Example Usage
Here is a demonstartion of the potential usage of GOBLIN lite.
Imports
Firstly, we start of by importing the relevant classes.
import os
from IPython.display import Image
from goblin_lite.goblin import ScenarioRunner
from goblin_lite.scenario_analysis.data_grapher import DataGrapher
from goblin_lite.resource_manager.data_fetcher import DataFetcher
import shutil
import os
import warnings
# Filter out the RuntimeWarning related to unsupported linux distribution
warnings.filterwarnings("ignore", message="untested linux distribution:", category=RuntimeWarning)
The ScenarioRunner class is basically used to set to run the scenarios based on the configuration files for goblin and the CBM CFS3.
The DataFetcher class is used to retrieve the various results as dataframes from the scenario run. Each of the methods in the DataFetcher class relates to specific table.
The DataGrapher class is still being developed, but contains some basic plotting to assist the user in visualising the results tables.
The Scenario Runner
In the first example below, we will set the path to the configurations, define the emissions factor country and the base and target year. Finally, we will create a directory for our outputs
#configuration
goblin_config = "../tests/data/config.json" #location of test goblin configuration
cbm_config = "../tests/data/cbm_factory.yaml" #location of test cbm configuration
ef_country = "ireland" #emissions factor country
#years for the baseline, and the target year
baseline_year = 2020
target_year = 2050
#Make directory to store exported file
os.mkdir("jupyter_example")
data_path = "./jupyter_example/"
Define classes and Run Scenarios
#class instances
runner_class = ScenarioRunner(ef_country, baseline_year,target_year, goblin_config, cbm_config)
graph_class = DataGrapher()
fetcher_class = DataFetcher()
#Run Scenarios
runner_class.run_scenarios()
Existing tables have been deleted.
JSON file detected
... calibration year not present, 2015 default year used for Spread Manure Dictionary...
... calibration year not present, 2015 default year used for Spread Manure Dictionary...
... calibration year not present, 2015 default year used for Spread Manure Dictionary...
... calibration year not present, 2015 default year used for Spread Manure Dictionary...
... calibration year not present, 2015 default year used for Spread Manure Dictionary...
... calibration year not present, 2015 default year used for Spread Manure Dictionary...
... calibration year not present, 2015 default year used for Spread Manure Dictionary...
... calibration year not present, 2015 default year used for Spread Manure Dictionary...
... calibration year not present, 2015 default year used for Spread Manure Dictionary...
... calibration year not present, 2015 default year used for Spread Manure Dictionary...
Scenario Generation Complete ...
If the calibration year is not present for a specific dataset, the default year of 2015 is used.
The Data Fetcher
The Scenario Run is now complete, and we can use the DataFectcher class to retrieve our data.
Note: Once a scenario run is complete, when the user again runs scenarios, table output from the previous run is completely deleted.
Here we will retrieve data for:
- Livestock outputs
- Aggregated emissions
- Animal emissions by category
- Land use emissions by category
- Crop emissions by category
- Forest carbon flux
These are not the only potential returns, as there are several additional methods returning results out. Please consult the documentation for further details.
#Livestock outputs
animal_output_df = fetcher_class.get_livestock_output_summary()
print(animal_output_df)
# baseline is index -1
total_milk_kg total_beef_kg
Scenarios
-1 8.251443e+09 6.528033e+08
0 5.339220e+09 2.596987e+08
1 5.339220e+09 2.596987e+08
#Aggregated emissions
aggregated_emmissions_df = fetcher_class.get_climate_change_emission_totals()
print(aggregated_emmissions_df)
# baseline is index -1
CH4 N2O CO2 CO2e
index
-1 634.471263 23.086912 914.344304 24797.571275
0 345.156833 11.919926 -52312.890656 -39489.718905
1 345.254615 11.902066 -48716.729109 -35895.552370
#Animal emissions by category
animal_emissions_df = fetcher_class.get_climate_change_animal_emissions_by_category()
print(animal_emissions_df)
# baseline is index -1
enteric_ch4 manure_management_N2O manure_management_CH4 \
index
-1 465.584094 3.075505 42.622576
0 201.478299 1.314963 17.416696
1 201.562043 1.315096 17.430742
manure_applied_N N_direct_PRP N_indirect_PRP N_direct_fertiliser \
index
-1 0.920240 4.628077 0.765050 7.109264
0 0.396043 2.014180 0.334145 2.119746
1 0.396043 2.015091 0.334278 2.103677
N_indirect_fertiliser soils_CO2 soil_organic_N_direct \
index
-1 1.239013 508.885373 5.548316
0 0.391338 436.243167 2.410223
1 0.388371 435.964488 2.411134
soil_organic_N_indirect soil_inorganic_N_direct \
index
-1 0.765050 7.109264
0 0.334145 2.119746
1 0.334278 2.103677
soil_inorganic_N_indirect soil_histosol_N_direct crop_residue_direct \
index
-1 1.239013 1.203983 3.529468
0 0.391338 1.203983 3.529468
1 0.388371 1.203983 3.529468
soil_N_direct soil_N_indirect soils_N2O
index
-1 17.391033 2.004063 19.395095
0 9.263421 0.725483 9.988904
1 9.248262 0.722649 9.970911
#Land use emissions by category
land_emissions_df = fetcher_class.get_landuse_emissions_totals()
print(land_emissions_df)
# baseline is index -1
land_use year CO2 CH4 N2O CO2e
scenario
-1 cropland 2020 0.000000 0.000154 0.000004 0.005380
-1 grassland 2020 1061.271545 42.841175 0.001507 2261.223915
-1 forest 2020 -1509.617775 1.926434 0.512809 -1319.783364
-1 wetland 2020 853.805160 81.496830 0.101991 3162.744029
-1 total 2020 405.458930 126.264594 0.616311 4104.189960
0 cropland 2050 0.000000 0.000154 0.000004 0.005380
0 grassland 2050 2114.310751 42.838420 0.001256 3314.119304
0 forest 2050 -55717.249734 1.926434 0.512809 -55527.415323
0 wetland 2050 853.805160 81.496830 0.101991 3162.744029
0 total 2050 -52749.133823 126.261838 0.616059 -49050.546611
1 cropland 2050 0.000000 0.000154 0.000004 0.005380
1 grassland 2050 2117.334858 42.838412 0.001255 3317.142998
1 forest 2050 -52123.833616 1.926434 0.512809 -51933.999205
1 wetland 2050 853.805160 81.496830 0.101991 3162.744029
1 total 2050 -49152.693598 126.261830 0.616059 -45454.106798
# Crop emissions by category
crop_emissions_df= fetcher_class.get_climate_change_crop_emissions_by_category()
print(crop_emissions_df)
# baseline is index -1
crop_residue_direct N_direct_fertiliser N_indirect_fertiliser \
index
-1 3.529468 0.491902 0.090813
0 3.529468 0.491902 0.090813
1 3.529468 0.491902 0.090813
soils_CO2 soils_N2O
index
-1 6.166289 4.112183
0 6.166289 4.112183
1 6.166289 4.112183
#Forest carbon flux
forest_flux_df = fetcher_class.get_forest_flux()
print(forest_flux_df)
# baseline is index -1
Year AGB BGB Deadwood Litter \
index
0 2016 -1.266127e+06 -2.618384e+05 5102.027979 -1.184498e+05
1 2017 -7.805690e+05 -1.542363e+05 -61197.065313 -2.938623e+05
2 2018 -7.505445e+05 -1.593684e+05 -56187.842107 -2.723639e+05
3 2019 -5.924877e+05 -1.144305e+05 -86351.247069 -2.558944e+05
4 2020 -6.084902e+05 -1.147854e+05 -97224.693899 -1.998624e+05
... ... ... ... ... ...
100 2046 -1.002326e+07 -2.256911e+06 -491980.251238 -1.000019e+06
101 2047 -1.024281e+07 -2.293113e+06 -590479.773195 -1.160341e+06
102 2048 -9.884681e+06 -2.207148e+06 -751864.227561 -1.435174e+06
103 2049 -9.831562e+06 -2.188388e+06 -845030.698928 -1.520163e+06
104 2050 -9.708535e+06 -2.157711e+06 -941986.878994 -1.607536e+06
Soil Harvest Total Ecosystem Scenario
index
0 -20067.805281 -6.382280e+05 -1.661381e+06 -1
1 -26011.569723 -9.102110e+05 -1.315876e+06 -1
2 -26367.823978 -9.165912e+05 -1.264832e+06 -1
3 -27775.638553 -1.027711e+06 -1.076939e+06 -1
4 -27576.477034 -1.009912e+06 -1.047939e+06 -1
... ... ... ... ...
100 -203185.136292 -9.064669e+05 -1.397536e+07 1
101 -212966.329239 -1.025050e+06 -1.449971e+07 1
102 -224841.380814 -1.443944e+06 -1.450371e+07 1
103 -232678.633331 -1.636748e+06 -1.461782e+07 1
104 -241785.071075 -1.866628e+06 -1.465755e+07 1
[105 rows x 9 columns]
These are simple examples, additional information on inputs and outputs can be retrieved. Furhter impact categories related to eutrophication and air quality are also available.
DataGrapher
The data grapher allows the user to plot some of the data from the generated tables
#save image
graph_class.plot_animal_lca_emissions_by_category(data_path)
#retrieve an display
image_path = os.path.join(data_path,"Livestock_LCA.png")
# display the image
Image(filename=image_path)
#Land use emissions
graph_class.plot_land_use_emissions(data_path)
#retrieve an display
image_path = os.path.join(data_path,"climate_change_land_use.png")
# display the image
Image(filename=image_path)
# Remove Generated files
for filename in os.listdir(data_path):
file_path = os.path.join(data_path, filename)
try:
if os.path.isfile(file_path):
os.remove(file_path)
print(f"Deleted file: {file_path}")
except Exception as e:
print(f"Error deleting file: {file_path}. Reason: {e}")
#Remove direcotry
os.rmdir(data_path)
Deleted file: ./jupyter_example/climate_change_land_use.png
Deleted file: ./jupyter_example/Livestock_LCA.png