<jupyter_start><jupyter_text>UIUC GHG EmissionsThis notebook shows the emissions from UIUC by year and by goal. The data is not available in a downloadable format but can be accessed [here](https://icap.sustainability.illinois.edu/metric/total-campus-ghg-emissions).<jupyter_code>import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

plt.rcParams['figure.figsize'] = (12,9)
plt.rcParams['figure.facecolor'] = 'w'
plt.rcParams['pgf.rcfonts'] = False
plt.rcParams['text.usetex'] = True
plt.rcParams['font.family'] = 'serif'
# years of available data
years = np.arange(2008, 2020, 1).astype(int)
goalyr = np.arange(2008, 2026, 1).astype(int)
# in ktons of CO2eq
emissions = np.array([575.088, 546.778, 485.208, 471.628, 463.343, 503.747, 505.503, 
                      475.418, 477.287, 413.106, 433.751, 433.797])
goals = {2015:459.875, 2020:402.562, 2025:344.906}

plt.plot(years, emissions, label="CO2 equivalent", marker='o', lw=5, linestyle='--', 
         color='tab:purple', markersize=10)
plt.scatter(goals.keys(), goals.values(), label='iCAP Target', marker='*', s=500, color='tab:red')
plt.title("UIUC Emissions vs. iCAP Targets", fontsize=36)
plt.ylabel("ktons of CO$_2$ equivalent", fontsize=21)
plt.yticks(fontsize=21)
plt.xlabel("Year", fontsize=21)
plt.xticks(goalyr, fontsize=21, rotation=45)
plt.grid(which='major')
plt.legend(loc=(1.02,0.5),fancybox=True, shadow=True, fontsize=21,prop={'size': 21})
# plt.show()
plt.savefig("../publications/papers/optimal-sizing-paper/images/icap_goals.pgf")
plt.savefig("../publications/papers/optimal-sizing-paper/images/icap_goals.eps", bbox_inches='tight')<jupyter_output>The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.<jupyter_text>We have to interpolate the goals for years not listed by iCAP<jupyter_code>data = np.empty(36)
data[:] = np.NaN
data[0] = 459.875
data[5] = 402.562
data[10] = 344.906
data[-1] = 0.0
icap_df = pd.DataFrame({'year':np.arange(2015,2051,1), 'goal':data})
icap_df['goal'] = icap_df['goal'].interpolate(method='linear')
mask = (icap_df['year'] <= 2050) & (icap_df['year'] >= 2021)
icap_df[mask]<jupyter_output><empty_output>
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