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z42nPk?~o7zR22Rv9T3iT1H&Lu7%>bCjllq62}2(|>fA{W$_V=lqm3Y)mj8P`s`>*|7!rv9 z${Yj^^!*qF1O;3WEYY0vduMpb27N1X>IX!D9$ms3-=H zB#6QA2+WU_VQ&)%Q2E~jSeg9m*jq(%WjP=t3)_7QbH6V1?Z>S;D6TykYqhWD{5;Y`# z(VMWQ6PE(Ccyax_YvKn!m=<;D?=_H@HY531B7S6=X z+{^^nXF&hVB9C}V4|8lxbr;w&83Fc+_wW7G8Xic8n!5Y%S$qLl^pKa>#{i)s!S*u? zCi4SnP?y&-kprCIV)LKP?w?tRkWT4!-Dlk4paiaDfWCh}i*@G%>Cggvf&ZPi_Yw)t z=P>OBT+5yVHe|T>`NGWi_ZluK%PFX7Df}HeAkfbd|G&fizWmI6?e}1+3YV3X)s%ST zWwm8_WYy$(G%l;lDQIZ{zuT7r-G`R+pbwnZ;sIm41X4hL-*6rNpBk{%c(g4Z_kRXb z8=#TsrS>^fVB)p|jHI+rqgmYlkJDbr^4Q}Yfq&?|yBtL9J^nT%1!#nLN$*j7*hx=7 z>0!VtGW(PYCH()C{wHO4(AL)bw1Gh3KNYIue`)*fHqyl3Yg1F#RyeP&esQ0V5C9xu z5kK}mU65uF++e|sgj|8^gZi)6rh8wg|s{1gIpdIk6-2=qVcdYTac literal 0 HcmV?d00001 From d6ec2d145a0e1e5b9dc227fca8dea8969569afb6 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 23 Jul 2024 21:41:30 +0530 Subject: [PATCH 02/58] Create CH4N2O --- GLOBAL CLIMATE ANALYSIS/DATA/CH4N2O | 1 + 1 file changed, 1 insertion(+) create mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/CH4N2O diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/CH4N2O b/GLOBAL CLIMATE ANALYSIS/DATA/CH4N2O new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/DATA/CH4N2O @@ -0,0 +1 @@ + From 27525dc9bb751237009ccf6f2b2ecf02ea361025 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 23 Jul 2024 21:42:18 +0530 Subject: [PATCH 03/58] ADD DATA USED --- .../DATA/CH4_N2O_Emissions.csv | 205 +++++++++++++++++ .../DATA/CO2_Emissions.csv | 216 ++++++++++++++++++ .../DATA/NOx_Emissions.csv | 167 ++++++++++++++ .../DATA/ODS_Consumption.csv | 170 ++++++++++++++ .../DATA/SO2_emissions.csv | 136 +++++++++++ .../DATA/merged_ghg_data.csv | 189 +++++++++++++++ 6 files changed, 1083 insertions(+) create mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/CH4_N2O_Emissions.csv create mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/CO2_Emissions.csv create mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/NOx_Emissions.csv create mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/ODS_Consumption.csv create mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/SO2_emissions.csv create mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/merged_ghg_data.csv diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/CH4_N2O_Emissions.csv b/GLOBAL CLIMATE ANALYSIS/DATA/CH4_N2O_Emissions.csv new file mode 100644 index 000000000..75157d8de --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/DATA/CH4_N2O_Emissions.csv @@ -0,0 +1,205 @@ +Country,latest year available,CH4 emissions,CH4 emissions per capita, % change since 1990,N2O emissions,N2O emissions per capita, % change since 1990, +Afghanistan,2005,10.16,0.42,...,5.94,0.24,...,\ +Albania,1994,2.14,0.68,...,0.29,0.09,..., +Algeria,2000,32.92,1.06,...,6.50,0.21,..., +Angola,2005,19.93,1.11,...,13.87,0.77,..., +Antigua and Barbuda,2000,0.14,1.83,43.74,0.08,1.08,5197.47, +Argentina,2000,84.85,2.29,10.50,67.50,1.82,30.26, +Armenia,2010,2.26,0.76,-28.66,0.48,0.16,185.46, +Australia,2012,111.71,4.88,-3.02,25.78,1.13,40.43, +Austria,2012,5.31,0.63,-36.32,5.22,0.62,-15.75, +Azerbaijan,1994,9.29,1.21,-38.78,0.66,0.09,-26.30, +Bahamas,1994,0.02,0.08,0.00,0.31,1.12,..., +Bahrain,2000,3.91,7.11,...,0.04,0.07,..., +Bangladesh,2005,39.47,0.28,...,19.11,0.13,..., +Barbados,1997,1.81,6.77,8.78,0.05,0.19,0.00, +Belarus,2012,15.39,1.62,1.14,16.40,1.73,-18.52, +Belgium,2012,6.39,0.58,-33.82,6.99,0.63,-35.86, +Belize,1994,5.57,27.60,...,0.17,0.85,..., +Benin,2000,2.32,0.33,...,2.51,0.36,..., +Bhutan,2000,0.59,1.05,...,0.47,0.82,..., +Bolivia (Plurinational State of),2004,11.97,1.34,28.03,1.39,0.15,140.53, +Bosnia and Herzegovina,2001,2.42,0.64,-45.70,1.46,0.38,-53.32, +Botswana,2000,2.16,1.40,...,2.04,1.32,..., +Brazil,2005,316.28,1.68,34.49,162.78,0.86,45.06, +Bulgaria,2012,7.19,0.98,-56.60,5.24,0.72,-59.79, +Burkina Faso,1994,4.70,0.48,...,0.37,0.04,..., +Burundi,2005,0.53,0.07,...,25.77,3.25,..., +Cabo Verde,2000,0.07,0.16,...,0.09,0.21,..., +Cambodia,1994,7.77,0.75,...,3.67,0.35,..., +Cameroon,1994,17.71,1.31,...,145.25,10.72,..., +Canada,2012,90.56,2.60,25.78,47.73,1.37,-2.92, +Central African Republic,1994,11.88,3.65,...,25.64,7.88,..., +Chad,1993,6.94,1.06,...,0.77,0.12,..., +Chile,2010,11.46,0.67,...,9.70,0.57,..., +China,2005,932.86,0.71,...,394.10,0.30,..., +Colombia,2004,53.87,1.26,21.37,34.38,0.80,39.53, +Comoros,1994,0.06,0.12,...,0.39,0.83,..., +Congo,2000,0.50,0.16,...,0.27,0.09,..., +Cook Islands,1994,0.01,0.58,...,0.04,2.04,..., +Costa Rica,2005,3.53,0.83,11.89,2.59,0.61,1302.52, +Côte d'Ivoire,2000,25.15,1.52,...,185.68,11.24,..., +Croatia,2012,3.42,0.80,-7.41,3.30,0.77,-17.40, +Cuba,1996,6.64,0.61,-37.81,7.04,0.64,-59.67, +Cyprus,2012,1.30,1.15,43.28,0.61,0.54,11.11, +Czech Republic,2012,10.26,0.97,-42.67,7.73,0.73,-42.69, +Democratic People's Republic of Korea,2002,12.62,0.54,-49.11,0.93,0.04,-76.92, +Democratic Republic of the Congo,2003,37.29,0.71,...,6.06,0.12,..., +Denmark,2012,5.52,0.99,-7.26,5.99,1.07,-38.99, +Djibouti,2000,0.28,0.39,...,0.45,0.62,..., +Dominica,2005,0.03,0.46,...,0.03,0.43,..., +Dominican Republic,2000,4.84,0.56,59.11,3.18,0.37,279.36, +Ecuador,2006,17.17,1.23,31.61,201.48,14.42,33.12, +Egypt,2000,39.43,0.58,77.83,24.45,0.36,136.65, +El Salvador,2005,3.36,0.57,...,1.65,0.28,..., +Eritrea,2000,3.00,0.85,...,0.31,0.09,..., +Estonia,2012,0.93,0.70,-44.58,1.01,0.76,-55.03, +Ethiopia,1995,37.44,0.65,-0.45,7.44,0.13,140.00, +Fiji,2004,0.51,0.63,...,0.63,0.77,..., +Finland,2012,4.08,0.75,-33.82,5.18,0.96,-29.94, +France,2012,51.74,0.81,-13.40,57.77,0.91,-36.95, +Gabon,2000,0.71,0.58,...,0.29,0.24,..., +Gambia,2000,0.80,0.65,...,1.10,0.90,..., +Georgia,2006,4.14,0.93,-42.19,2.20,0.50,-8.84, +Germany,2012,48.71,0.61,-55.23,55.80,0.69,-34.60, +Ghana,2006,6.42,0.31,113.56,3.96,0.18,40.77, +Greece,2012,9.71,309.10,-8.53,6.81,0.61,-33.39, +Grenada,1994,1.47,14.79,...,0.00,0.02,..., +Guatemala,1990,4.09,0.45,0.00,6.41,0.70,0.00, +Guinea,1994,3.25,0.43,...,0.23,0.03,..., +Guinea-Bissau,1994,0.67,0.58,...,0.85,0.73,..., +Guyana,2004,1.19,1.60,11.74,0.23,0.31,6.94, +Haiti,2000,3.67,0.43,...,1.57,0.18,..., +Honduras,2000,3.85,0.62,...,2.29,0.37,..., +Hungary,2012,7.99,0.80,-32.72,6.76,0.68,-47.59, +Iceland,2012,0.46,1.41,4.63,0.46,1.42,-12.08, +India,2000,407.24,0.39,...,79.80,0.08,..., +Indonesia,2000,236.33,1.12,122.72,28.47,0.13,58.04, +Iran (Islamic Republic of),2000,75.71,1.15,...,40.15,0.61,..., +Ireland,2012,12.07,2.59,-11.70,7.42,1.59,-18.61, +Israel,2010,6.81,0.92,...,2.57,0.35,..., +Italy,2012,36.08,0.60,-17.56,27.53,0.46,-26.52, +Jamaica,1994,1.22,0.49,...,106.54,43.19,..., +Japan,2012,20.03,0.16,-38.32,20.23,0.16,-31.94, +Jordan,2006,3.07,0.55,...,1.55,0.28,..., +Kazakhstan,2012,49.03,2.91,-32.23,9.49,0.56,-47.13, +Kenya,1994,15.54,0.58,...,0.42,0.02,..., +Kiribati,1994,0.01,0.12,...,0.00,0.00,..., +Kuwait,1994,2.71,1.60,...,0.14,0.08,..., +Kyrgyzstan,2005,3.02,0.59,-50.42,0.15,0.03,-16.53, +Lao People's Democratic Republic,2000,5.34,1.00,-16.68,2.50,0.47,6625.00, +Latvia,2012,1.63,0.80,-51.23,1.82,0.89,-52.41, +Lebanon,2000,1.83,0.56,...,1.04,0.00,..., +Lesotho,2000,1.26,0.68,...,1.45,0.78,..., +Liberia,2000,4.14,1.43,...,0.31,0.11,..., +Liechtenstein,2012,0.02,0.43,8.92,0.01,0.34,-5.95, +Lithuania,2012,3.05,1.01,-46.93,4.14,1.37,-42.33, +Luxembourg,2012,0.43,0.80,-7.64,0.47,0.87,-2.36, +Madagascar,2000,6.92,0.44,...,20.68,1.31,..., +Malawi,1994,3.95,0.41,-43.98,2.41,0.25,625.23, +Malaysia,2000,51.65,2.21,...,2.90,0.12,..., +Maldives,1994,0.02,...,...,…,…,…, +Mali,2000,8.85,0.80,...,1.77,0.16,..., +Malta,2012,0.10,0.25,42.30,0.06,0.14,11.29, +Mauritania,2000,4.15,1.53,...,1.66,0.61,..., +Mauritius,2006,1.39,1.13,...,0.19,0.15,..., +Mexico,2006,187.78,1.69,76.22,20.34,110.55,96.25, +Micronesia (Federated States of),1994,0.01,0.07,...,0.00,0.03,..., +Monaco,2012,0.00,0.02,-58.15,0.00,0.08,64.61, +Mongolia,2006,6.53,2.55,15.83,0.46,0.18,1380.00, +Montenegro,2003,0.53,0.87,-6.26,0.29,0.46,-22.03, +Morocco,2000,9.09,0.31,...,17.06,0.59,..., +Mozambique,1994,5.66,0.37,13.51,0.98,0.06,37.01, +Myanmar,2005,26.58,0.53,...,3.31,0.07,..., +Namibia,2000,6.76,3.56,...,0.31,0.16,..., +Nauru,1994,0.01,0.74,...,0.00,0.03,..., +Nepal,1994,19.92,0.95,...,9.64,0.46,..., +Netherlands,2012,14.94,0.89,-41.86,9.06,0.54,-54.68, +New Zealand,2012,29.04,6.55,8.21,10.89,2.45,32.02, +Nicaragua,2000,4.27,0.85,...,3.87,0.77,..., +Niger,2000,6.76,0.60,96.78,4.96,0.44,506.68, +Nigeria,2000,88.18,0.72,...,7.44,0.06,..., +Niue,1994,0.01,6.35,...,0.01,5.59,..., +Norway,2012,4.23,0.84,-14.76,3.20,0.64,-36.55, +Oman,1994,2.61,1.22,...,7.09,3.31,..., +Pakistan,1994,60.70,0.51,...,11.45,0.10,..., +Palau,2000,0.03,1.59,...,0.06,3.24,..., +Panama,2000,3.15,1.04,...,1.38,0.46,..., +Papua New Guinea,1994,0.09,0.02,...,3.78,0.82,..., +Paraguay,2000,11.48,2.16,-32.19,8.31,1.57,-77.55, +Peru,2010,21.86,0.74,...,13.78,0.47,..., +Philippines,2000,31.80,0.41,...,12.38,0.16,..., +Poland,2012,41.03,1.06,-17.36,29.59,0.77,-29.19, +Portugal,2012,12.25,1.17,20.03,4.48,0.43,-19.32, +Qatar,2007,3.53,2.99,...,0.45,0.38,..., +Republic of Korea,2012,29.78,0.60,-6.81,14.24,0.29,48.96, +Republic of Moldova,2009,2.68,0.66,-41.57,1.61,0.39,-51.53, +Romania,2012,22.24,1.11,-48.22,11.61,0.58,-52.59, +Russian Federation,2012,502.56,3.51,-15.31,115.95,0.81,-48.07, +Rwanda,2005,1.51,0.17,...,4.14,0.46,..., +Saint Kitts and Nevis,1994,0.06,1.40,...,0.03,0.80,..., +Saint Lucia,2000,0.16,1.05,...,0.04,0.25,..., +Saint Vincent and the Grenadines,1997,0.06,0.60,3.65,0.24,2.21,-3.77, +Samoa,1994,0.07,0.41,...,0.39,2.31,..., +San Marino,2007,0.00,0.11,...,0.00,0.06,..., +Sao Tome and Principe,2005,0.03,0.17,...,0.01,0.04,..., +Saudi Arabia,2000,27.55,1.29,66.71,11.79,0.55,12.52, +Senegal,2000,6.48,0.66,...,3.62,0.37,..., +Serbia,1998,8.91,0.92,-1.84,6.82,0.71,-22.03, +Seychelles,2000,0.06,0.71,...,0.01,0.15,..., +Singapore,2010,0.11,0.02,...,0.44,0.09,..., +Slovakia,2012,4.33,0.80,-16.57,2.94,0.54,-53.55, +Slovenia,2012,1.87,0.91,-11.85,1.11,0.54,-12.55, +South Africa,1994,43.21,1.07,0.21,20.67,0.51,-11.28, +Spain,2012,32.32,0.69,23.27,24.02,0.52,-9.81, +Sri Lanka,2000,6.80,0.36,...,1.07,0.06,..., +Sudan,2000,43.99,1.57,...,17.67,0.63,..., +Suriname,2003,0.86,1.77,...,...,...,..., +Swaziland,1994,1.35,1.43,...,0.41,0.44,..., +Sweden,2012,4.81,0.50,-31.18,6.19,0.65,-23.70, +Switzerland,2012,3.72,0.46,-19.84,3.02,0.38,-13.13, +Tajikistan,2010,3.49,0.46,-5.68,2.79,0.37,0.00, +Thailand,2000,58.61,0.93,...,12.34,5.99,..., +The former Yugoslav Republic of Macedonia,2009,1.66,0.80,-3.62,0.91,0.01,-27.62, +Timor-Leste,2010,0.55,0.52,...,0.47,0.44,..., +Togo,2000,1.11,0.23,...,2.35,0.48,..., +Tonga,2000,0.09,0.96,...,0.06,0.57,..., +Trinidad and Tobago,1990,0.69,0.56,0.00,0.33,0.27,0.00, +Tunisia,2000,5.81,0.60,...,5.76,0.59,..., +Turkey,2012,61.62,0.82,80.96,14.79,0.20,21.04, +Turkmenistan,2004,33.26,7.08,...,1.78,0.38,..., +Tuvalu,1994,0.00,0.10,...,0.00,0.00,..., +Uganda,2000,9.46,0.40,...,16.72,0.70,..., +Ukraine,2012,66.17,1.46,-59.24,31.37,0.69,-46.59, +United Arab Emirates,2005,29.93,9.81,...,6.14,2.01,..., +United Kingdom of Great Britain and Northern Ireland,2012,52.78,0.83,-51.60,35.41,0.56,-48.74, +United Republic of Tanzania,1994,21.63,0.75,4.73,14.38,0.50,-4.68, +United States of America,2012,552.01,1.75,-12.82,395.79,1.26,0.07, +Uruguay,2004,18.63,5.61,14.95,11.79,3.55,-2.16, +Uzbekistan,2005,89.43,3.45,57.73,9.97,0.38,-22.78, +Vanuatu,1994,0.24,1.43,...,0.01,0.05,..., +Venezuela (Bolivarian Republic of),1999,61.92,2.58,...,16.14,0.67,..., +Viet Nam,2010,87.32,0.99,...,32.70,0.37,..., +Yemen,2000,4.42,0.25,...,3.77,0.21,..., +Zambia,2000,6.57,0.62,...,5.33,0.50,..., +Zimbabwe,2000,7.48,0.60,...,36.20,2.90,..., +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/CO2_Emissions.csv b/GLOBAL CLIMATE ANALYSIS/DATA/CO2_Emissions.csv new file mode 100644 index 000000000..803285a3a --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/DATA/CO2_Emissions.csv @@ -0,0 +1,216 @@ +Country,CO2 emissions ,% change since 1990,CO2 emissions per capita,CO2 emissions per km2 +,mio. tonnes ,%,tonnes,tonnes +Afghanistan,12.25,357.7,0.43,18.77 +Albania,4.67,-37.7,1.62,162.38 +Algeria,121.76,54.3,3.32,51.12 +Andorra,0.49,...,5.97, 1 050.00 +Angola,29.71,570.7,1.35,23.83 +Anguilla,0.14,...,10.25, 1 571.43 +Antigua and Barbuda,0.51,70.7,5.82, 1 161.54 +Argentina,190.03,68.7,4.56,68.35 +Armenia,4.96,...,1.67,166.81 +Aruba,2.44,32.5,23.92, 13 547.78 +Australia,398.16,44.2,17.66,51.76 +Austria,70.35,13.4,8.35,838.83 +Azerbaijan,33.46,...,3.63,386.35 +Bahamas,1.91,-2.3,5.2,136.76 +Bahrain,23.44,85.1,17.95, 30 943.23 +Bangladesh,57.07,267.4,0.37,386.73 +Barbados,1.57,45.7,5.58, 3 641.40 +Belarus,55.38,-46.7,5.84,266.77 +Belgium,104.27,-12.4,9.47, 3 415.58 +Belize,0.55,76.5,1.67,23.95 +Benin,4.99,597.4,0.51,43.46 +Bermuda,0.39,-34.3,6.17, 7 403.77 +Bhutan,0.56,337.3,0.77,14.61 +Bolivia (Plurinational State of),16.12,191.7,1.6,14.67 +Bosnia and Herzegovina,23.75,...,6.2,463.74 +Botswana,4.86,122.9,2.32,8.34 +Brazil,439.41,110.4,2.19,51.61 +British Virgin Islands,0.18,166.7,6.31, 1 165.56 +Brunei Darussalam,9.74,56.8,24.39, 1 690.06 +Bulgaria,53.2,-33.7,7.23,479.78 +Burkina Faso,1.93,229.4,0.12,7.08 +Burundi,0.21,-28.8,0.02,7.51 +Cabo Verde,0.43,383.4,0.86,105.48 +Cambodia,4.5,896.8,0.31,24.83 +Cameroon,5.66,225.7,0.27,11.9 +Canada,557.29,21.4,16.15,55.81 +Cayman Islands,0.58,130.5,10.31, 2 208.71 +Central African Republic,0.29,44.4,0.06,0.46 +Chad,0.54,267.4,0.04,0.42 +Chile,79.41,138.4,4.62,105.02 +China, 9 019.52,266.5,6.69,939.83 +"China, Hong Kong Special Administrative Region",40.27,45.6,5.72, 36 480.71 +"China, Macao Special Administrative Region",1.17,12.8,2.13, 38 870.00 +Colombia,72.42,26.3,1.56,63.43 +Comoros,0.16,104.8,0.22,70.56 +Congo,2.25,89.2,0.54,6.57 +Cook Islands,0.07,216.8,3.42,295.34 +Costa Rica,7.84,165.4,1.7,153.5 +Côte d'Ivoire,6.45,11.2,0.31,19.99 +Croatia,20.92,-10.4,4.86,369.62 +Cuba,35.92,7.2,3.17,326.91 +Cyprus,7.57,63.5,6.78,817.88 +Czech Republic,115.07,-30.1,10.92, 1 459.06 +Democratic People's Republic of Korea,73.58,-69.9,2.99,610.42 +Democratic Republic of the Congo,3.43,-15.9,0.05,1.46 +Denmark,45.48,-16.1,8.15, 1 055.26 +Djibouti,0.47,25.2,0.56,20.39 +Dominica,0.12,112.4,1.75,166.05 +Dominican Republic,21.89,137.1,2.18,449.72 +Ecuador,35.73,112.2,2.35,139.36 +Egypt,220.79,190.7,2.64,220.35 +El Salvador,6.68,155.3,1.1,317.71 +Equatorial Guinea,6.69, 5 427.8,8.91,238.44 +Eritrea,0.52,...,0.11,4.43 +Estonia,18.43,-49.8,13.88,407.44 +Ethiopia,7.54,149.9,0.08,6.83 +Faeroe Islands,0.57,-8.8,11.72,408.04 +Falkland Islands (Malvinas),0.06,49.9,19.26,4.52 +Fiji,1.24,51.1,1.42,67.63 +Finland,56.4,-0.4,10.45,167.44 +France,364.82,-8.5,5.77,661.5 +French Guiana,0.72,-11.7,2.99,8.6 +French Polynesia,0.86,36.1,3.17,214.53 +Gabon,2.24,-53.8,1.42,8.36 +Gambia,0.42,121.1,0.24,37.34 +Georgia,7.93,...,1.89,113.8 +Germany,810.44,-22.2,10.08, 2 269.37 +Ghana,10.08,156.4,0.4,42.26 +Gibraltar,0.45,377.1,14.64, 75 783.33 +Greece,94.25,13.6,8.45,714.25 +Greenland,0.71,27,12.54,0.33 +Grenada,0.25,130,2.41,735.47 +Guadeloupe,1.77,37.1,3.87, 1 040.94 +Guatemala,11.26,121.3,0.75,103.39 +Guinea,2.6,145.8,0.23,10.56 +Guinea-Bissau,0.25,-2.9,0.15,6.8 +Guyana,1.78,56.3,2.36,8.29 +Haiti,2.21,122.5,0.22,79.68 +Honduras,8.41,224.5,1.1,74.78 +Hungary,49.86,-31.2,4.99,535.96 +Iceland,3.33,54.3,10.38,32.36 +India, 2 074.34,200.4,1.66,631.02 +Indonesia,563.98,277.1,2.3,295.14 +Iran (Islamic Republic of),586.6,177.8,7.8,360.15 +Iraq,133.65,154.3,4.19,307.08 +Ireland,37.72,16.3,8.11,540.15 +Israel,69.52,90.3,9.19, 3 149.81 +Italy,413.38,-4.9,6.93, 1 371.82 +Jamaica,7.76,-2.6,2.82,705.67 +Japan, 1 240.63,8.7,9.75, 3 282.70 +Jordan,22.26,114,3.29,249.18 +Kazakhstan,261.76,...,15.81,96.06 +Kenya,13.57,133,0.33,23.34 +Kiribati,0.06,183.2,0.6,85.77 +Kuwait,91.03,88.4,28.1, 5 108.86 +Kyrgyzstan,6.62,...,1.19,33.08 +Lao People's Democratic Republic,1.2,412.5,0.19,5.08 +Latvia,7.75,-59.3,3.76,120.06 +Lebanon,20.49,125.1,4.46, 1 960.15 +Lesotho,2.2,...,1.08,72.48 +Liberia,0.89,84.1,0.22,8 +Libya,39.02,6.1,6.2,22.18 +Liechtenstein,0.18,-10.4,4.9, 1 125.00 +Lithuania,14.03,-60.8,4.57,214.85 +Luxembourg,11.14,-6.8,21.42, 4 307.15 +Madagascar,2.45,148.3,0.11,4.17 +Malawi,1.21,97,0.08,10.18 +Malaysia,225.69,298.8,7.9,682.26 +Maldives,1.1,616.8,3.26, 3 679.33 +Mali,1.25,196.5,0.08,1.01 +Malta,2.67,42.9,6.44, 8 442.72 +Marshall Islands,0.1,115.3,1.95,567.4 +Martinique,2.45,18.4,6.21, 2 171.63 +Mauritania,2.31,-13.3,0.63,2.24 +Mauritius,3.92,167.7,3.13, 1 989.03 +Mexico,466.55,48.4,3.88,237.5 +Micronesia (Federated States of),0.13,...,1.24,182.76 +Monaco,0.08,-24.9,2.13, 39 600.00 +Mongolia,19.08,90,6.92,12.2 +Montenegro,2.57,...,4.13,186.11 +Montserrat,0.08,100.2,16.16,791.18 +Morocco,56.54,140.2,1.74,126.61 +Mozambique,3.28,227.8,0.13,4.09 +Myanmar,10.44,144.2,0.2,15.43 +Namibia,2.78,10701.2,1.24,3.37 +Nauru,0.05,-67.5,5.11, 2 442.86 +Nepal,4.33,583.2,0.16,29.45 +Netherlands,168.06,5.5,10.07, 4 499.07 +New Caledonia,3.85,137.2,15.43,207.48 +New Zealand,33.26,33.5,7.55,122.97 +Nicaragua,4.9,92.2,0.84,37.58 +Niger,1.42,70.9,0.08,1.12 +Nigeria,88.03,94,0.54,95.29 +Niue,0.01,197.3,6.81,42.31 +Norway,44.6,27.8,9,137.73 +Oman,64.85,469.6,20.2,209.55 +Pakistan,163.45,138.4,0.94,205.32 +Palau,0.22,...,10.86,487.36 +Panama,9.67,249.1,2.63,128.17 +Papua New Guinea,5.23,144.2,0.75,11.3 +Paraguay,5.3,134.2,0.84,13.03 +Peru,53.07,150.7,1.78,41.29 +Philippines,82.01,96.4,0.87,273.38 +Poland,327.72,-12.6,8.49, 1 050.77 +Portugal,51.24,13.6,4.85,555.71 +Qatar,83.88,612.3,44.02, 7 226.27 +Republic of Korea,589.43,138.7,11.94, 5 892.32 +Republic of Moldova,4.98,...,1.22,147.13 +Réunion,4.51,210.6,5.39, 1 794.83 +Romania,85.6,-51.9,4.26,359.09 +Russian Federation, 1 650.27,-34.2,11.52,96.52 +Rwanda,0.66,22.3,0.06,25.2 +Saint Helena,0.01,50.7,2.66,90.16 +Saint Kitts and Nevis,0.27,305.6,5.05, 1 025.67 +Saint Lucia,0.41,146.7,2.27,755.1 +Saint Pierre and Miquelon,0.07,-24,11.11,288.02 +Saint Vincent and the Grenadines,0.24,195.4,2.18,612.85 +Samoa,0.23,88.2,1.25,82.59 +Sao Tome and Principe,0.1,115.3,0.59,106.54 +Saudi Arabia,520.28,138.7,18.07,242.02 +Senegal,7.86,146.9,0.59,39.95 +Serbia,49.19,...,5.45,556.64 +Seychelles,0.6,425.7,6.37, 1 323.32 +Sierra Leone,0.9,131.1,0.15,12.43 +Singapore,22.39,-52.3,4.31, 31 351.53 +Slovakia,37.23,-39.8,6.88,759.3 +Slovenia,16.18,9.4,7.86,798 +Solomon Islands,0.2,22.8,0.37,6.85 +Somalia,0.58,3045.9,0.06,0.9 +South Africa,477.24,49.2,9.14,390.85 +Spain,280.92,23.5,6.01,555.19 +Sri Lanka,15.23,293.7,0.75,232.17 +State of Palestine,2.25,...,0.54,373.41 +Suriname,1.91,5.5,3.65,11.66 +Swaziland,1.05,146.5,0.87,60.4 +Sweden,48.48,-15.2,5.12,107.67 +Switzerland,41.85,-6.3,5.28, 1 013.63 +Syrian Arab Republic,57.67,54,2.81,311.43 +Tajikistan,2.78,...,0.36,19.45 +Thailand,303.37,216.6,4.53,591.23 +The former Yugoslav Republic of Macedonia,9.34,...,4.52,363.09 +Timor-Leste,0.18,...,0.17,12.29 +Togo,2.1,171.1,0.32,36.94 +Tonga,0.1,33.4,0.98,137.48 +Trinidad and Tobago,49.57,192.3,37.14, 9 663.59 +Tunisia,25.64,93.3,2.38,156.73 +Turkey,345.73,144.2,4.7,441.23 +Turkmenistan,62.22,...,12.18,127.47 +Turks and Caicos Islands,0.19,...,6.01,201.11 +Uganda,3.8,373,0.11,15.73 +Ukraine,306.53,-57.6,6.74,507.93 +United Arab Emirates,178.48,243.2,20.43, 2 134.98 +United Kingdom of Great Britain and Northern Ireland,464.04,-21.5,7.35, 1 913.59 +United Republic of Tanzania,7.3,207.7,0.2,7.73 +United States of America, 5 583.38,9.5,17.87,579.84 +Uruguay,7.77,94.7,2.3,44.12 +Uzbekistan,114.86,...,4.08,256.73 +Vanuatu,0.14,105.2,0.59,11.73 +Venezuela (Bolivarian Republic of),188.82,54.6,6.42,207.03 +Viet Nam,173.21,709.1,1.94,523.36 +Wallis and Futuna Islands,0.03,...,1.91,180.99 +Yemen,22.3,-843.3,0.92,42.23 +Zambia,3.05,24.6,0.21,4.05 +Zimbabwe,9.86,-36.4,0.69,25.23 diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/NOx_Emissions.csv b/GLOBAL CLIMATE ANALYSIS/DATA/NOx_Emissions.csv new file mode 100644 index 000000000..8629e843c --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/DATA/NOx_Emissions.csv @@ -0,0 +1,167 @@ +Country,latest year available,NOx emissions,% change since 1990,NOx emissions per capita +,,1000 tonnes,%,kg +Afghanistan,2005,62.58,…,2.56 +Albania,1994,18.01,…,5.73 +Algeria,2000,283.21,…,9.08 +Andorra*,1997,0.71,…,11.07 +Angola,2005,154,…,8.6 +Antigua and Barbuda,2000,2.27,…,29.23 +Argentina,2000,675.79,31.1,18.24 +Armenia,2010,17.21,-77.5,5.81 +Australia,2012, 2 536.45,44.6,110.71 +Austria,2012,178.26,-8.5,21.08 +Azerbaijan,1994,113,-28.2,14.72 +Bahrain,2000,52,…,77.98 +Bangladesh,2005,3.95,…,0.03 +Barbados,1997,0.05,-97.9,0.19 +Belarus,2012,189.92,-43.5,20.01 +Belgium,2012,193.31,-47.9,17.45 +Belize,1994,5.6,…,27.75 +Benin,2000,60.53,…,8.71 +Bhutan,2000,1.77,…,3.14 +Bolivia (Plurinational State of),2004,64.92,31.1,7.24 +Bosnia and Herzegovina,2001,40.07,-51.8,10.55 +Brazil,2005, 3 400.00,35.8,18.04 +Bulgaria,2012,148.48,-43.9,20.33 +Burkina Faso,1994,9.36,…,0.95 +Burundi,2005,11.23,…,1.42 +Cabo Verde,2000,2.03,…,4.62 +Cambodia,1994,38.02,…,3.67 +Cameroon,1994,252.22,…,18.62 +Central African Republic,1994,51.25,…,15.75 +Chad,1993,78.35,…,11.95 +Chile,2010,272.2,…,16 +Colombia,2004,335.16,24.5,7.84 +Comoros,1994,0.46,…,0.99 +Congo,2000,17.65,…,5.68 +Costa Rica,2005,26.88,-19.7,6.33 +Côte d'Ivoire,2000,290.49,…,17.59 +Croatia,2012,55.19,-40.9,12.87 +Cuba,1996,101.54,-28.4,9.27 +Cyprus,2012, 2 124.00, 13 321.3, 1 880.81 +Czech Republic,2012,210.77,-71.6,19.99 +Democratic People's Republic of Korea,2002,159,-65.4,6.84 +Democratic Republic of the Congo,2003,784.69,…,14.92 +Denmark,2012,119.79,-57.3,21.39 +Djibouti,2000,1..89,…,2.62 +Dominica,2005,0.63,…,8.93 +Dominican Republic,2000,93.12,68.3,10.88 +Ecuador,2006,231.77,47.7,16.59 +El Salvador,2005,40.42,…,6.8 +Eritrea,2000,6,…,1.7 +Estonia,2012, 3 182.00, 4 021.8, 2 403.25 +Ethiopia,1994,166,3.8,3 +Fiji,2004,11.49,…,14.04 +Finland,2012,146.75,-50.3,27.05 +France,2012, 1 074.74,-44.6,16.91 +Gabon,2000,7.54,…,6.12 +Gambia,2000,6.83,…,5.56 +Georgia,2006,27.67,-78.6,6.25 +Germany,2012, 1 269.26,-55.9,15.77 +Ghana,2000,205.64,…,10.92 +Greece,2012,258.91,-20.6,23.31 +Guatemala,1990,43.79,…,4.78 +Guinea,1994,70.42,…,9.34 +Guinea-Bissau,1994,4.88,…,4.22 +Guyana,2004,17,1.8,22.91 +Haiti,2000,14.7,…,1.72 +Honduras,2000,47.77,…,7.65 +Hungary,2012,109.41,-53,10.99 +Iceland,2012,20.55,-24.7,63.54 +Indonesia,2000,85.66,-28.9,0.4 +Iran (Islamic Republic of),2000,600.84,…,9.12 +Ireland,2012,72.87,-40.2,15.61 +Israel,2010,187.29,…,25.24 +Italy,2012,881.52,-57.4,14.76 +Jamaica,1994,30.86,…,12.51 +Japan,2012, 1 626.95,-20.4,12.8 +Jordan,2006,116,…,20.98 +Kazakhstan,2012,500.26,-25.9,29.74 +Kenya,1994,49.98,…,1.88 +Kiribati,1994,0,…,0 +Kuwait,1994,113,…,66.79 +Kyrgyzstan,2005,64.92,-44.5,12.69 +Lao People's Democratic Republic,2000,20.84,81.5,3.9 +Latvia,2012,34.87,-58.2,17.12 +Lebanon,2000,58.7,…,18.14 +Lesotho*,1998,5.05,…,2.77 +Liberia,2000,1,…,0.35 +Lithuania,2012,59.53,-63.4,19.74 +Luxembourg,2005,0.44,175,0.96 +Madagascar,2000,27.64,…,1.76 +Malawi,1994,26.31,-9,2.71 +Mali,2000,42.54,…,3.85 +Malta,2012,8.86,17.4,21.33 +Mauritania,2000,10.31,…,3.8 +Mauritius,2006,15.15,…,12.34 +Mexico,2002, 1 444.41,16.3,13.68 +Micronesia (Federated States of),1994,2.25,…,21.25 +Monaco,2012,0.33,-26.2,8.93 +Mongolia,1998,2.98,29.6,1.27 +Montenegro,2003,10.94,5.8,17.81 +Morocco,2000,190.91,…,6.46 +Mozambique,1994,93.81,22.1,6.11 +Myanmar,2005,0.03,…,0 +Namibia,2000,41.2,…,21.71 +Netherlands,2012,230.22,-59.4,13.75 +New Zealand,2012,158.5,57.7,35.73 +Nicaragua,2000,90.62,…,18.03 +Niger,2000,24,…,2.14 +Nigeria,2000, 1 008.00,…,8.2 +Niue,1994,26.3,…, 11 938.49 +Norway,2012,166.23,-13.3,33.12 +Oman,1994,0.22,…,0.1 +Pakistan,1994,410.26,…,3.43 +Panama*,2002,39.42,…,12.54 +Paraguay,2000,87.7,-20.3,16.54 +Peru,1994,181.66,…,7.69 +Philippines,1994,345.23,…,5.06 +Poland,2012,817.3,-36.1,21.17 +Portugal,2012,172.14,-30.4,16.37 +Qatar,2007,175.69,…,149.02 +Republic of Moldova,2010,37.06,-73,9.07 +Romania,2012,207.04,-54.7,10.38 +Russian Federation,2012, 5 603.13,-40.9,39.1 +Rwanda,2005,14.2,…,1.58 +Saint Lucia,2000,2,…,12.74 +Saint Vincent and the Grenadines,1997,27.88,-3.2,258.1 +Samoa,1994,0.97,…,5.75 +San Marino,2007,1.54,…,51.62 +Sao Tome and Principe,2005,0.77,…,5.03 +Senegal,2000,8.46,…,0.86 +Serbia,1998,164,-20.9,16.96 +Seychelles,2000,1.15,…,14.17 +Slovakia,2012,81.31,-64.1,15.01 +Slovenia,2012,44.84,-26.1,21.74 +Spain,2012,929.61,-31.1,19.93 +Sri Lanka,2000,83.69,…,4.46 +Sudan,2000,112,…,3.99 +Suriname,2003,10,…,20.51 +Swaziland,1994,19.93,…,21.11 +Sweden,2012,131.81,-51.2,13.81 +Switzerland,2012,73.71,-49.1,9.19 +Tajikistan,2010,6,-91.9,0.79 +Thailand,2000,907.1,…,14.47 +The former Yugoslav Republic of Macedonia,2009,34.11,-18,16.56 +Timor-Leste,2010,1.82,…,1.72 +Togo,2000,42.72,…,8.76 +Tonga,2000,0.59,…,6.07 +Trinidad and Tobago,1990,36.86,…,30.16 +Tunisia,2000,94.87,…,9.78 +Turkey,2012, 1 283.74,99.4,17.15 +Turkmenistan,2004,90.24,…,19.21 +Tuvalu,1994,0,…,0 +Uganda,2000,103.77,…,4.37 +Ukraine,2012, 1 205.57,-48.2,26.6 +United Arab Emirates,2005,332,…,74.07 +United Kingdom of Great Britain and Northern Ireland,2012, 1 067.63,-63.1,16.79 +United Republic of Tanzania,1994,979.07,524.9,33.73 +United States of America,2012, 11 882.02,-45.5,37.74 +Uruguay,2004,38.76,29.2,11.66 +Uzbekistan,2005,257.52,-37.5,9.93 +Vanuatu,1994,0.08,…,0.51 +Venezuela (Bolivarian Republic of),1999,395.79,…,16.48 +Viet Nam,2000,312.63,…,3.89 +Yemen,2000,115,…,6.46 +Zambia,2000, 1 276.67,…,120.61 +Zimbabwe,2000,148.83,...,11.91 diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/ODS_Consumption.csv b/GLOBAL CLIMATE ANALYSIS/DATA/ODS_Consumption.csv new file mode 100644 index 000000000..82c12bec0 --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/DATA/ODS_Consumption.csv @@ -0,0 +1,170 @@ +Country,Consumption of CFCs,,,Consumption of all ODS,, +,Baseline,2013,Reduction from baseline,2002,2013, Reduction from 2002 +,ODP tonnes,ODP tonnes,%,ODP tonnes,ODP tonnes, % +Afghanistan,380,0,100,181.5,17.7,90.2 +Albania,40.8,0,100,50.5,5.7,88.7 +Algeria, 2 119.5,0,100,1966.1,52,97.4 +Andorra,67.5,0,100,...,0,… +Angola,114.8,0,100,110,15.4,86 +Antigua and Barbuda,10.7,0,100,4,0.2,95 +Argentina, 4 697.2,0,100, 2 386.0,497.7,79.1 +Armenia,196.5,0,100,174.4,4.5,97.4 +Australia, 14 290.4,-7.5,100.1,389.7,48.3,87.6 +Azerbaijan,480.6,0,100,12.1,1.8,85.1 +Bahamas,64.9,0,100,58.4,2.7,95.4 +Bahrain,135.4,0,100,138.1,49.6,64.1 +Bangladesh,581.6,0,100,350.1,64.9,81.5 +Barbados,21.5,0,100,12.1,2.3,81 +Belarus, 2 510.9,0,100,2.7,7,-159.3 +Belize,24.4,0,100,21.7,2.4,88.9 +Benin,59.9,0,100,36,22.2,38.3 +Bhutan,0.2,0,100,0.1,0.3,-200 +Bolivia (Plurinational State of),75.7,0,100,67.4,0.4,99.4 +Bosnia and Herzegovina,24.2,0,100,259.2,5.1,98 +Botswana,6.9,0,100,10.2,10.8,-5.9 +Brazil, 10 525.8,0,100, 3 589.4, 1 189.3,66.9 +Brunei Darussalam,78.2,0,100,46.3,4.3,90.7 +Burkina Faso,36.3,0,100,27.9,14.9,46.6 +Burundi,59,0,100,19.2,7.1,63 +Cambodia,94.2,0,100,97,9.5,90.2 +Cameroon,256.9,0,100,261.7,82.3,68.6 +Canada, 19 958.2,0,100,923.1,65.9,92.9 +Cabo Verde,2.3,0,100,1.8,0.2,88.9 +Central African Republic,11.2,…,…,4.6,…,100 +Chad,34.6,0,100,27.3,15.2,44.3 +Chile,828.7,0,100,591.9,241.9,59.1 +China, 57 818.7,-386.6,100.7, 47 804.1, 15 690.6,67.2 +Colombia, 2 208.2,0,100, 1 002.2,176.7,82.4 +Comoros,2.5,0,100,1.9,0.1,94.7 +Congo,11.9,0,100,11.2,9.4,16.1 +Cook Islands,1.7,0,100,0,0,0 +Costa Rica,250.2,0,100,425.4,12.6,97 +Côte d'Ivoire,294.2,0,100,121.2,54.2,55.3 +Croatia,219.3,…,…,172.3,…,100 +Cuba,625.1,0,100,518,12.2,97.6 +Democratic People's Republic of Korea,441.7,0,100, 2 326.3,90.6,96.1 +Democratic Republic of the Congo,665.7,0,100, 1 081.3,35.9,96.7 +Djibouti,21,0,100,15.8,0.6,96.2 +Dominica,1.5,0,100,3.1,0.1,96.8 +Dominican Republic,539.8,0,100,406.9,34.8,91.4 +Ecuador,301.4,0,100,273.4,22,92 +Egypt, 1 668.0,0,100, 1 944.1,352.2,81.9 +El Salvador,306.5,0,100,108.1,8.1,92.5 +Equatorial Guinea,31.5,0,100,27.9,5.1,81.7 +Eritrea,41.1,0,100,32.2,1,96.9 +Ethiopia,33.8,0,100,86.6,5.5,93.6 +European Union (EU), 301 930.2,- 1 042.9,100.3,- 6 754.6,- 3 249.4,51.9 +Fiji,33.4,0,100,5.3,7.7,-45.3 +Gabon,10.3,0,100,6.9,28.6,-314.5 +Gambia,23.8,0,100,4.9,0.9,81.6 +Georgia,22.5,0,100,64.3,1.4,97.8 +Ghana,35.8,0,100,24,25.4,-5.8 +Grenada,6,0,100,2.3,0.3,87 +Guatemala,224.6,0,100,952.5,251.3,73.6 +Guinea,42.4,0,100,31.4,7.1,77.4 +Guinea-Bissau,26.3,0,100,27.2,2.3,91.5 +Guyana,53.2,0,100,15.6,1,93.6 +Haiti,169,0,100,197.7,2,99 +Honduras,331.6,0,100,555.7,18.9,96.6 +Iceland,195.1,0,100,2.6,0,100 +India, 6 681.0,-19.8,100.3, 15 026.9,956.1,93.6 +Indonesia, 8 332.7,0,100, 5 787.4,310.5,94.6 +Iran (Islamic Republic of), 4 571.7,0,100, 8 572.9,357.8,95.8 +Iraq, 1 517.0,0,100, 1 580.6,101.8,93.6 +Israel, 4 141.6,0,100, 1 241.3,94.5,92.4 +Jamaica,93.2,0,100,39.2,3.6,90.8 +Japan, 118 134.0,-181.3,100.2, 2 466.8,39.6,98.4 +Jordan,673.3,0,100,267,63,76.4 +Kazakhstan, 1 206.2,0,100,146.9,104.6,28.8 +Kenya,239.5,0,100,322,29.1,91 +Kiribati,0.7,0,100,0,0,0 +Kuwait,480.4,0,100,515.7,414.7,19.6 +Kyrgyzstan,72.8,0,100,50.2,4,92 +Lao People's Democratic Republic,43.3,0,100,42.9,1.6,96.3 +Lebanon,725.5,0,100,710.8,72.6,89.8 +Lesotho,5.1,0,100,4.6,2,56.5 +Liberia,56.1,0,100,54,4.5,91.7 +Libya,716.7,0,100, 1 596.5,144,91 +Liechtenstein,37.2,0,100,0.1,0,100 +Madagascar,47.9,0,100,8.8,16,-81.8 +Malawi,57.7,0,100,75.4,10.2,86.5 +Malaysia, 3 271.1,0,100, 1 966.3,449.9,77.1 +Maldives,4.6,0,100,4,3.2,20 +Mali,108.1,0,100,28.3,10.3,63.6 +Marshall Islands,1.1,0,100,0.3,0.1,66.7 +Mauritania,15.7,0,100,16.5,20.4,-23.6 +Mauritius,29.1,0,100,14.4,5.4,62.5 +Mexico, 4 624.9,0,100, 3 954.7, 1 106.2,72 +Micronesia (Federated States of),1.2,0,100,2.2,0,100 +Monaco,6.2,0,100,0.1,0,100 +Mongolia,10.6,0,100,7.3,0.9,87.7 +Montenegro,104.9,0,100,15.4,0.8,94.8 +Morocco,802.3,0,100, 1 070.0,49.4,95.4 +Mozambique,18.2,0,100,14.4,8.3,42.4 +Myanmar,54.3,0,100,45.7,3,93.4 +Namibia,21.9,0,100,20,7,65 +Nauru,0.5,0,100,0,0,… +Nepal,27,0,100,2.6,0.7,73.1 +New Zealand, 2 088.0,0,100,42.8,8.2,80.8 +Nicaragua,82.8,0,100,64.9,3.6,94.5 +Niger,32,0,100,27.6,14.6,47.1 +Nigeria, 3 650.0,0,100, 3 933.3,334.5,91.5 +Niue,0.1,0,100,0,0,0 +Norway, 1 313.0,0,100,-42.8,0,100 +Oman,248.4,0,100,201.5,28.9,85.7 +Pakistan, 1 679.4,0,100, 2 347.2,247,89.5 +Palau,1.6,0,100,0.2,0.1,50 +Panama,384.1,0,100,204.7,21.4,89.5 +Papua New Guinea,36.3,0,100,39.7,3,92.4 +Paraguay,210.6,0,100,105.5,16.5,84.4 +Peru,289.5,0,100,203.6,25.8,87.3 +Philippines, 3 055.8,0,100, 1 795.1,136.7,92.4 +Qatar,101.4,0,100,105.4,80.7,23.4 +Republic of Korea, 9 159.8,0,100, 11 745.9, 1 893.1,83.9 +Republic of Moldova,73.3,0,100,29.6,1,96.6 +Russian Federation, 100 352.0,288,99.7,892.3,836.5,6.3 +Rwanda,30.4,0,100,30.4,3.8,87.5 +Saint Kitts and Nevis,3.7,0,100,6.3,0.3,95.2 +Saint Lucia,8.3,0,100,7.7,0.6,92.2 +Samoa,4.5,0,100,2.6,0.1,96.2 +Sao Tome and Principe,4.7,0,100,4.4,0.1,97.7 +Saudi Arabia, 1 798.5,0,100, 1 926.4, 1 440.3,25.2 +Senegal,155.8,0,100,82.3,7.7,90.6 +Serbia,849.2,0,100,384.9,8.1,97.9 +Seychelles,2.9,0,100,166.1,0.6,99.6 +Sierra Leone,78.6,0,100,84.4,0.8,99.1 +Singapore, 2 718.2,0,100,146.7,116.7,20.4 +Solomon Islands,2.1,0,100,5.7,0.2,96.5 +Somalia,241.4,0,100,124.1,16.5,86.7 +South Africa,592.6,0,100,848.8,426.4,49.8 +Sri Lanka,445.6,0,100,227.4,13.4,94.1 +Saint Vincent and the Grenadines,1.8,0,100,6.4,0.2,96.9 +Sudan,456.8,0,100,258.2,51.9,79.9 +Suriname,41.3,0,100,51,1.2,97.6 +Swaziland,24.6,0,100,2.4,1.2,50 +Switzerland, 7 960.0,0,100,26.2,1.4,94.7 +Syrian Arab Republic, 2 224.6,0,100, 1 754.1,28,98.4 +Tajikistan,211,0,100,12.6,2.3,81.7 +Thailand, 6 082.1,0,100, 3 612.5,863.3,76.1 +The former Yugoslav Republic of Macedonia,519.7,0,100,44.6,0.7,98.4 +Timor-Leste,36,0,100,2.7,0.3,88.9 +Togo,39.8,0,100,35.3,19,46.2 +Tonga,1.3,0,100,1,0,100 +Trinidad and Tobago,120,0,100,93.9,39.5,57.9 +Tunisia,870.1,0,100,552.5,38.7,93 +Turkey, 3 805.7,0,100, 1 336.4,147,89 +Turkmenistan,37.3,0,100,10.9,4.2,61.5 +Tuvalu,0.3,0,100,0,0,-100 +Uganda,12.8,0,100,44.9,0,100 +Ukraine, 4 725.2,0,100,145.5,59.4,59.2 +United Arab Emirates,529.3,0,100,624.2,539.4,13.6 +United Republic of Tanzania,253.9,0,100,71.5,1.6,97.8 +United States of America, 305 963.6,-498.6,100.2, 16 206.4,711.3,95.6 +Uruguay,199.1,0,100,100.9,15.5,84.6 +Uzbekistan, 1 779.2,0,100,0.8,4.6,-475 +Vanuatu,0,0,0,0,0.1,-100 +Venezuela (Bolivarian Republic of), 3 322.4,0,100, 1 653.0,139.9,91.5 +Viet Nam,500,0,100,447.4,252.9,43.5 +Yemen, 1 796.1,0,100, 1 135.8,127.2,88.8 +Zambia,27.4,0,100,24,5,79.2 +Zimbabwe,451.4,0,100,345.8,15.8,95.4 diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/SO2_emissions.csv b/GLOBAL CLIMATE ANALYSIS/DATA/SO2_emissions.csv new file mode 100644 index 000000000..e08e2f854 --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/DATA/SO2_emissions.csv @@ -0,0 +1,136 @@ +Country,latest year available,SO2 emissions,% change since 1990,SO2 emissions per capita +,,1000 tonnes ,%,kg +Afghanistan,2005,13.86,…,0.57 +Algeria,2000,45.64,…,1.46 +Andorra*,1997,0.69,…,10.77 +Antigua and Barbuda,2000,2.75,-2.83,35.42 +Argentina,2000,87.62,10.63,2.36 +Armenia,2010,29.44, 7 448.72,9.93 +Australia,2012,797.76,-48.69,34.82 +Austria,2012,17.23,-76.84,2.04 +Azerbaijan,1994,48,-18.64,6.25 +Bahrain,2000,27,…,40.49 +Barbados,1997,0.05,…,0.19 +Belarus,2012,146.86,-86.44,15.47 +Belgium,2012,48.75,-86.42,4.4 +Belize,1994,0.53,…,2.63 +Benin,2000,13.88,…,2 +Bhutan,2000,1.06,…,1.88 +Bolivia (Plurinational State of),2000,12.1,8.42,1.45 +Bosnia and Herzegovina,2001,213.74,-52.83,56.25 +Bulgaria,2012, 1 335.49,-15.59,182.85 +Chile,2010,271.4,…,15.95 +Colombia,2004,142.81,0.71,3.34 +Costa Rica,2005,4.85,…,1.14 +Côte d'Ivoire,2000, 4 079.55,…,246.98 +Croatia,2012,25.58,-85.3,5.97 +Cuba,1996,432.38,-0.1,39.47 +Cyprus,2012,16.1,-45.9,14.26 +Czech Republic,2012,157.91,-91.58,14.97 +Democratic People's Republic of Korea,2002, 1 384.00,-55.66,59.53 +Democratic Republic of the Congo,2000,0.02,…,0 +Denmark,2012,13.43,-92.51,2.4 +Dominica,2005,0.22,…,3.12 +Dominican Republic,2000,110.15,42.94,12.86 +Ecuador,2006,8.87,37.73,0.64 +Estonia,2012,69.96,-62.03,52.84 +Ethiopia,1995,13.2,18.92,0.23 +Fiji,1994,0.03,…,0.04 +Finland,2012,52.06,-79.08,9.6 +France,2012,274.29,-79.68,4.32 +Gabon,2000,7.67,…,6.23 +Gambia,2000, 3 031.94,…, 2 467.27 +Georgia,2006,0.5,-99.8,0.11 +Germany,2012,427.07,-91.92,5.31 +Ghana,2000,0.5,…,0.03 +Greece,2012,244.9,-48.56,22.04 +Guatemala,1990,74.5,…,8.13 +Guinea,1994,0.44,…,0.06 +Guyana,2004,6.9,-8,9.3 +Haiti,2000,13.58,…,1.59 +Honduras,2000,0.38,…,0.06 +Hungary,2012,31.8,-96.15,3.19 +Iceland,2012,83.88,295.1,259.36 +Iran (Islamic Republic of),2000,139.46,…,2.12 +Ireland,2012,23.12,-87.31,4.95 +Israel,2010,164.46,…,22.16 +Italy,2012,181.73,-89.93,3.04 +Jamaica,1994,99.7,…,40.42 +Japan,2012,936.84,-25.29,7.37 +Jordan,2006,138,…,24.95 +Kazakhstan,2012,649.61,-37.92,38.62 +Kuwait,1994,319,…,188.55 +Kyrgyzstan,2005,26.9,-72.7,5.26 +Lao People's Democratic Republic,2000,1.59,…,0.3 +Latvia,2012,2.39,-97.66,1.17 +Lebanon,2000,93.42,…,28.87 +Lesotho*,1998,0,…,0 +Lithuania,2012,36.48,-82.78,12.09 +Luxembourg,2005,0.21,31.25,0.46 +Madagascar,2000,39.82,…,2.53 +Mali,1995,0,…,0 +Malta,2012,8.25,-47.72,19.85 +Mauritania,2000,0.09,…,0.03 +Mauritius,2006,11.44,…,9.32 +Mexico,2002, 2 612.91,-3.13,24.75 +Micronesia (Federated States of),1994,0.53,…,5 +Monaco,2012,0.04,-42.86,1.07 +Montenegro,2003,45.43,6.27,73.95 +Morocco,2000,484.09,…,16.39 +Namibia,2000,10.9,…,5.74 +Netherlands,2012,33.91,-82.86,2.02 +New Zealand,2012,78.16,33.84,17.62 +Nicaragua,2000,0.19,…,0.04 +Niger,2000, 2 140.00,…,190.65 +Nigeria,2000,190,…,1.55 +Niue,1994, 2 211.18,…,1 003 713.12 +Norway,2012,16.66,-68.1,3.32 +Oman,1994,3.38,,1.58 +Pakistan,1994,775.46,…,6.49 +Panama,2000,0.13,…,0.04 +Paraguay,2000,0.16,-46.67,0.03 +Peru,1994,123.26,…,5.22 +Philippines,1994,458.53,…,6.72 +Poland,2012,853.3,-73.42,22.1 +Portugal,2012,59.22,-81.71,5.63 +Qatar,2007,143.92,…,122.07 +Republic of Moldova,2010,18.78,-93.63,4.6 +Romania,2012,293.03,-66.35,14.69 +Russian Federation,2012,681.95,-16.02,4.76 +Rwanda,2005,18,…,2 +Saint Lucia,2000,0.1,…,1.86 +Saint Vincent and the Grenadines,1997,0.32,28,2.96 +Senegal,2000,41.96,…,4.26 +Serbia,1998,388,-20.98,40.13 +Slovakia,2012,58.52,-88.83,10.81 +Slovenia,2012,10.12,-94.91,4.91 +Spain,2012,407.94,-81.2,8.75 +Sri Lanka,2000,105.87,…,5.64 +Sudan,2000,1,…,0.04 +Swaziland,1994,1.97,…,2.09 +Sweden,2012,27.79,-73.59,2.91 +Switzerland,2012,10.67,-73.7,1.33 +Tajikistan,2010,9,-73.53,1.19 +Thailand,2000,618.9,…,9.87 +The former Yugoslav Republic of Macedonia,2009,205.83, 6 807.05,99.97 +Timor-Leste,2010,0.4,…,0.38 +Togo,2000,8.35,…,1.71 +Tonga,2000,0.1,…,1.02 +Trinidad and Tobago*,1996,8.6,-1.71,7.04 +Tunisia,2000,111.29,…,11.47 +Turkey,2012,248.83,-70.21,3.32 +Turkmenistan,2004,2.43,…,0.52 +Uganda,2000,4.1,…,0.17 +Ukraine,2012, 1 687.55,-68.15,37.24 +United Arab Emirates,2005, 10 354.00,…, 2 310.14 +United Kingdom of Great Britain and Northern Ireland,2012,429.44,-88.46,6.75 +United Republic of Tanzania,1994,175.74,8.39,6.05 +United States of America,2012, 4 739.47,-77.36,15.06 +Uruguay,2004,51.5,25.09,15.49 +Uzbekistan,2005,170.85,-74.93,6.59 +Viet Nam,2000,9.86,…,0.12 +Yemen,2000,12,…,0.67 +Zambia,2000,6.16,…,0.58 +Zimbabwe,2000,1.04,…,0.08 + ,,,, + ,,,, diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/merged_ghg_data.csv b/GLOBAL CLIMATE ANALYSIS/DATA/merged_ghg_data.csv new file mode 100644 index 000000000..aadaf1665 --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/DATA/merged_ghg_data.csv @@ -0,0 +1,189 @@ +Country,latest year available_perc,Total GHG emissions _perc,GHG from Energy_perc,"GHG from Energy +of which: from Transport_perc",GHG from Industrial Processes_perc,GHG from Agriculture_perc,GHG from Waste_perc,Total GHG emissions _tonnes,GHG from Energy_tonnes,"GHG from Energy +of which: from Transport_tonnes",GHG from Industrial Processes_tonnes,GHG from Agriculture_tonnes,GHG from Waste_tonnes +,,mio. tonnes of CO2 equivalent,%,%,%,%,%,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent +Afghanistan,2005,19.33,19.54,8.75,1.62,78.17,0.67,19.33,3.78,1.69,0.31,15.11,0.13 +Albania,1994,5.53,56.11,14.41,3.79,33.96,6.14,5.53,3.1,0.8,0.21,1.88,0.34 +Algeria,2000,111.02,78.9,11.52,4.92,5.89,10.29,111.02,87.6,12.79,5.46,6.53,11.43 +Angola,2005,61.61,61.24,...,0.57,36.64,1.54,61.61,37.73,...,0.35,22.58,0.95 +Antigua and Barbuda,2000,0.6,62.35,30.55,...,17.45,20.19,0.6,0.37,0.18,...,0.1,0.12 +Argentina,2000,282,46.79,14.27,3.94,44.3,4.97,282,131.96,40.24,11.11,124.92,14.01 +Armenia,2010,7.2,69.54,17.32,3.14,18.36,8.97,7.2,5.01,1.25,0.23,1.32,0.65 +Australia,2012,543.65,76.03,16.59,5.74,16.07,2.16,543.65,413.36,90.21,31.21,87.36,11.72 +Austria,2012,80.06,74.56,27.02,13.59,9.37,2.07,80.06,59.69,21.64,10.88,7.5,1.66 +Azerbaijan,1994,43.17,10.44,...,...,8.53,4.08,43.17,4.51,...,...,3.68,1.76 +Bahamas,1994,2.2,84.94,...,...,0.96,...,2.2,1.87,...,...,0.02,... +Bahrain,2000,22.37,77.12,6.79,11.24,...,11.64,22.37,17.25,1.52,2.52,...,2.6 +Bangladesh,2005,99.44,38.86,5.53,2.93,43.36,14.85,99.44,38.65,5.5,2.91,43.12,14.77 +Barbados,1997,4.06,49.98,6.2,4.22,1.65,44.16,4.06,2.03,0.25,0.17,0.07,1.79 +Belarus,2012,89.28,61.94,8.08,4.79,26.18,7.02,89.28,55.3,7.22,4.27,23.37,6.27 +Belgium,2012,116.52,81.02,21.41,9.59,7.94,1.29,116.52,94.4,24.95,11.17,9.26,1.51 +Belize,1994,6.34,9.58,4.96,0,4.27,86.15,6.34,0.61,0.31,0,0.27,5.46 +Benin,2000,6.25,30.09,14.53,...,67.81,2.1,6.25,1.88,0.91,...,4.24,0.13 +Bhutan,2000,1.56,17.23,7.59,15.28,64.59,2.9,1.56,0.27,0.12,0.24,1,0.05 +Bolivia (Plurinational State of),2004,43.67,20.69,9.79,48.71,26.7,3.9,43.67,9.03,4.27,21.27,11.66,1.7 +Bosnia and Herzegovina,2001,16.12,76.5,...,3.7,13.67,6.13,16.12,12.33,...,0.6,2.2,0.99 +Botswana,1994,9.29,41.35,8.62,2.27,54.53,1.85,9.29,3.84,0.8,0.21,5.07,0.17 +Brazil,2005,862.81,38.11,15.59,8.95,48.19,4.76,862.81,328.79,134.54,77.2,415.77,41.05 +Bulgaria,2012,61.26,77,13.75,6.36,10.67,5.9,61.26,47.17,8.42,3.9,6.54,3.61 +Burkina Faso,1994,5.97,15.22,5.41,...,78.89,5.89,5.97,0.91,0.32,...,4.71,0.35 +Burundi,2005,26.47,1.35,0.4,0,97.9,0.76,26.47,0.36,0.11,0,25.92,0.2 +Cabo Verde,2000,0.45,65.6,30.73,0.19,29.23,4.98,0.45,0.29,0.14,0,0.13,0.02 +Cambodia,1994,12.76,14.74,6.51,0.39,82.74,2.13,12.76,1.88,0.83,0.05,10.56,0.27 +Cameroon,1994,165.73,1.95,0.82,35.31,61.69,1.04,165.73,3.24,1.35,58.52,102.23,1.73 +Canada,2012,698.63,80.98,27.93,8.08,7.95,2.94,698.63,565.76,195.11,56.46,55.53,20.57 +Central African Republic,1994,37.74,50.16,0.32,...,43.04,6.8,37.74,18.93,0.12,...,16.24,2.57 +Chad,1993,8.02,3.86,...,...,91,5.14,8.02,0.31,...,...,7.3,0.41 +Chile,2006,78.96,73.23,21.63,6.64,16.97,3.15,78.96,57.82,17.08,5.24,13.4,2.49 +China,2005, 7 465.86,77.28,5.77,10.25,10.97,1.5, 7 465.86, 5 769.85,430.79,764.89,819.33,111.79 +Colombia,2004,153.88,42.87,14.15,5.89,44.56,6.68,153.88,65.97,21.77,9.07,68.57,10.28 +Comoros,1994,0.51,13.77,...,...,85.61,0.62,0.51,0.07,...,...,0.44,0 +Congo,2000,2.07,78.05,18.68,0.23,15.72,6,2.07,1.61,0.39,0,0.32,0.12 +Cook Islands,1994,0.08,40.55,19.99,...,12.85,46.59,0.08,0.03,0.02,...,0.01,0.04 +Costa Rica,2005,12.11,47,32.12,4.1,38,10.9,12.11,5.69,3.89,0.5,4.6,1.32 +Côte d'Ivoire,2000,271.2,24.55,0.81,0,71.76,3.69,271.2,66.59,2.2,0,194.61,10 +Croatia,2012,26.45,71.54,21.59,10.78,12.83,4.26,26.45,18.92,5.71,2.85,3.39,1.13 +Cuba,1996,40.19,66.29,...,3.03,25.56,5.12,40.19,26.64,...,1.22,10.27,2.06 +Cyprus,2012,9.26,70.8,22.32,8.79,8.81,10.81,9.26,6.56,2.07,0.81,0.82,1 +Czech Republic,2012,131.47,81.46,12.86,9.2,6.13,2.87,131.47,107.09,16.91,12.1,8.06,3.77 +Democratic People's Republic of Korea,2002,87.33,88.86,1.7,6.48,3.22,1.43,87.33,77.61,1.49,5.66,2.81,1.25 +Democratic Republic of the Congo,2003,46,7.82,1.81,0.34,75.18,16.66,46,3.6,0.83,0.16,34.58,7.66 +Denmark,2012,53.12,76.07,23.51,3.41,18.15,2.07,53.12,40.41,12.49,1.81,9.64,1.1 +Djibouti,2000,1.07,33.26,10.15,...,61.67,5.07,1.07,0.36,0.11,...,0.66,0.05 +Dominica,2005,0.18,66.91,25.73,0,22.76,10.32,0.18,0.12,0.05,0,0.04,0.02 +Dominican Republic,2000,26.43,69.03,23.39,3.07,21.57,6.33,26.43,18.25,6.18,0.81,5.7,1.67 +Ecuador,2006,247.99,10.85,5.16,1.11,84.73,3.32,247.99,26.9,12.78,2.75,210.11,8.23 +Egypt,2000,193.24,60.13,14.08,14.37,16.46,9.04,193.24,116.19,27.21,27.77,31.8,17.48 +El Salvador,2005,11.07,53.38,22.43,3.99,28.13,14.5,11.07,5.91,2.48,0.44,3.11,1.61 +Eritrea,2000,3.93,19.17,5.06,0.89,78.88,1.07,3.93,0.75,0.2,0.04,3.1,0.04 +Estonia,2012,19.19,87.93,11.88,3.45,6.91,1.61,19.19,16.87,2.28,0.66,1.33,0.31 +Ethiopia,1995,47.75,15.84,...,0.72,80.63,2.8,47.75,7.57,...,0.34,38.5,1.34 +Fiji,2004,2.71,60.98,27.05,...,35.52,3.5,2.71,1.65,0.73,...,0.96,0.09 +Finland,2012,60.97,78.43,20.8,8.71,9.36,3.39,60.97,47.81,12.68,5.31,5.71,2.07 +France,2012,496.4,71.87,26.98,7.26,18.07,2.57,496.4,356.76,133.93,36.03,89.71,12.76 +Gabon,2000,6.16,86.08,6.57,1.46,5.84,6.61,6.16,5.3,0.4,0.09,0.36,0.41 +Gambia,2000,19.38,1.75,0.51,89.08,8.09,1.07,19.38,0.34,0.1,17.27,1.57,0.21 +Georgia,2006,12.22,48.82,10.52,14.58,27.11,9.44,12.22,5.97,1.29,1.78,3.31,1.15 +Germany,2012,939.08,83.7,16.56,7.27,7.4,1.44,939.08,786.03,155.49,68.25,69.49,13.55 +Ghana,2006,18.23,50.66,17.12,1.34,35.57,12.43,18.23,9.23,3.12,0.24,6.48,2.27 +Greece,2012,110.99,78.61,14.5,8.66,8.18,4.27,110.99,87.26,16.1,9.61,9.08,4.74 +Grenada,1994,1.61,8.47,3.24,...,0.03,91.5,1.61,0.14,0.05,...,0,1.47 +Guatemala,1990,14.74,31.09,14.48,3.69,59.91,5.3,14.74,4.58,2.13,0.54,8.83,0.78 +Guinea,1994,5.06,40.4,12.36,2.84,50.02,6.75,5.06,2.04,0.63,0.14,2.53,0.34 +Guinea-Bissau,1994,1.69,10.62,0.02,0,86.75,2.63,1.69,0.18,0,0,1.47,0.04 +Guyana,2004,3.07,53.94,10.22,...,43.31,2.75,3.07,1.66,0.31,...,1.33,0.08 +Haiti,2000,6.68,23.47,11.22,...,71.39,2.84,6.68,1.57,0.75,...,4.77,0.19 +Honduras,2000,10.3,33.36,22.07,6.7,43.02,16.92,10.3,3.44,2.27,0.69,4.43,1.74 +Hungary,2012,61.98,73.37,17.5,6.9,14.05,5.12,61.98,45.47,10.85,4.27,8.71,3.18 +Iceland,2012,4.47,38.44,19.09,42.15,15.18,4.09,4.47,1.72,0.85,1.88,0.68,0.18 +India,2000, 1 523.77,67.4,6.44,5.81,23.34,3.45, 1 523.77, 1 027.02,98.1,88.6,355.6,52.55 +Indonesia,2000,554.33,50.68,10.25,7.7,13.24,28.38,554.33,280.94,56.82,42.67,73.4,157.33 +Iran (Islamic Republic of),2000,483.67,78.11,15.71,6.46,8.89,6.54,483.67,377.8,76.01,31.26,42.99,31.61 +Ireland,2012,58.53,63.32,18.62,4.14,30.7,1.72,58.53,37.06,10.9,2.42,17.97,1.01 +Israel,2010,75.42,84.98,21.7,3.91,3.17,7.93,75.42,64.09,16.37,2.95,2.39,5.98 +Italy,2012,461.19,82.37,23,6.11,7.68,3.52,461.19,379.86,106.06,28.2,35.4,16.21 +Jamaica,1994,116.31,7.08,1.09,0.33,92.27,0.33,116.31,8.23,1.27,0.38,107.32,0.38 +Japan,2012, 1 343.14,91.55,16.36,5.18,1.78,1.49, 1 343.14, 1 229.60,219.76,69.52,23.9,20.03 +Jordan,2006,27.75,75.37,17.03,9.19,4.47,10.97,27.75,20.92,4.73,2.55,1.24,3.05 +Kazakhstan,2012,283.55,85.08,8.2,5.9,7.59,1.43,283.55,241.23,23.25,16.74,21.53,4.06 +Kenya,1994,21.47,37.54,...,4.61,56.37,1.49,21.47,8.06,...,0.99,12.1,0.32 +Kiribati,1994,0.03,66.36,...,...,1.75,31.93,0.03,0.02,...,...,0,0.01 +Kuwait,1994,32.37,95.31,16.66,2.06,0.2,2.42,32.37,30.86,5.39,0.67,0.07,0.78 +Kyrgyzstan,2005,12.02,74.07,20.7,4.32,16.11,5.49,12.02,8.9,2.49,0.52,1.94,0.66 +Lao People's Democratic Republic,2000,8.9,11.68,5.02,0.54,86.26,1.51,8.9,1.04,0.45,0.05,7.68,0.13 +Latvia,2012,10.98,65.78,25.45,6.27,22.04,5.47,10.98,7.22,2.79,0.69,2.42,0.6 +Lebanon,2000,18.45,75.1,21.48,9.71,5.78,9.41,18.45,13.85,3.96,1.79,1.07,1.74 +Lesotho,2000,3.51,30.73,...,...,63.59,5.68,3.51,1.08,...,...,2.23,0.2 +Liberia,2000,8.02,67.49,27.09,...,31.94,0.57,8.02,5.41,2.17,...,2.56,0.05 +Liechtenstein,2012,0.23,84.66,36.61,3.72,10.32,0.89,0.23,0.19,0.08,0.01,0.02,0 +Lithuania,2012,21.62,54.97,20.99,16.78,23.4,4.47,21.62,11.89,4.54,3.63,5.06,0.97 +Luxembourg,2012,11.84,88.67,55.06,5.16,5.65,0.42,11.84,10.5,6.52,0.61,0.67,0.05 +Madagascar,2000,29.34,9.21,3.2,0.08,90.48,0.23,29.34,2.7,0.94,0.02,26.55,0.07 +Malawi,1994,7.07,52.58,...,0.83,45.32,1.27,7.07,3.72,...,0.06,3.2,0.09 +Malaysia,2000,193.4,76.01,...,7.31,3.05,13.63,193.4,147,...,14.13,5.9,26.36 +Maldives,1994,0.15,84.32,...,...,...,15.68,0.15,0.13,...,...,...,0.02 +Mali,2000,12.3,19.44,5.51,0.92,76.82,2.82,12.3,2.39,0.68,0.11,9.45,0.35 +Malta,2012,3.14,89.87,17.55,5.47,2.53,2.07,3.14,2.82,0.55,0.17,0.08,0.07 +Mauritania,2000,6.94,16.86,5.83,0.28,81.62,1.25,6.94,1.17,0.41,0.02,5.67,0.09 +Mauritius,2006,4.76,66.29,18.01,1.37,4.33,28.02,4.76,3.15,0.86,0.06,0.21,1.33 +Mexico,2006,641.45,67.05,22.56,9.9,7.1,15.94,641.45,430.1,144.69,63.53,45.55,102.27 +Micronesia (Federated States of),1994,0.25,97.96,0.26,0.01,0.34,1.69,0.25,0.24,0,0,0,0 +Monaco,2012,0.09,91.51,31.15,7.02,...,1.38,0.09,0.09,0.03,0.01,...,0 +Mongolia,2006,17.71,57.7,10.65,5.04,36.48,0.78,17.71,10.22,1.89,0.89,6.46,0.14 +Montenegro,2003,5.31,50,...,35.43,12.33,2.24,5.31,2.66,...,1.88,0.66,0.12 +Morocco,2000,59.7,53.79,9.99,6.32,35.05,4.84,59.7,32.11,5.96,3.77,20.93,2.89 +Mozambique,1994,8.22,22.64,10.39,0.62,56.2,20.53,8.22,1.86,0.85,0.05,4.62,1.69 +Myanmar,2005,38.37,21.4,6.47,1.32,69.13,8.14,38.37,8.21,2.48,0.51,26.53,3.12 +Namibia,2000,9.09,23.87,11.33,...,74.15,1.98,9.09,2.17,1.03,...,6.74,0.18 +Nauru,1994,0.04,78.89,...,...,13.68,7.44,0.04,0.03,...,...,0,0 +Nepal,1994,31.19,10.47,1.46,0.53,87.2,1.8,31.19,3.27,0.46,0.17,27.2,0.56 +Netherlands,2012,191.67,84.49,17.73,5.18,8.3,1.92,191.67,161.95,33.98,9.92,15.9,3.69 +New Zealand,2012,76.05,42.24,18.09,6.94,46.05,4.73,76.05,32.12,13.76,5.28,35.02,3.6 +Nicaragua,2000,11.98,32.73,10.3,2.55,59.27,5.44,11.98,3.92,1.23,0.31,7.1,0.65 +Niger,2000,13.63,19.24,5.56,0.13,78.04,2.58,13.63,2.62,0.76,0.02,10.64,0.35 +Nigeria,2000,212.44,71.63,...,0.99,26.27,1.12,212.44,152.16,...,2.1,55.81,2.37 +Niue,1994,4.42,99.94,32.16,...,0.02,0.03,4.42,4.42,1.42,...,0,0 +Norway,2012,52.76,74.32,28.74,14.55,8.54,2.26,52.76,39.21,15.16,7.67,4.5,1.19 +Oman,1994,20.88,59.61,7.93,2.84,35.77,1.78,20.88,12.44,1.66,0.59,7.47,0.37 +Pakistan,1994,160.59,51.84,11.63,7.02,38.57,2.57,160.59,83.26,18.68,11.27,61.94,4.12 +Palau,2000,0.09,...,...,0.01,99.99,...,0.09,...,...,0,0.09,... +Panama,2000,9.71,49.59,28.08,6.11,33.17,11.13,9.71,4.81,2.73,0.59,3.22,1.08 +Papua New Guinea,1994,5.01,18.91,...,3.85,77.24,...,5.01,0.95,...,0.19,3.87,... +Paraguay,2000,23.43,15.72,11.9,1.69,79.51,3.08,23.43,3.68,2.79,0.4,18.63,0.72 +Peru,2010,80.59,50.38,18.87,7.79,32.33,9.51,80.59,40.61,15.21,6.27,26.05,7.66 +Philippines,2000,126.88,54.91,20.44,6.79,29.16,9.14,126.88,69.67,25.94,8.61,37,11.6 +Poland,2012,399.27,80.06,11.73,6.75,9.18,3.82,399.27,319.66,46.82,26.96,36.65,15.24 +Portugal,2012,68.85,69.56,24.7,7.72,10.49,11.89,68.85,47.9,17,5.31,7.22,8.19 +Qatar,2007,61.59,91.27,8.67,7.92,0.14,0.67,61.59,56.22,5.34,4.88,0.08,0.41 +Republic of Korea,2012,688.43,87.19,12.55,7.46,3.19,2.15,688.43,600.25,86.36,51.37,21.99,14.81 +Republic of Moldova,2010,13.28,67.39,14.35,4.26,16.06,11.89,13.28,8.95,1.9,0.56,2.13,1.58 +Romania,2012,118.79,69.22,12.68,10.42,15.33,4.92,118.79,82.22,15.06,12.38,18.21,5.85 +Russian Federation,2012, 2 297.15,82.16,10.51,7.89,6.28,3.65, 2 297.15, 1 887.26,241.37,181.14,144.22,83.95 +Rwanda,2005,6.18,12.96,4.43,2.44,83.58,1.02,6.18,0.8,0.27,0.15,5.17,0.06 +Saint Kitts and Nevis,1994,0.16,44.97,15.98,...,25.78,29.25,0.16,0.07,0.03,...,0.04,0.05 +Saint Lucia,2000,0.55,63.55,...,...,7.33,29.01,0.55,0.35,...,...,0.04,0.16 +Saint Vincent and the Grenadines,1997,0.41,26.15,9.23,...,64.4,9.45,0.41,0.11,0.04,...,0.26,0.04 +Samoa,1994,0.56,18.34,12.7,...,76.79,4.87,0.56,0.1,0.07,...,0.43,0.03 +San Marino,2007,0.24,98.29,58.82,...,1.72,...,0.24,0.23,0.14,...,0,... +Sao Tome and Principe,2005,0.1,71.4,28.55,6.73,14.71,7.15,0.1,0.07,0.03,0.01,0.01,0.01 +Saudi Arabia,2000,296.06,82.84,19.62,6.56,4.17,6.44,296.06,245.25,58.09,19.41,12.33,19.07 +Senegal,2000,16.88,48.47,11.38,2.15,37.08,12.29,16.88,8.18,1.92,0.36,6.26,2.08 +Serbia,1998,66.34,76.19,5.84,5.46,14.32,4.04,66.34,50.55,3.88,3.62,9.5,2.68 +Seychelles,2000,0.33,79.31,20.13,...,4.72,15.97,0.33,0.26,0.07,...,0.02,0.05 +Singapore,2010,46.87,97.19,14.85,2.38,...,...,46.87,45.55,6.96,1.11,...,... +Slovakia,2012,43.12,68.5,15.25,18.54,7.56,5,43.12,29.53,6.57,7.99,3.26,2.16 +Slovenia,2012,18.91,81.84,30.53,5.36,9.9,2.58,18.91,15.48,5.77,1.01,1.87,0.49 +Solomon Islands,1994,0.29,100,65.49,...,...,...,0.29,0.29,0.19,...,...,... +South Africa,1994,379.84,78.34,11.46,8,9.33,4.33,379.84,297.57,43.52,30.39,35.46,16.43 +Spain,2012,340.81,77.92,23.67,6.87,11.07,3.78,340.81,265.55,80.67,23.41,37.71,12.87 +Sri Lanka,2000,18.8,61.51,27.05,2.62,25.05,10.82,18.8,11.56,5.08,0.49,4.71,2.03 +Sudan,2000,67.84,12.39,4.2,0.14,84.67,2.81,67.84,8.4,2.85,0.09,57.44,1.91 +Suriname,2003,3.33,72.19,10.54,1.95,25.23,0.63,3.33,2.4,0.35,0.07,0.84,0.02 +Swaziland,1994,7.54,14.01,5.87,65.03,16.36,4.6,7.54,1.06,0.44,4.9,1.23,0.35 +Sweden,2012,57.61,73.16,33.17,10.24,13.26,2.81,57.61,42.15,19.11,5.9,7.64,1.62 +Switzerland,2012,51.49,80.6,31.72,7.05,10.76,1.19,51.49,41.5,16.33,3.63,5.54,0.61 +Tajikistan,2010,8.18,15.55,4.96,8.02,69.76,6.67,8.18,1.27,0.41,0.66,5.71,0.55 +Thailand,2000,236.95,67.26,18.87,6.91,21.89,3.93,236.95,159.38,44.7,16.38,51.87,9.32 +The former Yugoslav Republic of Macedonia,2009,11.49,76.26,11.26,3.89,11.52,8.33,11.49,8.76,1.29,0.45,1.32,0.96 +Timor-Leste,2010,1.28,19.64,8.64,...,75.69,4.67,1.28,0.25,0.11,...,0.97,0.06 +Togo,2000,4.92,34.87,13.06,6.36,55.33,3.44,4.92,1.71,0.64,0.31,2.72,0.17 +Tonga,2000,0.25,40.13,23.02,...,38.12,21.75,0.25,0.1,0.06,...,0.09,0.05 +Trinidad and Tobago,1990,16.01,62.03,9.3,31.97,2.12,3.89,16.01,9.93,1.49,5.12,0.34,0.62 +Tunisia,2000,34.24,60.64,15.07,11.55,22.31,5.5,34.24,20.76,5.16,3.95,7.64,1.88 +Turkey,2012,439.87,70.16,14,14.27,7.34,8.23,439.87,308.6,61.56,62.77,32.28,36.22 +Turkmenistan,2004,75.41,69.63,3.14,20.67,9.02,0.68,75.41,52.51,2.37,15.58,6.81,0.51 +Tuvalu,1994,0.01,83.63,...,...,16.37,...,0.01,0,...,...,0,... +Uganda,2000,27.56,17.77,2.93,0.58,79.14,2.51,27.56,4.9,0.81,0.16,21.81,0.69 +Ukraine,2012,402.67,76.76,8.31,11.43,8.95,2.82,402.67,309.08,33.47,46.01,36.03,11.37 +United Arab Emirates,2005,195.31,89.48,14.96,4.83,2.04,3.65,195.31,174.76,29.22,9.44,3.99,7.12 +United Kingdom of Great Britain and Northern Ireland,2012,586.36,82.81,19.74,4.26,8.89,4.04,586.36,485.54,115.72,24.97,52.13,23.72 +United Republic of Tanzania,1994,39.24,17.56,4.26,0.94,75.77,5.73,39.24,6.89,1.67,0.37,29.73,2.25 +United States of America,2012, 6 487.85,84.76,26.77,5.15,8.11,1.91, 6 487.85, 5 498.88, 1 736.64,334.35,526.25,123.97 +Uruguay,2004,36.28,14.29,6.19,0.94,80.83,3.94,36.28,5.18,2.24,0.34,29.32,1.43 +Uzbekistan,2005,199.84,86.24,4.82,3.19,8.23,2.35,199.84,172.34,9.63,6.37,16.44,4.69 +Vanuatu,1994,0.3,21.45,15.13,...,78.55,...,0.3,0.06,0.05,...,0.24,... +Venezuela (Bolivarian Republic of),1999,192.19,74.7,17.69,4.79,17.15,3.36,192.19,143.56,33.99,9.21,32.96,6.47 +Viet Nam,2010,266.05,53.06,11.96,7.96,33.21,5.77,266.05,141.17,31.82,21.17,88.35,15.35 +Yemen,2000,25.74,69.07,19.25,2.85,23.34,4.74,25.74,17.78,4.96,0.73,6.01,1.22 +Zambia,2000,14.4,18.25,4.06,6.98,71.92,2.86,14.4,2.63,0.58,1.01,10.36,0.41 +Zimbabwe,2000,68.54,38.66,1.56,1.52,57.73,2.09,68.54,26.5,1.07,1.04,39.57,1.43 From 36652af2aa51ea476cffe6d9aaba2825c14bd8da Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 23 Jul 2024 21:44:12 +0530 Subject: [PATCH 04/58] Create CLEANING DATA --- GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED /MERGE ALL | 1 + 1 file changed, 1 insertion(+) create mode 100644 GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED /MERGE ALL diff --git a/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED /MERGE ALL b/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED /MERGE ALL new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED /MERGE ALL @@ -0,0 +1 @@ + From 4ede72c10183dd7dc7898ee92ae044eb89a2f287 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 23 Jul 2024 21:45:49 +0530 Subject: [PATCH 05/58] CREATING MERGE ALL.PY --- .../CLEAN AND PROCESSED/cleaned_data.csv | 46 +++++++++++++++++++ .../CLEAN AND PROCESSED/data perperation.py | 24 ++++++++++ .../CLEAN AND PROCESSED/merge all.py | 29 ++++++++++++ 3 files changed, 99 insertions(+) create mode 100644 GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/cleaned_data.csv create mode 100644 GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/data perperation.py create mode 100644 GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/merge all.py diff --git a/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/cleaned_data.csv b/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/cleaned_data.csv new file mode 100644 index 000000000..55ea7d043 --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/cleaned_data.csv @@ -0,0 +1,46 @@ +Country,latest year available_x,CH4 emissions,CH4 emissions per capita, % change since 1990,N2O emissions,N2O emissions per capita, % change since 1990.1,NOx emissions,% change since 1990_x,NOx emissions per capita,CO2 emissions ,% change since 1990_y,CO2 emissions per capita,CO2 emissions per km2,Total GHG emissions _perc,GHG from Energy_perc,"GHG from Energy +of which: from Transport_perc",GHG from Industrial Processes_perc,GHG from Agriculture_perc,GHG from Waste_perc,Total GHG emissions _tonnes,GHG from Energy_tonnes,"GHG from Energy +of which: from Transport_tonnes",GHG from Industrial Processes_tonnes,GHG from Agriculture_tonnes,GHG from Waste_tonnes,Consumption of CFCs_odptonnes,Consumption of all ODS,consumptionof all_odptonnesin 2013,reduction in odp,SO2 emissions,% change since 1990,SO2 emissions per capita +Argentina,2000,84.85,2.29,10.5,67.5,1.82,30.26,675.79,31.1,18.24,190.03,68.7,4.56,68.35,282,46.79,14.27,3.94,44.3,4.97,282,131.96,40.24,11.11,124.92,14.01, 4 697.2, 2 386.0,497.7,79.1,87.62,10.63,2.36 +Armenia,2010,2.26,0.76,-28.66,0.48,0.16,185.46,17.21,-77.5,5.81,4.96,0,1.67,166.81,7.2,69.54,17.32,3.14,18.36,8.97,7.2,5.01,1.25,0.23,1.32,0.65,196.5,174.4,4.5,97.4,29.44, 7 448.72,9.93 +Australia,2012,111.71,4.88,-3.02,25.78,1.13,40.43, 2 536.45,44.6,110.71,398.16,44.2,17.66,51.76,543.65,76.03,16.59,5.74,16.07,2.16,543.65,413.36,90.21,31.21,87.36,11.72, 14 290.4,389.7,48.3,87.6,797.76,-48.69,34.82 +Belarus,2012,15.39,1.62,1.14,16.4,1.73,-18.52,189.92,-43.5,20.01,55.38,-46.7,5.84,266.77,89.28,61.94,8.08,4.79,26.18,7.02,89.28,55.3,7.22,4.27,23.37,6.27, 2 510.9,2.7,7,-159.3,146.86,-86.44,15.47 +Bolivia (Plurinational State of),2004,11.97,1.34,28.03,1.39,0.15,140.53,64.92,31.1,7.24,16.12,191.7,1.6,14.67,43.67,20.69,9.79,48.71,26.7,3.9,43.67,9.03,4.27,21.27,11.66,1.7,75.7,67.4,0.4,99.4,12.1,8.42,1.45 +Brazil,2005,316.28,1.68,34.49,162.78,0.86,45.06, 3 400.00,35.8,18.04,439.41,110.4,2.19,51.61,862.81,38.11,15.59,8.95,48.19,4.76,862.81,328.79,134.54,77.2,415.77,41.05, 10 525.8, 3 589.4, 1 189.3,66.9,0,0,0 +Canada,2012,90.56,2.6,25.78,47.73,1.37,-2.92,0,0,0,557.29,21.4,16.15,55.81,698.63,80.98,27.93,8.08,7.95,2.94,698.63,565.76,195.11,56.46,55.53,20.57, 19 958.2,923.1,65.9,92.9,0,0,0 +Colombia,2004,53.87,1.26,21.37,34.38,0.8,39.53,335.16,24.5,7.84,72.42,26.3,1.56,63.43,153.88,42.87,14.15,5.89,44.56,6.68,153.88,65.97,21.77,9.07,68.57,10.28, 2 208.2, 1 002.2,176.7,82.4,142.81,0.71,3.34 +Costa Rica,2005,3.53,0.83,11.89,2.59,0.61,1302.52,26.88,-19.7,6.33,7.84,165.4,1.7,153.5,12.11,47.0,32.12,4.1,38.0,10.9,12.11,5.69,3.89,0.5,4.6,1.32,250.2,425.4,12.6,97.0,4.85,0,1.14 +Croatia,2012,3.42,0.8,-7.41,3.3,0.77,-17.4,55.19,-40.9,12.87,20.92,-10.4,4.86,369.62,26.45,71.54,21.59,10.78,12.83,4.26,26.45,18.92,5.71,2.85,3.39,1.13,219.3,172.3,0,100.0,25.58,-85.3,5.97 +Democratic People's Republic of Korea,2002,12.62,0.54,-49.11,0.93,0.04,-76.92,159,-65.4,6.84,73.58,-69.9,2.99,610.42,87.33,88.86,1.7,6.48,3.22,1.43,87.33,77.61,1.49,5.66,2.81,1.25,441.7, 2 326.3,90.6,96.1, 1 384.00,-55.66,59.53 +Dominican Republic,2000,4.84,0.56,59.11,3.18,0.37,279.36,93.12,68.3,10.88,21.89,137.1,2.18,449.72,26.43,69.03,23.39,3.07,21.57,6.33,26.43,18.25,6.18,0.81,5.7,1.67,539.8,406.9,34.8,91.4,110.15,42.94,12.86 +Ecuador,2006,17.17,1.23,31.61,201.48,14.42,33.12,231.77,47.7,16.59,35.73,112.2,2.35,139.36,247.99,10.85,5.16,1.11,84.73,3.32,247.99,26.9,12.78,2.75,210.11,8.23,301.4,273.4,22,92.0,8.87,37.73,0.64 +Egypt,2000,39.43,0.58,77.83,24.45,0.36,136.65,0,0,0,220.79,190.7,2.64,220.35,193.24,60.13,14.08,14.37,16.46,9.04,193.24,116.19,27.21,27.77,31.8,17.48, 1 668.0, 1 944.1,352.2,81.9,0,0,0 +Georgia,2006,4.14,0.93,-42.19,2.2,0.5,-8.84,27.67,-78.6,6.25,7.93,0,1.89,113.8,12.22,48.82,10.52,14.58,27.11,9.44,12.22,5.97,1.29,1.78,3.31,1.15,22.5,64.3,1.4,97.8,0.5,-99.8,0.11 +Ghana,2006,6.42,0.31,113.56,3.96,0.18,40.77,205.64,0,10.92,10.08,156.4,0.4,42.26,18.23,50.66,17.12,1.34,35.57,12.43,18.23,9.23,3.12,0.24,6.48,2.27,35.8,24,25.4,-5.8,0.5,0,0.03 +Iceland,2012,0.46,1.41,4.63,0.46,1.42,-12.08,20.55,-24.7,63.54,3.33,54.3,10.38,32.36,4.47,38.44,19.09,42.15,15.18,4.09,4.47,1.72,0.85,1.88,0.68,0.18,195.1,2.6,0,100.0,83.88,295.1,259.36 +Indonesia,2000,236.33,1.12,122.72,28.47,0.13,58.04,85.66,-28.9,0.4,563.98,277.1,2.3,295.14,554.33,50.68,10.25,7.7,13.24,28.38,554.33,280.94,56.82,42.67,73.4,157.33, 8 332.7, 5 787.4,310.5,94.6,0,0,0 +Japan,2012,20.03,0.16,-38.32,20.23,0.16,-31.94, 1 626.95,-20.4,12.8, 1 240.63,8.7,9.75, 3 282.70, 1 343.14,91.55,16.36,5.18,1.78,1.49, 1 343.14, 1 229.60,219.76,69.52,23.9,20.03, 118 134.0, 2 466.8,39.6,98.4,936.84,-25.29,7.37 +Kazakhstan,2012,49.03,2.91,-32.23,9.49,0.56,-47.13,500.26,-25.9,29.74,261.76,0,15.81,96.06,283.55,85.08,8.2,5.9,7.59,1.43,283.55,241.23,23.25,16.74,21.53,4.06, 1 206.2,146.9,104.6,28.8,649.61,-37.92,38.62 +Kyrgyzstan,2005,3.02,0.59,-50.42,0.15,0.03,-16.53,64.92,-44.5,12.69,6.62,0,1.19,33.08,12.02,74.07,20.7,4.32,16.11,5.49,12.02,8.9,2.49,0.52,1.94,0.66,72.8,50.2,4,92.0,26.9,-72.7,5.26 +Lao People's Democratic Republic,2000,5.34,1.0,-16.68,2.5,0.47,6625.0,20.84,81.5,3.9,1.2,412.5,0.19,5.08,8.9,11.68,5.02,0.54,86.26,1.51,8.9,1.04,0.45,0.05,7.68,0.13,43.3,42.9,1.6,96.3,1.59,0,0.3 +Mexico,2006,187.78,1.69,76.22,20.34,110.55,96.25, 1 444.41,16.3,13.68,466.55,48.4,3.88,237.5,641.45,67.05,22.56,9.9,7.1,15.94,641.45,430.1,144.69,63.53,45.55,102.27, 4 624.9, 3 954.7, 1 106.2,72.0, 2 612.91,-3.13,24.75 +Mongolia,2006,6.53,2.55,15.83,0.46,0.18,1380.0,2.98,29.6,1.27,19.08,90,6.92,12.2,17.71,57.7,10.65,5.04,36.48,0.78,17.71,10.22,1.89,0.89,6.46,0.14,10.6,7.3,0.9,87.7,0,0,0 +Mozambique,1994,5.66,0.37,13.51,0.98,0.06,37.01,93.81,22.1,6.11,3.28,227.8,0.13,4.09,8.22,22.64,10.39,0.62,56.2,20.53,8.22,1.86,0.85,0.05,4.62,1.69,18.2,14.4,8.3,42.4,0,0,0 +New Zealand,2012,29.04,6.55,8.21,10.89,2.45,32.02,158.5,57.7,35.73,33.26,33.5,7.55,122.97,76.05,42.24,18.09,6.94,46.05,4.73,76.05,32.12,13.76,5.28,35.02,3.6, 2 088.0,42.8,8.2,80.8,78.16,33.84,17.62 +Niger,2000,6.76,0.6,96.78,4.96,0.44,506.68,24,0,2.14,1.42,70.9,0.08,1.12,13.63,19.24,5.56,0.13,78.04,2.58,13.63,2.62,0.76,0.02,10.64,0.35,32,27.6,14.6,47.1, 2 140.00,0,190.65 +Norway,2012,4.23,0.84,-14.76,3.2,0.64,-36.55,166.23,-13.3,33.12,44.6,27.8,9.0,137.73,52.76,74.32,28.74,14.55,8.54,2.26,52.76,39.21,15.16,7.67,4.5,1.19, 1 313.0,-42.8,0,100.0,16.66,-68.1,3.32 +Paraguay,2000,11.48,2.16,-32.19,8.31,1.57,-77.55,87.7,-20.3,16.54,5.3,134.2,0.84,13.03,23.43,15.72,11.9,1.69,79.51,3.08,23.43,3.68,2.79,0.4,18.63,0.72,210.6,105.5,16.5,84.4,0.16,-46.67,0.03 +Republic of Korea,2012,29.78,0.6,-6.81,14.24,0.29,48.96,0,0,0,589.43,138.7,11.94, 5 892.32,688.43,87.19,12.55,7.46,3.19,2.15,688.43,600.25,86.36,51.37,21.99,14.81, 9 159.8, 11 745.9, 1 893.1,83.9,0,0,0 +Republic of Moldova,2009,2.68,0.66,-41.57,1.61,0.39,-51.53,37.06,-73,9.07,4.98,0,1.22,147.13,13.28,67.39,14.35,4.26,16.06,11.89,13.28,8.95,1.9,0.56,2.13,1.58,73.3,29.6,1,96.6,18.78,-93.63,4.6 +Russian Federation,2012,502.56,3.51,-15.31,115.95,0.81,-48.07, 5 603.13,-40.9,39.1, 1 650.27,-34.2,11.52,96.52, 2 297.15,82.16,10.51,7.89,6.28,3.65, 2 297.15, 1 887.26,241.37,181.14,144.22,83.95, 100 352.0,892.3,836.5,6.3,681.95,-16.02,4.76 +Saudi Arabia,2000,27.55,1.29,66.71,11.79,0.55,12.52,0,0,0,520.28,138.7,18.07,242.02,296.06,82.84,19.62,6.56,4.17,6.44,296.06,245.25,58.09,19.41,12.33,19.07, 1 798.5, 1 926.4, 1 440.3,25.2,0,0,0 +Serbia,1998,8.91,0.92,-1.84,6.82,0.71,-22.03,164,-20.9,16.96,49.19,0,5.45,556.64,66.34,76.19,5.84,5.46,14.32,4.04,66.34,50.55,3.88,3.62,9.5,2.68,849.2,384.9,8.1,97.9,388,-20.98,40.13 +South Africa,1994,43.21,1.07,0.21,20.67,0.51,-11.28,0,0,0,477.24,49.2,9.14,390.85,379.84,78.34,11.46,8.0,9.33,4.33,379.84,297.57,43.52,30.39,35.46,16.43,592.6,848.8,426.4,49.8,0,0,0 +Switzerland,2012,3.72,0.46,-19.84,3.02,0.38,-13.13,73.71,-49.1,9.19,41.85,-6.3,5.28, 1 013.63,51.49,80.6,31.72,7.05,10.76,1.19,51.49,41.5,16.33,3.63,5.54,0.61, 7 960.0,26.2,1.4,94.7,10.67,-73.7,1.33 +The former Yugoslav Republic of Macedonia,2009,1.66,0.8,-3.62,0.91,0.01,-27.62,34.11,-18,16.56,9.34,0,4.52,363.09,11.49,76.26,11.26,3.89,11.52,8.33,11.49,8.76,1.29,0.45,1.32,0.96,519.7,44.6,0.7,98.4,205.83, 6 807.05,99.97 +Turkey,2012,61.62,0.82,80.96,14.79,0.2,21.04, 1 283.74,99.4,17.15,345.73,144.2,4.7,441.23,439.87,70.16,14.0,14.27,7.34,8.23,439.87,308.6,61.56,62.77,32.28,36.22, 3 805.7, 1 336.4,147,89.0,248.83,-70.21,3.32 +Ukraine,2012,66.17,1.46,-59.24,31.37,0.69,-46.59, 1 205.57,-48.2,26.6,306.53,-57.6,6.74,507.93,402.67,76.76,8.31,11.43,8.95,2.82,402.67,309.08,33.47,46.01,36.03,11.37, 4 725.2,145.5,59.4,59.2, 1 687.55,-68.15,37.24 +United Republic of Tanzania,1994,21.63,0.75,4.73,14.38,0.5,-4.68,979.07,524.9,33.73,7.3,207.7,0.2,7.73,39.24,17.56,4.26,0.94,75.77,5.73,39.24,6.89,1.67,0.37,29.73,2.25,253.9,71.5,1.6,97.8,175.74,8.39,6.05 +United States of America,2012,552.01,1.75,-12.82,395.79,1.26,0.07, 11 882.02,-45.5,37.74, 5 583.38,9.5,17.87,579.84, 6 487.85,84.76,26.77,5.15,8.11,1.91, 6 487.85, 5 498.88, 1 736.64,334.35,526.25,123.97, 305 963.6, 16 206.4,711.3,95.6, 4 739.47,-77.36,15.06 +Uruguay,2004,18.63,5.61,14.95,11.79,3.55,-2.16,38.76,29.2,11.66,7.77,94.7,2.3,44.12,36.28,14.29,6.19,0.94,80.83,3.94,36.28,5.18,2.24,0.34,29.32,1.43,199.1,100.9,15.5,84.6,51.5,25.09,15.49 +Uzbekistan,2005,89.43,3.45,57.73,9.97,0.38,-22.78,257.52,-37.5,9.93,114.86,0,4.08,256.73,199.84,86.24,4.82,3.19,8.23,2.35,199.84,172.34,9.63,6.37,16.44,4.69, 1 779.2,0.8,4.6,-475.0,170.85,-74.93,6.59 diff --git a/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/data perperation.py b/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/data perperation.py new file mode 100644 index 000000000..bf353c93d --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/data perperation.py @@ -0,0 +1,24 @@ +import pandas as pd +import numpy as np + +# Load the CSV data into a pandas DataFrame +data = pd.read_csv('merge.csv') + +# Replace 0 with NaN +data.replace(0, np.nan, inplace=True) + +# Display the first few rows of the DataFrame +print(data.head()) + +# Handle missing values, if any +# For simplicity, we'll drop rows with missing values, but you could also fill them +data = data.dropna() + +# Convert columns to appropriate data types if necessary +data['latest year available_x'] = data['latest year available_x'].astype(int) + +# Save cleaned data for later use +data.to_csv('cleaned_data.csv', index=False) + + + diff --git a/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/merge all.py b/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/merge all.py new file mode 100644 index 000000000..97fe9ca7b --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/merge all.py @@ -0,0 +1,29 @@ +##Data Merging + +## Merge GHG Emissions Data + +##```python +import pandas as pd +# Load the data from CSV files +ch4_data = pd.read_csv('CH4_N2O_Emissions.csv') +co2_data = pd.read_csv('CO2_Emissions.csv') +merged_ghg_emissions_data=pd.read_csv('merged_ghg_data.csv') +NOx_data=pd.read_csv('NOx_Emissions.csv') +Ods_data=pd.read_csv('ODS_Consumption.csv') +SO2_data=pd.read_csv('SO2_emissions.csv') + +# Merge the datasets on the 'Country' column with outer join +merged_data = ch4_data.merge(NOx_data, on='Country', how='outer') +merged_data = merged_data.merge(co2_data, on='Country', how='outer') +merged_data = merged_data.merge(merged_ghg_emissions_data, on='Country', how='outer') +merged_data = merged_data.merge(Ods_data, on='Country', how='outer') +merged_data = merged_data.merge(SO2_data, on='Country', how='outer') + +# Fill missing values with 0 +merged_data.fillna(0, inplace=True) + +# Save the merged data to a new CSV file +merged_data.to_csv('merge.csv', index=False) + +# View the merged data +print(merged_data.head()) \ No newline at end of file From 89e956f04c76d0a6c15760c45f0e308bda63b06a Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 23 Jul 2024 21:48:19 +0530 Subject: [PATCH 06/58] CREATE CODE NOTEBOOK --- .../{CLEAN AND PROCESSED /MERGE ALL => NOTEBOOK/NOTEBOOK} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename GLOBAL CLIMATE ANALYSIS/{CLEAN AND PROCESSED /MERGE ALL => NOTEBOOK/NOTEBOOK} (100%) diff --git a/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED /MERGE ALL b/GLOBAL CLIMATE ANALYSIS/NOTEBOOK/NOTEBOOK similarity index 100% rename from GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED /MERGE ALL rename to GLOBAL CLIMATE ANALYSIS/NOTEBOOK/NOTEBOOK From 929c0c5ba1b5d776c3b52d461c964dcbd35e4547 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 23 Jul 2024 21:49:28 +0530 Subject: [PATCH 07/58] UPLOADING CODE NOTEBOOK --- .../NOTEBOOK/GLOBAL_CLIMATE_ANALYIS.ipynb | 804 ++++++++++++++++++ 1 file changed, 804 insertions(+) create mode 100644 GLOBAL CLIMATE ANALYSIS/NOTEBOOK/GLOBAL_CLIMATE_ANALYIS.ipynb diff --git a/GLOBAL CLIMATE ANALYSIS/NOTEBOOK/GLOBAL_CLIMATE_ANALYIS.ipynb b/GLOBAL CLIMATE ANALYSIS/NOTEBOOK/GLOBAL_CLIMATE_ANALYIS.ipynb new file mode 100644 index 000000000..9861db0dd --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/NOTEBOOK/GLOBAL_CLIMATE_ANALYIS.ipynb @@ -0,0 +1,804 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 71, + "id": "1b516b50-eb14-42b6-a48e-5ec5785fdc7e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.decomposition import PCA\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "ecce1da9-dbeb-4de8-bdc6-6f4798b862ad", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the cleaned data\n", + "data = pd.read_csv('cleaned_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "3f85261f-697d-4ee6-9127-0987d561c9ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns in the DataFrame:\n", + "Index(['Country', 'latest year available_x', 'CH4 emissions',\n", + " 'CH4 emissions per capita', ' % change since 1990', 'N2O emissions',\n", + " 'N2O emissions per capita', ' % change since 1990.1', 'NOx emissions',\n", + " '% change since 1990_x', 'NOx emissions per capita', 'CO2 emissions ',\n", + " '% change since 1990_y', 'CO2 emissions per capita',\n", + " 'CO2 emissions per km2', 'Total GHG emissions _perc',\n", + " 'GHG from Energy_perc',\n", + " 'GHG from Energy \\nof which: from Transport_perc',\n", + " 'GHG from Industrial Processes_perc', 'GHG from Agriculture_perc',\n", + " 'GHG from Waste_perc', 'Total GHG emissions _tonnes',\n", + " 'GHG from Energy_tonnes',\n", + " 'GHG from Energy \\nof which: from Transport_tonnes',\n", + " 'GHG from Industrial Processes_tonnes', 'GHG from Agriculture_tonnes',\n", + " 'GHG from Waste_tonnes', 'Consumption of CFCs_odptonnes',\n", + " 'Consumption of all ODS', 'consumptionof all_odptonnesin 2013',\n", + " 'reduction in odp', 'SO2 emissions', '% change since 1990',\n", + " 'SO2 emissions per capita'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "# Print all column names to verify\n", + "print(\"Columns in the DataFrame:\")\n", + "print(data.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "889d4338-844b-4398-8e9e-c5db38bb996a", + "metadata": {}, + "outputs": [], + "source": [ + "# List of columns to normalize (update this list based on actual column names)\n", + "columns_to_normalize = [\n", + " 'CH4 emissions', 'CH4 emissions per capita', ' % change since 1990',\n", + " 'N2O emissions', 'N2O emissions per capita', ' % change since 1990.1',\n", + " 'NOx emissions', '% change since 1990_x', 'NOx emissions per capita',\n", + " 'CO2 emissions ', '% change since 1990_y', 'CO2 emissions per capita',\n", + " 'CO2 emissions per km2', 'Total GHG emissions _perc', 'GHG from Energy_perc',\n", + " '\"GHG from Energy of which: from Transport_perc\"', 'GHG from Industrial Processes_perc',\n", + " 'GHG from Agriculture_perc', 'GHG from Waste_perc', 'Total GHG emissions _tonnes',\n", + " 'GHG from Energy_tonnes', '\"GHG from Energy of which: from Transport_tonnes\"',\n", + " 'GHG from Industrial Processes_tonnes', 'GHG from Agriculture_tonnes', 'GHG from Waste_tonnes',\n", + " 'Consumption of CFCs_odptonnes', 'Consumption of all ODS', 'consumptionof all_odptonnesin 2013',\n", + " 'reduction in odp', 'SO2 emissions', '% change since 1990', 'SO2 emissions per capita'\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "403c474c-5730-49db-9e36-2e86f116cb37", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing columns:\n", + "['\"GHG from Energy of which: from Transport_perc\"', '\"GHG from Energy of which: from Transport_tonnes\"']\n" + ] + } + ], + "source": [ + "# Check for columns that exist in the DataFrame\n", + "valid_columns = [col for col in columns_to_normalize if col in data.columns]\n", + "missing_cols = [col for col in columns_to_normalize if col not in data.columns]\n", + "print(\"Missing columns:\")\n", + "print(missing_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "47ed30d2-8fb6-4457-988f-9194c28107e6", + "metadata": {}, + "outputs": [], + "source": [ + "# Function to clean non-numeric values in a column\n", + "def clean_numeric(column):\n", + " # Replace commas and spaces in the entire column\n", + " column = column.str.replace(',', '', regex=False)\n", + " column = column.str.replace(' ', '', regex=False)\n", + " # Convert to numeric, forcing errors to NaN\n", + " return pd.to_numeric(column, errors='coerce')" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "a1321187-cf52-4c82-a2bc-f74c284ff9d4", + "metadata": {}, + "outputs": [], + "source": [ + "# Apply cleaning function to valid columns\n", + "for col in valid_columns:\n", + " data[col] = data[col].astype(str) # Ensure column is treated as strings\n", + " data[col] = clean_numeric(data[col]) # Apply cleaning function\n", + "\n", + "# Handle any NaNs in valid columns\n", + "data[valid_columns] = data[valid_columns].fillna(0)\n", + "\n", + "# Normalize the data\n", + "scaler = MinMaxScaler()\n", + "data[valid_columns] = scaler.fit_transform(data[valid_columns])" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "166c43f2-0387-4105-86fb-ffd9093174b3", + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare data \n", + "X = data[valid_columns]" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "32172d0b-589b-4491-a608-7d9d8753a2e8", + "metadata": {}, + "outputs": [ + { + "data": { + 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explained Variance by Principal Component 1: 0.34\n", + "Explained Variance by Principal Component 2: 0.17\n" + ] + } + ], + "source": [ + "\n", + "# Apply PCA\n", + "pca = PCA(n_components=2)\n", + "X_pca = pca.fit_transform(X)\n", + "\n", + "# Explained variance\n", + "explained_variance = pca.explained_variance_ratio_\n", + "\n", + "# Plot PCA results\n", + "plt.figure(figsize=(10, 7))\n", + "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=data['CO2 emissions '], cmap='viridis')\n", + "plt.colorbar(label='CO2 Emissions')\n", + "plt.title('PCA of Emission Data')\n", + "plt.xlabel('Principal Component 1')\n", + "plt.ylabel('Principal Component 2')\n", + "plt.show()\n", + "\n", + "print(f'Explained Variance by Principal Component 1: {explained_variance[0]:.2f}')\n", + "print(f'Explained Variance by Principal Component 2: {explained_variance[1]:.2f}')" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "667131db-51f8-4bbc-91a6-23d49a15656e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\minal\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5s/step - loss: 0.0076\n", + "Epoch 2/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0066\n", + "Epoch 3/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0061\n", + "Epoch 4/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0060\n", + "Epoch 5/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0062\n", + "Epoch 6/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0063\n", + "Epoch 7/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0062\n", + "Epoch 8/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0061\n", + "Epoch 9/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0060\n", + "Epoch 10/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0059\n", + "Epoch 11/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0058\n", + "Epoch 12/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0056\n", + "Epoch 20/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0055\n", + "Epoch 21/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0055\n", + "Epoch 22/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0055\n", + "Epoch 23/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0055\n", + "Epoch 24/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0054\n", + "Epoch 25/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0049\n", + "Epoch 33/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0048\n", + "Epoch 34/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0047\n", + "Epoch 35/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0046\n", + "Epoch 36/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0045\n", + "Epoch 37/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0045\n", + "Epoch 38/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.0044\n", + "Epoch 46/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 0.0044\n", + "Epoch 47/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 0.0044\n", + "Epoch 48/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 0.0043\n", + "Epoch 49/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 0.0043\n", + "Epoch 50/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 0.0043\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 312ms/step\n" + ] + }, + { + "data": { + "image/png": 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tkqRZs2bp/vvv16mnnqrk5GSNHj1aS5cuVXl5uf76179K8raXnjFjhq6++mp9//vf17///W9dd911evXVV/vdda2hoUGJiYmqr68PGJmQpNbWVu3cuVNjx44dtL/mA+iJf9cAwHwtzg7NvO0tudzet285SdH64MavHOZVQHg5VDboasAjOp9++qlmzZqlWbNmSZKWLFmiWbNm6ZZbbpEk7d27N6B719ixY/Xqq69qxYoVKiws1H333ac///nPI761NAAAwEB9UdEgl9ujUTGRslik8rqDqm5sNbssICgNeI3OggULDtku+Mknn+z1NZ999tlAbwUAAIAujKlqx+Qna9e+Zm2patL60notnMZIO9BdUHZdAwAAQE/G+pzCvCQV5iZJ6uzCBiAQQQcAACBEGKGmMDdJBXlJkgKbEwDoNOTtpQEAAHD09jc7Vbrfu9n6zNxEJUZ7N5teX1Yvj8czoI3LgZGAER0AAIAQYIzmjEuLVWJ0pCZnxsseYVX9wXbt3tdibnFAECLoAAAAhACjEUGRb22OPcKq6dne1rqs0wF6IugAAACEgJIujQgMRkMC1ukAPRF0RoDvfe97Ouecc/xfL1iwQDfccMOw17Fq1SpZLBbV1dUN+73NMljf865du2SxWFRcXDwodQEAQovH41FJWb0kqSA30X++yBd6Sgg6QA8EHZN873vfk8VikcVikd1u14QJE3THHXeoo6NjyO/9wgsv6Ne//nW/nmtGOPnss8/0zW9+UxkZGYqKitLEiRN1xRVXaMuWLQHP+8tf/qJjjjlGMTExio+P1ymnnKJXXnnliO755JNP+v95dD2ioo5uX4Ljjz9ee/fuVWJi4uGffAh5eXnau3evZsyYcVTXAQCEprIDB7W/2alIm0VTszp3gjdCzxcVDWp3uc0qDwhKBB0TnXHGGdq7d6+2bt2qn/70p7rtttt077339vpcp9M5aPdNTk5WfHz8oF1vML3yyis67rjj1NbWpr/97W/auHGjnnrqKSUmJurmm2/2P+9nP/uZ/uu//ksXXXSR1q9frzVr1ujEE0/U2WefrQceeOCI7p2QkKC9e/cGHLt37z6q78dutyszM/OoO+HYbDZlZmYqIoJGiQAwEhlrcKZmJSgq0uY/n58Sq4SoCLV1uLW5stGk6oDgRNAxkcPhUGZmpsaMGaMrr7xSCxcu1Msvvyypc7rZnXfeqezsbE2ePFmSVFpaqgsvvFBJSUlKTk7W2WefrV27dvmv6XK5tGTJEiUlJSklJUW/+MUv5PF4Au7bfepaW1ubfvnLXyovL08Oh0MTJkzQ448/rl27dunUU0+VJI0aNUoWi0Xf+973JElut1vLli3T2LFjFR0drcLCQj333HMB93nttdc0adIkRUdH69RTTw2oszctLS26/PLLdeaZZ+rll1/WwoULNXbsWM2bN0+//e1v9ac//UmS9NFHH+m+++7Tvffeq5/97GeaMGGCpk6dqjvvvFM33HCDlixZotLS0oH+45DFYlFmZmbAkZGREfBzu/baa3XDDTdo1KhRysjI0GOPPabm5mZdfvnlio+P14QJE/T666/7X9N9RGz37t1avHixRo0apdjYWE2fPl2vvfaaJOnAgQP69re/rbS0NEVHR2vixIn63//9X0m9T1179913deyxx8rhcCgrK0s33nhjwIjgggULdN111+kXv/iFkpOTlZmZqdtuu83/uMfj0W233abRo0fL4XAoOztb11133YB/bgCAoedfn+Nbk2OwWi3+NTs0JAAChV/Q8XgkZ7M5R7dAMVDR0dEBIzcrV67U5s2btWLFCr3yyitqb2/XokWLFB8fr//85z/64IMPFBcXpzPOOMP/uvvuu09PPvmknnjiCb3//vvav3+/XnzxxUPe97vf/a7+8Y9/6A9/+IM2btyoP/3pT4qLi1NeXp6ef/55SdLmzZu1d+9e/f73v5ckLVu2TH/961/1yCOP6IsvvtBPfvITfec739G7774ryRvIzjvvPC1evFjFxcX64Q9/qBtvvPGQdbz55puqra3VL37xi14fT0pKkiT94x//UFxcnP7rv/6rx3N++tOfqr293V/3WWedpRNPPFGFhYV69dVXD3n//vjLX/6i1NRUrVmzRtdee62uvPJKffOb39Txxx+vdevW6fTTT9ell16qlpbe23xeffXVamtr03vvvacNGzboN7/5jeLi4iRJN998s7788ku9/vrr2rhxox5++GGlpqb2ep3y8nKdeeaZOuaYY1RSUqKHH35Yjz/+uP7nf/6nR72xsbH6+OOPdc899+iOO+7QihUrJEnPP/+8fve73+lPf/qTtm7dqpdeekkzZ8486p8RAGDwlZR61+d0bURgMMIP63SAQOE3D6a9Rbor25x7/6pCsscO+GUej0crV67Um2++qWuvvdZ/PjY2Vn/+859lt9slSU899ZTcbrf+/Oc/+6dC/e///q+SkpK0atUqnX766Vq+fLmWLl2q8847T5L0yCOP6M033+zz3lu2bNGzzz6rFStWaOHChZKkcePG+R9PTk6WJKWnp/uDRltbm+666y69/fbbmj9/vv8177//vv70pz/plFNO0cMPP6zx48frvvvukyRNnjzZ/8a+L1u3bpUkTZky5ZA/ry1btmj8+PH+n0tX2dnZSkhI8K/n+eMf/6ixY8dq48aNOu2001RWVtbndevr6/2hw3DSSScFjNAUFhbqpptukiQtXbpUd999t1JTU3XFFVdIkm655RY9/PDDWr9+vY477rge99izZ4/OP/98f6Do+rPes2ePZs2apblz50qS8vPz+6z1oYceUl5enh544AFZLBZNmTJFFRUV+uUvf6lbbrlFVqv3bxgFBQW69dZbJUkTJ07UAw88oJUrV+q0007Tnj17lJmZqYULFyoyMlKjR4/Wscce2+c9AQDm6HC5taHcG3SK8nqu+fSP6PjCEACv8As6IeSVV15RXFyc2tvb5Xa79a1vfStgatHMmTMD3syXlJRo27ZtPdbXtLa2avv27aqvr9fevXs1b948/2MRERGaO3duj+lrhuLiYtlsNp1yyin9rnvbtm1qaWnRaaedFnDe6XRq1qxZkqSNGzcG1CHJH4r60leNR/PcsWPHSvJOSzPe/PclPj5e69atCzgXHR0d8HVBQYH/c5vNppSUlIBREGOqW3V1da/3uO6663TllVfqrbfe0sKFC3X++ef7r3nllVfq/PPP948MnXPOOTr++ON7vc7GjRs1f/78gLU/J5xwgpqamlRWVqbRo0f3qFeSsrKy/LV985vf1PLlyzVu3DidccYZOvPMM7V48WLWAQFAkNlW06SD7S7FOSI0LjWux+OFvoYEW6sb1dzWoVgH/x0HpHAMOpEx3pEVs+49AKeeeqoefvhh2e12ZWdn93iDGRsbODrU1NSkOXPm6G9/+1uPa6WlpQ28XvV8I98fTU1NkqRXX31VOTk5AY85HI4jqkOSJk2aJEnatGnTIUPRpEmT9P7778vpdPYY1amoqFBDQ4P/WpI3FN1www36+c9/fsj7W61WTZgw4ZDPiYyMDPjaYrEEnDOCh9vde+ebH/7wh1q0aJFeffVVvfXWW1q2bJnuu+8+XXvttfra176m3bt367XXXtOKFSv01a9+VVdffbV++9vfHrKmgdZr1JaXl6fNmzfr7bff1ooVK3TVVVfp3nvv1bvvvtvjdQAA8xhT0mbmJMpq7dncJj0hSlmJUdpb36rPy+s1b1zKMFcIBKfwW6NjsXinj5lxDLCzVmxsrCZMmKDRo0f366/os2fP1tatW5Wenq4JEyYEHImJiUpMTFRWVpY+/vhj/2s6Ojq0du3aPq85c+ZMud1u/9qa7owg4XK5/OemTZsmh8OhPXv29KgjLy9PkjR16lStWbMm4FofffTRIb+/008/Xampqbrnnnt6fdxY0H/xxRerqanJ35ygq9/+9reKjIzU+eef7z939913y2Kx6Jprrjnk/YdLXl6efvzjH+uFF17QT3/6Uz322GP+x9LS0nTZZZfpqaee0vLly/Xoo4/2eo2pU6dq9erVASNbH3zwgeLj45Wbm9vvWqKjo7V48WL94Q9/0KpVq7R69Wpt2LDhyL85AMCgKz7E+hyDf50ODQkAv/Ab0Qlj3/72t3Xvvffq7LPP1h133KHc3Fzt3r1bL7zwgn7xi18oNzdX119/ve6++25NnDhRU6ZM0f3333/IPXDy8/N12WWX6fvf/77+8Ic/qLCwULt371Z1dbUuvPBCjRkzRhaLRa+88orOPPNMRUdHKz4+Xj/72c/0k5/8RG63WyeeeKLq6+v1wQcfKCEhQZdddpl+/OMf67777tPPf/5z/fCHP9TatWv15JNPHvL7M9YkffOb39Q3vvENXXfddZowYYJqa2v17LPPas+ePXr66ac1f/58XX/99fr5z38up9Opc845R+3t7Xrqqaf0+9//XsuXL/cHrpdeeknPPPOM3nvvvcO2ePZ4PKqsrOxxPj09/bDT3vrrhhtu0Ne+9jVNmjRJBw4c0DvvvKOpU6dK8q7vmTNnjqZPn662tja98sor/se6u+qqq7R8+XJde+21uuaaa7R582bdeuutWrJkSb9rffLJJ+VyuTRv3jzFxMToqaeeUnR0tMaMGTMo3ysAYHAYIzq9rc8xFOYl6Y0vKlmnA3QRfiM6YSwmJkbvvfeeRo8erfPOO09Tp07VD37wA7W2tiohwbt52E9/+lNdeumluuyyyzR//nzFx8fr3HPPPeR1H374YV1wwQW66qqrNGXKFF1xxRVqbm6WJOXk5Oj222/XjTfeqIyMDP+oyK9//WvdfPPNWrZsmaZOnaozzjhDr776qn9NzOjRo/X888/rpZdeUmFhoR555BHdddddh/0ezz77bH344YeKjIzUt771LU2ZMkWXXHKJ6uvrAzqKLV++XA899JD+8Y9/aMaMGZo7d67ee+89vfTSSwENHS699FK5XC594xvf0IIFC3Tw4ME+793Q0KCsrKweR1/rbY6Ey+XS1Vdf7f+ZTZo0SQ899JAk7+jZ0qVLVVBQoJNPPlk2m01PP/10r9fJycnRa6+9pjVr1qiwsFA//vGP9YMf/MDfKKE/kpKS9Nhjj+mEE05QQUGB3n77bf3rX/9SSgpTHgAgWBx0urS5yrs/ziFHdHwhqJjOa4CfxTOQFeAmaWhoUGJiourr6/1v6A2tra3auXOnxo4de9S72APoG/+uAcDw+3TXfl3wyGqlxTu05ldf7XN2QmNruwpuf0sej/TpTQuVGnfka2aBYHeobNAVIzoAAABBqqTMtz4nN+mQU7DjoyI1Ps3bkW0963QASQQdAACAoNWf9TkGoyFBMet0AEkEHQAAgKBldFE71PocgxGGSlinA0gi6AAAAASlA81O7d7XIkkqyEk67PONMFRSVjegTbiBcEXQAQAACELGaM7Y1Fglxhx+I+cpmQmy26yqa2nXnv0tQ1wdEPzCJujwlwtgaLndbrNLAIARZb2/EcHh1+dIkj3CqqnZ3g5URhMDYCQL+Q1DIyMjZbFYVFNTo7S0tMNuCglgYDwej5xOp2pqamS1WmW3280uCQBGBGOtTX/W5xiKchNVUlqnktI6faMwe2gKA0JEyAcdm82m3NxclZWVadeuXWaXA4StmJgYjR49WlZr2AwEA0DQ8ng8A2pEYCjMS5JW76YhAaAwCDqSFBcXp4kTJ6q9vd3sUoCwZLPZFBERwYgpAAyT8rqDqm1yKsJq0bSsvjdE7M4IRZ9X1Kvd5VakjT9OYeQKi6Ajed+I2Ww2s8sAAAA4aiW+vXCmZMUrKrL/72/GpsQqPipCja0d2lLVqOnZ/VvfA4QjYj4AAECQWW9MW/NtAtpfVqtFBbnGfjo0JMDIRtABAAAIMsVH0IjAYIQjIywBIxVBBwAAIIi43B5tKPeOxhQdSdDxvaaYhgQY4Qg6AAAAQWRbdZNanC7F2m0anxY34Ncb4WhLVaNanB2DXB0QOgg6AAAAQcRoDT0zN1E268C7XWYkRCkzIUpuj/R5ecMgVweEDoIOAABAECk5wkYEXXU2JKg7+oKAEEXQAQAACCJHslFod8ZrS2hIgBGMoAMAABAkWttd2rS3UdLRBZ0igg5A0AEAAAgWX1Q0qMPtUWqcQ9mJUUd8nZm+qWul+w9qX1PbYJUHhBSCDgAAQJAw1tQU5SXKYhl4IwJDQlSkxqfFSpLWl7FxKEYmgg4AAECQMDb5LDiKRgQGo5kB++lgpCLoAAAABIkS3+jL0azPMRjXWM86HYxQBB0AAIAgUNfi1M7aZklSoW+NzdHo7LxWL4/Hc9TXA0INQQcAACAIGGtp8lNilBRjP+rrTc2KV6TNov3NTpUdOHjU1wNCDUEHAAAgCBiNCAZj2pokOSJsmpaVIIl1OhiZCDoAAABBwFifMxiNCAz+6WsEHYxABB0AAACTeTwe/6hLUd7Rr88xGKGJFtMYiQg6AAAAJttb36rapjbZrBZNzx68oGOEpg3l9epwuQftukAoIOgAAACYzJhaNiUzXlGRtkG77rjUOMU5InSw3aWt1U2Ddl0gFBB0AAAATFbs2+tmsBoRGKxWiwp8rapZp4ORhqADAABgMn/HtUHYP6e7zv106gb92kAwI+gAAACYyOX26PPyBkmDP6IjdYan4lIaEmBkIegAAACYaEdNk5raOhRjt2lievygX98IT1uqGnXQ6Rr06wPBiqADAABgIqOt9IycRNmslkG/fmZClNLjHXK5PfqiglEdjBwEHQAAABMZa2eKhmDamiRZLBb/qE4xDQkwghB0AAAATFTiWztT6NvccygU+RsSMKKDkYOgAwAAYJLWdpc2VXobERQMQcc1Ay2mMRIRdAAAAEyycW+D2l0epcTalTsqesjuU5CTJEnas79FB5qdQ3YfIJgQdAAAAEzi3z8nL0kWy+A3IjAkxkRqXGqs957sp4MRgqADAABgEmPNzFCuzzH4Nw5lPx2MEAQdAAAAk3SO6Azd+hyDsXEoIzoYKQg6AAAAJqg/2K4dtc2SpIJhGNEp8I/o1Mnj8Qz5/QCzEXQAAABMsME3bW10coySY+1Dfr9pWQmKsFq0r9mp8rqDQ34/wGwEHQAAABMYU8gKh2ij0O6iIm2ampXgvTfrdDACEHQAAABMUGyszxnC/XO6M9YCsU4HIwFBBwAAwARGI4KiYRrRkTq7uxWzcShGgCMKOg8++KDy8/MVFRWlefPmac2aNYd8/vLlyzV58mRFR0crLy9PP/nJT9Ta2npEBQMAAIS6yvpWVTe2yWa1aHr28I3oGKHq8/J6udw0JEB4G3DQeeaZZ7RkyRLdeuutWrdunQoLC7Vo0SJVV1f3+vy///3vuvHGG3Xrrbdq48aNevzxx/XMM8/oV7/61VEXDwAAEIqMEZVJGfGKttuG7b7j0uIUa7epxenStuqmYbsvYIYBB537779fV1xxhS6//HJNmzZNjzzyiGJiYvTEE0/0+vwPP/xQJ5xwgr71rW8pPz9fp59+ui655JLDjgIBAACEK2ONTNEw7J/Tlc1q0UxjPx2mryHMDSjoOJ1OrV27VgsXLuy8gNWqhQsXavXq1b2+5vjjj9fatWv9wWbHjh167bXXdOaZZ/Z5n7a2NjU0NAQcAAAA4cK/Uegw7J/TndHlrZiGBAhzEQN5cm1trVwulzIyMgLOZ2RkaNOmTb2+5lvf+pZqa2t14oknyuPxqKOjQz/+8Y8POXVt2bJluv322wdSGgAAQEhwuz3+PXSGq7V0V0W+cMWIDsLdkHddW7Vqle666y499NBDWrdunV544QW9+uqr+vWvf93na5YuXar6+nr/UVpaOtRlAgAADIsdtc1qbOtQVKRVE9Pjhv3+RrjaVNmo1nbXsN8fGC4DGtFJTU2VzWZTVVVVwPmqqiplZmb2+pqbb75Zl156qX74wx9KkmbOnKnm5mb96Ec/0n//93/Lau2ZtRwOhxwOx0BKAwAACAnGSMrMnERF2IZ/p4+sxCilxjlU29SmLyrqNWdM8rDXAAyHAf3bZbfbNWfOHK1cudJ/zu12a+XKlZo/f36vr2lpaekRZmw2b3cRj4e2hgAAYGQxGhGYsT5HkiwWi78JQklpvSk1AMNhwH9GWLJkiR577DH95S9/0caNG3XllVequblZl19+uSTpu9/9rpYuXep//uLFi/Xwww/r6aef1s6dO7VixQrdfPPNWrx4sT/wAAAAjBT+RgQmrM8xGCGrhIYECGMDmromSRdddJFqamp0yy23qLKyUkVFRXrjjTf8DQr27NkTMIJz0003yWKx6KabblJ5ebnS0tK0ePFi3XnnnYP3XQAAAISAtg6Xvtzr7SZbZGbQ8d2bhgQIZxZPCMwfa2hoUGJiourr65WQkGB2OQAAAEekuLRO5zz4gUbFRGrdzafJYrGYUkddi1NFd6zw1nTLaUqKsZtSB3Ak+psNhn8FHAAAwAi13lifk5dkWsiRpKQYu/JTYiRJJWWs00F4IugAAAAMk2ITNwrtzpi+tp7pawhTBB0AAIBhYqyJMXN9joGGBAh3BB0AAIBh0NDaru01zZKkgtxEk6vpHNEpLq1nyw+EJYIOAADAMNjgWwuTlxytlDjzN0afnp2gCKtFtU1tqqhvNbscYNARdAAAAIaBMUWsIAjW50hSVKRNkzPjJdFmGuGJoAMAADAM/OtzgiToSF3202GdDsIQQQcAAGAYlJR6p64VBkEjAoMRuhjRQTgi6AAAAAyxyvpWVTa0ymqRZuQEz+bnRujaUFYvl5uGBAgvBB0AAIAhZkwNm5QRrxh7hLnFdDEhPU4xdpuanS5tr2kyuxxgUBF0AAAAhth6X9AJho1Cu7JZLZqZ4211Xcz0NYQZgg4AAMAQC8b1OQZ/QwKCDsIMQQcAAGAIud0e/9S1wjzzNwrtzhhlWu/b5wcIFwQdAACAIbRzX7MaWzsUFWnVpIx4s8vpwQhfG/c2qLXdZXI1wOAh6AAAAAwhY0rYjOxERdqC761XTlK0UuPs6nB79OXeBrPLAQZN8P3bBgAAEEaMKWEFQdaIwGCxWPzT11ing3BC0AEAABhCRjezYFyfYygg6CAMEXQAAACGiLPDrS8rvNPBioKw45rBCGE0JEA4IegAAAAMkU2VDXK63EqKidTo5Bizy+mTMXVtR22z6lvazS0GGCQEHQAAgCFiTAUrzE2SxWIxt5hDGBVr15gUbxBbX15nbjHAICHoAAAADJES31SwwtzgXZ9joCEBwg1BBwAAYIj4R3SCeH2OocAXxopLWaeD8EDQAQAAGAKNre3aVtMkKXhbS3dlNEsoKauTx+MxtxhgEBB0AAAAhsCG8np5PN4NOdPiHWaXc1jTsxNls1pU09imyoZWs8sBjhpBBwAAYAiU+KaABXNb6a6i7TZNzoiXxDodhAeCDgAAwBBYX1YnKbg3Cu3OWEvEOh2EA4IOAADAEDBGRUJhfY6hyBfKGNFBOCDoAAAADLLqhlZV1LfKapFm5oTOiI4RyjaU18vtpiEBQhtBBwAAYJAZ++dMTI9XrCPC5Gr6b2J6nKIjbWpq69CO2iazywGOCkEHAABgkHXunxM6ozmSFGGz+kegWKeDUEfQAQAAGGQl/kYESabWcSQKWaeDMEHQAQAAGEQej6dzRCeEGhEYCrtsHAqEMoIOAADAINq1r0UNrR2yR1g1OTPe7HIGzAhnG/c2qK3DZW4xwFEg6AAAAAwiYzRnRnaCIm2h91Yrd1S0kmPtand5tHFvo9nlAEcs9P7tAwAACGLF/kYESabWcaQsFosKc1mng9BH0AEAABhExtqWohANOlKXdToEHYQwgg4AAMAgaXe59UVFg6TOzTdDkRF0imlIgBBG0AEAABgkmysb5exwKyEqQvkpMWaXc8SMhgQ7appVf7Dd3GKAI0TQAQAAGCRd1+dYLBZzizkKybF25SVHS5I+L2fjUIQmgg4AAMAgMda0hPL6HIMxqlPMOh2EKIIOAADAIDEaEYTiRqHdFdGQACGOoAMAADAImto6tLW6SZJUkJdocjVHz995jYYECFEEHQAAgEHweXm9PB4pOzFK6fFRZpdz1KZnJ8hqkaoa2lRZ32p2OcCAEXQAAAAGQUmIbxTaXYw9QpMy4iUxqoPQRNABAAAYBP71OWESdCTW6SC0EXQAAAAGQUmptw1zODQiMLBOB6GMoAMAAHCUahrbVF53UBaLNDM39BsRGIzQtr60Xm63x9xigAEi6AAAAByl9b4RjwlpcYpzRJhbzCCalBGnqEirGts6tKO22exygAEh6AAAABylcGtEYIiwWTUj2ztCtZ7pawgxBB0AAICjVFzmW58TZkFH6rJOh4YECDEEHQAAgKPg8Xj8ox1FYdSIwGAEHSPMAaGCoAMAAHAU9uxvUV1Lu+wRVk3OjDe7nEFnhLeNFQ1q63CZWwwwAAQdAACAo1Dsm9I1LStB9ojwe2uVlxytUTGRcrrc2rS30exygH4Lv38bAQAAhpGxf05RGK7PkSSLxaIC36gO++kglBB0AAAAjoLx5r8wL3z2z+musyEB63QQOgg6AAAAR6jd5dbn5b6Oa2HYiMBQ5AtxjOgglBB0AAAAjtDmyka1dbiVEBWh/JRYs8sZMsbUte01TWpobTe3GKCfCDoAAABHaL2v5XJBbpKsVovJ1Qyd1DiHckdFy+ORPqfNNEIEQQcAAOAIGZtohvP6HIMxNa+Y6WsIEQQdAACAI+RvRBDG63MMRphbT0MChAiCDgAAwBFobuvQlirvvjLh2lq6q0JaTCPEEHQAAACOwOfl9XJ7pKzEKKUnRJldzpCbkZMoq0XaW9+qqoZWs8sBDougAwAAcAQ6GxGE//ocSYp1RGhSRrykzrVJQDAj6AAAAByBYv9GoUmm1jGcmL6GUHJEQefBBx9Ufn6+oqKiNG/ePK1Zs+aQz6+rq9PVV1+trKwsORwOTZo0Sa+99toRFQwAABAMjFGNohHQiMBQYDQkoMU0QkDEQF/wzDPPaMmSJXrkkUc0b948LV++XIsWLdLmzZuVnp7e4/lOp1OnnXaa0tPT9dxzzyknJ0e7d+9WUlLSYNQPAAAw7Gqb2lR24KAsFmnGCJm6JnUZ0Smtk9vtCeu9gxD6Bhx07r//fl1xxRW6/PLLJUmPPPKIXn31VT3xxBO68cYbezz/iSee0P79+/Xhhx8qMjJSkpSfn3/Ie7S1tamtrc3/dUNDw0DLBAAAGDLrfVO3xqfFKSEq0txihtHkzHg5IqxqaO3Qrn3NGpcWZ3ZJQJ8GNHXN6XRq7dq1WrhwYecFrFYtXLhQq1ev7vU1L7/8subPn6+rr75aGRkZmjFjhu666y65XK4+77Ns2TIlJib6j7y8vIGUCQAAMKRKSkdWIwJDpM2qGTne75l1Ogh2Awo6tbW1crlcysjICDifkZGhysrKXl+zY8cOPffcc3K5XHrttdd0880367777tP//M//9HmfpUuXqr6+3n+UlpYOpEwAAIAhZbzJHwn753TXOX2NdToIbgOeujZQbrdb6enpevTRR2Wz2TRnzhyVl5fr3nvv1a233trraxwOhxwOx1CXBgAAMGAej8ffiKBwBDUiMBT6GhIU02IaQW5AQSc1NVU2m01VVVUB56uqqpSZmdnra7KyshQZGSmbzeY/N3XqVFVWVsrpdMputx9B2QAAAOYo3X9QB1raZbdZNSUr3uxyhp0R7r7c2yBnh1v2CHYrQXAa0G+m3W7XnDlztHLlSv85t9utlStXav78+b2+5oQTTtC2bdvkdrv957Zs2aKsrCxCDgAACDnG/jlTsxPkiLAd+slhaExKjBKjI+XscGtzZaPZ5QB9GnAEX7JkiR577DH95S9/0caNG3XllVequbnZ34Xtu9/9rpYuXep//pVXXqn9+/fr+uuv15YtW/Tqq6/qrrvu0tVXXz143wUAAMAwWe/fP2dkNSIwWCwW/yapxTQkQBAb8Bqdiy66SDU1NbrllltUWVmpoqIivfHGG/4GBXv27JHV2pmf8vLy9Oabb+onP/mJCgoKlJOTo+uvv16//OUvB++7AAAAGCZGI4KCEbg+x1CUm6j3ttSopLROlx43xuxygF4dUTOCa665Rtdcc02vj61atarHufnz5+ujjz46klsBAAAEjQ6XWxvKvd3GCkdgxzVDQZeNQ4FgxeoxAACAftpS1aTWdrfiHREalxprdjmmKfB1XttW06Smtg6TqwF6R9ABAADoJ/+0tbxEWa0Wc4sxUXp8lHKSouXxSBvK2E8HwYmgAwAA0E/rfUFnJO6f052xn04JDQkQpAg6AAAA/VRc6h29GMmNCAyFrNNBkCPoAAAA9EOLs0Nbqrz7xhSN4EYEBhoSINgRdAAAAPrhi4oGudweZSQ4lJkYZXY5ppuZmyiLRaqob1V1Y6vZ5QA9EHQAAAD6wRi5YH2OV5wjQhPT4yRJ60tpSIDgQ9ABAADoh5Iy9s/pzr9Oh4YECEIEHQAAgH5gRKcnI/QVs04HQYigAwAAcBj7m53as79FkndtCryMpgzry+rl8XjMLQbohqADAABwGMbUrHFpsUqMjjS3mCAyOTNe9gir6g+2a/e+FrPLAQIQdAAAAA7DmLZWxLS1AJE2q6ZnJ0hinQ6CD0EHAADgMPzrc2hE0IOxZol1Ogg2BB0AAIBD8Hg8Wu/ruFbA+pwejHU6bByKYEPQAQAAOISyAwe1r9mpSJtFU7MSzC4n6BijXJ9XNKjd5Ta3GKALgg4AAMAhGGtPpmYlKCrSZm4xQSg/JUYJURFydri1ubLR7HIAP4IOAADAIbB/zqFZLBb/qA4NCRBMCDoAAACHUFLqXZ9DI4K+GSGQdToIJgQdAACAPnS43NpQ7g06RXk0IuiLf0THFwqBYEDQAQAA6MO2miYdbHcpzhGhcalxZpcTtAp93ei2VDeqqa3D5GoAL4IOAABAH4ypWDNzEmW1WswtJoilJ0QpKzFKHo/0eTmjOggOBB0AAIA+FLM+p9+MdTrraUiAIEHQAQAA6IMxosP6nMNjnQ6CDUEHAACgF63tLm2u8u4Lw4jO4RX6wmAxndcQJAg6AAAAvfiiol4ut0dp8Q5lJkSZXU7Qm5mTKItFKq87qJrGNrPLAQg6AAAAvfGvz8lNksVCI4LDiY+K1IQ0b2c61ukgGBB0AAAAesH6nIErMDYOLWOdDsxH0AEAAOhFiW9UgvU5/WeEwhLW6SAIEHQAAAC6qWtxave+FklSQU6SucWEEH/ntbI6eTwec4vBiEfQAQAA6MaYejU2NVaJMZEmVxM6pmQmyG6zqq6lXXv2t5hdDkY4gg4AAEA3xtSrwlzW5wyEPcKqadkJkmgzDfMRdAAAALrxBx3W5wyYEQ7ZOBRmI+gAAAB04fF4aERwFIyfGS2mYTaCDgAAQBcV9a2qbXIqwmrRtKwEs8sJOUbQ+byiXu0ut7nFYEQj6AAAAHRhTFubkhWvqEibucWEoLEpsYqPilBru1tbqhrNLgcjGEEHAACgi85GBEmm1hGqrFaL/2fHOh2YiaADAADQRTGNCI5aQS4bh8J8BB0AAAAfl9ujDeXeUYgigs4R67pxKGAWgg4AAIDPtuomtThdirXbND4tzuxyQpYRErdUNarF2WFuMRixCDoAAAA+xgjEzNxE2awWc4sJYRkJUcpMiJLbI31e3mB2ORihCDoAAAA+NCIYPIV5rNOBuQg6AAAAPmwUOngKfGGxmHU6MAlBBwAAQFJru0ub9nr3fSHoHD1jnc56gg5MQtABAACQ9EVFgzrcHqXGOZSdGGV2OSFvpq/FdOn+g9rX1GZyNRiJCDoAAADqHHkoykuUxUIjgqOVEBWp8WmxkqT1ZWwciuFH0AEAAFDnovkCGhEMGmMKYDENCWACgg4AAICkEt+oA+tzBk8RG4fCRAQdAAAw4tW1OLWztlmSVOhbW4KjZ4yOrS+rl8fjMbcYjDgEHQAAMOIZa0jyU2KUFGM3uZrwMTUrXpE2i/Y3O1V24KDZ5WCEIegAAIARbz375wwJR4RN07ISJLFOB8OPoAMAAEa84lLviA6NCAafER5LCDoYZgQdAAAwonk8Hv9oQ1Ee63MGW6EvPNKQAMONoAMAAEa0vfWtqm1qk81q0fRsgs5gK/SFxw3l9epwuU2uBiMJQQcAAIxoxpSqKZnxioq0mVtMGBqXGqc4R4Ra293aWt1kdjkYQQg6AABgRGP/nKFltVpU4GvZzTodDCeCDgAAGNGMN9/snzN0Ctk4FCYg6AAAgBHL5fZoQzkjOkPNaEhgdLcDhgNBBwAAjFg7aprU1NahGLtNE9PjzS4nbBkNCbZUNeqg02VyNRgpCDoAAGDEMtpKz8hJlM1qMbeYMJaZEKX0eIdcbo++qGBUB8ODoAMAAEas9b5GBEVMWxtSFovFPzWwmIYEGCYEHQAAMGIZi+MLaEQw5Ir8DQkY0cHwIOgAAIARqbXdpY17GyR1LpbH0DF+xrSYxnAh6AAAgBFp494Gtbs8Som1K3dUtNnlhL2ZvlGzPftbtL/ZaXI1GAmOKOg8+OCDys/PV1RUlObNm6c1a9b063VPP/20LBaLzjnnnCO5LQAAwKDx75+TlySLhUYEQy0xOlLjUmMlSevZTwfDYMBB55lnntGSJUt06623at26dSosLNSiRYtUXV19yNft2rVLP/vZz3TSSScdcbEAAACDxWhEwLS14ePfOJT9dDAMBhx07r//fl1xxRW6/PLLNW3aND3yyCOKiYnRE0880edrXC6Xvv3tb+v222/XuHHjjqpgAACAwVDsG1Uw9njB0Cv0TV8rYUQHw2BAQcfpdGrt2rVauHBh5wWsVi1cuFCrV6/u83V33HGH0tPT9YMf/KBf92lra1NDQ0PAAQAAMFjqD7ZrR02zJKmAEZ1h0zmiUyePx2NuMQh7Awo6tbW1crlcysjICDifkZGhysrKXl/z/vvv6/HHH9djjz3W7/ssW7ZMiYmJ/iMvL28gZQIAABzSBt+0tdHJMUqOtZtczcgxNStBkTaL9jU7VXbgoNnlIMwNade1xsZGXXrppXrssceUmpra79ctXbpU9fX1/qO0tHQIqwQAACNNiX/aWpKpdYw0UZE2TclMkNS5RgoYKhEDeXJqaqpsNpuqqqoCzldVVSkzM7PH87dv365du3Zp8eLF/nNut9t744gIbd68WePHj+/xOofDIYfDMZDSAAAA+q3Y6LjGRqHDrjAvURvK61VSVqevF2SZXQ7C2IBGdOx2u+bMmaOVK1f6z7ndbq1cuVLz58/v8fwpU6Zow4YNKi4u9h/f+MY3dOqpp6q4uJgpaQAAwBRGe+MiRnSGndHlrpiNQzHEBjSiI0lLlizRZZddprlz5+rYY4/V8uXL1dzcrMsvv1yS9N3vflc5OTlatmyZoqKiNGPGjIDXJyUlSVKP8wAAAMOhsr5VVQ1tslktmp7NiM5wM8LlhrJ6dbjcirCxfz2GxoCDzkUXXaSamhrdcsstqqysVFFRkd544w1/g4I9e/bIauUXFgAABCdjJGFSRryi7TZzixmBxqXFKc4Roaa2Dm2rafKv2QEG24CDjiRdc801uuaaa3p9bNWqVYd87ZNPPnkktwQAABgUJf5pa4zmmMFmtWhGToI+2rFfJaV1BB0MGYZeAADAiFLib0SQZGodI5l/Px06r2EIEXQAAMCI4XZ7/Hvo0FraPEW+kFlCQwIMIYIOAAAYMXbUNquxrUNRkVZNTI8zu5wRywiZmyob1druMrcYhC2CDgAAGDGMEYSZOYl0+zJRVmKU0uIdcrk9+qKC6WsYGvwbDgAARgyjEQHrc8xlsVj8m7UWlxJ0MDQIOgAAYMTwNyJgfY7pjLBpbN4KDDaCDgAAGBHaOlzauLdRUuemlTCPv/MaDQkwRAg6AABgRNi0t1FOl1ujYiKVOyra7HJGvALf1LVd+1pU1+I0uRqEI4IOAAAYEfzrc/KSZLFYzC0GSoqxa2xqrCT208HQIOgAAIARoZiNQoOO0ZCA6WsYCgQdAAAwIhhvplmfEzwKaEiAIUTQAQAAYa+htV07apslda4NgfmMhgTFpfXyeDzmFoOwQ9ABAABh7/Oyenk8Ul5ytFLiHGaXA5/p2QmKsFpU29SmivpWs8tBmCHoAACAsFfsmxpVwPqcoBIVadOUrHhJrNPB4CPoAACAsOdfn0PQCTpGcwiCDgYbQQcAAIS9klJv++JCGhEEHSPoFBN0MMgIOgAAIKxVNbSqsqFVVos0IyfB7HLQjRE+Py+vl8tNQwIMHoIOAAAIa8aUqEkZ8YqxR5hbDHqYkB6nGLtNzU6Xttc0mV0OwghBBwAAhLUSXyMCNgoNTjarRTNzvC2/mb6GwUTQAQAAYY31OcHP2MSVhgQYTAQdAAAQttxuT+eITh4bhQYro+238c8KGAwEHQAAELZ27mtWY2uHoiKtmpQRb3Y56IMRQjftbVRru8vkahAuCDoAACBsrfeNEMzITlSkjbc9wSonKVqpcXZ1uD36cm+D2eUgTPBvPAAACFvG+pwCGhEENYvFwsahGHQEHQAAELaMLl6szwl+hTQkwCAj6AAAgLDk7HDrywrvNKgiOq4FPX/QKas3txCEDYIOAAAIS5sqG+R0uZUUE6nRyTFml4PDKPDtpbOztln1Le0mV4NwQNABAABhyRgZKMxNksViMbkaHM6oWLvGpHgD6fryOnOLQVgg6AAAgLBkrPUozGV9TqigIQEGE0EHAACEJX/QYX1OyDD+WRWXsk4HR4+gAwAAwk5ja7u21TRJorV0KCnydccrKauTx+MxuRqEOoIOAAAIOxvK6+XxeDeiTIt3mF0O+mlaVqJsVotqGttU2dBqdjkIcQQdAAAQdtb7GhHQVjq0RNttmpwRL4l1Ojh6BB0AABB2StgoNGSxTgeDhaADAADCjhF0WJ8TevzrdBjRwVEi6AAAgLBS3dCqivpWWS3SzBxGdEKNMaKzobxeLjcNCXDkCDoAACCsGBuFTkyPV6wjwuRqMFAT0uIUHWlTU1uHdvg65wFHgqADAADCyvqyOkmszwlVETarfyTOCK3AkSDoAACAsFLMRqEhr5B1OhgEBB0AABA2PB5PZ8c1GhGELCOklvhG54AjQdABAABhY9e+FjW0dsgeYdXkzHizy8ERMkLqxr0Nam13mVsMQhZBBwAAhA1jNGdGdoIibbzNCVW5o6KVHGtXu8ujjXsbzC4HIYr/AgAAgLBR4m9EkGRqHTg6FotFhbnedTrraUiAI0TQAQAAYcMY0Ski6IQ8/zodGhLgCBF0AABAWGh3ufV5hXeaUwGNCEKeEXSKaUiAI0TQAQAAYWFzZaOcHW4lREUoPyXG7HJwlIyGBDtqmlV/sN3cYhCSCDoAACAsdN0/x2KxmFsMjlpyrF2jk72BdQPrdHAECDoAACAsrPdNcWJ9Tvgo8DUkYD8dHAmCDgAACAslpd6/+rNRaPgooiEBjgJBBwAAhLymtg5tqW6UJBXkJZpcDQaLv/MaIzo4AgQdAAAQ8j4vr5fHI2UnRik9PsrscjBIpmcnyGa1qKqhTZX1rWaXgxBD0AEAACGvpEsjAoSPGHuEJmXES+psNgH0F0EHAACEPGNqE0En/BT6GhKsZ/oaBoigAwAAQh6NCMIX63RwpAg6AAAgpNU0tqm87qAsFmlmLo0Iwo0RXteX1svt9phbDEIKQQcAAIQ0Y0rThLQ4xTkizC0Gg25SRpyiIq1qbOvQjtpms8tBCCHoAACAkEYjgvAWYbNqZo5v41AaEmAACDoAACCkFZf51ucQdMJWgW/6Gut0MBAEHQAAELI8Ho9/6loRjQjCVmdDgnpzC0FIIegAAICQtWd/i+pa2mWPsGpyZrzZ5WCIGCF2Y0WD2jpc5haDkEHQAQAAIcvYRHJaVoLsEbytCVd5ydEaFRMpp8utTXsbzS4HIYL/IgAAgJBl7J9TxPqcsGaxWNhPBwNG0AEAACHLeNNbmMf+OeHO2E+nmM5r6CeCDgAACEntLre+qPB1XKMRQdgzwux6GhKgn44o6Dz44IPKz89XVFSU5s2bpzVr1vT53Mcee0wnnXSSRo0apVGjRmnhwoWHfD4AAEB/bKlqVGu7WwlREcpPiTW7HAwxo8X09pomNbS2m1sMQsKAg84zzzyjJUuW6NZbb9W6detUWFioRYsWqbq6utfnr1q1SpdcconeeecdrV69Wnl5eTr99NNVXl5+1MUDAICRy1ifU5CbJKvVYnI1GGqpcQ7ljoqWxyN9zqgO+mHAQef+++/XFVdcocsvv1zTpk3TI488opiYGD3xxBO9Pv9vf/ubrrrqKhUVFWnKlCn685//LLfbrZUrVx518QAAYOQq8a3VYH3OyGE0JCimIQH6YUBBx+l0au3atVq4cGHnBaxWLVy4UKtXr+7XNVpaWtTe3q7k5OQ+n9PW1qaGhoaAAwAAoCt/IwLW54wYxn46JTQkQD8MKOjU1tbK5XIpIyMj4HxGRoYqKyv7dY1f/vKXys7ODghL3S1btkyJiYn+Iy8vbyBlAgCAMNfi7NCWKu9+KrSWHjkKcmlIgP4b1q5rd999t55++mm9+OKLioqK6vN5S5cuVX19vf8oLS0dxioBAECw+7y8QW6PlJUYpfSEvt9TILzMyEmU1SLtrW9VVUOr2eUgyA0o6KSmpspms6mqqirgfFVVlTIzMw/52t/+9re6++679dZbb6mgoOCQz3U4HEpISAg4AAAADMbUJeMv/BgZYh0RmpQRL4npazi8AQUdu92uOXPmBDQSMBoLzJ8/v8/X3XPPPfr1r3+tN954Q3Pnzj3yagEAANS5GL2QaWsjjrEmq4SGBDiMAU9dW7JkiR577DH95S9/0caNG3XllVequblZl19+uSTpu9/9rpYuXep//m9+8xvdfPPNeuKJJ5Sfn6/KykpVVlaqqalp8L4LAAAwohh/zS+iEcGIY4Rbo7040JeIgb7goosuUk1NjW655RZVVlaqqKhIb7zxhr9BwZ49e2S1duanhx9+WE6nUxdccEHAdW699VbddtttR1c9AAAYcfY1tanswEFZLNIMpq6NOMZ0xZKyOrndHvZQQp8GHHQk6ZprrtE111zT62OrVq0K+HrXrl1HcgsAAIBeGR23xqfFKSEq0uRqMNwmZ8bLEWFVY2uHdu1r1ri0OLNLQpAa1q5rAAAAR6uYRgQjWqTNqhk5naM6QF8IOgAAIKQYb27ZP2fk8jckYJ0ODoGgAwAAQobH4/E3IiikEcGIVZjnHdEppsU0DoGgAwAAQkbp/oM60NIuu82qKVnxZpcDkxgh98uKBjk73OYWg6BF0AEAACHDmLY2NTtBjgibucXANGNSYpQYHSmny63NlY1ml4MgRdABAAAho3P/HBoRjGQWi8W/n04xDQnQB4IOAAAIGcaITgHrc0Y8I+yWsE4HfSDoAACAkNDhcmtDubfLViEd10Y843eAoIO+EHQAAEBI2FLVpNZ2t+IdERqXGmt2OTCZMaq3raZJTW0d5haDoETQAQAAIWG9MW0tL1FWq8XcYmC6tHiHcpKi5fFIG8rYTwc9EXQAAEBIMNbnsH8ODMZ+OiU0JEAvCDoAACAkFJd6/2pPIwIYjNDLOh30hqADAACCXouzQ1uqvPulFNGIAD40JMChEHQAAEDQ+6KiQS63RxkJDmUmRpldDoLEzJxEWS1SRX2rqhtbzS4HQYagAwAAgp7xF3vW56CrWEeEJqTHSZLWl9KQAIEIOgAAIOiVlLF/DnrnX6dDQwJ0Q9ABAABBjxEd9MUIv8Ws00E3BB0AABDU9jc7tWd/iyRpZm6iydUg2BR1aUjg8XjMLQZBhaADAACCmjElaVxarBKjI80tBkFncma87BFWNbR2aNe+FrPLQRAh6AAAgKBmLDIvYtoaehFps2p6doIk2kwjEEEHAAAENWNEh0YE6AsNCdAbgg4AAAhaHo/H/1f6AtbnoA9FbByKXhB0AABA0Co7cFD7mp2KtFk0NSvB7HIQpIzRvs8rGtTucptbDIIGQQcAAAQtYyrS1KwERUXazC0GQSs/JUYJURFydri1ubLR7HIQJAg6AAAgaK03NgqlEQEOwWKxsJ8OeiDoAACAoGW8aaURAQ7HCMPraUgAH4IOAAAISh0utzb4RnSK8mhEgEMr9DckqDe3EAQNgg4AAAhK22qadLDdpThHhMalxpldDoJcoa8r35bqRjW1dZhcDYIBQQcAAAQlo1XwzJxEWa0Wc4tB0EtPiFJ2YpQ8HunzckZ1QNABAABBqsRoRMD6HPRTIfvpoAuCDgAACErGm1XW56C/CvwNCRjRAUEHAAAEodZ2lzb59kNhRAf9VegLxbSYhkTQAQAAQeiLinq53B6lxTuUmRBldjkIETNzEmWxSOV1B1XT2GZ2OTAZQQcAAASd4tLOjUItFhoRoH/ioyI1Ic3boY/9dEDQwZDbUdOk+9/arJ//s0RvflEpZ4fb7JIAAEGO9Tk4UjQkgCHC7AIQng40O/XK+go9v648YJ7sP9eWKTnWrm8UZuuCObmanp3AX+oAAD0Yf41nfQ4GqjA3Uc+tLfN37cPIRdDBoGnrcOmdTdV6YV253tlcrXaXR5Jks1p08sRU5afG6pX1e1XT2KYnP9ylJz/cpckZ8Tp/To7OKcpROnOwAQCS6lqc2rWvRZJUkJNkbjEIOf4RnbI6eTwe/qA6ghF0cFQ8Ho/W7anTC+vK9Mr6vao/2O5/bEZOgs6dlatvFGYrLd4hSfrvM6fqP9tq9fzaMr31ZZU2VzXqrtc26e7XN+nkSWk6f3auTpuWoahIm1nfEgDAZMZf4semxioxJtLkahBqpmQmyG6zqq6lXXv2t2hMSqzZJcEkBB0ckT37WvTiZ+V68bMy/1/dJCkzIUrnzMrRebNzNCkjvsfrImxWnTo5XadOTlf9wXa9un6vnl9XprW7D2jV5hqt2lyj+KgInVXgndo2ezSLUAFgpDHWVhTmsj4HA2ePsGpadoKKS+tUXFpH0BnBCDrot/qWdr26Ya9e/KxMn+w64D8fY7fpjBmZOn92ro4blyKbtX/BJDE6Ut+aN1rfmjdaO2ub9cK6Mr2wrlzldQf1jzV79I81ezQ2NVbnz87RubNzlZMUPVTfGgAgiPiDDutzcISK8pJUXFqnktJ6nV2UY3Y5MAlBB4fU7nLr3c01euGzMr29sdrfMc1qkU6YkKrzZudo0fRMxdiP7ldpbGqsfnr6ZP1k4SR9tGOfnltXpjc+r9TO2mb99q0tum/FFs0fl6LzZ+fqazOP/n4AgODk8Xj8U9cIOjhSBb7RwBJaTI9ovFtEDx6PR+vL6vXiZ+V6uaRC+5ud/semZMbrvNk5OrsoRxlD0DzAarXo+AmpOn5Cqn59dode/7xSz68t0+od+/Thdu9x8/99rq/NyNIFc3I1b2yyrP0cQQIABL+K+lbVNrUpwmrRtKwEs8tBiDJC8hcV9Wp3uRVpY0eVkYigA7/yuoN66bNyvbCuTNtrmv3n0+IdOrswW+fNztW07OH7n06sI0IXzMnVBXNyVXagRS+uK9fz67xrgp5fV6bn15UpJyla58/O0Xmzc5WfyhxcAAh1xrS1KVnxNKbBERubEqv4qAg1tnZoS1Wjpmez3mskIuiMcI2t7Xp9Q6Ve+KxMH+3Y7z8fFWnVoumZOndWjk6ckKoIk/8SkjsqRtd+daKu+coErd19QM+vK9MrJXtVXndQf/j3Nv3h39s0d8wonT8nV18vyFJCFF16gP5yuT0qLj2gdzbV6N0tNaqoOyhHhFVRkTbZfR8d3T5GRVrliAj8aDzu6PX5Pc8ZH/u7rg8jQ2cjgiRT60Bos1otKsxN0vvbalVSWk/QGaEIOuHA45HcLsnjktwdvsPlO3xfezq/7uho12e7a7Vq416t2V4jt6tDEXLreKtbMzJjddL4JM0ZnaCYiDKpY5f0ZZdr+e/R/WOH5HF3u39HYF02u5SYJ43K9x1jJEfPzmyHYrFYNDc/WXPzk3Xr4ul668sqPb+2TP/ZWqNPdx/Qp7sP6LaXv9Dp0zN1/uwcnTQxjTdRQC9qm9r07uYavbO5Wv/ZWhvQGn64RdosAaHJ0TU8+b7u8THSpqguocrR5euo7oGq2+uNx5j2GpyKaUSAQVKYl+gLOnX61rzRZpcDExB0BqKtUVrz2KFDRcB5d69BIyAc9LhW99f0FlrcPV8/ABGSjvEdilDgb8F+3/HJIP3MDicmxRt6ksZ0hh8jCCXkSra+f0WjIm36RmG2vlGYraqGVr30mXdq25aqJv2rpEL/KqlQRoJD58zK0QWzczWxl3bXwEjhcntUUlanVZuqtWpLjdZ32zE8MTpSJ09K04JJaZqWnSBnh1ttHW61trvU2u7yf971Y1uXr1vb3Wrr6P1ja4dLbb6v23xfGxsKS1K7y6N2V4ea2ob3Z2K3WbuNQPU+4tR9VKr3ESur4qMiVZSXpMRoRpSPlMvt0efl3t/NIoIOjpIxKkhDgpGLoDMQzmZp5e1mVzEgHotNLotN7W6r2j0WuWRTh6ySxSZ7pF1RUXbZIyNlsUZI1gjJapMsNt/nvq+tvq/957ucMw6LtdtrerlWe4tUt0c6sMt7HNwvtezzHuVrexZvsUmJuYHhJ2mMNGqs9/OYZMm3x05GQpT+65Tx+tHJ47ShvF7Pry3TyyUVqmpo05/e3aE/vbtDBbmJOn+2dwPTUbH24fuHAJhkX1Ob3tvq3Z/qvS01OtASOGozIydBCyal69QpaSrMTRrWKaoutycg+AQEpHaXWjs6P/YWrPwf/a/v/liXYNXlsQ53Z8ByutxyutxqbOsYtO/LapGmZydq/vgUzR+fomPykxXn4H+1/bW9pknNTpdi7TaNT4szuxyEOGNUcEtVo1qcHXRsHYEsHo/Hc/inmauhoUGJiYmqr69XQoKJHVhaG6Q3bgx8k2+xBb6x7/WN/yAHhu6v61ZDc7v05qZavfjZXr2/fZ+Mf8L2CKtOm5qh82bn6ORJaeZ3IGltkOp2Swd2d4afOuPz3ZLrMH/etcd1Gw3K7wxFSaPltDj0703Ven5dmd7ZVO1/gxNps+grU9J1/uxcnTol3fyfAzBI3G6P1pfXa9Xmar2zuUbry+rU9b/w8VEROnlimhZMTtMpk9OUHj/4nRODXYfLO1J1uNGqruHqkKNVXZ5f3dimnbXNAfezWS0qzPUFn3GpmjNmlKLtLLDvy7OfluoXz63XvLHJeua/5ptdDsLAcXetVGVDq579r/k6dmyy2eVgkPQ3GxBtByIqQTrnIbOr6JXL7dHq7fv0wroyvfFFpVqcndPZjs1P1rmzc3TmzKzgmlIRlSBlzvQe3bndUlNVt/CzqzMUNVZIziap6nPv0Qt7XKbOGJWvM0aNUcspuVrbkKhXSu16tzpWb33h0ptfVCk51q5vFGbrgjm5mp6dIIuFOfsILQeanQGjNvu6tIOXpKlZCTp1cpoWTE7X7NHDO2oTjCJsVkXYrIp1DM31K+tb9dGOffpwe61W79in0v0HtW5PndbtqdOD72yX3WZV0egkzR+XouPHp6hodJIcEQQfg9GIgGlrGCyFeYmq/KJVJaV1BJ0RiBGdELe5slEvfFamlz4rV1VD5whIfkqMzpudq3Nn5SgvOcbECodIe6tUX9olAO3qEop2S20Nh3x5hyVS5Z407XSlqdSTpj2edLkTx2j69AKdNG+u0lLTh+GbAAbO7fbo84p6rfI1EigprZO766iNI0InTkzVqZPTdcrktCHZ7wr9V7q/Rat37NNHvn3AKhtaAx6PirRqzphROn58qo4bl6KC3MQRPcp81h//o8/LG/TQt2frzJlZZpeDMPDQqm26543N+npBlh781myzy8EgYUQnjFU3turl4gq9+Fm5vqjofEOfGB2pxYVZOm92rmblJYX36ERklJQ60Xt05/FIBw/0Mh1ulzcE1Zcqwt2uMarQGFtF5+ta5G3C8InUZI1XR8JoxWdNkC05P3CNUGKeFMEaHwyf+pZ2vbfVG2ze21Kj2qbAUZspmfFaMDldCyanac6YUSP6jXKwyUuOUV5yjC6cmyePx6Nd+1q0ert3xOejHftU2+TUB9v26YNt+yRJsXabjhmb7BvxSdW07IQR0zmytd2lTXsbJdFxDYOnyGhI4BstxMhC0AkRre0uvfVllV5YV6b/bK2Vq9t6k3Nn5erUKWlMgZC8DQpikr1HTi9/vXF1eKe+dQk/ztodaqjYqoiGUiV56hTnbpTqvvAePa5vlRJyOtcEJeUHrg+KTfM3SQCOhMfj0RcVDVq1uVqrNtdo3Z4DAaM2sXabTpyY6g83WYnR5hWLfrNYLBqbGquxqbH61rzR8ng82lbdpA+379Pq7fv00c59qmtp16rN3qmIkpQQFaFjx3qnuc0fn6LJGfFh2xb7y70N6nB7lBrnUHYiI5EYHDNyvfvnlB04qH1NbUqJG6J5qwhKBJ0g5nZ79PHO/XrxszK9tqFSTV06A80anaTzZufqrJlZdBAbKFuElDTae4w9WZJkl5Tqe3hneZXeXbNWX35RoriD5cqzVCvPUq0JkbXKUbUi3W3eaXP1pdKu//S8fmRMHw0Sxng/t8cOz/eJkFJ/sF3vb631hpstNappDGzGMSkjzh9s5o5Jlj2CUZtQZ7FYNDEjXhMz4nXZ8flyuz3aWNngDT079unjHfvV0NqhtzdW6e2NVZKk5Fi7jhvnHfGZPz5V49Niw2b0vnN9TmLYfE8wX0JUpManxWp7TbPWl9Xr1ClMTR9JCDpBaFt1k178rEwvfVah8rqD/vO5o6J13qwcnTs7V2NTebM8VMbmZGjsuWfKffbX9NGOfXpubZnu+bxSB1tckjxKs9TrzNw2nZXnVGFcnewNpZ1T5OrLvG20azZ6j97EpvfSMjvfey4hx9tBD2HP4/Fo495GrdpSrVWbarR2zwH/SK0kxdhtOn58qk6d4m0kkJPEqE24s1otmp6dqOnZifrhSePU4XLri4oGrd7hXd/z6a792t/s1GsbKvXahkpJUlq8w9/YYP74FI1OjgnZkGAEnQLfVCNgsBTmJWl7TbOKS+sIOiMMzQiCxP5mp/5VUqEX1pWppMtGfvFRETqrIEvnzsrV3DGjwnbKQrBrauvQ6xv26vl1Zfpox37/+Vi7TV+bmaXzZ+dq3thkWd1Ob9jp0SBhl3d9UGvdoW9kjZSS8npuoJo8TkoeLznYVyKUNbS264Ottd6pSVuqAxqISNKE9DgtmJSmU6eka27+KKaiIkC7y631ZXX6cNs+rd6xT5/uPiBnhzvgOdmJUZo/PtW/j08oBeRTf7tKO2ub9ZfvH6tTJqWZXQ7CyF9X79It//eFFkxO05OXH2t2ORgE/c0GBB0Ttba79O9N1XphXZlWba7x7/MSYbXolElpOm92rr46NV1RkbzZCSal+1v04mflen5dmXbva/Gfzx0VrfNm5+r82Tkak9LHiNvBA97A0zX8+APRHsnd3vvrDHGZUsp4b/BJGe8NP8bXkaHzhmak8Hg82lzV6O2Qtqlaa3cfCNiwMjrSpuPHp2jBlHQtmJQWnh0SMWRa2136bE+dVu/Yp9Xba1VcWqd2V+D/0sekxPimuXmPYN07qb6lXYV3vCVJKr7lNCXFMCUbg6ektE5nP/iBRsVEat3Np4XsqCc6EXSClMfj0drdB/T8unK9ur5CDa2d624KchN17qwcLS7MViqL5YKe8c/yubVlenX93oDd1Y/JH6XzZ+fqzIIsJUT1c+8it0tq3BsYgOp2S/t3Svu3Sy37Dv36hFwpZVyX8OP7OCpfiuD3abg0tXXog221/kYCe+sD2wmPS431r7U5dmwyf8jAoGlxdmjt7gP+5gYbyusDpkNK0vi0WB3vG/E5blyKkoNkjed/ttbo0sfXKD8lRqt+fqrZ5SDMtHW4NOPWN9Xu8ui9n5+q0Sn8USnUEXSCzO59zXphXble/Kxce/Z3jgJkJ0bpnFk5Om92jiakx5tYIY5Ga7tLb35RqefXlev9rTX+DlmOCKsWTc/U+XNydeKE1KNrE3uwzht49u3wfdwm7dvu/by1vu/XWazeltgBI0C+j0mjJVsQbSIbgozOWe/4gs0nu/YH/FXdEWH1jtr4wk2fo33AIGtsbdcnu/Zr9XbvVLcvKhrU/f/4UzLjNX+8t5X1sWOTTdtU+oF/b9Vv39qis4uy9fuLZ5lSA8Lb2Q+8r5Kyev3xkllaXJhtdjk4SgSdIFDX4tQr6/fqxc/KtXb3Af95Y13HebNzdNzYFNbdhJmqhlbv1La1Zdpa3eQ/n5Hg0DmzcnTB7FxNzBjEUOvxSC37feHHF4CMz/fvkJxNfb/WGuFdD9R9GlzKeG84ojFCr5rbOvTh9n3+UZuuTUMk74a9RrA5blwKozYICnUtTn280xd8tu/T5qrGgMetFml6dqKOH5+i48an6Jj8ZMU5hqdn0Q//8qne3lilm8+aph+cOHZY7omR5Zb/+1x/Xb1bPzxxrG46a5rZ5QQ1j8ejprYO1TY5VdPYptom71HT2KZjxybrpInmr6Ej6JjE2eHWqs3VemFduf69qVpOl3ehqNUinTQxTefNztHp0zIVbeeNT7jzeDzaUF6v59eW6f9KKlTX0rn+pjA3UefPydXiguyhbQ/u8UhN1b2EoB3eENRxsO/X2uzSqLG9rwmKz5asI6e9scfj0faaZn+wWbNzv//fbUmyR1g1f1yKFkz2dkijKyJCQW1Tmz7esV8fbq/V6h37tKOmOeBxm9WiwtxE7/qecamaM2bUkPy/y+Px6Jg7V6q2qU3PXzlfc8YkD/o9gOfXlumn/yzRMfmj9M8fH292OcPO4/Go2elSbWNnaDE+1jQ5e5xr69boxPBfJ4/T0jOnDnP1PRF0hpHH41FxaZ1e/Kxc/yqp0IEub2inZiXo/Nk5+kZhttITgnMRKIZeW4dL72yq1nNry7Vqc7V/QXqkzaKvTsnQ+XNytWBy2vDuaO92e9cEBYSgHd7PD+yUXM6+XxsRLSWP7X06XFxGWGyY2uLs0Ort+7yNBDZXq+xAYCjMS47WqZPTderkdB03LoU/XiDkVTW0+kd7PtxRq9L9gb/zdptVRaOT/O2si0YnDUpnwIq6gzr+7n/LZrXoi9sXMQKKIbGtukkL739XUZFWfX7bIkUM5/9vh1CLs6NLQHGqpqlNtY1t/o+1TcbnTh1sdw3o2rF2m9LiHUqN8x5p8Q6dNDFVp0/PHKLvpv8IOsOgdH+LXvrMu+5mR23nX8LS471TlM6dlaOpWcFTL4JDbVObXi6u0PPryvRFRYP/fEqsXd8oytYFc3I1PTvRxArlbYxQX9YlBG3v/Lxut+Tu6Pu19jhfCJrQMwTFpARtCPJ4PNpZ26x3Ntdo1eZqfbxzf0DrXrvNqnnjkv1T0salhs9GjUBvyg60+IPP6h37ejTWiIq0au6YZH9jg4LcxCP6Y80bn+/Vj59ap+nZCXr1upMGq3wggNvtUeHtb6mxrUOvXXeSpmUH7/uzg06XapvaVN1t9KXz884pZS3OgYWXGLvNH1pS4+w9gkxqnENpcQ6lxtsVYw/e7TYJOkNVS2u7bz+Vcq3Z2bmfSnSkTWfMyNS5s3J0wtEuOseIsamyQc+vLdOLn1WotqlzT5UpmfG6YE6uzi7KUVp8kHVMc7V7W2Eboz9d1wTVl0qe3oe7JUmORG9nuB4haJwUPWr4vgefg06XPtrhXWvzzuaagEYhkpSTFO3dsHNSuo6fkBLU/9EHhpLH49HufS3ejm47vOGn63+zJO9ff48Zm+wb8UnVtOyEfv2/8O7XN+mRd7frW/NG665zZw7VtwDoW499pA+379Pd583UxceOHtZ7t7a7AqaG9bb+xfjYPMDwEhVpVVq8L6DEOZRqfO77mBZv94eZ2GFadzfUhjToPPjgg7r33ntVWVmpwsJC/fGPf9Sxx/a9AdM///lP3Xzzzdq1a5cmTpyo3/zmNzrzzDP7fb9gCTrFpXW66E+r/fMWLRbphPGpOndWjs6YkRk2vzwYfh0ut97bWqPn15ZrxZdV/vUfNqtFJ0xIVU5SlOw2q+wRvsNm6/w8wiqH7zFHRNfndHm822uM50VYLYM7KtHR5m2N3duaoIayQ782OtkbfPwhqEurbMfgNW/YVdvsDzYf7dgXMA850mbRsWOTtWBSuk6dkqbxaXGM2gC9MLoNGqFn9Y59AesQJSkhKkLHjvVOc5s/PkWTM+J7bb5zyaMfafWOffrN+TN10THD++YTI8tv3tikh1dt1yXH5mnZeQVHfb3WdpcvqDgDpovV+MJLbZepZF23oOgPR4S1c4TFP9Ji73EuNd6hWLttxP2/qr/ZYMDvzJ955hktWbJEjzzyiObNm6fly5dr0aJF2rx5s9LT03s8/8MPP9Qll1yiZcuW6ayzztLf//53nXPOOVq3bp1mzJgx0NubampWvKIibRqTEqPzZufq7KJsZSWySSOOXoTNqq9MydBXpmSovqVd/1rvndr22Z46vbelZsjua7HIH4gc3cKREZYcEb2Hqu5hynsNm+wRDjlsM2SPLpA93yr7eN9jcir+YKliG3crtnmXoup3yV6/UxF1O2RtrpIO7pfK9ktln/QsNDY9cAqc8XnyOMl+6P0QWttd+njnfr2zqVrvbqnRztrABdfZiVH+DTuPn5A6bF2mgFBmsVg0MSNeEzPi9d35+XK7PdpU2ejfvPTjHfvV0NqhtzdW6e2NVZKk5Fi7jhuX7NvANFXj02Ll9kgbyr3t8Qvzkkz8jjASFOYmSZKKS/vekqGtw6V9XUZbOkddnL6F+51hprF1YOHFHmHtMtLSS2jpMqUszhEx4sLLUBjwiM68efN0zDHH6IEHHpAkud1u5eXl6dprr9WNN97Y4/kXXXSRmpub9corr/jPHXfccSoqKtIjjzzSr3sGy4iOJFXWtyojwcEvH4bF9pomrdpco5a2Djldbjk73GrzHc4Ot++cq8vnnc/p+nxnl+d330AwGMSoVfmWSuVbKjXBVqXx1kqNsVRqtPYqWQ2HfG1dRJr2OXJ1ICpPddF5aogZo6a4MWqKztWn5Qf14fZatbZ3jtpEWC06Jj9ZCyan6dQp6ZqYzqgNMNg6XG59UdHgH/H5ZNf+HmsJ0uIdKsxN1NsbqxVjt2nDbYuY9o0hVVnfquOWrZTNatGS0yb1CDM1jW0BG7n3h91mDVjr0hla7EqLj+p8LN6heMLLoBmSER2n06m1a9dq6dKl/nNWq1ULFy7U6tWre33N6tWrtWTJkoBzixYt0ksvvdTnfdra2tTW1jn3t6Hh0G90hlNmIp3TMHzGp8VpfFrcoF7T5fZ0BiKXKyAEdQ9Gbd0ClLPD1SNQ9XxOt9Dlv27Pezk73Opwe9SiKH3pydeXnnyp2xKfeLUo31Kpsb4gNNa61/95kqVZSR01SuqokZo/C3id22NRpUap3JKqfTGZsqeMUXreRI2bOFUxaSlSYrYUyYgsMBQibFYV5iWpMC9JPz5lvNpdbq0vq/N2dNu+T2t3H1BNY5ve3lgtSZqRnUjIwZDLTIxSRoJDVQ1tuvfNzX0+L9Jm6SW09LJoP96hhCjCSzAbUNCpra2Vy+VSRkZGwPmMjAxt2rSp19dUVlb2+vzKyso+77Ns2TLdfvvtAykNQD/ZrBZF222+dsjm7ILelcvtUbsrcJSqrd3VIzy1dfl6e4dbG11uWVr2K6pxp2Kbdiu+eY8SWnYrqbVUya2linK3KFv7lW3ZL7m3SDXyHuu63Dw2XUrKk5JGezdITRrd5fO8QV0bBIxkkTar5oxJ1pwxybrmKxPV2u5ScWmdPty+T5v2NujyE9gkFMPj1sXT9cK6co2Kiexz0X5idCThJUwE5WT0pUuXBowCNTQ0KC8vz8SKAAwVm9Uim9V2hHtnjJZU1PO0xyM113i7w9XtlupKvR3h6vZ4P6/bI7U3S83V3qN8be+Xjx7VSwAa7Q1BiXnex/mfITBgUZE2HTfO25YaGE5nzszSmTOzzC4Dw2RAQSc1NVU2m01VVVUB56uqqpSZ2fvmQZmZmQN6viQ5HA45HEHWUhdA6LBYpLh075E7t+fjHo908IAv+OzxhSBfAKr3haHWOu9zDh6QKtf3fh97fGfoMQJQ0mgp0fd5bBpBCAAAkwwo6Njtds2ZM0crV67UOeecI8nbjGDlypW65ppren3N/PnztXLlSt1www3+cytWrND8+fOPuGgAOCoWixST7D2yi3p/TmtDLwFoT+foUHON5GyUqr/0Hr2JiJYSc/ueHhefKVnZBR4AgKEw4KlrS5Ys0WWXXaa5c+fq2GOP1fLly9Xc3KzLL79ckvTd735XOTk5WrZsmSTp+uuv1ymnnKL77rtPX//61/X000/r008/1aOPPjq43wkADKaoBClqupQxvffHnS1SfVnPAGR83rhX6jgo7dvqPXpjjZQSc3wBaEzP0aGEHMlm/joqAABC0YCDzkUXXaSamhrdcsstqqysVFFRkd544w1/w4E9e/bIarX6n3/88cfr73//u2666Sb96le/0sSJE/XSSy+F3B46ABDAHiOlTfIevelwejdJ9Y8IdRsdqi+X3O3SgV3eQ//peQ2LVYrP7jYi1GV6XGKuFEknSAAAejPgfXTMEEz76ADAoHB1eEd9uo4CBYwOlUmutsNfJy6jZ5OErqNDjsFtTw4AgNmGZB8dAMAgsUX4RmfypDHH93zc7fZ2hKsr9XaOCxgR8n3e3iw1VXmP8k97v090cs8mCV1Hh6KSaJgAAAhLBB0ACEZWq7dZQXymlHdMz8c9Hqllf99rhOr3SK310sH93mNvSe/3cSR0mxKX17k+KCGHhgkAgJBF0AGAUGSxSLEp3iN7Vu/Paa3vFoC6tdNuqZXaGqTqL7xHr/exSfFZ3qYJCdne8JOY6/voC0Ox6d5gBgBAECHoAEC4ikqUMhOlzD6avzibvWuBAqbH+RolNJRLDRWSx+VtqtBQ1vd9rJFSQpaUkNslEOV2BqGEHCk2lSlyAIBhRdABgJHKHiulTfYevXG7vOt//MGn3Pd5me9jhdRU6e0eZ4wW9cXm8AYgYzQoIdsXhLoEouhRhCEAwKAh6AAAeme1+UZnsiX1sk5IklztUmOlLwSVecOP/3MjDFV5O8gd2Ok9+hIZ0216XC9T5RwJhCEAQL8QdAAAR84W2dk9ri8dTqmxopeRoS6BqGWf1N4i7dvmPfpijwtcH9RbIKKlNgBABB0AwFCLsEuj8r1HX9pbe4ag7oGotU5yNkm1m71HXxyJXYKQb3pc16lyCdneDV8BAGGNoAMAMF9klJQy3nv0xdnsnQpnjAL1FojaGqS2eqm6Xqr+su9rRSd3Gxnq0jjB+DzCMfjfJwBg2BB0AAChwR4rpU70Hn1pbfCtEyrrY2So3LvRqrG/UNWGvq8Vm9azg1zXqXIJ2d6pewCAoETQAQCEj6gE75E+pffHPR7vFLiGim4d5LoFoo5WqbnGe/S12aosUlxG4B5DcenefYXi0r1ByfjI6BAADDuCDgBg5LBYvG2so0dJGdN7f47HI7Xs7xJ+uk6V840WNVRILqe3vXZTpVS+9tD3jUrsJQClS3FpneeNc5FRg/99A8AIRNABAKAri0WKTfEeWQW9P8ftllpqA1tqN5RLTTVSc7XUVN05IuTukFrrvce+rYe/vyMhcDSot1BknLfHDu73DgBhhKADAMBAWa2dozA5s/t+ntvtnSrXVB0YgPxf1wR+dDl9DRUapP3bD19HZGzvAcj4GJfRJRTFsQcRgBGFoAMAwFCxWqWYZO+hPtYNGTwe76hPr0Goqmco6mj1NlY40Cwd2HX4WiKi+whFvYwWRSUSigCEPIIOAADBwGKRopO8x6E6y0neUNTW2C0UdR0t6haW2puljoNS3R7vcTg2hy8IpfW9lsgIRdGjCEUAghJBBwCAUGOxdHaYO9TeQwZn8yGmzXUbLXI2Sq42X9OFssNf2xrZLRQdYrQoOtk7ygUMB49HOnjAt46uQmr0ffR4pHELpLxjaREf5iwej8djdhGH09DQoMTERNXX1yshIcHscgAACF/tBw8Rirqdb60f2LUtNik2tZdRogzf4fs8PkOKSmKkCH1zu7y/h0Z46X4Y5zta+76GI1Ga8BVp4unShNO8v5MICf3NBgQdAABwZDra+p4uZwQj49zBAwO7ts0eGH66hyEjENGSO/x0tEmNe/sOLw17vY97XP27XkxK515X8VneEc5tb3s3DfazeBuLTDzde2QVMfoYxAg6AAAgeLja+w5FTVW+wzeVrrVuYNeOSuwWhroGonQpPtP7OVPnzNfW6A0qDeW+MFPeGV6Mz1tq+3cti9UbXOKzOoNMQlZnoEnI9n7sLQi7Xd79r7a+JW15U6pcH/h4bLo08TRv6Bl/qvd3DEGDoAMAAEJTe2uXEaEuIaixsss530dXW/+va7F1NlSIy+w5bS6+yzn2KBoYY6Nd/6hLuS+8VHQJNRXe1un9YXP4wkuXIz47MNDEpku2QVpu3rBX2rbCG3p2rJKcTZ2PWSOk0fN9wWeRlDaZaZUmI+gAAIDw5vF07lPUNfz0Foj6O0pgsMf1EoiMNURdzsWkDt6b7WDldnl/hgFTybqEFyPU9Dd0OhJ6CS9djxxzu/l1OKU9H0pbfcGn+0a/SaN9U9wWSWNPkiKjzalzBCPoAAAAGPxT56oCR4oaq3qea28ZwIUt3gYLvQaiblPpHAnBNxLQ3hq4Hqa3xf1NVf1fDxOb5ps2luMLLV0+j/d97Ygf2u9psO3f0Rl6dr0fGOgioqSxJ3eu7Rk1xrw6RxCCDgAAwJFoazzMtLmqzjVGHnf/rxsR3TMMBQQi37nYdCnCfvTfR2tD3+tgjEDTsq9/17LYuqyF6bYOxphKFp8lRTiOvu5g5myWdr7nDT1bV/RswZ42pXOK2+jjaF89RAg6AAAAQ8nt8gaFw60jaqqW2gbYijs6ue+mCsY5l7NbeOkWapyN/btXRFRgV7KAhf2+z2PTJKtt4D+jcObxSNVf+hoavCWVfhw48uVI8DYymLhImrDQG2gxKAg6AAAAwcLZ0rPBQo9pc76P7vbBu29UYt/rYIxQY+Z6mHBy8IC0/d/e0LNtRc/RsuxZnWt7smfRAfAoEHQAAABCjdvta7DQfZSoeyCq9HYD86+F6WVxf3yW5Igz+zsamdwuqeKzzvbVe4sDH49J7dK++itSdJIZVYYsgg4AAAAQDBorvZuUbnlT2v5O4LRCi827nsdoaJA+lRG2wyDoAAAAAMGmwymVftTZ0KB2c+DjiXmdoWfsyZI9xpw6gxhBBwAAAAh2B3Z1aV/9H6mjtfMxm8O7V8/ERdKk06VR+WZVGVQIOgAAAEAocbZ4w86WN73re+pLAx9PndQ52jN6/uC0IQ9BBB0AAAAgVHk8Us2mzvbVe1YHtq+2x0vjF3hHeyae5m0/PkIQdAAAAIBwcbBO2vFOZ/vq5prAx7MKfaHndClndljve0TQAQAAAMKR2y3t/cwbera+JVWsC3w8JkWacJp3pGfCV717JYURgg4AAAAwEjRVd2lf/W+praHzMYtVypvnHemZtEhKnxby7asJOgAAAMBI42qXSj/ubF9dszHw8YTczs1Kx50i2WPNqfMoEHQAAACAke7Abu+ani1vSTvfkzoOdj5ms0v5J3a2r04eZ16dA0DQAQAAANCp/aC0633faM+bUt2ewMdTJnSGntHHB237aoIOAAAAgN55PFLtls49e/asltwdnY/b46RxCzr37UnIMq3U7gg6AAAAAPqntV7asaqzk1tzdeDjmQXS3Mulud83pbyu+psNIoaxJgAAAADBKCpRmna293C7pcqSztBTvlaqXC81VJhd5YAQdAAAAAB0slql7FneY8EvpaYaaftK79chhKADAAAAoG9xaVLhxWZXMWBWswsAAAAAgMFG0AEAAAAQdgg6AAAAAMIOQQcAAABA2CHoAAAAAAg7BB0AAAAAYYegAwAAACDsEHQAAAAAhB2CDgAAAICwQ9ABAAAAEHYIOgAAAADCDkEHAAAAQNgh6AAAAAAIOwQdAAAAAGGHoAMAAAAg7BB0AAAAAIQdgg4AAACAsBNhdgH94fF4JEkNDQ0mVwIAAADATEYmMDJCX0Ii6DQ2NkqS8vLyTK4EAAAAQDBobGxUYmJin49bPIeLQkHA7XaroqJC8fHxslgsptbS0NCgvLw8lZaWKiEhwdRaMDLwO4fhxO8bhhu/cxhO/L6FB4/Ho8bGRmVnZ8tq7XslTkiM6FitVuXm5ppdRoCEhAT+BcGw4ncOw4nfNww3fucwnPh9C32HGskx0IwAAAAAQNgh6AAAAAAIOwSdAXI4HLr11lvlcDjMLgUjBL9zGE78vmG48TuH4cTv28gSEs0IAAAAAGAgGNEBAAAAEHYIOgAAAADCDkEHAAAAQNgh6AAAAAAIOwQdAAAAAGGHoDMADz74oPLz8xUVFaV58+ZpzZo1ZpeEMLVs2TIdc8wxio+PV3p6us455xxt3rzZ7LIwQtx9992yWCy64YYbzC4FYay8vFzf+c53lJKSoujoaM2cOVOffvqp2WUhTLlcLt18880aO3asoqOjNX78eP36178WzYfDG0Gnn5555hktWbJEt956q9atW6fCwkItWrRI1dXVZpeGMPTuu+/q6quv1kcffaQVK1aovb1dp59+upqbm80uDWHuk08+0Z/+9CcVFBSYXQrC2IEDB3TCCScoMjJSr7/+ur788kvdd999GjVqlNmlIUz95je/0cMPP6wHHnhAGzdu1G9+8xvdc889+uMf/2h2aRhC7KPTT/PmzdMxxxyjBx54QJLkdruVl5ena6+9VjfeeKPJ1SHc1dTUKD09Xe+++65OPvlks8tBmGpqatLs2bP10EMP6X/+539UVFSk5cuXm10WwtCNN96oDz74QP/5z3/MLgUjxFlnnaWMjAw9/vjj/nPnn3++oqOj9dRTT5lYGYYSIzr94HQ6tXbtWi1cuNB/zmq1auHChVq9erWJlWGkqK+vlyQ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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import LSTM, Dense\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "# Prepare data for LSTM\n", + "def create_dataset(data, look_back=1):\n", + " X, y = [], []\n", + " for i in range(len(data) - look_back - 1):\n", + " a = data[i:(i + look_back), 0]\n", + " X.append(a)\n", + " y.append(data[i + look_back, 0])\n", + " return np.array(X), np.array(y)\n", + "\n", + "data_CO2 = data['CO2 emissions '].values.reshape(-1, 1)\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "data_scaled = scaler.fit_transform(data_CO2)\n", + "\n", + "look_back = 10\n", + "X, y = create_dataset(data_scaled, look_back)\n", + "X = X.reshape(X.shape[0], X.shape[1], 1)\n", + "X_train, X_test = X[:-10], X[-10:]\n", + "y_train, y_test = y[:-10], y[-10:]\n", + "\n", + "# Define LSTM model\n", + "lstm_model = Sequential()\n", + "lstm_model.add(LSTM(units=50, return_sequences=True, input_shape=(look_back, 1)))\n", + "lstm_model.add(LSTM(units=50))\n", + "lstm_model.add(Dense(1))\n", + "lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n", + "\n", + "# Train the model\n", + "lstm_model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=1)\n", + "\n", + "# Make predictions\n", + "predictions = lstm_model.predict(X_test)\n", + "predictions = scaler.inverse_transform(predictions)\n", + "\n", + "# Plot predictions\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(data_CO2[-10:], label='Actual CO₂ Emissions')\n", + "plt.plot(predictions, label='Predicted CO₂ Emissions')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "24d8daa2-001c-4538-90e6-56e1c8128210", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\minal\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0344\n", + "Epoch 2/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0135 \n", + "Epoch 3/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0131 \n", + "Epoch 4/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0082 \n", + "Epoch 5/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0050 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"Epoch 25/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 6.9772e-05 \n", + "Epoch 26/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.9341e-05 \n", + "Epoch 27/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.3905e-05 \n", + "Epoch 28/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.6566e-05 \n", + "Epoch 29/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 4.5848e-05 \n", + "Epoch 30/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.2623e-05 \n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Prepare data for ANN\n", + "X = data[valid_columns]\n", + "y = data['CO2 emissions ']\n", + "\n", + "# Split data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n", + "\n", + "# Define ANN model\n", + "ann_model = Sequential()\n", + "ann_model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", + "ann_model.add(Dense(32, activation='relu'))\n", + "ann_model.add(Dense(1))\n", + "ann_model.compile(optimizer='adam', loss='mean_squared_error')\n", + "ann_model.fit(X_train, y_train, epochs=30, batch_size=10, verbose=1)\n", + "\n", + "# Make predictions\n", + "ann_predictions = ann_model.predict(X_test)\n", + "\n", + "# Plot predictions\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(y_test.values[:50], label='Actual CO₂ Emissions')\n", + "plt.plot(ann_predictions[:50], label='Predicted CO₂ Emissions')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "13f8ba0b-f3f0-4ae9-9ba0-7d3ae352a1e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: geopandas in c:\\users\\minal\\anaconda3\\lib\\site-packages (1.0.1)\n", + "Requirement already satisfied: numpy>=1.22 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (1.26.4)\n", + "Requirement already satisfied: pyogrio>=0.7.2 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (0.9.0)\n", + "Requirement already satisfied: packaging in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (23.2)\n", + "Requirement already satisfied: pandas>=1.4.0 in c:\\users\\minal\\appdata\\roaming\\python\\python312\\site-packages (from geopandas) (2.2.2)\n", + "Requirement already satisfied: pyproj>=3.3.0 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (3.6.1)\n", + "Requirement already satisfied: shapely>=2.0.0 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (2.0.5)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2023.3)\n", + "Requirement already satisfied: certifi in c:\\users\\minal\\anaconda3\\lib\\site-packages (from pyogrio>=0.7.2->geopandas) (2024.7.4)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas>=1.4.0->geopandas) (1.16.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install geopandas\n" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "9abe4a63-840c-4126-ae19-6dd13e2924b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data Columns: Index(['Country', 'latest year available_x', 'CH4 emissions',\n", + " 'CH4 emissions per capita', ' % change since 1990', 'N2O emissions',\n", + " 'N2O emissions per capita', ' % change since 1990.1', 'NOx emissions',\n", + " '% change since 1990_x', 'NOx emissions per capita', 'CO2 emissions ',\n", + " '% change since 1990_y', 'CO2 emissions per capita',\n", + " 'CO2 emissions per km2', 'Total GHG emissions _perc',\n", + " 'GHG from Energy_perc',\n", + " 'GHG from Energy \\nof which: from Transport_perc',\n", + " 'GHG from Industrial Processes_perc', 'GHG from Agriculture_perc',\n", + " 'GHG from Waste_perc', 'Total GHG emissions _tonnes',\n", + " 'GHG from Energy_tonnes',\n", + " 'GHG from Energy \\nof which: from Transport_tonnes',\n", + " 'GHG from Industrial Processes_tonnes', 'GHG from Agriculture_tonnes',\n", + " 'GHG from Waste_tonnes', 'Consumption of CFCs_odptonnes',\n", + " 'Consumption of all ODS', 'consumptionof all_odptonnesin 2013',\n", + " 'reduction in odp', 'SO2 emissions', '% change since 1990',\n", + " 'SO2 emissions per capita'],\n", + " dtype='object')\n", + "World Map Columns: Index(['featurecla', 'scalerank', 'LABELRANK', 'SOVEREIGNT', 'SOV_A3',\n", + " 'ADM0_DIF', 'LEVEL', 'TYPE', 'TLC', 'Country',\n", + " ...\n", + " 'FCLASS_TR', 'FCLASS_ID', 'FCLASS_PL', 'FCLASS_GR', 'FCLASS_IT',\n", + " 'FCLASS_NL', 'FCLASS_SE', 'FCLASS_BD', 'FCLASS_UA', 'geometry'],\n", + " dtype='object', length=169)\n", + " featurecla scalerank LABELRANK SOVEREIGNT SOV_A3 \\\n", + "0 Admin-0 country 1 6 Fiji FJI \n", + "1 Admin-0 country 1 3 United Republic of Tanzania TZA \n", + "2 Admin-0 country 1 7 Western Sahara SAH \n", + "3 Admin-0 country 1 2 Canada CAN \n", + "4 Admin-0 country 1 2 United States of America US1 \n", + "\n", + " ADM0_DIF LEVEL TYPE TLC Country ... \\\n", + "0 0 2 Sovereign country 1 Fiji ... \n", + "1 0 2 Sovereign country 1 United Republic of Tanzania ... \n", + "2 0 2 Indeterminate 1 Western Sahara ... \n", + "3 0 2 Sovereign country 1 Canada ... \n", + "4 1 2 Country 1 United States of America ... \n", + "\n", + " GHG from Industrial Processes_tonnes GHG from Agriculture_tonnes \\\n", + "0 NaN NaN \n", + "1 0.37 29.73 \n", + "2 NaN NaN \n", + "3 56.46 55.53 \n", + "4 334.35 526.25 \n", + "\n", + " GHG from Waste_tonnes Consumption of CFCs_odptonnes Consumption of all ODS \\\n", + "0 NaN NaN NaN \n", + "1 2.25 253.9 71.5 \n", + "2 NaN NaN NaN \n", + "3 20.57 19 958.2 923.1 \n", + "4 123.97 305 963.6 16 206.4 \n", + "\n", + " consumptionof all_odptonnesin 2013 reduction in odp SO2 emissions \\\n", + "0 NaN NaN NaN \n", + "1 1.6 97.8 175.74 \n", + "2 NaN NaN NaN \n", + "3 65.9 92.9 0 \n", + "4 711.3 95.6 4 739.47 \n", + "\n", + " % change since 1990 SO2 emissions per capita \n", + "0 NaN NaN \n", + "1 8.39 6.05 \n", + "2 NaN NaN \n", + "3 0 0.00 \n", + "4 -77.36 15.06 \n", + "\n", + "[5 rows x 202 columns]\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def load_world_map(shapefile_path):\n", + " try:\n", + " world = gpd.read_file(shapefile_path)\n", + " return world\n", + " except Exception as e:\n", + " print(f\"Error loading shapefile: {e}\")\n", + " return None\n", + "\n", + "def plot_emissions(world, data):\n", + " if world is not None:\n", + " # Rename 'ADMIN' to 'Country' if needed\n", + " if 'ADMIN' in world.columns:\n", + " world = world.rename(columns={'ADMIN': 'Country',})\n", + " else:\n", + " print(\"Error: The world map does not have a suitable column for country names.\")\n", + " return\n", + "\n", + " # Ensure the cleaned data has a 'Country' column\n", + " if 'Country' not in data.columns:\n", + " print(\"Error: The cleaned data does not have a 'Country' column.\")\n", + " return\n", + "\n", + " # Print column names for debugging\n", + " print(\"Data Columns:\", data.columns)\n", + " print(\"World Map Columns:\", world.columns)\n", + "\n", + " # Merge on 'Country' column\n", + " world = world.merge(data,how='left',on='Country',)\n", + " print(world.head())\n", + "\n", + " # Check if 'CO2 emissions' exists in the merged DataFrame\n", + " if 'CO2 emissions ' in world.columns:\n", + " # Plot emissions data\n", + " world.plot(column='CO2 emissions ', cmap='Reds', legend=True, figsize=(15, 10))\n", + " plt.title('Global CO₂ Emissions')\n", + " plt.show()\n", + " else:\n", + " print(\"Error: The 'CO2 emissions' column is not in the world map DataFrame after merging.\")\n", + " else:\n", + " print(\"World map could not be loaded.\")\n", + "\n", + "# Load cleaned data\n", + "try:\n", + " data = pd.read_csv('cleaned_data.csv')\n", + "except Exception as e:\n", + " print(f\"Error loading cleaned data: {e}\")\n", + " data = pd.DataFrame() # Empty DataFrame if loading fails\n", + "\n", + "# Load world map\n", + "shapefile_path = 'ne_110m_admin_0_countries.shp'\n", + "world = load_world_map(shapefile_path)\n", + "\n", + "\n", + "# Plot emissions data\n", + "plot_emissions(world, data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "6191e829-9fc1-4115-bd17-fcd7cb794a98", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Function to clean numeric columns by removing spaces and converting to float\n", + "def clean_numeric_column(column):\n", + " # Remove spaces\n", + " column = column.str.replace(' ', '')\n", + " # Convert to numeric, coercing errors to NaN\n", + " return pd.to_numeric(column, errors='coerce')\n", + "\n", + "# List of columns to clean\n", + "columns_to_clean = ['CO2 emissions ', 'CH4 emissions', 'N2O emissions', 'NOx emissions', 'SO2 emissions']\n", + "\n", + "# Clean the columns\n", + "for col in columns_to_clean:\n", + " data[col] = clean_numeric_column(data[col].astype(str))\n", + "\n", + "# Now calculate the correlation matrix\n", + "correlation_matrix = data[columns_to_clean].corr()\n", + "\n", + "# Plot the correlation matrix\n", + "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n", + "plt.title('Correlation Matrix of Emissions')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "f564194a-1d9b-4a24-8aa0-261f0f7a5e30", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Projected CO₂ Emissions with 25% reduction: 86.145\n" + ] + } + ], + "source": [ + "# Historical policy assessment\n", + "data['Policy_Effect'] = data['CO2 emissions '].pct_change()\n", + "\n", + "# Simulate new policies\n", + "def simulate_policy_change(current_emissions, reduction_percentage):\n", + " return current_emissions * (1 - reduction_percentage / 100)\n", + "\n", + "# Example policy simulation\n", + "future_emissions = simulate_policy_change(data['CO2 emissions '].iloc[-1], 25)\n", + "print(f'Projected CO₂ Emissions with 25% reduction: {future_emissions}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d53ce7d8-11f1-4e38-abc0-bd86c8374187", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From c0a4afa20b6be9816c26c998a1ed059f90e91000 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 23 Jul 2024 21:51:27 +0530 Subject: [PATCH 08/58] CREATING MERGE_GHG_DATA FILE --- GLOBAL CLIMATE ANALYSIS/DATA/CH4N2O | 1 - 1 file changed, 1 deletion(-) delete mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/CH4N2O diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/CH4N2O b/GLOBAL CLIMATE ANALYSIS/DATA/CH4N2O deleted file mode 100644 index 8b1378917..000000000 --- a/GLOBAL CLIMATE ANALYSIS/DATA/CH4N2O +++ /dev/null @@ -1 +0,0 @@ - From fbb12c5fc75005d9598836eac4ff96881fd52875 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 23 Jul 2024 21:52:44 +0530 Subject: [PATCH 09/58] CHANGING GLOBAL CLIMATE ANALYSIS/NOTEBOOK/NOTEBOOK --- GLOBAL CLIMATE ANALYSIS/NOTEBOOK/NOTEBOOK | 1 - 1 file changed, 1 deletion(-) delete mode 100644 GLOBAL CLIMATE ANALYSIS/NOTEBOOK/NOTEBOOK diff --git a/GLOBAL CLIMATE ANALYSIS/NOTEBOOK/NOTEBOOK b/GLOBAL CLIMATE ANALYSIS/NOTEBOOK/NOTEBOOK deleted file mode 100644 index 8b1378917..000000000 --- a/GLOBAL CLIMATE ANALYSIS/NOTEBOOK/NOTEBOOK +++ /dev/null @@ -1 +0,0 @@ - From 3b79e69c6995bc7d469fbfaa5981f97cd986ba1d Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 23 Jul 2024 21:53:18 +0530 Subject: [PATCH 10/58] Add files via upload --- GLOBAL CLIMATE ANALYSIS/.gitattributes | 2 + GLOBAL CLIMATE ANALYSIS/.gitignore | 160 ++++++++++++++++++++++++ GLOBAL CLIMATE ANALYSIS/LICENSE | 21 ++++ GLOBAL CLIMATE ANALYSIS/requirment.txt | 7 ++ 4 files changed, 190 insertions(+) create mode 100644 GLOBAL CLIMATE ANALYSIS/.gitattributes create mode 100644 GLOBAL CLIMATE ANALYSIS/.gitignore create mode 100644 GLOBAL CLIMATE ANALYSIS/LICENSE create mode 100644 GLOBAL CLIMATE ANALYSIS/requirment.txt diff --git a/GLOBAL CLIMATE ANALYSIS/.gitattributes b/GLOBAL CLIMATE ANALYSIS/.gitattributes new file mode 100644 index 000000000..dfe077042 --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/.gitattributes @@ -0,0 +1,2 @@ +# Auto detect text files and perform LF normalization +* text=auto diff --git a/GLOBAL CLIMATE ANALYSIS/.gitignore b/GLOBAL CLIMATE ANALYSIS/.gitignore new file mode 100644 index 000000000..51e9d59e6 --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/.gitignore @@ -0,0 +1,160 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ diff --git a/GLOBAL CLIMATE ANALYSIS/LICENSE b/GLOBAL CLIMATE ANALYSIS/LICENSE new file mode 100644 index 000000000..e817fb42a --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 minal + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/GLOBAL CLIMATE ANALYSIS/requirment.txt b/GLOBAL CLIMATE ANALYSIS/requirment.txt new file mode 100644 index 000000000..3e857db0b --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/requirment.txt @@ -0,0 +1,7 @@ +pandas +numpy +scikit-learn +tensorflow +geopandas +matplotlib +seaborn From 8eade5f2e5af822e5fab549497d67afe65dc3ea4 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 23 Jul 2024 21:55:49 +0530 Subject: [PATCH 11/58] Create README.md --- GLOBAL CLIMATE ANALYSIS/README.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 GLOBAL CLIMATE ANALYSIS/README.md diff --git a/GLOBAL CLIMATE ANALYSIS/README.md b/GLOBAL CLIMATE ANALYSIS/README.md new file mode 100644 index 000000000..4c94c4bde --- /dev/null +++ b/GLOBAL CLIMATE ANALYSIS/README.md @@ -0,0 +1,18 @@ +# Global Emission Analysis + +This project analyzes global emission trends and forecasts future emissions using various methods. The analysis is performed in a Jupyter notebook. + +## Files + +- `data/`: Contains the CSV file with emission data. +- `clean and processe`:Contains the main csv after cleaning and processing +- `notebook/`: Jupyter notebook with the complete analysis. +- `requirements.txt`: List of required Python packages. +- `README.md`: Project overview and instructions. + +## Installation + +Install the necessary packages using: + +```bash +pip install -r requirements.txt From 5e0a82a900884e11ad95b401c283259eb69cb6f6 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 23 Jul 2024 22:05:19 +0530 Subject: [PATCH 12/58] rename GLOBL CLIMATE ANALYSIS TO GLOBAL EMISSION ANALYSIS --- .../CLEAN AND PROCESSED/ cleaned_data.csv | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) rename GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/cleaned_data.csv => GLOBAL EMISSION ANALSIS/CLEAN AND PROCESSED/ cleaned_data.csv (99%) diff --git a/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/cleaned_data.csv b/GLOBAL EMISSION ANALSIS/CLEAN AND PROCESSED/ cleaned_data.csv similarity index 99% rename from GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/cleaned_data.csv rename to GLOBAL EMISSION ANALSIS/CLEAN AND PROCESSED/ cleaned_data.csv index 55ea7d043..875484f29 100644 --- a/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/cleaned_data.csv +++ b/GLOBAL EMISSION ANALSIS/CLEAN AND PROCESSED/ cleaned_data.csv @@ -1,5 +1,5 @@ -Country,latest year available_x,CH4 emissions,CH4 emissions per capita, % change since 1990,N2O emissions,N2O emissions per capita, % change since 1990.1,NOx emissions,% change since 1990_x,NOx emissions per capita,CO2 emissions ,% change since 1990_y,CO2 emissions per capita,CO2 emissions per km2,Total GHG emissions _perc,GHG from Energy_perc,"GHG from Energy -of which: from Transport_perc",GHG from Industrial Processes_perc,GHG from Agriculture_perc,GHG from Waste_perc,Total GHG emissions _tonnes,GHG from Energy_tonnes,"GHG from Energy +Country,latest year available_x,CH4 emissions,CH4 emissions per capita, % change since 1990,N2O emissions,N2O emissions per capita, % change since 1990.1,NOx emissions,% change since 1990_x,NOx emissions per capita,CO2 emissions ,% change since 1990_y,CO2 emissions per capita,CO2 emissions per km2,Total GHG emissions _perc,GHG from Energy_perc,"GHG from Energy +of which: from Transport_perc",GHG from Industrial Processes_perc,GHG from Agriculture_perc,GHG from Waste_perc,Total GHG emissions _tonnes,GHG from Energy_tonnes,"GHG from Energy of which: from Transport_tonnes",GHG from Industrial Processes_tonnes,GHG from Agriculture_tonnes,GHG from Waste_tonnes,Consumption of CFCs_odptonnes,Consumption of all ODS,consumptionof all_odptonnesin 2013,reduction in odp,SO2 emissions,% change since 1990,SO2 emissions per capita Argentina,2000,84.85,2.29,10.5,67.5,1.82,30.26,675.79,31.1,18.24,190.03,68.7,4.56,68.35,282,46.79,14.27,3.94,44.3,4.97,282,131.96,40.24,11.11,124.92,14.01, 4 697.2, 2 386.0,497.7,79.1,87.62,10.63,2.36 Armenia,2010,2.26,0.76,-28.66,0.48,0.16,185.46,17.21,-77.5,5.81,4.96,0,1.67,166.81,7.2,69.54,17.32,3.14,18.36,8.97,7.2,5.01,1.25,0.23,1.32,0.65,196.5,174.4,4.5,97.4,29.44, 7 448.72,9.93 From 257506d5965aa6d770310223f78f660d5e6800d6 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 24 Jul 2024 23:19:03 +0530 Subject: [PATCH 13/58] readme.md file --- GLOBAL EMISSION ANALSIS/README.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 GLOBAL EMISSION ANALSIS/README.md diff --git a/GLOBAL EMISSION ANALSIS/README.md b/GLOBAL EMISSION ANALSIS/README.md new file mode 100644 index 000000000..4c94c4bde --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/README.md @@ -0,0 +1,18 @@ +# Global Emission Analysis + +This project analyzes global emission trends and forecasts future emissions using various methods. The analysis is performed in a Jupyter notebook. + +## Files + +- `data/`: Contains the CSV file with emission data. +- `clean and processe`:Contains the main csv after cleaning and processing +- `notebook/`: Jupyter notebook with the complete analysis. +- `requirements.txt`: List of required Python packages. +- `README.md`: Project overview and instructions. + +## Installation + +Install the necessary packages using: + +```bash +pip install -r requirements.txt From 3dfa3a32d8a4039d787419e946c65cf82c857825 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 24 Jul 2024 23:20:37 +0530 Subject: [PATCH 14/58] Delete GLOBAL CLIMATE ANALYSIS directory --- GLOBAL CLIMATE ANALYSIS/.gitattributes | 2 - GLOBAL CLIMATE ANALYSIS/.gitignore | 160 ---- .../CLEAN AND PROCESSED/data perperation.py | 24 - .../CLEAN AND PROCESSED/merge all.py | 29 - .../DATA/CH4_N2O_Emissions.csv | 205 ----- .../DATA/CO2_Emissions.csv | 216 ----- .../DATA/NOx_Emissions.csv | 167 ---- .../DATA/ODS_Consumption.csv | 170 ---- .../DATA/SO2_emissions.csv | 136 --- .../DATA/merged_ghg_data.csv | 189 ---- GLOBAL CLIMATE ANALYSIS/LICENSE | 21 - .../NOTEBOOK/GLOBAL_CLIMATE_ANALYIS.ipynb | 804 ------------------ GLOBAL CLIMATE ANALYSIS/README.md | 18 - GLOBAL CLIMATE ANALYSIS/requirment.txt | 7 - 14 files changed, 2148 deletions(-) delete mode 100644 GLOBAL CLIMATE ANALYSIS/.gitattributes delete mode 100644 GLOBAL CLIMATE ANALYSIS/.gitignore delete mode 100644 GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/data perperation.py delete mode 100644 GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/merge all.py delete mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/CH4_N2O_Emissions.csv delete mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/CO2_Emissions.csv delete mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/NOx_Emissions.csv delete mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/ODS_Consumption.csv delete mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/SO2_emissions.csv delete mode 100644 GLOBAL CLIMATE ANALYSIS/DATA/merged_ghg_data.csv delete mode 100644 GLOBAL CLIMATE ANALYSIS/LICENSE delete mode 100644 GLOBAL CLIMATE ANALYSIS/NOTEBOOK/GLOBAL_CLIMATE_ANALYIS.ipynb delete mode 100644 GLOBAL CLIMATE ANALYSIS/README.md delete mode 100644 GLOBAL CLIMATE ANALYSIS/requirment.txt diff --git a/GLOBAL CLIMATE ANALYSIS/.gitattributes b/GLOBAL CLIMATE ANALYSIS/.gitattributes deleted file mode 100644 index dfe077042..000000000 --- a/GLOBAL CLIMATE ANALYSIS/.gitattributes +++ /dev/null @@ -1,2 +0,0 @@ -# Auto detect text files and perform LF normalization -* text=auto diff --git a/GLOBAL CLIMATE ANALYSIS/.gitignore b/GLOBAL CLIMATE ANALYSIS/.gitignore deleted file mode 100644 index 51e9d59e6..000000000 --- a/GLOBAL CLIMATE ANALYSIS/.gitignore +++ /dev/null @@ -1,160 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - -# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/#use-with-ide -.pdm.toml - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ diff --git a/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/data perperation.py b/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/data perperation.py deleted file mode 100644 index bf353c93d..000000000 --- a/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/data perperation.py +++ /dev/null @@ -1,24 +0,0 @@ -import pandas as pd -import numpy as np - -# Load the CSV data into a pandas DataFrame -data = pd.read_csv('merge.csv') - -# Replace 0 with NaN -data.replace(0, np.nan, inplace=True) - -# Display the first few rows of the DataFrame -print(data.head()) - -# Handle missing values, if any -# For simplicity, we'll drop rows with missing values, but you could also fill them -data = data.dropna() - -# Convert columns to appropriate data types if necessary -data['latest year available_x'] = data['latest year available_x'].astype(int) - -# Save cleaned data for later use -data.to_csv('cleaned_data.csv', index=False) - - - diff --git a/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/merge all.py b/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/merge all.py deleted file mode 100644 index 97fe9ca7b..000000000 --- a/GLOBAL CLIMATE ANALYSIS/CLEAN AND PROCESSED/merge all.py +++ /dev/null @@ -1,29 +0,0 @@ -##Data Merging - -## Merge GHG Emissions Data - -##```python -import pandas as pd -# Load the data from CSV files -ch4_data = pd.read_csv('CH4_N2O_Emissions.csv') -co2_data = pd.read_csv('CO2_Emissions.csv') -merged_ghg_emissions_data=pd.read_csv('merged_ghg_data.csv') -NOx_data=pd.read_csv('NOx_Emissions.csv') -Ods_data=pd.read_csv('ODS_Consumption.csv') -SO2_data=pd.read_csv('SO2_emissions.csv') - -# Merge the datasets on the 'Country' column with outer join -merged_data = ch4_data.merge(NOx_data, on='Country', how='outer') -merged_data = merged_data.merge(co2_data, on='Country', how='outer') -merged_data = merged_data.merge(merged_ghg_emissions_data, on='Country', how='outer') -merged_data = merged_data.merge(Ods_data, on='Country', how='outer') -merged_data = merged_data.merge(SO2_data, on='Country', how='outer') - -# Fill missing values with 0 -merged_data.fillna(0, inplace=True) - -# Save the merged data to a new CSV file -merged_data.to_csv('merge.csv', index=False) - -# View the merged data -print(merged_data.head()) \ No newline at end of file diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/CH4_N2O_Emissions.csv b/GLOBAL CLIMATE ANALYSIS/DATA/CH4_N2O_Emissions.csv deleted file mode 100644 index 75157d8de..000000000 --- a/GLOBAL CLIMATE ANALYSIS/DATA/CH4_N2O_Emissions.csv +++ /dev/null @@ -1,205 +0,0 @@ -Country,latest year available,CH4 emissions,CH4 emissions per capita, % change since 1990,N2O emissions,N2O emissions per capita, % change since 1990, -Afghanistan,2005,10.16,0.42,...,5.94,0.24,...,\ -Albania,1994,2.14,0.68,...,0.29,0.09,..., -Algeria,2000,32.92,1.06,...,6.50,0.21,..., -Angola,2005,19.93,1.11,...,13.87,0.77,..., -Antigua and Barbuda,2000,0.14,1.83,43.74,0.08,1.08,5197.47, -Argentina,2000,84.85,2.29,10.50,67.50,1.82,30.26, -Armenia,2010,2.26,0.76,-28.66,0.48,0.16,185.46, -Australia,2012,111.71,4.88,-3.02,25.78,1.13,40.43, -Austria,2012,5.31,0.63,-36.32,5.22,0.62,-15.75, -Azerbaijan,1994,9.29,1.21,-38.78,0.66,0.09,-26.30, -Bahamas,1994,0.02,0.08,0.00,0.31,1.12,..., -Bahrain,2000,3.91,7.11,...,0.04,0.07,..., -Bangladesh,2005,39.47,0.28,...,19.11,0.13,..., -Barbados,1997,1.81,6.77,8.78,0.05,0.19,0.00, -Belarus,2012,15.39,1.62,1.14,16.40,1.73,-18.52, -Belgium,2012,6.39,0.58,-33.82,6.99,0.63,-35.86, -Belize,1994,5.57,27.60,...,0.17,0.85,..., -Benin,2000,2.32,0.33,...,2.51,0.36,..., -Bhutan,2000,0.59,1.05,...,0.47,0.82,..., -Bolivia (Plurinational State of),2004,11.97,1.34,28.03,1.39,0.15,140.53, -Bosnia and Herzegovina,2001,2.42,0.64,-45.70,1.46,0.38,-53.32, -Botswana,2000,2.16,1.40,...,2.04,1.32,..., -Brazil,2005,316.28,1.68,34.49,162.78,0.86,45.06, -Bulgaria,2012,7.19,0.98,-56.60,5.24,0.72,-59.79, -Burkina Faso,1994,4.70,0.48,...,0.37,0.04,..., -Burundi,2005,0.53,0.07,...,25.77,3.25,..., -Cabo Verde,2000,0.07,0.16,...,0.09,0.21,..., -Cambodia,1994,7.77,0.75,...,3.67,0.35,..., -Cameroon,1994,17.71,1.31,...,145.25,10.72,..., -Canada,2012,90.56,2.60,25.78,47.73,1.37,-2.92, -Central African Republic,1994,11.88,3.65,...,25.64,7.88,..., -Chad,1993,6.94,1.06,...,0.77,0.12,..., -Chile,2010,11.46,0.67,...,9.70,0.57,..., -China,2005,932.86,0.71,...,394.10,0.30,..., -Colombia,2004,53.87,1.26,21.37,34.38,0.80,39.53, -Comoros,1994,0.06,0.12,...,0.39,0.83,..., -Congo,2000,0.50,0.16,...,0.27,0.09,..., -Cook Islands,1994,0.01,0.58,...,0.04,2.04,..., -Costa Rica,2005,3.53,0.83,11.89,2.59,0.61,1302.52, -Côte d'Ivoire,2000,25.15,1.52,...,185.68,11.24,..., -Croatia,2012,3.42,0.80,-7.41,3.30,0.77,-17.40, -Cuba,1996,6.64,0.61,-37.81,7.04,0.64,-59.67, -Cyprus,2012,1.30,1.15,43.28,0.61,0.54,11.11, -Czech Republic,2012,10.26,0.97,-42.67,7.73,0.73,-42.69, -Democratic People's Republic of Korea,2002,12.62,0.54,-49.11,0.93,0.04,-76.92, -Democratic Republic of the Congo,2003,37.29,0.71,...,6.06,0.12,..., -Denmark,2012,5.52,0.99,-7.26,5.99,1.07,-38.99, -Djibouti,2000,0.28,0.39,...,0.45,0.62,..., -Dominica,2005,0.03,0.46,...,0.03,0.43,..., -Dominican Republic,2000,4.84,0.56,59.11,3.18,0.37,279.36, -Ecuador,2006,17.17,1.23,31.61,201.48,14.42,33.12, -Egypt,2000,39.43,0.58,77.83,24.45,0.36,136.65, -El Salvador,2005,3.36,0.57,...,1.65,0.28,..., -Eritrea,2000,3.00,0.85,...,0.31,0.09,..., -Estonia,2012,0.93,0.70,-44.58,1.01,0.76,-55.03, -Ethiopia,1995,37.44,0.65,-0.45,7.44,0.13,140.00, -Fiji,2004,0.51,0.63,...,0.63,0.77,..., -Finland,2012,4.08,0.75,-33.82,5.18,0.96,-29.94, -France,2012,51.74,0.81,-13.40,57.77,0.91,-36.95, -Gabon,2000,0.71,0.58,...,0.29,0.24,..., -Gambia,2000,0.80,0.65,...,1.10,0.90,..., -Georgia,2006,4.14,0.93,-42.19,2.20,0.50,-8.84, -Germany,2012,48.71,0.61,-55.23,55.80,0.69,-34.60, -Ghana,2006,6.42,0.31,113.56,3.96,0.18,40.77, -Greece,2012,9.71,309.10,-8.53,6.81,0.61,-33.39, -Grenada,1994,1.47,14.79,...,0.00,0.02,..., -Guatemala,1990,4.09,0.45,0.00,6.41,0.70,0.00, -Guinea,1994,3.25,0.43,...,0.23,0.03,..., -Guinea-Bissau,1994,0.67,0.58,...,0.85,0.73,..., -Guyana,2004,1.19,1.60,11.74,0.23,0.31,6.94, -Haiti,2000,3.67,0.43,...,1.57,0.18,..., -Honduras,2000,3.85,0.62,...,2.29,0.37,..., -Hungary,2012,7.99,0.80,-32.72,6.76,0.68,-47.59, -Iceland,2012,0.46,1.41,4.63,0.46,1.42,-12.08, -India,2000,407.24,0.39,...,79.80,0.08,..., -Indonesia,2000,236.33,1.12,122.72,28.47,0.13,58.04, -Iran (Islamic Republic of),2000,75.71,1.15,...,40.15,0.61,..., -Ireland,2012,12.07,2.59,-11.70,7.42,1.59,-18.61, -Israel,2010,6.81,0.92,...,2.57,0.35,..., -Italy,2012,36.08,0.60,-17.56,27.53,0.46,-26.52, -Jamaica,1994,1.22,0.49,...,106.54,43.19,..., -Japan,2012,20.03,0.16,-38.32,20.23,0.16,-31.94, -Jordan,2006,3.07,0.55,...,1.55,0.28,..., -Kazakhstan,2012,49.03,2.91,-32.23,9.49,0.56,-47.13, -Kenya,1994,15.54,0.58,...,0.42,0.02,..., -Kiribati,1994,0.01,0.12,...,0.00,0.00,..., -Kuwait,1994,2.71,1.60,...,0.14,0.08,..., -Kyrgyzstan,2005,3.02,0.59,-50.42,0.15,0.03,-16.53, -Lao People's Democratic Republic,2000,5.34,1.00,-16.68,2.50,0.47,6625.00, -Latvia,2012,1.63,0.80,-51.23,1.82,0.89,-52.41, -Lebanon,2000,1.83,0.56,...,1.04,0.00,..., -Lesotho,2000,1.26,0.68,...,1.45,0.78,..., -Liberia,2000,4.14,1.43,...,0.31,0.11,..., -Liechtenstein,2012,0.02,0.43,8.92,0.01,0.34,-5.95, -Lithuania,2012,3.05,1.01,-46.93,4.14,1.37,-42.33, -Luxembourg,2012,0.43,0.80,-7.64,0.47,0.87,-2.36, -Madagascar,2000,6.92,0.44,...,20.68,1.31,..., -Malawi,1994,3.95,0.41,-43.98,2.41,0.25,625.23, -Malaysia,2000,51.65,2.21,...,2.90,0.12,..., -Maldives,1994,0.02,...,...,…,…,…, -Mali,2000,8.85,0.80,...,1.77,0.16,..., -Malta,2012,0.10,0.25,42.30,0.06,0.14,11.29, -Mauritania,2000,4.15,1.53,...,1.66,0.61,..., -Mauritius,2006,1.39,1.13,...,0.19,0.15,..., -Mexico,2006,187.78,1.69,76.22,20.34,110.55,96.25, -Micronesia (Federated States of),1994,0.01,0.07,...,0.00,0.03,..., -Monaco,2012,0.00,0.02,-58.15,0.00,0.08,64.61, -Mongolia,2006,6.53,2.55,15.83,0.46,0.18,1380.00, -Montenegro,2003,0.53,0.87,-6.26,0.29,0.46,-22.03, -Morocco,2000,9.09,0.31,...,17.06,0.59,..., -Mozambique,1994,5.66,0.37,13.51,0.98,0.06,37.01, -Myanmar,2005,26.58,0.53,...,3.31,0.07,..., -Namibia,2000,6.76,3.56,...,0.31,0.16,..., -Nauru,1994,0.01,0.74,...,0.00,0.03,..., -Nepal,1994,19.92,0.95,...,9.64,0.46,..., -Netherlands,2012,14.94,0.89,-41.86,9.06,0.54,-54.68, -New Zealand,2012,29.04,6.55,8.21,10.89,2.45,32.02, -Nicaragua,2000,4.27,0.85,...,3.87,0.77,..., -Niger,2000,6.76,0.60,96.78,4.96,0.44,506.68, -Nigeria,2000,88.18,0.72,...,7.44,0.06,..., -Niue,1994,0.01,6.35,...,0.01,5.59,..., -Norway,2012,4.23,0.84,-14.76,3.20,0.64,-36.55, -Oman,1994,2.61,1.22,...,7.09,3.31,..., -Pakistan,1994,60.70,0.51,...,11.45,0.10,..., -Palau,2000,0.03,1.59,...,0.06,3.24,..., -Panama,2000,3.15,1.04,...,1.38,0.46,..., -Papua New Guinea,1994,0.09,0.02,...,3.78,0.82,..., -Paraguay,2000,11.48,2.16,-32.19,8.31,1.57,-77.55, -Peru,2010,21.86,0.74,...,13.78,0.47,..., -Philippines,2000,31.80,0.41,...,12.38,0.16,..., -Poland,2012,41.03,1.06,-17.36,29.59,0.77,-29.19, -Portugal,2012,12.25,1.17,20.03,4.48,0.43,-19.32, -Qatar,2007,3.53,2.99,...,0.45,0.38,..., -Republic of Korea,2012,29.78,0.60,-6.81,14.24,0.29,48.96, -Republic of Moldova,2009,2.68,0.66,-41.57,1.61,0.39,-51.53, -Romania,2012,22.24,1.11,-48.22,11.61,0.58,-52.59, -Russian Federation,2012,502.56,3.51,-15.31,115.95,0.81,-48.07, -Rwanda,2005,1.51,0.17,...,4.14,0.46,..., -Saint Kitts and Nevis,1994,0.06,1.40,...,0.03,0.80,..., -Saint Lucia,2000,0.16,1.05,...,0.04,0.25,..., -Saint Vincent and the Grenadines,1997,0.06,0.60,3.65,0.24,2.21,-3.77, -Samoa,1994,0.07,0.41,...,0.39,2.31,..., -San Marino,2007,0.00,0.11,...,0.00,0.06,..., -Sao Tome and Principe,2005,0.03,0.17,...,0.01,0.04,..., -Saudi Arabia,2000,27.55,1.29,66.71,11.79,0.55,12.52, -Senegal,2000,6.48,0.66,...,3.62,0.37,..., -Serbia,1998,8.91,0.92,-1.84,6.82,0.71,-22.03, -Seychelles,2000,0.06,0.71,...,0.01,0.15,..., -Singapore,2010,0.11,0.02,...,0.44,0.09,..., -Slovakia,2012,4.33,0.80,-16.57,2.94,0.54,-53.55, -Slovenia,2012,1.87,0.91,-11.85,1.11,0.54,-12.55, -South Africa,1994,43.21,1.07,0.21,20.67,0.51,-11.28, -Spain,2012,32.32,0.69,23.27,24.02,0.52,-9.81, -Sri Lanka,2000,6.80,0.36,...,1.07,0.06,..., -Sudan,2000,43.99,1.57,...,17.67,0.63,..., -Suriname,2003,0.86,1.77,...,...,...,..., -Swaziland,1994,1.35,1.43,...,0.41,0.44,..., -Sweden,2012,4.81,0.50,-31.18,6.19,0.65,-23.70, -Switzerland,2012,3.72,0.46,-19.84,3.02,0.38,-13.13, -Tajikistan,2010,3.49,0.46,-5.68,2.79,0.37,0.00, -Thailand,2000,58.61,0.93,...,12.34,5.99,..., -The former Yugoslav Republic of Macedonia,2009,1.66,0.80,-3.62,0.91,0.01,-27.62, -Timor-Leste,2010,0.55,0.52,...,0.47,0.44,..., -Togo,2000,1.11,0.23,...,2.35,0.48,..., -Tonga,2000,0.09,0.96,...,0.06,0.57,..., -Trinidad and Tobago,1990,0.69,0.56,0.00,0.33,0.27,0.00, -Tunisia,2000,5.81,0.60,...,5.76,0.59,..., -Turkey,2012,61.62,0.82,80.96,14.79,0.20,21.04, -Turkmenistan,2004,33.26,7.08,...,1.78,0.38,..., -Tuvalu,1994,0.00,0.10,...,0.00,0.00,..., -Uganda,2000,9.46,0.40,...,16.72,0.70,..., -Ukraine,2012,66.17,1.46,-59.24,31.37,0.69,-46.59, -United Arab Emirates,2005,29.93,9.81,...,6.14,2.01,..., -United Kingdom of Great Britain and Northern Ireland,2012,52.78,0.83,-51.60,35.41,0.56,-48.74, -United Republic of Tanzania,1994,21.63,0.75,4.73,14.38,0.50,-4.68, -United States of America,2012,552.01,1.75,-12.82,395.79,1.26,0.07, -Uruguay,2004,18.63,5.61,14.95,11.79,3.55,-2.16, -Uzbekistan,2005,89.43,3.45,57.73,9.97,0.38,-22.78, -Vanuatu,1994,0.24,1.43,...,0.01,0.05,..., -Venezuela (Bolivarian Republic of),1999,61.92,2.58,...,16.14,0.67,..., -Viet Nam,2010,87.32,0.99,...,32.70,0.37,..., -Yemen,2000,4.42,0.25,...,3.77,0.21,..., -Zambia,2000,6.57,0.62,...,5.33,0.50,..., -Zimbabwe,2000,7.48,0.60,...,36.20,2.90,..., -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, -,,,,,,,, diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/CO2_Emissions.csv b/GLOBAL CLIMATE ANALYSIS/DATA/CO2_Emissions.csv deleted file mode 100644 index 803285a3a..000000000 --- a/GLOBAL CLIMATE ANALYSIS/DATA/CO2_Emissions.csv +++ /dev/null @@ -1,216 +0,0 @@ -Country,CO2 emissions ,% change since 1990,CO2 emissions per capita,CO2 emissions per km2 -,mio. tonnes ,%,tonnes,tonnes -Afghanistan,12.25,357.7,0.43,18.77 -Albania,4.67,-37.7,1.62,162.38 -Algeria,121.76,54.3,3.32,51.12 -Andorra,0.49,...,5.97, 1 050.00 -Angola,29.71,570.7,1.35,23.83 -Anguilla,0.14,...,10.25, 1 571.43 -Antigua and Barbuda,0.51,70.7,5.82, 1 161.54 -Argentina,190.03,68.7,4.56,68.35 -Armenia,4.96,...,1.67,166.81 -Aruba,2.44,32.5,23.92, 13 547.78 -Australia,398.16,44.2,17.66,51.76 -Austria,70.35,13.4,8.35,838.83 -Azerbaijan,33.46,...,3.63,386.35 -Bahamas,1.91,-2.3,5.2,136.76 -Bahrain,23.44,85.1,17.95, 30 943.23 -Bangladesh,57.07,267.4,0.37,386.73 -Barbados,1.57,45.7,5.58, 3 641.40 -Belarus,55.38,-46.7,5.84,266.77 -Belgium,104.27,-12.4,9.47, 3 415.58 -Belize,0.55,76.5,1.67,23.95 -Benin,4.99,597.4,0.51,43.46 -Bermuda,0.39,-34.3,6.17, 7 403.77 -Bhutan,0.56,337.3,0.77,14.61 -Bolivia (Plurinational State of),16.12,191.7,1.6,14.67 -Bosnia and Herzegovina,23.75,...,6.2,463.74 -Botswana,4.86,122.9,2.32,8.34 -Brazil,439.41,110.4,2.19,51.61 -British Virgin Islands,0.18,166.7,6.31, 1 165.56 -Brunei Darussalam,9.74,56.8,24.39, 1 690.06 -Bulgaria,53.2,-33.7,7.23,479.78 -Burkina Faso,1.93,229.4,0.12,7.08 -Burundi,0.21,-28.8,0.02,7.51 -Cabo Verde,0.43,383.4,0.86,105.48 -Cambodia,4.5,896.8,0.31,24.83 -Cameroon,5.66,225.7,0.27,11.9 -Canada,557.29,21.4,16.15,55.81 -Cayman Islands,0.58,130.5,10.31, 2 208.71 -Central African Republic,0.29,44.4,0.06,0.46 -Chad,0.54,267.4,0.04,0.42 -Chile,79.41,138.4,4.62,105.02 -China, 9 019.52,266.5,6.69,939.83 -"China, Hong Kong Special Administrative Region",40.27,45.6,5.72, 36 480.71 -"China, Macao Special Administrative Region",1.17,12.8,2.13, 38 870.00 -Colombia,72.42,26.3,1.56,63.43 -Comoros,0.16,104.8,0.22,70.56 -Congo,2.25,89.2,0.54,6.57 -Cook Islands,0.07,216.8,3.42,295.34 -Costa Rica,7.84,165.4,1.7,153.5 -Côte d'Ivoire,6.45,11.2,0.31,19.99 -Croatia,20.92,-10.4,4.86,369.62 -Cuba,35.92,7.2,3.17,326.91 -Cyprus,7.57,63.5,6.78,817.88 -Czech Republic,115.07,-30.1,10.92, 1 459.06 -Democratic People's Republic of Korea,73.58,-69.9,2.99,610.42 -Democratic Republic of the Congo,3.43,-15.9,0.05,1.46 -Denmark,45.48,-16.1,8.15, 1 055.26 -Djibouti,0.47,25.2,0.56,20.39 -Dominica,0.12,112.4,1.75,166.05 -Dominican Republic,21.89,137.1,2.18,449.72 -Ecuador,35.73,112.2,2.35,139.36 -Egypt,220.79,190.7,2.64,220.35 -El Salvador,6.68,155.3,1.1,317.71 -Equatorial Guinea,6.69, 5 427.8,8.91,238.44 -Eritrea,0.52,...,0.11,4.43 -Estonia,18.43,-49.8,13.88,407.44 -Ethiopia,7.54,149.9,0.08,6.83 -Faeroe Islands,0.57,-8.8,11.72,408.04 -Falkland Islands (Malvinas),0.06,49.9,19.26,4.52 -Fiji,1.24,51.1,1.42,67.63 -Finland,56.4,-0.4,10.45,167.44 -France,364.82,-8.5,5.77,661.5 -French Guiana,0.72,-11.7,2.99,8.6 -French Polynesia,0.86,36.1,3.17,214.53 -Gabon,2.24,-53.8,1.42,8.36 -Gambia,0.42,121.1,0.24,37.34 -Georgia,7.93,...,1.89,113.8 -Germany,810.44,-22.2,10.08, 2 269.37 -Ghana,10.08,156.4,0.4,42.26 -Gibraltar,0.45,377.1,14.64, 75 783.33 -Greece,94.25,13.6,8.45,714.25 -Greenland,0.71,27,12.54,0.33 -Grenada,0.25,130,2.41,735.47 -Guadeloupe,1.77,37.1,3.87, 1 040.94 -Guatemala,11.26,121.3,0.75,103.39 -Guinea,2.6,145.8,0.23,10.56 -Guinea-Bissau,0.25,-2.9,0.15,6.8 -Guyana,1.78,56.3,2.36,8.29 -Haiti,2.21,122.5,0.22,79.68 -Honduras,8.41,224.5,1.1,74.78 -Hungary,49.86,-31.2,4.99,535.96 -Iceland,3.33,54.3,10.38,32.36 -India, 2 074.34,200.4,1.66,631.02 -Indonesia,563.98,277.1,2.3,295.14 -Iran (Islamic Republic of),586.6,177.8,7.8,360.15 -Iraq,133.65,154.3,4.19,307.08 -Ireland,37.72,16.3,8.11,540.15 -Israel,69.52,90.3,9.19, 3 149.81 -Italy,413.38,-4.9,6.93, 1 371.82 -Jamaica,7.76,-2.6,2.82,705.67 -Japan, 1 240.63,8.7,9.75, 3 282.70 -Jordan,22.26,114,3.29,249.18 -Kazakhstan,261.76,...,15.81,96.06 -Kenya,13.57,133,0.33,23.34 -Kiribati,0.06,183.2,0.6,85.77 -Kuwait,91.03,88.4,28.1, 5 108.86 -Kyrgyzstan,6.62,...,1.19,33.08 -Lao People's Democratic Republic,1.2,412.5,0.19,5.08 -Latvia,7.75,-59.3,3.76,120.06 -Lebanon,20.49,125.1,4.46, 1 960.15 -Lesotho,2.2,...,1.08,72.48 -Liberia,0.89,84.1,0.22,8 -Libya,39.02,6.1,6.2,22.18 -Liechtenstein,0.18,-10.4,4.9, 1 125.00 -Lithuania,14.03,-60.8,4.57,214.85 -Luxembourg,11.14,-6.8,21.42, 4 307.15 -Madagascar,2.45,148.3,0.11,4.17 -Malawi,1.21,97,0.08,10.18 -Malaysia,225.69,298.8,7.9,682.26 -Maldives,1.1,616.8,3.26, 3 679.33 -Mali,1.25,196.5,0.08,1.01 -Malta,2.67,42.9,6.44, 8 442.72 -Marshall Islands,0.1,115.3,1.95,567.4 -Martinique,2.45,18.4,6.21, 2 171.63 -Mauritania,2.31,-13.3,0.63,2.24 -Mauritius,3.92,167.7,3.13, 1 989.03 -Mexico,466.55,48.4,3.88,237.5 -Micronesia (Federated States of),0.13,...,1.24,182.76 -Monaco,0.08,-24.9,2.13, 39 600.00 -Mongolia,19.08,90,6.92,12.2 -Montenegro,2.57,...,4.13,186.11 -Montserrat,0.08,100.2,16.16,791.18 -Morocco,56.54,140.2,1.74,126.61 -Mozambique,3.28,227.8,0.13,4.09 -Myanmar,10.44,144.2,0.2,15.43 -Namibia,2.78,10701.2,1.24,3.37 -Nauru,0.05,-67.5,5.11, 2 442.86 -Nepal,4.33,583.2,0.16,29.45 -Netherlands,168.06,5.5,10.07, 4 499.07 -New Caledonia,3.85,137.2,15.43,207.48 -New Zealand,33.26,33.5,7.55,122.97 -Nicaragua,4.9,92.2,0.84,37.58 -Niger,1.42,70.9,0.08,1.12 -Nigeria,88.03,94,0.54,95.29 -Niue,0.01,197.3,6.81,42.31 -Norway,44.6,27.8,9,137.73 -Oman,64.85,469.6,20.2,209.55 -Pakistan,163.45,138.4,0.94,205.32 -Palau,0.22,...,10.86,487.36 -Panama,9.67,249.1,2.63,128.17 -Papua New Guinea,5.23,144.2,0.75,11.3 -Paraguay,5.3,134.2,0.84,13.03 -Peru,53.07,150.7,1.78,41.29 -Philippines,82.01,96.4,0.87,273.38 -Poland,327.72,-12.6,8.49, 1 050.77 -Portugal,51.24,13.6,4.85,555.71 -Qatar,83.88,612.3,44.02, 7 226.27 -Republic of Korea,589.43,138.7,11.94, 5 892.32 -Republic of Moldova,4.98,...,1.22,147.13 -Réunion,4.51,210.6,5.39, 1 794.83 -Romania,85.6,-51.9,4.26,359.09 -Russian Federation, 1 650.27,-34.2,11.52,96.52 -Rwanda,0.66,22.3,0.06,25.2 -Saint Helena,0.01,50.7,2.66,90.16 -Saint Kitts and Nevis,0.27,305.6,5.05, 1 025.67 -Saint Lucia,0.41,146.7,2.27,755.1 -Saint Pierre and Miquelon,0.07,-24,11.11,288.02 -Saint Vincent and the Grenadines,0.24,195.4,2.18,612.85 -Samoa,0.23,88.2,1.25,82.59 -Sao Tome and Principe,0.1,115.3,0.59,106.54 -Saudi Arabia,520.28,138.7,18.07,242.02 -Senegal,7.86,146.9,0.59,39.95 -Serbia,49.19,...,5.45,556.64 -Seychelles,0.6,425.7,6.37, 1 323.32 -Sierra Leone,0.9,131.1,0.15,12.43 -Singapore,22.39,-52.3,4.31, 31 351.53 -Slovakia,37.23,-39.8,6.88,759.3 -Slovenia,16.18,9.4,7.86,798 -Solomon Islands,0.2,22.8,0.37,6.85 -Somalia,0.58,3045.9,0.06,0.9 -South Africa,477.24,49.2,9.14,390.85 -Spain,280.92,23.5,6.01,555.19 -Sri Lanka,15.23,293.7,0.75,232.17 -State of Palestine,2.25,...,0.54,373.41 -Suriname,1.91,5.5,3.65,11.66 -Swaziland,1.05,146.5,0.87,60.4 -Sweden,48.48,-15.2,5.12,107.67 -Switzerland,41.85,-6.3,5.28, 1 013.63 -Syrian Arab Republic,57.67,54,2.81,311.43 -Tajikistan,2.78,...,0.36,19.45 -Thailand,303.37,216.6,4.53,591.23 -The former Yugoslav Republic of Macedonia,9.34,...,4.52,363.09 -Timor-Leste,0.18,...,0.17,12.29 -Togo,2.1,171.1,0.32,36.94 -Tonga,0.1,33.4,0.98,137.48 -Trinidad and Tobago,49.57,192.3,37.14, 9 663.59 -Tunisia,25.64,93.3,2.38,156.73 -Turkey,345.73,144.2,4.7,441.23 -Turkmenistan,62.22,...,12.18,127.47 -Turks and Caicos Islands,0.19,...,6.01,201.11 -Uganda,3.8,373,0.11,15.73 -Ukraine,306.53,-57.6,6.74,507.93 -United Arab Emirates,178.48,243.2,20.43, 2 134.98 -United Kingdom of Great Britain and Northern Ireland,464.04,-21.5,7.35, 1 913.59 -United Republic of Tanzania,7.3,207.7,0.2,7.73 -United States of America, 5 583.38,9.5,17.87,579.84 -Uruguay,7.77,94.7,2.3,44.12 -Uzbekistan,114.86,...,4.08,256.73 -Vanuatu,0.14,105.2,0.59,11.73 -Venezuela (Bolivarian Republic of),188.82,54.6,6.42,207.03 -Viet Nam,173.21,709.1,1.94,523.36 -Wallis and Futuna Islands,0.03,...,1.91,180.99 -Yemen,22.3,-843.3,0.92,42.23 -Zambia,3.05,24.6,0.21,4.05 -Zimbabwe,9.86,-36.4,0.69,25.23 diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/NOx_Emissions.csv b/GLOBAL CLIMATE ANALYSIS/DATA/NOx_Emissions.csv deleted file mode 100644 index 8629e843c..000000000 --- a/GLOBAL CLIMATE ANALYSIS/DATA/NOx_Emissions.csv +++ /dev/null @@ -1,167 +0,0 @@ -Country,latest year available,NOx emissions,% change since 1990,NOx emissions per capita -,,1000 tonnes,%,kg -Afghanistan,2005,62.58,…,2.56 -Albania,1994,18.01,…,5.73 -Algeria,2000,283.21,…,9.08 -Andorra*,1997,0.71,…,11.07 -Angola,2005,154,…,8.6 -Antigua and Barbuda,2000,2.27,…,29.23 -Argentina,2000,675.79,31.1,18.24 -Armenia,2010,17.21,-77.5,5.81 -Australia,2012, 2 536.45,44.6,110.71 -Austria,2012,178.26,-8.5,21.08 -Azerbaijan,1994,113,-28.2,14.72 -Bahrain,2000,52,…,77.98 -Bangladesh,2005,3.95,…,0.03 -Barbados,1997,0.05,-97.9,0.19 -Belarus,2012,189.92,-43.5,20.01 -Belgium,2012,193.31,-47.9,17.45 -Belize,1994,5.6,…,27.75 -Benin,2000,60.53,…,8.71 -Bhutan,2000,1.77,…,3.14 -Bolivia (Plurinational State of),2004,64.92,31.1,7.24 -Bosnia and Herzegovina,2001,40.07,-51.8,10.55 -Brazil,2005, 3 400.00,35.8,18.04 -Bulgaria,2012,148.48,-43.9,20.33 -Burkina Faso,1994,9.36,…,0.95 -Burundi,2005,11.23,…,1.42 -Cabo Verde,2000,2.03,…,4.62 -Cambodia,1994,38.02,…,3.67 -Cameroon,1994,252.22,…,18.62 -Central African Republic,1994,51.25,…,15.75 -Chad,1993,78.35,…,11.95 -Chile,2010,272.2,…,16 -Colombia,2004,335.16,24.5,7.84 -Comoros,1994,0.46,…,0.99 -Congo,2000,17.65,…,5.68 -Costa Rica,2005,26.88,-19.7,6.33 -Côte d'Ivoire,2000,290.49,…,17.59 -Croatia,2012,55.19,-40.9,12.87 -Cuba,1996,101.54,-28.4,9.27 -Cyprus,2012, 2 124.00, 13 321.3, 1 880.81 -Czech Republic,2012,210.77,-71.6,19.99 -Democratic People's Republic of Korea,2002,159,-65.4,6.84 -Democratic Republic of the Congo,2003,784.69,…,14.92 -Denmark,2012,119.79,-57.3,21.39 -Djibouti,2000,1..89,…,2.62 -Dominica,2005,0.63,…,8.93 -Dominican Republic,2000,93.12,68.3,10.88 -Ecuador,2006,231.77,47.7,16.59 -El Salvador,2005,40.42,…,6.8 -Eritrea,2000,6,…,1.7 -Estonia,2012, 3 182.00, 4 021.8, 2 403.25 -Ethiopia,1994,166,3.8,3 -Fiji,2004,11.49,…,14.04 -Finland,2012,146.75,-50.3,27.05 -France,2012, 1 074.74,-44.6,16.91 -Gabon,2000,7.54,…,6.12 -Gambia,2000,6.83,…,5.56 -Georgia,2006,27.67,-78.6,6.25 -Germany,2012, 1 269.26,-55.9,15.77 -Ghana,2000,205.64,…,10.92 -Greece,2012,258.91,-20.6,23.31 -Guatemala,1990,43.79,…,4.78 -Guinea,1994,70.42,…,9.34 -Guinea-Bissau,1994,4.88,…,4.22 -Guyana,2004,17,1.8,22.91 -Haiti,2000,14.7,…,1.72 -Honduras,2000,47.77,…,7.65 -Hungary,2012,109.41,-53,10.99 -Iceland,2012,20.55,-24.7,63.54 -Indonesia,2000,85.66,-28.9,0.4 -Iran (Islamic Republic of),2000,600.84,…,9.12 -Ireland,2012,72.87,-40.2,15.61 -Israel,2010,187.29,…,25.24 -Italy,2012,881.52,-57.4,14.76 -Jamaica,1994,30.86,…,12.51 -Japan,2012, 1 626.95,-20.4,12.8 -Jordan,2006,116,…,20.98 -Kazakhstan,2012,500.26,-25.9,29.74 -Kenya,1994,49.98,…,1.88 -Kiribati,1994,0,…,0 -Kuwait,1994,113,…,66.79 -Kyrgyzstan,2005,64.92,-44.5,12.69 -Lao People's Democratic Republic,2000,20.84,81.5,3.9 -Latvia,2012,34.87,-58.2,17.12 -Lebanon,2000,58.7,…,18.14 -Lesotho*,1998,5.05,…,2.77 -Liberia,2000,1,…,0.35 -Lithuania,2012,59.53,-63.4,19.74 -Luxembourg,2005,0.44,175,0.96 -Madagascar,2000,27.64,…,1.76 -Malawi,1994,26.31,-9,2.71 -Mali,2000,42.54,…,3.85 -Malta,2012,8.86,17.4,21.33 -Mauritania,2000,10.31,…,3.8 -Mauritius,2006,15.15,…,12.34 -Mexico,2002, 1 444.41,16.3,13.68 -Micronesia (Federated States of),1994,2.25,…,21.25 -Monaco,2012,0.33,-26.2,8.93 -Mongolia,1998,2.98,29.6,1.27 -Montenegro,2003,10.94,5.8,17.81 -Morocco,2000,190.91,…,6.46 -Mozambique,1994,93.81,22.1,6.11 -Myanmar,2005,0.03,…,0 -Namibia,2000,41.2,…,21.71 -Netherlands,2012,230.22,-59.4,13.75 -New Zealand,2012,158.5,57.7,35.73 -Nicaragua,2000,90.62,…,18.03 -Niger,2000,24,…,2.14 -Nigeria,2000, 1 008.00,…,8.2 -Niue,1994,26.3,…, 11 938.49 -Norway,2012,166.23,-13.3,33.12 -Oman,1994,0.22,…,0.1 -Pakistan,1994,410.26,…,3.43 -Panama*,2002,39.42,…,12.54 -Paraguay,2000,87.7,-20.3,16.54 -Peru,1994,181.66,…,7.69 -Philippines,1994,345.23,…,5.06 -Poland,2012,817.3,-36.1,21.17 -Portugal,2012,172.14,-30.4,16.37 -Qatar,2007,175.69,…,149.02 -Republic of Moldova,2010,37.06,-73,9.07 -Romania,2012,207.04,-54.7,10.38 -Russian Federation,2012, 5 603.13,-40.9,39.1 -Rwanda,2005,14.2,…,1.58 -Saint Lucia,2000,2,…,12.74 -Saint Vincent and the Grenadines,1997,27.88,-3.2,258.1 -Samoa,1994,0.97,…,5.75 -San Marino,2007,1.54,…,51.62 -Sao Tome and Principe,2005,0.77,…,5.03 -Senegal,2000,8.46,…,0.86 -Serbia,1998,164,-20.9,16.96 -Seychelles,2000,1.15,…,14.17 -Slovakia,2012,81.31,-64.1,15.01 -Slovenia,2012,44.84,-26.1,21.74 -Spain,2012,929.61,-31.1,19.93 -Sri Lanka,2000,83.69,…,4.46 -Sudan,2000,112,…,3.99 -Suriname,2003,10,…,20.51 -Swaziland,1994,19.93,…,21.11 -Sweden,2012,131.81,-51.2,13.81 -Switzerland,2012,73.71,-49.1,9.19 -Tajikistan,2010,6,-91.9,0.79 -Thailand,2000,907.1,…,14.47 -The former Yugoslav Republic of Macedonia,2009,34.11,-18,16.56 -Timor-Leste,2010,1.82,…,1.72 -Togo,2000,42.72,…,8.76 -Tonga,2000,0.59,…,6.07 -Trinidad and Tobago,1990,36.86,…,30.16 -Tunisia,2000,94.87,…,9.78 -Turkey,2012, 1 283.74,99.4,17.15 -Turkmenistan,2004,90.24,…,19.21 -Tuvalu,1994,0,…,0 -Uganda,2000,103.77,…,4.37 -Ukraine,2012, 1 205.57,-48.2,26.6 -United Arab Emirates,2005,332,…,74.07 -United Kingdom of Great Britain and Northern Ireland,2012, 1 067.63,-63.1,16.79 -United Republic of Tanzania,1994,979.07,524.9,33.73 -United States of America,2012, 11 882.02,-45.5,37.74 -Uruguay,2004,38.76,29.2,11.66 -Uzbekistan,2005,257.52,-37.5,9.93 -Vanuatu,1994,0.08,…,0.51 -Venezuela (Bolivarian Republic of),1999,395.79,…,16.48 -Viet Nam,2000,312.63,…,3.89 -Yemen,2000,115,…,6.46 -Zambia,2000, 1 276.67,…,120.61 -Zimbabwe,2000,148.83,...,11.91 diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/ODS_Consumption.csv b/GLOBAL CLIMATE ANALYSIS/DATA/ODS_Consumption.csv deleted file mode 100644 index 82c12bec0..000000000 --- a/GLOBAL CLIMATE ANALYSIS/DATA/ODS_Consumption.csv +++ /dev/null @@ -1,170 +0,0 @@ -Country,Consumption of CFCs,,,Consumption of all ODS,, -,Baseline,2013,Reduction from baseline,2002,2013, Reduction from 2002 -,ODP tonnes,ODP tonnes,%,ODP tonnes,ODP tonnes, % -Afghanistan,380,0,100,181.5,17.7,90.2 -Albania,40.8,0,100,50.5,5.7,88.7 -Algeria, 2 119.5,0,100,1966.1,52,97.4 -Andorra,67.5,0,100,...,0,… -Angola,114.8,0,100,110,15.4,86 -Antigua and Barbuda,10.7,0,100,4,0.2,95 -Argentina, 4 697.2,0,100, 2 386.0,497.7,79.1 -Armenia,196.5,0,100,174.4,4.5,97.4 -Australia, 14 290.4,-7.5,100.1,389.7,48.3,87.6 -Azerbaijan,480.6,0,100,12.1,1.8,85.1 -Bahamas,64.9,0,100,58.4,2.7,95.4 -Bahrain,135.4,0,100,138.1,49.6,64.1 -Bangladesh,581.6,0,100,350.1,64.9,81.5 -Barbados,21.5,0,100,12.1,2.3,81 -Belarus, 2 510.9,0,100,2.7,7,-159.3 -Belize,24.4,0,100,21.7,2.4,88.9 -Benin,59.9,0,100,36,22.2,38.3 -Bhutan,0.2,0,100,0.1,0.3,-200 -Bolivia (Plurinational State of),75.7,0,100,67.4,0.4,99.4 -Bosnia and Herzegovina,24.2,0,100,259.2,5.1,98 -Botswana,6.9,0,100,10.2,10.8,-5.9 -Brazil, 10 525.8,0,100, 3 589.4, 1 189.3,66.9 -Brunei Darussalam,78.2,0,100,46.3,4.3,90.7 -Burkina Faso,36.3,0,100,27.9,14.9,46.6 -Burundi,59,0,100,19.2,7.1,63 -Cambodia,94.2,0,100,97,9.5,90.2 -Cameroon,256.9,0,100,261.7,82.3,68.6 -Canada, 19 958.2,0,100,923.1,65.9,92.9 -Cabo Verde,2.3,0,100,1.8,0.2,88.9 -Central African Republic,11.2,…,…,4.6,…,100 -Chad,34.6,0,100,27.3,15.2,44.3 -Chile,828.7,0,100,591.9,241.9,59.1 -China, 57 818.7,-386.6,100.7, 47 804.1, 15 690.6,67.2 -Colombia, 2 208.2,0,100, 1 002.2,176.7,82.4 -Comoros,2.5,0,100,1.9,0.1,94.7 -Congo,11.9,0,100,11.2,9.4,16.1 -Cook Islands,1.7,0,100,0,0,0 -Costa Rica,250.2,0,100,425.4,12.6,97 -Côte d'Ivoire,294.2,0,100,121.2,54.2,55.3 -Croatia,219.3,…,…,172.3,…,100 -Cuba,625.1,0,100,518,12.2,97.6 -Democratic People's Republic of Korea,441.7,0,100, 2 326.3,90.6,96.1 -Democratic Republic of the Congo,665.7,0,100, 1 081.3,35.9,96.7 -Djibouti,21,0,100,15.8,0.6,96.2 -Dominica,1.5,0,100,3.1,0.1,96.8 -Dominican Republic,539.8,0,100,406.9,34.8,91.4 -Ecuador,301.4,0,100,273.4,22,92 -Egypt, 1 668.0,0,100, 1 944.1,352.2,81.9 -El Salvador,306.5,0,100,108.1,8.1,92.5 -Equatorial Guinea,31.5,0,100,27.9,5.1,81.7 -Eritrea,41.1,0,100,32.2,1,96.9 -Ethiopia,33.8,0,100,86.6,5.5,93.6 -European Union (EU), 301 930.2,- 1 042.9,100.3,- 6 754.6,- 3 249.4,51.9 -Fiji,33.4,0,100,5.3,7.7,-45.3 -Gabon,10.3,0,100,6.9,28.6,-314.5 -Gambia,23.8,0,100,4.9,0.9,81.6 -Georgia,22.5,0,100,64.3,1.4,97.8 -Ghana,35.8,0,100,24,25.4,-5.8 -Grenada,6,0,100,2.3,0.3,87 -Guatemala,224.6,0,100,952.5,251.3,73.6 -Guinea,42.4,0,100,31.4,7.1,77.4 -Guinea-Bissau,26.3,0,100,27.2,2.3,91.5 -Guyana,53.2,0,100,15.6,1,93.6 -Haiti,169,0,100,197.7,2,99 -Honduras,331.6,0,100,555.7,18.9,96.6 -Iceland,195.1,0,100,2.6,0,100 -India, 6 681.0,-19.8,100.3, 15 026.9,956.1,93.6 -Indonesia, 8 332.7,0,100, 5 787.4,310.5,94.6 -Iran (Islamic Republic of), 4 571.7,0,100, 8 572.9,357.8,95.8 -Iraq, 1 517.0,0,100, 1 580.6,101.8,93.6 -Israel, 4 141.6,0,100, 1 241.3,94.5,92.4 -Jamaica,93.2,0,100,39.2,3.6,90.8 -Japan, 118 134.0,-181.3,100.2, 2 466.8,39.6,98.4 -Jordan,673.3,0,100,267,63,76.4 -Kazakhstan, 1 206.2,0,100,146.9,104.6,28.8 -Kenya,239.5,0,100,322,29.1,91 -Kiribati,0.7,0,100,0,0,0 -Kuwait,480.4,0,100,515.7,414.7,19.6 -Kyrgyzstan,72.8,0,100,50.2,4,92 -Lao People's Democratic Republic,43.3,0,100,42.9,1.6,96.3 -Lebanon,725.5,0,100,710.8,72.6,89.8 -Lesotho,5.1,0,100,4.6,2,56.5 -Liberia,56.1,0,100,54,4.5,91.7 -Libya,716.7,0,100, 1 596.5,144,91 -Liechtenstein,37.2,0,100,0.1,0,100 -Madagascar,47.9,0,100,8.8,16,-81.8 -Malawi,57.7,0,100,75.4,10.2,86.5 -Malaysia, 3 271.1,0,100, 1 966.3,449.9,77.1 -Maldives,4.6,0,100,4,3.2,20 -Mali,108.1,0,100,28.3,10.3,63.6 -Marshall Islands,1.1,0,100,0.3,0.1,66.7 -Mauritania,15.7,0,100,16.5,20.4,-23.6 -Mauritius,29.1,0,100,14.4,5.4,62.5 -Mexico, 4 624.9,0,100, 3 954.7, 1 106.2,72 -Micronesia (Federated States of),1.2,0,100,2.2,0,100 -Monaco,6.2,0,100,0.1,0,100 -Mongolia,10.6,0,100,7.3,0.9,87.7 -Montenegro,104.9,0,100,15.4,0.8,94.8 -Morocco,802.3,0,100, 1 070.0,49.4,95.4 -Mozambique,18.2,0,100,14.4,8.3,42.4 -Myanmar,54.3,0,100,45.7,3,93.4 -Namibia,21.9,0,100,20,7,65 -Nauru,0.5,0,100,0,0,… -Nepal,27,0,100,2.6,0.7,73.1 -New Zealand, 2 088.0,0,100,42.8,8.2,80.8 -Nicaragua,82.8,0,100,64.9,3.6,94.5 -Niger,32,0,100,27.6,14.6,47.1 -Nigeria, 3 650.0,0,100, 3 933.3,334.5,91.5 -Niue,0.1,0,100,0,0,0 -Norway, 1 313.0,0,100,-42.8,0,100 -Oman,248.4,0,100,201.5,28.9,85.7 -Pakistan, 1 679.4,0,100, 2 347.2,247,89.5 -Palau,1.6,0,100,0.2,0.1,50 -Panama,384.1,0,100,204.7,21.4,89.5 -Papua New Guinea,36.3,0,100,39.7,3,92.4 -Paraguay,210.6,0,100,105.5,16.5,84.4 -Peru,289.5,0,100,203.6,25.8,87.3 -Philippines, 3 055.8,0,100, 1 795.1,136.7,92.4 -Qatar,101.4,0,100,105.4,80.7,23.4 -Republic of Korea, 9 159.8,0,100, 11 745.9, 1 893.1,83.9 -Republic of Moldova,73.3,0,100,29.6,1,96.6 -Russian Federation, 100 352.0,288,99.7,892.3,836.5,6.3 -Rwanda,30.4,0,100,30.4,3.8,87.5 -Saint Kitts and Nevis,3.7,0,100,6.3,0.3,95.2 -Saint Lucia,8.3,0,100,7.7,0.6,92.2 -Samoa,4.5,0,100,2.6,0.1,96.2 -Sao Tome and Principe,4.7,0,100,4.4,0.1,97.7 -Saudi Arabia, 1 798.5,0,100, 1 926.4, 1 440.3,25.2 -Senegal,155.8,0,100,82.3,7.7,90.6 -Serbia,849.2,0,100,384.9,8.1,97.9 -Seychelles,2.9,0,100,166.1,0.6,99.6 -Sierra Leone,78.6,0,100,84.4,0.8,99.1 -Singapore, 2 718.2,0,100,146.7,116.7,20.4 -Solomon Islands,2.1,0,100,5.7,0.2,96.5 -Somalia,241.4,0,100,124.1,16.5,86.7 -South Africa,592.6,0,100,848.8,426.4,49.8 -Sri Lanka,445.6,0,100,227.4,13.4,94.1 -Saint Vincent and the Grenadines,1.8,0,100,6.4,0.2,96.9 -Sudan,456.8,0,100,258.2,51.9,79.9 -Suriname,41.3,0,100,51,1.2,97.6 -Swaziland,24.6,0,100,2.4,1.2,50 -Switzerland, 7 960.0,0,100,26.2,1.4,94.7 -Syrian Arab Republic, 2 224.6,0,100, 1 754.1,28,98.4 -Tajikistan,211,0,100,12.6,2.3,81.7 -Thailand, 6 082.1,0,100, 3 612.5,863.3,76.1 -The former Yugoslav Republic of Macedonia,519.7,0,100,44.6,0.7,98.4 -Timor-Leste,36,0,100,2.7,0.3,88.9 -Togo,39.8,0,100,35.3,19,46.2 -Tonga,1.3,0,100,1,0,100 -Trinidad and Tobago,120,0,100,93.9,39.5,57.9 -Tunisia,870.1,0,100,552.5,38.7,93 -Turkey, 3 805.7,0,100, 1 336.4,147,89 -Turkmenistan,37.3,0,100,10.9,4.2,61.5 -Tuvalu,0.3,0,100,0,0,-100 -Uganda,12.8,0,100,44.9,0,100 -Ukraine, 4 725.2,0,100,145.5,59.4,59.2 -United Arab Emirates,529.3,0,100,624.2,539.4,13.6 -United Republic of Tanzania,253.9,0,100,71.5,1.6,97.8 -United States of America, 305 963.6,-498.6,100.2, 16 206.4,711.3,95.6 -Uruguay,199.1,0,100,100.9,15.5,84.6 -Uzbekistan, 1 779.2,0,100,0.8,4.6,-475 -Vanuatu,0,0,0,0,0.1,-100 -Venezuela (Bolivarian Republic of), 3 322.4,0,100, 1 653.0,139.9,91.5 -Viet Nam,500,0,100,447.4,252.9,43.5 -Yemen, 1 796.1,0,100, 1 135.8,127.2,88.8 -Zambia,27.4,0,100,24,5,79.2 -Zimbabwe,451.4,0,100,345.8,15.8,95.4 diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/SO2_emissions.csv b/GLOBAL CLIMATE ANALYSIS/DATA/SO2_emissions.csv deleted file mode 100644 index e08e2f854..000000000 --- a/GLOBAL CLIMATE ANALYSIS/DATA/SO2_emissions.csv +++ /dev/null @@ -1,136 +0,0 @@ -Country,latest year available,SO2 emissions,% change since 1990,SO2 emissions per capita -,,1000 tonnes ,%,kg -Afghanistan,2005,13.86,…,0.57 -Algeria,2000,45.64,…,1.46 -Andorra*,1997,0.69,…,10.77 -Antigua and Barbuda,2000,2.75,-2.83,35.42 -Argentina,2000,87.62,10.63,2.36 -Armenia,2010,29.44, 7 448.72,9.93 -Australia,2012,797.76,-48.69,34.82 -Austria,2012,17.23,-76.84,2.04 -Azerbaijan,1994,48,-18.64,6.25 -Bahrain,2000,27,…,40.49 -Barbados,1997,0.05,…,0.19 -Belarus,2012,146.86,-86.44,15.47 -Belgium,2012,48.75,-86.42,4.4 -Belize,1994,0.53,…,2.63 -Benin,2000,13.88,…,2 -Bhutan,2000,1.06,…,1.88 -Bolivia (Plurinational State of),2000,12.1,8.42,1.45 -Bosnia and Herzegovina,2001,213.74,-52.83,56.25 -Bulgaria,2012, 1 335.49,-15.59,182.85 -Chile,2010,271.4,…,15.95 -Colombia,2004,142.81,0.71,3.34 -Costa Rica,2005,4.85,…,1.14 -Côte d'Ivoire,2000, 4 079.55,…,246.98 -Croatia,2012,25.58,-85.3,5.97 -Cuba,1996,432.38,-0.1,39.47 -Cyprus,2012,16.1,-45.9,14.26 -Czech Republic,2012,157.91,-91.58,14.97 -Democratic People's Republic of Korea,2002, 1 384.00,-55.66,59.53 -Democratic Republic of the Congo,2000,0.02,…,0 -Denmark,2012,13.43,-92.51,2.4 -Dominica,2005,0.22,…,3.12 -Dominican Republic,2000,110.15,42.94,12.86 -Ecuador,2006,8.87,37.73,0.64 -Estonia,2012,69.96,-62.03,52.84 -Ethiopia,1995,13.2,18.92,0.23 -Fiji,1994,0.03,…,0.04 -Finland,2012,52.06,-79.08,9.6 -France,2012,274.29,-79.68,4.32 -Gabon,2000,7.67,…,6.23 -Gambia,2000, 3 031.94,…, 2 467.27 -Georgia,2006,0.5,-99.8,0.11 -Germany,2012,427.07,-91.92,5.31 -Ghana,2000,0.5,…,0.03 -Greece,2012,244.9,-48.56,22.04 -Guatemala,1990,74.5,…,8.13 -Guinea,1994,0.44,…,0.06 -Guyana,2004,6.9,-8,9.3 -Haiti,2000,13.58,…,1.59 -Honduras,2000,0.38,…,0.06 -Hungary,2012,31.8,-96.15,3.19 -Iceland,2012,83.88,295.1,259.36 -Iran (Islamic Republic of),2000,139.46,…,2.12 -Ireland,2012,23.12,-87.31,4.95 -Israel,2010,164.46,…,22.16 -Italy,2012,181.73,-89.93,3.04 -Jamaica,1994,99.7,…,40.42 -Japan,2012,936.84,-25.29,7.37 -Jordan,2006,138,…,24.95 -Kazakhstan,2012,649.61,-37.92,38.62 -Kuwait,1994,319,…,188.55 -Kyrgyzstan,2005,26.9,-72.7,5.26 -Lao People's Democratic Republic,2000,1.59,…,0.3 -Latvia,2012,2.39,-97.66,1.17 -Lebanon,2000,93.42,…,28.87 -Lesotho*,1998,0,…,0 -Lithuania,2012,36.48,-82.78,12.09 -Luxembourg,2005,0.21,31.25,0.46 -Madagascar,2000,39.82,…,2.53 -Mali,1995,0,…,0 -Malta,2012,8.25,-47.72,19.85 -Mauritania,2000,0.09,…,0.03 -Mauritius,2006,11.44,…,9.32 -Mexico,2002, 2 612.91,-3.13,24.75 -Micronesia (Federated States of),1994,0.53,…,5 -Monaco,2012,0.04,-42.86,1.07 -Montenegro,2003,45.43,6.27,73.95 -Morocco,2000,484.09,…,16.39 -Namibia,2000,10.9,…,5.74 -Netherlands,2012,33.91,-82.86,2.02 -New Zealand,2012,78.16,33.84,17.62 -Nicaragua,2000,0.19,…,0.04 -Niger,2000, 2 140.00,…,190.65 -Nigeria,2000,190,…,1.55 -Niue,1994, 2 211.18,…,1 003 713.12 -Norway,2012,16.66,-68.1,3.32 -Oman,1994,3.38,,1.58 -Pakistan,1994,775.46,…,6.49 -Panama,2000,0.13,…,0.04 -Paraguay,2000,0.16,-46.67,0.03 -Peru,1994,123.26,…,5.22 -Philippines,1994,458.53,…,6.72 -Poland,2012,853.3,-73.42,22.1 -Portugal,2012,59.22,-81.71,5.63 -Qatar,2007,143.92,…,122.07 -Republic of Moldova,2010,18.78,-93.63,4.6 -Romania,2012,293.03,-66.35,14.69 -Russian Federation,2012,681.95,-16.02,4.76 -Rwanda,2005,18,…,2 -Saint Lucia,2000,0.1,…,1.86 -Saint Vincent and the Grenadines,1997,0.32,28,2.96 -Senegal,2000,41.96,…,4.26 -Serbia,1998,388,-20.98,40.13 -Slovakia,2012,58.52,-88.83,10.81 -Slovenia,2012,10.12,-94.91,4.91 -Spain,2012,407.94,-81.2,8.75 -Sri Lanka,2000,105.87,…,5.64 -Sudan,2000,1,…,0.04 -Swaziland,1994,1.97,…,2.09 -Sweden,2012,27.79,-73.59,2.91 -Switzerland,2012,10.67,-73.7,1.33 -Tajikistan,2010,9,-73.53,1.19 -Thailand,2000,618.9,…,9.87 -The former Yugoslav Republic of Macedonia,2009,205.83, 6 807.05,99.97 -Timor-Leste,2010,0.4,…,0.38 -Togo,2000,8.35,…,1.71 -Tonga,2000,0.1,…,1.02 -Trinidad and Tobago*,1996,8.6,-1.71,7.04 -Tunisia,2000,111.29,…,11.47 -Turkey,2012,248.83,-70.21,3.32 -Turkmenistan,2004,2.43,…,0.52 -Uganda,2000,4.1,…,0.17 -Ukraine,2012, 1 687.55,-68.15,37.24 -United Arab Emirates,2005, 10 354.00,…, 2 310.14 -United Kingdom of Great Britain and Northern Ireland,2012,429.44,-88.46,6.75 -United Republic of Tanzania,1994,175.74,8.39,6.05 -United States of America,2012, 4 739.47,-77.36,15.06 -Uruguay,2004,51.5,25.09,15.49 -Uzbekistan,2005,170.85,-74.93,6.59 -Viet Nam,2000,9.86,…,0.12 -Yemen,2000,12,…,0.67 -Zambia,2000,6.16,…,0.58 -Zimbabwe,2000,1.04,…,0.08 - ,,,, - ,,,, diff --git a/GLOBAL CLIMATE ANALYSIS/DATA/merged_ghg_data.csv b/GLOBAL CLIMATE ANALYSIS/DATA/merged_ghg_data.csv deleted file mode 100644 index aadaf1665..000000000 --- a/GLOBAL CLIMATE ANALYSIS/DATA/merged_ghg_data.csv +++ /dev/null @@ -1,189 +0,0 @@ -Country,latest year available_perc,Total GHG emissions _perc,GHG from Energy_perc,"GHG from Energy -of which: from Transport_perc",GHG from Industrial Processes_perc,GHG from Agriculture_perc,GHG from Waste_perc,Total GHG emissions _tonnes,GHG from Energy_tonnes,"GHG from Energy -of which: from Transport_tonnes",GHG from Industrial Processes_tonnes,GHG from Agriculture_tonnes,GHG from Waste_tonnes -,,mio. tonnes of CO2 equivalent,%,%,%,%,%,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent -Afghanistan,2005,19.33,19.54,8.75,1.62,78.17,0.67,19.33,3.78,1.69,0.31,15.11,0.13 -Albania,1994,5.53,56.11,14.41,3.79,33.96,6.14,5.53,3.1,0.8,0.21,1.88,0.34 -Algeria,2000,111.02,78.9,11.52,4.92,5.89,10.29,111.02,87.6,12.79,5.46,6.53,11.43 -Angola,2005,61.61,61.24,...,0.57,36.64,1.54,61.61,37.73,...,0.35,22.58,0.95 -Antigua and Barbuda,2000,0.6,62.35,30.55,...,17.45,20.19,0.6,0.37,0.18,...,0.1,0.12 -Argentina,2000,282,46.79,14.27,3.94,44.3,4.97,282,131.96,40.24,11.11,124.92,14.01 -Armenia,2010,7.2,69.54,17.32,3.14,18.36,8.97,7.2,5.01,1.25,0.23,1.32,0.65 -Australia,2012,543.65,76.03,16.59,5.74,16.07,2.16,543.65,413.36,90.21,31.21,87.36,11.72 -Austria,2012,80.06,74.56,27.02,13.59,9.37,2.07,80.06,59.69,21.64,10.88,7.5,1.66 -Azerbaijan,1994,43.17,10.44,...,...,8.53,4.08,43.17,4.51,...,...,3.68,1.76 -Bahamas,1994,2.2,84.94,...,...,0.96,...,2.2,1.87,...,...,0.02,... -Bahrain,2000,22.37,77.12,6.79,11.24,...,11.64,22.37,17.25,1.52,2.52,...,2.6 -Bangladesh,2005,99.44,38.86,5.53,2.93,43.36,14.85,99.44,38.65,5.5,2.91,43.12,14.77 -Barbados,1997,4.06,49.98,6.2,4.22,1.65,44.16,4.06,2.03,0.25,0.17,0.07,1.79 -Belarus,2012,89.28,61.94,8.08,4.79,26.18,7.02,89.28,55.3,7.22,4.27,23.37,6.27 -Belgium,2012,116.52,81.02,21.41,9.59,7.94,1.29,116.52,94.4,24.95,11.17,9.26,1.51 -Belize,1994,6.34,9.58,4.96,0,4.27,86.15,6.34,0.61,0.31,0,0.27,5.46 -Benin,2000,6.25,30.09,14.53,...,67.81,2.1,6.25,1.88,0.91,...,4.24,0.13 -Bhutan,2000,1.56,17.23,7.59,15.28,64.59,2.9,1.56,0.27,0.12,0.24,1,0.05 -Bolivia (Plurinational State of),2004,43.67,20.69,9.79,48.71,26.7,3.9,43.67,9.03,4.27,21.27,11.66,1.7 -Bosnia and Herzegovina,2001,16.12,76.5,...,3.7,13.67,6.13,16.12,12.33,...,0.6,2.2,0.99 -Botswana,1994,9.29,41.35,8.62,2.27,54.53,1.85,9.29,3.84,0.8,0.21,5.07,0.17 -Brazil,2005,862.81,38.11,15.59,8.95,48.19,4.76,862.81,328.79,134.54,77.2,415.77,41.05 -Bulgaria,2012,61.26,77,13.75,6.36,10.67,5.9,61.26,47.17,8.42,3.9,6.54,3.61 -Burkina Faso,1994,5.97,15.22,5.41,...,78.89,5.89,5.97,0.91,0.32,...,4.71,0.35 -Burundi,2005,26.47,1.35,0.4,0,97.9,0.76,26.47,0.36,0.11,0,25.92,0.2 -Cabo Verde,2000,0.45,65.6,30.73,0.19,29.23,4.98,0.45,0.29,0.14,0,0.13,0.02 -Cambodia,1994,12.76,14.74,6.51,0.39,82.74,2.13,12.76,1.88,0.83,0.05,10.56,0.27 -Cameroon,1994,165.73,1.95,0.82,35.31,61.69,1.04,165.73,3.24,1.35,58.52,102.23,1.73 -Canada,2012,698.63,80.98,27.93,8.08,7.95,2.94,698.63,565.76,195.11,56.46,55.53,20.57 -Central African Republic,1994,37.74,50.16,0.32,...,43.04,6.8,37.74,18.93,0.12,...,16.24,2.57 -Chad,1993,8.02,3.86,...,...,91,5.14,8.02,0.31,...,...,7.3,0.41 -Chile,2006,78.96,73.23,21.63,6.64,16.97,3.15,78.96,57.82,17.08,5.24,13.4,2.49 -China,2005, 7 465.86,77.28,5.77,10.25,10.97,1.5, 7 465.86, 5 769.85,430.79,764.89,819.33,111.79 -Colombia,2004,153.88,42.87,14.15,5.89,44.56,6.68,153.88,65.97,21.77,9.07,68.57,10.28 -Comoros,1994,0.51,13.77,...,...,85.61,0.62,0.51,0.07,...,...,0.44,0 -Congo,2000,2.07,78.05,18.68,0.23,15.72,6,2.07,1.61,0.39,0,0.32,0.12 -Cook Islands,1994,0.08,40.55,19.99,...,12.85,46.59,0.08,0.03,0.02,...,0.01,0.04 -Costa Rica,2005,12.11,47,32.12,4.1,38,10.9,12.11,5.69,3.89,0.5,4.6,1.32 -Côte d'Ivoire,2000,271.2,24.55,0.81,0,71.76,3.69,271.2,66.59,2.2,0,194.61,10 -Croatia,2012,26.45,71.54,21.59,10.78,12.83,4.26,26.45,18.92,5.71,2.85,3.39,1.13 -Cuba,1996,40.19,66.29,...,3.03,25.56,5.12,40.19,26.64,...,1.22,10.27,2.06 -Cyprus,2012,9.26,70.8,22.32,8.79,8.81,10.81,9.26,6.56,2.07,0.81,0.82,1 -Czech Republic,2012,131.47,81.46,12.86,9.2,6.13,2.87,131.47,107.09,16.91,12.1,8.06,3.77 -Democratic People's Republic of Korea,2002,87.33,88.86,1.7,6.48,3.22,1.43,87.33,77.61,1.49,5.66,2.81,1.25 -Democratic Republic of the Congo,2003,46,7.82,1.81,0.34,75.18,16.66,46,3.6,0.83,0.16,34.58,7.66 -Denmark,2012,53.12,76.07,23.51,3.41,18.15,2.07,53.12,40.41,12.49,1.81,9.64,1.1 -Djibouti,2000,1.07,33.26,10.15,...,61.67,5.07,1.07,0.36,0.11,...,0.66,0.05 -Dominica,2005,0.18,66.91,25.73,0,22.76,10.32,0.18,0.12,0.05,0,0.04,0.02 -Dominican Republic,2000,26.43,69.03,23.39,3.07,21.57,6.33,26.43,18.25,6.18,0.81,5.7,1.67 -Ecuador,2006,247.99,10.85,5.16,1.11,84.73,3.32,247.99,26.9,12.78,2.75,210.11,8.23 -Egypt,2000,193.24,60.13,14.08,14.37,16.46,9.04,193.24,116.19,27.21,27.77,31.8,17.48 -El Salvador,2005,11.07,53.38,22.43,3.99,28.13,14.5,11.07,5.91,2.48,0.44,3.11,1.61 -Eritrea,2000,3.93,19.17,5.06,0.89,78.88,1.07,3.93,0.75,0.2,0.04,3.1,0.04 -Estonia,2012,19.19,87.93,11.88,3.45,6.91,1.61,19.19,16.87,2.28,0.66,1.33,0.31 -Ethiopia,1995,47.75,15.84,...,0.72,80.63,2.8,47.75,7.57,...,0.34,38.5,1.34 -Fiji,2004,2.71,60.98,27.05,...,35.52,3.5,2.71,1.65,0.73,...,0.96,0.09 -Finland,2012,60.97,78.43,20.8,8.71,9.36,3.39,60.97,47.81,12.68,5.31,5.71,2.07 -France,2012,496.4,71.87,26.98,7.26,18.07,2.57,496.4,356.76,133.93,36.03,89.71,12.76 -Gabon,2000,6.16,86.08,6.57,1.46,5.84,6.61,6.16,5.3,0.4,0.09,0.36,0.41 -Gambia,2000,19.38,1.75,0.51,89.08,8.09,1.07,19.38,0.34,0.1,17.27,1.57,0.21 -Georgia,2006,12.22,48.82,10.52,14.58,27.11,9.44,12.22,5.97,1.29,1.78,3.31,1.15 -Germany,2012,939.08,83.7,16.56,7.27,7.4,1.44,939.08,786.03,155.49,68.25,69.49,13.55 -Ghana,2006,18.23,50.66,17.12,1.34,35.57,12.43,18.23,9.23,3.12,0.24,6.48,2.27 -Greece,2012,110.99,78.61,14.5,8.66,8.18,4.27,110.99,87.26,16.1,9.61,9.08,4.74 -Grenada,1994,1.61,8.47,3.24,...,0.03,91.5,1.61,0.14,0.05,...,0,1.47 -Guatemala,1990,14.74,31.09,14.48,3.69,59.91,5.3,14.74,4.58,2.13,0.54,8.83,0.78 -Guinea,1994,5.06,40.4,12.36,2.84,50.02,6.75,5.06,2.04,0.63,0.14,2.53,0.34 -Guinea-Bissau,1994,1.69,10.62,0.02,0,86.75,2.63,1.69,0.18,0,0,1.47,0.04 -Guyana,2004,3.07,53.94,10.22,...,43.31,2.75,3.07,1.66,0.31,...,1.33,0.08 -Haiti,2000,6.68,23.47,11.22,...,71.39,2.84,6.68,1.57,0.75,...,4.77,0.19 -Honduras,2000,10.3,33.36,22.07,6.7,43.02,16.92,10.3,3.44,2.27,0.69,4.43,1.74 -Hungary,2012,61.98,73.37,17.5,6.9,14.05,5.12,61.98,45.47,10.85,4.27,8.71,3.18 -Iceland,2012,4.47,38.44,19.09,42.15,15.18,4.09,4.47,1.72,0.85,1.88,0.68,0.18 -India,2000, 1 523.77,67.4,6.44,5.81,23.34,3.45, 1 523.77, 1 027.02,98.1,88.6,355.6,52.55 -Indonesia,2000,554.33,50.68,10.25,7.7,13.24,28.38,554.33,280.94,56.82,42.67,73.4,157.33 -Iran (Islamic Republic of),2000,483.67,78.11,15.71,6.46,8.89,6.54,483.67,377.8,76.01,31.26,42.99,31.61 -Ireland,2012,58.53,63.32,18.62,4.14,30.7,1.72,58.53,37.06,10.9,2.42,17.97,1.01 -Israel,2010,75.42,84.98,21.7,3.91,3.17,7.93,75.42,64.09,16.37,2.95,2.39,5.98 -Italy,2012,461.19,82.37,23,6.11,7.68,3.52,461.19,379.86,106.06,28.2,35.4,16.21 -Jamaica,1994,116.31,7.08,1.09,0.33,92.27,0.33,116.31,8.23,1.27,0.38,107.32,0.38 -Japan,2012, 1 343.14,91.55,16.36,5.18,1.78,1.49, 1 343.14, 1 229.60,219.76,69.52,23.9,20.03 -Jordan,2006,27.75,75.37,17.03,9.19,4.47,10.97,27.75,20.92,4.73,2.55,1.24,3.05 -Kazakhstan,2012,283.55,85.08,8.2,5.9,7.59,1.43,283.55,241.23,23.25,16.74,21.53,4.06 -Kenya,1994,21.47,37.54,...,4.61,56.37,1.49,21.47,8.06,...,0.99,12.1,0.32 -Kiribati,1994,0.03,66.36,...,...,1.75,31.93,0.03,0.02,...,...,0,0.01 -Kuwait,1994,32.37,95.31,16.66,2.06,0.2,2.42,32.37,30.86,5.39,0.67,0.07,0.78 -Kyrgyzstan,2005,12.02,74.07,20.7,4.32,16.11,5.49,12.02,8.9,2.49,0.52,1.94,0.66 -Lao People's Democratic Republic,2000,8.9,11.68,5.02,0.54,86.26,1.51,8.9,1.04,0.45,0.05,7.68,0.13 -Latvia,2012,10.98,65.78,25.45,6.27,22.04,5.47,10.98,7.22,2.79,0.69,2.42,0.6 -Lebanon,2000,18.45,75.1,21.48,9.71,5.78,9.41,18.45,13.85,3.96,1.79,1.07,1.74 -Lesotho,2000,3.51,30.73,...,...,63.59,5.68,3.51,1.08,...,...,2.23,0.2 -Liberia,2000,8.02,67.49,27.09,...,31.94,0.57,8.02,5.41,2.17,...,2.56,0.05 -Liechtenstein,2012,0.23,84.66,36.61,3.72,10.32,0.89,0.23,0.19,0.08,0.01,0.02,0 -Lithuania,2012,21.62,54.97,20.99,16.78,23.4,4.47,21.62,11.89,4.54,3.63,5.06,0.97 -Luxembourg,2012,11.84,88.67,55.06,5.16,5.65,0.42,11.84,10.5,6.52,0.61,0.67,0.05 -Madagascar,2000,29.34,9.21,3.2,0.08,90.48,0.23,29.34,2.7,0.94,0.02,26.55,0.07 -Malawi,1994,7.07,52.58,...,0.83,45.32,1.27,7.07,3.72,...,0.06,3.2,0.09 -Malaysia,2000,193.4,76.01,...,7.31,3.05,13.63,193.4,147,...,14.13,5.9,26.36 -Maldives,1994,0.15,84.32,...,...,...,15.68,0.15,0.13,...,...,...,0.02 -Mali,2000,12.3,19.44,5.51,0.92,76.82,2.82,12.3,2.39,0.68,0.11,9.45,0.35 -Malta,2012,3.14,89.87,17.55,5.47,2.53,2.07,3.14,2.82,0.55,0.17,0.08,0.07 -Mauritania,2000,6.94,16.86,5.83,0.28,81.62,1.25,6.94,1.17,0.41,0.02,5.67,0.09 -Mauritius,2006,4.76,66.29,18.01,1.37,4.33,28.02,4.76,3.15,0.86,0.06,0.21,1.33 -Mexico,2006,641.45,67.05,22.56,9.9,7.1,15.94,641.45,430.1,144.69,63.53,45.55,102.27 -Micronesia (Federated States of),1994,0.25,97.96,0.26,0.01,0.34,1.69,0.25,0.24,0,0,0,0 -Monaco,2012,0.09,91.51,31.15,7.02,...,1.38,0.09,0.09,0.03,0.01,...,0 -Mongolia,2006,17.71,57.7,10.65,5.04,36.48,0.78,17.71,10.22,1.89,0.89,6.46,0.14 -Montenegro,2003,5.31,50,...,35.43,12.33,2.24,5.31,2.66,...,1.88,0.66,0.12 -Morocco,2000,59.7,53.79,9.99,6.32,35.05,4.84,59.7,32.11,5.96,3.77,20.93,2.89 -Mozambique,1994,8.22,22.64,10.39,0.62,56.2,20.53,8.22,1.86,0.85,0.05,4.62,1.69 -Myanmar,2005,38.37,21.4,6.47,1.32,69.13,8.14,38.37,8.21,2.48,0.51,26.53,3.12 -Namibia,2000,9.09,23.87,11.33,...,74.15,1.98,9.09,2.17,1.03,...,6.74,0.18 -Nauru,1994,0.04,78.89,...,...,13.68,7.44,0.04,0.03,...,...,0,0 -Nepal,1994,31.19,10.47,1.46,0.53,87.2,1.8,31.19,3.27,0.46,0.17,27.2,0.56 -Netherlands,2012,191.67,84.49,17.73,5.18,8.3,1.92,191.67,161.95,33.98,9.92,15.9,3.69 -New Zealand,2012,76.05,42.24,18.09,6.94,46.05,4.73,76.05,32.12,13.76,5.28,35.02,3.6 -Nicaragua,2000,11.98,32.73,10.3,2.55,59.27,5.44,11.98,3.92,1.23,0.31,7.1,0.65 -Niger,2000,13.63,19.24,5.56,0.13,78.04,2.58,13.63,2.62,0.76,0.02,10.64,0.35 -Nigeria,2000,212.44,71.63,...,0.99,26.27,1.12,212.44,152.16,...,2.1,55.81,2.37 -Niue,1994,4.42,99.94,32.16,...,0.02,0.03,4.42,4.42,1.42,...,0,0 -Norway,2012,52.76,74.32,28.74,14.55,8.54,2.26,52.76,39.21,15.16,7.67,4.5,1.19 -Oman,1994,20.88,59.61,7.93,2.84,35.77,1.78,20.88,12.44,1.66,0.59,7.47,0.37 -Pakistan,1994,160.59,51.84,11.63,7.02,38.57,2.57,160.59,83.26,18.68,11.27,61.94,4.12 -Palau,2000,0.09,...,...,0.01,99.99,...,0.09,...,...,0,0.09,... -Panama,2000,9.71,49.59,28.08,6.11,33.17,11.13,9.71,4.81,2.73,0.59,3.22,1.08 -Papua New Guinea,1994,5.01,18.91,...,3.85,77.24,...,5.01,0.95,...,0.19,3.87,... -Paraguay,2000,23.43,15.72,11.9,1.69,79.51,3.08,23.43,3.68,2.79,0.4,18.63,0.72 -Peru,2010,80.59,50.38,18.87,7.79,32.33,9.51,80.59,40.61,15.21,6.27,26.05,7.66 -Philippines,2000,126.88,54.91,20.44,6.79,29.16,9.14,126.88,69.67,25.94,8.61,37,11.6 -Poland,2012,399.27,80.06,11.73,6.75,9.18,3.82,399.27,319.66,46.82,26.96,36.65,15.24 -Portugal,2012,68.85,69.56,24.7,7.72,10.49,11.89,68.85,47.9,17,5.31,7.22,8.19 -Qatar,2007,61.59,91.27,8.67,7.92,0.14,0.67,61.59,56.22,5.34,4.88,0.08,0.41 -Republic of Korea,2012,688.43,87.19,12.55,7.46,3.19,2.15,688.43,600.25,86.36,51.37,21.99,14.81 -Republic of Moldova,2010,13.28,67.39,14.35,4.26,16.06,11.89,13.28,8.95,1.9,0.56,2.13,1.58 -Romania,2012,118.79,69.22,12.68,10.42,15.33,4.92,118.79,82.22,15.06,12.38,18.21,5.85 -Russian Federation,2012, 2 297.15,82.16,10.51,7.89,6.28,3.65, 2 297.15, 1 887.26,241.37,181.14,144.22,83.95 -Rwanda,2005,6.18,12.96,4.43,2.44,83.58,1.02,6.18,0.8,0.27,0.15,5.17,0.06 -Saint Kitts and Nevis,1994,0.16,44.97,15.98,...,25.78,29.25,0.16,0.07,0.03,...,0.04,0.05 -Saint Lucia,2000,0.55,63.55,...,...,7.33,29.01,0.55,0.35,...,...,0.04,0.16 -Saint Vincent and the Grenadines,1997,0.41,26.15,9.23,...,64.4,9.45,0.41,0.11,0.04,...,0.26,0.04 -Samoa,1994,0.56,18.34,12.7,...,76.79,4.87,0.56,0.1,0.07,...,0.43,0.03 -San Marino,2007,0.24,98.29,58.82,...,1.72,...,0.24,0.23,0.14,...,0,... -Sao Tome and Principe,2005,0.1,71.4,28.55,6.73,14.71,7.15,0.1,0.07,0.03,0.01,0.01,0.01 -Saudi Arabia,2000,296.06,82.84,19.62,6.56,4.17,6.44,296.06,245.25,58.09,19.41,12.33,19.07 -Senegal,2000,16.88,48.47,11.38,2.15,37.08,12.29,16.88,8.18,1.92,0.36,6.26,2.08 -Serbia,1998,66.34,76.19,5.84,5.46,14.32,4.04,66.34,50.55,3.88,3.62,9.5,2.68 -Seychelles,2000,0.33,79.31,20.13,...,4.72,15.97,0.33,0.26,0.07,...,0.02,0.05 -Singapore,2010,46.87,97.19,14.85,2.38,...,...,46.87,45.55,6.96,1.11,...,... -Slovakia,2012,43.12,68.5,15.25,18.54,7.56,5,43.12,29.53,6.57,7.99,3.26,2.16 -Slovenia,2012,18.91,81.84,30.53,5.36,9.9,2.58,18.91,15.48,5.77,1.01,1.87,0.49 -Solomon Islands,1994,0.29,100,65.49,...,...,...,0.29,0.29,0.19,...,...,... -South Africa,1994,379.84,78.34,11.46,8,9.33,4.33,379.84,297.57,43.52,30.39,35.46,16.43 -Spain,2012,340.81,77.92,23.67,6.87,11.07,3.78,340.81,265.55,80.67,23.41,37.71,12.87 -Sri Lanka,2000,18.8,61.51,27.05,2.62,25.05,10.82,18.8,11.56,5.08,0.49,4.71,2.03 -Sudan,2000,67.84,12.39,4.2,0.14,84.67,2.81,67.84,8.4,2.85,0.09,57.44,1.91 -Suriname,2003,3.33,72.19,10.54,1.95,25.23,0.63,3.33,2.4,0.35,0.07,0.84,0.02 -Swaziland,1994,7.54,14.01,5.87,65.03,16.36,4.6,7.54,1.06,0.44,4.9,1.23,0.35 -Sweden,2012,57.61,73.16,33.17,10.24,13.26,2.81,57.61,42.15,19.11,5.9,7.64,1.62 -Switzerland,2012,51.49,80.6,31.72,7.05,10.76,1.19,51.49,41.5,16.33,3.63,5.54,0.61 -Tajikistan,2010,8.18,15.55,4.96,8.02,69.76,6.67,8.18,1.27,0.41,0.66,5.71,0.55 -Thailand,2000,236.95,67.26,18.87,6.91,21.89,3.93,236.95,159.38,44.7,16.38,51.87,9.32 -The former Yugoslav Republic of Macedonia,2009,11.49,76.26,11.26,3.89,11.52,8.33,11.49,8.76,1.29,0.45,1.32,0.96 -Timor-Leste,2010,1.28,19.64,8.64,...,75.69,4.67,1.28,0.25,0.11,...,0.97,0.06 -Togo,2000,4.92,34.87,13.06,6.36,55.33,3.44,4.92,1.71,0.64,0.31,2.72,0.17 -Tonga,2000,0.25,40.13,23.02,...,38.12,21.75,0.25,0.1,0.06,...,0.09,0.05 -Trinidad and Tobago,1990,16.01,62.03,9.3,31.97,2.12,3.89,16.01,9.93,1.49,5.12,0.34,0.62 -Tunisia,2000,34.24,60.64,15.07,11.55,22.31,5.5,34.24,20.76,5.16,3.95,7.64,1.88 -Turkey,2012,439.87,70.16,14,14.27,7.34,8.23,439.87,308.6,61.56,62.77,32.28,36.22 -Turkmenistan,2004,75.41,69.63,3.14,20.67,9.02,0.68,75.41,52.51,2.37,15.58,6.81,0.51 -Tuvalu,1994,0.01,83.63,...,...,16.37,...,0.01,0,...,...,0,... -Uganda,2000,27.56,17.77,2.93,0.58,79.14,2.51,27.56,4.9,0.81,0.16,21.81,0.69 -Ukraine,2012,402.67,76.76,8.31,11.43,8.95,2.82,402.67,309.08,33.47,46.01,36.03,11.37 -United Arab Emirates,2005,195.31,89.48,14.96,4.83,2.04,3.65,195.31,174.76,29.22,9.44,3.99,7.12 -United Kingdom of Great Britain and Northern Ireland,2012,586.36,82.81,19.74,4.26,8.89,4.04,586.36,485.54,115.72,24.97,52.13,23.72 -United Republic of Tanzania,1994,39.24,17.56,4.26,0.94,75.77,5.73,39.24,6.89,1.67,0.37,29.73,2.25 -United States of America,2012, 6 487.85,84.76,26.77,5.15,8.11,1.91, 6 487.85, 5 498.88, 1 736.64,334.35,526.25,123.97 -Uruguay,2004,36.28,14.29,6.19,0.94,80.83,3.94,36.28,5.18,2.24,0.34,29.32,1.43 -Uzbekistan,2005,199.84,86.24,4.82,3.19,8.23,2.35,199.84,172.34,9.63,6.37,16.44,4.69 -Vanuatu,1994,0.3,21.45,15.13,...,78.55,...,0.3,0.06,0.05,...,0.24,... -Venezuela (Bolivarian Republic of),1999,192.19,74.7,17.69,4.79,17.15,3.36,192.19,143.56,33.99,9.21,32.96,6.47 -Viet Nam,2010,266.05,53.06,11.96,7.96,33.21,5.77,266.05,141.17,31.82,21.17,88.35,15.35 -Yemen,2000,25.74,69.07,19.25,2.85,23.34,4.74,25.74,17.78,4.96,0.73,6.01,1.22 -Zambia,2000,14.4,18.25,4.06,6.98,71.92,2.86,14.4,2.63,0.58,1.01,10.36,0.41 -Zimbabwe,2000,68.54,38.66,1.56,1.52,57.73,2.09,68.54,26.5,1.07,1.04,39.57,1.43 diff --git a/GLOBAL CLIMATE ANALYSIS/LICENSE b/GLOBAL CLIMATE ANALYSIS/LICENSE deleted file mode 100644 index e817fb42a..000000000 --- a/GLOBAL CLIMATE ANALYSIS/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2024 minal - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/GLOBAL CLIMATE ANALYSIS/NOTEBOOK/GLOBAL_CLIMATE_ANALYIS.ipynb b/GLOBAL CLIMATE ANALYSIS/NOTEBOOK/GLOBAL_CLIMATE_ANALYIS.ipynb deleted file mode 100644 index 9861db0dd..000000000 --- a/GLOBAL CLIMATE ANALYSIS/NOTEBOOK/GLOBAL_CLIMATE_ANALYIS.ipynb +++ /dev/null @@ -1,804 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 71, - "id": "1b516b50-eb14-42b6-a48e-5ec5785fdc7e", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.preprocessing import MinMaxScaler\n", - "from sklearn.decomposition import PCA\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "ecce1da9-dbeb-4de8-bdc6-6f4798b862ad", - "metadata": {}, - "outputs": [], - "source": [ - "# Load the cleaned data\n", - "data = pd.read_csv('cleaned_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "3f85261f-697d-4ee6-9127-0987d561c9ce", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Columns in the DataFrame:\n", - "Index(['Country', 'latest year available_x', 'CH4 emissions',\n", - " 'CH4 emissions per capita', ' % change since 1990', 'N2O emissions',\n", - " 'N2O emissions per capita', ' % change since 1990.1', 'NOx emissions',\n", - " '% change since 1990_x', 'NOx emissions per capita', 'CO2 emissions ',\n", - " '% change since 1990_y', 'CO2 emissions per capita',\n", - " 'CO2 emissions per km2', 'Total GHG emissions _perc',\n", - " 'GHG from Energy_perc',\n", - " 'GHG from Energy \\nof which: from Transport_perc',\n", - " 'GHG from Industrial Processes_perc', 'GHG from Agriculture_perc',\n", - " 'GHG from Waste_perc', 'Total GHG emissions _tonnes',\n", - " 'GHG from Energy_tonnes',\n", - " 'GHG from Energy \\nof which: from Transport_tonnes',\n", - " 'GHG from Industrial Processes_tonnes', 'GHG from Agriculture_tonnes',\n", - " 'GHG from Waste_tonnes', 'Consumption of CFCs_odptonnes',\n", - " 'Consumption of all ODS', 'consumptionof all_odptonnesin 2013',\n", - " 'reduction in odp', 'SO2 emissions', '% change since 1990',\n", - " 'SO2 emissions per capita'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "# Print all column names to verify\n", - "print(\"Columns in the DataFrame:\")\n", - "print(data.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "889d4338-844b-4398-8e9e-c5db38bb996a", - "metadata": {}, - "outputs": [], - "source": [ - "# List of columns to normalize (update this list based on actual column names)\n", - "columns_to_normalize = [\n", - " 'CH4 emissions', 'CH4 emissions per capita', ' % change since 1990',\n", - " 'N2O emissions', 'N2O emissions per capita', ' % change since 1990.1',\n", - " 'NOx emissions', '% change since 1990_x', 'NOx emissions per capita',\n", - " 'CO2 emissions ', '% change since 1990_y', 'CO2 emissions per capita',\n", - " 'CO2 emissions per km2', 'Total GHG emissions _perc', 'GHG from Energy_perc',\n", - " '\"GHG from Energy of which: from Transport_perc\"', 'GHG from Industrial Processes_perc',\n", - " 'GHG from Agriculture_perc', 'GHG from Waste_perc', 'Total GHG emissions _tonnes',\n", - " 'GHG from Energy_tonnes', '\"GHG from Energy of which: from Transport_tonnes\"',\n", - " 'GHG from Industrial Processes_tonnes', 'GHG from Agriculture_tonnes', 'GHG from Waste_tonnes',\n", - " 'Consumption of CFCs_odptonnes', 'Consumption of all ODS', 'consumptionof all_odptonnesin 2013',\n", - " 'reduction in odp', 'SO2 emissions', '% change since 1990', 'SO2 emissions per capita'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "403c474c-5730-49db-9e36-2e86f116cb37", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Missing columns:\n", - "['\"GHG from Energy of which: from Transport_perc\"', '\"GHG from Energy of which: from Transport_tonnes\"']\n" - ] - } - ], - "source": [ - "# Check for columns that exist in the DataFrame\n", - "valid_columns = [col for col in columns_to_normalize if col in data.columns]\n", - "missing_cols = [col for col in columns_to_normalize if col not in data.columns]\n", - "print(\"Missing columns:\")\n", - "print(missing_cols)" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "47ed30d2-8fb6-4457-988f-9194c28107e6", - "metadata": {}, - "outputs": [], - "source": [ - "# Function to clean non-numeric values in a column\n", - "def clean_numeric(column):\n", - " # Replace commas and spaces in the entire column\n", - " column = column.str.replace(',', '', regex=False)\n", - " column = column.str.replace(' ', '', regex=False)\n", - " # Convert to numeric, forcing errors to NaN\n", - " return pd.to_numeric(column, errors='coerce')" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "a1321187-cf52-4c82-a2bc-f74c284ff9d4", - "metadata": {}, - "outputs": [], - "source": [ - "# Apply cleaning function to valid columns\n", - "for col in valid_columns:\n", - " data[col] = data[col].astype(str) # Ensure column is treated as strings\n", - " data[col] = clean_numeric(data[col]) # Apply cleaning function\n", - "\n", - "# Handle any NaNs in valid columns\n", - "data[valid_columns] = data[valid_columns].fillna(0)\n", - "\n", - "# Normalize the data\n", - "scaler = MinMaxScaler()\n", - "data[valid_columns] = scaler.fit_transform(data[valid_columns])" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "166c43f2-0387-4105-86fb-ffd9093174b3", - "metadata": {}, - "outputs": [], - "source": [ - "# Prepare data \n", - "X = data[valid_columns]" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "32172d0b-589b-4491-a608-7d9d8753a2e8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Explained Variance by Principal Component 1: 0.34\n", - "Explained Variance by Principal Component 2: 0.17\n" - ] - } - ], - "source": [ - "\n", - "# Apply PCA\n", - "pca = PCA(n_components=2)\n", - "X_pca = pca.fit_transform(X)\n", - "\n", - "# Explained variance\n", - "explained_variance = pca.explained_variance_ratio_\n", - "\n", - "# Plot PCA results\n", - "plt.figure(figsize=(10, 7))\n", - "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=data['CO2 emissions '], cmap='viridis')\n", - "plt.colorbar(label='CO2 Emissions')\n", - "plt.title('PCA of Emission Data')\n", - "plt.xlabel('Principal Component 1')\n", - "plt.ylabel('Principal Component 2')\n", - "plt.show()\n", - "\n", - "print(f'Explained Variance by Principal Component 1: {explained_variance[0]:.2f}')\n", - "print(f'Explained Variance by Principal Component 2: {explained_variance[1]:.2f}')" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "667131db-51f8-4bbc-91a6-23d49a15656e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/50\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\minal\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", - " super().__init__(**kwargs)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5s/step - loss: 0.0076\n", - "Epoch 2/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0066\n", - "Epoch 3/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0061\n", - "Epoch 4/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0060\n", - "Epoch 5/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0062\n", - "Epoch 6/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0063\n", - "Epoch 7/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0062\n", - "Epoch 8/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0061\n", - "Epoch 9/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0060\n", - "Epoch 10/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0059\n", - "Epoch 11/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0058\n", - "Epoch 12/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0056\n", - "Epoch 20/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0055\n", - "Epoch 21/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0055\n", - "Epoch 22/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0055\n", - "Epoch 23/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0055\n", - "Epoch 24/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0054\n", - "Epoch 25/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0049\n", - "Epoch 33/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0048\n", - "Epoch 34/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0047\n", - "Epoch 35/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0046\n", - "Epoch 36/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0045\n", - "Epoch 37/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0045\n", - "Epoch 38/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.0044\n", - "Epoch 46/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 0.0044\n", - "Epoch 47/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 0.0044\n", - "Epoch 48/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 0.0043\n", - "Epoch 49/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 0.0043\n", - "Epoch 50/50\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 0.0043\n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 312ms/step\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import LSTM, Dense\n", - "from sklearn.preprocessing import MinMaxScaler\n", - "\n", - "# Prepare data for LSTM\n", - "def create_dataset(data, look_back=1):\n", - " X, y = [], []\n", - " for i in range(len(data) - look_back - 1):\n", - " a = data[i:(i + look_back), 0]\n", - " X.append(a)\n", - " y.append(data[i + look_back, 0])\n", - " return np.array(X), np.array(y)\n", - "\n", - "data_CO2 = data['CO2 emissions '].values.reshape(-1, 1)\n", - "scaler = MinMaxScaler(feature_range=(0, 1))\n", - "data_scaled = scaler.fit_transform(data_CO2)\n", - "\n", - "look_back = 10\n", - "X, y = create_dataset(data_scaled, look_back)\n", - "X = X.reshape(X.shape[0], X.shape[1], 1)\n", - "X_train, X_test = X[:-10], X[-10:]\n", - "y_train, y_test = y[:-10], y[-10:]\n", - "\n", - "# Define LSTM model\n", - "lstm_model = Sequential()\n", - "lstm_model.add(LSTM(units=50, return_sequences=True, input_shape=(look_back, 1)))\n", - "lstm_model.add(LSTM(units=50))\n", - "lstm_model.add(Dense(1))\n", - "lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n", - "\n", - "# Train the model\n", - "lstm_model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=1)\n", - "\n", - "# Make predictions\n", - "predictions = lstm_model.predict(X_test)\n", - "predictions = scaler.inverse_transform(predictions)\n", - "\n", - "# Plot predictions\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(data_CO2[-10:], label='Actual CO₂ Emissions')\n", - "plt.plot(predictions, label='Predicted CO₂ Emissions')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "24d8daa2-001c-4538-90e6-56e1c8128210", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/30\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\minal\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", - " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0344\n", - "Epoch 2/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0135 \n", - "Epoch 3/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0131 \n", - "Epoch 4/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0082 \n", - "Epoch 5/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0050 \n", - "Epoch 6/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0037 \n", - "Epoch 7/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0022 \n", - "Epoch 8/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0022 \n", - "Epoch 9/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0010 \n", - "Epoch 10/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.5139e-04 \n", - "Epoch 11/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.5110e-04 \n", - "Epoch 12/30\n", - "\u001b[1m4/4\u001b[0m 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"Epoch 25/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 6.9772e-05 \n", - "Epoch 26/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.9341e-05 \n", - "Epoch 27/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.3905e-05 \n", - "Epoch 28/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.6566e-05 \n", - "Epoch 29/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 4.5848e-05 \n", - "Epoch 30/30\n", - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.2623e-05 \n", - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n" - ] - }, - { - "data": { - "image/png": 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d2G28NXcrl7UI55Mby9bUqjKYXqpXUFDA2rVr6d+///EXs1rp37//aSfqL1++vNTxAAMGDCg53m63M2vWLJo1a8aAAQMICwujS5cuZ53En5+fT1ZWVqmbiIiIiIhUnth4o+rM1brpFauwxCk9PR2bzUZ4eOn6xfDw8JPaPhdLTk4+4/GpqakcOXKEV199lYEDB/Lnn38yYsQIRo4cyaJFi04byyuvvEJgYGDJLTIy8gLfnYiIiIiIlFVadj4bkw4D0Ke5EqcKZ7fbARg2bBgPPvgg7dq14/HHH2fw4MFnXOR0/PjxZGZmltxOXDxUREREREQq1oIEY7SpTb1AwgJcs0S/whbADQkJwc3NjZSUlFL7U1JSiIiIOOVzIiIiznh8SEgI7u7utGjRotQxMTExLF269LSxeHl54eXldT5vQ0RERERELtD8OCNx6hvtmqNNUIEjTp6enlx00UXExsaW7LPb7cTGxtKtW7dTPqdbt26ljgeYO3duyfGenp506tSJhISEUsds3bqV+vXrl/M7EBERERGRC5VfZGPJtjQA+kW7XhvyYhVaqjdu3DgmTZrEV199RVxcHHfddRc5OTncfPPNANx4442MHz++5Pj777+fOXPmMGHCBOLj43nuuedYs2YN9957b8kxjzzyCJMnT2bSpEls376d999/n99++4277767It+KnOCmm25i+PDhJV/37t2bBx54oNLjWLhwIRaLhcOHD1f6a5ulvN7z7t27sVgsbNiwoVziEhERETmdVbsyyCmwEebvRcs6p+9a5+wqNHEaPXo0b775Js888wzt2rVjw4YNzJkzp6QBRGJiIgcOHCg5vnv37nz//fd88skntG3bll9++YXp06fTqtXxVYVHjBjBRx99xOuvv07r1q359NNPmTJlCj179qzIt+L0brrpJiwWCxaLBU9PT5o0acILL7xAUVFRhb/21KlTefHFF8t0rBnJzvr167nqqqsIDw/H29ubpk2bcvvtt7N169ZSx3311Vd06tQJX19f/P396dWrV6nW+Ofiyy+/LPn3OPF2oW23u3fvzoEDBwgMDLyg80RGRnLgwIFS/7dEREREKsL8+ONlelarxeRozl+FzXEqdu+995YaMTrRwoULT9p31VVXcdVVV53xnLfccgu33HJLeYRXpQwcOJAvvviC/Px8Zs+ezT333IOHh0epUb1iBQUFeHp6lsvr1qrlvAuYzZw5k1GjRjFgwAC+++47GjduTGpqKj///DNPP/00kydPBuDhhx/m/fff56WXXmL48OEUFhby7bffMmzYMN55553T/gyfSUBAwEllpRbLhf2y8PT0PO0cwXPh5uZWLucREREROROHw0FsFZjfBC7WVU/OzMvLi4iICOrXr89dd91F//79mTFjBnC8vO7ll1+mTp06NG/eHICkpCSuvvpqgoKCqFWrFsOGDWP37t0l57TZbIwbN46goCCCg4N59NFH+feayf8u1cvPz+exxx4jMjISLy8vmjRpwmeffcbu3bvp06cPADVr1sRisXDTTTcBxvy3V155hYYNG+Lj41My4nii2bNn06xZM3x8fOjTp0+pOE8lNzeXm2++mUGDBjFjxgz69+9Pw4YN6dKlC2+++SYff/wxACtWrGDChAm88cYbPPzwwzRp0oSYmBhefvllHnjgAcaNG3denRgtFgsRERGlbie22+/duzdjx47lgQceoGbNmoSHhzNp0qSSclZ/f3+aNGnC77//XvKcf4/Y7dmzhyFDhlCzZk38/Pxo2bIls2fPBuDQoUNcd911hIaG4uPjQ9OmTfniiy+AU5fqLVq0iM6dO+Pl5UXt2rV5/PHHS41Y9u7dm/vuu49HH32UWrVqERERwXPPPVfyuMPh4LnnniMqKgovLy/q1KnDfffdd87fNxEREak6dqTlkJiRi6e7lR5NQswO54IocTobhwMKcsy5/StBOVc+Pj4UFBSUfB0bG0tCQgJz585l5syZFBYWMmDAAPz9/VmyZAl//fUXNWrUYODAgSXPmzBhAl9++SWff/45S5cuJSMjg2nTpp3xdW+88UZ++OEH3n33XeLi4vj444+pUaMGkZGRTJkyBYCEhAQOHDjAO++8AxhrbX399dd89NFHbN68mQcffJDrr7++ZH2upKQkRo4cyZAhQ9iwYQO33XYbjz/++Bnj+OOPP0hPT+fRRx895eNBQUEA/PDDD9SoUYP//Oc/Jx3z0EMPUVhYWBL34MGD6dmzJ23btmXWrFlnfP2y+OqrrwgJCWHVqlWMHTuWu+66i6uuuoru3buzbt06LrvsMm644QZyc3NP+fx77rmH/Px8Fi9ezN9//81rr71GjRo1AHj66afZsmULv//+O3FxcXz44YeEhJz6F9a+ffsYNGgQnTp1YuPGjXz44Yd89tlnvPTSSyfF6+fnx8qVK3n99dd54YUXmDt3LgBTpkzh7bff5uOPP2bbtm1Mnz6d1q1bX/D3SERERFzX/HijY3a3RsH4eVV4sVuFcu3oK0NhLvy3jjmv/cR+8PQ756c5HA5iY2P5448/GDt2bMl+Pz8/Pv3005ISvW+//Ra73c6nn35aUkL2xRdfEBQUxMKFC7nsssuYOHEi48ePZ+TIkQB89NFH/PHHH6d97a1bt/LTTz8xd+5c+vfvD0CjRo1KHi8u6wsLCytJXPLz8/nvf//LvHnzSjooNmrUiKVLl/Lxxx/Tq1cvPvzwQxo3bsyECRMAaN68eUmicDrbtm0DIDo6+ozfr61bt9K4ceNTli7WqVOHgICAkvlQ7733Hg0bNiQuLo5LL72UvXv3nva8mZmZJUlMsYsvvrjUCFLbtm156qmnAGO9sVdffZWQkBBuv/12AJ555hk+/PBDNm3aRNeuXU96jcTEREaNGlWSoJz4vU5MTKR9+/Z07NgRgAYNGpw21v/9739ERkby/vvvY7FYiI6OZv/+/Tz22GM888wzWK3GNZY2bdrw7LPPAtC0aVPef/99YmNjufTSS0lMTCQiIoL+/fvj4eFBVFQUnTt3Pu1rioiISNVXXKbXL8a1y/RAiVOVMnPmTGrUqEFhYSF2u53/+7//K1VK1bp161LJwcaNG9m+fTv+/v6lzpOXl8eOHTvIzMzkwIEDdOnSpeQxd3d3OnbseFK5XrENGzbg5uZGr169yhz39u3byc3N5dJLLy21v6CggPbt2wMQFxdXKg7gtG3ti50uxgs5tmHDhoBRhlecTJyOv78/69atK7XPx8en1Ndt2rQp2XZzcyM4OLjUKE1xaV9qauopX+O+++7jrrvu4s8//6R///6MGjWq5Jx33XUXo0aNKhm5Gj58ON27dz/leeLi4ujWrVupOVg9evTgyJEj7N27l6ioqJPiBahdu3ZJbFdddRUTJ06kUaNGDBw4kEGDBjFkyBDc3fVrRkREpDrKzC1kzZ5DAPRprsSp6vPwNUZ+zHrtc9CnTx8+/PBDPD09qVOnzkkfWP38So9eHTlyhIsuuojvvvvupHOFhoaee7ycnBiUxZEjRwCYNWsWdevWLfXYhSxc3KxZMwDi4+PPmGQ1a9aMpUuXnrJhxv79+8nKyio5FxhJ1gMPPMAjjzxyxte3Wq00adLkjMd4eHiU+tpisZTaV5zI2O32Uz7/tttuY8CAAcyaNYs///yTV155hQkTJjB27Fguv/xy9uzZw+zZs5k7dy79+vXjnnvu4c033zxjTOcab3FskZGRJCQkMG/ePObOncvdd9/NG2+8waJFi056noiIiFR9i7alYbM7aB7uT2Stc/tc64w0x+lsLBajXM6M2zl2YPPz86NJkyZERUWV6Sp/hw4d2LZtG2FhYTRp0qTULTAwkMDAQGrXrs3KlStLnlNUVMTatWtPe87WrVtjt9tL5ib9W3FiYrPZSva1aNECLy8vEhMTT4ojMjISgJiYGFatWlXqXCtWrDjj+7vssssICQnh9ddfP+XjxQ0WrrnmGo4cOVLSLOJEb775Jh4eHowaNapk36uvvorFYjmvTnsVITIykjvvvJOpU6fy0EMPMWnSpJLHQkNDGTNmDN9++y0TJ07kk08+OeU5YmJiWL58eamRt7/++gt/f3/q1atX5lh8fHwYMmQI7777LgsXLmT58uX8/fff5//mRERExGXNjzPmN/WtAmV6oBGnau26667jjTfeYNiwYbzwwgvUq1ePPXv2MHXqVB599FHq1avH/fffz6uvvkrTpk2Jjo7mrbfeOuMaTA0aNGDMmDHccsstvPvuu7Rt25Y9e/aQmprK1VdfTf369bFYLMycOZNBgwbh4+ODv78/Dz/8MA8++CB2u52ePXuSmZnJX3/9RUBAAGPGjOHOO+9kwoQJPPLII9x2222sXbuWL7/88ozvr3hO11VXXcXQoUO57777aNKkCenp6fz0008kJiby448/0q1bN+6//34eeeQRCgoKSrUjf+edd5g4cWJJAjd9+nQmT57M4sWLz9pa3OFwkJycfNL+sLCws5b5ldUDDzzA5ZdfTrNmzTh06BALFiwgJiYGMOZHXXTRRbRs2ZL8/HxmzpxZ8ti/3X333UycOJGxY8dy7733kpCQwLPPPsu4cePKHOuXX36JzWajS5cu+Pr68u233+Lj40P9+vXL5b2KiIiI6yiy2Vm4NQ2Afi7ehryYRpyqMV9fXxYvXkxUVBQjR44kJiaGW2+9lby8PAICjFWdH3roIW644QbGjBlDt27d8Pf3Z8SIEWc874cffsiVV17J3XffTXR0NLfffjs5OTkA1K1bl+eff57HH3+c8PDwklGbF198kaeffppXXnmFmJgYBg4cyKxZs0rmFEVFRTFlyhSmT59O27Zt+eijj/jvf/971vc4bNgwli1bhoeHB//3f/9HdHQ01157LZmZmaU6xk2cOJH//e9//PDDD7Rq1YqOHTuyePFipk+fXqrBxg033IDNZmPo0KH07t2bo0ePnva1s7KyqF279km3081XOh82m4177rmn5HvWrFkz/ve//wHG6N748eNp06YNl1xyCW5ubvz444+nPE/dunWZPXs2q1atom3bttx5553ceuutJY0ryiIoKIhJkybRo0cP2rRpw7x58/jtt98IDg4ul/cqIiIirmN90mEO5xYS5OtB+6iaZodTLiyOc5lBX0VkZWURGBhIZmZmSYJQLC8vj127dtGwYUO8vb1NilCk6tP/NRERkarr1d/j+WjRDka0r8vbo9uZHc4ZnSk3OJFGnEREREREpFwVr9/Up4qU6YESJxERERERKUdJGblsTTmCm9VCr6bn16nZGSlxEhERERGRcjM/3pjP3bF+TQJ9q86SJEqcRERERESk3MQeS5z6VZE25MWUOImIiIiISLnIyS9ixY6DAPSNDjc5mvKlxOk07Ha72SGIVGn6PyYiIlL1LN2eToHNTv1gXxqH+pkdTrnSArj/4unpidVqZf/+/YSGhuLp6XnWhU5FpOwcDgcFBQWkpaVhtVrx9PQ0OyQREREpJ/PjjDK9vtFhVe4ztBKnf7FarTRs2JADBw6wf/9+s8MRqbJ8fX2JiorCatXAt4iISFVgtzuYn3BsflMVK9MDJU6n5OnpSVRUFEVFRdhsNrPDEaly3NzccHd3r3JXokRERKqzf/Znkpadj5+nG50b1jI7nHKnxOk0LBYLHh4eeHhUnRaKIiIiIiIVJfZYmd4lzULxdK96FSVV7x2JiIiIiEilK16/qW901WpDXkyJk4iIiIiIXJCUrDz+3peJxQK9mytxEhEREREROcmCY6NNbesFEervZXI0FUOJk4iIiIiIXJDY+OJuelVztAmUOImIiIiIyAXIK7SxdFs6AH1jlDiJiIiIlF12Csx7Hg4nmh2JiFSwFTsPcrTQRkSANy1qB5gdToVRO3IREREpXw4HTLkVdi+BIykw/H9mRyQiFaikm15MWJVeo1EjTiIiIlK+1n9jJE0Ae5aZG4uIVCiHw1GyflNVnt8ESpxERESkPGWnwJ9PHf/60C5jn4hUSVtTjrDv8FG83K10bxxidjgVSomTiIiIlJ/fH4W8TKjdFkJjjH1JK8yNSUQqTGy8cWGkR5MQfDzdTI6mYilxEhERkfIRPxu2TAeLGwx9D+p3N/YnrjQ1LBGpOPOPlen1reJleqDESURERMpDXhbMesjY7n6vMeIU1c34OnG5eXGJSIXJyClgXeIhQImTiIiISNnEvgDZ+6FmQ+j1uLEvqotxn7wJCnLMi01EKsSiranYHRBTO4A6QT5mh1PhlDiJiIjIhUlcCas/NbaHTARPX2M7MBIC6oK9CPatNS08EakY1aWbXjElTiIiInL+ivJhxljAAe2uh0a9jz9msUDksVEnzXMSqVIKbXYWbU0DjPWbqgMlTiIiInL+lr4N6QngFwqXvXjy45rnJFIlrdl9iOy8IoL9PGlbL8jscCqFEicRERE5P6nxsPhNY/vy18C31snHFM9z2rsa7LbKi01EKtT8Y23IezcPw81qMTmayqHESURERM6d3Q6/3Qf2Qmg6AFqOPPVxYS3B0x/ysyB1S+XGKCIVJjb+2PymalKmB0qcRERE5Hys/RySVoJnDbhigjGf6VTc3KFeR2M7UQvhilQFu9Jz2JmWg7vVwsVNQ8wOp9IocRIREZFzk7Uf5j5nbPd7BoIiz3x8yTwnJU4iVcH8Y6NNXRrVwt/bw+RoKo8SJxERESk7hwNmPQwF2VCvE3S67ezPKZ7nlKTOeiJVQfH8pr7R4SZHUrmUOImIiEjZxc2AhFlg9YAh74LV7ezPqdsRLG6QmQSZeys+RhGpMNl5hazcmQFUn/WbiilxEhERkbI5eghmP2Js93wAwluU7XleNSCitbGtcj0Rl7ZkWzpFdgeNQv1oEOJndjiVSomTiIiIlM3cZ+FICgQ3hYsfPrfnap6TSJUQG3esm141G20CJU4iIiJSFruXwrqvjO2h74KH97k9v2SekxInEVdlsztYmGAkTtVtfhMocRIREZGzKcyDGfcZ2xfdDPW7n/s5Irsa9ymbIS+z/GITkUqzce9hDuYU4O/tTscGNc0Op9IpcRIREZEzW/w6ZOyAGhFw6fPnd46A2hBUHxx22Lu6fOMTkUox/1iZXq9moXi4Vb80ovq9YxERESm75H/gr3eM7SveBO/A8z9XyTwntSUXcUWxx9Zv6hdT/eY3gRInEREROR27DWaMBXsRRA+GmCEXdr6oY+V6icsvPDYRqVT7Dx8l7kAWVgv0aqbESUREROS4VZ/A/nXgFQCD3rzw8xUnTvvWgq3wws8nIpVm/rHRpg5RNanl52lyNOZQ4iQiIiInO5wIsS8a25c+b8xRulAhzcE7CApzIXnThZ9PRCpNceLUt5qW6YESJxEREfk3hwNmjoPCHIjqDh1uKp/zWq0QeawtueY5ibiMowU2/tqeDkC/atiGvJgSJxERESntnymwfS64ecKQd4yEp7xonpOIy1m2I538Ijt1g3xoFl7D7HBMo8RJREREjsvNgN8fM7YveRRCm5Xv+YsTp6SVxsiWiDi9E7vpWSwWk6MxjxInEREROe6PJyE3HcJaQI/7y//8dToYI1lHUuDQrvI/v4iUK4fDUbJ+U9/o6ju/CZQ4iYiISLEd82Hj94AFhrwL7hXQOcvDG2q3M7Y1z0nE6W05kEVyVh4+Hm50bRRsdjimUuIkIiIiUJALvz1gbHe+AyI7VdxraZ6TiMsoHm3q2TQEbw83k6MxlxInERERgYX/hcN7IKAe9Hu6Yl/rxHlOIuLUSuY3VfMyPVDiJCIiIvs3wPIPjO0rJoCXf8W+XnFL8rR4oxmFiDiltOx8Nu49DEAfJU5KnERERKo1WxHMGAsOO7QcCc0HVvxr+oVAcFNjO2lVxb+eiJyXhQmpOBzQum4g4QHeZodjOiVOIiIi1dmKDyB5E3gHweWvVd7rap6TiNObH69ueidS4iQiIlJdZeyEBf81tge8DDUq8cOR5jmJOLWCIjuLt6YBxvpNosRJRESkenI4jC56RXnQ8BJod13lvn5UN+N+3zooyq/c1xaRs1q1K4OcAhuh/l60qhNodjhOQYmTiIhIdbThe9i1CNy9YfBEsFgq9/VrNQLfELDlG80pRMSpxManANC3eRhWayX/fnBSSpxERESqmyOp8McTxnbv8RDcuPJjsFg0z0nESTkcDmKPrd/UV2V6JZQ4iYiIVDdzHoe8wxDRGrrda14cmuck4pR2pOWQmJGLp5uVnk1CzA7HaShxEhERqU62/gn/TAGLFYa+B27u5sVSPM8pcYUx50pEnML8Y2V6XRsH4+dl4u8IJ6PESUREpLrIz4aZDxrbXe+GOu3NjSeijTHH6mgGpG8zNxYRKVFcptdPbchLUeIkIiJSXcx/CbL2QlAU9HnC7GjA3RPqdjS2Nc9JxClk5hayZs8hQOs3/ZsSJxERkepg7xpY+bGxPXgiePqZGk6JqC7GveY5iTiFRdvSsNkdNAuvQWQtX7PDcSpKnERERKq6ogKYMRZwQJtroEk/syM6rmSek0acRJzB/Lhjbcijw02OxPkocRIREanqlr0DqVvANxgG/NfsaEqr1wmwQMZOo026iJimyGZn4dY0APqpDflJlDiJiIhUZenbYNHrxvbAV8Ev2Nx4/s0nCMJaGNuJK0wNRaS6W590mMO5hQT5etA+MsjscJyOEicREZGqym6H3+4HWwE06Q+trzI7olPTPCcRp1DcTa93s1Dc3ZQm/Ju+IyIiIlXVuq9gz1/g4QtXvAUWi9kRnZrmOYk4hQXxRuLUN0bzm05FiZOIiEhVlHUA5j5rbPd9GmrWNzeeM4k8NuJ0YCMU5Jobi0g1lZSRS0JKNm5WC72ahpodjlNS4iQiIlIV/f4I5GdCnQ7Q5T9mR3NmQVHgXwfsRbBvrdnRiFRLCxKM0aaO9WsS6OthcjTOSYmTiIhIVRP3m3GzusPQd8HqZnZEZ2axnDDPSQ0iRMxQPL9J3fROT4mTiIhIVZKXCbMeNra73wcRrc2Np6xK5jkpcRKpbDn5RSzfcRDQ+k1nosRJRESkKpn3HBxJhlqNodejZkdTdsXznJJWg91mbiwi1cxf29MpsNmpH+xL41A/s8NxWkqcREREqoo9y2HN58b2kHfAw8fceM5FeCvwrGHMy0qNMzsakWplfnE3vegwLM7afdMJKHESERGpCory4bf7jO32N0DDi82N51y5uUO9jsa25jmJVBq73VGSOPVTmd4ZKXESERGpCpZMgPSt4BcGl71odjTnR/OcRCrd5v1ZpGbn4+fpRueGtcwOx6kpcRIREXF1qXGw5C1je9Dr4FPT3HjOV/E8p8SV5sYhUo3ExqcAcHHTUDzdlRqcib47IiIirsxugxljwV4IzQdBi+FmR3T+6nUEixtkJkLmPrOjEakWSuY3qQ35WSlxEhERcWWrP4O9q8HTHwa9aayJ5Kq8/CGilbGteU4iFS41K49NezMB6NNcidPZKHESERFxVZl7IfZ5Y7v/sxBY19x4yoPmOYlUmgUJxmhT28ggQv29TI7G+SlxEhERcUUOB8x6CAqOGHODOt5qdkTlo2SekxInkYoWG1fcTU+jTWWhxElERMQVbZ4GW+eA1QOGvAvWKvInPaqrcZ/yD+RnmxuLSBWWV2hj6fZ0wFi/Sc6uivyWFRERqUZyM+D3R43tix+CsGhz4ylPAXUgKAocdmPulohUiJW7MsgtsBEe4EXLOgFmh+MSlDiJiIi4mrlPQ04ahDSHi8eZHU350zwnkQo3P85oQ943OhyLKzeVqURKnERERFzJzkWw/ltje+i74F4FJ3RrnpNIhXI4HMTGa37TuVLiJCIi4ioKj8Jv9xvbnW47Ph+oqikecdq7BmxF5sYiUgVtSz3C3kNH8XK30qNJiNnhuIxKSZw++OADGjRogLe3N126dGHVqlVnPP7nn38mOjoab29vWrduzezZs0977J133onFYmHixInlHLWIiIiTWfQaHNoF/nWg37NmR1NxQqPBOxAKcyDlb7OjEalyirvpdW8cjI+nmzlBOBwud2GkwhOnyZMnM27cOJ599lnWrVtH27ZtGTBgAKmpqac8ftmyZVx77bXceuutrF+/nuHDhzN8+HD++eefk46dNm0aK1asoE6dOhX9NkRERMx1YBP89a6xfcWb4F2FJ3NbrSrXE6lA8+OPzW+KCTcngMQV8OVgWPyGOa9/nio8cXrrrbe4/fbbufnmm2nRogUfffQRvr6+fP7556c8/p133mHgwIE88sgjxMTE8OKLL9KhQwfef//9Usft27ePsWPH8t133+Hh4XHGGPLz88nKyip1ExERcRm2IvjtPnDYoMUwiL7C7IgqnhInkQpxKKeAtXsOASa0Id+3Dr4dBZ8PgD1LYfUkKMqv3BguQIUmTgUFBaxdu5b+/fsff0Grlf79+7N8+fJTPmf58uWljgcYMGBAqePtdjs33HADjzzyCC1btjxrHK+88gqBgYElt8jIyPN8RyIiIiZY+RHsXw9egXD562ZHUzlO7KzncJgbi0gVsmhrGnYHREf4UzfIp3JeNGUL/HgdTOoD2+eB1R0uugn+s9ilGtxUaOKUnp6OzWYjPLz0MGB4eDjJycmnfE5ycvJZj3/ttddwd3fnvvvuK1Mc48ePJzMzs+SWlJR0ju9ERETEJId2w4KXje3LXgT/CFPDqTR1OxiL+x5JhsN7zI5GpMoo6aYXUwmjTenb4Zdb4cPuED8TsECba+De1TDkHQisV/ExlCN3swM4V2vXruWdd95h3bp1Ze457+XlhZeX62SzIiIigDHSMvNBKMyF+j2hw41mR1R5PHygTjtjEdzEFVCzgdkRibi8QpudRQlG4tQ3ugLnNx3aA4tfhw0/GCXGAC2GQ+/xLr1gd4WOOIWEhODm5kZKSkqp/SkpKUREnPqKWURExBmPX7JkCampqURFReHu7o67uzt79uzhoYceokGDBhXyPkREREyx6SfYMR/cvIyrs9Vtkcriduua5yRSLtbuOURWXhG1/DxpFxlU/i+QdQBmPQTvXWSsN+ewQbOBRkne1V+5dNIEFZw4eXp6ctFFFxEbG1uyz263ExsbS7du3U75nG7dupU6HmDu3Lklx99www1s2rSJDRs2lNzq1KnDI488wh9//FFxb0ZERKQy5aTDnMeN7V6PQkgTc+MxQ6QSJ5HyNP9YmV7v5qG4WcvxQkxOOvzxJLzbDlZ/CvZCaNQbbp0H/zcZarctv9cyUYWX6o0bN44xY8bQsWNHOnfuzMSJE8nJyeHmm28G4MYbb6Ru3bq88sorANx///306tWLCRMmcMUVV/Djjz+yZs0aPvnkEwCCg4MJDg4u9RoeHh5ERETQvHnzin47IiIileOPJ+BoBoS1hB73mx2NOYpHnNLi4Ogh8KlpbjwiLi42zqjq6ldeZXpHD8Gy92HFh8a6a2B0xOz7NDS8uHxew4lUeOI0evRo0tLSeOaZZ0hOTqZdu3bMmTOnpAFEYmIiVuvxga/u3bvz/fff89RTT/HEE0/QtGlTpk+fTqtWrSo6VBEREeewfR5smgxYYOh74HbmZTeqLL8QCG4CB7dD0ipoNsDsiERc1u70HHak5eButXBxs5ALO1l+ttHtc9l7kJdp7KvdzkiYmvSrsmXFFoej+vX4zMrKIjAwkMzMTAICqvACgiIi4nryj8D/ukFmInS9Gwa+YnZE5vr1HmOuRM9x0P9Zs6MRcVmfL93FCzO30L1xMN/f3vX8TlJ41CjFW/o25B409oXGQN8nIXqwyyZMZc0NXK6rnoiISJW24L9G0hQYBX2eNDsa80V2NRInzXMSuSDF85vOa9HbogJY9xUsftNYIgCgVmPo8wS0HAFWt3KM1HkpcRIREXEW+9bCyg+N7cFvgVcNc+NxBsUL4e5bC0X5LrVYpoizyM4rZOUuY4SoX8w5zG+yFcGmH2Hha8YFHYDASOj1GLS9FtyqVypRvd6tiIiIs7IVwoz7wWGH1ldB00vNjsg5BDcG3xDITYcDGyGys9kRibicpdvSKbQ5aBTiR8MQv7M/wW6HzVONEfCMHca+GhFwycPGenLV9AKGEicRERFnsOw9SPnb6Bw3oJrPazqRxWJ014ufCYnLlTiJnIfYspbpORwQPwsWvAypW4x9vsHQ80HoeCt4+lZwpM5NiZOIiIjZDu6ARa8Z2wNegRqh5sbjbCK7HEucVkIPs4MRcS12u4MFxYlTzGkSJ4cDtsfCgpdg/3pjn1cg9BgLXe4EL/9Kita5KXESERExk8MBv90PRXnQqA+0vcbsiJxP8TynpBXG98tFO3eJmGHj3sMczCnA38udTg1qnXzA7qUw/yVjRBfAww+63gXd79Xaaf+ixElERMRM67+F3UvA3QcGv62k4FRqtwV3b6P98cHtENLU7IhEXEZxN71Lmofi4XZ87VSSVhsjTDsXGl+7e0On24yyPL8LXOepilLiJCIiYpbsFPjzWMvxPk9ArYbmxuOs3D2h7kWw5y/jqrgSJ5Eyi40zEqd+xfObDmwy5jBtnWN8bfWAi8bAxQ9BQB2TonQNSpxERETMMucxyMs0RlS63m12NM4tssuxxGml0dVLRM7qQOZRthzIwmKBviGH4acnYct040GLFdr+H/R6FGrWNzNMl6HESURExAwJv8PmaWBxg6HvVbv1UM5Z8Tyn4nkYInJW8+NTibSk8GLgTIK+WGAsd4AFWo2C3uMhpInZIboU/ZYWERGpbHlZMOshY7v7vcaIk5xZZCfjPmMHHElT50GRs8ncS90l45nvOQePPJuxL3qwURYc3tLc2FyUEicREZHKFvsCZO2Dmg2g1+NmR+MafGpCWAtjbZmkFRAzxOyIRJzTkVRY8haONZ/R21YAFjgS2ZsaA5+Fuh3Mjs6lWc9+iIiIiJSbxJWw+lNje8g71X5ByXMS2cW4T1xhbhwizig3A+Y+C++0hZUfYrEVsMIew12eL+N3y3QlTeVAI04iIiKVpSgffrsPcEC766BRb7Mjci1R3WDtF0qcRE6UlwUr/gfLP4D8LGNf3Yv43Ot6XtgSxg0dGmDRMgflQomTiIhIZVk6EdLiwS8ULnvJ7GhcT9SxEacDG6EgV6N1Ur0V5MCqT+Cvd+DoIWNfeGvo+xSOppcx6bUFQB59Y8JMDbMqUeIkIiJSGdISYMmbxvbAV8G3lrnxuKKg+uBfG7IPwP510KCn2RGJVL7CPFj7JSyZADnGGk2ENDOaPsQMA6uVuP1ZHMjMw8fDjW6Ngk0NtypR4iQiIlLR7HaYcR/YCqDpAKMVsJw7i8WY57RlulGup8RJqhNbIaz/Fha/YTSXAeNiQu/x0OZqsLqVHDo/PgWAHk1C8PZwO9XZ5DwocRIREaloa78wOsF51oArJhgJgJyfqG7HEyeR6sBug79/hoWvwKHdxr6AunDJI9D+enDzOOkpsfHGSFQ/lemVKyVOIiIiFSlrv9HpCqDv0xAUaW48rq54nlPSKmMkz6oGwVJF2e0QNwMW/BfSE4x9fqFw8UNw0c3g4X3Kp6UfyWdD0mEA+jRX4lSelDiJiIhUFIcDZj0MBdlQtyN0vt3siFxfeGvw8IP8TEiL00KeUvU4HLD1D1jwEiT/bezzDoKeD0DnO8DT74xPX5iQhsMBreoGEBF46uRKzo8SJxERkYoSNwMSZoHVHYa+W2oOgpwnN3eo1xF2LTLK9ZQ4SVXhcBg/1/Nfgr2rjX2e/tDtHuh2N3gHluk0xfOb+kaHV1Sk1ZYSJxERkYpw9DDMfsTY7vmgPuCXp6huxxOnTreaHY3IhUtcYSRMu5cYX7v7QJf/QI/7z6kDZ0GRncVb0wHoF60yvfKmxElERKQizH0GjqRAcFO4+GGzo6laSuY5qUGEuLj9642Eafs842s3T+h4C/QcB/7nPmK0encGR/KLCKnhReu6ZRuhkrJT4iQiIlLedi+FdV8Z20PeOe0kbjlP9TqBxQqHE43mGwF1zI5I5NykbIEFL0P8TONrq7vRIe+SRyCw3nmfNjbO6KbXNzoUq1XdO8ubEicREZHyVJgHv91vbF90EzToYWo4VZKXP4S3guRNRolTq5FmRyRSNgd3GF3y/pkCOAALtBkNvR+DWo0u6NQOh4NYzW+qUEqcREREytPiN+DgdqgRAf2fNzuaqiuqmxIncR2H9sDi12HDD+CwGftaDDcWrw2LLpeX2Jmew56DuXi6WenZNKRczimlKXESEREpL8n/wF8Tje1Bb4BPkJnRVG1RXWDVx5rnJM4t6wAseRPWfgX2QmNfs4HQ5wmo3bZcX2r+sTK9Lo1qUcNLH/Ergr6rIiIi5cFug9/uA3sRRA+GFkPNjqhqi+xq3Cf/DfnZRvmeiLPISYelb8PqT6Eoz9jXqDf0eQoiO1XISxaX6ambXsVR4iQiIlIeVk2CfWvBKwAGvWl2NFVfYF0IjILMRNi7Bhr3MTsiEWMZgmXvwYoPoTDH2BfZBfo+DQ0vrrCXzTxayOrdhwDNb6pISpxEREQu1OFEiH3B2L70eQiobW481UVUV/g70ZjnpMRJzJSfDSs/MpKmvExjX+12RsLUpB9YKrbD3eKtadjsDpqG1SAq2LdCX6s6U+IkIiJyIRwOmPWQcXU5qjt0uMnsiKqPqC7w90+a5yTmKTwKqz+DpW9B7kFjX2gM9H3SKNmt4ISp2Pz4Y23IY1SmV5GUOImIiFyIf6bAtj+NhSuHvANWq9kRVR9R3Yz7pNVgKwI3fayRSlJUYKzVtvhNOJJs7KvV2Gj60HIEWN0qLRSb3cGCBCNx6qcyvQql3zAiIiLnKzcDfn/M2L7kEQhtZm481U1oDHgFQn4mpPwDddqZHZFUdbYi2PQjLHzNmF8HEBgJvR6DtteakryvTzzE4dxCAn086BAVVOmvX50ocRIRETlffzwJuenGB/geD5gdTfVjtUJkZ9g+15jnpMRJKordDpunGovXZuww9tWIgEsehg43gruXaaHFHivT6908FHc3jXhXJCVOIiIi52PHAtj4PWCBoe+Cu6fZEVVPUV2MxClpBXS90+xopKpxOCB+Fix4GVK3GPt8g6Hng9DxVvA0vxFD8fpNfdWGvMIpcRIRETlXBbkw8wFju/PtxqiHmKN4nlPiCuNDbiVNxpdqYHsszH8R9q83vvYKhB5jocudTrNuWFJGLgkp2bhZLfRqFmp2OFWeEicREZFztfAVOLQbAupCv2fMjqZ6q9MBrO6QfcBoC1+zvtkRSVWwYz58O9LY9vCDrndB93vBp6a5cf1LcVOIi+rXJMhXo94VTYmTiIjIudi/AZa/b2xf8ZbTXHmutjx9jfVy9q0xRp2UOEl5WPeNcd98EAx9D/xCzI3nNGLjirvpqUyvMmgGmYiISFnZimDGWHDYoeVIaD7Q7IgEjIVwQes5SfkoyIWtc4ztix922qQpJ7+I5TuMtaP6af2mSqHESUREpKxWfADJm8A7CC5/zexopFhx4pSoxEnKwfa5UJgLgVFQt4PZ0ZzWX9vTKbDZiarlS+PQGmaHUy0ocRIRESmLjF2w4BVje8DLUENXeJ1GZBfjPjUOjh4yNxZxfZunG/cthzl1s5H58ce76VmcOM6qRImTiIjI2TgcRhe9oqPQ8BJod53ZEcmJaoRBrcaAA5JWmx2NuLKCXNj6h7HdYoS5sZyB3e4oSZxUpld5lDiJiIiczcYfYOdCcPeGwROd+ip0taV5TlIets+FwhynL9PbvD+L1Ox8/Dzd6NywltnhVBtKnERERM7kSBr88YSx3ftxCG5sbjxyaprnJOXBRcr0YuNTALi4aShe7m4mR1N9KHESERE5kzmPG/NmwltDt3vNjkZOp3gh3H1roajA3FjENRUedYkyPThhfpPK9CqVEicREZHT2fon/PMLWKww9F1w8zA7Ijmd4CbgGwxFeXBgo9nRiCva5hpleqlZeWzamwlAn+ZKnCqTEicREZFTyT8Cs8YZ213vduoPUoJRVhWpeU5yATZPM+5bDHXqMr2FCWkAtI0MItTfy+RoqhclTiIiIqcy/yXITIKgKOjzhNnRSFlEHWtLrnlOcq5OLNNrOdLcWM6ieH5Tv2iNNlU2JU4iIiL/tncNrPzI2B48ETz9TA1Hyqh4nlPiCqOFvEhZuUiZXn6RjSXb0gFj/SapXEqcRERETlRUADPuAxzQ5hpo0s/siKSsarcFNy/ITYeDO8yORlzJlunGvZOX6a3cmUFugY3wAC9a1gkwO5xqR4mTiIjIiZa9A6mbjUYDA/5rdjRyLty9oO5FxrbmOUlZFR6FhDnGdksX6aYXHYbFiRO8qkqJk4iISLH0bbDoDWN74KvgF2xuPHLuSuY5LTc3DnEdJWV6kccTbyfkcDhK5jf1jQ43OZrqSYmTiIgIgN0Ov90Ptnxo3A9aX2V2RHI+SuY5rTQ3DnEdJWV6zr3o7fbUIyRlHMXT3UqPJrqoYwYlTiIiIgDrv4Y9f4GHLwx+26k/QMkZ1Otk3B/cBjnp5sYizs+FyvRij5XpdW8cjK+nu8nRVE9KnERERLKT4c9njO2+T0HN+ubGI+fPtxaExhjbSRp1krNwkTI9gPlxRuKkNuTmUeIkIiIy+xHIz4Q6HaDLnWZHIxdK85ykrFykTO9wbgFr9mQA0EeJk2mUOImISPUWNxPiZoDFDYa+C1Y3syOSC6V5TlIWLlSmt2hrGnYHREf4U6+mr9nhVFtKnEREpPrKy4TZDxvbPe6DiNbmxiPlI/LYiNP+9caHY5FT2T7PZcr0YuOOtyEX8yhxEhGR6mve85B9AGo1gl6PmR2NlJeaDaBGBNgLjeRJ5FQ2TzPunbxMr8hmZ2HCsflNMUqczKSWHCIiYg6HA+xFYCsEW8EptguOfV1ofAA+6et/P3bsOSc9VnjCeU/4uigfEmYZsQx5Bzx8zP1+SPmxWIx5Tlt+NeY51e9udkTibFyoTG/tnkNk5RVR09eDdpE1zQ6nWlPiJCLiyhwOsNtOTgpOm3ycLTEpOnMictbE5BzP6wza3wANLzE7CilvUd2OJU4rzI5EnJELlenNP9aGvE/zMNyszjsyVh0ocRIRcVZb/4SFr0B+1plHV6oSqztYPcDNE9zcjXurx7+2i2+exvFunsbXJ267eZTtPF7+EDPU7HctFaF4nlPSSmNxY6tmJ8gJNk837p28TA+Or9/UV2V6plPiJCLijFLj4OcxUJh77s+t7OTjlOc5w2P/ftx6wjH6cCvlJaINePgZDUDS4iG8hdkRibMoPAoJvxvbLYabGsrZ7DmYw/bUI7hbLVzcNNTscKo9JU4iIs4mPxsm32AkTY16G00LlHyInBs3d6h3EexaDEkrlDjJcSeW6dXraHY0Z1RcptepQS0CfTxMjkaUOImIOBOHA367Hw5uA/86MOoz8AsxOyoR1xTVzUicEldAx1vMjkachQuV6RUnTuqm5xx0WVJExJms/hT+mWKMHF31pZImkQtRPM9JDSKkWOFR2Hqsm56Tl+kdyS9ixc6DgNZvchZKnEREnMXetTBnvLF96YtGO2UROX/1OoHFCof3QNYBs6MRZ7B9HhQcgYB6Tl+mt3RbGoU2Bw1D/GgUWsPscAQlTiIiziE3w2gGYS80urx1vcvsiERcn3cAhLc0tpM06iQcL9NrOdzpy/Ri445109Nok9NQ4iQiYja7Hab9BzKToFYjGPa+0/9BF3EZUd2Me5XriQuV6dntDhYkHJvfpMTJaShxEhEx29K3YNuf4O4NV38N3oFmRyRSdWiekxTbHusyZXqb9mWSfqQAfy93OjaoZXY4cowSJxERM+1cBAteNravmAARrc2NR6SqKR5xSv4b8o+YG4uYa/M0494VuunFpQBwSbNQPN31cd1Z6F9CRMQsWQdgyq3gsEP7642biJSvwLrGej0OG+xbY3Y0YpYTy/RaDjc1lLKIjdf8JmekxElExAy2IvjlFshJg/BWMOhNsyMSqbqiuhr3Ktervk4s06vr3GV6yZl5bN6fhcUCvZuHmh2OnECJk4iIGea/AInLwNPfmNfk4WN2RCJVl+Y5yZbpxn2LYWB17o+/xYveto8MIriGl8nRyImc+ydHRKQqip8Ff71jbA//AIIbmxuPSFVXPM9p72pjtFeql8KjkPC7se0CZXrz4435Tf1iwk2ORP5NiZOISGXK2AXTjq3R1PUe4+qniFSssBjwCjBKtVI3mx2NVLaSMr26Tl+ml1doY+n2dEDzm5yREicRkcpSmAc/3Qj5mUbp0KXPmx2RSPVgdYPIzsa2yvWqn5IyveFOX6a3fMdB8grt1An0JjrC3+xw5F+c+6dHRKQqmfMYJG8C32C48gtw8zA7IpHqI1INIqolFyvTiz1Wptc3JgyLk7dMr46UOJnt6CGw282OQkQq2sYfYe2XgAVGfWq0SBaRynNiZz2Hw9xYpPK4UJmew+FgfpzRGKJftOY3OSMlTmb79V54tx0seMWY+yAiVU/KFpj5oLHd+3Fo3NfceESqo7oXgdUdsvdDZpLZ0UhlcaFuevHJ2ezPzMPbw0q3xsFmhyOn4Nw/QVVdYR7sWQaH98CiV40E6otBsP5byM82OzoRKQ/52ca8psJcI2G65BGzIxKpnjx9oXZbY1vletVDYR4kFC96O8LcWMqguA15zyYheHu4mRyNnIoSJzN5eMODm2HkJGjUB7DAnr/g13vgzWYw9T+wc5FK+URclcMBM8bCwW1GmcjIScYkdRExh+Y5VS87YqEg2yXK9ABi447Nb1KZntNS4mQ2T19oczXcOB0e/Af6PQPBTYyr05t+hK+HwjttYP5LcHCH2dGKyLlYNQk2TzPKg676EvxCzI5IpHqLUuJUrWyeZty7QJnewSP5rE86DKgNuTNz7p+i6iawHlz8ENy7Bm6dBxfdDF6BRi324jfgvQ7w2QBY+xXkZZodrYicyd618McTxvalLx5vhSwi5ilOnFK3wNHDpoYiFezEMr0Ww00NpSwWJqThcEDLOgFEBHqbHY6chhInZ2SxQGQnGDIRHk6AKz+HJv3BYoWkFfDbfUYp35TbjG4xdpvZEYvIiXIz4OcxYC+EmKHQ9S6zIxIRgBphUKsR4IC9q82ORirSiWV69TqZHc1ZFc9v6qfRJqfmbnYAchYePtBqlHHLOgCbJsOG7yE9Af7+2bj514G2o6Ht/0FoM7MjFqne7HaYeocxUlyrMQz7wLgYIiLOIbIrZOw0yvWaXmp2NFJRNk837l2gTK+gyM7irWkA9I3R/CZn5tw/SVJaQG3o+QDcsxJunw+dbgPvIKO16tK34YNOMKkfrP7MWB9KRCrf0gmwfS64e8PVX4N3gNkRiciJNM+p6ivMO77orQuU6a3ZnUF2fhEhNTxpUzfQ7HDkDDTi5IosFmM9iroXwYD/Gr8cNv4A2+bCvjXGbc54iB5kjEI17gtu+qcWqXA7F8GC/xrbV0yAiFbmxiMiJytOnPathaICcPc0Nx4pf8Vlev51XKJML/ZYmV6f5mFYrapQcGb6NO3q3L2g5XDjlp1ilO5t+B5SNxvdZDZPgxrh0GY0tPs/CIsxO2KRqinrAEy5FRx2aH+9cRMR5xPSDHxqwdEMSN4E9Zy/TbWco+IyvZbDnb5MD06Y3xSj+U3Ozvl/mqTs/MOh+71w11/wn8XQ5U7wDYYjKbDsXfhfV/ikN6z8xJi8LiLlw1YIv9wMOWkQ3goGvWl2RCJyOhYLRHYxtlWuV/W4WJnezrQj7ErPwcPNQs+moWaHI2ehxKkqsliM1dEvfw3GxcPo76D5FcZaMvvXw++PGF35Jt9g/HKxFZodsYhri30BEpeDV4Axr8nDx+yIRORMSuY5LTc3Dil/LlamVzza1LVRMDW8VAjm7Colcfrggw9o0KAB3t7edOnShVWrVp3x+J9//pno6Gi8vb1p3bo1s2fPLnmssLCQxx57jNatW+Pn50edOnW48cYb2b9/f0W/Ddfk7gkxg+Ha7+GhBBj4KkS0Mdokx82AH66Bt2JgzhOQ/I/Z0Yq4nriZxoguGB30ghubG4+InF1x4pS0EhwOc2OR8uVC3fQAYuOMxEmL3rqGCv+Jmjx5MuPGjePZZ59l3bp1tG3blgEDBpCamnrK45ctW8a1117Lrbfeyvr16xk+fDjDhw/nn3+MD/W5ubmsW7eOp59+mnXr1jF16lQSEhIYOnRoRb8V1+cXYqwnc+cSuHMpdL0H/EKN8qIVH8BHPeCjnrDiQ8hJNztaEeeXsQum321sd70HWuj3kIhLqNMe3LyMv38ZO82ORsrLiWV6LUeYG0sZZB4tZPVuY+qEEifXYHE4KvZSS5cuXejUqRPvv/8+AHa7ncjISMaOHcvjjz9+0vGjR48mJyeHmTNnluzr2rUr7dq146OPPjrla6xevZrOnTuzZ88eoqKizhpTVlYWgYGBZGZmEhBQzVsF2wph+zyjoUTC78ZIFBhlfU0HQLtrjXt1HRIprTAPPrvUmFwe2QVumgVuHmZHJSJl9dkAY1H5Yf+D9teZHY2Uh/jZ8OO1Rpneg5udfsRp5qb93Pv9epqE1WDeuF5mh1OtlTU3qNCfqIKCAtauXUv//v2Pv6DVSv/+/Vm+/NR1xcuXLy91PMCAAQNOezxAZmYmFouFoKCgUz6en59PVlZWqZsc4+YBzS+H0d/Aw1uNSe112oO9CBJmweTrYUJzmP0o7N+gkgaRYnMeM5Im32C48gslTSKuRvOcqp7N04x7FynTm3+sTK+fRptcRoX+VKWnp2Oz2QgPL70Kcnh4OMnJyad8TnJy8jkdn5eXx2OPPca111572gzxlVdeITAwsOQWGRl5Hu+mGvCtBZ1vhzsWwt0roPt9UCPCaNm66mP4pBd82AOWvWe0Pheprjb8AGu/BCww6lMIrGt2RCJyrqK6GfdJK82NQ8pHqTK94aaGUhY2u4MFCZrf5GqcPx0/g8LCQq6++mocDgcffvjhaY8bP348mZmZJbekpKRKjNJFhcXAZS8aQ93X/QItRxr14Kmb4c+njIYS3482JmEW5ZsdrUjlSdkMMx80tnuPNxaYFhHXE9nZuE/fCjkHzY1FLtyO+Sd00+tsdjRntSHpEIdyCwnwduei+jXNDkfKqEL7HoaEhODm5kZKSunRiZSUFCIiIk75nIiIiDIdX5w07dmzh/nz55+xHtHLywsvL6/zfBfVnJs7NL3UuB09BP9MhY0/wN7VsHWOcfMOgtZXGgvs1ulgtEMXqYrys+GnMVB01EiYLnnE7IhE5Hz51oLQaEiLN0adogeZHZFciC3TjXsXKdMr7qbXu3kY7m7OH68YKvRfytPTk4suuojY2NiSfXa7ndjYWLp163bK53Tr1q3U8QBz584tdXxx0rRt2zbmzZtHcHBwxbwBKc2nJnS6FW6bB/eshp7jjCs7eYdh9acwqS980AWWToSsA2ZHK1K+HA6YMRYOboOAujBykkv8cRaRMyhZCFfznFyai5XpwfH1m/rFqEzPlVT4X/1x48YxadIkvvrqK+Li4rjrrrvIycnh5ptvBuDGG29k/PjxJcfff//9zJkzhwkTJhAfH89zzz3HmjVruPfeewEjabryyitZs2YN3333HTabjeTkZJKTkykoKKjotyPFQptB/2fhwX/g+qnQ+ipw94b0BJj3LLzdAr4dBX//AoVHzY5W5MKtmmRMPLa6w1VfGu39RcS1aZ5T1bBjPuRnuUyZ3t5DucQnZ2O1QK9moWaHI+egwpcoHj16NGlpaTzzzDMkJyfTrl075syZU9IAIjExEesJV227d+/O999/z1NPPcUTTzxB06ZNmT59Oq1atQJg3759zJgxA4B27dqVeq0FCxbQu3fvin5LciKrGzTpZ9zyMo05Txu+N1q8bp9n3LwCodUIaHedsYq3SvnE1exdA388YWxf9tLxuREi4tqijo047V9vjFp4eJsbj5wfFyvTW3BstKlj/VoE+Wq5F1dS4es4OSOt41QJDu4w5kJt/BEyT2jGEdwE2l4Lba+BwHrmxSdSVrkZ8NHFkLXX+KN81VdK/kWqCofDWHLjSArcPAfqn3oagTixonx4o4kx4nTLH8fbzDuxm75YxcKENB6/PJo7ezU2OxzBSdZxkmosuDH0fQru3wQ3zjCSJQ9fOLgd5r8Ib7eCr4fBpp+gINfsaEVOzW6HqXcYSVOtxjD0fSVNIlWJxaJ5Tq7Oxcr0cguKWLbD6OKo9ZtcT4WX6kk1Z7VCo17GbdAbsGWGUcq3ZynsXGjcPP2NyZztrjOuFOmDqTiLpRNg+1xj/t7VX4O3RqhFqpyobhA3Q/OcXFXJordDXaJM76/tBykoshNZy4cmYTXMDkfOkRInqTxe/tD+OuOWsQs2TTaSqMN7YP03xq1mQ6OtedtrICjK7IilOtu5EBb819i+4i2IaGVqOCJSQYrnOSWuMEaZXeDDtxxTlH9CN70R5sZSRvPjjSV3+kWHY9GFYpej3w5ijloNoffjcN8GuGk2tLsePGvAoV2w4GWY2Bq+HGwkVvlHzI5Wqpus/TDlNnDYof0NRrIvIlVTRBujlDzvsLEYrrgOFyvTczgcJes39VWZnktS4iTmslqhQQ8Y/gE8vBVGfAwNLwEssHsJTL8L3mwG0+6CXUuMq4EiFclWCL/cAjlpEN7aKDEVkarLzQPqXmRsa56Ta9k83bh3kTK9zfuzSM3Ox9fTjS6NapkdjpwH5/8pk+rD088o0RvzGzywCfo8BbUaQWEObPwevhoM77Y1yqcydpodrVRVsc8bH568AuDqr8DDx+yIRKSiaT0n11OUDwmzje0Ww00NpayKR5subhqCl7ubydHI+VDiJM4pKAp6PQJj1xntRTuMMT7IHk6ERa/Bu+3h88th3TeQn212tFJVxM2EZe8Z28M+MLpDikjVF6XOei6npEyv9vHOiE7uxPlN4pqUOIlzs1iMTntD3zVK+UZ9Bo37AhZIXAYz7oU3mhoto3csUCmfnL+MnTD9bmO7271G6YeIVA/1OoPFCod2Q3ay2dFIWZSU6bnGorep2Xls3JsJQO/oUJOjkfPl/D9pIsU8fKD1lXDDNHhwM/R7FoKbQtFRo0PfN8ONphKxLxoL8IqUVeFR+OlGyM80rlz2f87siESkMnkHQFhLYztxhbmxyNm5YJnewvg0ANrWCyTM39vkaOR8KXES1xRYFy4eB/euhttioeMt4B1oLFS65E14rwN8dhms+QKOHjY7WnF2vz8GyX+Dbwhc9aUxWVxEqpeorsa95jk5Pxcs04s9VqbXV2V6Lk2Jk7g2iwXqdYTBb8NDW+HKL6DpZUbJRdJKmPkAvNUC1n4FDofZ0Yoz2vADrPsKsMCoTyGgjtkRiYgZihMnzXNyfi5WppdfZGPJtnQA+sWoDbkrc/6fNpGy8vCGViPhup9hXBxc+gKERhtd+X67D365GfIyzY5SnEnKZpj5oLHdezw07mNuPCJinuLE6cAmKMgxNxY5PRcs01u5M4PcAhvhAV60rBNgdjhyAZQ4SdXkHwE97oe7lhvzVazusHkafNQTklabHZ04g/xsY15T0VFo3A8uecTsiETETIH1IKAeOGywd43Z0cjp7FjgcmV68+OPL3prsVhMjkYuhBInqdqsVuj5INw8x2hxfjgRvhgIS95SB77qzOGAGWPh4HYIqAsjJ7lEuYeIVDDNc3J+m6cZ9zGuseitw+HQ/KYqxPl/4kTKQ2QnuHMptBwJ9iJjkdNvR0B2itmRiRlWfWL88bW6w1VfgV+w2RGJiDPQPCfndmKZXssR5sZSRttTj5CUcRRPdys9muhvjatT4iTVh3cgXPk5DH0P3H1g50L4sDtsm2d2ZFKZ9q6BP540ti97yUiqRUTghBGn1WC3mRuLnMwFy/Rij5XpdW8cjK+nu8nRyIVS4iTVi8UCHW6E/yyC8FaQmw7fjYI/n4KiArOjk4qWmwE/jQF7odGNqcudZkckIs4krAV4BUBBttE8RpzLlunGvYuU6QHMjzMSp37R6qZXFbjGT51IeQttbqz/1Ol24+tl78HnAyBjp7lxScWx22Hq7cZaX7Uaw9D3jURaRKSY1Q3qHRuF1kK4zqUoH+JnGdsth5saSlkdzi1gzZ4MAPoocaoSlDhJ9eXhDVe8CaO/Be8g2L8OProENv1sdmRSEZZMgO3zjDLN0d+At1rCisgplJTrKXFyKsVlejUiILKr2dGUyaKtadgdEB3hT72avmaHI+VAiZNIzBCjcURUN6M8Y+ptMP1uyD9idmRSXnYsgAUvG9uD34LwlubGIyLOq6RBhBInp1Jcpucii95C6TbkUjW4xk+eSEULioQxM6HXY2Cxwobv4JPexkKI4tqy9sOU2wAHtL8B2v2f2RGJiDOrexFY3CBrHxxOMjsagWNlesXd9IabGkpZFdnsLExIA5Q4VSVKnESKublDnydgzG/gXwcOboNP+8GKj4x1f8T12Arhl1uMJiDhrWHQG2ZHJCLOztMParc1tjXq5Bx2LID8TJcq01uXeJjMo4UE+XrQPqqm2eFIOVHiJPJvDXoapXvNLgdbAcx5DH64FnIOmh2ZnKvY5431WLwC4OqvwMPH7IhExBVonpNzccEyveJFb/s0D8PNqkZEVYVr/PSJVDa/YLj2B7j8dXDzhK2/w0c9YfdSsyOTsor7zeiWCDD8fxDc2Nx4RMR1aJ6T83DBMj043oZcZXpVixInkdOxWKDLf4y25cFNIXs/fDUEFvwXbEVmRydnkrHTaPAB0O1eowGIiEhZFZeDpWyGvExzY6nudi50uTK9xIO5bEs9gpvVwiXNQs0OR8qREieRs6ndBu5YCO2uA4cdFr0GXw2GzL1mRyanUngUfrrRaFsb2RX6P2d2RCLiavzDoWZDwAFJq82OpnrbPM24b+FCi94eK9Pr1KAmgT4eJkcj5ck1fgJFzOZVwyj3GvkpePob82Y+7AFxM82OTP7t98cg+W/wDYGrvgA3/dESkfOgeU7mK1WmN8LcWM5B7LE25P2iw02ORMqbEieRc9HmKrhzMdRpD3mHYfJ1MOshY5RDzLfhe1j3FWCBUZ9CQB2zIxIndvBIPlPX7SUlK8/sUMQZaZ6T+VywTO9IfhErd2YA0DdG85uqGnezAxBxObUawS1/wvwXjOYDqz+FPcuN0Y3Q5mZHV32lbIaZ44ztPk9A4z7mxiNOy2Z38N3KPbz5RwJZeUW4Wy0MaBXBjV3r07lhLSwWdcASjn9Q37vGWNpAo9eVb/N0496FyvSWbkunwGanQbAvjUL8zA5Hyplr/BSKOBt3T7jsJbhuilESlroZPu4Fa7/Smk9myMuCyTdA0VFo3A8uftjsiMRJrd2TwZD3lvLMr5vJyisipIYXRXYHszYdYPQnK7j8nSV8t3IPOflqAFPthTQDn5rG7xUthl75ivIhfpax3WK4qaGci+L5TX2jw3URpgpS4iRyIZr2h7uWQaPexh/X3+4zFlxVF6bK43DAjLGQsQMC6sLISS5zZVIqT1p2Pg/9tJFRHy5ny4EsArzdeXFYS1Y+0Y/Z913MtZ0j8fFwIz45myen/UPX/8by/G+b2Zl2xOzQxSxWK0R2MbY1z6nynVimF+UaZXp2u4P58WkA9FOZXpWkTxciF8o/HK6fZnRvs7rD5qnw0cVGeYdUvFWfGIsjWt3hqq+MNbhEjimy2fnir130nbCQKeuMTpijO0ay4OHe3NCtAW5WCy3qBPDKyDasGN+Pp66IoUGwL9n5RXzx1276TljEDZ+tZO6WFGx2jSZXOyXznJabG0d1VKpMz83UUMrq732ZpB/Jp4aXO50a1DI7HKkAmuMkUh6sVuj5INTvCVNugcN74PMB0Pcp6H6/RkAqStJq+ONJY/uylyGyk7nxiFNZtSuDZ379h/jkbABa1w3khWEtaR9V85THB/p6cNvFjbilR0MWb0vjm+V7mJ+QypJt6SzZlk7dIB+u71qf0Z0iqeXnWZlvRcxSPM8pcaUxuq3Sq8pRVOCSZXrF3fQuaRaCp7v+7ldFSpxEylNkJ/jPEpj5gLH2xLznYOciGPGxMTIl5SfnIPx8E9gLjT+sXf5jdkTiJFKz8njl93imrd8HQJCvB48MaM41naJws579g6/VaqF38zB6Nw8jKSOXb1fsYfKaJPYdPsprc+J5e95WBrepzZhuDWgbGVTB70ZMVac9uHlCTqqxsHZwY7Mjqh52LjhWphfuMmV6UHp+k1RNSodFyptPEFz5BQx9D9x9jD8AH/WA7fPMjqzqsNth6u2QtReCmxjfa10JrvYKbXY+XbKTvhMWMW39PiwWuLZzFAse6s11XeqXKWn6t8havowfFMOK8f1448o2tK4bSEGRnanr9jHsg78Y9v5SpqzdS16hrQLekZjOw9tIngCSVpobS3VSUqY3zGXK9JIz8/hnXxYWC/RuHmp2OFJBlDiJVASLBTrcCHcshLCWkJMG346CP58yShDkwix5E3bEGonp1V+Dd4DZEYnJVuw8yOB3l/LSrDiO5BfRtl4g0+/uwSsjW1OzHMrqvD3cuKpjJDPu7cG0u7szon1dPN2sbNybyUM/b6T7q/N5bU48ew/llsO7EaeieU6Vq6gAElyvTG9BglGm1y4yiJAaXiZHIxVFpXoiFSksGm6PNRKm1Z8a6z7t/guu/MxYD0rO3Y4FsOC/xvbgtyC8pbnxiKlSsvJ4eVYcMzbuB6CmrwePDYzm6o6RWM9jhOlsLBYL7aNq0j6qJk9eEcPk1Ul8t2IP+zPz+HDhDj5etIO+0eHc2K0+PZuEVEgMUskiuwLvGPOcpOLtXGh0pnWxMr3YOCNx6hetbnpVmRInkYrm4QNXTDBalv96L+xfBx9dAkMmQusrzY7OtWTthym3AQ5jRK/d/5kdkZik8Fi3vHfmbSOnwIbFAtd1ieLhy5oT5Fs5jRtCanhxT58m/OeSRsTGp/LN8j0s3Z7OvLgU5sWl0CjEj+u71ufKjvUI8NbiqS6ruCV5egLkZoCvuqVVqM3TjPsY1+mml1do46/t6YDmN1V1SpxEKkvMEKjdzpibk7gcptxqjJ5c/hp41TA7OudnK4Sfb4bcdIhoDZe/bnZEYpJl29N5ZsZmtqcaayy1jwrixWGtaFU30JR43N2sDGgZwYCWEWxPPcK3K/bwy9q97EzP4YWZW3jzzwSGt6/Ljd3qEx2hslKX4xdsLIabvtWY59T8crMjqrpOLNNrOdzUUM7F8p0HOVpoo3agNzG1/c0ORyqQ5jiJVKagSBgzE3o9Blhgw7fwSW+tSl8W854zFqH0CjTmNXn4mB2RVLIDmUe55/t1/N+nK9meeoRafp68fmUbptzZ3bSk6d+ahNXguaEtWfFEP14c3opm4TXILbDx/cpEBk5cwtUfL2fmpv0U2uxmhyrnQvOcKkepMr1uZkdTZvOPlen1jQ7DokZFVZpGnEQqm5s79HkCGlwMU++Ag9vg035w2UvQ+Q51hzuVuN9g+fvG9vAPND+smikosvPZ0l28N38buQU2rBa4vmt9Hrq0OYG+zlkCV8PLnRu61uf6LlGs2JnBNyt288fmFFbtymDVrgzCA7y4tnMU/9c5irAAb7PDlbOJ7ArrvtY8p4q2Zbpx70Jleg6Hg/nH1m/qF6P5TVWdEicRszS8GO5cCr/eA1t/h98fNa62DftANfQnytgJ0+82trvda5Q8SrWxZFsaz87YzM60HAAuql+TF4a1pGUd5xhhOhuLxUK3xsF0axzMgcyj/LAyke9XJZGSlc/Eedt4f/52BraK4MZuDejUoKauVjur4hGn/eugMM9oUy7lq6gA4mca2y5UppeQks2+w0fx9rDSvXGI2eFIBVOpnoiZ/ILh2h9g4GvGIosJs+HDHrB7qdmROYfCo/DTjZCfZVzx7f+c2RFJJdl/+Ch3f7eWGz5bxc60HEJqeDLhqrb8cmc3l0ma/q12oA/jLmvOssf78s417ehYvyZFdgczNx3g6o+Xc/k7S/h+ZSK5BUVmhyr/VqsR+IWCrQAObDA7mqqpuEzPL8ylyvSKu+n1aByCt4drjJLJ+VPiJGI2iwW63gm3zTMWc83eD18NMVpu26r5B6jfH4Xkv8E3BK76AtycsyxLyk9+kY0PFmyn34RFzP47GasFburegNiHejPqonpVYkTG093KsHZ1+eWu7sy6ryfXdIrE28NKfHI2T0z7my7/jeWF37awKz3H7FClmMWieU4VrbhMz4UWvQVKyvT6qkyvWlDiJOIsareFOxZBu+vAYYdFrxkJVOZesyMzx/rvjDkFWIx1rwLqmB2RVLCFCakMnLiEN/5I4GihjU4NajLrvot5bmhLAn2qZtLcsk4gr45qw8rx/XnqihjqB/uSnVfE53/tos+bC7nhs5XM25KCze4wO1QpHgXRPKfy56Jlehk5BaxLPAQYjSGk6tMcJxFn4lUDhv8PGvWBmQ9C4jKjdG/YBxAz2OzoKk/KZpj1kLHd5wljDSypsvYeyuXFmVv4Y3MKAKH+XjwxKJrh7epWiRGmsgj09eC2ixtxS4+GLN6WxtfL97AgIZUl29JZsi2dejV9uL5rfUZ3jKSmX+WsUyX/EnlsxClpBdjtYNW153LjomV6CxNScTigRe0Aageq02t1oMRJxBm1uQrqXQS/3AL718Pk66DTbXDZy1V/UnJeFky+AYqOQuN+cPHDZkckFSSv0MakxTv5YOF28grtuFkt3NS9AQ/0b4p/NV0w1mq10Lt5GL2bh5F4MJdvV+7hpzVJ7D10lFd/j+etuVsZ0qYOY7rXp029ILPDrV5qtwF3Hzh6yOiGGtrc7IiqjpIyPdfppgcQq2561Y4ul4g4q1qN4JY/oftY4+vVnxpty9MSzI2rIjkcMGMsZOyAgHowcpKu6lZRC+JTGTBxMRPmbiWv0E6XhrWYfd/FPD24RbVNmv4tKtiXJwbFsGJ8P16/sg2t6gZQUGRnyrq9DH3/L4Z98BdT1u4lr9BmdqjVg5sH1OtobGueU/kpVaY3wtxYzkGhzc7ihDRAZXrViT6RiDgzd09jfafrphgNElL+MRbMXfe1kWRUNSs/Nq48Wj3gqi+NroNSpSRl5HLbV2u4+cvV7DmYS5i/F+9c044f7+hK8wh/s8NzSt4eblzdMZLf7u3J1Lu7M6J9XTzdrGxMOsxDP2+k+6vzeW1OPHsP5ZodatVX0iBC85zKza5FLlmmt3p3Btn5RQT7edJWo7/Vhkr1RFxB0/5w118w7T9GLfiMsbBjAQyZCN6u2Zr5JEmr4M8nje0BL0NkJ3PjkXKVV2jjo0U7+HDhDvKL7LhbLdzSsyH39WtKDS/9KSoLi8VCh6iadIiqyZNXxDB5dRLfrtjDgcw8Ply4g48X7aBfTDhjujWgR5PgajM/rFJFqrNeuds8zbh3sTK9+cfakPeJDsNq1f+16kJ/rURchX8EXD8Nlr0D81+CzVNh31q48vPj5SOuKucg/HwT2IugxXDofIfZEUk5mrclhednbiYp4ygA3RsH8/zQljQN1wjT+Qqp4cU9fZrwn0saMS8ulW9W7Oav7QeZuyWFuVtSaBTqxw1d6zPqonoEqPSx/ER2AixwaBdkp4B/uNkRubYTy/RaDDc1lHNV3Ia8n8r0qhWV6om4EqsVej4IN8+BoCg4vAc+HwBL3za6PLkiux2m3g5Z+4x1rIa+Z6yZIi5vz8EcbvlyNbd9vYakjKNEBHjz/v+157vbuihpKifublYGtorgu9u6Mm/cJYzpVp8aXu7sTMvh+d+20PW/sTw57W8SkrPNDrVq8A6E8JbGdtIKc2OpCk4s06vf3exoymxn2hF2pufg4WahZ9MQs8ORSqTEScQVRXaC/ywxJtLai2Dec/DtSOMKqKtZ8ibsiDW6VV39NXgHmB2RXKCjBTbe+jOBS99ezPz4VDzcLNzZqzGxD/VicJs6KiGrIE3C/Hl+WCtWPNGPF4e1pGlYDXILbHy3MpEBExcz+uPlzNp0gEKbi15kcRaa51R+Nk837l2tTO/YaFOXhsFqZlPNqFRPxFX5BMGVXxhrPv3+GOxcAB/1gBEfQZP+ZkdXNjsWwIL/GtuD3z5+JVdcksPh4M8tKbzw2xb2HTbK8i5uGsKzQ1rSJKyGydFVHzW83LmhWwOu71qf5TsP8s3yPfy5JYWVuzJYuSuD8AAv/q9zfa7tHElYQBVf3qAiRHY1upxqntOFKSqA+N+MbRct01M3vepHiZOIK7NY4KIxENnFWPMpdTN8Owq63wd9nza68lUAm93B8h0HmbpuLwkp2XSsX5O+MeF0aVgLb48yXjXM3AdTbgUc0GEMtLu2QmKVyrErPYfnZmxm0VajPW+dQG+eHtyCga0iNMJkEovFQvfGIXRvHMKBzKN8vzKRH1YlkpKVz9vztvLe/G1c3ro2N3arT8f6NfXvVFbFI07Jm6AgBzz9zI3HVblomV5WXiGrdmUAWr+pOrI4HFWxp/GZZWVlERgYSGZmJgEBKguSKqLwKPz5lHElFKBOB7jyM2M9qHKyNSWbqev2MX39PpKz8k563NfTjZ5NQugXE0af5mGnv5ptK4Qvr4CklRDRGm6dV/UX9q2icguK+GDBdiYt3kWBzY6nm5XbL2nIPX2a4Oupa3POJr/Ixpx/kvl6+R7W7jlUsj86wp8x3RswrF0d/buVxVstjHmZY36DhpeYHY1rmn4PbPjWWNz9iglmR1NmszYd4J7v19E41I/Yh3qbHY6Uk7LmBvrtKFJVePgYf3wa9YZf74H96+CjS4yW5a2vPO/Tph/JZ8aG/Uxbv4+/92WW7A/08WBI29p0rF+LlbsOEhuXSmp2Pn9uSeHPLcZcq9Z1A+kbHUa/mDBa1Qk83rJ13nNG0uQVaMxrUtLkchwOB3P+SebFmVvYn2kk0Zc0C+W5IS1oFKqyPGfl5e7GsHZ1GdauLv/sy+Sb5Xv4deM+4pOzGT/1b/47O46rO0Zyfdf6NAzRSMppRXWFf6YY85yUOJ07F+6mFxtv/H3rF6OOitWRRpw04iRV0eEko1NdcQ1+u+th0OtlLinJK7QRG5fK1HV7Wbg1DZvd+DXhbrXQJzqMUR3q0ic6DC/342V5DoeDzfuziI1LZX58Chv3ZpY6Z6i/F32bh3GN/wbaLx9r7Bz9HcQMvvD3K5VqR9oRnpuxmSXb0gGoG+TDM0NacFmLcJV7uaDDuQX8vGYv36zYQ2LG8UV0L2kWyphu9endPAw3rVNT2spP4PdHoHE/uGGq2dG4nm1z4bsrjTK9h+JdpjGEze6g08vzyMgp4Mc7utK1kRZpryrKmhsocVLiJFWVrQgWvw6LXgccENzUWPOpdptTHu5wOFi75xBT1u1j5qb9ZOcVlTzWtl4gIzvUY0jbOtTyK9u8qdTsPBYmpDE/LpUl29LIKbBR35LMb55PEmA5yiz/K0nr+hT9YsKJrOVbHu9YKlhOfhHvzd/OZ0t3Umhz4Olu5c5LGnFX7yb4eLrGBx85PbvdwaJtaXy9bDcLt6ZR/OmgXk0fbuhan6s7RlKzjP//q7wDm+Dji8ErAB7b7TIf/J1GcZlex1th8FtmR1Nma/ccYtSHywjwdmft05fi4abm1FWFEqczUOIk1cquJcboU/YBcPOEy14yFpg9NjKw52AO09bvY+q6faWuNtcJ9GZEh7qMaF/vgjui5RfZWLNtP41njCDi6DZW2ZvzfwVPUnSsWrhpWA36xoTRLzqcDlFBuOuPkVNxOBzM+vsAL8+K48Cxsrw+zUN5dkhLGqicq0raczCHb1fs4ac1e8k8WgiAl7uVIW3rMKZbA1rXCzQ5QpPZbfBqfSjIhjuXGnM1pWxshfBGE8g7DGNmQsOLzY6ozN74I54PFuxgSNs6vHdte7PDkXKkxOkMlDhJtZNz0Jj3tPV3AAqbDOTX+k/y4z9HWHPCBHE/Tzcub12bkR3q0rVh8PE5SeXh13th/Tc4fEPYfeUc5u61EhuXypo9h0pKAcGYO9WrWSj9YsLo1SyUIF9d4TbT9tRsnp2xmb+2HwSM0Ydnh7Skf0yYyvKqgaMFNn7buJ+vlu9m8/6skv3tIoO4sVt9rmhTu1TJbrXyzQjYMR8GvQmdbzc7GtexbR58N8rlyvQABk5cTHxyNhNHt2N4+7pmhyPlSInTGShxkuqosMjGzllv0WjDa3g4CjngqMUDBfewmhh6NAlhVId6XNYyvGI6aq3/Dn69G7DAjdONBhbHZOYWsmhbGvPjUli4NY3DuYUlj1kt0LF+rWOjUWE0CauhD+uV5Eh+Ee/GbuPzpbsoshtleXf1asxdvRuXveW8VBkOh4N1iYf5ZvluZv19gEKb8dEh2M+T0Z0iua5rfeoG+ZgcZSVb+Bos/C+0utLoYCpl46JlevsOH6XHq/OxWmDtU5eqbLWKUeJ0BkqcpLoobtgwZd1eZmzYz8GcAlpadvOux3s0th7AjpXcruOocel4cKugJpvJ/8Cn/aHoKPR5Cno9ctpDi2x2NiQdJjY+lflxqSSkZJd6PLKWD32bh537mlFSZg6Hgxkb9/Pf2XGkZOUD0D8mjGcGtyQqWHPRBNKy85m8OpHvViaWlG5aLdA/JpwbuzWgR5Pg6nGBY+ci+HooBEbCg/+YHY1rKFWm51qt3L9ZsYenp/9DpwY1+flO11l3SspGidMZKHGSqi45M4/pG/Yxdd1etqYcKdkfUsOTYe3qcmXrIKLXv4hlw/fGA1HdYdQkCKxXvoHkZcEnvSFjBzS5FP7vJ7CWff5SUkYuCxJSiY1LZfnOgxQU2UseK/OaUVJmW1OyeebXf1ix01jcMaqWL88NbUHfaLXdlZMV2ezMi0vh6+V7WLbjYMn+xqF+3NC1PqMuqoe/t4eJEVawghx4JRIcNnhwc/n//qyKSsr0QuGhBJcq07v5i1UsSEjjsYHR3NW7sdnhSDlT4nQGSpykKsrJL+KPzclMXbePv3akl3TE8nS3clmLcEZ1qMfFTUNKN17Y9BPMHGdMcPYOguH/g+gryicghwN+HgNbfoWAenDnEvCtdd6nyy0o4q/tB5kfn1KyZtSJTrtmlJxVdl4h78zbxhfLdmOzO/Byt3JPnybccUkjjepJmWxLyeabFXuYsnYvOQU2wLi4MaJ9XYa0rcNF9WtWzQ5kn/SG/eth1GcXtF5etfHrPbDe9cr0cguKaPfCXAqK7Pz54CU0C/c3OyQpZ0qczkCJk1QVNruDFTsPMmXdXub8k0zusQ8sAJ0b1GJkh7pc3ro2gT5nuOqbsRN+ucX44w/Q6Xaj896FLkq74kOY8zhYPeDm3yGy04Wd7wRlXTOqb0wYPZuE4Oeltb5PxeFw8OuG/bw8O460Y4noZS3CeXpwC7WIl/OSnVfItPX7+Hr5HranHh/t9vd255KmofRuHkrv5mGE+nuZGGU5+v1xWPmh8XvzijfNjsa5uXCZ3rwtKdz29Rrq1fRhyaN9qkcpajWjxOkMlDiJq9uWks3U9fuYvn5fyRwDgAbBvozsUI8R7eue2wffogKIfR6Wv298Hd7KWPMptPn5BZi0Cr64HOxFcPnr0OU/53eeMjrVmlHFPN2sdGlUi37RYVoz6gTxyVk88+tmVu0yyvIaBPvy3NCW9G4eZnJkUhU4HA6W7zjIL2uNRbQzcgpKPd6mXiB9mofRJzqMNnVdeIR483RjZD2itdGWXE7Phcv0xk/9mx9WJTKmW32eH9bK7HCkAihxOgMlTuKKDh7J57eN+5m6fh+bThhhCfB2Z0jbOozsUI8OUUEXdiVs21yYdifkpoOHL1z+GrS/oWTNpzLJOWgsDJm1D1qOgCu/OLfnX6D8IhurdmUcG41KLbU2FWjNqKy8Qt6eu5Wvl+/BZnfg7WFlbN+m3HZxw+rbVloqlM3uYNPewyyIT2VBQhp/7ys9Qhzs50mv5qH0jQ7j4qahZx4hdzbZyTChOVis8Nge8NZnitNy0TI9h8NB11diScnK56tbOtOrWajZIUkFUOJ0BkqcxFXkFdqYH5/K1HV7WZiQRtGx9Y7crRZ6Nw9jVIe69I0JK98PvNnJMO0/sHOh8XXLkTBkIniXYcFLuw2+u9JY2yS4KdyxALzMqwV3OBzsSMspmRdVndeMcjgcTF23j1d+jyf9iFGWd3mrCJ4a3KL6tZEWU6Vm5bFwaxoL4lNZsi2dI/lFJY+5WS1cVL/msdGoUJqH+zt/WdQ7beHQbrh+KjTpZ3Y0zslWCG82haOHXK5M7599mQx+bym+nm6se/pSzfusopQ4nYESJ3Fmxnoph5iybh8zN+4nK+/4h4q29QIZ2aEeg9vUJrhGBc4RsNvhr4kw/yWjY1RQfaN0r17HMz+veF0Tdx+4fT6Et6i4GM9DdV0zasv+LJ759Z+SxY4bhfrx/NCWXNxUV07FXAVFdtbsyTBKbeNTS82LAqgT6E3v6DD6Ng+je5Pgilln7kJN/Q9s+hEueRT6Pml2NM5p+zz41jXL9N6N3cZbc7dyWYtwPrnxLH8DxWUpcToDJU7ijBIP5jJt/T6mrt/LnoPHy8tqB3ozon1dRnaoS5OwSh69SVoNU26Bw4lgdYe+T0H3+0/dUnzHfPhmJOCA4R9Bu2srN9ZzVB3WjMo8WshbfybwzYo92B1Gl7OxfZtya8+GeLpXrxJFcQ3FSxAsiE9l2Y6D5J+wBIGnu5WujYLpc6ysr36wn4mRnmDNFzDzAWMUZcxvZkfjnErK9G6BwW+bHc05GfbBX2xMOsxro1ozulOU2eFIBVHidAZKnMRZZOUVMnvTAaau28eq3Rkl+3093bi8VW1GdahL10bB5k6cPnrY+FCweZrxdaM+MOJj8D9hbZ/Mfca8ptyD0GEMDH3XjEgvSFVaM8pud/DLur289ns8B49Nyr+iTW2euiKG2oEqyxPXkFdoY/mOgyxIMOYr7j10tNTjjUL86BNt/J/s1LCmeXP0UuPgf12NeaGPJ4KbC83RqgwuXKaXlp1Pp5fnAbDqiX5O/7tfzp8SpzNQ4iRmKrTZWbItjSnr9jF3S0rJB3SLBXo2CWFkh7oMaBnhXCUpDges+xp+fwyKjhrlFiM+gib9jT+KX14BSSshog3cOvfCW5mbzJXXjPpnXyZP//oP6xMPA9AkrAbPD21JjyYh5gYmcgGM+YpHmB+fyoL4NFbvziiZ8wng5+lGjyYhJYlURGAl/g6y2+H1hkab7dsXQN0OlffarsCFy/R+WpPEo79sok29QGbc29PscKQClTU3cKJPZiJVV/G6Q1PX7WPGxn2kHznemrdZeA1GdajHsHZ1K/eP/bmwWOCiMRDZBX65GVK3GH8Iu481EqekleAVCFd/5fJJE4CvpzuXtgjn0hbhp1wz6u99xu2d2G1Os2bU4dwC3vwzge9WJuJwGB8k7+/flJu6qyxPXJ/FYqFJmD9Nwvy545LGZOUV8te2dCORSkgj/Ug+f25J4c8tKQDE1A6gb3QofZqH0T6qJm4VeXHDajV+N277AxJXKHH6t83TjfuYIS6VNAHMj0sFoG+0lmkQg0acNOIkFSglK4/p6/cxdd2+UnNoQmp4MrStMW+pZZ0A12pCUHgU/nwKVn9aev/o7yBmsDkxVaKzrRnVtXEwfZuHVtqaUXa7g5/WJPH6Hwkla+UMbVuHJwbFOG8iLlKO7Hbj4kZxSd/GvYc58ZNNkK8HlzQ15kVd0iyUWn4V0D1zyVvGWngthsHVX5f/+V3ViWV6N86ARr3MjqjM8otsdHhhLjkFNn67tyet65Whs6y4LJXqnYESJ6lIuQVF/LE5manr9vHX9nSKq0k83a1c2iKcUR3qcnHTUDxcff2guN+MCb95mdDtXhjwstkRVTqz14zatPcwT/+6mY1JhwFj9PL5oa3o1ji4XF9HxJUcPJLPoq1pLEhIY1FCaqnOpBYLtIsMou+xxXfL7cLVnmXGot81wo1yNFe6GFaRTizTGxcPbs5Z6JRXaGNH2hG2pmSTkGzcxx/IYn9mHmH+XqwY38+pSrKl/ClxOgMlTlLe7HYHK3YeZMq6fcz550CpUYjODWoxskNdLm9d27UWdiyL7GQ4sMmY63SqTnvVSFnWjOp9rBvYha4ZdSingNf/SODH1UZZXg0vdx7o35Qx3Ru4fkIuUo6KbHbWJxmL786PTyU+uXT3zDB/r5L/lz2ahODvfZ6/owvz4NVIsBXAfRugVsMLD74q+PVeWP+N03TTK7LZ2ZORy9bkbBJSso0EKTmb3ek52E/zafjhy5pxb9+mlRuoVDolTmegxEnKy/bUbKau28f09fvYn5lXsr9+sC8j29djRPu6RAVXfLmWOJ+KWDPKZnfw4+pE3vgjoeR8I9rXZfzl0er2JFIGBzKPsiA+jQUJqfy1PZ3cEy5yebhZ6NSg1rHFd8NoHOp3bqNRn14Ke1e5xHIMlcLEMj2Hw8H+zLzjCVKykSBtTztSqmPqiQJ9PGge4U/zcH+aRfgTHeFPszB/An2r2AVPOSUlTmegxEkuREZOAb9t3M/UdXvZuDezZH+AtzuD29ZhVIe6dIiq6VrzlqRClWXNqH7R4fSNDqNLo1qnbKu8PvEQz87YzKZjP3PREf68MKwVnRvWqpT3IFLVFJfazo9PZWFCGrvSc0o9XryWW+/oMLo1Cj77Wm5/Pg3L3nXZJRnKXXGZnm+IUb5YQWV6B4/klyRHCSlGmd3W5Gyy84tOebyPhxvNwmvQLNyf5hH+NAs3kqRQfy/93a7GlDidgRInOVf5RTYWxKcyZd0+FsSnlrTBdbda6N08jJEd6tI3OswlF0mVyncua0a5WS28PieByWuSAPD3cufBS5txY7f65T5nSqQ625Wew4L4VBYkpLJyZwYFtuP/L709rHRvXNzuPJR6NU9RSRA/C378PwiNhntWVmLkTqqcy/SO5BeVJEXFZXYJydmlutSeyN1qoXFoDZpF+NP8WKIUHRFAvZo+mq8kJ1HidAZKnKQsHA4H6xIPM3XdXmZuOkDm0eOlVm3qBTKyfV2GtK1DcA0vE6MUV3e2NaO8PazkFRof4EZ1qMdjlzcnzF9leSIVKSe/iGU7Dh4bjUrlwAml2HCs8Ut0GL2bh9GxQU1jbmFOOrzR2Djg0V3gW41Hgy+gTC+/yMaO1BwjMTohUfr3AsjFLBaIquVrjCCdUGbXINhPSzFImSlxOgMlTnImSRm5TFu/j6nr9rL74PEuaREB3ozoUJeR7evSNNzfxAilqjrVmlEALWoH8MKwlnRsUI0/iImYxOFwEJ+czYKEVBbEp7J2z6FSjQT8vdy5uFkIfZqHMeKv4bgf2g7XTobmA80L2mzbY+HbkWcs07PZHew5mFOqk11CSja70nNKNdY5UXiAV6kEqXm4P03DazjXgvHikrQArsg5yMor5Pe/DzBl3T5W7coo2e/r6cbAVhGM6lCPro2CK3YRRan2LBYLreoG0qpuIPf3b0pqdh77Dh2ldd1AleWJmMRisRBTO4CY2gHc3btJSeOXhfGpLNyaRkZOAbP/Tmb238nY3Otxjft2Vi2ehYdPF9rWC6qeZWGbpxn3MUNwWN04cPhoqdGjrSnZbEs5Qv5pGjUEeLsTHRFAs4gaRpJ0bD7ShXQjFSkPGnHSiFO1VWSzs2RbOlPX7+PPzcklv8AtFujROISRHeoyoGUEfl66viAiIiez2R1s2nuYBQlpLIhPJTr5V97w+IRV9uZcXfAswX6e9GoWSp/oMC5pGlrlO7Rl5BSwdX8G7X7qjHdhJs/VfIUphxqTnXfqRg3eHlaaFSdGJ4wihQeoUYNULpXqnYESp+rL4XCw5UAWU9ft49cN+0k/cnw+SdOwGoy6qB7D2tWhdqCPiVGKiIgrOpi4heDPu1Fo8aCL/Usy8o9/+HezWrgoqia9o411o5qH+7tscpBT3KjhX2V2adn5XGzdxDeer5LuCKBL/gfYcMPdaqFRqN9JCVJkLV9VcohTUKmeyAlSsvL4dcM+pq7bV2oBxGA/T4a2q8OoDvXKbwV5ERGploIjY8A3BI/cdFbdHMxqW7OSuVHbUo+wancGq3Zn8PqcBOoEetM72uie2b1xsFNWNxQU2dmRdqSkg11xgpSUcepGDQBX+6wFG+yN6Mdb3S6ieYQ/jUJqqFGDVAkacdKIk0txOBzkF9nJLbCRk1/E0ULjPrfAduxWVPJY8b4tB7JYui2tZDKvp7uVS2PCGdmhLpc0CzW6IYmIiJSHH6+D+JnQ/3no+UDJ7qSMXBYmpDI/PpVlOw6Wmt/j6WalSyNj8d2+0WE0CPGr1JBtdgeJGbmlkqOtyUajhqLTNGoI9fcyFok9YRSpabAXfu/FHOum9ys06l2p70PkfGnESUxVnOCcmMDkFBRxtOA0iU5BEbn5pfflFhSRk28rSY6OHjvuNL/Dz6pTg5qMaF+PK1rXrvJ15iIiYpKorkbilFR6LafIWr7c0K0BN3RrQF6hjeU7DrLgWCK199BRlmxLZ8m2dF6YuYWGIX70aR5Gn+hQOjc89aLY58PhcJCclXc8QTpWZrctNbtk2YN/8/d2P54gHbtvFu5PLb9TNGrYHmskTb4hUL9nucQs4kyUOFVzDoeDvEL78aSmOGnJt5Xel3+aRKfQRm7+CYlOgY2jx7bPN8EpK28PK36e7vh6ueHrceze0w1fT3f8PN3wOXYf4u/F5a0iqB9cuVfwRESkGorqZtwnrgCHw+g49C/eHm7GYrrRYTw/1MGOtCPMj09lQXwaq3dnsCs9h13pu/j8r134errRo0nIsXWjQss8B/dQTkGphWKL77NO06jBy/2ERg0RNUoSpYgA77KXsW+ZbtzHDDllC3IRV6efahfhcDg4WmgrldSccnSm4NQJz/F9Jzwnv4jcQhsVXazp43EsofFyw8/THR/PE+/d8PVyx9fDuPfzPJ78+Hoe31f8nOJ9Ph5umlAqIiLOJ6INuHvD0QxI3wahzc54uMVioUmYP03C/LnjksZk5xWydFu6MTcqIY207Hzmbklh7pYUAGJqB9CnudFgol1kEAU2O1tTjpRq9Z2QnH3SYtrF3KwWGob40fxYg4biBCnqQhs12Aohbqax3XL4+Z9HxIkpcTLZ+/O3cSAzr/RoT6n5O0aic7SSEhw/r38lKcfu/byOJzrFIznFCY/xnOKkx710kuThVj3XsBARkerJ3RPqdoQ9SyFx+VkTp3/z9/bg8ta1ubx1bex2oxPs/PhUFiSksiHpMHEHsog7kMX/Fu7A19ON3ALbac9Vr6bPSWV2jUL9yq30r5Rdi41k0TdYZXpSZVVK4vTBBx/wxhtvkJycTNu2bXnvvffo3LnzaY//+eefefrpp9m9ezdNmzbltddeY9CgQSWPOxwOnn32WSZNmsThw4fp0aMHH374IU2bNq2Mt1Oupq7fx860nHN6ju+/R2WKExsP4/6kx/81kuPn5YaPh3upJEkJjoiISDmJ6mIkTkkr4aIx530aq/X4otj39WvKwSP5LN6Wxvz4NBZvTSPzaCEAITW8aB5Rg+bhASVldk3D/alRmZ36Ssr0hqpMT6qsCv/Jnjx5MuPGjeOjjz6iS5cuTJw4kQEDBpCQkEBYWNhJxy9btoxrr72WV155hcGDB/P9998zfPhw1q1bR6tWrQB4/fXXeffdd/nqq69o2LAhTz/9NAMGDGDLli14e3tX9FsqV9d1qU/W0UL8vE5MhIpHeUrv8/Nyw9tdCY6IiIhTK5nntLxcTxtcw4sR7esxon09imx2dqTlEFLDk+AaXuX6OudMZXpSTVR4O/IuXbrQqVMn3n//fQDsdjuRkZGMHTuWxx9//KTjR48eTU5ODjNnzizZ17VrV9q1a8dHH32Ew+GgTp06PPTQQzz88MMAZGZmEh4ezpdffsk111xz1pjUjlxEREQqzNHD8FoDwAEPb4MaJ18orlJ2zIdvRhhleg9t1YiTuJyy5gYVuoBNQUEBa9eupX///sdf0Gqlf//+LF9+6qswy5cvL3U8wIABA0qO37VrF8nJyaWOCQwMpEuXLqc9Z35+PllZWaVuIiIiIhXCJwjCWhjbiStMDaVSbJ5m3KubnlRxFZo4paenY7PZCA8PL7U/PDyc5OTkUz4nOTn5jMcX35/LOV955RUCAwNLbpGRkef1fkRERETKJKqrcf+v9ZyqnFJleiPMjUWkglVo4uQsxo8fT2ZmZsktKSnJ7JBERESkKitOnMp5npPT2b1E3fSk2qjQxCkkJAQ3NzdSUlJK7U9JSSEiIuKUz4mIiDjj8cX353JOLy8vAgICSt1EREREKkxx4nRgIxTkmhtLRdo83bhXmZ5UAxWaOHl6enLRRRcRGxtbss9utxMbG0u3bt1O+Zxu3bqVOh5g7ty5Jcc3bNiQiIiIUsdkZWWxcuXK055TREREpFIFRoJ/HbAXwb61ZkdTMWyFEPebsd1iuKmhiFSGCi/VGzduHJMmTeKrr74iLi6Ou+66i5ycHG6++WYAbrzxRsaPH19y/P3338+cOXOYMGEC8fHxPPfcc6xZs4Z7770XMFbYfuCBB3jppZeYMWMGf//9NzfeeCN16tRh+PDhFf12RERERM7OYjlhnlMVbRBxYpleg4vNjkakwlX4mOro0aNJS0vjmWeeITk5mXbt2jFnzpyS5g6JiYlYrcfzt+7du/P999/z1FNP8cQTT9C0aVOmT59esoYTwKOPPkpOTg533HEHhw8fpmfPnsyZM8fl1nASERGRKiyqK2yeWnU766lMT6qZCl/HyRlpHScRERGpcAc2wseXgFcgPLYLrG5mR1R+bEXwZlNjxOmG6dC4j9kRiZw3p1jHSURERKTaCmsJnjUgPxNS48yOpnztXqwyPal2lDiJiIiIVAQ3d6jXydiuavOcisv0ogerTE+qDSVOIiIiIhWlZD2nKpQ42YogXoveSvWjxElERESkopQkTivNjaM87V4CuQdVpifVjhInERERkYpStyNY3CAzETL3mR1N+dg8zbhXmZ5UM0qcRERERCqKVw2IaG1sV4V5Tv/f3v3HVlXffxx/3ba0VOkPiv0JtwU0Un4rLdRKpt9RIl9EIo4xZ7qkionJUhzIZlLmFlw2RGe26URRFoNZauMPXKuQOcdgg6FllGIJ/QGCc6Xyo4BKfzFK6T3fPy69tl+wt4V77ufe0+cjac7x9v549aQxvHren3P6jOktMhoFCDaKEwAAgJ2ctM6pZ0wvNkkae4fpNEBQUZwAAADs5KTiVF/h3XLTWwxBFCcAAAA7uS8Vp+ZaqbPNbJZr0X1Ratjs3WdMD0MQxQkAAMBO8elSYpZkeaTPq0ynuXqM6WGIozgBAADYzQnjeozpYYijOAEAANgt3IsTY3oAxQkAAMB2PeucPt/rLSHhhjE9gOIEAABgu+RsaXiC1NUhNR8wnWbwfGN63PQWQxfFCQAAwG4REZI7z7sfbuN6fcb07jObBTCI4gQAABAM4brOqXEXY3qAKE4AAADB4e5VnCzLbJbBqCv3bhnTwxBHcQIAAAiG0TOkiGFS+0npbKPpNAPTe0xv0iKjUQDTKE4AAADBMCxWyrjFux8u43q9x/TGMaaHoY3iBAAAECzhts6prsK7nXiPFDnMaBTANIoTAABAsLjDqDh1X5Qa3vPuM6YHUJwAAACCpueM0+kG6b9fmc3ij29MbyRjeoAoTgAAAMFz/Q3SqJu8+017zGbxxzemt5AxPUAUJwAAgOAKh3VOXE0PuAzFCQAAIJjCYZ1T4y7p3BnG9IBeKE4AAADBlJnv3R7fJ13sNJvlm/SM6WVzNT2gB8UJAAAgmEbdKF13g3TxvHRiv+k0l+s9pjf5PrNZgBBCcQIAAAgmlyu01zk1fsiYHnAFFCcAAIBgc+d5t6FYnOrKvVvG9IA+KE4AAADB1rPOqWm3ZFlms/TWZ0xvkdEoQKihOAEAAARb+nQparj3BrNfHDGd5mt9xvTuNJ0GCCkUJwAAgGCLipZG53j3Q2lcr77Cu2VMD7gMxQkAAMCEUFvn1H1Rqn/Pu8+YHnAZihMAAIAJvdc5hQLG9IB+UZwAAABMcM/0br84IrWfNptFYkwP8IPiBAAAYELsSCllknff9FknTzdX0wP8oDgBAACYEirrnBo/lDpOM6YH9IPiBAAAYErPOifTxcl309sFjOkB34DiBAAAYErmpTNOJ/ZLF86ZydBnTO8+MxmAMEBxAgAAMCUxS4pLlzxd0vF9ZjIwpgcMCMUJAADAFJfL/DqnugrvljE9oF8UJwAAAJNMrnPydEsNl256O4kxPaA/FCcAAACTMm/zbpv2SB5PcD+7Z0xveKI0njE9oD8UJwAAAJNSp0jDrpc6W6TTDcH97J4xvYnc9Bbwh+IEAABgUmSU5J7p3Q/muB5jesCgUJwAAABMc18a1wtmcWJMDxgUihMAAIBpvnVOQSxOjOkBg0JxAgAAMG1MruSKkM4elVqP2/95vW96y5geMCAUJwAAANNi4qS0qd79YIzrNX4kdZxiTA8YBIoTAABAKAjmOqe6cu82mzE9YKAoTgAAAKEgWOuceo/pTWZMDxgoihMAAEAo6ClOJw9InW32fQ5jesBVoTgBAACEgvgMKTFTsjzS53vt+5z6Cu+WMT1gUChOAAAAocLudU6ebqn+0k1vJy+y5zMAh6I4AQAAhAq71zn1HtMbx5geMBgUJwAAgFDhK05VUvfFwL9/7zG9qOjAvz/gYBQnAACAUJE8UYpJkLo6pObawL43Y3rANaE4AQAAhIqICMk9y7sf6HVOvjG9BMb0gKtAcQIAAAgldq1z8o3pLWRMD7gKFCcAAIBQktnrynqWFZj3ZEwPuGYUJwAAgFCSMUOKGCa1nZDOHg3Mex6tZEwPuEYUJwAAgFASfZ2UPt27H6h1TnXl3i1X0wOuGsUJAAAg1ARynVOfMb37rv39gCGK4gQAABBqeq9zulaM6QEBQXECAAAINe5LxelUg/Tfr67tveoqvFvG9IBrQnECAAAINSOSpaQbJVlSU9XVv4+nW6p/17s/aVEgkgFDFsUJAAAgFGXme7fXss6p95je+P8JSCxgqKI4AQAAhKLMPO/2WtY5MaYHBAzFCQAAIBT1nHE6Vi1dvDD413u6pYZLV9NjTA+4ZhQnAACAUDTqJum6UdLF89KJ/YN//dFKqb2ZMT0gQChOAAAAocjl+vrqelezzqlnTG/CAsb0gACgOAEAAISqq13n1HtMj5veAgFBcQIAAAhVPeucju6WLGvgrzu6mzE9IMAoTgAAAKEqfboUGSOdOyN98enAX1dX7t0ypgcEDMUJAAAgVEXFSKNzvPsDXefUZ0xvkS2xgKGI4gQAABDKfOucKgf2/J4xvZgEafy37csFDDEUJwAAgFDmW+f0r4E9v77Cu81mTA8IJIoTAABAKBsz07v94rDUcab/53q6pfp3vfuM6QEBRXECAAAIZdclSckTvftNfs46MaYH2IbiBAAAEOoGus6JMT3ANhQnAACAUDeQdU6ebqmeq+kBdqE4AQAAhDr3pTNOxz+Wuv575ecc3S21n2RMD7AJxQkAACDUjRwrjUiTPF3e8nQlvjG9uxnTA2xAcQIAAAh1Llf/65w8nl5jevcFLxcwhFCcAAAAwkF/65yaGNMD7EZxAgAACAc965yadnvPMPVWV+7dMqYH2IbiBAAAEA7SpknDrpfOt0hnDn39eO8xvUmLjEQDhgKKEwAAQDiIjJLG5Hj3e69z6j2mdyNjeoBdKE4AAADh4krrnOoqvNvsu6WomKBHAoYKihMAAEC4cP+/K+t5PFL9u959xvQAW0WZDgAAAIABGjNTckVIZxul1hPSV59dGtOLZ0wPsJltZ5y+/PJLFRYWKj4+XomJiXr44YfV3t7e72vOnz+v4uJijRo1SiNGjNDixYvV3Nzs+/7+/fv1wAMPyO12KzY2VhMnTtTzzz9v148AAAAQWobHS6mTvftNu3uN6S1gTA+wmW3FqbCwUHV1ddq6dau2bNminTt36pFHHun3NY899pg2b96st99+Wzt27NDx48f1ne98x/f96upqpaSkqLS0VHV1dXriiSe0atUqrVu3zq4fAwAAILT0rHNqrGRMDwgil2VZVqDftKGhQZMmTVJVVZVyc3MlSX/5y19099136/PPP1dGRsZlr2lpaVFycrLKysr03e9+V5J08OBBTZw4UZWVlbrtttuu+FnFxcVqaGjQ9u3bB5yvtbVVCQkJamlpUXx8/FX8hAAAAIbUviNtWipFx0kX2rxjeo8f4YwTcJUG2g1sOeNUWVmpxMREX2mSpLlz5yoiIkL/+tcV7nYt79mkrq4uzZ071/dYdna2MjMzVVlZecXXSN7ClZSU1G+ezs5Otba29vkCAAAIS+5Lf0y+0ObdTuBqekAw2FKcTp48qZSUlD6PRUVFKSkpSSdPnvzG10RHRysxMbHP46mpqd/4mo8++khvvvmm3xHAtWvXKiEhwffldrsH/sMAAACEkoTRUkLm1/89+T5zWYAhZFDFqaSkRC6Xq9+vgwcP2pW1j9raWt17771avXq17rrrrn6fu2rVKrW0tPi+mpqagpIRAADAFpmXLkvO1fSAoBnU5ch//OMf68EHH+z3OePHj1daWppOnTrV5/GLFy/qyy+/VFpa2hVfl5aWpgsXLujs2bN9zjo1Nzdf9pr6+noVFBTokUce0c9+9jO/uWNiYhQTwylsAADgEDf/r3TgbWnqEsb0gCAZVHFKTk5WcnKy3+fl5+fr7Nmzqq6uVk5OjiRp+/bt8ng8ysvLu+JrcnJyNGzYMG3btk2LFy+WJB06dEhHjx5Vfn6+73l1dXWaM2eOioqKtGbNmsHEBwAAcIYpi6WR476+NDkA29lyVT1Jmj9/vpqbm/Xyyy+rq6tLDz30kHJzc1VWViZJOnbsmAoKCvTHP/5Rs2bNkiT98Ic/1J///Ge99tprio+P16OPPirJu5ZJ8o7nzZkzR/PmzdOzzz7r+6zIyMgBFboeXFUPAAAAgDTwbjCoM06D8frrr2vZsmUqKChQRESEFi9erN///ve+73d1denQoUM6d+6c77Hf/e53vud2dnZq3rx5eumll3zf37Rpk06fPq3S0lKVlpb6Hs/KytJ//vMfu34UAAAAAEOcbWecQhlnnAAAAABIhu/jBAAAAABOQnECAAAAAD8oTgAAAADgB8UJAAAAAPygOAEAAACAHxQnAAAAAPCD4gQAAAAAflCcAAAAAMAPihMAAAAA+EFxAgAAAAA/KE4AAAAA4AfFCQAAAAD8oDgBAAAAgB8UJwAAAADwg+IEAAAAAH5EmQ5ggmVZkqTW1lbDSQAAAACY1NMJejrCNxmSxamtrU2S5Ha7DScBAAAAEAra2tqUkJDwjd93Wf6qlQN5PB4dP35ccXFxcrlcRrO0trbK7XarqalJ8fHxRrM4EcfXXhxfe3F87cXxtR/H2F4cX3txfO0VSsfXsiy1tbUpIyNDERHfvJJpSJ5xioiI0JgxY0zH6CM+Pt74L42TcXztxfG1F8fXXhxf+3GM7cXxtRfH116hcnz7O9PUg4tDAAAAAIAfFCcAAAAA8IPiZFhMTIxWr16tmJgY01EcieNrL46vvTi+9uL42o9jbC+Or704vvYKx+M7JC8OAQAAAACDwRknAAAAAPCD4gQAAAAAflCcAAAAAMAPihMAAAAA+EFxAgAAAAA/KE6Gvfjiixo7dqyGDx+uvLw87dmzx3QkR9i5c6cWLlyojIwMuVwuVVRUmI7kKGvXrtXMmTMVFxenlJQULVq0SIcOHTIdyzHWr1+vadOm+e6mnp+fr/fff990LMd6+umn5XK5tGLFCtNRHOHJJ5+Uy+Xq85WdnW06lqMcO3ZMP/jBDzRq1CjFxsZq6tSp2rt3r+lYjjB27NjLfn9dLpeKi4tNR3OE7u5u/fznP9e4ceMUGxurG2+8Ub/85S8VLhf5pjgZ9Oabb2rlypVavXq19u3bp+nTp2vevHk6deqU6Whhr6OjQ9OnT9eLL75oOooj7dixQ8XFxdq9e7e2bt2qrq4u3XXXXero6DAdzRHGjBmjp59+WtXV1dq7d6/mzJmje++9V3V1daajOU5VVZVeeeUVTZs2zXQUR5k8ebJOnDjh+9q1a5fpSI7x1Vdfafbs2Ro2bJjef/991dfX6ze/+Y1GjhxpOpojVFVV9fnd3bp1qyRpyZIlhpM5wzPPPKP169dr3bp1amho0DPPPKNf//rXeuGFF0xHGxDu42RQXl6eZs6cqXXr1kmSPB6P3G63Hn30UZWUlBhO5xwul0vl5eVatGiR6SiOdfr0aaWkpGjHjh264447TMdxpKSkJD377LN6+OGHTUdxjPb2ds2YMUMvvfSSfvWrX+mWW27Rc889ZzpW2HvyySdVUVGhmpoa01EcqaSkRB9++KH++c9/mo4yJKxYsUJbtmzR4cOH5XK5TMcJe/fcc49SU1P16quv+h5bvHixYmNjVVpaajDZwHDGyZALFy6ourpac+fO9T0WERGhuXPnqrKy0mAyYPBaWlokef9xj8Dq7u7WG2+8oY6ODuXn55uO4yjFxcVasGBBn/8PIzAOHz6sjIwMjR8/XoWFhTp69KjpSI7x3nvvKTc3V0uWLFFKSopuvfVW/eEPfzAdy5EuXLig0tJSLV26lNIUILfffru2bdumTz75RJK0f/9+7dq1S/PnzzecbGCiTAcYqs6cOaPu7m6lpqb2eTw1NVUHDx40lAoYPI/HoxUrVmj27NmaMmWK6TiOceDAAeXn5+v8+fMaMWKEysvLNWnSJNOxHOONN97Qvn37VFVVZTqK4+Tl5em1117ThAkTdOLECf3iF7/Qt771LdXW1iouLs50vLD373//W+vXr9fKlSv105/+VFVVVfrRj36k6OhoFRUVmY7nKBUVFTp79qwefPBB01Eco6SkRK2trcrOzlZkZKS6u7u1Zs0aFRYWmo42IBQnANekuLhYtbW1rGEIsAkTJqimpkYtLS3atGmTioqKtGPHDspTADQ1NWn58uXaunWrhg8fbjqO4/T+y/G0adOUl5enrKwsvfXWW4yaBoDH41Fubq6eeuopSdKtt96q2tpavfzyyxSnAHv11Vc1f/58ZWRkmI7iGG+99ZZef/11lZWVafLkyaqpqdGKFSuUkZERFr+/FCdDbrjhBkVGRqq5ubnP483NzUpLSzOUChicZcuWacuWLdq5c6fGjBljOo6jREdH66abbpIk5eTkqKqqSs8//7xeeeUVw8nCX3V1tU6dOqUZM2b4Huvu7tbOnTu1bt06dXZ2KjIy0mBCZ0lMTNTNN9+sI0eOmI7iCOnp6Zf9AWXixIl65513DCVypsbGRv3tb3/Tn/70J9NRHOXxxx9XSUmJvv/970uSpk6dqsbGRq1duzYsihNrnAyJjo5WTk6Otm3b5nvM4/Fo27ZtrGNAyLMsS8uWLVN5ebm2b9+ucePGmY7keB6PR52dnaZjOEJBQYEOHDigmpoa31dubq4KCwtVU1NDaQqw9vZ2ffrpp0pPTzcdxRFmz5592e0fPvnkE2VlZRlK5EwbN25USkqKFixYYDqKo5w7d04REX3rR2RkpDwej6FEg8MZJ4NWrlypoqIi5ebmatasWXruuefU0dGhhx56yHS0sNfe3t7nr5ufffaZampqlJSUpMzMTIPJnKG4uFhlZWV69913FRcXp5MnT0qSEhISFBsbazhd+Fu1apXmz5+vzMxMtbW1qaysTP/4xz/0wQcfmI7mCHFxcZetx7v++us1atQo1ukFwE9+8hMtXLhQWVlZOn78uFavXq3IyEg98MADpqM5wmOPPabbb79dTz31lL73ve9pz5492rBhgzZs2GA6mmN4PB5t3LhRRUVFiorin8qBtHDhQq1Zs0aZmZmaPHmyPv74Y/32t7/V0qVLTUcbGAtGvfDCC1ZmZqYVHR1tzZo1y9q9e7fpSI7w97//3ZJ02VdRUZHpaI5wpWMrydq4caPpaI6wdOlSKysry4qOjraSk5OtgoIC669//avpWI525513WsuXLzcdwxHuv/9+Kz093YqOjrZGjx5t3X///daRI0dMx3KUzZs3W1OmTLFiYmKs7Oxsa8OGDaYjOcoHH3xgSbIOHTpkOorjtLa2WsuXL7cyMzOt4cOHW+PHj7eeeOIJq7Oz03S0AeE+TgAAAADgB2ucAAAAAMAPihMAAAAA+EFxAgAAAAA/KE4AAAAA4AfFCQAAAAD8oDgBAAAAgB8UJwAAAADwg+IEAAAAAH5QnAAAAADAD4oTAAAAAPhBcQIAAAAAP/4PnsFx2/ZgVoMAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import Dense\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# Prepare data for ANN\n", - "X = data[valid_columns]\n", - "y = data['CO2 emissions ']\n", - "\n", - "# Split data\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n", - "\n", - "# Define ANN model\n", - "ann_model = Sequential()\n", - "ann_model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", - "ann_model.add(Dense(32, activation='relu'))\n", - "ann_model.add(Dense(1))\n", - "ann_model.compile(optimizer='adam', loss='mean_squared_error')\n", - "ann_model.fit(X_train, y_train, epochs=30, batch_size=10, verbose=1)\n", - "\n", - "# Make predictions\n", - "ann_predictions = ann_model.predict(X_test)\n", - "\n", - "# Plot predictions\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(y_test.values[:50], label='Actual CO₂ Emissions')\n", - "plt.plot(ann_predictions[:50], label='Predicted CO₂ Emissions')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "13f8ba0b-f3f0-4ae9-9ba0-7d3ae352a1e5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: geopandas in c:\\users\\minal\\anaconda3\\lib\\site-packages (1.0.1)\n", - "Requirement already satisfied: numpy>=1.22 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (1.26.4)\n", - "Requirement already satisfied: pyogrio>=0.7.2 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (0.9.0)\n", - "Requirement already satisfied: packaging in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (23.2)\n", - "Requirement already satisfied: pandas>=1.4.0 in c:\\users\\minal\\appdata\\roaming\\python\\python312\\site-packages (from geopandas) (2.2.2)\n", - "Requirement already satisfied: pyproj>=3.3.0 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (3.6.1)\n", - "Requirement already satisfied: shapely>=2.0.0 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (2.0.5)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2.9.0.post0)\n", - "Requirement already satisfied: pytz>=2020.1 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2024.1)\n", - "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2023.3)\n", - "Requirement already satisfied: certifi in c:\\users\\minal\\anaconda3\\lib\\site-packages (from pyogrio>=0.7.2->geopandas) (2024.7.4)\n", - "Requirement already satisfied: six>=1.5 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas>=1.4.0->geopandas) (1.16.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "pip install geopandas\n" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "9abe4a63-840c-4126-ae19-6dd13e2924b6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data Columns: Index(['Country', 'latest year available_x', 'CH4 emissions',\n", - " 'CH4 emissions per capita', ' % change since 1990', 'N2O emissions',\n", - " 'N2O emissions per capita', ' % change since 1990.1', 'NOx emissions',\n", - " '% change since 1990_x', 'NOx emissions per capita', 'CO2 emissions ',\n", - " '% change since 1990_y', 'CO2 emissions per capita',\n", - " 'CO2 emissions per km2', 'Total GHG emissions _perc',\n", - " 'GHG from Energy_perc',\n", - " 'GHG from Energy \\nof which: from Transport_perc',\n", - " 'GHG from Industrial Processes_perc', 'GHG from Agriculture_perc',\n", - " 'GHG from Waste_perc', 'Total GHG emissions _tonnes',\n", - " 'GHG from Energy_tonnes',\n", - " 'GHG from Energy \\nof which: from Transport_tonnes',\n", - " 'GHG from Industrial Processes_tonnes', 'GHG from Agriculture_tonnes',\n", - " 'GHG from Waste_tonnes', 'Consumption of CFCs_odptonnes',\n", - " 'Consumption of all ODS', 'consumptionof all_odptonnesin 2013',\n", - " 'reduction in odp', 'SO2 emissions', '% change since 1990',\n", - " 'SO2 emissions per capita'],\n", - " dtype='object')\n", - "World Map Columns: Index(['featurecla', 'scalerank', 'LABELRANK', 'SOVEREIGNT', 'SOV_A3',\n", - " 'ADM0_DIF', 'LEVEL', 'TYPE', 'TLC', 'Country',\n", - " ...\n", - " 'FCLASS_TR', 'FCLASS_ID', 'FCLASS_PL', 'FCLASS_GR', 'FCLASS_IT',\n", - " 'FCLASS_NL', 'FCLASS_SE', 'FCLASS_BD', 'FCLASS_UA', 'geometry'],\n", - " dtype='object', length=169)\n", - " featurecla scalerank LABELRANK SOVEREIGNT SOV_A3 \\\n", - "0 Admin-0 country 1 6 Fiji FJI \n", - "1 Admin-0 country 1 3 United Republic of Tanzania TZA \n", - "2 Admin-0 country 1 7 Western Sahara SAH \n", - "3 Admin-0 country 1 2 Canada CAN \n", - "4 Admin-0 country 1 2 United States of America US1 \n", - "\n", - " ADM0_DIF LEVEL TYPE TLC Country ... \\\n", - "0 0 2 Sovereign country 1 Fiji ... \n", - "1 0 2 Sovereign country 1 United Republic of Tanzania ... \n", - "2 0 2 Indeterminate 1 Western Sahara ... \n", - "3 0 2 Sovereign country 1 Canada ... \n", - "4 1 2 Country 1 United States of America ... \n", - "\n", - " GHG from Industrial Processes_tonnes GHG from Agriculture_tonnes \\\n", - "0 NaN NaN \n", - "1 0.37 29.73 \n", - "2 NaN NaN \n", - "3 56.46 55.53 \n", - "4 334.35 526.25 \n", - "\n", - " GHG from Waste_tonnes Consumption of CFCs_odptonnes Consumption of all ODS \\\n", - "0 NaN NaN NaN \n", - "1 2.25 253.9 71.5 \n", - "2 NaN NaN NaN \n", - "3 20.57 19 958.2 923.1 \n", - "4 123.97 305 963.6 16 206.4 \n", - "\n", - " consumptionof all_odptonnesin 2013 reduction in odp SO2 emissions \\\n", - "0 NaN NaN NaN \n", - "1 1.6 97.8 175.74 \n", - "2 NaN NaN NaN \n", - "3 65.9 92.9 0 \n", - "4 711.3 95.6 4 739.47 \n", - "\n", - " % change since 1990 SO2 emissions per capita \n", - "0 NaN NaN \n", - "1 8.39 6.05 \n", - "2 NaN NaN \n", - "3 0 0.00 \n", - "4 -77.36 15.06 \n", - "\n", - "[5 rows x 202 columns]\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def load_world_map(shapefile_path):\n", - " try:\n", - " world = gpd.read_file(shapefile_path)\n", - " return world\n", - " except Exception as e:\n", - " print(f\"Error loading shapefile: {e}\")\n", - " return None\n", - "\n", - "def plot_emissions(world, data):\n", - " if world is not None:\n", - " # Rename 'ADMIN' to 'Country' if needed\n", - " if 'ADMIN' in world.columns:\n", - " world = world.rename(columns={'ADMIN': 'Country',})\n", - " else:\n", - " print(\"Error: The world map does not have a suitable column for country names.\")\n", - " return\n", - "\n", - " # Ensure the cleaned data has a 'Country' column\n", - " if 'Country' not in data.columns:\n", - " print(\"Error: The cleaned data does not have a 'Country' column.\")\n", - " return\n", - "\n", - " # Print column names for debugging\n", - " print(\"Data Columns:\", data.columns)\n", - " print(\"World Map Columns:\", world.columns)\n", - "\n", - " # Merge on 'Country' column\n", - " world = world.merge(data,how='left',on='Country',)\n", - " print(world.head())\n", - "\n", - " # Check if 'CO2 emissions' exists in the merged DataFrame\n", - " if 'CO2 emissions ' in world.columns:\n", - " # Plot emissions data\n", - " world.plot(column='CO2 emissions ', cmap='Reds', legend=True, figsize=(15, 10))\n", - " plt.title('Global CO₂ Emissions')\n", - " plt.show()\n", - " else:\n", - " print(\"Error: The 'CO2 emissions' column is not in the world map DataFrame after merging.\")\n", - " else:\n", - " print(\"World map could not be loaded.\")\n", - "\n", - "# Load cleaned data\n", - "try:\n", - " data = pd.read_csv('cleaned_data.csv')\n", - "except Exception as e:\n", - " print(f\"Error loading cleaned data: {e}\")\n", - " data = pd.DataFrame() # Empty DataFrame if loading fails\n", - "\n", - "# Load world map\n", - "shapefile_path = 'ne_110m_admin_0_countries.shp'\n", - "world = load_world_map(shapefile_path)\n", - "\n", - "\n", - "# Plot emissions data\n", - "plot_emissions(world, data)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "id": "6191e829-9fc1-4115-bd17-fcd7cb794a98", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Function to clean numeric columns by removing spaces and converting to float\n", - "def clean_numeric_column(column):\n", - " # Remove spaces\n", - " column = column.str.replace(' ', '')\n", - " # Convert to numeric, coercing errors to NaN\n", - " return pd.to_numeric(column, errors='coerce')\n", - "\n", - "# List of columns to clean\n", - "columns_to_clean = ['CO2 emissions ', 'CH4 emissions', 'N2O emissions', 'NOx emissions', 'SO2 emissions']\n", - "\n", - "# Clean the columns\n", - "for col in columns_to_clean:\n", - " data[col] = clean_numeric_column(data[col].astype(str))\n", - "\n", - "# Now calculate the correlation matrix\n", - "correlation_matrix = data[columns_to_clean].corr()\n", - "\n", - "# Plot the correlation matrix\n", - "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n", - "plt.title('Correlation Matrix of Emissions')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "f564194a-1d9b-4a24-8aa0-261f0f7a5e30", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Projected CO₂ Emissions with 25% reduction: 86.145\n" - ] - } - ], - "source": [ - "# Historical policy assessment\n", - "data['Policy_Effect'] = data['CO2 emissions '].pct_change()\n", - "\n", - "# Simulate new policies\n", - "def simulate_policy_change(current_emissions, reduction_percentage):\n", - " return current_emissions * (1 - reduction_percentage / 100)\n", - "\n", - "# Example policy simulation\n", - "future_emissions = simulate_policy_change(data['CO2 emissions '].iloc[-1], 25)\n", - "print(f'Projected CO₂ Emissions with 25% reduction: {future_emissions}')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d53ce7d8-11f1-4e38-abc0-bd86c8374187", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/GLOBAL CLIMATE ANALYSIS/README.md b/GLOBAL CLIMATE ANALYSIS/README.md deleted file mode 100644 index 4c94c4bde..000000000 --- a/GLOBAL CLIMATE ANALYSIS/README.md +++ /dev/null @@ -1,18 +0,0 @@ -# Global Emission Analysis - -This project analyzes global emission trends and forecasts future emissions using various methods. The analysis is performed in a Jupyter notebook. - -## Files - -- `data/`: Contains the CSV file with emission data. -- `clean and processe`:Contains the main csv after cleaning and processing -- `notebook/`: Jupyter notebook with the complete analysis. -- `requirements.txt`: List of required Python packages. -- `README.md`: Project overview and instructions. - -## Installation - -Install the necessary packages using: - -```bash -pip install -r requirements.txt diff --git a/GLOBAL CLIMATE ANALYSIS/requirment.txt b/GLOBAL CLIMATE ANALYSIS/requirment.txt deleted file mode 100644 index 3e857db0b..000000000 --- a/GLOBAL CLIMATE ANALYSIS/requirment.txt +++ /dev/null @@ -1,7 +0,0 @@ -pandas -numpy -scikit-learn -tensorflow -geopandas -matplotlib -seaborn From bc092d8aeb8c1d36578ee6d7ea2a91a18bc51152 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 24 Jul 2024 23:21:29 +0530 Subject: [PATCH 15/58] Create ch4no2 --- GLOBAL EMISSION ANALSIS/Dataset/ch4no2 | 1 + 1 file changed, 1 insertion(+) create mode 100644 GLOBAL EMISSION ANALSIS/Dataset/ch4no2 diff --git a/GLOBAL EMISSION ANALSIS/Dataset/ch4no2 b/GLOBAL EMISSION ANALSIS/Dataset/ch4no2 new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/Dataset/ch4no2 @@ -0,0 +1 @@ + From 0e2063811ed28fa1d2c0ee8de74136b61a8985cb Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 24 Jul 2024 23:22:30 +0530 Subject: [PATCH 16/58] adding dataset used --- .../Dataset/CH4_N2O_Emissions.csv | 205 +++++++++++++++++ .../Dataset/CO2_Emissions.csv | 216 ++++++++++++++++++ .../Dataset/NOx_Emissions.csv | 167 ++++++++++++++ .../Dataset/ODS_Consumption.csv | 170 ++++++++++++++ .../Dataset/SO2_emissions.csv | 136 +++++++++++ .../Dataset/merged_ghg_data.csv | 189 +++++++++++++++ 6 files changed, 1083 insertions(+) create mode 100644 GLOBAL EMISSION ANALSIS/Dataset/CH4_N2O_Emissions.csv create mode 100644 GLOBAL EMISSION ANALSIS/Dataset/CO2_Emissions.csv create mode 100644 GLOBAL EMISSION ANALSIS/Dataset/NOx_Emissions.csv create mode 100644 GLOBAL EMISSION ANALSIS/Dataset/ODS_Consumption.csv create mode 100644 GLOBAL EMISSION ANALSIS/Dataset/SO2_emissions.csv create mode 100644 GLOBAL EMISSION ANALSIS/Dataset/merged_ghg_data.csv diff --git a/GLOBAL EMISSION ANALSIS/Dataset/CH4_N2O_Emissions.csv b/GLOBAL EMISSION ANALSIS/Dataset/CH4_N2O_Emissions.csv new file mode 100644 index 000000000..75157d8de --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/Dataset/CH4_N2O_Emissions.csv @@ -0,0 +1,205 @@ +Country,latest year available,CH4 emissions,CH4 emissions per capita, % change since 1990,N2O emissions,N2O emissions per capita, % change since 1990, +Afghanistan,2005,10.16,0.42,...,5.94,0.24,...,\ +Albania,1994,2.14,0.68,...,0.29,0.09,..., +Algeria,2000,32.92,1.06,...,6.50,0.21,..., +Angola,2005,19.93,1.11,...,13.87,0.77,..., +Antigua and Barbuda,2000,0.14,1.83,43.74,0.08,1.08,5197.47, +Argentina,2000,84.85,2.29,10.50,67.50,1.82,30.26, +Armenia,2010,2.26,0.76,-28.66,0.48,0.16,185.46, +Australia,2012,111.71,4.88,-3.02,25.78,1.13,40.43, +Austria,2012,5.31,0.63,-36.32,5.22,0.62,-15.75, +Azerbaijan,1994,9.29,1.21,-38.78,0.66,0.09,-26.30, +Bahamas,1994,0.02,0.08,0.00,0.31,1.12,..., +Bahrain,2000,3.91,7.11,...,0.04,0.07,..., +Bangladesh,2005,39.47,0.28,...,19.11,0.13,..., +Barbados,1997,1.81,6.77,8.78,0.05,0.19,0.00, +Belarus,2012,15.39,1.62,1.14,16.40,1.73,-18.52, +Belgium,2012,6.39,0.58,-33.82,6.99,0.63,-35.86, +Belize,1994,5.57,27.60,...,0.17,0.85,..., +Benin,2000,2.32,0.33,...,2.51,0.36,..., +Bhutan,2000,0.59,1.05,...,0.47,0.82,..., +Bolivia (Plurinational State of),2004,11.97,1.34,28.03,1.39,0.15,140.53, +Bosnia and Herzegovina,2001,2.42,0.64,-45.70,1.46,0.38,-53.32, +Botswana,2000,2.16,1.40,...,2.04,1.32,..., +Brazil,2005,316.28,1.68,34.49,162.78,0.86,45.06, +Bulgaria,2012,7.19,0.98,-56.60,5.24,0.72,-59.79, +Burkina Faso,1994,4.70,0.48,...,0.37,0.04,..., +Burundi,2005,0.53,0.07,...,25.77,3.25,..., +Cabo Verde,2000,0.07,0.16,...,0.09,0.21,..., +Cambodia,1994,7.77,0.75,...,3.67,0.35,..., +Cameroon,1994,17.71,1.31,...,145.25,10.72,..., +Canada,2012,90.56,2.60,25.78,47.73,1.37,-2.92, +Central African Republic,1994,11.88,3.65,...,25.64,7.88,..., +Chad,1993,6.94,1.06,...,0.77,0.12,..., +Chile,2010,11.46,0.67,...,9.70,0.57,..., +China,2005,932.86,0.71,...,394.10,0.30,..., +Colombia,2004,53.87,1.26,21.37,34.38,0.80,39.53, +Comoros,1994,0.06,0.12,...,0.39,0.83,..., +Congo,2000,0.50,0.16,...,0.27,0.09,..., +Cook Islands,1994,0.01,0.58,...,0.04,2.04,..., +Costa Rica,2005,3.53,0.83,11.89,2.59,0.61,1302.52, +Côte d'Ivoire,2000,25.15,1.52,...,185.68,11.24,..., +Croatia,2012,3.42,0.80,-7.41,3.30,0.77,-17.40, +Cuba,1996,6.64,0.61,-37.81,7.04,0.64,-59.67, +Cyprus,2012,1.30,1.15,43.28,0.61,0.54,11.11, +Czech Republic,2012,10.26,0.97,-42.67,7.73,0.73,-42.69, +Democratic People's Republic of Korea,2002,12.62,0.54,-49.11,0.93,0.04,-76.92, +Democratic Republic of the Congo,2003,37.29,0.71,...,6.06,0.12,..., +Denmark,2012,5.52,0.99,-7.26,5.99,1.07,-38.99, +Djibouti,2000,0.28,0.39,...,0.45,0.62,..., +Dominica,2005,0.03,0.46,...,0.03,0.43,..., +Dominican Republic,2000,4.84,0.56,59.11,3.18,0.37,279.36, +Ecuador,2006,17.17,1.23,31.61,201.48,14.42,33.12, +Egypt,2000,39.43,0.58,77.83,24.45,0.36,136.65, +El Salvador,2005,3.36,0.57,...,1.65,0.28,..., +Eritrea,2000,3.00,0.85,...,0.31,0.09,..., +Estonia,2012,0.93,0.70,-44.58,1.01,0.76,-55.03, +Ethiopia,1995,37.44,0.65,-0.45,7.44,0.13,140.00, +Fiji,2004,0.51,0.63,...,0.63,0.77,..., +Finland,2012,4.08,0.75,-33.82,5.18,0.96,-29.94, +France,2012,51.74,0.81,-13.40,57.77,0.91,-36.95, +Gabon,2000,0.71,0.58,...,0.29,0.24,..., +Gambia,2000,0.80,0.65,...,1.10,0.90,..., +Georgia,2006,4.14,0.93,-42.19,2.20,0.50,-8.84, +Germany,2012,48.71,0.61,-55.23,55.80,0.69,-34.60, +Ghana,2006,6.42,0.31,113.56,3.96,0.18,40.77, +Greece,2012,9.71,309.10,-8.53,6.81,0.61,-33.39, +Grenada,1994,1.47,14.79,...,0.00,0.02,..., +Guatemala,1990,4.09,0.45,0.00,6.41,0.70,0.00, +Guinea,1994,3.25,0.43,...,0.23,0.03,..., +Guinea-Bissau,1994,0.67,0.58,...,0.85,0.73,..., +Guyana,2004,1.19,1.60,11.74,0.23,0.31,6.94, +Haiti,2000,3.67,0.43,...,1.57,0.18,..., +Honduras,2000,3.85,0.62,...,2.29,0.37,..., +Hungary,2012,7.99,0.80,-32.72,6.76,0.68,-47.59, +Iceland,2012,0.46,1.41,4.63,0.46,1.42,-12.08, +India,2000,407.24,0.39,...,79.80,0.08,..., +Indonesia,2000,236.33,1.12,122.72,28.47,0.13,58.04, +Iran (Islamic Republic of),2000,75.71,1.15,...,40.15,0.61,..., +Ireland,2012,12.07,2.59,-11.70,7.42,1.59,-18.61, +Israel,2010,6.81,0.92,...,2.57,0.35,..., +Italy,2012,36.08,0.60,-17.56,27.53,0.46,-26.52, +Jamaica,1994,1.22,0.49,...,106.54,43.19,..., +Japan,2012,20.03,0.16,-38.32,20.23,0.16,-31.94, +Jordan,2006,3.07,0.55,...,1.55,0.28,..., +Kazakhstan,2012,49.03,2.91,-32.23,9.49,0.56,-47.13, +Kenya,1994,15.54,0.58,...,0.42,0.02,..., +Kiribati,1994,0.01,0.12,...,0.00,0.00,..., +Kuwait,1994,2.71,1.60,...,0.14,0.08,..., +Kyrgyzstan,2005,3.02,0.59,-50.42,0.15,0.03,-16.53, +Lao People's Democratic Republic,2000,5.34,1.00,-16.68,2.50,0.47,6625.00, +Latvia,2012,1.63,0.80,-51.23,1.82,0.89,-52.41, +Lebanon,2000,1.83,0.56,...,1.04,0.00,..., +Lesotho,2000,1.26,0.68,...,1.45,0.78,..., +Liberia,2000,4.14,1.43,...,0.31,0.11,..., +Liechtenstein,2012,0.02,0.43,8.92,0.01,0.34,-5.95, +Lithuania,2012,3.05,1.01,-46.93,4.14,1.37,-42.33, +Luxembourg,2012,0.43,0.80,-7.64,0.47,0.87,-2.36, +Madagascar,2000,6.92,0.44,...,20.68,1.31,..., +Malawi,1994,3.95,0.41,-43.98,2.41,0.25,625.23, +Malaysia,2000,51.65,2.21,...,2.90,0.12,..., +Maldives,1994,0.02,...,...,…,…,…, +Mali,2000,8.85,0.80,...,1.77,0.16,..., +Malta,2012,0.10,0.25,42.30,0.06,0.14,11.29, +Mauritania,2000,4.15,1.53,...,1.66,0.61,..., +Mauritius,2006,1.39,1.13,...,0.19,0.15,..., +Mexico,2006,187.78,1.69,76.22,20.34,110.55,96.25, +Micronesia (Federated States of),1994,0.01,0.07,...,0.00,0.03,..., +Monaco,2012,0.00,0.02,-58.15,0.00,0.08,64.61, +Mongolia,2006,6.53,2.55,15.83,0.46,0.18,1380.00, +Montenegro,2003,0.53,0.87,-6.26,0.29,0.46,-22.03, +Morocco,2000,9.09,0.31,...,17.06,0.59,..., +Mozambique,1994,5.66,0.37,13.51,0.98,0.06,37.01, +Myanmar,2005,26.58,0.53,...,3.31,0.07,..., +Namibia,2000,6.76,3.56,...,0.31,0.16,..., +Nauru,1994,0.01,0.74,...,0.00,0.03,..., +Nepal,1994,19.92,0.95,...,9.64,0.46,..., +Netherlands,2012,14.94,0.89,-41.86,9.06,0.54,-54.68, +New Zealand,2012,29.04,6.55,8.21,10.89,2.45,32.02, +Nicaragua,2000,4.27,0.85,...,3.87,0.77,..., +Niger,2000,6.76,0.60,96.78,4.96,0.44,506.68, +Nigeria,2000,88.18,0.72,...,7.44,0.06,..., +Niue,1994,0.01,6.35,...,0.01,5.59,..., +Norway,2012,4.23,0.84,-14.76,3.20,0.64,-36.55, +Oman,1994,2.61,1.22,...,7.09,3.31,..., +Pakistan,1994,60.70,0.51,...,11.45,0.10,..., +Palau,2000,0.03,1.59,...,0.06,3.24,..., +Panama,2000,3.15,1.04,...,1.38,0.46,..., +Papua New Guinea,1994,0.09,0.02,...,3.78,0.82,..., +Paraguay,2000,11.48,2.16,-32.19,8.31,1.57,-77.55, +Peru,2010,21.86,0.74,...,13.78,0.47,..., +Philippines,2000,31.80,0.41,...,12.38,0.16,..., +Poland,2012,41.03,1.06,-17.36,29.59,0.77,-29.19, +Portugal,2012,12.25,1.17,20.03,4.48,0.43,-19.32, +Qatar,2007,3.53,2.99,...,0.45,0.38,..., +Republic of Korea,2012,29.78,0.60,-6.81,14.24,0.29,48.96, +Republic of Moldova,2009,2.68,0.66,-41.57,1.61,0.39,-51.53, +Romania,2012,22.24,1.11,-48.22,11.61,0.58,-52.59, +Russian Federation,2012,502.56,3.51,-15.31,115.95,0.81,-48.07, +Rwanda,2005,1.51,0.17,...,4.14,0.46,..., +Saint Kitts and Nevis,1994,0.06,1.40,...,0.03,0.80,..., +Saint Lucia,2000,0.16,1.05,...,0.04,0.25,..., +Saint Vincent and the Grenadines,1997,0.06,0.60,3.65,0.24,2.21,-3.77, +Samoa,1994,0.07,0.41,...,0.39,2.31,..., +San Marino,2007,0.00,0.11,...,0.00,0.06,..., +Sao Tome and Principe,2005,0.03,0.17,...,0.01,0.04,..., +Saudi Arabia,2000,27.55,1.29,66.71,11.79,0.55,12.52, +Senegal,2000,6.48,0.66,...,3.62,0.37,..., +Serbia,1998,8.91,0.92,-1.84,6.82,0.71,-22.03, +Seychelles,2000,0.06,0.71,...,0.01,0.15,..., +Singapore,2010,0.11,0.02,...,0.44,0.09,..., +Slovakia,2012,4.33,0.80,-16.57,2.94,0.54,-53.55, +Slovenia,2012,1.87,0.91,-11.85,1.11,0.54,-12.55, +South Africa,1994,43.21,1.07,0.21,20.67,0.51,-11.28, +Spain,2012,32.32,0.69,23.27,24.02,0.52,-9.81, +Sri Lanka,2000,6.80,0.36,...,1.07,0.06,..., +Sudan,2000,43.99,1.57,...,17.67,0.63,..., +Suriname,2003,0.86,1.77,...,...,...,..., +Swaziland,1994,1.35,1.43,...,0.41,0.44,..., +Sweden,2012,4.81,0.50,-31.18,6.19,0.65,-23.70, +Switzerland,2012,3.72,0.46,-19.84,3.02,0.38,-13.13, +Tajikistan,2010,3.49,0.46,-5.68,2.79,0.37,0.00, +Thailand,2000,58.61,0.93,...,12.34,5.99,..., +The former Yugoslav Republic of Macedonia,2009,1.66,0.80,-3.62,0.91,0.01,-27.62, +Timor-Leste,2010,0.55,0.52,...,0.47,0.44,..., +Togo,2000,1.11,0.23,...,2.35,0.48,..., +Tonga,2000,0.09,0.96,...,0.06,0.57,..., +Trinidad and Tobago,1990,0.69,0.56,0.00,0.33,0.27,0.00, +Tunisia,2000,5.81,0.60,...,5.76,0.59,..., +Turkey,2012,61.62,0.82,80.96,14.79,0.20,21.04, +Turkmenistan,2004,33.26,7.08,...,1.78,0.38,..., +Tuvalu,1994,0.00,0.10,...,0.00,0.00,..., +Uganda,2000,9.46,0.40,...,16.72,0.70,..., +Ukraine,2012,66.17,1.46,-59.24,31.37,0.69,-46.59, +United Arab Emirates,2005,29.93,9.81,...,6.14,2.01,..., +United Kingdom of Great Britain and Northern Ireland,2012,52.78,0.83,-51.60,35.41,0.56,-48.74, +United Republic of Tanzania,1994,21.63,0.75,4.73,14.38,0.50,-4.68, +United States of America,2012,552.01,1.75,-12.82,395.79,1.26,0.07, +Uruguay,2004,18.63,5.61,14.95,11.79,3.55,-2.16, +Uzbekistan,2005,89.43,3.45,57.73,9.97,0.38,-22.78, +Vanuatu,1994,0.24,1.43,...,0.01,0.05,..., +Venezuela (Bolivarian Republic of),1999,61.92,2.58,...,16.14,0.67,..., +Viet Nam,2010,87.32,0.99,...,32.70,0.37,..., +Yemen,2000,4.42,0.25,...,3.77,0.21,..., +Zambia,2000,6.57,0.62,...,5.33,0.50,..., +Zimbabwe,2000,7.48,0.60,...,36.20,2.90,..., +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, +,,,,,,,, diff --git a/GLOBAL EMISSION ANALSIS/Dataset/CO2_Emissions.csv b/GLOBAL EMISSION ANALSIS/Dataset/CO2_Emissions.csv new file mode 100644 index 000000000..803285a3a --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/Dataset/CO2_Emissions.csv @@ -0,0 +1,216 @@ +Country,CO2 emissions ,% change since 1990,CO2 emissions per capita,CO2 emissions per km2 +,mio. tonnes ,%,tonnes,tonnes +Afghanistan,12.25,357.7,0.43,18.77 +Albania,4.67,-37.7,1.62,162.38 +Algeria,121.76,54.3,3.32,51.12 +Andorra,0.49,...,5.97, 1 050.00 +Angola,29.71,570.7,1.35,23.83 +Anguilla,0.14,...,10.25, 1 571.43 +Antigua and Barbuda,0.51,70.7,5.82, 1 161.54 +Argentina,190.03,68.7,4.56,68.35 +Armenia,4.96,...,1.67,166.81 +Aruba,2.44,32.5,23.92, 13 547.78 +Australia,398.16,44.2,17.66,51.76 +Austria,70.35,13.4,8.35,838.83 +Azerbaijan,33.46,...,3.63,386.35 +Bahamas,1.91,-2.3,5.2,136.76 +Bahrain,23.44,85.1,17.95, 30 943.23 +Bangladesh,57.07,267.4,0.37,386.73 +Barbados,1.57,45.7,5.58, 3 641.40 +Belarus,55.38,-46.7,5.84,266.77 +Belgium,104.27,-12.4,9.47, 3 415.58 +Belize,0.55,76.5,1.67,23.95 +Benin,4.99,597.4,0.51,43.46 +Bermuda,0.39,-34.3,6.17, 7 403.77 +Bhutan,0.56,337.3,0.77,14.61 +Bolivia (Plurinational State of),16.12,191.7,1.6,14.67 +Bosnia and Herzegovina,23.75,...,6.2,463.74 +Botswana,4.86,122.9,2.32,8.34 +Brazil,439.41,110.4,2.19,51.61 +British Virgin Islands,0.18,166.7,6.31, 1 165.56 +Brunei Darussalam,9.74,56.8,24.39, 1 690.06 +Bulgaria,53.2,-33.7,7.23,479.78 +Burkina Faso,1.93,229.4,0.12,7.08 +Burundi,0.21,-28.8,0.02,7.51 +Cabo Verde,0.43,383.4,0.86,105.48 +Cambodia,4.5,896.8,0.31,24.83 +Cameroon,5.66,225.7,0.27,11.9 +Canada,557.29,21.4,16.15,55.81 +Cayman Islands,0.58,130.5,10.31, 2 208.71 +Central African Republic,0.29,44.4,0.06,0.46 +Chad,0.54,267.4,0.04,0.42 +Chile,79.41,138.4,4.62,105.02 +China, 9 019.52,266.5,6.69,939.83 +"China, Hong Kong Special Administrative Region",40.27,45.6,5.72, 36 480.71 +"China, Macao Special Administrative Region",1.17,12.8,2.13, 38 870.00 +Colombia,72.42,26.3,1.56,63.43 +Comoros,0.16,104.8,0.22,70.56 +Congo,2.25,89.2,0.54,6.57 +Cook Islands,0.07,216.8,3.42,295.34 +Costa Rica,7.84,165.4,1.7,153.5 +Côte d'Ivoire,6.45,11.2,0.31,19.99 +Croatia,20.92,-10.4,4.86,369.62 +Cuba,35.92,7.2,3.17,326.91 +Cyprus,7.57,63.5,6.78,817.88 +Czech Republic,115.07,-30.1,10.92, 1 459.06 +Democratic People's Republic of Korea,73.58,-69.9,2.99,610.42 +Democratic Republic of the Congo,3.43,-15.9,0.05,1.46 +Denmark,45.48,-16.1,8.15, 1 055.26 +Djibouti,0.47,25.2,0.56,20.39 +Dominica,0.12,112.4,1.75,166.05 +Dominican Republic,21.89,137.1,2.18,449.72 +Ecuador,35.73,112.2,2.35,139.36 +Egypt,220.79,190.7,2.64,220.35 +El Salvador,6.68,155.3,1.1,317.71 +Equatorial Guinea,6.69, 5 427.8,8.91,238.44 +Eritrea,0.52,...,0.11,4.43 +Estonia,18.43,-49.8,13.88,407.44 +Ethiopia,7.54,149.9,0.08,6.83 +Faeroe Islands,0.57,-8.8,11.72,408.04 +Falkland Islands (Malvinas),0.06,49.9,19.26,4.52 +Fiji,1.24,51.1,1.42,67.63 +Finland,56.4,-0.4,10.45,167.44 +France,364.82,-8.5,5.77,661.5 +French Guiana,0.72,-11.7,2.99,8.6 +French Polynesia,0.86,36.1,3.17,214.53 +Gabon,2.24,-53.8,1.42,8.36 +Gambia,0.42,121.1,0.24,37.34 +Georgia,7.93,...,1.89,113.8 +Germany,810.44,-22.2,10.08, 2 269.37 +Ghana,10.08,156.4,0.4,42.26 +Gibraltar,0.45,377.1,14.64, 75 783.33 +Greece,94.25,13.6,8.45,714.25 +Greenland,0.71,27,12.54,0.33 +Grenada,0.25,130,2.41,735.47 +Guadeloupe,1.77,37.1,3.87, 1 040.94 +Guatemala,11.26,121.3,0.75,103.39 +Guinea,2.6,145.8,0.23,10.56 +Guinea-Bissau,0.25,-2.9,0.15,6.8 +Guyana,1.78,56.3,2.36,8.29 +Haiti,2.21,122.5,0.22,79.68 +Honduras,8.41,224.5,1.1,74.78 +Hungary,49.86,-31.2,4.99,535.96 +Iceland,3.33,54.3,10.38,32.36 +India, 2 074.34,200.4,1.66,631.02 +Indonesia,563.98,277.1,2.3,295.14 +Iran (Islamic Republic of),586.6,177.8,7.8,360.15 +Iraq,133.65,154.3,4.19,307.08 +Ireland,37.72,16.3,8.11,540.15 +Israel,69.52,90.3,9.19, 3 149.81 +Italy,413.38,-4.9,6.93, 1 371.82 +Jamaica,7.76,-2.6,2.82,705.67 +Japan, 1 240.63,8.7,9.75, 3 282.70 +Jordan,22.26,114,3.29,249.18 +Kazakhstan,261.76,...,15.81,96.06 +Kenya,13.57,133,0.33,23.34 +Kiribati,0.06,183.2,0.6,85.77 +Kuwait,91.03,88.4,28.1, 5 108.86 +Kyrgyzstan,6.62,...,1.19,33.08 +Lao People's Democratic Republic,1.2,412.5,0.19,5.08 +Latvia,7.75,-59.3,3.76,120.06 +Lebanon,20.49,125.1,4.46, 1 960.15 +Lesotho,2.2,...,1.08,72.48 +Liberia,0.89,84.1,0.22,8 +Libya,39.02,6.1,6.2,22.18 +Liechtenstein,0.18,-10.4,4.9, 1 125.00 +Lithuania,14.03,-60.8,4.57,214.85 +Luxembourg,11.14,-6.8,21.42, 4 307.15 +Madagascar,2.45,148.3,0.11,4.17 +Malawi,1.21,97,0.08,10.18 +Malaysia,225.69,298.8,7.9,682.26 +Maldives,1.1,616.8,3.26, 3 679.33 +Mali,1.25,196.5,0.08,1.01 +Malta,2.67,42.9,6.44, 8 442.72 +Marshall Islands,0.1,115.3,1.95,567.4 +Martinique,2.45,18.4,6.21, 2 171.63 +Mauritania,2.31,-13.3,0.63,2.24 +Mauritius,3.92,167.7,3.13, 1 989.03 +Mexico,466.55,48.4,3.88,237.5 +Micronesia (Federated States of),0.13,...,1.24,182.76 +Monaco,0.08,-24.9,2.13, 39 600.00 +Mongolia,19.08,90,6.92,12.2 +Montenegro,2.57,...,4.13,186.11 +Montserrat,0.08,100.2,16.16,791.18 +Morocco,56.54,140.2,1.74,126.61 +Mozambique,3.28,227.8,0.13,4.09 +Myanmar,10.44,144.2,0.2,15.43 +Namibia,2.78,10701.2,1.24,3.37 +Nauru,0.05,-67.5,5.11, 2 442.86 +Nepal,4.33,583.2,0.16,29.45 +Netherlands,168.06,5.5,10.07, 4 499.07 +New Caledonia,3.85,137.2,15.43,207.48 +New Zealand,33.26,33.5,7.55,122.97 +Nicaragua,4.9,92.2,0.84,37.58 +Niger,1.42,70.9,0.08,1.12 +Nigeria,88.03,94,0.54,95.29 +Niue,0.01,197.3,6.81,42.31 +Norway,44.6,27.8,9,137.73 +Oman,64.85,469.6,20.2,209.55 +Pakistan,163.45,138.4,0.94,205.32 +Palau,0.22,...,10.86,487.36 +Panama,9.67,249.1,2.63,128.17 +Papua New Guinea,5.23,144.2,0.75,11.3 +Paraguay,5.3,134.2,0.84,13.03 +Peru,53.07,150.7,1.78,41.29 +Philippines,82.01,96.4,0.87,273.38 +Poland,327.72,-12.6,8.49, 1 050.77 +Portugal,51.24,13.6,4.85,555.71 +Qatar,83.88,612.3,44.02, 7 226.27 +Republic of Korea,589.43,138.7,11.94, 5 892.32 +Republic of Moldova,4.98,...,1.22,147.13 +Réunion,4.51,210.6,5.39, 1 794.83 +Romania,85.6,-51.9,4.26,359.09 +Russian Federation, 1 650.27,-34.2,11.52,96.52 +Rwanda,0.66,22.3,0.06,25.2 +Saint Helena,0.01,50.7,2.66,90.16 +Saint Kitts and Nevis,0.27,305.6,5.05, 1 025.67 +Saint Lucia,0.41,146.7,2.27,755.1 +Saint Pierre and Miquelon,0.07,-24,11.11,288.02 +Saint Vincent and the Grenadines,0.24,195.4,2.18,612.85 +Samoa,0.23,88.2,1.25,82.59 +Sao Tome and Principe,0.1,115.3,0.59,106.54 +Saudi Arabia,520.28,138.7,18.07,242.02 +Senegal,7.86,146.9,0.59,39.95 +Serbia,49.19,...,5.45,556.64 +Seychelles,0.6,425.7,6.37, 1 323.32 +Sierra Leone,0.9,131.1,0.15,12.43 +Singapore,22.39,-52.3,4.31, 31 351.53 +Slovakia,37.23,-39.8,6.88,759.3 +Slovenia,16.18,9.4,7.86,798 +Solomon Islands,0.2,22.8,0.37,6.85 +Somalia,0.58,3045.9,0.06,0.9 +South Africa,477.24,49.2,9.14,390.85 +Spain,280.92,23.5,6.01,555.19 +Sri Lanka,15.23,293.7,0.75,232.17 +State of Palestine,2.25,...,0.54,373.41 +Suriname,1.91,5.5,3.65,11.66 +Swaziland,1.05,146.5,0.87,60.4 +Sweden,48.48,-15.2,5.12,107.67 +Switzerland,41.85,-6.3,5.28, 1 013.63 +Syrian Arab Republic,57.67,54,2.81,311.43 +Tajikistan,2.78,...,0.36,19.45 +Thailand,303.37,216.6,4.53,591.23 +The former Yugoslav Republic of Macedonia,9.34,...,4.52,363.09 +Timor-Leste,0.18,...,0.17,12.29 +Togo,2.1,171.1,0.32,36.94 +Tonga,0.1,33.4,0.98,137.48 +Trinidad and Tobago,49.57,192.3,37.14, 9 663.59 +Tunisia,25.64,93.3,2.38,156.73 +Turkey,345.73,144.2,4.7,441.23 +Turkmenistan,62.22,...,12.18,127.47 +Turks and Caicos Islands,0.19,...,6.01,201.11 +Uganda,3.8,373,0.11,15.73 +Ukraine,306.53,-57.6,6.74,507.93 +United Arab Emirates,178.48,243.2,20.43, 2 134.98 +United Kingdom of Great Britain and Northern Ireland,464.04,-21.5,7.35, 1 913.59 +United Republic of Tanzania,7.3,207.7,0.2,7.73 +United States of America, 5 583.38,9.5,17.87,579.84 +Uruguay,7.77,94.7,2.3,44.12 +Uzbekistan,114.86,...,4.08,256.73 +Vanuatu,0.14,105.2,0.59,11.73 +Venezuela (Bolivarian Republic of),188.82,54.6,6.42,207.03 +Viet Nam,173.21,709.1,1.94,523.36 +Wallis and Futuna Islands,0.03,...,1.91,180.99 +Yemen,22.3,-843.3,0.92,42.23 +Zambia,3.05,24.6,0.21,4.05 +Zimbabwe,9.86,-36.4,0.69,25.23 diff --git a/GLOBAL EMISSION ANALSIS/Dataset/NOx_Emissions.csv b/GLOBAL EMISSION ANALSIS/Dataset/NOx_Emissions.csv new file mode 100644 index 000000000..8629e843c --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/Dataset/NOx_Emissions.csv @@ -0,0 +1,167 @@ +Country,latest year available,NOx emissions,% change since 1990,NOx emissions per capita +,,1000 tonnes,%,kg +Afghanistan,2005,62.58,…,2.56 +Albania,1994,18.01,…,5.73 +Algeria,2000,283.21,…,9.08 +Andorra*,1997,0.71,…,11.07 +Angola,2005,154,…,8.6 +Antigua and Barbuda,2000,2.27,…,29.23 +Argentina,2000,675.79,31.1,18.24 +Armenia,2010,17.21,-77.5,5.81 +Australia,2012, 2 536.45,44.6,110.71 +Austria,2012,178.26,-8.5,21.08 +Azerbaijan,1994,113,-28.2,14.72 +Bahrain,2000,52,…,77.98 +Bangladesh,2005,3.95,…,0.03 +Barbados,1997,0.05,-97.9,0.19 +Belarus,2012,189.92,-43.5,20.01 +Belgium,2012,193.31,-47.9,17.45 +Belize,1994,5.6,…,27.75 +Benin,2000,60.53,…,8.71 +Bhutan,2000,1.77,…,3.14 +Bolivia (Plurinational State of),2004,64.92,31.1,7.24 +Bosnia and Herzegovina,2001,40.07,-51.8,10.55 +Brazil,2005, 3 400.00,35.8,18.04 +Bulgaria,2012,148.48,-43.9,20.33 +Burkina Faso,1994,9.36,…,0.95 +Burundi,2005,11.23,…,1.42 +Cabo Verde,2000,2.03,…,4.62 +Cambodia,1994,38.02,…,3.67 +Cameroon,1994,252.22,…,18.62 +Central African Republic,1994,51.25,…,15.75 +Chad,1993,78.35,…,11.95 +Chile,2010,272.2,…,16 +Colombia,2004,335.16,24.5,7.84 +Comoros,1994,0.46,…,0.99 +Congo,2000,17.65,…,5.68 +Costa Rica,2005,26.88,-19.7,6.33 +Côte d'Ivoire,2000,290.49,…,17.59 +Croatia,2012,55.19,-40.9,12.87 +Cuba,1996,101.54,-28.4,9.27 +Cyprus,2012, 2 124.00, 13 321.3, 1 880.81 +Czech Republic,2012,210.77,-71.6,19.99 +Democratic People's Republic of Korea,2002,159,-65.4,6.84 +Democratic Republic of the Congo,2003,784.69,…,14.92 +Denmark,2012,119.79,-57.3,21.39 +Djibouti,2000,1..89,…,2.62 +Dominica,2005,0.63,…,8.93 +Dominican Republic,2000,93.12,68.3,10.88 +Ecuador,2006,231.77,47.7,16.59 +El Salvador,2005,40.42,…,6.8 +Eritrea,2000,6,…,1.7 +Estonia,2012, 3 182.00, 4 021.8, 2 403.25 +Ethiopia,1994,166,3.8,3 +Fiji,2004,11.49,…,14.04 +Finland,2012,146.75,-50.3,27.05 +France,2012, 1 074.74,-44.6,16.91 +Gabon,2000,7.54,…,6.12 +Gambia,2000,6.83,…,5.56 +Georgia,2006,27.67,-78.6,6.25 +Germany,2012, 1 269.26,-55.9,15.77 +Ghana,2000,205.64,…,10.92 +Greece,2012,258.91,-20.6,23.31 +Guatemala,1990,43.79,…,4.78 +Guinea,1994,70.42,…,9.34 +Guinea-Bissau,1994,4.88,…,4.22 +Guyana,2004,17,1.8,22.91 +Haiti,2000,14.7,…,1.72 +Honduras,2000,47.77,…,7.65 +Hungary,2012,109.41,-53,10.99 +Iceland,2012,20.55,-24.7,63.54 +Indonesia,2000,85.66,-28.9,0.4 +Iran (Islamic Republic of),2000,600.84,…,9.12 +Ireland,2012,72.87,-40.2,15.61 +Israel,2010,187.29,…,25.24 +Italy,2012,881.52,-57.4,14.76 +Jamaica,1994,30.86,…,12.51 +Japan,2012, 1 626.95,-20.4,12.8 +Jordan,2006,116,…,20.98 +Kazakhstan,2012,500.26,-25.9,29.74 +Kenya,1994,49.98,…,1.88 +Kiribati,1994,0,…,0 +Kuwait,1994,113,…,66.79 +Kyrgyzstan,2005,64.92,-44.5,12.69 +Lao People's Democratic Republic,2000,20.84,81.5,3.9 +Latvia,2012,34.87,-58.2,17.12 +Lebanon,2000,58.7,…,18.14 +Lesotho*,1998,5.05,…,2.77 +Liberia,2000,1,…,0.35 +Lithuania,2012,59.53,-63.4,19.74 +Luxembourg,2005,0.44,175,0.96 +Madagascar,2000,27.64,…,1.76 +Malawi,1994,26.31,-9,2.71 +Mali,2000,42.54,…,3.85 +Malta,2012,8.86,17.4,21.33 +Mauritania,2000,10.31,…,3.8 +Mauritius,2006,15.15,…,12.34 +Mexico,2002, 1 444.41,16.3,13.68 +Micronesia (Federated States of),1994,2.25,…,21.25 +Monaco,2012,0.33,-26.2,8.93 +Mongolia,1998,2.98,29.6,1.27 +Montenegro,2003,10.94,5.8,17.81 +Morocco,2000,190.91,…,6.46 +Mozambique,1994,93.81,22.1,6.11 +Myanmar,2005,0.03,…,0 +Namibia,2000,41.2,…,21.71 +Netherlands,2012,230.22,-59.4,13.75 +New Zealand,2012,158.5,57.7,35.73 +Nicaragua,2000,90.62,…,18.03 +Niger,2000,24,…,2.14 +Nigeria,2000, 1 008.00,…,8.2 +Niue,1994,26.3,…, 11 938.49 +Norway,2012,166.23,-13.3,33.12 +Oman,1994,0.22,…,0.1 +Pakistan,1994,410.26,…,3.43 +Panama*,2002,39.42,…,12.54 +Paraguay,2000,87.7,-20.3,16.54 +Peru,1994,181.66,…,7.69 +Philippines,1994,345.23,…,5.06 +Poland,2012,817.3,-36.1,21.17 +Portugal,2012,172.14,-30.4,16.37 +Qatar,2007,175.69,…,149.02 +Republic of Moldova,2010,37.06,-73,9.07 +Romania,2012,207.04,-54.7,10.38 +Russian Federation,2012, 5 603.13,-40.9,39.1 +Rwanda,2005,14.2,…,1.58 +Saint Lucia,2000,2,…,12.74 +Saint Vincent and the Grenadines,1997,27.88,-3.2,258.1 +Samoa,1994,0.97,…,5.75 +San Marino,2007,1.54,…,51.62 +Sao Tome and Principe,2005,0.77,…,5.03 +Senegal,2000,8.46,…,0.86 +Serbia,1998,164,-20.9,16.96 +Seychelles,2000,1.15,…,14.17 +Slovakia,2012,81.31,-64.1,15.01 +Slovenia,2012,44.84,-26.1,21.74 +Spain,2012,929.61,-31.1,19.93 +Sri Lanka,2000,83.69,…,4.46 +Sudan,2000,112,…,3.99 +Suriname,2003,10,…,20.51 +Swaziland,1994,19.93,…,21.11 +Sweden,2012,131.81,-51.2,13.81 +Switzerland,2012,73.71,-49.1,9.19 +Tajikistan,2010,6,-91.9,0.79 +Thailand,2000,907.1,…,14.47 +The former Yugoslav Republic of Macedonia,2009,34.11,-18,16.56 +Timor-Leste,2010,1.82,…,1.72 +Togo,2000,42.72,…,8.76 +Tonga,2000,0.59,…,6.07 +Trinidad and Tobago,1990,36.86,…,30.16 +Tunisia,2000,94.87,…,9.78 +Turkey,2012, 1 283.74,99.4,17.15 +Turkmenistan,2004,90.24,…,19.21 +Tuvalu,1994,0,…,0 +Uganda,2000,103.77,…,4.37 +Ukraine,2012, 1 205.57,-48.2,26.6 +United Arab Emirates,2005,332,…,74.07 +United Kingdom of Great Britain and Northern Ireland,2012, 1 067.63,-63.1,16.79 +United Republic of Tanzania,1994,979.07,524.9,33.73 +United States of America,2012, 11 882.02,-45.5,37.74 +Uruguay,2004,38.76,29.2,11.66 +Uzbekistan,2005,257.52,-37.5,9.93 +Vanuatu,1994,0.08,…,0.51 +Venezuela (Bolivarian Republic of),1999,395.79,…,16.48 +Viet Nam,2000,312.63,…,3.89 +Yemen,2000,115,…,6.46 +Zambia,2000, 1 276.67,…,120.61 +Zimbabwe,2000,148.83,...,11.91 diff --git a/GLOBAL EMISSION ANALSIS/Dataset/ODS_Consumption.csv b/GLOBAL EMISSION ANALSIS/Dataset/ODS_Consumption.csv new file mode 100644 index 000000000..82c12bec0 --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/Dataset/ODS_Consumption.csv @@ -0,0 +1,170 @@ +Country,Consumption of CFCs,,,Consumption of all ODS,, +,Baseline,2013,Reduction from baseline,2002,2013, Reduction from 2002 +,ODP tonnes,ODP tonnes,%,ODP tonnes,ODP tonnes, % +Afghanistan,380,0,100,181.5,17.7,90.2 +Albania,40.8,0,100,50.5,5.7,88.7 +Algeria, 2 119.5,0,100,1966.1,52,97.4 +Andorra,67.5,0,100,...,0,… +Angola,114.8,0,100,110,15.4,86 +Antigua and Barbuda,10.7,0,100,4,0.2,95 +Argentina, 4 697.2,0,100, 2 386.0,497.7,79.1 +Armenia,196.5,0,100,174.4,4.5,97.4 +Australia, 14 290.4,-7.5,100.1,389.7,48.3,87.6 +Azerbaijan,480.6,0,100,12.1,1.8,85.1 +Bahamas,64.9,0,100,58.4,2.7,95.4 +Bahrain,135.4,0,100,138.1,49.6,64.1 +Bangladesh,581.6,0,100,350.1,64.9,81.5 +Barbados,21.5,0,100,12.1,2.3,81 +Belarus, 2 510.9,0,100,2.7,7,-159.3 +Belize,24.4,0,100,21.7,2.4,88.9 +Benin,59.9,0,100,36,22.2,38.3 +Bhutan,0.2,0,100,0.1,0.3,-200 +Bolivia (Plurinational State of),75.7,0,100,67.4,0.4,99.4 +Bosnia and Herzegovina,24.2,0,100,259.2,5.1,98 +Botswana,6.9,0,100,10.2,10.8,-5.9 +Brazil, 10 525.8,0,100, 3 589.4, 1 189.3,66.9 +Brunei Darussalam,78.2,0,100,46.3,4.3,90.7 +Burkina Faso,36.3,0,100,27.9,14.9,46.6 +Burundi,59,0,100,19.2,7.1,63 +Cambodia,94.2,0,100,97,9.5,90.2 +Cameroon,256.9,0,100,261.7,82.3,68.6 +Canada, 19 958.2,0,100,923.1,65.9,92.9 +Cabo Verde,2.3,0,100,1.8,0.2,88.9 +Central African Republic,11.2,…,…,4.6,…,100 +Chad,34.6,0,100,27.3,15.2,44.3 +Chile,828.7,0,100,591.9,241.9,59.1 +China, 57 818.7,-386.6,100.7, 47 804.1, 15 690.6,67.2 +Colombia, 2 208.2,0,100, 1 002.2,176.7,82.4 +Comoros,2.5,0,100,1.9,0.1,94.7 +Congo,11.9,0,100,11.2,9.4,16.1 +Cook Islands,1.7,0,100,0,0,0 +Costa Rica,250.2,0,100,425.4,12.6,97 +Côte d'Ivoire,294.2,0,100,121.2,54.2,55.3 +Croatia,219.3,…,…,172.3,…,100 +Cuba,625.1,0,100,518,12.2,97.6 +Democratic People's Republic of Korea,441.7,0,100, 2 326.3,90.6,96.1 +Democratic Republic of the Congo,665.7,0,100, 1 081.3,35.9,96.7 +Djibouti,21,0,100,15.8,0.6,96.2 +Dominica,1.5,0,100,3.1,0.1,96.8 +Dominican Republic,539.8,0,100,406.9,34.8,91.4 +Ecuador,301.4,0,100,273.4,22,92 +Egypt, 1 668.0,0,100, 1 944.1,352.2,81.9 +El Salvador,306.5,0,100,108.1,8.1,92.5 +Equatorial Guinea,31.5,0,100,27.9,5.1,81.7 +Eritrea,41.1,0,100,32.2,1,96.9 +Ethiopia,33.8,0,100,86.6,5.5,93.6 +European Union (EU), 301 930.2,- 1 042.9,100.3,- 6 754.6,- 3 249.4,51.9 +Fiji,33.4,0,100,5.3,7.7,-45.3 +Gabon,10.3,0,100,6.9,28.6,-314.5 +Gambia,23.8,0,100,4.9,0.9,81.6 +Georgia,22.5,0,100,64.3,1.4,97.8 +Ghana,35.8,0,100,24,25.4,-5.8 +Grenada,6,0,100,2.3,0.3,87 +Guatemala,224.6,0,100,952.5,251.3,73.6 +Guinea,42.4,0,100,31.4,7.1,77.4 +Guinea-Bissau,26.3,0,100,27.2,2.3,91.5 +Guyana,53.2,0,100,15.6,1,93.6 +Haiti,169,0,100,197.7,2,99 +Honduras,331.6,0,100,555.7,18.9,96.6 +Iceland,195.1,0,100,2.6,0,100 +India, 6 681.0,-19.8,100.3, 15 026.9,956.1,93.6 +Indonesia, 8 332.7,0,100, 5 787.4,310.5,94.6 +Iran (Islamic Republic of), 4 571.7,0,100, 8 572.9,357.8,95.8 +Iraq, 1 517.0,0,100, 1 580.6,101.8,93.6 +Israel, 4 141.6,0,100, 1 241.3,94.5,92.4 +Jamaica,93.2,0,100,39.2,3.6,90.8 +Japan, 118 134.0,-181.3,100.2, 2 466.8,39.6,98.4 +Jordan,673.3,0,100,267,63,76.4 +Kazakhstan, 1 206.2,0,100,146.9,104.6,28.8 +Kenya,239.5,0,100,322,29.1,91 +Kiribati,0.7,0,100,0,0,0 +Kuwait,480.4,0,100,515.7,414.7,19.6 +Kyrgyzstan,72.8,0,100,50.2,4,92 +Lao People's Democratic Republic,43.3,0,100,42.9,1.6,96.3 +Lebanon,725.5,0,100,710.8,72.6,89.8 +Lesotho,5.1,0,100,4.6,2,56.5 +Liberia,56.1,0,100,54,4.5,91.7 +Libya,716.7,0,100, 1 596.5,144,91 +Liechtenstein,37.2,0,100,0.1,0,100 +Madagascar,47.9,0,100,8.8,16,-81.8 +Malawi,57.7,0,100,75.4,10.2,86.5 +Malaysia, 3 271.1,0,100, 1 966.3,449.9,77.1 +Maldives,4.6,0,100,4,3.2,20 +Mali,108.1,0,100,28.3,10.3,63.6 +Marshall Islands,1.1,0,100,0.3,0.1,66.7 +Mauritania,15.7,0,100,16.5,20.4,-23.6 +Mauritius,29.1,0,100,14.4,5.4,62.5 +Mexico, 4 624.9,0,100, 3 954.7, 1 106.2,72 +Micronesia (Federated States of),1.2,0,100,2.2,0,100 +Monaco,6.2,0,100,0.1,0,100 +Mongolia,10.6,0,100,7.3,0.9,87.7 +Montenegro,104.9,0,100,15.4,0.8,94.8 +Morocco,802.3,0,100, 1 070.0,49.4,95.4 +Mozambique,18.2,0,100,14.4,8.3,42.4 +Myanmar,54.3,0,100,45.7,3,93.4 +Namibia,21.9,0,100,20,7,65 +Nauru,0.5,0,100,0,0,… +Nepal,27,0,100,2.6,0.7,73.1 +New Zealand, 2 088.0,0,100,42.8,8.2,80.8 +Nicaragua,82.8,0,100,64.9,3.6,94.5 +Niger,32,0,100,27.6,14.6,47.1 +Nigeria, 3 650.0,0,100, 3 933.3,334.5,91.5 +Niue,0.1,0,100,0,0,0 +Norway, 1 313.0,0,100,-42.8,0,100 +Oman,248.4,0,100,201.5,28.9,85.7 +Pakistan, 1 679.4,0,100, 2 347.2,247,89.5 +Palau,1.6,0,100,0.2,0.1,50 +Panama,384.1,0,100,204.7,21.4,89.5 +Papua New Guinea,36.3,0,100,39.7,3,92.4 +Paraguay,210.6,0,100,105.5,16.5,84.4 +Peru,289.5,0,100,203.6,25.8,87.3 +Philippines, 3 055.8,0,100, 1 795.1,136.7,92.4 +Qatar,101.4,0,100,105.4,80.7,23.4 +Republic of Korea, 9 159.8,0,100, 11 745.9, 1 893.1,83.9 +Republic of Moldova,73.3,0,100,29.6,1,96.6 +Russian Federation, 100 352.0,288,99.7,892.3,836.5,6.3 +Rwanda,30.4,0,100,30.4,3.8,87.5 +Saint Kitts and Nevis,3.7,0,100,6.3,0.3,95.2 +Saint Lucia,8.3,0,100,7.7,0.6,92.2 +Samoa,4.5,0,100,2.6,0.1,96.2 +Sao Tome and Principe,4.7,0,100,4.4,0.1,97.7 +Saudi Arabia, 1 798.5,0,100, 1 926.4, 1 440.3,25.2 +Senegal,155.8,0,100,82.3,7.7,90.6 +Serbia,849.2,0,100,384.9,8.1,97.9 +Seychelles,2.9,0,100,166.1,0.6,99.6 +Sierra Leone,78.6,0,100,84.4,0.8,99.1 +Singapore, 2 718.2,0,100,146.7,116.7,20.4 +Solomon Islands,2.1,0,100,5.7,0.2,96.5 +Somalia,241.4,0,100,124.1,16.5,86.7 +South Africa,592.6,0,100,848.8,426.4,49.8 +Sri Lanka,445.6,0,100,227.4,13.4,94.1 +Saint Vincent and the Grenadines,1.8,0,100,6.4,0.2,96.9 +Sudan,456.8,0,100,258.2,51.9,79.9 +Suriname,41.3,0,100,51,1.2,97.6 +Swaziland,24.6,0,100,2.4,1.2,50 +Switzerland, 7 960.0,0,100,26.2,1.4,94.7 +Syrian Arab Republic, 2 224.6,0,100, 1 754.1,28,98.4 +Tajikistan,211,0,100,12.6,2.3,81.7 +Thailand, 6 082.1,0,100, 3 612.5,863.3,76.1 +The former Yugoslav Republic of Macedonia,519.7,0,100,44.6,0.7,98.4 +Timor-Leste,36,0,100,2.7,0.3,88.9 +Togo,39.8,0,100,35.3,19,46.2 +Tonga,1.3,0,100,1,0,100 +Trinidad and Tobago,120,0,100,93.9,39.5,57.9 +Tunisia,870.1,0,100,552.5,38.7,93 +Turkey, 3 805.7,0,100, 1 336.4,147,89 +Turkmenistan,37.3,0,100,10.9,4.2,61.5 +Tuvalu,0.3,0,100,0,0,-100 +Uganda,12.8,0,100,44.9,0,100 +Ukraine, 4 725.2,0,100,145.5,59.4,59.2 +United Arab Emirates,529.3,0,100,624.2,539.4,13.6 +United Republic of Tanzania,253.9,0,100,71.5,1.6,97.8 +United States of America, 305 963.6,-498.6,100.2, 16 206.4,711.3,95.6 +Uruguay,199.1,0,100,100.9,15.5,84.6 +Uzbekistan, 1 779.2,0,100,0.8,4.6,-475 +Vanuatu,0,0,0,0,0.1,-100 +Venezuela (Bolivarian Republic of), 3 322.4,0,100, 1 653.0,139.9,91.5 +Viet Nam,500,0,100,447.4,252.9,43.5 +Yemen, 1 796.1,0,100, 1 135.8,127.2,88.8 +Zambia,27.4,0,100,24,5,79.2 +Zimbabwe,451.4,0,100,345.8,15.8,95.4 diff --git a/GLOBAL EMISSION ANALSIS/Dataset/SO2_emissions.csv b/GLOBAL EMISSION ANALSIS/Dataset/SO2_emissions.csv new file mode 100644 index 000000000..e08e2f854 --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/Dataset/SO2_emissions.csv @@ -0,0 +1,136 @@ +Country,latest year available,SO2 emissions,% change since 1990,SO2 emissions per capita +,,1000 tonnes ,%,kg +Afghanistan,2005,13.86,…,0.57 +Algeria,2000,45.64,…,1.46 +Andorra*,1997,0.69,…,10.77 +Antigua and Barbuda,2000,2.75,-2.83,35.42 +Argentina,2000,87.62,10.63,2.36 +Armenia,2010,29.44, 7 448.72,9.93 +Australia,2012,797.76,-48.69,34.82 +Austria,2012,17.23,-76.84,2.04 +Azerbaijan,1994,48,-18.64,6.25 +Bahrain,2000,27,…,40.49 +Barbados,1997,0.05,…,0.19 +Belarus,2012,146.86,-86.44,15.47 +Belgium,2012,48.75,-86.42,4.4 +Belize,1994,0.53,…,2.63 +Benin,2000,13.88,…,2 +Bhutan,2000,1.06,…,1.88 +Bolivia (Plurinational State of),2000,12.1,8.42,1.45 +Bosnia and Herzegovina,2001,213.74,-52.83,56.25 +Bulgaria,2012, 1 335.49,-15.59,182.85 +Chile,2010,271.4,…,15.95 +Colombia,2004,142.81,0.71,3.34 +Costa Rica,2005,4.85,…,1.14 +Côte d'Ivoire,2000, 4 079.55,…,246.98 +Croatia,2012,25.58,-85.3,5.97 +Cuba,1996,432.38,-0.1,39.47 +Cyprus,2012,16.1,-45.9,14.26 +Czech Republic,2012,157.91,-91.58,14.97 +Democratic People's Republic of Korea,2002, 1 384.00,-55.66,59.53 +Democratic Republic of the Congo,2000,0.02,…,0 +Denmark,2012,13.43,-92.51,2.4 +Dominica,2005,0.22,…,3.12 +Dominican Republic,2000,110.15,42.94,12.86 +Ecuador,2006,8.87,37.73,0.64 +Estonia,2012,69.96,-62.03,52.84 +Ethiopia,1995,13.2,18.92,0.23 +Fiji,1994,0.03,…,0.04 +Finland,2012,52.06,-79.08,9.6 +France,2012,274.29,-79.68,4.32 +Gabon,2000,7.67,…,6.23 +Gambia,2000, 3 031.94,…, 2 467.27 +Georgia,2006,0.5,-99.8,0.11 +Germany,2012,427.07,-91.92,5.31 +Ghana,2000,0.5,…,0.03 +Greece,2012,244.9,-48.56,22.04 +Guatemala,1990,74.5,…,8.13 +Guinea,1994,0.44,…,0.06 +Guyana,2004,6.9,-8,9.3 +Haiti,2000,13.58,…,1.59 +Honduras,2000,0.38,…,0.06 +Hungary,2012,31.8,-96.15,3.19 +Iceland,2012,83.88,295.1,259.36 +Iran (Islamic Republic of),2000,139.46,…,2.12 +Ireland,2012,23.12,-87.31,4.95 +Israel,2010,164.46,…,22.16 +Italy,2012,181.73,-89.93,3.04 +Jamaica,1994,99.7,…,40.42 +Japan,2012,936.84,-25.29,7.37 +Jordan,2006,138,…,24.95 +Kazakhstan,2012,649.61,-37.92,38.62 +Kuwait,1994,319,…,188.55 +Kyrgyzstan,2005,26.9,-72.7,5.26 +Lao People's Democratic Republic,2000,1.59,…,0.3 +Latvia,2012,2.39,-97.66,1.17 +Lebanon,2000,93.42,…,28.87 +Lesotho*,1998,0,…,0 +Lithuania,2012,36.48,-82.78,12.09 +Luxembourg,2005,0.21,31.25,0.46 +Madagascar,2000,39.82,…,2.53 +Mali,1995,0,…,0 +Malta,2012,8.25,-47.72,19.85 +Mauritania,2000,0.09,…,0.03 +Mauritius,2006,11.44,…,9.32 +Mexico,2002, 2 612.91,-3.13,24.75 +Micronesia (Federated States of),1994,0.53,…,5 +Monaco,2012,0.04,-42.86,1.07 +Montenegro,2003,45.43,6.27,73.95 +Morocco,2000,484.09,…,16.39 +Namibia,2000,10.9,…,5.74 +Netherlands,2012,33.91,-82.86,2.02 +New Zealand,2012,78.16,33.84,17.62 +Nicaragua,2000,0.19,…,0.04 +Niger,2000, 2 140.00,…,190.65 +Nigeria,2000,190,…,1.55 +Niue,1994, 2 211.18,…,1 003 713.12 +Norway,2012,16.66,-68.1,3.32 +Oman,1994,3.38,,1.58 +Pakistan,1994,775.46,…,6.49 +Panama,2000,0.13,…,0.04 +Paraguay,2000,0.16,-46.67,0.03 +Peru,1994,123.26,…,5.22 +Philippines,1994,458.53,…,6.72 +Poland,2012,853.3,-73.42,22.1 +Portugal,2012,59.22,-81.71,5.63 +Qatar,2007,143.92,…,122.07 +Republic of Moldova,2010,18.78,-93.63,4.6 +Romania,2012,293.03,-66.35,14.69 +Russian Federation,2012,681.95,-16.02,4.76 +Rwanda,2005,18,…,2 +Saint Lucia,2000,0.1,…,1.86 +Saint Vincent and the Grenadines,1997,0.32,28,2.96 +Senegal,2000,41.96,…,4.26 +Serbia,1998,388,-20.98,40.13 +Slovakia,2012,58.52,-88.83,10.81 +Slovenia,2012,10.12,-94.91,4.91 +Spain,2012,407.94,-81.2,8.75 +Sri Lanka,2000,105.87,…,5.64 +Sudan,2000,1,…,0.04 +Swaziland,1994,1.97,…,2.09 +Sweden,2012,27.79,-73.59,2.91 +Switzerland,2012,10.67,-73.7,1.33 +Tajikistan,2010,9,-73.53,1.19 +Thailand,2000,618.9,…,9.87 +The former Yugoslav Republic of Macedonia,2009,205.83, 6 807.05,99.97 +Timor-Leste,2010,0.4,…,0.38 +Togo,2000,8.35,…,1.71 +Tonga,2000,0.1,…,1.02 +Trinidad and Tobago*,1996,8.6,-1.71,7.04 +Tunisia,2000,111.29,…,11.47 +Turkey,2012,248.83,-70.21,3.32 +Turkmenistan,2004,2.43,…,0.52 +Uganda,2000,4.1,…,0.17 +Ukraine,2012, 1 687.55,-68.15,37.24 +United Arab Emirates,2005, 10 354.00,…, 2 310.14 +United Kingdom of Great Britain and Northern Ireland,2012,429.44,-88.46,6.75 +United Republic of Tanzania,1994,175.74,8.39,6.05 +United States of America,2012, 4 739.47,-77.36,15.06 +Uruguay,2004,51.5,25.09,15.49 +Uzbekistan,2005,170.85,-74.93,6.59 +Viet Nam,2000,9.86,…,0.12 +Yemen,2000,12,…,0.67 +Zambia,2000,6.16,…,0.58 +Zimbabwe,2000,1.04,…,0.08 + ,,,, + ,,,, diff --git a/GLOBAL EMISSION ANALSIS/Dataset/merged_ghg_data.csv b/GLOBAL EMISSION ANALSIS/Dataset/merged_ghg_data.csv new file mode 100644 index 000000000..aadaf1665 --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/Dataset/merged_ghg_data.csv @@ -0,0 +1,189 @@ +Country,latest year available_perc,Total GHG emissions _perc,GHG from Energy_perc,"GHG from Energy +of which: from Transport_perc",GHG from Industrial Processes_perc,GHG from Agriculture_perc,GHG from Waste_perc,Total GHG emissions _tonnes,GHG from Energy_tonnes,"GHG from Energy +of which: from Transport_tonnes",GHG from Industrial Processes_tonnes,GHG from Agriculture_tonnes,GHG from Waste_tonnes +,,mio. tonnes of CO2 equivalent,%,%,%,%,%,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent,mio. tonnes of CO2 equivalent +Afghanistan,2005,19.33,19.54,8.75,1.62,78.17,0.67,19.33,3.78,1.69,0.31,15.11,0.13 +Albania,1994,5.53,56.11,14.41,3.79,33.96,6.14,5.53,3.1,0.8,0.21,1.88,0.34 +Algeria,2000,111.02,78.9,11.52,4.92,5.89,10.29,111.02,87.6,12.79,5.46,6.53,11.43 +Angola,2005,61.61,61.24,...,0.57,36.64,1.54,61.61,37.73,...,0.35,22.58,0.95 +Antigua and Barbuda,2000,0.6,62.35,30.55,...,17.45,20.19,0.6,0.37,0.18,...,0.1,0.12 +Argentina,2000,282,46.79,14.27,3.94,44.3,4.97,282,131.96,40.24,11.11,124.92,14.01 +Armenia,2010,7.2,69.54,17.32,3.14,18.36,8.97,7.2,5.01,1.25,0.23,1.32,0.65 +Australia,2012,543.65,76.03,16.59,5.74,16.07,2.16,543.65,413.36,90.21,31.21,87.36,11.72 +Austria,2012,80.06,74.56,27.02,13.59,9.37,2.07,80.06,59.69,21.64,10.88,7.5,1.66 +Azerbaijan,1994,43.17,10.44,...,...,8.53,4.08,43.17,4.51,...,...,3.68,1.76 +Bahamas,1994,2.2,84.94,...,...,0.96,...,2.2,1.87,...,...,0.02,... +Bahrain,2000,22.37,77.12,6.79,11.24,...,11.64,22.37,17.25,1.52,2.52,...,2.6 +Bangladesh,2005,99.44,38.86,5.53,2.93,43.36,14.85,99.44,38.65,5.5,2.91,43.12,14.77 +Barbados,1997,4.06,49.98,6.2,4.22,1.65,44.16,4.06,2.03,0.25,0.17,0.07,1.79 +Belarus,2012,89.28,61.94,8.08,4.79,26.18,7.02,89.28,55.3,7.22,4.27,23.37,6.27 +Belgium,2012,116.52,81.02,21.41,9.59,7.94,1.29,116.52,94.4,24.95,11.17,9.26,1.51 +Belize,1994,6.34,9.58,4.96,0,4.27,86.15,6.34,0.61,0.31,0,0.27,5.46 +Benin,2000,6.25,30.09,14.53,...,67.81,2.1,6.25,1.88,0.91,...,4.24,0.13 +Bhutan,2000,1.56,17.23,7.59,15.28,64.59,2.9,1.56,0.27,0.12,0.24,1,0.05 +Bolivia (Plurinational State of),2004,43.67,20.69,9.79,48.71,26.7,3.9,43.67,9.03,4.27,21.27,11.66,1.7 +Bosnia and Herzegovina,2001,16.12,76.5,...,3.7,13.67,6.13,16.12,12.33,...,0.6,2.2,0.99 +Botswana,1994,9.29,41.35,8.62,2.27,54.53,1.85,9.29,3.84,0.8,0.21,5.07,0.17 +Brazil,2005,862.81,38.11,15.59,8.95,48.19,4.76,862.81,328.79,134.54,77.2,415.77,41.05 +Bulgaria,2012,61.26,77,13.75,6.36,10.67,5.9,61.26,47.17,8.42,3.9,6.54,3.61 +Burkina Faso,1994,5.97,15.22,5.41,...,78.89,5.89,5.97,0.91,0.32,...,4.71,0.35 +Burundi,2005,26.47,1.35,0.4,0,97.9,0.76,26.47,0.36,0.11,0,25.92,0.2 +Cabo Verde,2000,0.45,65.6,30.73,0.19,29.23,4.98,0.45,0.29,0.14,0,0.13,0.02 +Cambodia,1994,12.76,14.74,6.51,0.39,82.74,2.13,12.76,1.88,0.83,0.05,10.56,0.27 +Cameroon,1994,165.73,1.95,0.82,35.31,61.69,1.04,165.73,3.24,1.35,58.52,102.23,1.73 +Canada,2012,698.63,80.98,27.93,8.08,7.95,2.94,698.63,565.76,195.11,56.46,55.53,20.57 +Central African Republic,1994,37.74,50.16,0.32,...,43.04,6.8,37.74,18.93,0.12,...,16.24,2.57 +Chad,1993,8.02,3.86,...,...,91,5.14,8.02,0.31,...,...,7.3,0.41 +Chile,2006,78.96,73.23,21.63,6.64,16.97,3.15,78.96,57.82,17.08,5.24,13.4,2.49 +China,2005, 7 465.86,77.28,5.77,10.25,10.97,1.5, 7 465.86, 5 769.85,430.79,764.89,819.33,111.79 +Colombia,2004,153.88,42.87,14.15,5.89,44.56,6.68,153.88,65.97,21.77,9.07,68.57,10.28 +Comoros,1994,0.51,13.77,...,...,85.61,0.62,0.51,0.07,...,...,0.44,0 +Congo,2000,2.07,78.05,18.68,0.23,15.72,6,2.07,1.61,0.39,0,0.32,0.12 +Cook Islands,1994,0.08,40.55,19.99,...,12.85,46.59,0.08,0.03,0.02,...,0.01,0.04 +Costa Rica,2005,12.11,47,32.12,4.1,38,10.9,12.11,5.69,3.89,0.5,4.6,1.32 +Côte d'Ivoire,2000,271.2,24.55,0.81,0,71.76,3.69,271.2,66.59,2.2,0,194.61,10 +Croatia,2012,26.45,71.54,21.59,10.78,12.83,4.26,26.45,18.92,5.71,2.85,3.39,1.13 +Cuba,1996,40.19,66.29,...,3.03,25.56,5.12,40.19,26.64,...,1.22,10.27,2.06 +Cyprus,2012,9.26,70.8,22.32,8.79,8.81,10.81,9.26,6.56,2.07,0.81,0.82,1 +Czech Republic,2012,131.47,81.46,12.86,9.2,6.13,2.87,131.47,107.09,16.91,12.1,8.06,3.77 +Democratic People's Republic of Korea,2002,87.33,88.86,1.7,6.48,3.22,1.43,87.33,77.61,1.49,5.66,2.81,1.25 +Democratic Republic of the Congo,2003,46,7.82,1.81,0.34,75.18,16.66,46,3.6,0.83,0.16,34.58,7.66 +Denmark,2012,53.12,76.07,23.51,3.41,18.15,2.07,53.12,40.41,12.49,1.81,9.64,1.1 +Djibouti,2000,1.07,33.26,10.15,...,61.67,5.07,1.07,0.36,0.11,...,0.66,0.05 +Dominica,2005,0.18,66.91,25.73,0,22.76,10.32,0.18,0.12,0.05,0,0.04,0.02 +Dominican Republic,2000,26.43,69.03,23.39,3.07,21.57,6.33,26.43,18.25,6.18,0.81,5.7,1.67 +Ecuador,2006,247.99,10.85,5.16,1.11,84.73,3.32,247.99,26.9,12.78,2.75,210.11,8.23 +Egypt,2000,193.24,60.13,14.08,14.37,16.46,9.04,193.24,116.19,27.21,27.77,31.8,17.48 +El Salvador,2005,11.07,53.38,22.43,3.99,28.13,14.5,11.07,5.91,2.48,0.44,3.11,1.61 +Eritrea,2000,3.93,19.17,5.06,0.89,78.88,1.07,3.93,0.75,0.2,0.04,3.1,0.04 +Estonia,2012,19.19,87.93,11.88,3.45,6.91,1.61,19.19,16.87,2.28,0.66,1.33,0.31 +Ethiopia,1995,47.75,15.84,...,0.72,80.63,2.8,47.75,7.57,...,0.34,38.5,1.34 +Fiji,2004,2.71,60.98,27.05,...,35.52,3.5,2.71,1.65,0.73,...,0.96,0.09 +Finland,2012,60.97,78.43,20.8,8.71,9.36,3.39,60.97,47.81,12.68,5.31,5.71,2.07 +France,2012,496.4,71.87,26.98,7.26,18.07,2.57,496.4,356.76,133.93,36.03,89.71,12.76 +Gabon,2000,6.16,86.08,6.57,1.46,5.84,6.61,6.16,5.3,0.4,0.09,0.36,0.41 +Gambia,2000,19.38,1.75,0.51,89.08,8.09,1.07,19.38,0.34,0.1,17.27,1.57,0.21 +Georgia,2006,12.22,48.82,10.52,14.58,27.11,9.44,12.22,5.97,1.29,1.78,3.31,1.15 +Germany,2012,939.08,83.7,16.56,7.27,7.4,1.44,939.08,786.03,155.49,68.25,69.49,13.55 +Ghana,2006,18.23,50.66,17.12,1.34,35.57,12.43,18.23,9.23,3.12,0.24,6.48,2.27 +Greece,2012,110.99,78.61,14.5,8.66,8.18,4.27,110.99,87.26,16.1,9.61,9.08,4.74 +Grenada,1994,1.61,8.47,3.24,...,0.03,91.5,1.61,0.14,0.05,...,0,1.47 +Guatemala,1990,14.74,31.09,14.48,3.69,59.91,5.3,14.74,4.58,2.13,0.54,8.83,0.78 +Guinea,1994,5.06,40.4,12.36,2.84,50.02,6.75,5.06,2.04,0.63,0.14,2.53,0.34 +Guinea-Bissau,1994,1.69,10.62,0.02,0,86.75,2.63,1.69,0.18,0,0,1.47,0.04 +Guyana,2004,3.07,53.94,10.22,...,43.31,2.75,3.07,1.66,0.31,...,1.33,0.08 +Haiti,2000,6.68,23.47,11.22,...,71.39,2.84,6.68,1.57,0.75,...,4.77,0.19 +Honduras,2000,10.3,33.36,22.07,6.7,43.02,16.92,10.3,3.44,2.27,0.69,4.43,1.74 +Hungary,2012,61.98,73.37,17.5,6.9,14.05,5.12,61.98,45.47,10.85,4.27,8.71,3.18 +Iceland,2012,4.47,38.44,19.09,42.15,15.18,4.09,4.47,1.72,0.85,1.88,0.68,0.18 +India,2000, 1 523.77,67.4,6.44,5.81,23.34,3.45, 1 523.77, 1 027.02,98.1,88.6,355.6,52.55 +Indonesia,2000,554.33,50.68,10.25,7.7,13.24,28.38,554.33,280.94,56.82,42.67,73.4,157.33 +Iran (Islamic Republic of),2000,483.67,78.11,15.71,6.46,8.89,6.54,483.67,377.8,76.01,31.26,42.99,31.61 +Ireland,2012,58.53,63.32,18.62,4.14,30.7,1.72,58.53,37.06,10.9,2.42,17.97,1.01 +Israel,2010,75.42,84.98,21.7,3.91,3.17,7.93,75.42,64.09,16.37,2.95,2.39,5.98 +Italy,2012,461.19,82.37,23,6.11,7.68,3.52,461.19,379.86,106.06,28.2,35.4,16.21 +Jamaica,1994,116.31,7.08,1.09,0.33,92.27,0.33,116.31,8.23,1.27,0.38,107.32,0.38 +Japan,2012, 1 343.14,91.55,16.36,5.18,1.78,1.49, 1 343.14, 1 229.60,219.76,69.52,23.9,20.03 +Jordan,2006,27.75,75.37,17.03,9.19,4.47,10.97,27.75,20.92,4.73,2.55,1.24,3.05 +Kazakhstan,2012,283.55,85.08,8.2,5.9,7.59,1.43,283.55,241.23,23.25,16.74,21.53,4.06 +Kenya,1994,21.47,37.54,...,4.61,56.37,1.49,21.47,8.06,...,0.99,12.1,0.32 +Kiribati,1994,0.03,66.36,...,...,1.75,31.93,0.03,0.02,...,...,0,0.01 +Kuwait,1994,32.37,95.31,16.66,2.06,0.2,2.42,32.37,30.86,5.39,0.67,0.07,0.78 +Kyrgyzstan,2005,12.02,74.07,20.7,4.32,16.11,5.49,12.02,8.9,2.49,0.52,1.94,0.66 +Lao People's Democratic Republic,2000,8.9,11.68,5.02,0.54,86.26,1.51,8.9,1.04,0.45,0.05,7.68,0.13 +Latvia,2012,10.98,65.78,25.45,6.27,22.04,5.47,10.98,7.22,2.79,0.69,2.42,0.6 +Lebanon,2000,18.45,75.1,21.48,9.71,5.78,9.41,18.45,13.85,3.96,1.79,1.07,1.74 +Lesotho,2000,3.51,30.73,...,...,63.59,5.68,3.51,1.08,...,...,2.23,0.2 +Liberia,2000,8.02,67.49,27.09,...,31.94,0.57,8.02,5.41,2.17,...,2.56,0.05 +Liechtenstein,2012,0.23,84.66,36.61,3.72,10.32,0.89,0.23,0.19,0.08,0.01,0.02,0 +Lithuania,2012,21.62,54.97,20.99,16.78,23.4,4.47,21.62,11.89,4.54,3.63,5.06,0.97 +Luxembourg,2012,11.84,88.67,55.06,5.16,5.65,0.42,11.84,10.5,6.52,0.61,0.67,0.05 +Madagascar,2000,29.34,9.21,3.2,0.08,90.48,0.23,29.34,2.7,0.94,0.02,26.55,0.07 +Malawi,1994,7.07,52.58,...,0.83,45.32,1.27,7.07,3.72,...,0.06,3.2,0.09 +Malaysia,2000,193.4,76.01,...,7.31,3.05,13.63,193.4,147,...,14.13,5.9,26.36 +Maldives,1994,0.15,84.32,...,...,...,15.68,0.15,0.13,...,...,...,0.02 +Mali,2000,12.3,19.44,5.51,0.92,76.82,2.82,12.3,2.39,0.68,0.11,9.45,0.35 +Malta,2012,3.14,89.87,17.55,5.47,2.53,2.07,3.14,2.82,0.55,0.17,0.08,0.07 +Mauritania,2000,6.94,16.86,5.83,0.28,81.62,1.25,6.94,1.17,0.41,0.02,5.67,0.09 +Mauritius,2006,4.76,66.29,18.01,1.37,4.33,28.02,4.76,3.15,0.86,0.06,0.21,1.33 +Mexico,2006,641.45,67.05,22.56,9.9,7.1,15.94,641.45,430.1,144.69,63.53,45.55,102.27 +Micronesia (Federated States of),1994,0.25,97.96,0.26,0.01,0.34,1.69,0.25,0.24,0,0,0,0 +Monaco,2012,0.09,91.51,31.15,7.02,...,1.38,0.09,0.09,0.03,0.01,...,0 +Mongolia,2006,17.71,57.7,10.65,5.04,36.48,0.78,17.71,10.22,1.89,0.89,6.46,0.14 +Montenegro,2003,5.31,50,...,35.43,12.33,2.24,5.31,2.66,...,1.88,0.66,0.12 +Morocco,2000,59.7,53.79,9.99,6.32,35.05,4.84,59.7,32.11,5.96,3.77,20.93,2.89 +Mozambique,1994,8.22,22.64,10.39,0.62,56.2,20.53,8.22,1.86,0.85,0.05,4.62,1.69 +Myanmar,2005,38.37,21.4,6.47,1.32,69.13,8.14,38.37,8.21,2.48,0.51,26.53,3.12 +Namibia,2000,9.09,23.87,11.33,...,74.15,1.98,9.09,2.17,1.03,...,6.74,0.18 +Nauru,1994,0.04,78.89,...,...,13.68,7.44,0.04,0.03,...,...,0,0 +Nepal,1994,31.19,10.47,1.46,0.53,87.2,1.8,31.19,3.27,0.46,0.17,27.2,0.56 +Netherlands,2012,191.67,84.49,17.73,5.18,8.3,1.92,191.67,161.95,33.98,9.92,15.9,3.69 +New Zealand,2012,76.05,42.24,18.09,6.94,46.05,4.73,76.05,32.12,13.76,5.28,35.02,3.6 +Nicaragua,2000,11.98,32.73,10.3,2.55,59.27,5.44,11.98,3.92,1.23,0.31,7.1,0.65 +Niger,2000,13.63,19.24,5.56,0.13,78.04,2.58,13.63,2.62,0.76,0.02,10.64,0.35 +Nigeria,2000,212.44,71.63,...,0.99,26.27,1.12,212.44,152.16,...,2.1,55.81,2.37 +Niue,1994,4.42,99.94,32.16,...,0.02,0.03,4.42,4.42,1.42,...,0,0 +Norway,2012,52.76,74.32,28.74,14.55,8.54,2.26,52.76,39.21,15.16,7.67,4.5,1.19 +Oman,1994,20.88,59.61,7.93,2.84,35.77,1.78,20.88,12.44,1.66,0.59,7.47,0.37 +Pakistan,1994,160.59,51.84,11.63,7.02,38.57,2.57,160.59,83.26,18.68,11.27,61.94,4.12 +Palau,2000,0.09,...,...,0.01,99.99,...,0.09,...,...,0,0.09,... +Panama,2000,9.71,49.59,28.08,6.11,33.17,11.13,9.71,4.81,2.73,0.59,3.22,1.08 +Papua New Guinea,1994,5.01,18.91,...,3.85,77.24,...,5.01,0.95,...,0.19,3.87,... +Paraguay,2000,23.43,15.72,11.9,1.69,79.51,3.08,23.43,3.68,2.79,0.4,18.63,0.72 +Peru,2010,80.59,50.38,18.87,7.79,32.33,9.51,80.59,40.61,15.21,6.27,26.05,7.66 +Philippines,2000,126.88,54.91,20.44,6.79,29.16,9.14,126.88,69.67,25.94,8.61,37,11.6 +Poland,2012,399.27,80.06,11.73,6.75,9.18,3.82,399.27,319.66,46.82,26.96,36.65,15.24 +Portugal,2012,68.85,69.56,24.7,7.72,10.49,11.89,68.85,47.9,17,5.31,7.22,8.19 +Qatar,2007,61.59,91.27,8.67,7.92,0.14,0.67,61.59,56.22,5.34,4.88,0.08,0.41 +Republic of Korea,2012,688.43,87.19,12.55,7.46,3.19,2.15,688.43,600.25,86.36,51.37,21.99,14.81 +Republic of Moldova,2010,13.28,67.39,14.35,4.26,16.06,11.89,13.28,8.95,1.9,0.56,2.13,1.58 +Romania,2012,118.79,69.22,12.68,10.42,15.33,4.92,118.79,82.22,15.06,12.38,18.21,5.85 +Russian Federation,2012, 2 297.15,82.16,10.51,7.89,6.28,3.65, 2 297.15, 1 887.26,241.37,181.14,144.22,83.95 +Rwanda,2005,6.18,12.96,4.43,2.44,83.58,1.02,6.18,0.8,0.27,0.15,5.17,0.06 +Saint Kitts and Nevis,1994,0.16,44.97,15.98,...,25.78,29.25,0.16,0.07,0.03,...,0.04,0.05 +Saint Lucia,2000,0.55,63.55,...,...,7.33,29.01,0.55,0.35,...,...,0.04,0.16 +Saint Vincent and the Grenadines,1997,0.41,26.15,9.23,...,64.4,9.45,0.41,0.11,0.04,...,0.26,0.04 +Samoa,1994,0.56,18.34,12.7,...,76.79,4.87,0.56,0.1,0.07,...,0.43,0.03 +San Marino,2007,0.24,98.29,58.82,...,1.72,...,0.24,0.23,0.14,...,0,... +Sao Tome and Principe,2005,0.1,71.4,28.55,6.73,14.71,7.15,0.1,0.07,0.03,0.01,0.01,0.01 +Saudi Arabia,2000,296.06,82.84,19.62,6.56,4.17,6.44,296.06,245.25,58.09,19.41,12.33,19.07 +Senegal,2000,16.88,48.47,11.38,2.15,37.08,12.29,16.88,8.18,1.92,0.36,6.26,2.08 +Serbia,1998,66.34,76.19,5.84,5.46,14.32,4.04,66.34,50.55,3.88,3.62,9.5,2.68 +Seychelles,2000,0.33,79.31,20.13,...,4.72,15.97,0.33,0.26,0.07,...,0.02,0.05 +Singapore,2010,46.87,97.19,14.85,2.38,...,...,46.87,45.55,6.96,1.11,...,... +Slovakia,2012,43.12,68.5,15.25,18.54,7.56,5,43.12,29.53,6.57,7.99,3.26,2.16 +Slovenia,2012,18.91,81.84,30.53,5.36,9.9,2.58,18.91,15.48,5.77,1.01,1.87,0.49 +Solomon Islands,1994,0.29,100,65.49,...,...,...,0.29,0.29,0.19,...,...,... +South Africa,1994,379.84,78.34,11.46,8,9.33,4.33,379.84,297.57,43.52,30.39,35.46,16.43 +Spain,2012,340.81,77.92,23.67,6.87,11.07,3.78,340.81,265.55,80.67,23.41,37.71,12.87 +Sri Lanka,2000,18.8,61.51,27.05,2.62,25.05,10.82,18.8,11.56,5.08,0.49,4.71,2.03 +Sudan,2000,67.84,12.39,4.2,0.14,84.67,2.81,67.84,8.4,2.85,0.09,57.44,1.91 +Suriname,2003,3.33,72.19,10.54,1.95,25.23,0.63,3.33,2.4,0.35,0.07,0.84,0.02 +Swaziland,1994,7.54,14.01,5.87,65.03,16.36,4.6,7.54,1.06,0.44,4.9,1.23,0.35 +Sweden,2012,57.61,73.16,33.17,10.24,13.26,2.81,57.61,42.15,19.11,5.9,7.64,1.62 +Switzerland,2012,51.49,80.6,31.72,7.05,10.76,1.19,51.49,41.5,16.33,3.63,5.54,0.61 +Tajikistan,2010,8.18,15.55,4.96,8.02,69.76,6.67,8.18,1.27,0.41,0.66,5.71,0.55 +Thailand,2000,236.95,67.26,18.87,6.91,21.89,3.93,236.95,159.38,44.7,16.38,51.87,9.32 +The former Yugoslav Republic of Macedonia,2009,11.49,76.26,11.26,3.89,11.52,8.33,11.49,8.76,1.29,0.45,1.32,0.96 +Timor-Leste,2010,1.28,19.64,8.64,...,75.69,4.67,1.28,0.25,0.11,...,0.97,0.06 +Togo,2000,4.92,34.87,13.06,6.36,55.33,3.44,4.92,1.71,0.64,0.31,2.72,0.17 +Tonga,2000,0.25,40.13,23.02,...,38.12,21.75,0.25,0.1,0.06,...,0.09,0.05 +Trinidad and Tobago,1990,16.01,62.03,9.3,31.97,2.12,3.89,16.01,9.93,1.49,5.12,0.34,0.62 +Tunisia,2000,34.24,60.64,15.07,11.55,22.31,5.5,34.24,20.76,5.16,3.95,7.64,1.88 +Turkey,2012,439.87,70.16,14,14.27,7.34,8.23,439.87,308.6,61.56,62.77,32.28,36.22 +Turkmenistan,2004,75.41,69.63,3.14,20.67,9.02,0.68,75.41,52.51,2.37,15.58,6.81,0.51 +Tuvalu,1994,0.01,83.63,...,...,16.37,...,0.01,0,...,...,0,... +Uganda,2000,27.56,17.77,2.93,0.58,79.14,2.51,27.56,4.9,0.81,0.16,21.81,0.69 +Ukraine,2012,402.67,76.76,8.31,11.43,8.95,2.82,402.67,309.08,33.47,46.01,36.03,11.37 +United Arab Emirates,2005,195.31,89.48,14.96,4.83,2.04,3.65,195.31,174.76,29.22,9.44,3.99,7.12 +United Kingdom of Great Britain and Northern Ireland,2012,586.36,82.81,19.74,4.26,8.89,4.04,586.36,485.54,115.72,24.97,52.13,23.72 +United Republic of Tanzania,1994,39.24,17.56,4.26,0.94,75.77,5.73,39.24,6.89,1.67,0.37,29.73,2.25 +United States of America,2012, 6 487.85,84.76,26.77,5.15,8.11,1.91, 6 487.85, 5 498.88, 1 736.64,334.35,526.25,123.97 +Uruguay,2004,36.28,14.29,6.19,0.94,80.83,3.94,36.28,5.18,2.24,0.34,29.32,1.43 +Uzbekistan,2005,199.84,86.24,4.82,3.19,8.23,2.35,199.84,172.34,9.63,6.37,16.44,4.69 +Vanuatu,1994,0.3,21.45,15.13,...,78.55,...,0.3,0.06,0.05,...,0.24,... +Venezuela (Bolivarian Republic of),1999,192.19,74.7,17.69,4.79,17.15,3.36,192.19,143.56,33.99,9.21,32.96,6.47 +Viet Nam,2010,266.05,53.06,11.96,7.96,33.21,5.77,266.05,141.17,31.82,21.17,88.35,15.35 +Yemen,2000,25.74,69.07,19.25,2.85,23.34,4.74,25.74,17.78,4.96,0.73,6.01,1.22 +Zambia,2000,14.4,18.25,4.06,6.98,71.92,2.86,14.4,2.63,0.58,1.01,10.36,0.41 +Zimbabwe,2000,68.54,38.66,1.56,1.52,57.73,2.09,68.54,26.5,1.07,1.04,39.57,1.43 From 71399c5d9d3f3372b316b62dda47d8452437afc3 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 24 Jul 2024 23:23:04 +0530 Subject: [PATCH 17/58] Delete ch4no2 --- GLOBAL EMISSION ANALSIS/Dataset/ch4no2 | 1 - 1 file changed, 1 deletion(-) delete mode 100644 GLOBAL EMISSION ANALSIS/Dataset/ch4no2 diff --git a/GLOBAL EMISSION ANALSIS/Dataset/ch4no2 b/GLOBAL EMISSION ANALSIS/Dataset/ch4no2 deleted file mode 100644 index 8b1378917..000000000 --- a/GLOBAL EMISSION ANALSIS/Dataset/ch4no2 +++ /dev/null @@ -1 +0,0 @@ - From e3d761dcfc2b3e211953f0ea9d583cd4a5b4f4cf Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 24 Jul 2024 23:25:00 +0530 Subject: [PATCH 18/58] forming data perparingand cleaning --- .../cleaned_data.csv} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename GLOBAL EMISSION ANALSIS/{CLEAN AND PROCESSED/ cleaned_data.csv => data perparing and cleaning/cleaned_data.csv} (100%) diff --git a/GLOBAL EMISSION ANALSIS/CLEAN AND PROCESSED/ cleaned_data.csv b/GLOBAL EMISSION ANALSIS/data perparing and cleaning/cleaned_data.csv similarity index 100% rename from GLOBAL EMISSION ANALSIS/CLEAN AND PROCESSED/ cleaned_data.csv rename to GLOBAL EMISSION ANALSIS/data perparing and cleaning/cleaned_data.csv From 83891d50dab0b8ab249d97c1900b4e32274c530e Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 24 Jul 2024 23:25:56 +0530 Subject: [PATCH 19/58] adding data perpetration code --- .../data perperation.py | 24 +++++++++++++++ .../data perparing and cleaning/merge all.py | 29 +++++++++++++++++++ 2 files changed, 53 insertions(+) create mode 100644 GLOBAL EMISSION ANALSIS/data perparing and cleaning/data perperation.py create mode 100644 GLOBAL EMISSION ANALSIS/data perparing and cleaning/merge all.py diff --git a/GLOBAL EMISSION ANALSIS/data perparing and cleaning/data perperation.py b/GLOBAL EMISSION ANALSIS/data perparing and cleaning/data perperation.py new file mode 100644 index 000000000..bf353c93d --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/data perparing and cleaning/data perperation.py @@ -0,0 +1,24 @@ +import pandas as pd +import numpy as np + +# Load the CSV data into a pandas DataFrame +data = pd.read_csv('merge.csv') + +# Replace 0 with NaN +data.replace(0, np.nan, inplace=True) + +# Display the first few rows of the DataFrame +print(data.head()) + +# Handle missing values, if any +# For simplicity, we'll drop rows with missing values, but you could also fill them +data = data.dropna() + +# Convert columns to appropriate data types if necessary +data['latest year available_x'] = data['latest year available_x'].astype(int) + +# Save cleaned data for later use +data.to_csv('cleaned_data.csv', index=False) + + + diff --git a/GLOBAL EMISSION ANALSIS/data perparing and cleaning/merge all.py b/GLOBAL EMISSION ANALSIS/data perparing and cleaning/merge all.py new file mode 100644 index 000000000..97fe9ca7b --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/data perparing and cleaning/merge all.py @@ -0,0 +1,29 @@ +##Data Merging + +## Merge GHG Emissions Data + +##```python +import pandas as pd +# Load the data from CSV files +ch4_data = pd.read_csv('CH4_N2O_Emissions.csv') +co2_data = pd.read_csv('CO2_Emissions.csv') +merged_ghg_emissions_data=pd.read_csv('merged_ghg_data.csv') +NOx_data=pd.read_csv('NOx_Emissions.csv') +Ods_data=pd.read_csv('ODS_Consumption.csv') +SO2_data=pd.read_csv('SO2_emissions.csv') + +# Merge the datasets on the 'Country' column with outer join +merged_data = ch4_data.merge(NOx_data, on='Country', how='outer') +merged_data = merged_data.merge(co2_data, on='Country', how='outer') +merged_data = merged_data.merge(merged_ghg_emissions_data, on='Country', how='outer') +merged_data = merged_data.merge(Ods_data, on='Country', how='outer') +merged_data = merged_data.merge(SO2_data, on='Country', how='outer') + +# Fill missing values with 0 +merged_data.fillna(0, inplace=True) + +# Save the merged data to a new CSV file +merged_data.to_csv('merge.csv', index=False) + +# View the merged data +print(merged_data.head()) \ No newline at end of file From ea9cae8a8e8634f8cba35b37f053829b5380876e Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 24 Jul 2024 23:27:01 +0530 Subject: [PATCH 20/58] Create notebook --- GLOBAL EMISSION ANALSIS/models/notebook | 1 + 1 file changed, 1 insertion(+) create mode 100644 GLOBAL EMISSION ANALSIS/models/notebook diff --git a/GLOBAL EMISSION ANALSIS/models/notebook b/GLOBAL EMISSION ANALSIS/models/notebook new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/models/notebook @@ -0,0 +1 @@ + From e99d73d3e5fd1f38f740640914053692f768db01 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 24 Jul 2024 23:27:39 +0530 Subject: [PATCH 21/58] addiing model notebook --- .../models/GLOBAL_CLIMATE_ANALYIS.ipynb | 804 ++++++++++++++++++ 1 file changed, 804 insertions(+) create mode 100644 GLOBAL EMISSION ANALSIS/models/GLOBAL_CLIMATE_ANALYIS.ipynb diff --git a/GLOBAL EMISSION ANALSIS/models/GLOBAL_CLIMATE_ANALYIS.ipynb b/GLOBAL EMISSION ANALSIS/models/GLOBAL_CLIMATE_ANALYIS.ipynb new file mode 100644 index 000000000..9861db0dd --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/models/GLOBAL_CLIMATE_ANALYIS.ipynb @@ -0,0 +1,804 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 71, + "id": "1b516b50-eb14-42b6-a48e-5ec5785fdc7e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.decomposition import PCA\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "ecce1da9-dbeb-4de8-bdc6-6f4798b862ad", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the cleaned data\n", + "data = pd.read_csv('cleaned_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "3f85261f-697d-4ee6-9127-0987d561c9ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns in the DataFrame:\n", + "Index(['Country', 'latest year available_x', 'CH4 emissions',\n", + " 'CH4 emissions per capita', ' % change since 1990', 'N2O emissions',\n", + " 'N2O emissions per capita', ' % change since 1990.1', 'NOx emissions',\n", + " '% change since 1990_x', 'NOx emissions per capita', 'CO2 emissions ',\n", + " '% change since 1990_y', 'CO2 emissions per capita',\n", + " 'CO2 emissions per km2', 'Total GHG emissions _perc',\n", + " 'GHG from Energy_perc',\n", + " 'GHG from Energy \\nof which: from Transport_perc',\n", + " 'GHG from Industrial Processes_perc', 'GHG from Agriculture_perc',\n", + " 'GHG from Waste_perc', 'Total GHG emissions _tonnes',\n", + " 'GHG from Energy_tonnes',\n", + " 'GHG from Energy \\nof which: from Transport_tonnes',\n", + " 'GHG from Industrial Processes_tonnes', 'GHG from Agriculture_tonnes',\n", + " 'GHG from Waste_tonnes', 'Consumption of CFCs_odptonnes',\n", + " 'Consumption of all ODS', 'consumptionof all_odptonnesin 2013',\n", + " 'reduction in odp', 'SO2 emissions', '% change since 1990',\n", + " 'SO2 emissions per capita'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "# Print all column names to verify\n", + "print(\"Columns in the DataFrame:\")\n", + "print(data.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "889d4338-844b-4398-8e9e-c5db38bb996a", + "metadata": {}, + "outputs": [], + "source": [ + "# List of columns to normalize (update this list based on actual column names)\n", + "columns_to_normalize = [\n", + " 'CH4 emissions', 'CH4 emissions per capita', ' % change since 1990',\n", + " 'N2O emissions', 'N2O emissions per capita', ' % change since 1990.1',\n", + " 'NOx emissions', '% change since 1990_x', 'NOx emissions per capita',\n", + " 'CO2 emissions ', '% change since 1990_y', 'CO2 emissions per capita',\n", + " 'CO2 emissions per km2', 'Total GHG emissions _perc', 'GHG from Energy_perc',\n", + " '\"GHG from Energy of which: from Transport_perc\"', 'GHG from Industrial Processes_perc',\n", + " 'GHG from Agriculture_perc', 'GHG from Waste_perc', 'Total GHG emissions _tonnes',\n", + " 'GHG from Energy_tonnes', '\"GHG from Energy of which: from Transport_tonnes\"',\n", + " 'GHG from Industrial Processes_tonnes', 'GHG from Agriculture_tonnes', 'GHG from Waste_tonnes',\n", + " 'Consumption of CFCs_odptonnes', 'Consumption of all ODS', 'consumptionof all_odptonnesin 2013',\n", + " 'reduction in odp', 'SO2 emissions', '% change since 1990', 'SO2 emissions per capita'\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "403c474c-5730-49db-9e36-2e86f116cb37", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing columns:\n", + "['\"GHG from Energy of which: from Transport_perc\"', '\"GHG from Energy of which: from Transport_tonnes\"']\n" + ] + } + ], + "source": [ + "# Check for columns that exist in the DataFrame\n", + "valid_columns = [col for col in columns_to_normalize if col in data.columns]\n", + "missing_cols = [col for col in columns_to_normalize if col not in data.columns]\n", + "print(\"Missing columns:\")\n", + "print(missing_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "47ed30d2-8fb6-4457-988f-9194c28107e6", + "metadata": {}, + "outputs": [], + "source": [ + "# Function to clean non-numeric values in a column\n", + "def clean_numeric(column):\n", + " # Replace commas and spaces in the entire column\n", + " column = column.str.replace(',', '', regex=False)\n", + " column = column.str.replace(' ', '', regex=False)\n", + " # Convert to numeric, forcing errors to NaN\n", + " return pd.to_numeric(column, errors='coerce')" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "a1321187-cf52-4c82-a2bc-f74c284ff9d4", + "metadata": {}, + "outputs": [], + "source": [ + "# Apply cleaning function to valid columns\n", + "for col in valid_columns:\n", + " data[col] = data[col].astype(str) # Ensure column is treated as strings\n", + " data[col] = clean_numeric(data[col]) # Apply cleaning function\n", + "\n", + "# Handle any NaNs in valid columns\n", + "data[valid_columns] = data[valid_columns].fillna(0)\n", + "\n", + "# Normalize the data\n", + "scaler = MinMaxScaler()\n", + "data[valid_columns] = scaler.fit_transform(data[valid_columns])" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "166c43f2-0387-4105-86fb-ffd9093174b3", + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare data \n", + "X = data[valid_columns]" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "32172d0b-589b-4491-a608-7d9d8753a2e8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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KlStnxcfHW3379rXmzZtnORyOPNd899131iWXXGKFhoZaVatWtf7zn/9YX375Za6fjWVZ1vHjx62BAwdaUVFRuX4vTNO0nnjiCatGjRpWcHCw1bx5c+vzzz+3brzxxnxLOQMA3MOwrHNcWQkAAAAAXsCaGQAAAAA+iWQGAAAAgE8imQEAAADgk0hmAAAAAD/09ddfq0ePHqpataoMw9Ann3xy1mtWrVqliy66SMHBwapTp45mzZrl9jgLQzIDAAAA+KETJ06oadOmeumll4rUf9euXerWrZuuuOIKbdy4UXfddZduvvlmffnll26OtGBUMwMAAAD8nGEYWrBgQaH7mt1333364osvtGnTJmfbgAEDlJqaqsWLF3sgyrzYNPMsTNPUgQMHVL58eRmG4e1wAAAA8C+WZenYsWOqWrWqbLbS9+DR6dOnlZmZ6ZF7WZaV5zNrcHCwgoODz3nsNWvWqGPHjrnaOnfurLvuuuucxy4ukpmzOHDggOLi4rwdBgAAAM5i3759ql69urfDyOX06dOqWSNcSYccHrlfeHi4jh8/nqtt0qRJeuihh8557KSkJMXExORqi4mJUXp6uk6dOqXQ0NBzvoerSGbOonz58pJy/nJERER4ORoAAAD8W3p6uuLi4pyf20qTzMxMJR1yaM/6eEWUd++sUfoxUzVa7M7zubUkZmVKK5KZszgzTRcREUEyAwAAUIqV5iUB4eUNhZd3b3ym3Pu5NTY2VsnJybnakpOTFRER4ZVZGYlqZgAAAACKICEhQcuXL8/VtnTpUiUkJHgpImZmAAAAALdzWKYcbq4h7LBMl/ofP35cO3bscL7etWuXNm7cqAoVKuj888/X+PHjtX//fr3zzjuSpFtvvVUvvvii/vOf/+imm27SihUr9OGHH+qLL74o0ffhCmZmAAAAAD/0448/qnnz5mrevLkkaezYsWrevLkmTpwoSTp48KD27t3r7F+zZk198cUXWrp0qZo2baqpU6dq5syZ6ty5s1fil9hn5qzS09MVGRmptLQ01swAAACUQqX589qZ2JK2nu+RAgCx9faWyp+DuzAzAwAAAMAnsWYGAAAAcDNTplxb0VK8e/gbZmYAAAAA+CRmZgAAAAA3c1iWHG5equ7u8UsjZmYAAAAA+CRmZgAAAAA3M2XJlHtnTtw9fmnEzAwAAAAAn8TMDAAAAOBmpiw5mJkpcczMAAAAAPBJJDMAAAAAfJLPJTMvvfSS4uPjFRISotatW2vdunWF9k9NTdUdd9yhKlWqKDg4WHXr1tWiRYs8FC0AAADw/wIA7j78jU+tmZk7d67Gjh2rGTNmqHXr1po+fbo6d+6srVu3qnLlynn6Z2Zm6qqrrlLlypU1b948VatWTXv27FFUVJTngwcAAABQonwqmZk2bZpGjBihYcOGSZJmzJihL774Qm+++abGjRuXp/+bb76plJQUrV69WoGBgZKk+Ph4T4YMlCrpKce19IPV2v37fgWHBimhazM1v7yBbDafm6QFAMCnsGmme/hMMpOZman169dr/PjxzjabzaaOHTtqzZo1+V7z6aefKiEhQXfccYcWLlyoSpUqaeDAgbrvvvtkt9vzvSYjI0MZGRnO1+np6SX7RgAvWTlvraYmzpIjyyGbzZAM6fM3V6l2k/P12IejFV05wtshAgAAuMRnvo49cuSIHA6HYmJicrXHxMQoKSkp32v++OMPzZs3Tw6HQ4sWLdKECRM0depUPfbYYwXeZ/LkyYqMjHQecXFxJfo+AG/4dfU2PXXLG8rOzJZlWXI4TDmyTUnS7t/+1IPXPSfTNL0cJQAAZZfpocPf+EwyUxymaapy5cp67bXX1KJFC/Xv318PPPCAZsyYUeA148ePV1pamvPYt2+fByOGP8jOytaPS3/Rsve+0cZVv8nhcP8/PXOfXSTDZuR7zuEwtfOXvfpp1Wa3xwEAAFCSfOYxs4oVK8putys5OTlXe3JysmJjY/O9pkqVKgoMDMz1SFmDBg2UlJSkzMxMBQUF5bkmODhYwcHBJRs88Ldl732j18bNVuqh/z++WKn6eUp8bqgSerRwyz0zT2fpxxW/qbACJ/YAm1Z/8ZNaXHmhW2IAAMDfOTywaaa7xy+NfGZmJigoSC1atNDy5cudbaZpavny5UpISMj3mrZt22rHjh25Hp/Ztm2bqlSpkm8iA7jT0ne/1lM3vZIrkZGkw/v/0kP9pmntop/cct+szKxCExlJsiwp43SmW+4PAADgLj6TzEjS2LFj9frrr+vtt9/W5s2bddttt+nEiRPO6mZDhgzJVSDgtttuU0pKikaPHq1t27bpiy++0BNPPKE77rjDW28BfiorM1uv/ue9/E9aOf8z4553ZbmhCkm58qGqEBNZaB/LtBRfv1qJ3xsAAORwWJ45/I3PPGYmSf3799fhw4c1ceJEJSUlqVmzZlq8eLGzKMDevXtzlZiNi4vTl19+qTFjxqhJkyaqVq2aRo8erfvuu89bbwF+av3SX5T+1/ECz1uWtH9Hkrat/0P1WtYu0XsbhqGeI67Q208slGXm/6+cPdCuqwa2KdH7AgAAuJtPJTOSlJiYqMTExHzPrVq1Kk9bQkKCvv/+ezdHBRTuaHJakfqlHEx1y/373N5JPyzbpN/X7cyV0NjsNlmmpTHP36jI88q75d4AAMAz1caoZgbALc6rElW0flWj3XL/oJBAPfHxGN14/zW5Hjlrdml9PbnwbnW47hK33BcAAMCdfG5mBvBFF3VsrMhKEUo7nP8mrIZhqNoFsbrgoppuiyE4NEgDxnbTdXddrZPHTiswKEDBoRTCAADAE0wZcij/bRJK8h7+hpkZwAMCAgN0+9Qh+Z4zDEMypNunDcn5tZvZbDaFR5YjkQEAAD6PmRnAQ64Y0EY2u02v/uc9Hdmf4myPrVlZic8NVctOTb0YHQAAcCfTyjncfQ9/QzIDeNBl116idn1a6bfVW5WanKaK1SuoQesLPDIjAwAAUNaQzAAeZrfb1KR9A2+HAQAAPMjhgTUz7h6/NGLNDAAAAACfRDIDAAAAwCfxmBkAAADgZjxm5h7MzAAAAADwSczMAAAAAG5mWoZMy82bZrp5/NKImRkAAAAAPomZGQAAAMDNWDPjHszMAAAAAPBJzMwAAAAAbuaQTQ43zyM43Dp66cTMDAAAAACfxMwMAAAA4GaWB6qZWVQzAwAAAADfwMwMAAAA4GZUM3MPZmYAAAAA+CRmZgAAAAA3c1g2OSw3VzOz3Dp8qcTMDAAAAACfxMwM4CLTNGWz8T0AAAAoOlOGTDfPI5jyv6kZkhmgCHb/tk8fTf1MX324RhmnMlWlZmX1uL2Trrm9s4JCgrwdXpl0NPWEtu1Ils1mU8P6VRRWLtjbIQEAgFKGZAY4i40rN+n+bpNlOkw5sk1J0sFdh/T6f2br2/nrNGXJgwrhg3aJOXbstJ6bsUwrvtosh5nzDVNwUICu6dZMI4depsBAu5cjBADAdVQzcw+elQEKkXk6U49c96yysxzOROYMy7K0Ze12vf/EAi9FV/acOp2p0eM+0PJ/JDKSlJGZrY8++VETHv9Epul/U+gAACB/zMzALx38I1nL3vtaR5PTVLFaBXUcfKkqx1XM0++bj9fqWMrxAscxTUufz1iiwRP7KTCIv07natGSX/XH7sOy8slXLEtas26nfvxpt1q1qOn54AAAOAeeqWbmf1/48ekLfsXhcOiVu2Zp4cuLZbPZZNgMWaalWRPmaMC4Xhr22PUyjP9P0e7YuFsBgXZlZzkKHPPY0RM68udfqlIrxhNvoUz77L8/F3reZjP0+eKfSWYAAIAkHjODn3ln0oda+PJiyVLOGpgsh0yHKcuy9MHkBZo37fNc/QOCAvKdJfi30ycz9PNXv+v3NduUmZHlpujLvkNHjhX68zZNS0mH0j0XEAAAKNWYmYHfOJF2QvOmfabCqha+/8THuiaxi4KCAyVJl3S7SHOe/KTA/obNUFhEqG5rcZ9zTU356DD1HdNNA8b1lt3O9wWuiIospxMnMgo8b7MZOi86zIMRAQBQMnJKM7t3gb67xy+N+KQFv7F+6S/KPF34rMnxoye06ZvNztcNE+qqwSUXyB6Q/18Vy7R0Iu1UruIAx46e0KxJH2r6La/J8sNnV89F16sa53rM799M01KXjo08GBEAACjNSGbgN04dP+1yP8Mw9PCCexV/YZwkyfb3TMuZ/5eh/BMWS1r81kptWbfj3IL2M9d0babKFcvLbsub0NhshhrWr6K2CRd4ITIAAM6NKZscbj7cvSlnaeR/7xh+6/wG1YvYr1qu19GVI/XSD0/q4QX36ooBbdWmZ0tde3cPVaxeodBH1uwBdi1+c+W5hOx3ypcP0YvPDFSjhrl/DwxDap9wgZ5+9DoF8OgeAAD4G2tm4Dfqt6qjGhfGad+W/TIdZp7zNrtNF7app7h61fKcs9ttatOzpdr0bOls+/jZz/P0+ydHtkMHdyWfe+B+pnKlCD3/1EDt2nNEv23eL7vdpuZNzldsTKS3QwMAoNgozeweJDPwG4Zh6L63EzX2sonKPJ2VK6GxBdhUrnyo7nr1liKPFx4dptRCKmvZ7DZFVeIDeHHVrFFRNWvk3fsHAADgDJ7XgF+54KJaenHtZLXv29q57iUg0K4rr2+nl3+YovPr552VKUinIZf9f+1MPkyHqQ6D2p9zzAAAwPeZf69pcffhb5iZgd+p0TBOD84Zq1PHT+nY0ROKOK+8QsoFuzxO71Fd9eWsVTp29ESex9ZsdpsaXHKBWnZuWlJhAwAA4F9IZuC3QsNDFRoeWuzrK1aroKmrHtJjA6Zr96Z9MmyGZFmyJCX0bKl737yNfWbc4MTx01rxyQZt2bhXNrtNzdteoHZdGisoiH/OAACll8My5LDcuw+Mu8cvjfivP3AOajSortc2Pq3f12zT1h93KjAwQC06NVHV2rHeDq1M+um77Xr0jnd06lSmbDabDEnLFqzXm08t0mNvDld8XX7uAAD4E5IZ4BwZhqEL29TThW3qeTuUMu3PXYc16Za3lJ1lSpZyPdp39K/jGn/j65q55B6FlS/+bBsAAO5yZi8Y997D/6qZ8QwMAJ+w8O3v5HBY+W5SajpMpaYc1/JPfvJCZAAAwFtIZgD4hO+W/Jrv/kD/tHrpJg9FAwCAa0zL5pHD3/jfOwbgkzIzsgvvYEkZp7M8EwwAACgVSGaAMigrM1sph9J0+mSmt0MpMbUaVJHNVnCVFrvdpjoNi75PEAAAnnRmzYy7D39DAQCgDEk5lKY5zy7Wkg9WK+NUpmw2QwlXN9PAu7uq1oXVvR3eOelxQxv9um5XgecdDlNdr2/twYgAAIC3+V/6BpRRRw6malSnJ/XF218r41TOjIxpWlqz+GfddfUUbfp+u5cjPDftOjdWxz4tJEnGPyZozszWDLvnatWsV8UboQEAcFam/r/XjLuOwleWlk0kM0AZ8eqDH+roofQ8i+RNh6nsLIem3PamHGdZQF+aGYahMU/0012P99X5dWKc7fWbna9Jr9yo60Ze7r3gAACAV/CYGVAGpB5O1+r/bpTpyL++vGVaOnIgVRtW/a6LOzTycHQlx2azqfO1rdT52lbKOJ0lm81QYBD/jAEA4K+YmQHKgD//OFRgInOGzW7T3m1JHorI/YJDAklkAAA+w5TNI4e/8b93DJRBIeWCz9rHMk0FhwZ6IBoAAADP4GtNoAyo2bCaKlWN1uEDRwvuZBhq3alJse+RcTpLmRlZCisfIpuN70EAAHCFw7LJ4eZNLd09fmnkc+/4pZdeUnx8vEJCQtS6dWutW7euSNfNmTNHhmGoV69e7g0Q8AK73aaBd3ct8LxhM3RV/wRVqhrt8tg/r9mh+4e8ql4Nx+u65hN1Q8Kj+uClZcrMYINKAADgXT6VzMydO1djx47VpEmTtGHDBjVt2lSdO3fWoUOHCr1u9+7duueee9S+fXsPRQp4Xpcb2unG8T1l2AzZ7IbsATbZA3L+irfvcZHumDLA5TGXL1ivcTfM0M9rdjjbjh4+pnef/VL3D35VGadJaAAAKApThkcOf2NYllX4quFSpHXr1rr44ov14osvSpJM01RcXJzuvPNOjRs3Lt9rHA6HLr30Ut1000365ptvlJqaqk8++aTI90xPT1dkZKTS0tIUERFREm8DfsayLBmG5/5xOXIwVcs//F5Je/9SRHSYLu/dUjWLsWFm6l/HNbjNo8rOcuR73rAZGjKmiwbc0eFcQwYA4JyU5s9rZ2J7fv0lCg137wqPU8ezNarF96Xy5+AuPrNmJjMzU+vXr9f48eOdbTabTR07dtSaNWsKvO6RRx5R5cqVNXz4cH3zzTdnvU9GRoYyMjKcr9PT088tcPiM0yczlH4kXeHR4SpXPvScxsrMyNLnry3XZzOW6eCuZAWFBKl931a69q6uir8wroQizl/FKlHqP7rLOY+zbP6PcmQXvC+NZVr67N3v1P/2Kz2arAEA4ItYM+MePpPMHDlyRA6HQzExMbnaY2JitGXLlnyv+fbbb/XGG29o48aNRb7P5MmT9fDDD59LqPAgy7L06zebtX/7QYVFltPFXZopNNy1RCRp9yG989CHWvnBt8rOcshmM9Tmmos15KHrVLNxDZdjyjiVqft7PKXfVm+TJUuyctpWzlmtrz76Xo8uuEfNr7jQ5XE9bffWgzJshqxCSj6nHErXyeMZCisf4sHIAAAAcvhMMuOqY8eOafDgwXr99ddVsWLFIl83fvx4jR071vk6PT1dcXHu/SYdxfPL179r6vCXdWBnsrMtuFywrh/fWwPv71Ok2YI/tx/UqIT7dTL9pHMWwjQtrf70R61bvFFPL5+khpfUlWVZ2vHTbiXvPazIiuXVMKGe7Pb8v/2Y+8xn+m3NNv37CU5HtinTtPTYwOf1/h8vKDg06BzevfsFhwTprD9CQ+z1AgBAEThkk8PNy9XdPX5p5DOfQipWrCi73a7k5ORc7cnJyYqNjc3Tf+fOndq9e7d69OjhbDPNnA+rAQEB2rp1q2rXrp3nuuDgYAUHn33PDnjXlnXbdV+nR+XIzr2eI+NkhmZNmKOs01ka+ujZF7y/cMfrOpF2UqYj9+NUpsNUdkaWnrrxRY2deZteSHxDe37/03n+vKrRGjnlBl0xoG2u6xzZDn02Y5ksM//ZDMu0dDz1pL6ev05XDWpX1LfrFW06NdKi9wt+hNNmN3RRu3oKCvaZf0YAAEAZ4zPpW1BQkFq0aKHly5c720zT1PLly5WQkJCnf/369fXrr79q48aNzqNnz5664oortHHjRmZbfNybD3wg02EWmDTMmbJARw+lFTrGwT+StWHZr3kSmTNM09L+7Qf1n6se1d4t+3Od++vAUU0e/IK+nLUqT3t6yvFC72sPtGvHT7sL7VMaNG93garXqlTgedNhqcv1l3gwIgAAfJdpGR45/I3PJDOSNHbsWL3++ut6++23tXnzZt122206ceKEhg0bJkkaMmSIs0BASEiIGjVqlOuIiopS+fLl1ahRIwUFle5HfFCwvw4e1U/LC05CpJxE5Ku5qwsdZ9/WA2e/mc0my7QKTJpm3POOMk9nOl8HFOWRK0sK9IHZDMMw5LAsFVTl0bDb9NXnGz0aEwAAwD+V/k9U/9C/f38dPnxYEydOVFJSkpo1a6bFixc7iwLs3buXncn9QNrhs1eYs9ttSkk6Wmif0PCzL1o3DCPP2pd/OpF2Ut9/sUGX9s2ZoYiOiVTNxnHa/dufBSZAjmyHWl/d7Kz39rbfftytg3tTJMPISWicP4ec15ak7778VUePHFN0xfJejBQAgNLP9MCaGdO35ilKhE8lM5KUmJioxMTEfM+tWrWq0GtnzZpV8gHB4ypUiTprkuHINlUprvDCDw0uuUCRlSKKlBwVxLAZ+mt/yv9fG4YG3neNHr/hxXz72wNsqt20hhq1rVfse3rKH1sO5PyczzTkUw3ANC3t2Z5MMgMAALzC/9I3+LyoSpFq3e0i2QqoJiZJ9kCbAoMDNKrtA+oS1F9Xh1yvCT2f1M9f/ebsExAYoBsm9CtwjKJsnWKZlqJjo3K1Xdq3tW56tL9kyBnjmf+Pq1dVD88b6xP7sgQFBxSaMP6zHwAAKJxp2Txy+Bs+hcAn3fTEQG1cuUmZp7PyXTtTt2UdTR3+imw2Q6ZpSTK17r8/6fvP12v0KyPV/ZarJEnX3NFFx1KO671H50mWJcOes0bGNE11HXmVfv9+u/b89meBH+pDw0N0SfcWedr739Nd7ftcrP++uUp7tx5QaFiwLu3TSq27Npc9wO7y+00/ekKnT2YqulJ5j5VCbtG+Xs4+MwU8LidJEdFhqtuYYhoAAMA7SGbgk2o2Ol/PfvOonr/9dW3+fruzPTomUm17t9bnM5ZI0t+JTI4zSc/zt7+uZlc2UvULqsgwDA2eeK2uvrmDlr37tQ7vO6KoypHqMKi9qtaO1Yblv2p81ydkKP/H2m56bIBCyuVfyrtqrRgNf6z/Ob3P9V9v0QfPL9FvP+6SJJULD1aX6xM08M5OCotwbXNQV1WqEqUO11ykFQs35Po5/tO1Iy9XQKDryRkAAP7GIUOOgqrqlOA9/I1hFeU5Ej+Wnp6uyMhIpaWlKSIiwtvhIB97ft+n/TuSFBZZTo3a1te4zo/pl69/L7Damc1uU+9RXXXr1BuLNP66//6k5++YqUP7/nK2hUeH6aZHBzhneNxh2cc/aOo978tmGLmSCZvdprjalTV13ii3JzQZp7P0xKj3tG7lZtntNpmWJcMwZDpM9RraTiPv7+ETj8wBAMq20vx57Uxsj667UiHh7p1HOH08WxNarSiVPwd3YWYGPq9GwzjVaPj/R522/biz8LLNDlNbf9hR5PFbXd1c7+x4QT9/9bsO7TmiiIrl1eKqJgoKDjynuAtzLPWEnh//oWRJ5r++bzAdpvbtTNacl5Zp+PgeBYxQMoJDAvXQq0O1ZeNerVi4QWkpJxRTLVpX9W2p8+vEuPXeAACUJZ5Y08KaGaAMCAg6+2NPru7zYrPZ1PyKRsUNyWUrFqxXdpajwPOmw9J/P1itG+/p6vbHvAzDUIPmNdSgeQ233gcAAMBV/pe+ocxL6NFS9oCC/2gbhqFLurf0YESu27sjWTZ74Y9vnUg/rfSjxz0UEQAAQOlDMoMyp89d3SUp37UcNrtN4VFh6nTj5R6OyjWhYcFSEVazBYfmX3wAAACULg79vwiA+w7/QzKDMqdWkxp6YM5Y2QPtMmw5CY3x9y724VFhmrJ0gsKjwrwcZeHadmkiRyHrfmx2m5q1vUBh5UM8GBUAAEDpwpoZlEnt+7TW+3te0X/fWKHN32+TPdCuFlc1Vccb2is03L0VwEpC/eY11LTNBfp17Q6Zjn9N0Rg5m3Ven9jJO8EBAACXUQDAPUhmUGZFx0Rp4P19vB1GsRiGoQkzhunx22fpp2+3yR5gkyFD2Q6HgoMDNebp69UkoY63wwQAAPAqkhmglAqLCNUT792mrT/v1erFv+jUyQzVuCBWl1/TgsfLAADwMQ7LJoebZ07cPX5pRDIDlHL1mp6vek3P93YYAAAApQ7JDAAAAOBmlgyZKnzbhZK4h7/xv7koAAAAAJKkl156SfHx8QoJCVHr1q21bt26QvtPnz5d9erVU2hoqOLi4jRmzBidPn3aQ9HmxcwMAAAA4Galcc3M3LlzNXbsWM2YMUOtW7fW9OnT1blzZ23dulWVK1fO0//999/XuHHj9Oabb6pNmzbatm2bhg4dKsMwNG3atJJ6Gy4hmQHgNqlHjmn5xz/o4J4jCo8sp8t6XqSaDap6OywAACBp2rRpGjFihIYNGyZJmjFjhr744gu9+eabGjduXJ7+q1evVtu2bTVw4EBJUnx8vK6//nqtXbvWo3H/E8kMALdY+MZXev3RBTJNSzabTbIszX1hidp1a6Z7nxusoJBAb4cIAIDHmJYh03LvmpYz46enp+dqDw4OVnBwcK62zMxMrV+/XuPHj3e22Ww2dezYUWvWrMl3/DZt2ui9997TunXr1KpVK/3xxx9atGiRBg8eXMLvpOhYMwOgxK1auF4zJn0sR7Ypy7TkyHbI4TAlSd/992c9958PvBwhAABlV1xcnCIjI53H5MmT8/Q5cuSIHA6HYmJicrXHxMQoKSkp33EHDhyoRx55RO3atVNgYKBq166tyy+/XPfff79b3kdRkMwAKFGWZem9qYtkFPDlk2VaWrHgRyXt/cuzgQEA4EUO2TxySNK+ffuUlpbmPP45+3IuVq1apSeeeEIvv/yyNmzYoPnz5+uLL77Qo48+WiLjFwePmaFMObTviFIOHlV0TJRialTydjh+6c+dh7T/j8OF9jEMQ2u+/EW9R1zhoagAAPAfERERioiIKLRPxYoVZbfblZycnKs9OTlZsbGx+V4zYcIEDR48WDfffLMkqXHjxjpx4oRGjhypBx54IOexcg8jmUGZsH3DH3rt3ne1ceUmZ9uFbetpxJTBurBNPS9G5n9On8w4ax+bzdCpE2fvBwBAWeHJNTNFERQUpBYtWmj58uXq1atXzvWmqeXLlysxMTHfa06ePJknYbHb7ZJynszwBh4zg8/bvHa7Rrd7UL98/Xvu9jXbdPflk3IlOHC/2PMryh5Q+D8tjmxT51+Q/7c+AADAM8aOHavXX39db7/9tjZv3qzbbrtNJ06ccFY3GzJkSK5H1Hr06KFXXnlFc+bM0a5du7R06VJNmDBBPXr0cCY1nsbMDHzec7e9Jkdmtkwz9zcCppmz1+6zI2do1rYXZBS0iAMlqnxUTgnmVQs3yPx70f8/GTZD5aPKqfVVjbwQHQAA3mHKJtPN8wiujt+/f38dPnxYEydOVFJSkpo1a6bFixc7iwLs3bs310zMgw8+KMMw9OCDD2r//v2qVKmSevTooccff7xE34crDMtbc0I+Ij09XZGRkUpLSzvrs4fwvJ0/79atze89a79pXz2ixu0beCAiSFJKcpru6jlNfyWl5UpobHZDhmHo4Vm3qMXl/H4AAEpGaf68dia2xG97KzjcvdsSZBzP0ovtFpTKn4O78JgZfNqBncln7yTp4B9F64eSUSEmUs99fre63tBGwaFBOY2G1OKyBnpm/l0kMgAAoETwmBl8WvnosCL1C48qWj+UnOhKEbrj8es0YmJvpaecUGh4sMLKh3o7LAAAvMJhGXK4uQCAu8cvjZiZgU9r1K6+oipHFtqnXESoWnRq4qGI8G9BwYGqWCWKRAYAAJQ4Zmbg0wICAzTssev17MgZBfYZPPFaBYcGS8opObj2iw36fMYS7d28X2FR5XTl9e3UZfiViqhQ3lNhAwAAP1PaSjOXFSQz8Hldb+6gzFOZmjnuPWWczpTdbpfD4VBgUICGTLpOfcd0lyQ5HA5NGfyCVs75Tja7zbkw/Y9f9ujjZz/X1FUPq3rdqt58KwAAAHAByQzKhF53Xq2rbrxM33y8Vn/tT1F0bJTa922t8tHhzj4fT/tcK+d+J0m5KmxZpqXUw+ma0PNJvfH7dK/sXgsAAMo2y7LJtNz7GcNy8/ilEckMyoywiHLqMuyKfM85HA59PP1zqYBC5KbD1J/bDmrDsl/VslNTN0YJAACAkuJ/6Rv8UtKuQ0o5mFpoH3uAXb989ZtnAgIAAH7FIcMjh78hmQEAAADgk3jMDH4htmZlVagSVejsjCPboSaXXei5oAAAgN8wLfdXGzMLeJy+LGNmBn7Bbrer713dVdDsq81uU/W6VXRRx8aeDQwAAADFRjIDv9F3bHdd0b+tpJzk5QzDZiiqUoQe/XQclcwAAIBbmH9XM3P34W94zAx+w263a/zs0bpyYHt99uoS7f39T4VHhTk3zfxnGWcAAACUfiQz8CuGYeiS7i10SfcW3g4FAAD4EVOGTDdXG3P3+KURyQwg6YcvN2r+84u0ec122eyGWnZupj6juqp+qzreDg0AAAAFIJmB33tr4lx9MHmBbHabTIcpSfrm4+/11YdrdPfrt6jTjZd7N0AAAODzHJYhh5urmbl7/NLI/1YJAf/w49Kf9cHkBZLkTGQkyZFtyrIsTRv5qg7sTPJWeAAAACgEyQz82icvLs5V2SwPw9Dnry3zXEAAAKBMopqZe/jfOwb+4ffV23LNyPyb6TD12+qtHowIAAAARcWaGfg1e8DZ83l7gN0DkQAAgLLMlCHTzWta/LGaGTMz8Gutul5UaEJj2Ay17trcgxEBAACgqEhm4Nd6J3aRaVr5njNshkLKBavz0Cs8HBUAAACKgmQGfq1O85oa93ai7AG2XIUADJuhkLBgPf7ZOEVVivBihAAAoCyw/t40052H5YePmbFmBn7vigFt1TChrhbNXK5N321VQKBdLa5qos5DL1dkRRIZAACA0opkBpAUU6OShj06wNthAACAMsq0PFAAgE0zAQAAAMA3+Fwy89JLLyk+Pl4hISFq3bq11q1bV2Df119/Xe3bt1d0dLSio6PVsWPHQvsDAAAA7sCmme7hU+947ty5Gjt2rCZNmqQNGzaoadOm6ty5sw4dOpRv/1WrVun666/XypUrtWbNGsXFxalTp07av3+/hyMHAAAAUNIMy7Lyr0tbCrVu3VoXX3yxXnzxRUmSaZqKi4vTnXfeqXHjxp31eofDoejoaL344osaMmRIvn0yMjKUkZHhfJ2enq64uDilpaUpIoLF4AAAAKVNenq6IiMjS+XntTOxXbPkJgWGBbn1XlknMrWw05ul8ufgLj4zM5OZman169erY8eOzjabzaaOHTtqzZo1RRrj5MmTysrKUoUKFQrsM3nyZEVGRjqPuLi4c44dAAAAQMnzmWTmyJEjcjgciomJydUeExOjpKSkIo1x3333qWrVqrkSon8bP3680tLSnMe+ffvOKW4AAADA3XvMnDn8jd+UZn7yySc1Z84crVq1SiEhIQX2Cw4OVnBwsAcjAwAAAFAcPpPMVKxYUXa7XcnJybnak5OTFRsbW+i1zzzzjJ588kktW7ZMTZo0cWeYAAAAQB7sM+MePvOYWVBQkFq0aKHly5c720zT1PLly5WQkFDgdU899ZQeffRRLV68WC1btvREqAAAAAA8wGdmZiRp7NixuvHGG9WyZUu1atVK06dP14kTJzRs2DBJ0pAhQ1StWjVNnjxZkjRlyhRNnDhR77//vuLj451ra8LDwxUeHu619wEAAAD/wsyMe/hUMtO/f38dPnxYEydOVFJSkpo1a6bFixc7iwLs3btXNtv/J5teeeUVZWZmql+/frnGmTRpkh566CFPhg4AAACghPlUMiNJiYmJSkxMzPfcqlWrcr3evXu3+wMCAAAAzoKZGffwmTUzAAAAAPBPPjczAwAAAPgaZmbcg5kZAAAAAD6JZAYAAACAT+IxMwAAAMDNLEmm3PsYmOXW0UsnZmYAAAAA+CRmZlDqHfwjWX8dSFFUTJSqX1DF2+EAAAC4jAIA7kEyg1Jr89rtevWet/Xbd1udbfUurqORTw9Wk0sbejEyAAAAlAY8ZoZS6bfVWzX2sonavGZbrvZt63fqPx0f1oZlv3gpMgAAANedmZlx9+FvSGZQ6liWpedue01mtkOmmXspm2VaMk1Lz97yqkzT9FKEAAAAKA1IZlDq7Ny4W7t+3ZsnkTnDMi0l7TqkTd9u8XBkAAAAxcPMjHuQzKDUObjrUNH6/ZHs5kgAAABQmlEAAKVORIXwovU7r7ybIwEAACgZVDNzD2ZmUOo0aldf0bFRhfYJjwpTi6uaeCYgAAAAlEokMyh17AF2DX9iYKF9bny4v4JCgjwUEQAAwLmxLMMjh78hmUGp1HnoFRr18giFhAVLkmz2nD+qwaFBuuWZIbomsYs3wwMAAEApwJoZlFo9bu2kjoMv1epPftCR/SmqEBulNr0uVlhEOW+HBgAA4BJThky5ec2Mm8cvjUhmUKqFhoWow6D23g4DAAAApRDJDAAAAOBmVDNzD9bMAAAAAPBJzMwAAAAAbuaJamNUMwMAAAAAH8HMDAAAAOBmrJlxD2ZmAAAAAPgkZmbgkyzL0vqlv+iL15bqz20HVL5CuDoMbK8rB7VXaFiIt8MDAACAB5DMwOc4HA5NGfyCVs75TrYAm8xsU4Zh6NdvNuvDpxfqmZUPq1L187wdJgAAgBMFANyDx8zgc+ZOWahVc7+TJJnZpqScmRpZUvKew3q479M5rwEAAFCmkczAp2RnZWv+9M9VUK7iyDa19Yed2rJuh2cDAwAAKIT1dwEAdx7MzACl3L4t+5V25FihfWx2mzau2OShiAAAAOAtrJmBTzHNIjw+Zkimabo/GAAAgCKypAKfLCnJe/gbZmbgU+LqVVVYZLlC+5jZphq1q++hiAAAAOAtJDPwKUEhQep5e2cZtvyfCbXZbarRsLqaXNrQw5EBAAAUzJThkcPfkMzA59ww8Vpd1KGxJMn2j6TGsBmKrFheD82/V4bhf3+ZAQAA/A1rZuBzgoID9fgX92vV3NX6bMYS7dvyp0LCQnR5/7a67t6eiqwY4e0QAQAAcmGfGfdgZgY+yR5gV1TlCGWczFD6X8d1aO8Rffj0Qk0e9Jx2/brH2+EBAADAA0hm4JNWL/xB47s8rp0/787V/tOKTRrV5gH98QsJDQAAKD3cvcfMmcPfkMzA52RnZWvayBmyZMn6V6lm02Eq83SWXhr9ppeic80fv/2pOc8t1ntPf67vv/xFjmyHt0MCAADwGayZgc9Z99+flHY4vcDzpsPUL1/9roN/JKtKrRgPRlZ0x46e0OSRb+inb7bIZrfJMCRHtqnzqkTpgZkj1KBFTW+HCAAASpBleWCfGT/caIaZGficpF2HCizN/E8Hdx3yQDSuczhMPTjwRf28epuknOTLkZ2zyefR5DTdf+1z2v9H6YwdAACgNCGZgc8pXyE8z+Nl+YmoEO6BaFy3fsVv2vbTHpkOM88507SUlZGt+TOWeSEyAADgLmeqmbn78DckM/A5CT1aKjAksOAOhlTtgiqq3SzeYzG54uuF62WzF/xXz+EwteLjHzwYEQAAgG9yOZkxzbzfJp9p37t37zkHBJxNeFSYrr+vd8EdLGn4EwNL7caZx9NP5Tsr80+nT2TI8scHXwEAKKOYmXGPIicz6enpuu666xQWFqaYmBhNnDhRDsf/Ky8dPnxYNWuyaBmeccPEfrphQj8FBNolQ7IH5PxRLhcRqnvfukPt+17i5QgLVrVmJdkLmZmRpJi4CqU2GQMAACgtilzNbMKECfr555/17rvvKjU1VY899pg2bNig+fPnKygoSJL4JhkeYxiGbny4v3qP6qpv569V+l/HVPn8imrbu5WCQ4O9HV6hugxqqwWvrijwvGEz1HXIpR6MCAAAwDcVOZn55JNP9Pbbb+vyyy+XJPXq1UvdunVTjx499Omnn0oS3yTD4yLOK6+uIzp6OwyXnF+3ivqP7qy5z30pGZL+8R2AzW6o1oXV1fOmy7wWHwAAKHmmZchw82NgbJpZiMOHD6tGjRrO1xUrVtSyZct07Ngxde3aVSdPnnRLgEBZdOO4nhr1zEDFVD/P2RZSLkg9hl2mKfPHKCSsdM8uAQAAlAZFnpk5//zztXnz5lzrYsqXL68lS5aoU6dO6t27kAXZAHIxDENX39BOnQe20YE/DisrM0tValQiiQEAoIxi00z3KPLMTKdOnfTWW2/laQ8PD9eXX36pkJCQEg0M8Ac2m03V68SoZsPqJDIAAAAuKvLMzMMPP6wDBw7ke658+fJaunSpNmzYUGKBAQAAAGVFzsyMe9e0+OPMTJGTmejoaEVHRxd4vnz58rrsMhYtAwAAAPAMlzfN9LaXXnpJ8fHxCgkJUevWrbVu3bpC+3/00UeqX7++QkJC1LhxYy1atMhDkQIAAAA52DTTPXwqmZk7d67Gjh2rSZMmacOGDWratKk6d+6sQ4cO5dt/9erVuv766zV8+HD99NNP6tWrl3r16qVNmzZ5OHIAAAAAJc2nkplp06ZpxIgRGjZsmBo2bKgZM2aoXLlyevPNN/Pt/9xzz6lLly6699571aBBAz366KO66KKL9OKLL3o4cgAAAPgzy0OHv/GZZCYzM1Pr169Xx47/3yDRZrOpY8eOWrNmTb7XrFmzJld/SercuXOB/SUpIyND6enpuQ4AAAAApY/LyYzdbs/3sa6//vpLdru9RILKz5EjR+RwOBQTE5OrPSYmRklJSflek5SU5FJ/SZo8ebIiIyOdR1xc3LkHDwAAAL/Gmhn3cDmZsQqo+ZaRkaGgoKBzDsjbxo8fr7S0NOexb98+b4cEAAAAIB9FLs38/PPPS8rZuXzmzJkKDw93nnM4HPr6669Vv379ko/wbxUrVpTdbldycnKu9uTkZMXGxuZ7TWxsrEv9JSk4OFjBwWxeCAAAgBLkiUUtfrhopsjJzLPPPispZ2ZmxowZuR4pCwoKUnx8vGbMmFHyEf7jHi1atNDy5cvVq1cvSZJpmlq+fLkSExPzvSYhIUHLly/XXXfd5WxbunSpEhIS3BYnAAAAAM8ocjKza9cuSdIVV1yh+fPnF7qBpruMHTtWN954o1q2bKlWrVpp+vTpOnHihIYNGyZJGjJkiKpVq6bJkydLkkaPHq3LLrtMU6dOVbdu3TRnzhz9+OOPeu211zweOwAAAPyYJ9a0sGbm7FauXOmVREaS+vfvr2eeeUYTJ05Us2bNtHHjRi1evNi5yH/v3r06ePCgs3+bNm30/vvv67XXXlPTpk01b948ffLJJ2rUqJFX4gcAAABKE1c3pE9NTdUdd9yhKlWqKDg4WHXr1vXqpvSGVdCK/gI4HA7NmjVLy5cv16FDh2SaZq7zK1asKNEAvS09PV2RkZFKS0tTRESEt8MBAADAv5Tmz2tnYqv51gOylQtx673Mk6e1a9jjRf45zJ07V0OGDNGMGTPUunVrTZ8+XR999JG2bt2qypUr5+mfmZmptm3bqnLlyrr//vtVrVo17dmzR1FRUWratKk73tJZFfkxszNGjx6tWbNmqVu3bmrUqJEMw/+mswAAAABf988N6SVpxowZ+uKLL/Tmm29q3Lhxefq/+eabSklJ0erVqxUYGChJio+P92TIebiczMyZM0cffvihunbt6o54AAAAgDLHE/vAnBn/35u+51et98yG9OPHj3e2nW1D+k8//VQJCQm64447tHDhQlWqVEkDBw7Ufffd59b9Jgvj8pqZoKAg1alTxx2xAAAAADhHcXFxuTaBP1Mc65+KsyH9H3/8oXnz5snhcGjRokWaMGGCpk6dqscee8wt76MoXJ6Zufvuu/Xcc8/pxRdf5BEzAAAAoJTZt29frjUzJbWHommaqly5sl577TXZ7Xa1aNFC+/fv19NPP61JkyaVyD1c5XIy8+2332rlypX673//qwsvvND5vNwZ8+fPL7HgAE8wTVO/fLtVyXv/UkSFMF10xYUKDg3ydlgAAKAssQz3l07+e/yIiIizFgAozob0VapUUWBgYK5Hyho0aKCkpCRlZmYqKMjzn59cTmaioqLUu3dvd8QCeNyPyzfpubve0eE/U5xt5SJCNeT+a3TNyA7MPgIAgDKpOBvSt23bVu+//75M05TNlrNaZdu2bapSpYpXEhmpGMnMW2+95Y44AI/7+Zstmtj/eVlm7urkJ9NPaca4OXJkOdQ3sbOXogMAAGWJZeUc7r6HK1zdkP62227Tiy++qNGjR+vOO+/U9u3b9cQTT2jUqFEl/VaKzOUCAJKUnZ2tZcuW6dVXX9WxY8ckSQcOHNDx48dLNDjAnV6f8JEsy1JBWy2988QnOpF+ysNRAQAAeIarG9LHxcXpyy+/1A8//KAmTZpo1KhRGj16dL5lnPOzYcMG/frrr87XCxcuVK9evXT//fcrMzOzWO/B5ZmZPXv2qEuXLtq7d68yMjJ01VVXqXz58poyZYoyMjI0Y8aMYgUCeNKfO5K04+c9hfbJOJWlNYt+UscBbTwUFQAAKLOsvw9338NFiYmJBT5WtmrVqjxtCQkJ+v77712/kaRbbrlF48aNU+PGjfXHH39owIAB6t27tz766COdPHlS06dPd3lMl2dmRo8erZYtW+ro0aMKDQ11tvfu3VvLly93OQDAG1IPpZ+1j81u09Ei9AMAAMDZbdu2Tc2aNZMkffTRR7r00kv1/vvva9asWfr444+LNabLMzPffPONVq9enWeRT3x8vPbv31+sIABPO69q9Fn7mA5TFYvQDwAA4Gw8uWlmaWVZlkzTlCQtW7ZM3bt3l5Tz+NqRI0eKNabLMzOmacrhcORp//PPP1W+fPliBQF4WpX4SmrYuo5stoL/0pcrH6KErs08FxQAAEAZ1rJlSz322GN699139dVXX6lbt26SpF27duXZvLOoXE5mOnXqlOt5NsMwdPz4cU2aNEldu3YtVhCAN9zy+HWyB9gLTGhGPHqdQsqVzCZTAAAAznUz7jpKuenTp2vDhg1KTEzUAw88oDp16kiS5s2bpzZtirdG2bAKKuVUgD///FOdO3eWZVnavn27WrZsqe3bt6tixYr6+uuvVbly5WIFUlqlp6crMjJSaWlpZ918CL7n93U79cLYd7Xrtz+dbefFRmnYpL7qOCDBi5EBAICiKs2f187Edv5rE2ULDXHrvcxTp7V35COl8udQmNOnT8tutyswMNDla11eM1O9enX9/PPPmjNnjn755RcdP35cw4cP16BBg3IVBAB8QcNWtfXyN5P0x6Z9St77lyKiw9SgdR3Z7cWqWg4AAJAv1sz8X2Zmpg4dOuRcP3PG+eef7/JYLiczkhQQEKAbbrihOJcCpY5hGKrd+HzVbuz6XyAAAAAUzbZt2zR8+HCtXr06V7tlWTIMI991+WdTrGRm+/btWrlyZb4Z1cSJE4szJAAAAFB2ldJ9Zjxp2LBhCggI0Oeff64qVarIMM59JsnlZOb111/XbbfdpooVKyo2NjZXEIZhkMwAAAAAyGPjxo1av3696tevX2JjupzMPPbYY3r88cd13333lVgQAADfkZGZrf+u/l0LVv2qpL+OKbp8qHq0v1DXXNZY4VQABIACGH8f7r5H6dWwYcNi7ydTEJeTmaNHj+raa68t0SAAAL7hxKlM3fHUPP3+R5IMQ7Is6Wj6ST0/92t9tHyjXru/vypXYM8xAEBeU6ZM0X/+8x898cQTaty4cZ7qZcWpwOZyyaZrr71WS5YscflGAADfN/2DVdqyO1lSTiJzhmVJyX8d04QZ//VSZABQyrl7jxkf2GumY8eO+v7779WhQwdVrlxZ0dHRio6OVlRUlKKjo4s1psszM3Xq1NGECRP0/fff55tRjRo1qliBAO5w9FCa/jpwVJEVy6tS9fO8HQ7g09KOn9Ki736Xaeb/X0uHaemnrX9q559HVLt6RQ9HBwAo7VauXFniY7qczLz22msKDw/XV199pa+++irXOcMwSGZQKuzZ/KdmjputtV/8pDP7wjZqV1/DH79ejdqV3KIzwJ9s3XNYWdnmWfv9vP0AyQwAII/LLrusxMd0OZnZtWtXiQcBlKRdv+7V6PYTlHEy05nISNLvq7fqng4P69FP79PFnZt5L0DAR9mK+GCyrXSvPwUA76A0syQpNTVVb7zxhjZv3ixJuvDCC3XTTTcpMjKyWOOd0zbnlmXl+rAIlAYvjn5LGSczZTpyf4NsmpZMh6VpI16Vw3H2b5cB5NYgPlbBQWf/Duyi+nEeiAYA4Gt+/PFH1a5dW88++6xSUlKUkpKiadOmqXbt2tqwYUOxxixWMvPOO++ocePGCg0NVWhoqJo0aaJ33323WAEAJenAziT98tXveRKZMyzL0pH9Kdqw7BcPRwb4vrDQIPW5okmBm5zZbYbaNa2p82OLt4gTAMo0y/DMUYqNGTNGPXv21O7duzV//nzNnz9fu3btUvfu3XXXXXcVa0yXHzObNm2aJkyYoMTERLVt21aS9O233+rWW2/VkSNHNGbMmGIFApSEAzuTz9rHMIwi9QOQ1x3XttPuAyla8+tu2WyGTNOSzTBkWpZqVa+oiSO7eDtEAEAp9eOPP+r1119XQMD/U5CAgAD95z//UcuWLYs1psvJzAsvvKBXXnlFQ4YMcbb17NlTF154oR566CGSGXhVeFTYWftYlqXwyHIeiAYoe4ICAzRtbC+t/nmXPln1qw4cTlOFyDB1b9dQHVrVVVCgy/9ZAQC/YFm5S9q76x6lWUREhPbu3av69XMXY9q3b5/Kly/eHmUu/1fn4MGDatOmTZ72Nm3a6ODBg8UKAigpdVvWUqW483R4318F9gkMDtQl3Vt4MCqgbLHbbGrfvLbaN6/t7VAAAD6kf//+Gj58uJ555hlnPvHdd9/p3nvv1fXXX1+sMV1eM1OnTh19+OGHedrnzp2rCy64oFhBACXFZrNp2KMDCu3T/z89FcbMDAAA8CQ2zdQzzzyjPn36aMiQIYqPj1d8fLyGDh2qfv36acqUKcUa0+WZmYcfflj9+/fX119/7Vwz891332n58uX5JjmAp101+FKdOn5Kr97zrjIzsmQPsMt0mLLZDF17dw8NntjP2yECAAD4naCgID333HOaPHmydu7cKUmqXbu2ypUr/pfMLiczffv21dq1a/Xss8/qk08+kSQ1aNBA69atU/PmzYsdCFCSet7WWR0GtddXH67RoX1/KapSeV12bYKiY6K8HRoAAPBHnqg2VsqrmZ1Rrlw5NW7cuETGKtZKzRYtWui9994rkQAAdwmLKKeuN3fwdhgAAAB+q0+fPpo1a5YiIiLUp0+fQvvOnz/f5fGLlcw4HA4tWLDAuXNnw4YNdc011+QqswacsfWHHVrw/CJtWP6rZFlq3qGxeo/qqvqtWGMFAAD8g2HlHO6+R2kTGRnp3J8sMjKyxMc3LMu1Im6//fabevbsqaSkJNWrV0+StG3bNlWqVEmfffaZGjVqVOJBelN6eroiIyOVlpamiIgIb4fjcz6bsUTP3/G67HabHNk5G1naA2xyOEyNevFm9bits5cjBAAAvq40f147E1vcc4/IFhri1nuZp05r3+iJpfLn4C4uVzO7+eabdeGFF+rPP//Uhg0btGHDBu3bt09NmjTRyJEj3REjfNTOn3fr+Ttelyw5Exnp719b0vOJM7Xjp11ejBAAAMBDqGamU6dO6eTJk87Xe/bs0fTp07VkyZJij+lyMrNx40ZNnjxZ0dHRzrbo6Gg9/vjj+umnn4odCMqehS8tlt1e8B8xu92mhS8t9mBEAAAA8JZrrrlG77zzjiQpNTVVrVq10tSpU3XNNdfolVdeKdaYLiczdevWVXJycp72Q4cOqU6dOsUKAmXTL1/9lmtG5t8c2aZ++ep3D0YEAADgJWeqmbn7KMU2bNig9u3bS5LmzZun2NhY7dmzR++8846ef/75Yo3pcjIzefJkjRo1SvPmzdOff/6pP//8U/PmzdNdd92lKVOmKD093XnAv9ns9rP2Meyl+y8dAAAASsbJkydVvnx5SdKSJUvUp08f2Ww2XXLJJdqzZ0+xxnS5/Fj37t0lSdddd52zMsGZGgI9evRwvjYMQw6Ho1hBoWxo1aWZ9m8/KNOR/+yMLcCmizs382xQAAAA3uCJNS2lfM1MnTp19Mknn6h379768ssvNWbMGEk5T3gVt2CBy8nMypUri3Uj+J8et3fWJy8ulgzl/ctlSIYMXXNHF2+EBgAAAA+bOHGiBg4cqDFjxqhDhw5KSEiQlDNL07x582KN6XIyc9lllxXrRvA/1epU0YNzx+jxAc/KNC3nDI3NbpNhM/TAB2NUvW5VL0cJAADgAczMqF+/fmrXrp0OHjyopk2bOts7dOig3r17F2vMYu1yefr0af3yyy86dOiQTDP3I0Q9e/YsViAom9r1bq1Z217Q5zOW6KcVv0qSml3RSN1v7aTY+Mpejg4AAACeFBsbq9jYWEk5e/CsWLFC9erVU/369Ys1nsvJzOLFizVkyBAdOXIkzznWySA/MTUqafjkQd4OAwAAAF503XXX6dJLL1ViYqJOnTqlli1bavfu3bIsS3PmzFHfvn1dHtPlamZ33nmnrr32Wh08eFCmaeY6SGQAAACAfLBppr7++mtnaeYFCxbIsiylpqbq+eef12OPPVasMV1OZpKTkzV27FjFxMQU64YAAAAA/E9aWpoqVKggKedpr759+6pcuXLq1q2btm/fXqwxXU5m+vXrp1WrVhXrZgAAAIBfYtNMxcXFac2aNTpx4oQWL16sTp06SZKOHj2qkJCQYo3p8pqZF198Uddee62++eYbNW7cWIGBgbnOjxo1qliBAAAAACi77rrrLg0aNEjh4eGqUaOGLr/8ckk5j581bty4WGO6nMx88MEHWrJkiUJCQrRq1SrnxplSTgEAkhkAAAAgN8PKOdx9j9Ls9ttvV6tWrbRv3z5dddVVstlyHhKrVatWsdfMuJzMPPDAA3r44Yc1btw4ZwBAfhwOh06knVRwaJCCQ4O9HQ4AAAC8rGXLlmrZsmWutm7duhV7PJeTmczMTPXv359EBgU6eeyU5k75RJ/NWKJjKcdl2AxdfHVzDbq/jxom1PN2eCgldmzap0/f/lab1u6UzW5Ty8sbqMeQdqpWs5K3QwMAoOT56aaZY8eO1aOPPqqwsDCNHTu20L7Tpk1zeXyXM5Ibb7xRc+fOdflG5yolJUWDBg1SRESEoqKiNHz4cB0/frzQ/nfeeafq1aun0NBQnX/++Ro1apTS0tI8GLX/OZF+UmPaT9CcKZ/oWErO749lWvpx8UaNuXSiVi/8wcsRojRYOOtr3dl9mlbM/1EH9/6l/bsO67N3vtWtnaZozZJfvR0eAAAoIT/99JOysrKcvy7o2LhxY7HGd3lmxuFw6KmnntKXX36pJk2a5CkAUJyMqigGDRqkgwcPaunSpcrKytKwYcM0cuRIvf/++/n2P3DggA4cOKBnnnlGDRs21J49e3TrrbfqwIEDmjdvnltihPTeI/O0+7d9Mh1mrnbTYcowpCcHP6+5B19XaFjxKlbA9/2+fpdmPLRAkuT4x58T02HKNKUn7nhbb371gCpVjfZWiAAAoISsXLky31+XFJeTmV9//VXNmzeXJG3atCnXuX8WAyhJmzdv1uLFi/XDDz84n7F74YUX1LVrVz3zzDOqWrVqnmsaNWqkjz/+2Pm6du3aevzxx3XDDTcoOztbAQEuv3WcRWZGlhbNXJYnkTnDsqRTx0/rq7mr1eWmK0vknifSTur0iQxFViqvgEB+T33BJ29+LbvdliuRcbJykppF76/Rjfd09XxwAADAp7j86c8dGdXZrFmzRlFRUbkWC3Xs2FE2m01r165V7969izROWlqaIiIiCk1kMjIylJGR4Xydnp5e/MD9zF8HUnQy/dRZ+33/2Y/nnMz8/NXveu/x+fp51e+SpHIRoep685UaNL63wiLLndPYcK+fV2/PP5H5m2la+nl18TbOAgCgtDLkgWpm7h3+nJ0+fVovvPCCVq5cqUOHDsk0c38e2LBhg8tjntNX2X/++ackqXr16ucyzFklJSWpcuXKudoCAgJUoUIFJSUlFWmMI0eO6NFHH9XIkSML7Td58mQ9/PDDxY7Vn4WUK1rFstWf/qiNKzep2RWNinWfVR+u0eTBL8qw/f+v7Mn0U5r/3H/1w+KfNf2rh0hoSrPS/i8tAABwi+HDh2vJkiXq16+fWrVqVSJPdbmczJimqccee0xTp051LsAvX7687r77bj3wwAMuVTkbN26cpkyZUmifzZs3uxpiHunp6erWrZsaNmyohx56qNC+48ePz1VpIT09XXFxceccgz+IjolS3Za1tX39TlmFffNgSDPHzdaLaye7fI8T6Sc1dcSrsmTJcuS+iekwtW/rAc1+YoFGThnk8tjwjGZtLtB3//2lwNkZm81Qs7YXeDgqAADczDJyDnffoxT7/PPPtWjRIrVt27bExizWPjNvvPGGnnzySWcg3377rR566CGdPn1ajz/+eJHHuvvuuzV06NBC+9SqVUuxsbE6dOhQrvbs7GylpKQoNja20OuPHTumLl26qHz58lqwYEGeggX/FhwcrOBg9kQprsETr9WEnk8W2scyLW39YYf27zioanWquDT+yjmrlXE6s8DSg6bD1KKZKzTs0f4KDGINTWnUa9hl+vrzjfmfNCSb3aarr0/waEwAAMD9qlWrpvLly5fomC5/2nv77bc1c+ZM9ezZ09nWpEkTVatWTbfffrtLyUylSpVUqdLZ95RISEhQamqq1q9frxYtWkiSVqxYIdM01bp16wKvS09PV+fOnRUcHKxPP/1UISFU0HK3S7q3UJtrLi5SCea0w+kuJzN7Nu+XPcAuR5ajwD4nj53S0aRUVT6/oktjwzMatIjX7Y/01cuTPpbd9v9CADa7TTaboQdeHkolMwBA2eOn+8z809SpU3XfffdpxowZqlGjRomM6XIyk5KSovr16+dpr1+/vlJSUkokqH9r0KCBunTpohEjRmjGjBnKyspSYmKiBgwY4Kxktn//fnXo0EHvvPOOWrVqpfT0dHXq1EknT57Ue++9p/T0dOdi/kqVKslut7slVkjt+15SpGSmYvXzXB47pFywCn+G7e9+YcyulWY9hrRTwxY19dk73+jXtTtl/3vTzO6D26pqPJtmAgBQFrVs2VKnT59WrVq1VK5cuTxPTBUnl3A5mWnatKlefPFFPf/887naX3zxRTVt2tTlAIpq9uzZSkxMVIcOHWSz2dS3b99cMWRlZWnr1q06efKkpJxqCGvXrpUk1alTJ9dYu3btUnx8vNti9Xdte7dSSFiwTp/IyPe8zW5T08svVOU412dO2vW+WHOf/rTA8zabTQ0SLlDEeSU7hYmSV/vCarprygBvhwEAgGcwM6Prr79e+/fv1xNPPKGYmBjvFAB46qmn1K1bNy1btkwJCTnPta9Zs0b79u3TokWLzjmgglSoUKHADTIlKT4+XtY/vrG//PLLc72G54SGhei2Z4fp2ZEz8pyz2W0KDArQyKcHF2vsei1r66KOjbVx5W/57mdjWqYG3V+0Ut0AAADwnNWrV2vNmjUlOgFS9NJjf7vsssu0bds29e7dW6mpqUpNTVWfPn20detWtW/fvsQCg2/renMHjZ89WpVr5J59qXdxbT37zaOq06xmscee8MFoNb28oSTJHmCTPdAuwzAUGByge2feqpZXNTmn2AEAAFDy6tevr1Onzr4noSsMi+mLQqWnpysyMtK54SZcY5qmtqzboWMpx1WlVozOr1+tRMa1LEtbf9ypbz5ep1MnTuv8+tXUYWBblY8OL5HxC+NwmPr9++06dvSEYmtUVK3G57v9ngAAoGCl+fPamdjiH39cNjcXozJPn9buBx4olT8HSVqyZIkefvhhPf7442rcuHGeNTPFibnIj5lt375dEydO1KuvvprnRmlpabrtttv02GOPqVatWi4HgbLLZrOp4SV1S3xcwzBU/+I6qn9xnbN3LkFLZ3+rWQ9/rL+SUp1ttZucr8RpQ9SgVW2PxgIAAOBLunTpIknq0KFDrnbLsmQYhhyOgqvVFqTIyczTTz+tuLi4fDOmyMhIxcXF6emnn9Yrr7zichCAL/jizZV64a538rTv2rRP/+n2pJ5ZPF71WpDMAwCAfFAAQCtXrizxMYuczHz11Vd67733Cjx/3XXXaeDAgSUSFFDanD6Rodfvn5vvOdO0pGxTMyd8qKcXjfNwZAAAAL7hsssuK/Exi1wAYO/evapcuXKB5ytWrKh9+/aVSFBAafPdZ+t1+mT+paYlyXSY+vXbrUrac8SDUQEAAJ9heegohZ566qlcC/+/++47ZWT8/3PVsWPHdPvttxdr7CInM5GRkdq5c2eB53fs2FEqFxoBJeGvg0dlDzj7X5e/Dh71QDQAAAC+Y/z48Tp27Jjz9dVXX639+/c7X588eVKvvvpqscYucjJz6aWX6oUXXijw/PPPP09pZpRZUZUj5cjOu6/Nv0VXIqEHAAB5GZZnjtLo38WTS7KYcpGTmfHjx+u///2v+vXrp3Xr1iktLU1paWlau3at+vbtqy+//FLjx48vscCA0qRt94sUGBxY4HnDZqhey1qqWjvGg1EBAAD4tyInM82bN9e8efP09ddfKyEhQRUqVFCFChXUpk0bffPNN/rwww910UUXuTNWwGvCIstpyAO98j1nGIYMw9DwR671bFAAAMB3WIZnDj9T5GpmktS9e3ft2bNHixcv1o4dO2RZlurWratOnTqpXLly7ooRKBX6jb5aAYEBeveJBTp57LSzvVL1CrrrhWFq0q6+F6MDAAAovWbOnKnw8JzNzbOzszVr1ixVrFhRknKtp3GVYZXkQ2tlUGneURbekXEqU+uXb9Kxo8cVW6OSGrerJ5utyJOcAACghJXmz2tnYqv50BOyhYS49V7m6dPa9dD9pe7nEB8fL8M4+6zRrl27XB7bpZkZAFJwaJDadOeRSgAAgKLYvXu328YmmQEAAADczBPVxkprNTN34tkYAAAAAD6JmRkAAADA3ay/D3ffw88UKZlJT08v8oClabERAAAAgLKrSMlMVFTUWSsQWJYlwzDkcDhKJDAAAACgzPDAmhlmZgqwcuVKd8cBAAAAAC4pUjJz2WWXuTsOAAAAoOzy4zUzWVlZeuCBBzR//nxVqFBBt956q2666Sbn+eTkZFWtWrVYT3gVuwDAyZMntXfvXmVmZuZqb9KkSXGHBAAAAFDGPP7443rnnXd0zz33KDU1VWPHjtXatWv16quvOvtYVvEyMZeTmcOHD2vYsGH673//m+951swAAAAAOGP27NmaOXOmunfvLkkaOnSorr76ag0bNkxvvvmmJJ11fX5BXN5n5q677lJqaqrWrl2r0NBQLV68WG+//bYuuOACffrpp8UKAgAAACjTLA8dpdD+/fvVqFEj5+s6depo1apVWr16tQYPHnxOkyEuz8ysWLFCCxcuVMuWLWWz2VSjRg1dddVVioiI0OTJk9WtW7diBwMAAACgbImNjdXOnTsVHx/vbKtWrZpWrlypK664QkOHDi322C7PzJw4cUKVK1eWJEVHR+vw4cOSpMaNG2vDhg3FDgQAAAAoqwzLM0dpdOWVV+r999/P0161alWtWLFCu3btKvbYLs/M1KtXT1u3blV8fLyaNm2qV199VfHx8ZoxY4aqVKlS7EAAAAAAlD0TJkzQli1b8j1XrVo1ffXVV1q6dGmxxnY5mRk9erQOHjwoSZo0aZK6dOmi2bNnKygoSLNmzSpWEAAAAADKpho1aqhGjRoFnq9atapuvPHGYo3tcjJzww03OH/dokUL7dmzR1u2bNH555+vihUrFisIAAAAAGXbRx99pA8++EDbtm2TJNWtW1cDBw5Uv379ij2my2tm/smyLIWGhuqiiy4ikQEAAAAK4sfVzEzTVP/+/dW/f3/9/vvvqlOnjurUqaPffvtN/fv314ABA4q9z0yxkpk33nhDjRo1UkhIiEJCQtSoUSPNnDmzWAEAAAAAKLuee+45LVu2TJ9++qm2bNmiTz75RJ988om2bt2qBQsWaOnSpXruueeKNbbLyczEiRM1evRo9ejRQx999JE++ugj9ejRQ2PGjNHEiROLFQQAAABQlvlzNbO33npLTz/9tHPTzH/q2bOnnnrqKefmma5yec3MK6+8otdff13XX399riCaNGmiO++8U4888kixAgEAAABQ9mzfvl0dO3Ys8HzHjh2VmJhYrLFdnpnJyspSy5Yt87S3aNFC2dnZxQoCAAAAKPP8cL2MJIWGhio1NbXA8+np6QoJCSnW2C4nM4MHD9Yrr7ySp/21117ToEGDihUEAAAAgLIpISEh3/zhjJdeekkJCQnFGtvlx8yknAIAS5Ys0SWXXCJJWrt2rfbu3ashQ4Zo7Nixzn7Tpk0rVlAAAABAmeKJ2ZNSOjvzwAMP6PLLL9dff/2le+65R/Xr15dlWdq8ebOmTp2qhQsXauXKlcUa2+VkZtOmTbroooskSTt37pQkVaxYURUrVtSmTZuc/QzDKFZAAAAAAMqONm3aaO7cuRo5cqQ+/vjjXOeio6P1wQcfqG3btsUa2+VkprhZEwAAAOCvPFFtrLRWM5Ok3r17q3Pnzvryyy+1fft2STmbZnbq1EnlypUr9rjFeswMAAAAAFxRrlw59e7du0THLFIy06dPH82aNUsRERHq06dPoX3nz59fIoEBAAAAZYYfr5lZsWKFEhMT9f333ysiIiLXubS0NLVp00YzZsxQ+/btXR67SMlMZGSkcw1MZGSkyzcBAAAA4J+mT5+uESNG5ElkpJzc4pZbbtG0adPcl8y89dZb+f4aAAAAAArz888/a8qUKQWe79Spk5555plije3ympldu3YpOztbF1xwQa727du3KzAwUPHx8cUKBAAAACir/LkAQHJysgIDAws8HxAQoMOHDxdrbJc3zRw6dKhWr16dp33t2rUaOnRosYIAAAAAUDZVq1Yt1xYu//bLL7+oSpUqxRrb5WTmp59+yrcO9CWXXKKNGzcWKwgAAACgTLM8dJRCXbt21YQJE3T69Ok8506dOqVJkyape/fuxRrb5WTGMAwdO3YsT3taWpocDkexggAAAADgeS+99JLi4+MVEhKi1q1ba926dUW6bs6cOTIMQ7169Tpr3wcffFApKSmqW7eunnrqKS1cuFALFy7UlClTVK9ePaWkpOiBBx4oVvwuJzOXXnqpJk+enCtxcTgcmjx5stq1a1esIAAAAIAyrRTOzMydO1djx47VpEmTtGHDBjVt2lSdO3fWoUOHCr1u9+7duueee4pcfSwmJkarV69Wo0aNNH78ePXu3Vu9e/fW/fffr0aNGunbb79VTEyMa8H/zbAsy6W3/fvvv+vSSy9VVFSU8w188803Sk9P14oVK9SoUaNiBVJapaenKzIyUmlpafmWkwMAAIB3lebPa2diqzv2CdmDQ9x6L0fGaW2bdn+Rfw6tW7fWxRdfrBdffFGSZJqm4uLidOedd2rcuHH538Ph0KWXXqqbbrpJ33zzjVJTU/XJJ58UOcajR49qx44dsixLF1xwgaKjo4t8bX5cnplp2LChfvnlF1133XU6dOiQjh07piFDhmjLli1lLpEBAAAASsKZambuPqScBOqfR0ZGRp54MjMztX79enXs2NHZZrPZ1LFjR61Zs6bA9/HII4+ocuXKGj58eLF+DtHR0br44ovVqlWrc05kpGKUZpakqlWr6oknnjjnmwMAAAAoWXFxcbleT5o0SQ899FCutiNHjsjhcOR5vCsmJkZbtmzJd9xvv/1Wb7zxRqkq+lWsZCY1NVXr1q3ToUOHZJpmrnNDhgwpkcAAAACAMsMT1cb+Hn/fvn25HjMLDg4+56GPHTumwYMH6/XXX1fFihXPebyS4nIy89lnn2nQoEE6fvy4IiIiZBiG85xhGCQzAAAAgBdFREScdc1MxYoVZbfblZycnKs9OTlZsbGxefrv3LlTu3fvVo8ePZxtZyY1AgICtHXrVtWuXbsEoneNy2tm7r77bt100006fvy4UlNTdfToUeeRkpLijhglSSkpKRo0aJAiIiIUFRWl4cOH6/jx40W61rIsXX311TIMw6UFSgAAAECJKGXVzIKCgtSiRQstX77c2WaappYvX66EhIQ8/evXr69ff/1VGzdudB49e/bUFVdcoY0bN+Z5tM1TXJ6Z2b9/v0aNGqVy5cq5I54CDRo0SAcPHtTSpUuVlZWlYcOGaeTIkXr//ffPeu306dNzzSABAAAA/m7s2LG68cYb1bJlS7Vq1UrTp0/XiRMnNGzYMEk5y0eqVaumyZMnKyQkJE+xr6ioKEnyahEwl5OZzp0768cff1StWrXcEU++Nm/erMWLF+uHH35Qy5YtJUkvvPCCunbtqmeeeUZVq1Yt8NqNGzdq6tSp+vHHH1WlShVPhQwAAAA4/bPamDvv4Yr+/fvr8OHDmjhxopKSktSsWTMtXrzYWRRg7969stlcfpDLo1xOZrp166Z7771Xv//+uxo3bqzAwMBc53v27FliwZ2xZs0aRUVFORMZSerYsaNsNpvWrl2r3r1753vdyZMnNXDgQL300kv5PvuXn4yMjFzl69LT088teAAAAKCUSkxMVGJiYr7nVq1aVei1s2bNKvmAXORyMjNixAhJOTWm/80wDDkcjnOP6l+SkpJUuXLlXG0BAQGqUKGCkpKSCrxuzJgxatOmja655poi32vy5Ml6+OGHix0rAAAAkIcHq5n5E5fnjUzTLPBwNZEZN26cDMMo9CiozvXZfPrpp1qxYoWmT5/u0nXjx49XWlqa89i3b1+x7g8AAADAvYq1z0xJufvuuzV06NBC+9SqVUuxsbE6dOhQrvbs7GylpKQU+PjYihUrtHPnTufCpDP69u2r9u3bFzhtFhwcXCK1uAEAAIAzSuOambKgSMnM888/r5EjRyokJETPP/98oX1HjRpV5JtXqlRJlSpVOmu/hIQEpaamav369WrRooWknGTFNE21bt0632vGjRunm2++OVdb48aN9eyzz+aqjw0AAADANxUpmXn22Wc1aNAghYSE6Nlnny2wn2EYLiUzRdWgQQN16dJFI0aM0IwZM5SVlaXExEQNGDDAWcls//796tChg9555x21atVKsbGx+c7anH/++apZs2aJxwgAAAAUiDUzblGkZGbXrl35/tqTZs+ercTERHXo0EE2m019+/bNNUuUlZWlrVu36uTJk16JDwAAAIBnubRmJisrS/Xr19fnn3+uBg0auCumfFWoUKHQDTLj4+NlWYWno2c7DwAAAMB3uJTMBAYG6vTp0+6KBQAAACibeMzMLVwuzXzHHXdoypQpys7Odkc8AAAAAFAkLpdm/uGHH7R8+XItWbJEjRs3VlhYWK7z8+fPL7HgAAAAgLLA+Ptw9z38jcvJTFRUlPr27euOWAAAAACgyFxOZt566y13xAEAAACUXayZcYsir5kxTVNTpkxR27ZtdfHFF2vcuHE6deqUO2MDAAAAgAIVOZl5/PHHdf/99ys8PFzVqlXTc889pzvuuMOdsQEAAABlgmF55vA3RU5m3nnnHb388sv68ssv9cknn+izzz7T7NmzZZqmO+MDAAAAgHwVOZnZu3evunbt6nzdsWNHGYahAwcOuCUwAAAAoMywPHT4mSInM9nZ2QoJCcnVFhgYqKysrBIPCgAAAADOpsjVzCzL0tChQxUcHOxsO336tG699dZce82wzwwAAACQDz+cOXG3IiczN954Y562G264oUSDAQAAAICiKnIyw/4yAAAAQPF4otoY1cwAAAAAwEcUeWYGZcef2w/qk+cX6dsFa5WVka06zeN1TeLVSujRUoZheDs8AACAsscT1cb8cGaGZMbPrF/6syZcM0VmtkOO7Jw9gjau/E0blv2qbrdcpdEvjyChAQAAgE/gMTM/cjz1hB7q87SyM7KdiYwkmY6cX3/x6lIte+9rb4UHAABQZp1ZM+Puw9+QzPiRpe98pYyTmbKs/P+kGzZDHz/7uYejAgAAAIqHZMaP/P79NhX2BJllWtq5cbeys7I9FxQAAABQTKyZ8SN2u0052UzBc5CGIdbMAAAAlDQKALgFMzN+pHmHxs71Mfmx2W1q1L6B7AF2D0YFAAAAFA/JjB+5vH8bRVWOlM2e/2+76TDV/95rPBwVAABA2UcBAPcgmfEjwaHBmrz4AYVHheV6lMxmz/l11xEd1bJLMy9FBwAAALiGZMbP1GlWU7O2Pa+RTw9WjYbVFRAUINORk8Yven2Zbqh5h76et8bLUQIAAJQxlocOP0My44fKR4eret2q2rt5vxz/qlx25M+/9Oh10/TVRyQ0AAAAKN1IZvyQaZp6afSbkiwVsOWMXr7rLTkcDo/GBQAAUGYxM+MWJDN+6Pc125S061CBiYwkpRw8qp9X/ua5oAAAAAAXsc+MH0o5eLRI/f46ULR+AAAAKJwnqo1RzQx+oUJsVNH6VSlaPwAAAMAbSGb8UMM29RQTX0kyCu4THRulZlc08lxQAAAAZRlrZtyCZMYP2Ww23f7ssJwXBSQ0t00bKnuA3XNBAQAAAC4imfFTba65WA99fK8qVjsvV3uFKtG6//27dMWAtl6KDAAAoOwxLMsjh7+hAIAfa9urlS7p0UKbvtmiI/tTFB0bpaaXNWRGBgAAAD6BZMbP2e12Nb38Qm+HAQAAULZ5Yk2L/03MkMwAZYllWfrtx906uO8vlY8sp4vaXaCg4EBvhwUAAOAWJDNAGbFxzQ49/+DHOrj3L2dbWPkQ3TCqk665sa0Mo5DydQAAwK3YZ8Y9SGaAMmDTj7v04E0zZZq5/xU7cey0Xn38U2VnZavfiMu9ExwAAICbUM0MKAPefGqRTNOSZeb/lcy7zy3RiWOnPBwVAABwYp8ZtyCZAXxc0r4Ubf5pT4GJjCRlZmTruy83eTAqAAAA9yOZAXzc0SPHztrHbrcp5fDZ+wEAAPgS1swAPq5C5Yiz9nE4TJ0Xc/Z+AADAPSgA4B7MzAA+LqZatBpdXFM2W8HVyoJDA9W2UyMPRgUAAOB+JDNAGXDzfd1kD7DJKCChGXZPV5ULD/FwVAAAwIkCAG5BMgOUAfWanq8n371FNS6IydUeVTFcox/vq2uGtPVSZAAAAO7DmhmgjGh4Ubxe/myMdv5+QEn7UlQ+KlSNWtaUPcDu7dAAAPB7rJlxD5IZFOhocqqOJqcpOiZS0TFR3g4HRWAYhupcWE11Lqzm7VAAAADcjmQGeez4aZfeGD9bPy79OefZS0Nq2bmZhj8xUHWa1fR2eAAAAL7HE2ta/HBmhjUzyGXz2u0a1fYBbVj+6///QljShqW/aHSbB7V57XavxgcAAACcQTIDJ8uyNG3EK3JkZst0mLnOmQ5T2ZlZenbkDFmWH6b9AAAA5+jMuhl3Hf6IZAZOW9Zt1+5N+2Sa+f9tME1Lu37dq23r//BwZAAAAEBeJDOQJDmyHXrhjplF6ntgR5KbowEAAChjLMszh5/xmWQmJSVFgwYNUkREhKKiojR8+HAdP378rNetWbNGV155pcLCwhQREaFLL71Up06d8kDEvuXzV5dq+4ZdReobFlnOzdEAAAAAZ+czycygQYP022+/aenSpfr888/19ddfa+TIkYVes2bNGnXp0kWdOnXSunXr9MMPPygxMVE2m8+8bY9Z8MKiIvULjwpTsysudHM0AAAAZYu718v467oZnyjNvHnzZi1evFg//PCDWrZsKUl64YUX1LVrVz3zzDOqWrVqvteNGTNGo0aN0rhx45xt9erVK/ReGRkZysjIcL5OT08vgXdQumVlZmn/toNF6jt44rUKCglyc0QAAADA2fnEFMWaNWsUFRXlTGQkqWPHjrLZbFq7dm2+1xw6dEhr165V5cqV1aZNG8XExOiyyy7Tt99+W+i9Jk+erMjISOcRFxdXou+lNLIH2GWzGWftV7tZvHqP7uqBiAAAAMoYy0OHn/GJZCYpKUmVK1fO1RYQEKAKFSooKSn/xeh//JFTceuhhx7SiBEjtHjxYl100UXq0KGDtm8veK+U8ePHKy0tzXns27ev5N5IKWWz2dTy6uay2Qv/43D9+D4yjLMnPQAAAIAneDWZGTdunAzDKPTYsmVLscY2zZx9Um655RYNGzZMzZs317PPPqt69erpzTffLPC64OBgRURE5Dr8wYD/9JJVQElme4BNVWrFqG2viz0cFQAAQNlgmJ45/I1X18zcfffdGjp0aKF9atWqpdjYWB06dChXe3Z2tlJSUhQbG5vvdVWqVJEkNWzYMFd7gwYNtHfv3uIHXUY1bt9A9751h6be/LIs05JlSYbNkOkwVfn8SpqyZIICAn1iiRUAAAD8hFc/nVaqVEmVKlU6a7+EhASlpqZq/fr1atGihSRpxYoVMk1TrVu3zvea+Ph4Va1aVVu3bs3Vvm3bNl199dXnHnwZdNWQy3TRVU20+M0V2rlxl4JCgpTQo6Xa9LpYgUGB3g4PAAAAyMUnvmpv0KCBunTpohEjRmjGjBnKyspSYmKiBgwY4Kxktn//fnXo0EHvvPOOWrVqJcMwdO+992rSpElq2rSpmjVrprfffltbtmzRvHnzvPyOSq/zqkRr0AN9vR0GAABA2eKJBfp+WADAJ5IZSZo9e7YSExPVoUMH2Ww29e3bV88//7zzfFZWlrZu3aqTJ0862+666y6dPn1aY8aMUUpKipo2baqlS5eqdu3a3ngLAAAAAEqQYVmWH+ZwRZeenq7IyEilpaX5TTEAAAAAX1KaP6+dia3VNY8pIDDErffKzjqtdQsfLJU/B3fxidLMAAAAAPBvPvOYGQAAAOCzLCvncPc9/AwzMwAAAAB8EjMzAAAAgJsZVs7h7nv4G2ZmAAAAAPgkZmYAAAAAd2OfGbdgZgYAAACAT2JmBgAAAHAz1sy4BzMzAAAAAHwSMzMAAACAu7HPjFswMwMAAADAJzEzAwAAALgZa2bcg5kZAAAAAD6JmRkAAADA3dhnxi2YmQEAAADgk5iZAQAAANyMNTPuwcwMAAAAAJ9EMgMAAADAJ/GYGQAAAOBuppVzuPsefoaZGQAAAAA+iZkZAAAAwN0ozewWzMwAAAAA8EnMzPihk8dOKSsjS+UrhMtmI58FAABwN0MeKM3s3uFLJZIZP7J20QZ9MHm+fvtuqySpQpVo9Uq8Wn3HdldQcKCXowMAAABcQzLjJxa+tFgv3vmGbPb/z8SkHDyqtyZ8oA3Lf9ETi+5XYBAJDQAAgFtYVs7h7nv4GZ4x8gOH9h3Ry6PflCSZDjPXOcu09PPK3/TZK0u8ERoAAABQbCQzfuC/M5dLRsFPUVqytPClxR6MCAAAwL8YlmcOf0My4wd2/7ZPVmGbKFnSgR1JcjgcngsKAAAAOEckM34gJCxYhq3w+hYBgXYqmwEAALiL5aHDRS+99JLi4+MVEhKi1q1ba926dQX2ff3119W+fXtFR0crOjpaHTt2LLS/J/Dp1Q+07dUqz1qZf7IH2NS2T2sZhTyKBgAAgLJl7ty5Gjt2rCZNmqQNGzaoadOm6ty5sw4dOpRv/1WrVun666/XypUrtWbNGsXFxalTp07av3+/hyP/P5IZP5DQo6XOb1BNtoC8v905+Yuh6+7p6fG4AAAA/IVhWR45XDFt2jSNGDFCw4YNU8OGDTVjxgyVK1dOb775Zr79Z8+erdtvv13NmjVT/fr1NXPmTJmmqeXLl5fEj6hYSGb8gD3ArilLJqhGg+rO1/YAu2RIwaHBmvTxParboraXowQAAEBJSE9Pz3VkZGTk6ZOZman169erY8eOzjabzaaOHTtqzZo1RbrPyZMnlZWVpQoVKpRY7K5inxk/UbHaeZrx09PasOxXrf18vTJPZ6pO85q6clB7hUWU83Z4AAAAZZv59+Hue0iKi4vL1Txp0iQ99NBDudqOHDkih8OhmJiYXO0xMTHasmVLkW533333qWrVqrkSIk8jmfEjNptNLTs1VctOTb0dCgAAANxk3759ioiIcL4ODg4u8Xs8+eSTmjNnjlatWqWQkJASH7+oSGYAAAAANyvOmpbi3EOSIiIiciUz+alYsaLsdruSk5NztScnJys2NrbQa5955hk9+eSTWrZsmZo0aXJuQZ8j1swAAAAAfiYoKEgtWrTItXj/zGL+hISEAq976qmn9Oijj2rx4sVq2bKlJ0ItFDMzAAAAgB8aO3asbrzxRrVs2VKtWrXS9OnTdeLECQ0bNkySNGTIEFWrVk2TJ0+WJE2ZMkUTJ07U+++/r/j4eCUlJUmSwsPDFR4e7pX3QDIDAAAAuFsxN7V0+R4u6N+/vw4fPqyJEycqKSlJzZo10+LFi51FAfbu3ZtrU/VXXnlFmZmZ6tevX65x8isw4CkkMwAAAICfSkxMVGJiYr7nVq1alev17t273R+Qi0hmABRL+tET+uK977Tso3VKSzmhylWj1WVggjr1b62Q0CBvhwcAQOliWTmHu+/hZ0hmALgs+c8U3dP3ef2VnCbLzPmHc9exU3pl0sda8uFaTZlzh8IiQr0cJQAAKOuoZgbAZU8mvqOUQ+nOREaS81ngXZsP6NVHFngtNgAASiPD8szhb0hmALhk529/asuG3TId+W9jbDpMrVywXulHT3g4MgAA4G9IZgC4ZPOG3ZJReJ/sLId2/rbfI/EAAOATzqyZcffhZ0hmALjEZrMVqfSjPYB/XgAAgHtRAACAS5q3q5szM1NIQhNSLkh1m8R5LCYAAEo7w8w53H0Pf8NXpwBcUqVGRV3SsZFs9vz/+TAMQz1ubK+QcsEejgwAAPgbkhkALhs7daBqX1hNkmSz5SygOZPcXNKpkYbc09VrsQEAUCqxZsYteMwMgMvKR5XTtAV3ac2Xv2rZx+t09PAxxcadp87XX6Lm7ermrKsBAABwM5IZAMUSEGhX++7N1L57M2+HAgBA6ff3fmxuv4ef8ZmvT1NSUjRo0CBFREQoKipKw4cP1/Hjxwu9JikpSYMHD1ZsbKzCwsJ00UUX6eOPP/ZQxAAAAADcyWeSmUGDBum3337T0qVL9fnnn+vrr7/WyJEjC71myJAh2rp1qz799FP9+uuv6tOnj6677jr99NNPHooaAAAAkAzL8sjhb3wimdm8ebMWL16smTNnqnXr1mrXrp1eeOEFzZkzRwcOHCjwutWrV+vOO+9Uq1atVKtWLT344IOKiorS+vXrPRg9AAAAAHfwiWRmzZo1ioqKUsuWLZ1tHTt2lM1m09q1awu8rk2bNpo7d65SUlJkmqbmzJmj06dP6/LLLy/wmoyMDKWnp+c6AAAAgHNCNTO38IlkJikpSZUrV87VFhAQoAoVKigpKanA6z788ENlZWXpvPPOU3BwsG655RYtWLBAderUKfCayZMnKzIy0nnExbHxHwAAAFAaeTWZGTdunAzDKPTYsmVLscefMGGCUlNTtWzZMv34448aO3asrrvuOv36668FXjN+/HilpaU5j3379hX7/gAAAADcx6ulme+++24NHTq00D61atVSbGysDh06lKs9OztbKSkpio2Nzfe6nTt36sUXX9SmTZt04YUXSpKaNm2qb775Ri+99JJmzJiR73XBwcEKDmbncgAAAJQgS5LpgXv4Ga8mM5UqVVKlSpXO2i8hIUGpqalav369WrRoIUlasWKFTNNU69at873m5MmTkpRn8z673S7TdPefJOQnec9hffrKEq1b9JOys7J1Ydt6uub2zrrgolreDg0AAAA+yCfWzDRo0EBdunTRiBEjtG7dOn333XdKTEzUgAEDVLVqVUnS/v37Vb9+fa1bt06SVL9+fdWpU0e33HKL1q1bp507d2rq1KlaunSpevXq5cV3459++HKjhjUco3nTPtfu3/bpz20Htezdr3X7xeM1/7kvvB0eAACAW1Ga2T18IpmRpNmzZ6t+/frq0KGDunbtqnbt2um1115zns/KytLWrVudMzKBgYFatGiRKlWqpB49eqhJkyZ655139Pbbb6tr167eeht+6Whyqh7qO1XZGdkyHf+fFXNk5/z6lbHv6Jevf/dWeAAAAPBRXn3MzBUVKlTQ+++/X+D5+Ph4Wf/KRi+44AJ9/PHH7g4NZ7HojRXKysjK8/tzhj3ApvnPLVKTSxt6ODIAAAAPseT+0sn+NzHjOzMz8F0bV2ySZRb8t8uRbeqnFZs8GBEAAADKAp+ZmYHvKtKXEH74TQIAAPAjntjUkjUzQMlrenlD2WxGgeftATY1uYxHzAAAAOAakhm4XdebO8geaJdRQD7jyDbVZzRFGQAAQBlmeujwMyQzcLvzqkRr4odjZQ8MkM3+/z9y9oCcX4+YMkjNr2zkrfAAAADgo1gzA4+4pHsLzfx1qj57ZYm+/2K9HFkOXdimnnre0VkNL6nr7fAAAADcyhP7wPjjPjMkM/CYanVidevUIbp16hBvhwIAAIAygGQGAAAAcDeqmbkFa2YAAAAA+CRmZkqJk8dOafnsb7R+yUY5sk01TKinLjddoeiYKG+HBgAAgHPFzIxbkMyUAtvW79S4zo/p2NHjMmTIsiytXbRB7zz8oR744C61693a2yECAAAApQ6PmXnZsaPHNa7TozqRdlKyJOvvjNoyLTmyHHqs/zTt+nWPl6MEAADAOTkzM+Puw8+QzHjZklmrdDz1pExH3l2OziQ2859b5OmwAAAAgFKPZMbLvv98vTNpyY8j29TqT3/wYEQAAAAocaaHDj9DMuNlWRlZZ+2TnZntgUgAAAAA30Iy42X1W9WRLaDg3wab3aZ6LWt7MCIAAADAN5DMeFn3WzvJchT8mJnpMNVrVFcPRgQAAICSZliWRw5/QzLjZdXrVtWdLw6XpFwzNDZ7zq+vSeyihB4tvRIbAAAAUJqxz0wp0OO2zjq/QXXNm/aZflzysyzTVL2L66jP6G669NoEGYbh7RABAABwLtg00y1IZkqJppdfqKaXXygppyQzCQwAAABQOJKZUohEBgAAoIwxLclw88yJ6X8zM6yZAQAAAOCTmJkBAAAA3I01M27BzAwAAAAAn8TMDAAAAOB2HpiZETMzAAAAAOATmJkBAAAA3I01M27BzAwAAAAAn8TMDAAAAOBupiW3r2lhnxkAAAAA8A3MzAAAAADuZpk5h7vv4WeYmQEAAADgk5iZAQAAANyNamZuwcwMAAAAAJ9EMgMAAADAJ/GYGQAAAOBulGZ2C2ZmAAAAAPgkZmYAAAAAd6MAgFswMwMAAADAJzEzAwAAALibJQ/MzLh3+NKImRkAAAAAPomZGQAAAMDdWDPjFszMAAAAAPBJzMwAAAAA7maakkwP3MO/MDMDAAAAwCcxMwMAAAC4G2tm3IKZGQAAAAA+iZkZAAAAwN2YmXELZmYAAAAA+CRmZgAAAFAiLMcR6dR8WY4/JKOcjJAuUuDFMgzD26F5n2lJcvPMiel/MzMkMwAAADhn1sk5stIfVs4HduPvtvekwBZS9AwZtkivxoeyyWceM3v88cfVpk0blStXTlFRUUW6xrIsTZw4UVWqVFFoaKg6duyo7du3uzdQAAAAP2OdXikrfaIkh3L2UnH8fUjK2ijr6B3eC66UsCzTI4e/8ZlkJjMzU9dee61uu+22Il/z1FNP6fnnn9eMGTO0du1ahYWFqXPnzjp9+rQbIwUAAPAv1omXVfDHSoeUtU5W1i+eDAl+wmceM3v44YclSbNmzSpSf8uyNH36dD344IO65pprJEnvvPOOYmJi9Mknn2jAgAHuChUAAMBvWGaKlPXzWXrZZZ1eIiOwiUdiKpUsy/1rWqhmVnbs2rVLSUlJ6tixo7MtMjJSrVu31po1awq8LiMjQ+np6bkOAAAAFMA6VYROhmRluD0U+J8ym8wkJSVJkmJiYnK1x8TEOM/lZ/LkyYqMjHQecXFxbo0TAADAp9kqSUb4WTplywio65Fw4F+8msyMGzdOhmEUemzZssWjMY0fP15paWnOY9++fR69PwAAgC8xjCCp3AAV/LHSkIxyUkg3T4ZV+pzZNNPdh5/x6pqZu+++W0OHDi20T61atYo1dmxsrCQpOTlZVapUcbYnJyerWbNmBV4XHBys4ODgYt0TAADAHxlht8vK+E7K3qqcamZn2CVZMiKfkWEr56XoUJZ5NZmpVKmSKlWq5Jaxa9asqdjYWC1fvtyZvKSnp2vt2rUuVUQDAABA4QxbuFRhtqwTb0gn35eso5IMKfgyGWG3yghq5u0Qvc80JcPNpZP9sDSzz1Qz27t3r1JSUrR37145HA5t3LhRklSnTh2Fh+c8p1m/fn1NnjxZvXv3lmEYuuuuu/TYY4/pggsuUM2aNTVhwgRVrVpVvXr18t4bAQAAKIMMW7iM8qNlhd8pWcckI0SGwdMucC+fSWYmTpyot99+2/m6efPmkqSVK1fq8ssvlyRt3bpVaWlpzj7/+c9/dOLECY0cOVKpqalq166dFi9erJCQEI/GDgAA4C8MwyYZkd4Oo/SxLEmUZi5phmX54bt2QXp6uiIjI5WWlqaIiAhvhwMAAIB/Kc2f187E1iF8oAKMILfeK9vK1PLj75fKn4O7+MzMDAAAAPC/9u48Kqrz/AP4d0BnUNYgyBIFVBRwRVEQrIKViNVaaM9xr8GlWo1aLEaDHhXFpi7RGGs8LrENHmujsXWrSyxBUYOIiqBoENHgEg9g3dhcUOb5/ZHD/TmyCJRhHOb7OWfO4b73vXee+8zrlYf33jvGSrRaiJ7vmRETvGemyX7PDBERERERNW2cmSEiIiIi0jfeM6MXnJkhIiIiIiKjxJkZIiIiIiJ90wqg4sxMQ+PMDBERERERGSXOzBARERER6ZsIAD0/bYwzM0RERERERMaBMzNERERERHomWoHo+Z4Z4cwMERERERGRceDMDBERERGRvokW+r9nRs/7fwtxZoaIiIiIiIwSixkiIiIiIjJKLGaIiIiIiPRMtNIor7rasGEDPDw8YGFhgYCAAJw9e7bG/rt374a3tzcsLCzQrVs3HD58uL4paRAsZoiIiIiITNCuXbsQHR2N2NhYXLhwAT169EBYWBju3btXZf/Tp09jzJgxmDx5MtLT0xEREYGIiAhcvny5kSP/fyoxxWe41UFRURFsbW1RWFgIGxsbQ4dDRERERK95m39fq4gtBOFopmqu1/d6KS+QhP21zkNAQAD69OmDzz//HACg1WrRtm1bzJo1CzExMZX6jxo1CqWlpTh48KDS1rdvX/j6+mLTpk0NdyB1wKeZvUFFrVdUVGTgSIiIiIioKhW/p73Nf6N/iReAnsN7iRcAKv/eqtFooNFodNrKysqQlpaG+fPnK21mZmYIDQ1FSkpKlftPSUlBdHS0TltYWBj27dvXANHXD4uZNyguLgYAtG3b1sCREBEREVFNiouLYWtra+gwdKjVajg7O+O7/Ma5t8TKyqrS762xsbFYsmSJTtv9+/dRXl4OJycnnXYnJydcvXq1yn3n5+dX2T8/P/9/D7yeWMy8gaurK+7cuQMRgZubG+7cufPWTV8aWlFREdq2bcvcVIG5qRrzUj3mpnrMTfWYm6oxL9VrarkRERQXF8PV1dXQoVRiYWGB3NxclJWVNcr7iQhUKpVO2+uzMk0Ji5k3MDMzQ5s2bZTpOhsbmybxj14fmJvqMTdVY16qx9xUj7mpHnNTNealek0pN2/bjMyrLCwsYGFhYegwdDg4OMDc3BwFBQU67QUFBXB2dq5yG2dn5zr1bwx8mhkRERERkYlRq9Xw8/NDYmKi0qbVapGYmIjAwMAqtwkMDNTpDwAJCQnV9m8MnJkhIiIiIjJB0dHRiIyMRO/eveHv74/PPvsMpaWlmDhxIgDg/fffx7vvvovly5cDAKKiohAcHIw1a9Zg2LBh2LlzJ86fP48tW7YY7BhYzNSSRqNBbGxsk77msL6Ym+oxN1VjXqrH3FSPuakec1M15qV6zA0BPz1q+b///S8WL16M/Px8+Pr64ptvvlFu8r99+zbMzP7/Qq6goCD84x//wMKFC7FgwQJ07NgR+/btQ9euXQ11CPyeGSIiIiIiMk68Z4aIiIiIiIwSixkiIiIiIjJKLGaIiIiIiMgosZghIiIiIiKjxGKmBh9//DGCgoLQsmVL2NnZ1WobEcHixYvh4uKCFi1aIDQ0FDk5OfoN1AAePnyIcePGwcbGBnZ2dpg8eTJKSkpq3CYkJAQqlUrnNW3atEaKWH82bNgADw8PWFhYICAgAGfPnq2x/+7du+Ht7Q0LCwt069YNhw8fbqRIG1dd8hIfH19pbLxtXy7WUE6ePInhw4fD1dUVKpUK+/bte+M2SUlJ6NWrFzQaDTw9PREfH6/3OBtbXfOSlJRUacyoVCrk5+c3TsCNaPny5ejTpw+sra3RunVrREREIDs7+43bNfVzTX3yYirnmo0bN6J79+7KF2IGBgbiyJEjNW7T1McLNV0sZmpQVlaGESNGYPr06bXeZtWqVfjLX/6CTZs2ITU1FZaWlggLC8OzZ8/0GGnjGzduHK5cuYKEhAQcPHgQJ0+exNSpU9+43ZQpU5CXl6e8Vq1a1QjR6s+uXbsQHR2N2NhYXLhwAT169EBYWBju3btXZf/Tp09jzJgxmDx5MtLT0xEREYGIiAhcvny5kSPXr7rmBfjpW6hfHRu3bt1qxIgbT2lpKXr06IENGzbUqn9ubi6GDRuGgQMHIiMjA7Nnz8bvfvc7HD16VM+RNq665qVCdna2zrhp3bq1niI0nBMnTmDGjBk4c+YMEhIS8OLFCwwePBilpaXVbmMK55r65AUwjXNNmzZtsGLFCqSlpeH8+fP4+c9/jvDwcFy5cqXK/qYwXqgJE3qjL7/8Umxtbd/YT6vVirOzs3zyySdK2+PHj0Wj0chXX32lxwgb1/fffy8A5Ny5c0rbkSNHRKVSyd27d6vdLjg4WKKiohohwsbj7+8vM2bMUJbLy8vF1dVVli9fXmX/kSNHyrBhw3TaAgIC5Pe//71e42xsdc1Lbf+NNTUAZO/evTX2mTdvnnTp0kWnbdSoURIWFqbHyAyrNnk5fvy4AJBHjx41Skxvk3v37gkAOXHiRLV9TOVc86ra5MVUzzUiIu+8845s3bq1ynWmOF6o6eDMTAPKzc1Ffn4+QkNDlTZbW1sEBAQgJSXFgJE1rJSUFNjZ2aF3795KW2hoKMzMzJCamlrjtjt27ICDgwO6du2K+fPn48mTJ/oOV2/KysqQlpam83mbmZkhNDS02s87JSVFpz8AhIWFNanxUZ+8AEBJSQnc3d3Rtm3bGv+CaGpMYcz8L3x9feHi4oL33nsPycnJhg6nURQWFgIA7O3tq+1jiuOmNnkBTO9cU15ejp07d6K0tBSBgYFV9jHF8UJNRzNDB9CUVFyrXfGtqRWcnJya1HXc+fn5lS7laNasGezt7Ws8zrFjx8Ld3R2urq64dOkSPvroI2RnZ2PPnj36Dlkv7t+/j/Ly8io/76tXr1a5TX5+fpMfH/XJi5eXF/72t7+he/fuKCwsxOrVqxEUFIQrV66gTZs2jRH2W6u6MVNUVISnT5+iRYsWBorMsFxcXLBp0yb07t0bz58/x9atWxESEoLU1FT06tXL0OHpjVarxezZs9GvX78av3HbFM41r6ptXkzpXJOZmYnAwEA8e/YMVlZW2Lt3Lzp37lxlX1MbL9S0mFwxExMTg5UrV9bYJysrC97e3o0U0dujtrmpr1fvqenWrRtcXFwwaNAg3LhxAx06dKj3fsn4BQYG6vzFMCgoCD4+Pti8eTOWLVtmwMjobeXl5QUvLy9lOSgoCDdu3MDatWuxfft2A0amXzNmzMDly5fx3XffGTqUt0pt82JK5xovLy9kZGSgsLAQ//znPxEZGYkTJ05UW9AQGSuTK2bmzJmDCRMm1Ninffv29dq3s7MzAKCgoAAuLi5Ke0FBAXx9feu1z8ZU29w4OztXupH75cuXePjwoZKD2ggICAAAXL9+3SiLGQcHB5ibm6OgoECnvaCgoNo8ODs716m/MapPXl7XvHlz9OzZE9evX9dHiEalujFjY2NjsrMy1fH392/Sv+TPnDlTeeDKm2YRTOFcU6EueXldUz7XqNVqeHp6AgD8/Pxw7tw5rFu3Dps3b67U15TGCzU9JnfPjKOjI7y9vWt8qdXqeu27Xbt2cHZ2RmJiotJWVFSE1NTUaq9TfZvUNjeBgYF4/Pgx0tLSlG2PHTsGrVarFCi1kZGRAQA6hZ8xUavV8PPz0/m8tVotEhMTq/28AwMDdfoDQEJCglGMj9qqT15eV15ejszMTKMdGw3JFMZMQ8nIyGiSY0ZEMHPmTOzduxfHjh1Du3bt3riNKYyb+uTldaZ0rtFqtXj+/HmV60xhvFATZugnELzNbt26Jenp6bJ06VKxsrKS9PR0SU9Pl+LiYqWPl5eX7NmzR1lesWKF2NnZyf79++XSpUsSHh4u7dq1k6dPnxriEPRmyJAh0rNnT0lNTZXvvvtOOnbsKGPGjFHW//jjj+Ll5SWpqakiInL9+nWJi4uT8+fPS25uruzfv1/at28vAwYMMNQhNIidO3eKRqOR+Ph4+f7772Xq1KliZ2cn+fn5IiIyfvx4iYmJUfonJydLs2bNZPXq1ZKVlSWxsbHSvHlzyczMNNQh6EVd87J06VI5evSo3LhxQ9LS0mT06NFiYWEhV65cMdQh6E1xcbFyLgEgn376qaSnp8utW7dERCQmJkbGjx+v9P/hhx+kZcuWMnfuXMnKypINGzaIubm5fPPNN4Y6BL2oa17Wrl0r+/btk5ycHMnMzJSoqCgxMzOTb7/91lCHoDfTp08XW1tbSUpKkry8POX15MkTpY8pnmvqkxdTOdfExMTIiRMnJDc3Vy5duiQxMTGiUqnkP//5j4iY5nihpovFTA0iIyMFQKXX8ePHlT4A5Msvv1SWtVqtLFq0SJycnESj0cigQYMkOzu78YPXswcPHsiYMWPEyspKbGxsZOLEiTpFXm5urk6ubt++LQMGDBB7e3vRaDTi6ekpc+fOlcLCQgMdQcNZv369uLm5iVqtFn9/fzlz5oyyLjg4WCIjI3X6f/3119KpUydRq9XSpUsXOXToUCNH3DjqkpfZs2crfZ2cnGTo0KFy4cIFA0StfxWPFH79VZGPyMhICQ4OrrSNr6+vqNVqad++vc45p6moa15WrlwpHTp0EAsLC7G3t5eQkBA5duyYYYLXs6ry8vr/PaZ4rqlPXkzlXDNp0iRxd3cXtVotjo6OMmjQIKWQETHN8UJNl0pEpBEmgIiIiIiIiBqUyd0zQ0RERERETQOLGSIiIiIiMkosZoiIiIiIyCixmCEiIiIiIqPEYoaIiIiIiIwSixkiIiIiIjJKLGaIiIiIiMgosZghIiIiIiKjxGKGiEySh4cHPvvsswbb34QJExAREdFg+wOApKQkqFQqPH78uEH3S0RE1FSwmCEiozZhwgSoVCqoVCqo1Wp4enoiLi4OL1++rHG7c+fOYerUqQ0Wx7p16xAfH99g+6uL9PR0jBgxAk5OTrCwsEDHjh0xZcoUXLt2zSDxvK1qW8Bu2bIFISEhsLGxYTFJRPSWYzFDREZvyJAhyMvLQ05ODubMmYMlS5bgk08+qbJvWVkZAMDR0REtW7ZssBhsbW1hZ2fXYPurrYMHD6Jv3754/vw5duzYgaysLPz973+Hra0tFi1a1OjxNAVPnjzBkCFDsGDBAkOHQkREb8BihoiMnkajgbOzM9zd3TF9+nSEhobiwIEDAP7/8q+PP/4Yrq6u8PLyAlD5r/QqlQpbt27Fr3/9a7Rs2RIdO3ZU9lHhypUr+OUvfwkbGxtYW1ujf//+uHHjhs77VAgJCcHMmTMxc+ZM2NrawsHBAYsWLYKIKH22b9+O3r17w9raGs7Ozhg7dizu3btX6+N+8uQJJk6ciKFDh+LAgQMIDQ1Fu3btEBAQgNWrV2Pz5s1K3xMnTsDf3x8ajQYuLi6IiYnRmb0KCQnBrFmzMHv2bLzzzjtwcnLCF198gdLSUkycOBHW1tbw9PTEkSNHlG0qLoM7dOgQunfvDgsLC/Tt2xeXL1/WifNf//oXunTpAo1GAw8PD6xZs0ZnvYeHB/785z9j0qRJsLa2hpubG7Zs2aLT586dOxg5ciTs7Oxgb2+P8PBw3Lx5U1lfkf/Vq1fDxcUFrVq1wowZM/DixQvl+G7duoU//vGPykxedWbPno2YmBj07du31p8FEREZBosZImpyWrRooczAAEBiYiKys7ORkJCAgwcPVrvd0qVLMXLkSFy6dAlDhw7FuHHj8PDhQwDA3bt3MWDAAGg0Ghw7dgxpaWmYNGlSjZezbdu2Dc2aNcPZs2exbt06fPrpp9i6dauy/sWLF1i2bBkuXryIffv24ebNm5gwYUKtj/Po0aO4f/8+5s2bV+X6ipmiu3fvYujQoejTpw8uXryIjRs34q9//Sv+9Kc/VYrXwcEBZ8+exaxZszB9+nSMGDECQUFBuHDhAgYPHozx48fjyZMnOtvNnTsXa9aswblz5+Do6Ijhw4crRURaWhpGjhyJ0aNHIzMzE0uWLMGiRYsqXZK3Zs0a9O7dG+np6fjggw8wffp0ZGdnK3kKCwuDtbU1Tp06heTkZFhZWWHIkCE6n/Px48dx48YNHD9+HNu2bUN8fLzyPnv27EGbNm0QFxeHvLw85OXl1TrPRET0FhMiIiMWGRkp4eHhIiKi1WolISFBNBqNfPjhh8p6Jycnef78uc527u7usnbtWmUZgCxcuFBZLikpEQBy5MgRERGZP3++tGvXTsrKyt4Yh4hIcHCw+Pj4iFarVdo++ugj8fHxqfZYzp07JwCkuLhYRESOHz8uAOTRo0dV9l+5cqUAkIcPH1a7TxGRBQsWiJeXl04sGzZsECsrKykvL1fi/dnPfqasf/nypVhaWsr48eOVtry8PAEgKSkpOvHt3LlT6fPgwQNp0aKF7Nq1S0RExo4dK++9955OPHPnzpXOnTsry+7u7vLb3/5WWdZqtdK6dWvZuHGjiIhs3769UvzPnz+XFi1ayNGjR0Xkp/y7u7vLy5cvlT4jRoyQUaNG6bzPq5/5m7wp/0REZHicmSEio3fw4EFYWVnBwsICv/jFLzBq1CgsWbJEWd+tWzeo1eo37qd79+7Kz5aWlrCxsVEu+8rIyED//v3RvHnzWsfVt29fncuZAgMDkZOTg/LycgA/zVoMHz4cbm5usLa2RnBwMADg9u3btdq/vHLJWk2ysrIQGBioE0u/fv1QUlKCH3/8UWl79fjNzc3RqlUrdOvWTWlzcnICgEqXwgUGBio/29vbw8vLC1lZWcp79+vXT6d/v379dPLw+nurVCo4Ozsr73Px4kVcv34d1tbWsLKygpWVFezt7fHs2TPlMj8A6NKlC8zNzZVlFxeXOl22R0RExqeZoQMgIvpfDRw4EBs3boRarYarqyuaNdM9tVlaWtZqP68XKiqVClqtFsBPl641pNLSUoSFhSEsLAw7duyAo6Mjbt++jbCwMJ1Lp2rSqVMnAMDVq1d1Cor6qur4X22rKIYqctKQasp9SUkJ/Pz8sGPHjkrbOTo61mofRETUNHFmhoiMnqWlJTw9PeHm5lapkGko3bt3x6lTp5R7QWojNTVVZ/nMmTPo2LEjzM3NcfXqVTx48AArVqxA//794e3tXedZhMGDB8PBwQGrVq2qcn3FI4V9fHyQkpKiM5OTnJwMa2trtGnTpk7vWZUzZ84oPz969AjXrl2Dj4+P8t7Jyck6/ZOTk9GpUyedWZSa9OrVCzk5OWjdujU8PT11Xra2trWOU61W68wGERGR8WMxQ0RUCzNnzkRRURFGjx6N8+fPIycnB9u3b1duUq/K7du3ER0djezsbHz11VdYv349oqKiAABubm5Qq9VYv349fvjhBxw4cADLli2rU0yWlpbYunUrDh06hF/96lf49ttvcfPmTZw/fx7z5s3DtGnTAAAffPAB7ty5g1mzZuHq1avYv38/YmNjER0dDTOz//2/gbi4OCQmJuLy5cuYMGECHBwclCe7zZkzB4mJiVi2bBmuXbuGbdu24fPPP8eHH35Y6/2PGzcODg4OCA8Px6lTp5Cbm4ukpCT84Q9/0LlM7k08PDxw8uRJ3L17F/fv36+2X35+PjIyMnD9+nUAQGZmJjIyMpSHQRAR0duDxQwRUS20atUKx44dQ0lJCYKDg+Hn54cvvviixnto3n//fTx9+hT+/v6YMWMGoqKilC/qdHR0RHx8PHbv3o3OnTtjxYoVWL16dZ3jCg8Px+nTp9G8eXOMHTsW3t7eGDNmDAoLC5Wnlb377rs4fPgwzp49ix49emDatGmYPHkyFi5cWL9kvGbFihWIioqCn58f8vPz8e9//1u5R6lXr174+uuvsXPnTnTt2hWLFy9GXFxcnZ7a1rJlS5w8eRJubm74zW9+Ax8fH0yePBnPnj2DjY1NrfcTFxeHmzdvokOHDjqXp71u06ZN6NmzJ6ZMmQIAGDBgAHr27FnpUd1ERGR4KqntHaRERFRrISEh8PX1rdU3zhurpKQkDBw4EI8ePTLIF4YSERFxZoaIiIiIiIwSixkiIiIiIjJKvMyMiIiIiIiMEmdmiIiIiIjIKLGYISIiIiIio8RihoiIiIiIjBKLGSIiIiIiMkosZoiIiIiIyCixmCEiIiIiIqPEYoaIiIiIiIwSixkiIiIiIjJK/wdVYwnUkdws+QAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explained Variance by Principal Component 1: 0.34\n", + "Explained Variance by Principal Component 2: 0.17\n" + ] + } + ], + "source": [ + "\n", + "# Apply PCA\n", + "pca = PCA(n_components=2)\n", + "X_pca = pca.fit_transform(X)\n", + "\n", + "# Explained variance\n", + "explained_variance = pca.explained_variance_ratio_\n", + "\n", + "# Plot PCA results\n", + "plt.figure(figsize=(10, 7))\n", + "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=data['CO2 emissions '], cmap='viridis')\n", + "plt.colorbar(label='CO2 Emissions')\n", + "plt.title('PCA of Emission Data')\n", + "plt.xlabel('Principal Component 1')\n", + "plt.ylabel('Principal Component 2')\n", + "plt.show()\n", + "\n", + "print(f'Explained Variance by Principal Component 1: {explained_variance[0]:.2f}')\n", + "print(f'Explained Variance by Principal Component 2: {explained_variance[1]:.2f}')" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "667131db-51f8-4bbc-91a6-23d49a15656e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\minal\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5s/step - loss: 0.0076\n", + "Epoch 2/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0066\n", + "Epoch 3/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0061\n", + "Epoch 4/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0060\n", + "Epoch 5/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0062\n", + "Epoch 6/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0063\n", + "Epoch 7/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0062\n", + "Epoch 8/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0061\n", + "Epoch 9/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0060\n", + "Epoch 10/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0059\n", + "Epoch 11/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0058\n", + "Epoch 12/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0056\n", + "Epoch 20/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0055\n", + "Epoch 21/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0055\n", + "Epoch 22/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0055\n", + "Epoch 23/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0055\n", + "Epoch 24/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0054\n", + "Epoch 25/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0049\n", + "Epoch 33/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0048\n", + "Epoch 34/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0047\n", + "Epoch 35/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0046\n", + "Epoch 36/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0045\n", + "Epoch 37/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0045\n", + "Epoch 38/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.0044\n", + "Epoch 46/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 0.0044\n", + "Epoch 47/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 0.0044\n", + "Epoch 48/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 0.0043\n", + "Epoch 49/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 0.0043\n", + "Epoch 50/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 0.0043\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 312ms/step\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import LSTM, Dense\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "# Prepare data for LSTM\n", + "def create_dataset(data, look_back=1):\n", + " X, y = [], []\n", + " for i in range(len(data) - look_back - 1):\n", + " a = data[i:(i + look_back), 0]\n", + " X.append(a)\n", + " y.append(data[i + look_back, 0])\n", + " return np.array(X), np.array(y)\n", + "\n", + "data_CO2 = data['CO2 emissions '].values.reshape(-1, 1)\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "data_scaled = scaler.fit_transform(data_CO2)\n", + "\n", + "look_back = 10\n", + "X, y = create_dataset(data_scaled, look_back)\n", + "X = X.reshape(X.shape[0], X.shape[1], 1)\n", + "X_train, X_test = X[:-10], X[-10:]\n", + "y_train, y_test = y[:-10], y[-10:]\n", + "\n", + "# Define LSTM model\n", + "lstm_model = Sequential()\n", + "lstm_model.add(LSTM(units=50, return_sequences=True, input_shape=(look_back, 1)))\n", + "lstm_model.add(LSTM(units=50))\n", + "lstm_model.add(Dense(1))\n", + "lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n", + "\n", + "# Train the model\n", + "lstm_model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=1)\n", + "\n", + "# Make predictions\n", + "predictions = lstm_model.predict(X_test)\n", + "predictions = scaler.inverse_transform(predictions)\n", + "\n", + "# Plot predictions\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(data_CO2[-10:], label='Actual CO₂ Emissions')\n", + "plt.plot(predictions, label='Predicted CO₂ Emissions')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "24d8daa2-001c-4538-90e6-56e1c8128210", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\minal\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0344\n", + "Epoch 2/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0135 \n", + "Epoch 3/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0131 \n", + "Epoch 4/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0082 \n", + "Epoch 5/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0050 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"Epoch 25/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 6.9772e-05 \n", + "Epoch 26/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.9341e-05 \n", + "Epoch 27/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.3905e-05 \n", + "Epoch 28/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.6566e-05 \n", + "Epoch 29/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 4.5848e-05 \n", + "Epoch 30/30\n", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.2623e-05 \n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Prepare data for ANN\n", + "X = data[valid_columns]\n", + "y = data['CO2 emissions ']\n", + "\n", + "# Split data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n", + "\n", + "# Define ANN model\n", + "ann_model = Sequential()\n", + "ann_model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", + "ann_model.add(Dense(32, activation='relu'))\n", + "ann_model.add(Dense(1))\n", + "ann_model.compile(optimizer='adam', loss='mean_squared_error')\n", + "ann_model.fit(X_train, y_train, epochs=30, batch_size=10, verbose=1)\n", + "\n", + "# Make predictions\n", + "ann_predictions = ann_model.predict(X_test)\n", + "\n", + "# Plot predictions\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(y_test.values[:50], label='Actual CO₂ Emissions')\n", + "plt.plot(ann_predictions[:50], label='Predicted CO₂ Emissions')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "13f8ba0b-f3f0-4ae9-9ba0-7d3ae352a1e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: geopandas in c:\\users\\minal\\anaconda3\\lib\\site-packages (1.0.1)\n", + "Requirement already satisfied: numpy>=1.22 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (1.26.4)\n", + "Requirement already satisfied: pyogrio>=0.7.2 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (0.9.0)\n", + "Requirement already satisfied: packaging in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (23.2)\n", + "Requirement already satisfied: pandas>=1.4.0 in c:\\users\\minal\\appdata\\roaming\\python\\python312\\site-packages (from geopandas) (2.2.2)\n", + "Requirement already satisfied: pyproj>=3.3.0 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (3.6.1)\n", + "Requirement already satisfied: shapely>=2.0.0 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from geopandas) (2.0.5)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2023.3)\n", + "Requirement already satisfied: certifi in c:\\users\\minal\\anaconda3\\lib\\site-packages (from pyogrio>=0.7.2->geopandas) (2024.7.4)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\minal\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas>=1.4.0->geopandas) (1.16.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install geopandas\n" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "9abe4a63-840c-4126-ae19-6dd13e2924b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data Columns: Index(['Country', 'latest year available_x', 'CH4 emissions',\n", + " 'CH4 emissions per capita', ' % change since 1990', 'N2O emissions',\n", + " 'N2O emissions per capita', ' % change since 1990.1', 'NOx emissions',\n", + " '% change since 1990_x', 'NOx emissions per capita', 'CO2 emissions ',\n", + " '% change since 1990_y', 'CO2 emissions per capita',\n", + " 'CO2 emissions per km2', 'Total GHG emissions _perc',\n", + " 'GHG from Energy_perc',\n", + " 'GHG from Energy \\nof which: from Transport_perc',\n", + " 'GHG from Industrial Processes_perc', 'GHG from Agriculture_perc',\n", + " 'GHG from Waste_perc', 'Total GHG emissions _tonnes',\n", + " 'GHG from Energy_tonnes',\n", + " 'GHG from Energy \\nof which: from Transport_tonnes',\n", + " 'GHG from Industrial Processes_tonnes', 'GHG from Agriculture_tonnes',\n", + " 'GHG from Waste_tonnes', 'Consumption of CFCs_odptonnes',\n", + " 'Consumption of all ODS', 'consumptionof all_odptonnesin 2013',\n", + " 'reduction in odp', 'SO2 emissions', '% change since 1990',\n", + " 'SO2 emissions per capita'],\n", + " dtype='object')\n", + "World Map Columns: Index(['featurecla', 'scalerank', 'LABELRANK', 'SOVEREIGNT', 'SOV_A3',\n", + " 'ADM0_DIF', 'LEVEL', 'TYPE', 'TLC', 'Country',\n", + " ...\n", + " 'FCLASS_TR', 'FCLASS_ID', 'FCLASS_PL', 'FCLASS_GR', 'FCLASS_IT',\n", + " 'FCLASS_NL', 'FCLASS_SE', 'FCLASS_BD', 'FCLASS_UA', 'geometry'],\n", + " dtype='object', length=169)\n", + " featurecla scalerank LABELRANK SOVEREIGNT SOV_A3 \\\n", + "0 Admin-0 country 1 6 Fiji FJI \n", + "1 Admin-0 country 1 3 United Republic of Tanzania TZA \n", + "2 Admin-0 country 1 7 Western Sahara SAH \n", + "3 Admin-0 country 1 2 Canada CAN \n", + "4 Admin-0 country 1 2 United States of America US1 \n", + "\n", + " ADM0_DIF LEVEL TYPE TLC Country ... \\\n", + "0 0 2 Sovereign country 1 Fiji ... \n", + "1 0 2 Sovereign country 1 United Republic of Tanzania ... \n", + "2 0 2 Indeterminate 1 Western Sahara ... \n", + "3 0 2 Sovereign country 1 Canada ... \n", + "4 1 2 Country 1 United States of America ... \n", + "\n", + " GHG from Industrial Processes_tonnes GHG from Agriculture_tonnes \\\n", + "0 NaN NaN \n", + "1 0.37 29.73 \n", + "2 NaN NaN \n", + "3 56.46 55.53 \n", + "4 334.35 526.25 \n", + "\n", + " GHG from Waste_tonnes Consumption of CFCs_odptonnes Consumption of all ODS \\\n", + "0 NaN NaN NaN \n", + "1 2.25 253.9 71.5 \n", + "2 NaN NaN NaN \n", + "3 20.57 19 958.2 923.1 \n", + "4 123.97 305 963.6 16 206.4 \n", + "\n", + " consumptionof all_odptonnesin 2013 reduction in odp SO2 emissions \\\n", + "0 NaN NaN NaN \n", + "1 1.6 97.8 175.74 \n", + "2 NaN NaN NaN \n", + "3 65.9 92.9 0 \n", + "4 711.3 95.6 4 739.47 \n", + "\n", + " % change since 1990 SO2 emissions per capita \n", + "0 NaN NaN \n", + "1 8.39 6.05 \n", + "2 NaN NaN \n", + "3 0 0.00 \n", + "4 -77.36 15.06 \n", + "\n", + "[5 rows x 202 columns]\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def load_world_map(shapefile_path):\n", + " try:\n", + " world = gpd.read_file(shapefile_path)\n", + " return world\n", + " except Exception as e:\n", + " print(f\"Error loading shapefile: {e}\")\n", + " return None\n", + "\n", + "def plot_emissions(world, data):\n", + " if world is not None:\n", + " # Rename 'ADMIN' to 'Country' if needed\n", + " if 'ADMIN' in world.columns:\n", + " world = world.rename(columns={'ADMIN': 'Country',})\n", + " else:\n", + " print(\"Error: The world map does not have a suitable column for country names.\")\n", + " return\n", + "\n", + " # Ensure the cleaned data has a 'Country' column\n", + " if 'Country' not in data.columns:\n", + " print(\"Error: The cleaned data does not have a 'Country' column.\")\n", + " return\n", + "\n", + " # Print column names for debugging\n", + " print(\"Data Columns:\", data.columns)\n", + " print(\"World Map Columns:\", world.columns)\n", + "\n", + " # Merge on 'Country' column\n", + " world = world.merge(data,how='left',on='Country',)\n", + " print(world.head())\n", + "\n", + " # Check if 'CO2 emissions' exists in the merged DataFrame\n", + " if 'CO2 emissions ' in world.columns:\n", + " # Plot emissions data\n", + " world.plot(column='CO2 emissions ', cmap='Reds', legend=True, figsize=(15, 10))\n", + " plt.title('Global CO₂ Emissions')\n", + " plt.show()\n", + " else:\n", + " print(\"Error: The 'CO2 emissions' column is not in the world map DataFrame after merging.\")\n", + " else:\n", + " print(\"World map could not be loaded.\")\n", + "\n", + "# Load cleaned data\n", + "try:\n", + " data = pd.read_csv('cleaned_data.csv')\n", + "except Exception as e:\n", + " print(f\"Error loading cleaned data: {e}\")\n", + " data = pd.DataFrame() # Empty DataFrame if loading fails\n", + "\n", + "# Load world map\n", + "shapefile_path = 'ne_110m_admin_0_countries.shp'\n", + "world = load_world_map(shapefile_path)\n", + "\n", + "\n", + "# Plot emissions data\n", + "plot_emissions(world, data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "6191e829-9fc1-4115-bd17-fcd7cb794a98", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Function to clean numeric columns by removing spaces and converting to float\n", + "def clean_numeric_column(column):\n", + " # Remove spaces\n", + " column = column.str.replace(' ', '')\n", + " # Convert to numeric, coercing errors to NaN\n", + " return pd.to_numeric(column, errors='coerce')\n", + "\n", + "# List of columns to clean\n", + "columns_to_clean = ['CO2 emissions ', 'CH4 emissions', 'N2O emissions', 'NOx emissions', 'SO2 emissions']\n", + "\n", + "# Clean the columns\n", + "for col in columns_to_clean:\n", + " data[col] = clean_numeric_column(data[col].astype(str))\n", + "\n", + "# Now calculate the correlation matrix\n", + "correlation_matrix = data[columns_to_clean].corr()\n", + "\n", + "# Plot the correlation matrix\n", + "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n", + "plt.title('Correlation Matrix of Emissions')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "f564194a-1d9b-4a24-8aa0-261f0f7a5e30", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Projected CO₂ Emissions with 25% reduction: 86.145\n" + ] + } + ], + "source": [ + "# Historical policy assessment\n", + "data['Policy_Effect'] = data['CO2 emissions '].pct_change()\n", + "\n", + "# Simulate new policies\n", + "def simulate_policy_change(current_emissions, reduction_percentage):\n", + " return current_emissions * (1 - reduction_percentage / 100)\n", + "\n", + "# Example policy simulation\n", + "future_emissions = simulate_policy_change(data['CO2 emissions '].iloc[-1], 25)\n", + "print(f'Projected CO₂ Emissions with 25% reduction: {future_emissions}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d53ce7d8-11f1-4e38-abc0-bd86c8374187", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From d503f97eed578786f480ebb267d0ae4c501348dd Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 24 Jul 2024 23:28:17 +0530 Subject: [PATCH 22/58] Add files via upload --- GLOBAL EMISSION ANALSIS/.gitattributes | 2 + GLOBAL EMISSION ANALSIS/.gitignore | 160 ++++++++++++++++++++++++ GLOBAL EMISSION ANALSIS/requirment.txt | 7 ++ 3 files changed, 169 insertions(+) create mode 100644 GLOBAL EMISSION ANALSIS/.gitattributes create mode 100644 GLOBAL EMISSION ANALSIS/.gitignore create mode 100644 GLOBAL EMISSION ANALSIS/requirment.txt diff --git a/GLOBAL EMISSION ANALSIS/.gitattributes b/GLOBAL EMISSION ANALSIS/.gitattributes new file mode 100644 index 000000000..dfe077042 --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/.gitattributes @@ -0,0 +1,2 @@ +# Auto detect text files and perform LF normalization +* text=auto diff --git a/GLOBAL EMISSION ANALSIS/.gitignore b/GLOBAL EMISSION ANALSIS/.gitignore new file mode 100644 index 000000000..51e9d59e6 --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/.gitignore @@ -0,0 +1,160 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ diff --git a/GLOBAL EMISSION ANALSIS/requirment.txt b/GLOBAL EMISSION ANALSIS/requirment.txt new file mode 100644 index 000000000..3e857db0b --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/requirment.txt @@ -0,0 +1,7 @@ +pandas +numpy +scikit-learn +tensorflow +geopandas +matplotlib +seaborn From 4574ab36ae3d10b6ec8b032768eeea956f742e6b Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 24 Jul 2024 23:33:00 +0530 Subject: [PATCH 23/58] Delete notebook --- GLOBAL EMISSION ANALSIS/models/notebook | 1 - 1 file changed, 1 deletion(-) delete mode 100644 GLOBAL EMISSION ANALSIS/models/notebook diff --git a/GLOBAL EMISSION ANALSIS/models/notebook b/GLOBAL EMISSION ANALSIS/models/notebook deleted file mode 100644 index 8b1378917..000000000 --- a/GLOBAL EMISSION ANALSIS/models/notebook +++ /dev/null @@ -1 +0,0 @@ - From 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file changed, 0 insertions(+), 0 deletions(-) rename GLOBAL EMISSION ANALSIS/{requirment.txt => requirments.txt} (100%) diff --git a/GLOBAL EMISSION ANALSIS/requirment.txt b/GLOBAL EMISSION ANALSIS/requirments.txt similarity index 100% rename from GLOBAL EMISSION ANALSIS/requirment.txt rename to GLOBAL EMISSION ANALSIS/requirments.txt From 052fd0d6b34a22e70bc4ba548ac9a05b99959eea Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 30 Jul 2024 18:26:59 +0530 Subject: [PATCH 26/58] Delete GLOBAL EMISSION ANALSIS/data perparing and cleaning directory --- .../cleaned_data.csv | 46 ------------------- .../data perperation.py | 24 ---------- .../data perparing and cleaning/merge all.py | 29 ------------ 3 files changed, 99 deletions(-) delete mode 100644 GLOBAL EMISSION ANALSIS/data perparing and cleaning/cleaned_data.csv delete mode 100644 GLOBAL EMISSION ANALSIS/data perparing and cleaning/data perperation.py delete mode 100644 GLOBAL EMISSION ANALSIS/data perparing and cleaning/merge all.py diff --git a/GLOBAL EMISSION ANALSIS/data perparing and cleaning/cleaned_data.csv b/GLOBAL EMISSION ANALSIS/data perparing and cleaning/cleaned_data.csv deleted file mode 100644 index 875484f29..000000000 --- a/GLOBAL EMISSION ANALSIS/data perparing and cleaning/cleaned_data.csv +++ /dev/null @@ -1,46 +0,0 @@ -Country,latest year available_x,CH4 emissions,CH4 emissions per capita, % change since 1990,N2O emissions,N2O emissions per capita, % change since 1990.1,NOx emissions,% change since 1990_x,NOx emissions per capita,CO2 emissions ,% change since 1990_y,CO2 emissions per capita,CO2 emissions per km2,Total GHG emissions _perc,GHG from Energy_perc,"GHG from Energy -of which: from Transport_perc",GHG from Industrial Processes_perc,GHG from Agriculture_perc,GHG from Waste_perc,Total GHG emissions _tonnes,GHG from Energy_tonnes,"GHG from Energy -of which: from Transport_tonnes",GHG from Industrial Processes_tonnes,GHG from Agriculture_tonnes,GHG from Waste_tonnes,Consumption of CFCs_odptonnes,Consumption of all ODS,consumptionof all_odptonnesin 2013,reduction in odp,SO2 emissions,% change since 1990,SO2 emissions per capita -Argentina,2000,84.85,2.29,10.5,67.5,1.82,30.26,675.79,31.1,18.24,190.03,68.7,4.56,68.35,282,46.79,14.27,3.94,44.3,4.97,282,131.96,40.24,11.11,124.92,14.01, 4 697.2, 2 386.0,497.7,79.1,87.62,10.63,2.36 -Armenia,2010,2.26,0.76,-28.66,0.48,0.16,185.46,17.21,-77.5,5.81,4.96,0,1.67,166.81,7.2,69.54,17.32,3.14,18.36,8.97,7.2,5.01,1.25,0.23,1.32,0.65,196.5,174.4,4.5,97.4,29.44, 7 448.72,9.93 -Australia,2012,111.71,4.88,-3.02,25.78,1.13,40.43, 2 536.45,44.6,110.71,398.16,44.2,17.66,51.76,543.65,76.03,16.59,5.74,16.07,2.16,543.65,413.36,90.21,31.21,87.36,11.72, 14 290.4,389.7,48.3,87.6,797.76,-48.69,34.82 -Belarus,2012,15.39,1.62,1.14,16.4,1.73,-18.52,189.92,-43.5,20.01,55.38,-46.7,5.84,266.77,89.28,61.94,8.08,4.79,26.18,7.02,89.28,55.3,7.22,4.27,23.37,6.27, 2 510.9,2.7,7,-159.3,146.86,-86.44,15.47 -Bolivia (Plurinational State of),2004,11.97,1.34,28.03,1.39,0.15,140.53,64.92,31.1,7.24,16.12,191.7,1.6,14.67,43.67,20.69,9.79,48.71,26.7,3.9,43.67,9.03,4.27,21.27,11.66,1.7,75.7,67.4,0.4,99.4,12.1,8.42,1.45 -Brazil,2005,316.28,1.68,34.49,162.78,0.86,45.06, 3 400.00,35.8,18.04,439.41,110.4,2.19,51.61,862.81,38.11,15.59,8.95,48.19,4.76,862.81,328.79,134.54,77.2,415.77,41.05, 10 525.8, 3 589.4, 1 189.3,66.9,0,0,0 -Canada,2012,90.56,2.6,25.78,47.73,1.37,-2.92,0,0,0,557.29,21.4,16.15,55.81,698.63,80.98,27.93,8.08,7.95,2.94,698.63,565.76,195.11,56.46,55.53,20.57, 19 958.2,923.1,65.9,92.9,0,0,0 -Colombia,2004,53.87,1.26,21.37,34.38,0.8,39.53,335.16,24.5,7.84,72.42,26.3,1.56,63.43,153.88,42.87,14.15,5.89,44.56,6.68,153.88,65.97,21.77,9.07,68.57,10.28, 2 208.2, 1 002.2,176.7,82.4,142.81,0.71,3.34 -Costa Rica,2005,3.53,0.83,11.89,2.59,0.61,1302.52,26.88,-19.7,6.33,7.84,165.4,1.7,153.5,12.11,47.0,32.12,4.1,38.0,10.9,12.11,5.69,3.89,0.5,4.6,1.32,250.2,425.4,12.6,97.0,4.85,0,1.14 -Croatia,2012,3.42,0.8,-7.41,3.3,0.77,-17.4,55.19,-40.9,12.87,20.92,-10.4,4.86,369.62,26.45,71.54,21.59,10.78,12.83,4.26,26.45,18.92,5.71,2.85,3.39,1.13,219.3,172.3,0,100.0,25.58,-85.3,5.97 -Democratic People's Republic of Korea,2002,12.62,0.54,-49.11,0.93,0.04,-76.92,159,-65.4,6.84,73.58,-69.9,2.99,610.42,87.33,88.86,1.7,6.48,3.22,1.43,87.33,77.61,1.49,5.66,2.81,1.25,441.7, 2 326.3,90.6,96.1, 1 384.00,-55.66,59.53 -Dominican Republic,2000,4.84,0.56,59.11,3.18,0.37,279.36,93.12,68.3,10.88,21.89,137.1,2.18,449.72,26.43,69.03,23.39,3.07,21.57,6.33,26.43,18.25,6.18,0.81,5.7,1.67,539.8,406.9,34.8,91.4,110.15,42.94,12.86 -Ecuador,2006,17.17,1.23,31.61,201.48,14.42,33.12,231.77,47.7,16.59,35.73,112.2,2.35,139.36,247.99,10.85,5.16,1.11,84.73,3.32,247.99,26.9,12.78,2.75,210.11,8.23,301.4,273.4,22,92.0,8.87,37.73,0.64 -Egypt,2000,39.43,0.58,77.83,24.45,0.36,136.65,0,0,0,220.79,190.7,2.64,220.35,193.24,60.13,14.08,14.37,16.46,9.04,193.24,116.19,27.21,27.77,31.8,17.48, 1 668.0, 1 944.1,352.2,81.9,0,0,0 -Georgia,2006,4.14,0.93,-42.19,2.2,0.5,-8.84,27.67,-78.6,6.25,7.93,0,1.89,113.8,12.22,48.82,10.52,14.58,27.11,9.44,12.22,5.97,1.29,1.78,3.31,1.15,22.5,64.3,1.4,97.8,0.5,-99.8,0.11 -Ghana,2006,6.42,0.31,113.56,3.96,0.18,40.77,205.64,0,10.92,10.08,156.4,0.4,42.26,18.23,50.66,17.12,1.34,35.57,12.43,18.23,9.23,3.12,0.24,6.48,2.27,35.8,24,25.4,-5.8,0.5,0,0.03 -Iceland,2012,0.46,1.41,4.63,0.46,1.42,-12.08,20.55,-24.7,63.54,3.33,54.3,10.38,32.36,4.47,38.44,19.09,42.15,15.18,4.09,4.47,1.72,0.85,1.88,0.68,0.18,195.1,2.6,0,100.0,83.88,295.1,259.36 -Indonesia,2000,236.33,1.12,122.72,28.47,0.13,58.04,85.66,-28.9,0.4,563.98,277.1,2.3,295.14,554.33,50.68,10.25,7.7,13.24,28.38,554.33,280.94,56.82,42.67,73.4,157.33, 8 332.7, 5 787.4,310.5,94.6,0,0,0 -Japan,2012,20.03,0.16,-38.32,20.23,0.16,-31.94, 1 626.95,-20.4,12.8, 1 240.63,8.7,9.75, 3 282.70, 1 343.14,91.55,16.36,5.18,1.78,1.49, 1 343.14, 1 229.60,219.76,69.52,23.9,20.03, 118 134.0, 2 466.8,39.6,98.4,936.84,-25.29,7.37 -Kazakhstan,2012,49.03,2.91,-32.23,9.49,0.56,-47.13,500.26,-25.9,29.74,261.76,0,15.81,96.06,283.55,85.08,8.2,5.9,7.59,1.43,283.55,241.23,23.25,16.74,21.53,4.06, 1 206.2,146.9,104.6,28.8,649.61,-37.92,38.62 -Kyrgyzstan,2005,3.02,0.59,-50.42,0.15,0.03,-16.53,64.92,-44.5,12.69,6.62,0,1.19,33.08,12.02,74.07,20.7,4.32,16.11,5.49,12.02,8.9,2.49,0.52,1.94,0.66,72.8,50.2,4,92.0,26.9,-72.7,5.26 -Lao People's Democratic Republic,2000,5.34,1.0,-16.68,2.5,0.47,6625.0,20.84,81.5,3.9,1.2,412.5,0.19,5.08,8.9,11.68,5.02,0.54,86.26,1.51,8.9,1.04,0.45,0.05,7.68,0.13,43.3,42.9,1.6,96.3,1.59,0,0.3 -Mexico,2006,187.78,1.69,76.22,20.34,110.55,96.25, 1 444.41,16.3,13.68,466.55,48.4,3.88,237.5,641.45,67.05,22.56,9.9,7.1,15.94,641.45,430.1,144.69,63.53,45.55,102.27, 4 624.9, 3 954.7, 1 106.2,72.0, 2 612.91,-3.13,24.75 -Mongolia,2006,6.53,2.55,15.83,0.46,0.18,1380.0,2.98,29.6,1.27,19.08,90,6.92,12.2,17.71,57.7,10.65,5.04,36.48,0.78,17.71,10.22,1.89,0.89,6.46,0.14,10.6,7.3,0.9,87.7,0,0,0 -Mozambique,1994,5.66,0.37,13.51,0.98,0.06,37.01,93.81,22.1,6.11,3.28,227.8,0.13,4.09,8.22,22.64,10.39,0.62,56.2,20.53,8.22,1.86,0.85,0.05,4.62,1.69,18.2,14.4,8.3,42.4,0,0,0 -New Zealand,2012,29.04,6.55,8.21,10.89,2.45,32.02,158.5,57.7,35.73,33.26,33.5,7.55,122.97,76.05,42.24,18.09,6.94,46.05,4.73,76.05,32.12,13.76,5.28,35.02,3.6, 2 088.0,42.8,8.2,80.8,78.16,33.84,17.62 -Niger,2000,6.76,0.6,96.78,4.96,0.44,506.68,24,0,2.14,1.42,70.9,0.08,1.12,13.63,19.24,5.56,0.13,78.04,2.58,13.63,2.62,0.76,0.02,10.64,0.35,32,27.6,14.6,47.1, 2 140.00,0,190.65 -Norway,2012,4.23,0.84,-14.76,3.2,0.64,-36.55,166.23,-13.3,33.12,44.6,27.8,9.0,137.73,52.76,74.32,28.74,14.55,8.54,2.26,52.76,39.21,15.16,7.67,4.5,1.19, 1 313.0,-42.8,0,100.0,16.66,-68.1,3.32 -Paraguay,2000,11.48,2.16,-32.19,8.31,1.57,-77.55,87.7,-20.3,16.54,5.3,134.2,0.84,13.03,23.43,15.72,11.9,1.69,79.51,3.08,23.43,3.68,2.79,0.4,18.63,0.72,210.6,105.5,16.5,84.4,0.16,-46.67,0.03 -Republic of Korea,2012,29.78,0.6,-6.81,14.24,0.29,48.96,0,0,0,589.43,138.7,11.94, 5 892.32,688.43,87.19,12.55,7.46,3.19,2.15,688.43,600.25,86.36,51.37,21.99,14.81, 9 159.8, 11 745.9, 1 893.1,83.9,0,0,0 -Republic of Moldova,2009,2.68,0.66,-41.57,1.61,0.39,-51.53,37.06,-73,9.07,4.98,0,1.22,147.13,13.28,67.39,14.35,4.26,16.06,11.89,13.28,8.95,1.9,0.56,2.13,1.58,73.3,29.6,1,96.6,18.78,-93.63,4.6 -Russian Federation,2012,502.56,3.51,-15.31,115.95,0.81,-48.07, 5 603.13,-40.9,39.1, 1 650.27,-34.2,11.52,96.52, 2 297.15,82.16,10.51,7.89,6.28,3.65, 2 297.15, 1 887.26,241.37,181.14,144.22,83.95, 100 352.0,892.3,836.5,6.3,681.95,-16.02,4.76 -Saudi Arabia,2000,27.55,1.29,66.71,11.79,0.55,12.52,0,0,0,520.28,138.7,18.07,242.02,296.06,82.84,19.62,6.56,4.17,6.44,296.06,245.25,58.09,19.41,12.33,19.07, 1 798.5, 1 926.4, 1 440.3,25.2,0,0,0 -Serbia,1998,8.91,0.92,-1.84,6.82,0.71,-22.03,164,-20.9,16.96,49.19,0,5.45,556.64,66.34,76.19,5.84,5.46,14.32,4.04,66.34,50.55,3.88,3.62,9.5,2.68,849.2,384.9,8.1,97.9,388,-20.98,40.13 -South Africa,1994,43.21,1.07,0.21,20.67,0.51,-11.28,0,0,0,477.24,49.2,9.14,390.85,379.84,78.34,11.46,8.0,9.33,4.33,379.84,297.57,43.52,30.39,35.46,16.43,592.6,848.8,426.4,49.8,0,0,0 -Switzerland,2012,3.72,0.46,-19.84,3.02,0.38,-13.13,73.71,-49.1,9.19,41.85,-6.3,5.28, 1 013.63,51.49,80.6,31.72,7.05,10.76,1.19,51.49,41.5,16.33,3.63,5.54,0.61, 7 960.0,26.2,1.4,94.7,10.67,-73.7,1.33 -The former Yugoslav Republic of Macedonia,2009,1.66,0.8,-3.62,0.91,0.01,-27.62,34.11,-18,16.56,9.34,0,4.52,363.09,11.49,76.26,11.26,3.89,11.52,8.33,11.49,8.76,1.29,0.45,1.32,0.96,519.7,44.6,0.7,98.4,205.83, 6 807.05,99.97 -Turkey,2012,61.62,0.82,80.96,14.79,0.2,21.04, 1 283.74,99.4,17.15,345.73,144.2,4.7,441.23,439.87,70.16,14.0,14.27,7.34,8.23,439.87,308.6,61.56,62.77,32.28,36.22, 3 805.7, 1 336.4,147,89.0,248.83,-70.21,3.32 -Ukraine,2012,66.17,1.46,-59.24,31.37,0.69,-46.59, 1 205.57,-48.2,26.6,306.53,-57.6,6.74,507.93,402.67,76.76,8.31,11.43,8.95,2.82,402.67,309.08,33.47,46.01,36.03,11.37, 4 725.2,145.5,59.4,59.2, 1 687.55,-68.15,37.24 -United Republic of Tanzania,1994,21.63,0.75,4.73,14.38,0.5,-4.68,979.07,524.9,33.73,7.3,207.7,0.2,7.73,39.24,17.56,4.26,0.94,75.77,5.73,39.24,6.89,1.67,0.37,29.73,2.25,253.9,71.5,1.6,97.8,175.74,8.39,6.05 -United States of America,2012,552.01,1.75,-12.82,395.79,1.26,0.07, 11 882.02,-45.5,37.74, 5 583.38,9.5,17.87,579.84, 6 487.85,84.76,26.77,5.15,8.11,1.91, 6 487.85, 5 498.88, 1 736.64,334.35,526.25,123.97, 305 963.6, 16 206.4,711.3,95.6, 4 739.47,-77.36,15.06 -Uruguay,2004,18.63,5.61,14.95,11.79,3.55,-2.16,38.76,29.2,11.66,7.77,94.7,2.3,44.12,36.28,14.29,6.19,0.94,80.83,3.94,36.28,5.18,2.24,0.34,29.32,1.43,199.1,100.9,15.5,84.6,51.5,25.09,15.49 -Uzbekistan,2005,89.43,3.45,57.73,9.97,0.38,-22.78,257.52,-37.5,9.93,114.86,0,4.08,256.73,199.84,86.24,4.82,3.19,8.23,2.35,199.84,172.34,9.63,6.37,16.44,4.69, 1 779.2,0.8,4.6,-475.0,170.85,-74.93,6.59 diff --git a/GLOBAL EMISSION ANALSIS/data perparing and cleaning/data perperation.py b/GLOBAL EMISSION ANALSIS/data perparing and cleaning/data perperation.py deleted file mode 100644 index bf353c93d..000000000 --- a/GLOBAL EMISSION ANALSIS/data perparing and cleaning/data perperation.py +++ /dev/null @@ -1,24 +0,0 @@ -import pandas as pd -import numpy as np - -# Load the CSV data into a pandas DataFrame -data = pd.read_csv('merge.csv') - -# Replace 0 with NaN -data.replace(0, np.nan, inplace=True) - -# Display the first few rows of the DataFrame -print(data.head()) - -# Handle missing values, if any -# For simplicity, we'll drop rows with missing values, but you could also fill them -data = data.dropna() - -# Convert columns to appropriate data types if necessary -data['latest year available_x'] = data['latest year available_x'].astype(int) - -# Save cleaned data for later use -data.to_csv('cleaned_data.csv', index=False) - - - diff --git a/GLOBAL EMISSION ANALSIS/data perparing and cleaning/merge all.py b/GLOBAL EMISSION ANALSIS/data perparing and cleaning/merge all.py deleted file mode 100644 index 97fe9ca7b..000000000 --- a/GLOBAL EMISSION ANALSIS/data perparing and cleaning/merge all.py +++ /dev/null @@ -1,29 +0,0 @@ -##Data Merging - -## Merge GHG Emissions Data - -##```python -import pandas as pd -# Load the data from CSV files -ch4_data = pd.read_csv('CH4_N2O_Emissions.csv') -co2_data = pd.read_csv('CO2_Emissions.csv') -merged_ghg_emissions_data=pd.read_csv('merged_ghg_data.csv') -NOx_data=pd.read_csv('NOx_Emissions.csv') -Ods_data=pd.read_csv('ODS_Consumption.csv') -SO2_data=pd.read_csv('SO2_emissions.csv') - -# Merge the datasets on the 'Country' column with outer join -merged_data = ch4_data.merge(NOx_data, on='Country', how='outer') -merged_data = merged_data.merge(co2_data, on='Country', how='outer') -merged_data = merged_data.merge(merged_ghg_emissions_data, on='Country', how='outer') -merged_data = merged_data.merge(Ods_data, on='Country', how='outer') -merged_data = merged_data.merge(SO2_data, on='Country', how='outer') - -# Fill missing values with 0 -merged_data.fillna(0, inplace=True) - -# Save the merged data to a new CSV file -merged_data.to_csv('merge.csv', index=False) - -# View the merged data -print(merged_data.head()) \ No newline at end of file From e3bf0f8a84c916f2a71848bf1554cb902da0e1a8 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 30 Jul 2024 22:03:33 +0530 Subject: [PATCH 27/58] Update README.md --- GLOBAL EMISSION ANALSIS/README.md | 96 ++++++++++++++++++++++++++---- 1 file changed, 84 insertions(+), 12 deletions(-) diff --git a/GLOBAL EMISSION ANALSIS/README.md b/GLOBAL EMISSION ANALSIS/README.md index 4c94c4bde..855cb67ba 100644 --- a/GLOBAL EMISSION ANALSIS/README.md +++ b/GLOBAL EMISSION ANALSIS/README.md @@ -1,18 +1,90 @@ -# Global Emission Analysis + **Greenhouse Gas Emissions Analysis** -This project analyzes global emission trends and forecasts future emissions using various methods. The analysis is performed in a Jupyter notebook. +## 🎯 **Goal** -## Files +The main goal of this project is to analyze and forecast greenhouse gas (GHG) emissions, including CH4, N2O, CO2, NOx, ODS, and SO2, using various machine learning algorithms and geospatial analysis. The purpose is to understand the trends in emissions and identify key factors contributing to these trends, ultimately aiding in the formulation of policies for emission reduction. -- `data/`: Contains the CSV file with emission data. -- `clean and processe`:Contains the main csv after cleaning and processing -- `notebook/`: Jupyter notebook with the complete analysis. -- `requirements.txt`: List of required Python packages. -- `README.md`: Project overview and instructions. +## 🧵 **Dataset** -## Installation +The dataset used in this project is compiled from multiple sources including governmental and environmental organizations. The data includes emissions of different gases from various countries over multiple years. -Install the necessary packages using: +- [CH4 Emissions](link_to_dataset) +- [N2O Emissions](link_to_dataset) +- [CO2 Emissions](link_to_dataset) +- [NOx Emissions](link_to_dataset) +- [ODS Consumption](link_to_dataset) +- [SO2 Emissions](link_to_dataset) -```bash -pip install -r requirements.txt +## 🧾 **Description** + +This project involves the collection, merging, and analysis of greenhouse gas emissions data from various sources. Machine learning models such as LSTM and ANN are implemented for time-series forecasting, while geospatial analysis is conducted to visualize the data. Principal Component Analysis (PCA) is used to reduce dimensionality and identify key patterns. + +## 🧮 **What I Had Done!** + +1. **Data Collection and Preprocessing:** + - Collected data from various sources and cleaned it. + - Normalized the data for better performance of machine learning models. + +2. **Data Merging:** + - Merged datasets on the basis of the 'Country' column. + +3. **Exploratory Data Analysis (EDA):** + - Performed EDA to visualize trends and patterns in the data. + +4. **Feature Engineering:** + - Conducted feature engineering to create relevant features for modeling. + +5. **Modeling:** + - Implemented LSTM and ANN for time-series forecasting. + - Applied PCA for dimensionality reduction. + - Conducted geospatial analysis to visualize emissions geographically. + +6. **Model Evaluation:** + - Evaluated the performance of the models using accuracy scores and other relevant metrics. + +## 🚀 **Models Implemented** + +- **LSTM (Long Short-Term Memory):** Chosen for its effectiveness in handling time-series data and capturing long-term dependencies. +- **ANN (Artificial Neural Network):** Used for its flexibility and ability to model complex relationships in the data. +- **Geospatial Analysis:** Implemented to visualize emissions data geographically and identify regional trends. +- **PCA (Principal Component Analysis):** Applied for dimensionality reduction and to identify the most important features in the dataset. + +## 📚 **Libraries Needed** + +- `numpy` +- `pandas` +- `matplotlib` +- `seaborn` +- `tensorflow` +- `scikit-learn` +- `geopandas` +- `pysal` +- `libpysal` + +## 📊 **Exploratory Data Analysis Results** + +![EDA Results](images/eda_result.png) + +*Include various images showcasing the EDA results here.* + +## 📈 **Performance of the Models based on the Accuracy Scores** + +- **LSTM:** + - Accuracy: 85% + - Other relevant metrics: Precision, Recall, F1-Score + +- **ANN:** + - Accuracy: 82% + - Other relevant metrics: Precision, Recall, F1-Score + +- **PCA:** + - Explained Variance Ratio: [0.45, 0.35] *Add more details as applicable* + +## 📢 **Conclusion** + +The project successfully analyzed and forecasted greenhouse gas emissions using various machine learning models and geospatial analysis. The LSTM model performed slightly better than the ANN model in terms of accuracy. The PCA analysis helped in identifying the key factors contributing to emissions. The geospatial analysis provided valuable insights into regional emission trends, which can aid in policy-making for emission reduction. + +## ✒️ **Your Signature** + +Minal Jain +[LinkedIn](https://linkedin.com/in/minal-631400259) | [GitHub](https://github.com/minal2577) From cf1088cbec352c8d5617af7f4b2243a06dacaf30 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Tue, 30 Jul 2024 23:28:35 +0530 Subject: [PATCH 28/58] Add files via upload --- .../Dataset/cleaned_data.csv | 46 +++++++++++++++++++ .../Dataset/data perperation.py | 24 ++++++++++ GLOBAL EMISSION ANALSIS/Dataset/merge all.py | 29 ++++++++++++ 3 files changed, 99 insertions(+) create mode 100644 GLOBAL EMISSION ANALSIS/Dataset/cleaned_data.csv create mode 100644 GLOBAL EMISSION ANALSIS/Dataset/data perperation.py create mode 100644 GLOBAL EMISSION ANALSIS/Dataset/merge all.py diff --git a/GLOBAL EMISSION ANALSIS/Dataset/cleaned_data.csv b/GLOBAL EMISSION ANALSIS/Dataset/cleaned_data.csv new file mode 100644 index 000000000..55ea7d043 --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/Dataset/cleaned_data.csv @@ -0,0 +1,46 @@ +Country,latest year available_x,CH4 emissions,CH4 emissions per capita, % change since 1990,N2O emissions,N2O emissions per capita, % change since 1990.1,NOx emissions,% change since 1990_x,NOx emissions per capita,CO2 emissions ,% change since 1990_y,CO2 emissions per capita,CO2 emissions per km2,Total GHG emissions _perc,GHG from Energy_perc,"GHG from Energy +of which: from Transport_perc",GHG from Industrial Processes_perc,GHG from Agriculture_perc,GHG from Waste_perc,Total GHG emissions _tonnes,GHG from Energy_tonnes,"GHG from Energy +of which: from Transport_tonnes",GHG from Industrial Processes_tonnes,GHG from Agriculture_tonnes,GHG from Waste_tonnes,Consumption of CFCs_odptonnes,Consumption of all ODS,consumptionof all_odptonnesin 2013,reduction in odp,SO2 emissions,% change since 1990,SO2 emissions per capita +Argentina,2000,84.85,2.29,10.5,67.5,1.82,30.26,675.79,31.1,18.24,190.03,68.7,4.56,68.35,282,46.79,14.27,3.94,44.3,4.97,282,131.96,40.24,11.11,124.92,14.01, 4 697.2, 2 386.0,497.7,79.1,87.62,10.63,2.36 +Armenia,2010,2.26,0.76,-28.66,0.48,0.16,185.46,17.21,-77.5,5.81,4.96,0,1.67,166.81,7.2,69.54,17.32,3.14,18.36,8.97,7.2,5.01,1.25,0.23,1.32,0.65,196.5,174.4,4.5,97.4,29.44, 7 448.72,9.93 +Australia,2012,111.71,4.88,-3.02,25.78,1.13,40.43, 2 536.45,44.6,110.71,398.16,44.2,17.66,51.76,543.65,76.03,16.59,5.74,16.07,2.16,543.65,413.36,90.21,31.21,87.36,11.72, 14 290.4,389.7,48.3,87.6,797.76,-48.69,34.82 +Belarus,2012,15.39,1.62,1.14,16.4,1.73,-18.52,189.92,-43.5,20.01,55.38,-46.7,5.84,266.77,89.28,61.94,8.08,4.79,26.18,7.02,89.28,55.3,7.22,4.27,23.37,6.27, 2 510.9,2.7,7,-159.3,146.86,-86.44,15.47 +Bolivia (Plurinational State of),2004,11.97,1.34,28.03,1.39,0.15,140.53,64.92,31.1,7.24,16.12,191.7,1.6,14.67,43.67,20.69,9.79,48.71,26.7,3.9,43.67,9.03,4.27,21.27,11.66,1.7,75.7,67.4,0.4,99.4,12.1,8.42,1.45 +Brazil,2005,316.28,1.68,34.49,162.78,0.86,45.06, 3 400.00,35.8,18.04,439.41,110.4,2.19,51.61,862.81,38.11,15.59,8.95,48.19,4.76,862.81,328.79,134.54,77.2,415.77,41.05, 10 525.8, 3 589.4, 1 189.3,66.9,0,0,0 +Canada,2012,90.56,2.6,25.78,47.73,1.37,-2.92,0,0,0,557.29,21.4,16.15,55.81,698.63,80.98,27.93,8.08,7.95,2.94,698.63,565.76,195.11,56.46,55.53,20.57, 19 958.2,923.1,65.9,92.9,0,0,0 +Colombia,2004,53.87,1.26,21.37,34.38,0.8,39.53,335.16,24.5,7.84,72.42,26.3,1.56,63.43,153.88,42.87,14.15,5.89,44.56,6.68,153.88,65.97,21.77,9.07,68.57,10.28, 2 208.2, 1 002.2,176.7,82.4,142.81,0.71,3.34 +Costa Rica,2005,3.53,0.83,11.89,2.59,0.61,1302.52,26.88,-19.7,6.33,7.84,165.4,1.7,153.5,12.11,47.0,32.12,4.1,38.0,10.9,12.11,5.69,3.89,0.5,4.6,1.32,250.2,425.4,12.6,97.0,4.85,0,1.14 +Croatia,2012,3.42,0.8,-7.41,3.3,0.77,-17.4,55.19,-40.9,12.87,20.92,-10.4,4.86,369.62,26.45,71.54,21.59,10.78,12.83,4.26,26.45,18.92,5.71,2.85,3.39,1.13,219.3,172.3,0,100.0,25.58,-85.3,5.97 +Democratic People's Republic of Korea,2002,12.62,0.54,-49.11,0.93,0.04,-76.92,159,-65.4,6.84,73.58,-69.9,2.99,610.42,87.33,88.86,1.7,6.48,3.22,1.43,87.33,77.61,1.49,5.66,2.81,1.25,441.7, 2 326.3,90.6,96.1, 1 384.00,-55.66,59.53 +Dominican Republic,2000,4.84,0.56,59.11,3.18,0.37,279.36,93.12,68.3,10.88,21.89,137.1,2.18,449.72,26.43,69.03,23.39,3.07,21.57,6.33,26.43,18.25,6.18,0.81,5.7,1.67,539.8,406.9,34.8,91.4,110.15,42.94,12.86 +Ecuador,2006,17.17,1.23,31.61,201.48,14.42,33.12,231.77,47.7,16.59,35.73,112.2,2.35,139.36,247.99,10.85,5.16,1.11,84.73,3.32,247.99,26.9,12.78,2.75,210.11,8.23,301.4,273.4,22,92.0,8.87,37.73,0.64 +Egypt,2000,39.43,0.58,77.83,24.45,0.36,136.65,0,0,0,220.79,190.7,2.64,220.35,193.24,60.13,14.08,14.37,16.46,9.04,193.24,116.19,27.21,27.77,31.8,17.48, 1 668.0, 1 944.1,352.2,81.9,0,0,0 +Georgia,2006,4.14,0.93,-42.19,2.2,0.5,-8.84,27.67,-78.6,6.25,7.93,0,1.89,113.8,12.22,48.82,10.52,14.58,27.11,9.44,12.22,5.97,1.29,1.78,3.31,1.15,22.5,64.3,1.4,97.8,0.5,-99.8,0.11 +Ghana,2006,6.42,0.31,113.56,3.96,0.18,40.77,205.64,0,10.92,10.08,156.4,0.4,42.26,18.23,50.66,17.12,1.34,35.57,12.43,18.23,9.23,3.12,0.24,6.48,2.27,35.8,24,25.4,-5.8,0.5,0,0.03 +Iceland,2012,0.46,1.41,4.63,0.46,1.42,-12.08,20.55,-24.7,63.54,3.33,54.3,10.38,32.36,4.47,38.44,19.09,42.15,15.18,4.09,4.47,1.72,0.85,1.88,0.68,0.18,195.1,2.6,0,100.0,83.88,295.1,259.36 +Indonesia,2000,236.33,1.12,122.72,28.47,0.13,58.04,85.66,-28.9,0.4,563.98,277.1,2.3,295.14,554.33,50.68,10.25,7.7,13.24,28.38,554.33,280.94,56.82,42.67,73.4,157.33, 8 332.7, 5 787.4,310.5,94.6,0,0,0 +Japan,2012,20.03,0.16,-38.32,20.23,0.16,-31.94, 1 626.95,-20.4,12.8, 1 240.63,8.7,9.75, 3 282.70, 1 343.14,91.55,16.36,5.18,1.78,1.49, 1 343.14, 1 229.60,219.76,69.52,23.9,20.03, 118 134.0, 2 466.8,39.6,98.4,936.84,-25.29,7.37 +Kazakhstan,2012,49.03,2.91,-32.23,9.49,0.56,-47.13,500.26,-25.9,29.74,261.76,0,15.81,96.06,283.55,85.08,8.2,5.9,7.59,1.43,283.55,241.23,23.25,16.74,21.53,4.06, 1 206.2,146.9,104.6,28.8,649.61,-37.92,38.62 +Kyrgyzstan,2005,3.02,0.59,-50.42,0.15,0.03,-16.53,64.92,-44.5,12.69,6.62,0,1.19,33.08,12.02,74.07,20.7,4.32,16.11,5.49,12.02,8.9,2.49,0.52,1.94,0.66,72.8,50.2,4,92.0,26.9,-72.7,5.26 +Lao People's Democratic Republic,2000,5.34,1.0,-16.68,2.5,0.47,6625.0,20.84,81.5,3.9,1.2,412.5,0.19,5.08,8.9,11.68,5.02,0.54,86.26,1.51,8.9,1.04,0.45,0.05,7.68,0.13,43.3,42.9,1.6,96.3,1.59,0,0.3 +Mexico,2006,187.78,1.69,76.22,20.34,110.55,96.25, 1 444.41,16.3,13.68,466.55,48.4,3.88,237.5,641.45,67.05,22.56,9.9,7.1,15.94,641.45,430.1,144.69,63.53,45.55,102.27, 4 624.9, 3 954.7, 1 106.2,72.0, 2 612.91,-3.13,24.75 +Mongolia,2006,6.53,2.55,15.83,0.46,0.18,1380.0,2.98,29.6,1.27,19.08,90,6.92,12.2,17.71,57.7,10.65,5.04,36.48,0.78,17.71,10.22,1.89,0.89,6.46,0.14,10.6,7.3,0.9,87.7,0,0,0 +Mozambique,1994,5.66,0.37,13.51,0.98,0.06,37.01,93.81,22.1,6.11,3.28,227.8,0.13,4.09,8.22,22.64,10.39,0.62,56.2,20.53,8.22,1.86,0.85,0.05,4.62,1.69,18.2,14.4,8.3,42.4,0,0,0 +New Zealand,2012,29.04,6.55,8.21,10.89,2.45,32.02,158.5,57.7,35.73,33.26,33.5,7.55,122.97,76.05,42.24,18.09,6.94,46.05,4.73,76.05,32.12,13.76,5.28,35.02,3.6, 2 088.0,42.8,8.2,80.8,78.16,33.84,17.62 +Niger,2000,6.76,0.6,96.78,4.96,0.44,506.68,24,0,2.14,1.42,70.9,0.08,1.12,13.63,19.24,5.56,0.13,78.04,2.58,13.63,2.62,0.76,0.02,10.64,0.35,32,27.6,14.6,47.1, 2 140.00,0,190.65 +Norway,2012,4.23,0.84,-14.76,3.2,0.64,-36.55,166.23,-13.3,33.12,44.6,27.8,9.0,137.73,52.76,74.32,28.74,14.55,8.54,2.26,52.76,39.21,15.16,7.67,4.5,1.19, 1 313.0,-42.8,0,100.0,16.66,-68.1,3.32 +Paraguay,2000,11.48,2.16,-32.19,8.31,1.57,-77.55,87.7,-20.3,16.54,5.3,134.2,0.84,13.03,23.43,15.72,11.9,1.69,79.51,3.08,23.43,3.68,2.79,0.4,18.63,0.72,210.6,105.5,16.5,84.4,0.16,-46.67,0.03 +Republic of Korea,2012,29.78,0.6,-6.81,14.24,0.29,48.96,0,0,0,589.43,138.7,11.94, 5 892.32,688.43,87.19,12.55,7.46,3.19,2.15,688.43,600.25,86.36,51.37,21.99,14.81, 9 159.8, 11 745.9, 1 893.1,83.9,0,0,0 +Republic of Moldova,2009,2.68,0.66,-41.57,1.61,0.39,-51.53,37.06,-73,9.07,4.98,0,1.22,147.13,13.28,67.39,14.35,4.26,16.06,11.89,13.28,8.95,1.9,0.56,2.13,1.58,73.3,29.6,1,96.6,18.78,-93.63,4.6 +Russian Federation,2012,502.56,3.51,-15.31,115.95,0.81,-48.07, 5 603.13,-40.9,39.1, 1 650.27,-34.2,11.52,96.52, 2 297.15,82.16,10.51,7.89,6.28,3.65, 2 297.15, 1 887.26,241.37,181.14,144.22,83.95, 100 352.0,892.3,836.5,6.3,681.95,-16.02,4.76 +Saudi Arabia,2000,27.55,1.29,66.71,11.79,0.55,12.52,0,0,0,520.28,138.7,18.07,242.02,296.06,82.84,19.62,6.56,4.17,6.44,296.06,245.25,58.09,19.41,12.33,19.07, 1 798.5, 1 926.4, 1 440.3,25.2,0,0,0 +Serbia,1998,8.91,0.92,-1.84,6.82,0.71,-22.03,164,-20.9,16.96,49.19,0,5.45,556.64,66.34,76.19,5.84,5.46,14.32,4.04,66.34,50.55,3.88,3.62,9.5,2.68,849.2,384.9,8.1,97.9,388,-20.98,40.13 +South Africa,1994,43.21,1.07,0.21,20.67,0.51,-11.28,0,0,0,477.24,49.2,9.14,390.85,379.84,78.34,11.46,8.0,9.33,4.33,379.84,297.57,43.52,30.39,35.46,16.43,592.6,848.8,426.4,49.8,0,0,0 +Switzerland,2012,3.72,0.46,-19.84,3.02,0.38,-13.13,73.71,-49.1,9.19,41.85,-6.3,5.28, 1 013.63,51.49,80.6,31.72,7.05,10.76,1.19,51.49,41.5,16.33,3.63,5.54,0.61, 7 960.0,26.2,1.4,94.7,10.67,-73.7,1.33 +The former Yugoslav Republic of Macedonia,2009,1.66,0.8,-3.62,0.91,0.01,-27.62,34.11,-18,16.56,9.34,0,4.52,363.09,11.49,76.26,11.26,3.89,11.52,8.33,11.49,8.76,1.29,0.45,1.32,0.96,519.7,44.6,0.7,98.4,205.83, 6 807.05,99.97 +Turkey,2012,61.62,0.82,80.96,14.79,0.2,21.04, 1 283.74,99.4,17.15,345.73,144.2,4.7,441.23,439.87,70.16,14.0,14.27,7.34,8.23,439.87,308.6,61.56,62.77,32.28,36.22, 3 805.7, 1 336.4,147,89.0,248.83,-70.21,3.32 +Ukraine,2012,66.17,1.46,-59.24,31.37,0.69,-46.59, 1 205.57,-48.2,26.6,306.53,-57.6,6.74,507.93,402.67,76.76,8.31,11.43,8.95,2.82,402.67,309.08,33.47,46.01,36.03,11.37, 4 725.2,145.5,59.4,59.2, 1 687.55,-68.15,37.24 +United Republic of Tanzania,1994,21.63,0.75,4.73,14.38,0.5,-4.68,979.07,524.9,33.73,7.3,207.7,0.2,7.73,39.24,17.56,4.26,0.94,75.77,5.73,39.24,6.89,1.67,0.37,29.73,2.25,253.9,71.5,1.6,97.8,175.74,8.39,6.05 +United States of America,2012,552.01,1.75,-12.82,395.79,1.26,0.07, 11 882.02,-45.5,37.74, 5 583.38,9.5,17.87,579.84, 6 487.85,84.76,26.77,5.15,8.11,1.91, 6 487.85, 5 498.88, 1 736.64,334.35,526.25,123.97, 305 963.6, 16 206.4,711.3,95.6, 4 739.47,-77.36,15.06 +Uruguay,2004,18.63,5.61,14.95,11.79,3.55,-2.16,38.76,29.2,11.66,7.77,94.7,2.3,44.12,36.28,14.29,6.19,0.94,80.83,3.94,36.28,5.18,2.24,0.34,29.32,1.43,199.1,100.9,15.5,84.6,51.5,25.09,15.49 +Uzbekistan,2005,89.43,3.45,57.73,9.97,0.38,-22.78,257.52,-37.5,9.93,114.86,0,4.08,256.73,199.84,86.24,4.82,3.19,8.23,2.35,199.84,172.34,9.63,6.37,16.44,4.69, 1 779.2,0.8,4.6,-475.0,170.85,-74.93,6.59 diff --git a/GLOBAL EMISSION ANALSIS/Dataset/data perperation.py b/GLOBAL EMISSION ANALSIS/Dataset/data perperation.py new file mode 100644 index 000000000..bf353c93d --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/Dataset/data perperation.py @@ -0,0 +1,24 @@ +import pandas as pd +import numpy as np + +# Load the CSV data into a pandas DataFrame +data = pd.read_csv('merge.csv') + +# Replace 0 with NaN +data.replace(0, np.nan, inplace=True) + +# Display the first few rows of the DataFrame +print(data.head()) + +# Handle missing values, if any +# For simplicity, we'll drop rows with missing values, but you could also fill them +data = data.dropna() + +# Convert columns to appropriate data types if necessary +data['latest year available_x'] = data['latest year available_x'].astype(int) + +# Save cleaned data for later use +data.to_csv('cleaned_data.csv', index=False) + + + diff --git a/GLOBAL EMISSION ANALSIS/Dataset/merge all.py b/GLOBAL EMISSION ANALSIS/Dataset/merge all.py new file mode 100644 index 000000000..97fe9ca7b --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/Dataset/merge all.py @@ -0,0 +1,29 @@ +##Data Merging + +## Merge GHG Emissions Data + +##```python +import pandas as pd +# Load the data from CSV files +ch4_data = pd.read_csv('CH4_N2O_Emissions.csv') +co2_data = pd.read_csv('CO2_Emissions.csv') +merged_ghg_emissions_data=pd.read_csv('merged_ghg_data.csv') +NOx_data=pd.read_csv('NOx_Emissions.csv') +Ods_data=pd.read_csv('ODS_Consumption.csv') +SO2_data=pd.read_csv('SO2_emissions.csv') + +# Merge the datasets on the 'Country' column with outer join +merged_data = ch4_data.merge(NOx_data, on='Country', how='outer') +merged_data = merged_data.merge(co2_data, on='Country', how='outer') +merged_data = merged_data.merge(merged_ghg_emissions_data, on='Country', how='outer') +merged_data = merged_data.merge(Ods_data, on='Country', how='outer') +merged_data = merged_data.merge(SO2_data, on='Country', how='outer') + +# Fill missing values with 0 +merged_data.fillna(0, inplace=True) + +# Save the merged data to a new CSV file +merged_data.to_csv('merge.csv', index=False) + +# View the merged data +print(merged_data.head()) \ No newline at end of file From 96da3f993579999539f5243d90970a18701d3626 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 31 Jul 2024 16:26:03 +0530 Subject: [PATCH 29/58] Update README.md --- GLOBAL EMISSION ANALSIS/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/GLOBAL EMISSION ANALSIS/README.md b/GLOBAL EMISSION ANALSIS/README.md index 855cb67ba..f58c87eb5 100644 --- a/GLOBAL EMISSION ANALSIS/README.md +++ b/GLOBAL EMISSION ANALSIS/README.md @@ -70,8 +70,9 @@ This project involves the collection, merging, and analysis of greenhouse gas em ## 📈 **Performance of the Models based on the Accuracy Scores** - **LSTM:** - - Accuracy: 85% - - Other relevant metrics: Precision, Recall, F1-Score + - Mean Squared Error (MSE): 0.11771341085785052 + - Root Mean Squared Error (RMSE): 0.34309388053104434 + - Mean Absolute Error (MAE): 0.17082083003785606 - **ANN:** - Accuracy: 82% From 6cdd41ea41bd24ce9ef4e5ce5307e9867e2a7e5f Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 31 Jul 2024 16:28:54 +0530 Subject: [PATCH 30/58] Create ANN --- GLOBAL EMISSION ANALSIS/Images/ANN | 1 + 1 file changed, 1 insertion(+) create mode 100644 GLOBAL EMISSION ANALSIS/Images/ANN diff --git a/GLOBAL EMISSION ANALSIS/Images/ANN b/GLOBAL EMISSION ANALSIS/Images/ANN new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/GLOBAL EMISSION ANALSIS/Images/ANN @@ -0,0 +1 @@ + From 2a8e770c2fd11bc61988eb721310d7584bbf076e Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 31 Jul 2024 16:30:04 +0530 Subject: [PATCH 31/58] adding principal component analysis --- GLOBAL EMISSION ANALSIS/Images/ann.png | Bin 0 -> 71134 bytes .../Images/global carbon emission anlysis.png | Bin 0 -> 102758 bytes .../Images/principal component .png | Bin 0 -> 76457 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 GLOBAL EMISSION ANALSIS/Images/ann.png create mode 100644 GLOBAL EMISSION ANALSIS/Images/global carbon emission anlysis.png create mode 100644 GLOBAL EMISSION ANALSIS/Images/principal component .png diff --git a/GLOBAL EMISSION ANALSIS/Images/ann.png b/GLOBAL EMISSION ANALSIS/Images/ann.png new file mode 100644 index 0000000000000000000000000000000000000000..d6df918170a1f1547f0ee4f67814efb65ee976c4 GIT binary patch literal 71134 zcmb@uby(Bw8#YeIXc)~vLKuj&)aV$fNE)CBjOLLT>1c^jj#8vUS`1nkO1ILW0@67U zq&wda`uKhP{r-6WdN~fqG2Dl{uKSAfJTD^kb+xEKY#=;5JgV#38isgyL?(E61i@q^ 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Update README.md --- GLOBAL EMISSION ANALSIS/README.md | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/GLOBAL EMISSION ANALSIS/README.md b/GLOBAL EMISSION ANALSIS/README.md index f58c87eb5..82265901c 100644 --- a/GLOBAL EMISSION ANALSIS/README.md +++ b/GLOBAL EMISSION ANALSIS/README.md @@ -70,20 +70,21 @@ This project involves the collection, merging, and analysis of greenhouse gas em ## 📈 **Performance of the Models based on the Accuracy Scores** - **LSTM:** - - Mean Squared Error (MSE): 0.11771341085785052 - - Root Mean Squared Error (RMSE): 0.34309388053104434 - - Mean Absolute Error (MAE): 0.17082083003785606 + - Mean Squared Error (MSE): 0.11771341085785052 + - Root Mean Squared Error (RMSE): 0.34309388053104434 + - Mean Absolute Error (MAE): 0.17082083003785606 - **ANN:** - - Accuracy: 82% - - Other relevant metrics: Precision, Recall, F1-Score + - Mean Squared Error (MSE): 0.001151512867330171 + - Root Mean Squared Error (RMSE): 0.03393394859620924 + - Mean Absolute Error (MAE): 0.02689476781745063 - **PCA:** - - Explained Variance Ratio: [0.45, 0.35] *Add more details as applicable* + - Explained Variance Ratio: [0.34, 0.17] ## 📢 **Conclusion** -The project successfully analyzed and forecasted greenhouse gas emissions using various machine learning models and geospatial analysis. The LSTM model performed slightly better than the ANN model in terms of accuracy. The PCA analysis helped in identifying the key factors contributing to emissions. The geospatial analysis provided valuable insights into regional emission trends, which can aid in policy-making for emission reduction. +The project successfully analyzed and forecasted greenhouse gas emissions using various machine learning models and geospatial analysis. The ANN model performed slightly better than the LSTM model in terms of accuracy. The PCA analysis helped in identifying the key factors contributing to emissions. The geospatial analysis provided valuable insights into regional emission trends, which can aid in policy-making for emission reduction. ## ✒️ **Your Signature** From a44100ce670475987a8b096473f1036036d9a870 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 31 Jul 2024 16:42:50 +0530 Subject: [PATCH 33/58] Delete extra file --- GLOBAL EMISSION ANALSIS/Images/ANN | 1 - 1 file changed, 1 deletion(-) delete mode 100644 GLOBAL EMISSION ANALSIS/Images/ANN diff --git a/GLOBAL EMISSION ANALSIS/Images/ANN b/GLOBAL EMISSION ANALSIS/Images/ANN deleted file mode 100644 index 8b1378917..000000000 --- a/GLOBAL EMISSION ANALSIS/Images/ANN +++ /dev/null @@ -1 +0,0 @@ - From 92ac7199abc74e80c4548bc65c2efb51dbc607b0 Mon Sep 17 00:00:00 2001 From: minal2577 <119778266+minal2577@users.noreply.github.com> Date: Wed, 31 Jul 2024 16:43:56 +0530 Subject: [PATCH 34/58] Add files via upload --- GLOBAL EMISSION ANALSIS/Images/LSTM.png | Bin 0 -> 57635 bytes 1 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