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SM_plots.py
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import matplotlib.pyplot as plt
# This file is just used to generate some example plots for the supplementary materials.
# Define the Y-values for each scenario
rows1 = [[102.18957060431767, 94.8788031833301, 162.57516108499107, 154.8784227028888, 158.35107693832157, 189.0009168529093, 177.7290455104445, 85.44430570744306, 203.7387863495216, 157.7925483883392, 167.49315359896707, 147.5145436690785, 214.27042075334862, 166.3691911189883, 216.0130301819143, 132.9970717521781, 162.92491829080743, 117.63036286957934, 184.0497122054036, 94.2276342406811, 205.71894534586812, 110.16622053947246, 171.47505761217576, 100.13762035173103, 127.61927911218777, 169.8650441595272, 84.49727153337547],
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]
rows2 = [[100, 105.9712021744139, 117.4775126374767, 119.3773901514435, 135.63758173801955, 139.5449806550705, 146.64035901334705, 160.66933155781314, 159.29366146707778, 168.60268889579376, 178.19205578479313, 191.96112639826853, 194.23402857403923, 208.16061248830806, 211.5927303971241, 225.76077606230007, 232.21657748829307, 237.08265591053978, 244.13175032751204, 259.96522713588433, 266.9741064399666, 272.0971638346577, 266.78454838274814, 289.3244053572034, 285.63486434387323, 284.8694185362307, 296.33179161147115],
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]
rows3 = [[122.56465235478439, 123.51404853302584, 120.49062363224574, 123.27889935431969, 120.23107149416012, 120.15157838614047, 122.07138175329095, 123.58996314764165, 228.76619219653168, 121.818088152298, 121.89255318991475, 120.18498889646396, 120.95889530230271, 123.1479743260113, 120.90384082786711, 122.76932830606769, 122.89813258677461, 122.52102950615726, 121.00092938424116, 120.48234710764149, 120.08992787338555, 123.37181488426934, 121.43299184755074, 121.67986005454827, 120.67428787653641, 120.55949013051556, 120.66880384331388],
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]
rows4 = [[76.71076633621412, 107.0791533068457, 97.07831962743938, 79.21433802596395, 73.7077938865157, 97.2844884869719, 82.80860042991988, 76.45195198270432, 84.64563454685843, 88.17584326884781, 69.73615612341698, 70.62024659000339, 97.1873559379595, 93.25215157844684, 84.18696415089994, 95.4476282725233, 78.45439686760652, 62.07875921172309, 97.43027850487532, 91.22200908435411, 63.53911858432151, 106.75189718593896, 108.02242864234674, 94.93337560012908, 94.82998050532156, 96.81799121232125, 107.58372189378818],
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]
# Define the X-axis values (years)
years = range(2024, 2051)
# Define shades of pink for line colors
# colors = ['pink', 'lightpink', 'hotpink', 'deeppink', 'mediumvioletred', 'palevioletred', 'fuchsia', 'violet']
pink_colors = ['#FFC0CB', '#FFB6C1', '#FF69B4', '#FF1493', '#C71585', '#DB7093', '#FF00FF', '#EE82EE']
grey_colors = ['#404040', '#666666', '#808080', '#A0A0A0', '#B0B0B0', '#C0C0C0', '#D0D0D0', '#E0E0E0']
teal_colors = ['#008080', '#009090', '#00A0A0', '#00B0B0', '#00C0C0', '#00D0D0', '#00E0E0', '#00F0F0']
apricot_colors = ['#E57546', '#E88356', '#EDA265', '#F1B074', '#F4BE84', '#F8CB94', '#FCE9B3', '#FFF6C3']
colors = [pink_colors,grey_colors,teal_colors,apricot_colors]
# Create a figure and subplots
fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))
# Define the list of rows for each subplot
all_rows = [rows1, rows2, rows3, rows4]
x_labels = ["(A)","(B)","(C)","(D)"]
y_labels = ["Price of negative emissions [EUR/tCO2]", "Price of EU ETS allowances [EUR/tCO2]", "Price of electricity [EUR/MWh]", "Price of heat [EUR/MWh]"]
# Plot the line for each scenario in each subplot
for i, ax in enumerate(axs.flat):
for scenario, color in zip(all_rows[i], colors[i]):
ax.plot(years, scenario, color=color)
# Set labels and title for each subplot
# ax.set_xlabel('Year')
ax.set_ylabel(y_labels[i])
ax.set_xlabel(x_labels[i])
# Adjust spacing between subplots
plt.tight_layout()
# Display the combined figure
plt.show()