From 1ba224c4061cb465d4fc9ae973c5c59212e4f96e Mon Sep 17 00:00:00 2001
From: BenjaminBlanz <79572774+BenjaminBlanz@users.noreply.github.com>
Date: Tue, 16 Jul 2024 11:20:29 +0200
Subject: [PATCH] Initial version of the script to create figures
---
Raw Results Analysis_BB.py | 980 +++++++++++++++++++++++++++++++++++++
1 file changed, 980 insertions(+)
create mode 100644 Raw Results Analysis_BB.py
diff --git a/Raw Results Analysis_BB.py b/Raw Results Analysis_BB.py
new file mode 100644
index 0000000..4f09391
--- /dev/null
+++ b/Raw Results Analysis_BB.py
@@ -0,0 +1,980 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# code from Nike modified by Benjamin
+
+#%% In[2]:
+
+print('loading libs')
+
+from scipy.io import loadmat
+import numpy as np
+import pandas as pd
+import geopandas as gpd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+import matplotlib.colors as mcolors
+import matplotlib.patches as patches
+from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+import os
+
+#%%
+
+# show the plots?
+showPlots = False
+
+#%% function to find the shape of a nested list as python is defficient
+def shape(listOfLists):
+ shape = []
+ b = listOfLists
+ while type(b) == list:
+ shape.append(len(b))
+ b = b[0]
+ return shape
+
+#%% sector aggregation function
+def aggregateSectorNace62ToNace1(inArray):
+ shape = list(inArray.shape)
+ sectorDim = shape.index(len(sectors_nace_62))
+ shape[sectorDim] = len(sectors_nace_1)
+ aggArray = np.ones(shape)
+ for t in range(len(time_steps)):
+ for c in range(len(country_codes)):
+ for s in range(len(sectors_nace_1)):
+ matched_sector_idcs = [sectors_nace_62.index(sector) for sector in sectors_nace_62 if sector.startswith(sectors_nace_1[s])]
+ if len(shape)==4:
+ for e in range(len(experiments)):
+ aggArray[t,e,c,s] = sum(inArray[t,e,c,matched_sector_idcs])
+ else:
+ aggArray[t,c,s] = sum(inArray[t,c,matched_sector_idcs])
+ return aggArray
+
+#%% data description function
+def describeData(data):
+ for key in list(data.keys()):
+ print(key)
+ if(type(data[key]) != np.ndarray):
+ print(" Is not a data array, ignoring.")
+ else:
+ x = data[key]
+ print(" Is an array with shape: " + str(x.shape))
+ dimensions = ''
+ for j in range(0,(len(list(x.shape)))):
+ if key[-5:] == "_mean" and j==1 and x.shape[j]==1:
+ dimensions = dimensions + "experiment mean" + ', '
+ else:
+ dimensions = dimensions + list(dimensionLabels.keys())[dimensionLabelsLengths.index(x.shape[j])] + ', '
+ dimensions = dimensions[:-2]
+ print(" dimensions: " + dimensions)
+
+#%% area of circle wedge
+# use if you want the change in the piechart wedge radi to reflect the area change of the wedges
+# decided against this as with linear radius scaling, we can add concentric circles behind the pie for scales
+
+# def areaOfWedges(pieData, radius):
+# angles = 360 * pieData / sum(pieData)
+# areas = np.pi * radius**2 * angles/360
+# return areas, angles
+
+# def radiusOfWedgeWithArea(area,angle):
+# return np.sqrt((area*360)/(np.pi * angle))
+
+# def radiusOfRelChange(pieData, baseAreas, baseRadius, angles):
+# relChangedAreas = baseAreas * pieData
+# return radiusOfWedgeWithArea(relChangedAreas, angles)
+
+
+# baseAreas, baseAngles = areaOfWedges(piedata,base_radius)
+# relAdjArea = baseAreas[s] * (1+ scenarios_dif_rel[scenario_index][sector_thing][time_index,country_index,s]*10)
+# relAdjRadius = np.sqrt((relAdjArea*360)/(np.pi * baseAngles[s]))
+
+#%% Constants data labels
+# List of country codes
+country_codes = ['AT', 'BE', 'BG', 'CY', 'CZ', 'DE', 'DK', 'EE', 'EL', 'ES', 'FI', 'FR', 'HR',
+ 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'NL', 'PL', 'PT', 'RO', 'SE', 'SI', 'SK']
+country_codes_3 = ['AUT', 'BEL', 'BGR', 'CYP', 'CZE', 'DEU', 'DNK', 'EST', 'GRC', 'ESP', 'FIN', 'FRA', 'HRV',
+ 'HUN', 'IRL', 'ITA', 'LTU', 'LUX', 'LVA', 'NLD', 'POL', 'PRT', 'ROU', 'SWE', 'SVN', 'SVK']
+regions=['AT11', 'AT12', 'AT13', 'AT21', 'AT22', 'AT31', 'AT32', 'AT33', 'AT34', 'BE10', 'BE21', 'BE22', 'BE23',
+ 'BE24', 'BE25', 'BE31', 'BE32', 'BE33', 'BE34', 'BE35', 'BG31', 'BG32', 'BG33', 'BG34', 'BG41', 'BG42',
+ 'CY00', 'CZ01', 'CZ02', 'CZ03', 'CZ04', 'CZ05', 'CZ06', 'CZ07', 'CZ08', 'DE11', 'DE12', 'DE13', 'DE14',
+ 'DE21', 'DE22', 'DE23', 'DE24', 'DE25', 'DE26', 'DE27', 'DE30', 'DE40', 'DE50', 'DE60', 'DE71', 'DE72',
+ 'DE73', 'DE80', 'DE91', 'DE92', 'DE93', 'DE94', 'DEA1', 'DEA2', 'DEA3', 'DEA4', 'DEA5', 'DEB1', 'DEB2',
+ 'DEB3', 'DEC0', 'DED2', 'DED4', 'DED5', 'DEE0', 'DEF0', 'DEG0', 'DK01', 'DK02', 'DK03', 'DK04', 'DK05',
+ 'EE00', 'EL30', 'EL41', 'EL42', 'EL43', 'EL51', 'EL52', 'EL53', 'EL54', 'EL61', 'EL62', 'EL63', 'EL64',
+ 'EL65', 'ES11', 'ES12', 'ES13', 'ES21', 'ES22', 'ES23', 'ES24', 'ES30', 'ES41', 'ES42', 'ES43', 'ES51',
+ 'ES52', 'ES53', 'ES61', 'ES62', 'ES63', 'ES64', 'ES70', 'FI1B', 'FI1C', 'FI1D', 'FI19', 'FI20', 'FR10',
+ 'FRB0', 'FRC1', 'FRC2', 'FRD1', 'FRD2', 'FRE1', 'FRE2', 'FRF1', 'FRF2', 'FRF3', 'FRG0', 'FRH0', 'FRI1',
+ 'FRI2', 'FRI3', 'FRJ1', 'FRJ2', 'FRK1', 'FRK2', 'FRL0', 'FRM0', 'FRY1', 'FRY2', 'FRY3', 'FRY4', 'HR03',
+ 'HU11', 'HU12', 'HU21', 'HU22', 'HU23', 'HU31', 'HU32', 'HU33', 'IE04', 'IE05', 'IE06', 'ITC1', 'ITC2',
+ 'ITC3', 'ITC4', 'ITF1', 'ITF2', 'ITF3', 'ITF4', 'ITF5', 'ITF6', 'ITG1', 'ITG2', 'ITH1', 'ITH2', 'ITH3',
+ 'ITH4', 'ITH5', 'ITI1', 'ITI2', 'ITI3', 'ITI4', 'LT01', 'LT02', 'LU00', 'LV00', 'NL11', 'NL12', 'NL13',
+ 'NL21', 'NL22', 'NL23', 'NL31', 'NL32', 'NL33', 'NL34', 'NL41', 'NL42', 'PL21', 'PL22', 'PL41', 'PL42',
+ 'PL43', 'PL51', 'PL52', 'PL61', 'PL62', 'PL63', 'PL71', 'PL72', 'PL81', 'PL82', 'PL84', 'PL91', 'PL92',
+ 'PT11', 'PT15', 'PT16', 'PT17', 'PT18', 'PT20', 'PT30', 'RO11', 'RO12', 'RO21', 'RO22', 'RO31', 'RO32',
+ 'RO41', 'RO42', 'SE11', 'SE12', 'SE21', 'SE22', 'SE23', 'SE31', 'SE32', 'SE33', 'SI03', 'SI04', 'SK01',
+ 'SK02', 'SK03', 'SK04'];
+danube_countries = ['DE', 'AT', 'CZ', 'HU', 'SK', 'SI', 'RO', 'BG', 'HR']
+# List of NUTS-2 codes for the specified countries and German regions
+danube_nuts_2 = [
+ # Germany
+ 'DE11', 'DE13', 'DE14', 'DE21', 'DE22', 'DE23', 'DE24', 'DE25', 'DE26', 'DE27',
+ # Austria
+ 'AT11', 'AT12', 'AT13', 'AT21', 'AT22', 'AT31', 'AT32', 'AT33', 'AT34',
+ # Czechia
+ 'CZ01', 'CZ02', 'CZ03', 'CZ04', 'CZ05', 'CZ06', 'CZ07', 'CZ08',
+ # Hungary
+ 'HU11', 'HU12', 'HU21', 'HU22', 'HU23', 'HU31', 'HU32', 'HU33',
+ # Slovenia
+ 'SI03', 'SI04',
+ # Croatia
+ 'HR03', #'HR04',
+ # Romania
+ 'RO11', 'RO12', 'RO21', 'RO22', 'RO31', 'RO32', 'RO41', 'RO42',
+ # Bulgaria
+ 'BG31', 'BG32', 'BG33', 'BG34', 'BG41', 'BG42'
+]
+sectors_nace_62 = ['A01', 'A02', 'A03', 'B', 'C10-C12', 'C13-C15', 'C16', 'C17', 'C18', 'C19', 'C20', 'C21', 'C22', 'C23',
+ 'C24', 'C25', 'C26', 'C27', 'C28', 'C29', 'C30', 'C31_C32', 'C33', 'D', 'E36', 'E37-E39', 'F', 'G45',
+ 'G46', 'G47', 'H49', 'H50', 'H51', 'H52', 'H53', 'I', 'J58', 'J59_J60', 'J61', 'J62_J63', 'K64', 'K65',
+ 'K66', 'L', 'M69_M70', 'M71', 'M72', 'M73', 'M74_M75', 'N77', 'N78', 'N79', 'N80-N82', 'O', 'P',
+ 'Q86', 'Q87_Q88', 'R90-R92', 'R93', 'S94', 'S95', 'S96']
+sectors_nace_1 = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S']
+key_sectors = ['A01', 'H50', 'H51', 'H52', 'H53', 'K64', 'K65']
+
+time_steps = [f"Q{i}" for i in range(0,13)]
+
+experiments = [f"E{i}" for i in range(0,18)]
+
+dimensionLabels = {"countries":country_codes,"sectors_nace_62":sectors_nace_62,"sectors_nace_1":sectors_nace_1,"time":time_steps,"experiments":experiments}
+dimensionLabelsLengths = [len(dimensionLabels[i]) for i in dimensionLabels.keys()]
+
+sectors_nace_1_colors = cm.tab20.colors
+sectors_nace_62_colors = cm.tab20(np.linspace(0, 1, len(sectors_nace_62)))
+
+#%% load data
+print('loading data')
+# `data` is a dictionary with variable names as keys and loaded matrices as values
+base = loadmat('Baseline.mat')
+shock_eq = loadmat('Earthquake_Q1.mat')
+shock_f = loadmat('Flood_Q1.mat')
+shock_eq_f = loadmat('Earthquake_Q1_Flood_Q5.mat')
+shock_f_eq = loadmat('Flood_Q1_Earthquake_Q5.mat')
+
+
+scenarios = [shock_f, shock_eq, shock_f_eq, shock_eq_f]
+scenarios_names = ["flood", "earthquake", "compound flood earthquake", "compound earthquake flood"]
+scenarios_colors = ["deepskyblue", "indianred","deepskyblue", "indianred"]
+scenarios_linest = ["-","-","--","--"]
+
+#%% load and prepare map shape
+print('loading map')
+
+sns.set(style="whitegrid", palette="pastel", color_codes=True)
+sns.mpl.rc("figure", figsize=(10,6))
+world = gpd.read_file("ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp")
+europe = world#world[world['CONTINENT']=='Europe']
+europe.insert(len(europe.columns), 'INSET_FIG_X', europe['LABEL_X'])
+europe.insert(len(europe.columns), 'INSET_FIG_Y', europe['LABEL_Y'])
+
+europe.loc[europe['ADM0_A3']=='LUX','INSET_FIG_X'] = 2
+europe.loc[europe['ADM0_A3']=='LUX','INSET_FIG_Y'] = 54
+
+europe.loc[europe['ADM0_A3']=='NLD','INSET_FIG_X'] = 5.8
+europe.loc[europe['ADM0_A3']=='NLD','INSET_FIG_Y'] = 53
+
+europe.loc[europe['ADM0_A3']=='BEL','INSET_FIG_X'] = 2.4
+europe.loc[europe['ADM0_A3']=='BEL','INSET_FIG_Y'] = 56
+
+europe.loc[europe['ADM0_A3']=='HRV','INSET_FIG_X'] = 16.9
+europe.loc[europe['ADM0_A3']=='HRV','INSET_FIG_Y'] = 44.4
+
+europe.loc[europe['ADM0_A3']=='AUT','INSET_FIG_X'] = 12
+europe.loc[europe['ADM0_A3']=='AUT','INSET_FIG_Y'] = 47.5
+
+europe.loc[europe['ADM0_A3']=='ITA','INSET_FIG_X'] = 12
+europe.loc[europe['ADM0_A3']=='ITA','INSET_FIG_Y'] = 43.3
+
+europe.loc[europe['ADM0_A3']=='LVA','INSET_FIG_X'] = 21
+europe.loc[europe['ADM0_A3']=='LVA','INSET_FIG_Y'] = 58.2
+
+europe.loc[europe['ADM0_A3']=='LUX','INSET_FIG_X'] = 0.2
+europe.loc[europe['ADM0_A3']=='LUX','INSET_FIG_Y'] = 52.5
+
+europe.loc[europe['ADM0_A3']=='HUN','INSET_FIG_X'] = 19.45
+europe.loc[europe['ADM0_A3']=='HUN','INSET_FIG_Y'] = 47.2
+
+europe.loc[europe['ADM0_A3']=='ROU','INSET_FIG_X'] = 24.2
+europe.loc[europe['ADM0_A3']=='ROU','INSET_FIG_Y'] = 46.1
+
+europe.loc[europe['ADM0_A3']=='BGR','INSET_FIG_X'] = 25.5
+europe.loc[europe['ADM0_A3']=='BGR','INSET_FIG_Y'] = 42.5
+
+europe.loc[europe['ADM0_A3']=='SVN','INSET_FIG_X'] = 5
+europe.loc[europe['ADM0_A3']=='SVN','INSET_FIG_Y'] = 40
+
+europe.loc[europe['ADM0_A3']=='SVK','INSET_FIG_X'] = 26
+europe.loc[europe['ADM0_A3']=='SVK','INSET_FIG_Y'] = 50.5
+
+#%% iterate over entries of data and print out shape
+# print('Basic shape of Data')
+# describeData(base)
+
+#%% calculate means, differences to baseline, aggregate sectors
+print('calculating means and aggregating sectors')
+thingsWeCareAbout = ['capital_consumption', 'capital_loss', 'compensation_employees', 'euribor',
+ 'government_debt', 'government_deficit', 'nominal_capitalformation', 'nominal_exports',
+ 'nominal_fixed_capitalformation', 'nominal_fixed_capitalformation_dwellings',
+ 'nominal_gdp', 'nominal_government_consumption', 'nominal_gva',
+ 'nominal_household_consumption', 'nominal_imports', 'nominal_output', 'nominal_sector_gva',
+ 'nominal_sector_output', 'operating_surplus', 'real_capitalformation', 'real_exports',
+ 'real_fixed_capitalformation', 'real_fixed_capitalformation_dwellings', 'real_gdp',
+ 'real_government_consumption', 'real_gva', 'real_household_consumption', 'real_imports',
+ 'real_output', 'real_sector_gva', 'real_sector_output', 'sector_capital_consumption',
+ 'sector_capital_loss', 'sector_operating_surplus', 'taxes_production', 'unemployment_rate',
+ 'wages']
+
+scenarios_rel = []
+scenarios_dif = []
+scenarios_dif_rel = []
+for j in range(len(thingsWeCareAbout)):
+ base[thingsWeCareAbout[j]+"_mean"] = base[thingsWeCareAbout[j]].mean(axis=1)
+ for s in range(len(scenarios)):
+ scenarios[s][thingsWeCareAbout[j]+"_mean"] = scenarios[s][thingsWeCareAbout[j]].mean(axis=1)
+thingsWeCareAbout = thingsWeCareAbout + [s + "_mean" for s in thingsWeCareAbout]
+for j in range(len(thingsWeCareAbout)):
+ if 'sector' in thingsWeCareAbout[j]:
+ base[thingsWeCareAbout[j]+"_nace1"] = aggregateSectorNace62ToNace1(base[thingsWeCareAbout[j]])
+ thingsWeCareAbout.append(thingsWeCareAbout[j]+"_nace1")
+ for s in range(len(scenarios)):
+ if 'sector' in thingsWeCareAbout[j]:
+ scenarios[s][thingsWeCareAbout[j]+"_nace1"] = aggregateSectorNace62ToNace1(scenarios[s][thingsWeCareAbout[j]])
+for s in range(len(scenarios)):
+ scenarios_rel.append(dict())
+ scenarios_dif.append(dict())
+ scenarios_dif_rel.append(dict())
+ for j in range(len(thingsWeCareAbout)):
+ scenarios_rel[s][thingsWeCareAbout[j]] = scenarios[s][thingsWeCareAbout[j]]/base[thingsWeCareAbout[j]]
+ scenarios_dif[s][thingsWeCareAbout[j]] = scenarios[s][thingsWeCareAbout[j]]-base[thingsWeCareAbout[j]]
+ scenarios_dif_rel[s][thingsWeCareAbout[j]] = (scenarios[s][thingsWeCareAbout[j]]-base[thingsWeCareAbout[j]])/base[thingsWeCareAbout[j]]
+
+
+#%% grid plots
+print('plotting time series')
+figdir = 'figures/timeseries'
+
+thing = 'real_output_mean'
+plotType = 'dif_rel'
+
+num_countries = len(country_codes)
+
+# Creating a figure and a grid of subplots
+nrows=6
+ncols=5
+subfig_size = [5, 6]
+
+fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(subfig_size[0]*ncols,subfig_size[1]*nrows )) # Adjust nrows and ncols if needed
+fig.suptitle(thing + ' as ' + plotType)
+fig.subplots_adjust(hspace=0.5, wspace=0.5)
+
+max_val = 0
+min_val = 0
+for s in range(len(scenarios)):
+ max_val = max(max_val,np.max(scenarios_dif_rel[s][thing]))
+ min_val = min(min_val,np.min(scenarios_dif_rel[s][thing]))
+
+# Plotting data for each country in its own subplot
+row = 0
+col = 0
+for index, code in enumerate(country_codes):
+ if(col==ncols):
+ row+=1
+ col=0
+ ax = axes[row, col] # Determine the position of the subplot
+ if(plotType=='abs'):
+ ax.plot(base[thing][:,index], label='Baseline')
+ for s in range(len(scenarios)):
+ if plotType == 'abs':
+ ax.plot(scenarios[s][thing][:,index], label=scenarios_names[s],
+ color=scenarios_colors[s],linestyle=scenarios_linest[s])
+ if plotType == 'dif_rel':
+ ax.plot(scenarios_dif_rel[s][thing][:,index], label=scenarios_names[s],
+ color=scenarios_colors[s],linestyle=scenarios_linest[s])
+ ax.set_ylim(min_val,max_val)
+ ax.set_title(f'{code}')
+ ax.set_xlabel('Time Step')
+ ax.set_ylabel(thing)
+ col+=1
+handles, labels = ax.get_legend_handles_labels()
+fig.legend(handles, labels, loc='lower right')
+# Hide unused subplots
+for index in range(num_countries, nrows*ncols): # Adjust the range if the grid size is changed
+ if(col==ncols):
+ row+=1
+ col=0
+ axes[row, col].axis('off')
+ col+=1
+
+# Adjust layout
+plt.tight_layout()
+
+# Show the plot
+if showPlots: plt.show()
+
+
+if not os.path.exists(figdir):
+ os.makedirs(figdir)
+fig.savefig(figdir+'/timeseries_' + thing + '_as_' + plotType + '.png')
+fig.savefig(figdir+'/timeseries_' + thing + '_as_' + plotType + '.pdf')
+
+plt.close()
+
+#%% spatial plot
+print('plotting maps')
+basefigdir = 'figures/maps'
+
+nrows=3
+ncols=10
+subfig_size = [5, 5]
+
+max_val = max(abs(min_val),abs(max_val))
+min_val = -max_val
+
+scenariosToPlot = [0,1,2,3]
+
+for scenario_index in scenariosToPlot:
+ timestep = 'Q4'
+
+ if plotType == 'abs':
+ thing_df = pd.DataFrame({'time': np.repeat(time_steps, len(country_codes)), 'country': country_codes_3 * len(time_steps) , thing:base[thing].flatten()})
+ if plotType == 'dif_rel':
+ thing_df = pd.DataFrame({'time': np.repeat(time_steps, len(country_codes)), 'country': country_codes_3 * len(time_steps) , thing:scenarios_dif_rel[scenario_index][thing].flatten()})
+
+
+ # initialize the figure
+ nrows=3
+ ncols=6
+ fig, axes = plt.subplots(nrows, ncols, figsize=(subfig_size[0]*ncols,subfig_size[1]*nrows ))
+ fig.suptitle(thing + ' in ' + scenarios_names[scenario_index] + ' scenario as ' + plotType)
+ row = 0
+ col = 0
+ for index, timestep in enumerate(time_steps):
+
+ thing_df_1 = thing_df[thing_df['time']==timestep]
+
+ mergedData = europe.merge(thing_df_1, how='left', left_on='ADM0_A3', right_on='country')
+
+ # determine subfigure
+ if(col==ncols):
+ row+=1
+ col=0
+ if row>=nrows or col>=ncols:
+ break
+ if ncols > 1:
+ ax = axes[row, col]
+ else:
+ ax = axes[row]
+ col+=1
+ # define colors
+ if plotType == 'abs':
+ cmap = cm.Reds
+ min_val, max_val = min(thing_df[thing]), max(thing_df[thing])
+ if plotType == 'dif_rel':
+ cmap = cm.seismic_r
+ norm = mcolors.Normalize(vmin=min_val, vmax=max_val)
+
+ # create the plot
+ mergedData.plot(column=thing, cmap=cmap, norm=norm,
+ edgecolor='black', linewidth=0.2,ax=ax)
+
+ # custom axis
+ ax.set_xlim(-15, 35)
+ ax.set_ylim(32, 72)
+ ax.axis('off')
+
+ # define range and values for the legend
+ value_ranges = np.arange(min_val,max_val,(max_val-min_val)/10).tolist()
+ labels = ['%.2f' % elem for elem in value_ranges]
+
+ # parameters of the legend
+ rectangle_width = 2
+ rectangle_height = 1.5
+ legend_x = 30
+ legend_y_start = 55
+ legend_y_step = 1.5
+
+ # create the legend
+ for i in range(len(labels)):
+ value = (value_ranges[i]-min_val)/(max_val-min_val) # Normalize the value to [0, 1]
+ color = cmap(value)
+ ax.add_patch(plt.Rectangle((legend_x, legend_y_start - i * legend_y_step), rectangle_width, rectangle_height,
+ color=color, ec='black', lw=0.6))
+ ax.text(legend_x + 2.5, legend_y_start - i * legend_y_step + 0.7, labels[i],
+ fontsize=12, va='center')
+
+ # title
+ ax.title.set_text(timestep)
+ # Hide unused subplots
+ for index in range(len(time_steps), nrows*ncols): # Adjust the range if the grid size is changed
+ if(col==ncols):
+ row+=1
+ col=0
+ axes[row, col].axis('off')
+ col+=1
+
+ # display the plot
+ plt.tight_layout()
+ if showPlots: plt.show()
+
+ figdir = basefigdir
+ if not os.path.exists(figdir):
+ os.makedirs(figdir)
+ fig.savefig('figures/map-'+ thing + '_in_' + scenarios_names[scenario_index] + '_scenario_as_' + plotType + '.png')
+ fig.savefig('figures/map-'+ thing + '_in_' + scenarios_names[scenario_index] + '_scenario_as_' + plotType + '.pdf')
+
+ plt.close(fig)
+
+#%% pie chart config
+plotType = 'dif_rel'
+sector_thing = 'real_sector_output_mean_nace1'
+country_index = 22
+scenario_index = 1
+base_radius = 1
+rel_change_scaling = 5
+nrows=3
+ncols=6
+subfig_size=[3,3]
+
+wedgeprops={"edgecolor":"k","linewidth":0.5}
+
+if base[sector_thing].shape[2]==len(sectors_nace_1):
+ sector_colors= sectors_nace_1_colors
+ sector_names = sectors_nace_1
+else:
+ sector_colors= sectors_nace_62_colors
+ sector_names = sectors_nace_62
+
+#%% function pie charts real output per sector
+
+def addRelChangePiePlot(piedata,ax):
+ #circle scale lines
+ ax.add_patch(plt.Circle((0,0), base_radius*(1-0.1*rel_change_scaling), linestyle='--', fill = False, edgecolor='gray', linewidth=1))
+ ax.add_patch(plt.Circle((0,0), base_radius*(1-0.05*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+ # ax.add_patch(plt.Circle((0,0), base_radius*(1-0.01*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+ ax.add_patch(plt.Circle((0,0), base_radius, linestyle='-', fill = False, edgecolor='k' ))
+ # ax.add_patch(plt.Circle((0,0), base_radius*(1+0.01*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+ ax.add_patch(plt.Circle((0,0), base_radius*(1+0.05*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+ # ax.add_patch(plt.Circle((0,0), base_radius*(1+0.1*rel_change_scaling), linestyle='--', fill = False, edgecolor='gray', linewidth=1))
+
+ # the pie wedges
+ for s in range(len(piedata)):
+ wedges1, texts1 = ax.pie(piedata, radius=base_radius + rel_change_scaling* scenarios_dif_rel[scenario_index][sector_thing][time_index,country_index,s],
+ colors=sector_colors, wedgeprops=wedgeprops, counterclock=False, startangle=-270)
+ for wi in range(len(wedges1)):
+ if wi != s:
+ wedges1[wi].set_visible(False)
+
+#%% pie charts
+print('plotting pies')
+
+basefigdir = 'figures/pies'
+
+# initialize the figure
+for country_index in range(len(country_codes)):
+ for scenario_index in [0,1,2,3]:
+ print(sector_thing + ' in ' + scenarios_names[scenario_index] + ' scenario as ' + plotType + ' for country ' + country_codes_3[country_index])
+ fig, axes = plt.subplots(nrows, ncols, figsize=(subfig_size[0]*ncols,subfig_size[1]*nrows ))
+ fig.suptitle(sector_thing + ' in ' + scenarios_names[scenario_index] + ' scenario as ' + plotType + ' for country ' + country_codes_3[country_index])
+ row = 0
+ col = 0
+ for time_index, timestep in enumerate(time_steps):
+ # determine subfigure
+ if(col==ncols):
+ row+=1
+ col=0
+ if row>=nrows or col>=ncols:
+ break
+ if ncols > 1:
+ ax = axes[row, col]
+ else:
+ ax = axes[row]
+ col+=1
+ piedata = base[sector_thing][time_index,country_index,:]
+ addRelChangePiePlot(piedata,ax)
+ # title
+ ax.title.set_text(timestep)
+ # Hide unused subplots
+ for time_index in range(len(time_steps), nrows*ncols-2): # Adjust the range if the grid size is changed
+ if(col==ncols):
+ row+=1
+ col=0
+ axes[row, col].axis('off')
+ col+=1
+
+ # scale lines legend
+ ax = axes[nrows-1,ncols-2]
+ limits = 1+(0.09*rel_change_scaling)
+ ax.set_xlim(-limits, limits)
+ ax.set_ylim(-limits, limits)
+ ax.axis('off')
+ ax.add_patch(plt.Circle((0,0), (1-0.1*rel_change_scaling), linestyle='--', fill = False, edgecolor='gray', linewidth=1))
+ ax.add_patch(plt.Circle((0,0), (1-0.05*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+ ax.add_patch(plt.Circle((0,0), 1, linestyle='-', fill = False, edgecolor='k' ))
+ ax.add_patch(plt.Circle((0,0), (1+0.05*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+ ax.text(0,(1-0.1*rel_change_scaling),'-10%',fontsize=10,
+ backgroundcolor='white',color='gray',horizontalalignment='center',verticalalignment='center')
+ ax.text(0,(1-0.05*rel_change_scaling),'-5%',fontsize=10,
+ backgroundcolor='white',color='gray',horizontalalignment='center',verticalalignment='center')
+ ax.text(0,1,'reference',fontsize=10,
+ backgroundcolor='white',color='black',horizontalalignment='center',verticalalignment='center')
+ ax.text(0,(1+0.05*rel_change_scaling),'+5%',fontsize=10,
+ backgroundcolor='white',color='gray',horizontalalignment='center',verticalalignment='center')
+ # ax.text(0,(1+0.1*rel_change_scaling),'+10%',fontsize=10,
+ # backgroundcolor='white',color='gray',horizontalalignment='center',verticalalignment='center')
+
+ # sector color legends
+ ax = axes[nrows-1,ncols-1]
+ wedges, texts = ax.pie(np.ones(len(piedata)),radius=base_radius, colors=sector_colors, labels=sector_names,
+ wedgeprops=wedgeprops, counterclock=False, startangle=-270)
+ ax.title.set_text('Sectors')
+
+ # display the plot
+ plt.tight_layout()
+ if showPlots: plt.show()
+
+ # save the plot
+ figdir = basefigdir + '/' + scenarios_names[scenario_index]
+ if not os.path.exists(figdir):
+ os.makedirs(figdir)
+ fig.savefig(figdir+'/pie-'+ sector_thing + '_in_' + scenarios_names[scenario_index] + '_scenario_as_' + plotType + ' for country ' + country_codes_3[country_index] + '.png')
+ fig.savefig(figdir+'/pie-'+ sector_thing + '_in_' + scenarios_names[scenario_index] + '_scenario_as_' + plotType + ' for country ' + country_codes_3[country_index] + '.pdf')
+
+ plt.close(fig)
+
+
+#%% function pie charts real output per sector only rel diff
+
+def addBrokenDonutPlot(piedata,ax):
+ #circle scale lines
+ ax.add_patch(plt.Circle((0,0), base_radius*(1-0.1*rel_change_scaling), linestyle='--', fill = False, edgecolor='gray', linewidth=1))
+ ax.add_patch(plt.Circle((0,0), base_radius*(1-0.05*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+ ax.add_patch(plt.Circle((0,0), base_radius, linestyle='-', fill = False, linewidth=0.1 ))
+ ax.add_patch(plt.Circle((0,0), base_radius*(1+0.05*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+ for s in range(len(piedata)):
+ if scenarios_dif_rel[1][sector_thing][time_index,country_index,s] >= 0:
+ wedges1, texts1 = ax.pie(piedata, radius=base_radius * (1 + rel_change_scaling * scenarios_dif_rel[scenario_index][sector_thing][time_index,country_index,s]),
+ colors=sector_colors, wedgeprops=wedgeprops, counterclock=False, startangle=-270)
+ wedges1[s].set_width(base_radius*(1-1/1 + rel_change_scaling * scenarios_dif_rel[scenario_index][sector_thing][time_index,country_index,s]))
+ else:
+ wedges1, texts1 = ax.pie(piedata, radius=base_radius,
+ colors=sector_colors, wedgeprops=wedgeprops, counterclock=False, startangle=-270)
+ wedges1[s].set_width(- base_radius * rel_change_scaling * scenarios_dif_rel[scenario_index][sector_thing][time_index,country_index,s])
+ for wi in range(len(wedges1)):
+ if wi != s:
+ wedges1[wi].set_visible(False)
+
+#%% broken donut charts real output per sector only rel diff
+print('plotting broken donuts')
+
+basefigdir = 'figures/brokendonuts'
+
+max_val = 0
+min_val = 0
+for s in range(len(scenarios)):
+ max_val = max(max_val,np.max(scenarios_dif_rel[s][thing]))
+ min_val = min(min_val,np.min(scenarios_dif_rel[s][thing]))
+
+# initialize the figure
+for country_index in range(len(country_codes)):
+ for scenario_index in [0,1,2,3]:
+ print(sector_thing + ' in ' + scenarios_names[scenario_index] + ' scenario as ' + plotType + ' for country ' + country_codes_3[country_index])
+
+ # initialize the figure
+ nrows=3
+ ncols=6
+ fig, axes = plt.subplots(nrows, ncols, figsize=(subfig_size[0]*ncols,subfig_size[1]*nrows))
+ fig.suptitle(sector_thing + ' in ' + scenarios_names[scenario_index] + ' scenario as ' + plotType + ' for country ' + country_codes_3[country_index])
+ row = 0
+ col = 0
+ for time_index, timestep in enumerate(time_steps):
+ # determine subfigure
+ if(col==ncols):
+ row+=1
+ col=0
+ if row>=nrows or col>=ncols:
+ break
+ if ncols > 1:
+ ax = axes[row, col]
+ else:
+ ax = axes[row]
+ col+=1
+ piedata = base[sector_thing][time_index,country_index,:]
+ addBrokenDonutPlot(piedata,ax)
+ # title
+ ax.title.set_text(timestep)
+ # Hide unused subplots
+ for time_index in range(len(time_steps), nrows*ncols-2): # Adjust the range if the grid size is changed
+ if(col==ncols):
+ row+=1
+ col=0
+ axes[row, col].axis('off')
+ col+=1
+
+ # scale lines legend
+ ax = axes[nrows-1,ncols-2]
+ limits = 1+(0.09*rel_change_scaling)
+ ax.set_xlim(-limits, limits)
+ ax.set_ylim(-limits, limits)
+ ax.axis('off')
+ ax.add_patch(plt.Circle((0,0), (1-0.1*rel_change_scaling), linestyle='--', fill = False, edgecolor='gray', linewidth=1))
+ ax.add_patch(plt.Circle((0,0), (1-0.05*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+ ax.add_patch(plt.Circle((0,0), 1, linestyle='-', fill = False, edgecolor='k' ))
+ ax.add_patch(plt.Circle((0,0), (1+0.05*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+ ax.text(0,(1-0.1*rel_change_scaling),'-10%',fontsize=10,
+ backgroundcolor='white',color='gray',horizontalalignment='center',verticalalignment='center')
+ ax.text(0,(1-0.05*rel_change_scaling),'-5%',fontsize=10,
+ backgroundcolor='white',color='gray',horizontalalignment='center',verticalalignment='center')
+ ax.text(0,1,'reference',fontsize=10,
+ backgroundcolor='white',color='black',horizontalalignment='center',verticalalignment='center')
+ ax.text(0,(1+0.05*rel_change_scaling),'+5%',fontsize=10,
+ backgroundcolor='white',color='gray',horizontalalignment='center',verticalalignment='center')
+
+ # sector color legends
+ ax = axes[nrows-1,ncols-1]
+ wedges, texts = ax.pie(np.ones(len(piedata)), colors=sector_colors, labels=sector_names,
+ wedgeprops=wedgeprops, counterclock=False, startangle=-270)
+ ax.title.set_text('Sectors')
+
+ # display the plot
+ plt.tight_layout()
+ if showPlots: plt.show()
+
+ # save the plot
+ figdir = basefigdir + '/' + scenarios_names[scenario_index]
+ if not os.path.exists(figdir):
+ os.makedirs(figdir)
+ fig.savefig(figdir+'/brokendonut-'+ sector_thing + '_in_' + scenarios_names[scenario_index] + '_scenario_as_' + plotType + '_country_ ' + country_codes_3[country_index] + '.png')
+ fig.savefig(figdir+'/brokendonut-'+ sector_thing + '_in_' + scenarios_names[scenario_index] + '_scenario_as_' + plotType + '_country_' + country_codes_3[country_index] + '.pdf')
+
+ plt.close(fig)
+
+
+
+#%% spatial pie plot
+print('plotting maps with broken donuts')
+basefigdir='figures/maps-brokendonut'
+subfig_size = (15,15)
+insetfig_size = min(subfig_size)*0.06
+base_radius = 1
+
+thing = 'real_output_mean'
+sector_thing = 'real_sector_output_mean_nace1'
+sns.set(style="whitegrid", palette="pastel", color_codes=True)
+
+max_val = max(abs(min_val),abs(max_val))
+min_val = -max_val
+
+scenariosToPlot = [1]
+
+for scenario_index in scenariosToPlot:
+ print('running scenario '+scenarios_names[scenario_index])
+ if plotType == 'abs':
+ thing_df = pd.DataFrame({'time': np.repeat(time_steps, len(country_codes)), 'country': country_codes_3 * len(time_steps) , thing:base[thing].flatten()})
+ if plotType == 'dif_rel':
+ thing_df = pd.DataFrame({'time': np.repeat(time_steps, len(country_codes)), 'country': country_codes_3 * len(time_steps) , thing:scenarios_dif_rel[scenario_index][thing].flatten()})
+
+
+ # initialize the figure
+ nrows=3
+ ncols=6
+ fig, axes = plt.subplots(nrows, ncols, figsize=(subfig_size[0]*ncols,subfig_size[1]*nrows))
+ fig.suptitle(thing + ' in ' + scenarios_names[scenario_index] + ' scenario as ' + plotType)
+ row = 0
+ col = 0
+ for time_index, timestep in enumerate(time_steps):
+ print(' time step ' + timestep)
+ # determine subfigure
+ if(col==ncols):
+ row+=1
+ col=0
+ if row>=nrows or col>=ncols:
+ break
+ if ncols > 1:
+ ax = axes[row, col]
+ else:
+ ax = axes[row]
+ col+=1
+ # define colors
+ if plotType == 'abs':
+ cmap = cm.Reds
+ min_val, max_val = min(thing_df[thing]), max(thing_df[thing])
+ if plotType == 'dif_rel':
+ cmap = cm.seismic_r
+ norm = mcolors.Normalize(vmin=min_val, vmax=max_val)
+
+ # create the plot
+ thing_df_1 = thing_df[thing_df['time']==timestep]
+ mergedData = europe.merge(thing_df_1, how='left', left_on='ADM0_A3', right_on='country')
+ mergedData.plot(column=thing, cmap=cmap, norm=norm,
+ edgecolor='black', linewidth=0.2,ax=ax)
+
+ # custom axis
+ ax.set_xlim(-15, 35)
+ ax.set_ylim(32, 72)
+ ax.axis('off')
+
+ # arrow for BEL inset
+ ax.add_patch(patches.FancyArrowPatch((europe['LABEL_X'][europe['ADM0_A3']=='BEL'].iloc[0],
+ europe['LABEL_Y'][europe['ADM0_A3']=='BEL'].iloc[0]),
+ (europe['INSET_FIG_X'][europe['ADM0_A3']=='BEL'].iloc[0],
+ europe['INSET_FIG_Y'][europe['ADM0_A3']=='BEL'].iloc[0]-1.9),
+ color='k', linewidth=0.2,connectionstyle="arc3,rad=-.3"))
+
+ # arrow for LUX inset
+ ax.add_patch(patches.FancyArrowPatch((europe['LABEL_X'][europe['ADM0_A3']=='LUX'].iloc[0],
+ europe['LABEL_Y'][europe['ADM0_A3']=='LUX'].iloc[0]),
+ (europe['INSET_FIG_X'][europe['ADM0_A3']=='LUX'].iloc[0]+1.3,
+ europe['INSET_FIG_Y'][europe['ADM0_A3']=='LUX'].iloc[0]-1.3),
+ color='k', linewidth=0.2,connectionstyle="arc3,rad=-.3"))
+
+ # arrow for SVN inset
+ ax.add_patch(patches.FancyArrowPatch((europe['LABEL_X'][europe['ADM0_A3']=='SVN'].iloc[0],
+ europe['LABEL_Y'][europe['ADM0_A3']=='SVN'].iloc[0]),
+ (europe['INSET_FIG_X'][europe['ADM0_A3']=='SVN'].iloc[0]+1.9,
+ europe['INSET_FIG_Y'][europe['ADM0_A3']=='SVN'].iloc[0]),
+ color='k', linewidth=0.2,connectionstyle="arc3,rad=-.6"))
+
+ # arrow for SVK inset
+ ax.add_patch(patches.FancyArrowPatch((europe['LABEL_X'][europe['ADM0_A3']=='SVK'].iloc[0]+2,
+ europe['LABEL_Y'][europe['ADM0_A3']=='SVK'].iloc[0]),
+ (europe['INSET_FIG_X'][europe['ADM0_A3']=='SVK'].iloc[0]-1.3,
+ europe['INSET_FIG_Y'][europe['ADM0_A3']=='SVK'].iloc[0]-1.3),
+ color='k', linewidth=0.2,connectionstyle="arc3,rad=.1"))
+ # country inset plots
+ for country_index in range(len(country_codes)):
+ print('Making inset ' + country_codes_3[country_index])
+ ax_sub = inset_axes(ax, width=insetfig_size, height=insetfig_size, loc=10,
+ bbox_to_anchor=(europe[europe['ADM0_A3']==country_codes_3[country_index]]['INSET_FIG_X'],
+ europe[europe['ADM0_A3']==country_codes_3[country_index]]['INSET_FIG_Y']),
+ bbox_transform=ax.transData)
+ piedata = base[sector_thing][time_index,country_index,:]
+ addBrokenDonutPlot(piedata,ax_sub)
+
+ # define range and values for the legend
+ value_ranges = np.arange(min_val,max_val,(max_val-min_val)/10).tolist()
+ labels = ['%.2f' % elem for elem in value_ranges]
+
+ # parameters of the legend
+ rectangle_width = 2
+ rectangle_height = 1.5
+ legend_x = 30
+ legend_y_start = 55
+ legend_y_step = 1.5
+
+ # create the legend
+ for i in range(len(labels)):
+ value = (value_ranges[i]-min_val)/(max_val-min_val) # Normalize the value to [0, 1]
+ color = cmap(value)
+ ax.add_patch(plt.Rectangle((legend_x, legend_y_start - i * legend_y_step), rectangle_width, rectangle_height,
+ color=color, ec='black', lw=0.6))
+ ax.text(legend_x + 2.5, legend_y_start - i * legend_y_step + 0.7, labels[i],
+ fontsize=12, va='center')
+
+ # title
+ ax.title.set_text(timestep)
+
+ # scale lines legend
+ ax_sub = inset_axes(ax, width=insetfig_size*1.5, height=insetfig_size*1.5, loc=10,
+ bbox_to_anchor=(ax.get_xlim()[0]+3,ax.get_ylim()[1]-5),
+ bbox_transform=ax.transData)
+ limits = 1+(0.09*rel_change_scaling)
+ ax_sub.set_xlim(-limits, limits)
+ ax_sub.set_ylim(-limits, limits)
+ ax_sub.axis('off')
+ ax_sub.add_patch(plt.Circle((0,0), (1-0.1*rel_change_scaling), linestyle='--', fill = False, edgecolor='gray', linewidth=1))
+ ax_sub.add_patch(plt.Circle((0,0), (1-0.05*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+ ax_sub.add_patch(plt.Circle((0,0), 1, linestyle='-', fill = False, edgecolor='k' ))
+ ax_sub.add_patch(plt.Circle((0,0), (1+0.05*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+ ax_sub.title.set_text('Relative Change')
+ textkw = dict(facecolor='white',linewidth=0,pad=1)
+ txt=ax_sub.text(0,(1-0.11*rel_change_scaling),'-10%',fontsize=10,
+ backgroundcolor='white',color='gray',horizontalalignment='center',verticalalignment='center')
+ txt.set_bbox(textkw)
+ txt=ax_sub.text(0,(1-0.05*rel_change_scaling),'-5%',fontsize=10,
+ backgroundcolor='white',color='gray',horizontalalignment='center',verticalalignment='center')
+ txt.set_bbox(textkw)
+ # ax_sub.text(0,1,'reference',fontsize=10,
+ # backgroundcolor='white',color='black',horizontalalignment='center',verticalalignment='center')
+ txt=ax_sub.text(0,(1+0.05*rel_change_scaling),'+5%',fontsize=10,
+ backgroundcolor='white',color='gray',horizontalalignment='center',verticalalignment='center')
+ txt.set_bbox(textkw)
+
+ # sector color legends
+ ax_sub = inset_axes(ax, width=insetfig_size*1.5, height=insetfig_size*1.5, loc=10,
+ bbox_to_anchor=(ax.get_xlim()[0]+10,ax.get_ylim()[1]-5),
+ bbox_transform=ax.transData)
+ wedges, texts = ax_sub.pie(np.ones(len(piedata)), colors=sector_colors, labels=sector_names,
+ wedgeprops=wedgeprops, counterclock=False, startangle=-270)
+ ax_sub.title.set_text('Sectors')
+
+ # save just the subplot
+ figdir = basefigdir + '/' + scenarios_names[scenario_index]
+ if not os.path.exists(figdir):
+ os.makedirs(figdir)
+ fig.savefig(figdir+'/map-brokendonut-'+ sector_thing + '_in_' + scenarios_names[scenario_index] + '_scenario_as_' + plotType + '_time_step_ ' + timestep + '.png',
+ bbox_inches=ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted()).expanded(1, 1))
+ fig.savefig(figdir+'/map-brokendonut-'+ sector_thing + '_in_' + scenarios_names[scenario_index] + '_scenario_as_' + plotType + '_time_step_ ' + timestep + '.pdf',
+ bbox_inches=ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted()).expanded(1, 1))
+
+ # Hide unused subplots
+ for time_index in range(len(time_steps), nrows*ncols): # Adjust the range if the grid size is changed
+ if(col==ncols):
+ row+=1
+ col=0
+ axes[row, col].axis('off')
+ col+=1
+
+ # display the plot
+ plt.tight_layout()
+ if showPlots: plt.show()
+
+ # save just the whole figure
+ figdir = basefigdir
+ if not os.path.exists(figdir):
+ os.makedirs(figdir)
+ fig.savefig(figdir+'/map-brokendonut-'+ sector_thing + '_in_' + scenarios_names[scenario_index] + '_scenario_as_' + plotType + '.png')
+ fig.savefig(figdir+'/map-brokendonut-'+ sector_thing + '_in_' + scenarios_names[scenario_index] + '_scenario_as_' + plotType + '.pdf')
+
+ plt.close(fig)
+
+#%% test
+
+# subfig_width = 15
+# subfig_height = 15
+# insetfig_size = min(subfig_width,subfig_height)*0.06
+# base_radius = 1
+
+# fig, axes = plt.subplots(1, 1, figsize=(subfig_width, subfig_height))
+# fig.suptitle(thing + ' in ' + scenarios_names[scenario_index] + ' scenario as ' + plotType)
+# row = 1
+# col = 1
+# time_index, timestep = 5, 'Q5'
+# thing_df_1 = thing_df[thing_df['time']==timestep]
+
+# # determine subfigure
+# ax = axes
+
+# # create the plot
+# thing_df_1 = thing_df[thing_df['time']==timestep]
+# mergedData = europe.merge(thing_df_1, how='left', left_on='ADM0_A3', right_on='country')
+# mergedData.plot(column=thing, cmap=cmap, norm=norm,
+# edgecolor='black', linewidth=0.2,ax=ax)
+
+# # custom axis
+# ax.set_xlim(-15, 35)
+# ax.set_ylim(32, 72)
+# ax.axis('off')
+
+
+
+
+# # arrow for BEL inset
+# ax.add_patch(patches.FancyArrowPatch((europe['LABEL_X'][europe['ADM0_A3']=='BEL'].iloc[0],
+# europe['LABEL_Y'][europe['ADM0_A3']=='BEL'].iloc[0]),
+# (europe['INSET_FIG_X'][europe['ADM0_A3']=='BEL'].iloc[0],
+# europe['INSET_FIG_Y'][europe['ADM0_A3']=='BEL'].iloc[0]-1.9),
+# color='k', linewidth=0.2,connectionstyle="arc3,rad=-.3"))
+
+# # arrow for LUX inset
+# ax.add_patch(patches.FancyArrowPatch((europe['LABEL_X'][europe['ADM0_A3']=='LUX'].iloc[0],
+# europe['LABEL_Y'][europe['ADM0_A3']=='LUX'].iloc[0]),
+# (europe['INSET_FIG_X'][europe['ADM0_A3']=='LUX'].iloc[0]+1.3,
+# europe['INSET_FIG_Y'][europe['ADM0_A3']=='LUX'].iloc[0]-1.3),
+# color='k', linewidth=0.2,connectionstyle="arc3,rad=-.3"))
+
+# # arrow for SVN inset
+# ax.add_patch(patches.FancyArrowPatch((europe['LABEL_X'][europe['ADM0_A3']=='SVN'].iloc[0],
+# europe['LABEL_Y'][europe['ADM0_A3']=='SVN'].iloc[0]),
+# (europe['INSET_FIG_X'][europe['ADM0_A3']=='SVN'].iloc[0]+1.9,
+# europe['INSET_FIG_Y'][europe['ADM0_A3']=='SVN'].iloc[0]),
+# color='k', linewidth=0.2,connectionstyle="arc3,rad=-.6"))
+
+# # arrow for SVK inset
+# ax.add_patch(patches.FancyArrowPatch((europe['LABEL_X'][europe['ADM0_A3']=='SVK'].iloc[0]+2,
+# europe['LABEL_Y'][europe['ADM0_A3']=='SVK'].iloc[0]),
+# (europe['INSET_FIG_X'][europe['ADM0_A3']=='SVK'].iloc[0]-1.3,
+# europe['INSET_FIG_Y'][europe['ADM0_A3']=='SVK'].iloc[0]-1.3),
+# color='k', linewidth=0.2,connectionstyle="arc3,rad=.1"))
+
+# # country inset plots
+# for country_index in range(len(country_codes)):
+# print('Making inset ' + country_codes_3[country_index])
+# ax_sub = inset_axes(ax, width=insetfig_size, height=insetfig_size, loc=10,
+# bbox_to_anchor=(europe[europe['ADM0_A3']==country_codes_3[country_index]]['INSET_FIG_X'],
+# europe[europe['ADM0_A3']==country_codes_3[country_index]]['INSET_FIG_Y']),
+# bbox_transform=ax.transData)
+# piedata = base[sector_thing][time_index,country_index,:]
+# addBrokenDonutPlot(piedata,ax_sub)
+# # addRelChangePiePlot(piedata,ax_sub)
+# # limits = 1+(0.09*rel_change_scaling)
+# # ax_sub.set_xlim(-limits, limits)
+# # ax_sub.set_ylim(-limits, limits)
+# # ax_sub.axis('off')
+# # ax_sub.add_patch(plt.Circle((0,0), base_radius*(1+0.05*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+
+# # define range and values for the legend
+# value_ranges = np.arange(min_val,max_val,(max_val-min_val)/10).tolist()
+# labels = ['%.2f' % elem for elem in value_ranges]
+
+# # parameters of the legend
+# rectangle_width = 2
+# rectangle_height = 1.5
+# legend_x = 30
+# legend_y_start = 55
+# legend_y_step = 1.5
+
+# # create the legend
+# for i in range(len(labels)):
+# value = (value_ranges[i]-min_val)/(max_val-min_val) # Normalize the value to [0, 1]
+# color = cmap(value)
+# ax.add_patch(plt.Rectangle((legend_x, legend_y_start - i * legend_y_step), rectangle_width, rectangle_height,
+# color=color, ec='black', lw=0.6))
+# ax.text(legend_x + 2.5, legend_y_start - i * legend_y_step + 0.7, labels[i],
+# fontsize=12, va='center')
+
+# # title
+# ax.title.set_text(timestep)
+
+# # scale lines legend
+# ax_sub = inset_axes(ax, width=insetfig_size*1.5, height=insetfig_size*1.5, loc=10,
+# bbox_to_anchor=(ax.get_xlim()[0]+3,ax.get_ylim()[1]-5),
+# bbox_transform=ax.transData)
+# limits = 1+(0.09*rel_change_scaling)
+# ax_sub.set_xlim(-limits, limits)
+# ax_sub.set_ylim(-limits, limits)
+# ax_sub.axis('off')
+# ax_sub.add_patch(plt.Circle((0,0), (1-0.1*rel_change_scaling), linestyle='--', fill = False, edgecolor='gray', linewidth=1))
+# ax_sub.add_patch(plt.Circle((0,0), (1-0.05*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+# ax_sub.add_patch(plt.Circle((0,0), 1, linestyle='-', fill = False, edgecolor='k' ))
+# ax_sub.add_patch(plt.Circle((0,0), (1+0.05*rel_change_scaling), linestyle=':', fill = False, edgecolor='gray', linewidth=1))
+# ax_sub.title.set_text('Relative Change')
+# textkw = dict(facecolor='white',linewidth=0,pad=1)
+# txt=ax_sub.text(0,(1-0.11*rel_change_scaling),'-10%',fontsize=10,
+# backgroundcolor='white',color='gray',horizontalalignment='center',verticalalignment='center')
+# txt.set_bbox(textkw)
+# txt=ax_sub.text(0,(1-0.05*rel_change_scaling),'-5%',fontsize=10,
+# backgroundcolor='white',color='gray',horizontalalignment='center',verticalalignment='center')
+# txt.set_bbox(textkw)
+# # ax_sub.text(0,1,'reference',fontsize=10,
+# # backgroundcolor='white',color='black',horizontalalignment='center',verticalalignment='center')
+# txt=ax_sub.text(0,(1+0.05*rel_change_scaling),'+5%',fontsize=10,
+# backgroundcolor='white',color='gray',horizontalalignment='center',verticalalignment='center')
+# txt.set_bbox(textkw)
+
+# # sector color legends
+# ax_sub = inset_axes(ax, width=insetfig_size*1.5, height=insetfig_size*1.5, loc=10,
+# bbox_to_anchor=(ax.get_xlim()[0]+10,ax.get_ylim()[1]-5),
+# bbox_transform=ax.transData)
+# wedges, texts = ax_sub.pie(np.ones(len(piedata)), colors=sector_colors, labels=sector_names,
+# wedgeprops=wedgeprops, counterclock=False, startangle=-270)
+# ax_sub.title.set_text('Sectors')
--
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