline at end of file
diff --git a/src/clustering b/src/clustering
index 67d464a..09f483b 100644
--- a/src/clustering
+++ b/src/clustering
@@ -1,7 +1,7 @@
 # Load the config file at the top
 configfile: "clustering.yaml"
 
-out_path     = "Output/"
+out_path     = "ClusterOutput/"
 data_path    = "Data/"
 modules_path = "Modules/"
 submod_path  = "Modules/Submodules/"
@@ -12,7 +12,7 @@ balmorel_sc_folder = f"{balmorel_path}/{scenario}/model/"
 # 1. General Purpose
 rule all:
     input:
-        [f"{out_path}WND_VAR_T.inc",
+        [f"{out_path}DE.inc",
         ]
 
 rule collect_balmorel_input:
@@ -34,8 +34,14 @@ rule clustering:
         aggregation_functions=config['clustering']['aggregation_functions'],
         cluster_size=config['clustering']['cluster_size']
     output:
-        [f"{out_path}WND_VAR_T.inc"]
+        [f"{out_path}clustering.gpkg"]
     shell:
         """
         python {modules_path}clustering.py --model-path={balmorel_path} --scenario={scenario} --cluster-params "{params.cluster_params}" --aggregation-functions="{params.aggregation_functions}" --cluster-size={params.cluster_size}
         """
+
+rule convert_demands:
+    input:
+        f"{out_path}clustering.gpkg"
+    output:
+        f"{out_path}DE.inc"
\ No newline at end of file

From f701162131fc4cd4af6e8ff16d42d3711c851966 Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Tue, 1 Oct 2024 20:42:11 +0200
Subject: [PATCH 18/19] Made clustering example workflow

---
 src/Analysis/clustering_dag.pdf    | Bin 7427 -> 7583 bytes
 src/Analysis/preprocessing_dag.pdf | Bin 15182 -> 15181 bytes
 src/Modules/convert_demands.py     |  90 +++++++++++++++++++++++++++++
 src/clustering                     |   8 ++-
 src/clustering.yaml                |   2 +-
 5 files changed, 96 insertions(+), 4 deletions(-)
 create mode 100644 src/Modules/convert_demands.py

diff --git a/src/Analysis/clustering_dag.pdf b/src/Analysis/clustering_dag.pdf
index 724997ea85241e3b42b38a4ac3ca913e53b64007..ecf1a94534a0b89ea5bfebde279e7df132290b1b 100644
GIT binary patch
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diff --git a/src/Analysis/preprocessing_dag.pdf b/src/Analysis/preprocessing_dag.pdf
index 5794b0ed5766b6cb487016c28084f79fe2746861..8431397f54cd58c7d34e6b941b87d6f6882fd15b 100644
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diff --git a/src/Modules/convert_demands.py b/src/Modules/convert_demands.py
new file mode 100644
index 0000000..a85cc66
--- /dev/null
+++ b/src/Modules/convert_demands.py
@@ -0,0 +1,90 @@
+
+"""
+TITLE
+
+Description
+
+Created on 01.10.2024
+@author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+"""
+#%% ------------------------------- ###
+###        0. Script Settings       ###
+### ------------------------------- ###
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import geopandas as gpd
+import click
+from pybalmorel import Balmorel, IncFile
+from pybalmorel.utils import symbol_to_df
+import gams
+
+#%% ------------------------------- ###
+###        1. 
+### ------------------------------- ###
+
+def convert_parameter(db: gams.GamsDatabase, 
+                      symbol: str,
+                      clustering: gpd.GeoDataFrame):
+    
+    # Load dataframe
+    df = symbol_to_df(db, symbol, ['Y', 'R', 'DEUSER', 'Value'])
+
+    old_column = 'R'
+    new_column = 'R_new'
+    aggfunc = 'sum'
+
+    # Aggregate
+    # df = df.pivot_table(index='R')
+
+    # Convert names
+    clustering.columns = [old_column, new_column]
+    df = (
+        df
+        .merge(clustering, on=old_column, how='outer')
+        .drop(columns=old_column)
+        .rename(columns={new_column : old_column})
+        .groupby(['Y', 'R', 'DEUSER'])
+        .aggregate({'Value' : aggfunc})
+    )
+    
+    # Make IncFile
+    prefix = '\n'.join([
+        "TABLE DE(RRR,YYY,DEUSER) 'Annual electricity demand'",
+        ""
+    ])
+    suffix = '\n;'
+    f = IncFile(name=symbol, path='ClusterOutput',
+                prefix=prefix, suffix=suffix)
+    f.body = df
+    f.body_prepare(index=['Y', 'R'], columns='DEUSER', values='Value')
+    f.save()
+    
+    
+
+#%% ------------------------------- ###
+###            X. Main              ###
+### ------------------------------- ###
+
+@click.command()
+@click.option('--model-path', type=str, required=True, help='Balmorel model path')
+@click.option('--scenario', type=str, required=True, help='Balmorel scenario')
+def main(model_path: str, scenario: str):
+    
+    # Load files
+    m = Balmorel(model_path)
+    m.load_incfiles(scenario)
+
+    # Naming of clusters
+    clusters = gpd.read_file('ClusterOutput/clustering.gpkg')
+    clusters['cluster_name'] = ''
+    for cluster in clusters.cluster_group.unique():
+        idx = clusters.query('cluster_group == @cluster').index 
+        clusters.loc[idx, 'cluster_name'] = 'CL%d'%cluster
+        
+    convert_parameter(m.input_data[scenario],
+                      'DE', clusters[['index', 'cluster_name']])
+
+if __name__ == '__main__':
+    main()
diff --git a/src/clustering b/src/clustering
index 09f483b..914035d 100644
--- a/src/clustering
+++ b/src/clustering
@@ -12,8 +12,7 @@ balmorel_sc_folder = f"{balmorel_path}/{scenario}/model/"
 # 1. General Purpose
 rule all:
     input:
-        [f"{out_path}DE.inc",
-        ]
+        [f"{out_path}DE.inc"]
 
 rule collect_balmorel_input:
     output:
@@ -42,6 +41,9 @@ rule clustering:
 
 rule convert_demands:
     input:
-        f"{out_path}clustering.gpkg"
+        [
+            f"{balmorel_sc_folder}{scenario}_input_data.gdx",
+            f"{out_path}clustering.gpkg"
+        ]
     output:
         f"{out_path}DE.inc"
\ No newline at end of file
diff --git a/src/clustering.yaml b/src/clustering.yaml
index 54cecec..e0fd420 100644
--- a/src/clustering.yaml
+++ b/src/clustering.yaml
@@ -6,4 +6,4 @@ balmorel_input:
 clustering:
   data_for_clustering: "DE, DH, WNDFLH, SOLEFLH"
   aggregation_functions: "sum, sum, mean, mean"
-  cluster_size: 6
\ No newline at end of file
+  cluster_size: 20
\ No newline at end of file

From 749c1c1ec7cf6a7d1bb984101a3b763f4c9e9075 Mon Sep 17 00:00:00 2001
From: Mathias157 <mathiasros@gmail.com>
Date: Thu, 3 Oct 2024 13:47:30 +0200
Subject: [PATCH 19/19] Minor change

---
 src/Modules/clustering.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/Modules/clustering.py b/src/Modules/clustering.py
index 5dfd7fe..e96b351 100644
--- a/src/Modules/clustering.py
+++ b/src/Modules/clustering.py
@@ -16,6 +16,7 @@
 from pybalmorel import Balmorel
 from pybalmorel.utils import symbol_to_df
 from Submodules.utils import convert_names
+from typing import Tuple
 import click
 import pandas as pd
 import numpy as np
@@ -83,7 +84,7 @@ def correct_VRE_data(path_to_file, generation_name: str):
     return vredata
     
 def convert_municipal_code_to_name(to_be_converted: pd.DataFrame,
-                                    column_to_convert: (str, int),
+                                    column_to_convert: Tuple[str, int],
                                     pivot_table: bool = False,
                                     exclude_regions: list = ['Herlev', 'Christiansø'],
                                     muni_geofile_path: str = r'C:\Users\mberos\gitRepos\balmorel-preprocessing\src\Data\Shapefiles\Denmark\Adm\gadm36_DNK_2.shp'):