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.resource_manager.goblin_data_manager import GoblinDataManager
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
# create goblin data manager
goblin_data_manger = GoblinDataManager(
ef_country = ef_country,
calibration_year= baseline_year,
target_year= target_year,
configuration_path= goblin_config,
cbm_configuration_path= cbm_config,
)
# class instances
runner_class = ScenarioRunner(goblin_data_manger)
graph_class = DataGrapher()
fetcher_class = DataFetcher()
#Run Scenarios
runner_class.run_scenarios()
Existing tables have been deleted.
JSON file detected
Land use Assignment Solution is optimal!
Land use Assignment Solution is optimal!
Land use Assignment Solution is optimal!
Land use Assignment Solution is optimal!
Land use Assignment Solution is optimal!
Land use Assignment Solution is optimal!
Land use Assignment Solution is optimal!
Land use Assignment Solution is optimal!
Land use Assignment Solution is optimal!
Land use Assignment Solution is optimal!
Land use Assignment Solution is optimal!
Land use Assignment Solution is optimal!
🚀 Starting FM Simulation...
✅ FM Simulation Complete.
Cleaning scenario SIT data directories
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/home/colmduff/Dropbox/projects/FORESIGHT/packages/goblin_lite/.venv/lib/python3.10/site-packages/goblin_cbm_runner/cbm/data_processing/default_processing/SC_disturbances.py:145: UserWarning: Adjusted area for species 'Sitka' and yield class 'YC17_20' is 0 after adjustment.
warnings.warn(f"Adjusted area for species '{species}' and yield class '{yield_class}' is 0 after adjustment.")
/home/colmduff/Dropbox/projects/FORESIGHT/packages/goblin_lite/.venv/lib/python3.10/site-packages/goblin_cbm_runner/cbm/data_processing/default_processing/SC_disturbances.py:145: UserWarning: Adjusted area for species 'Sitka' and yield class 'YC20_24' is 0 after adjustment.
warnings.warn(f"Adjusted area for species '{species}' and yield class '{yield_class}' is 0 after adjustment.")
/home/colmduff/Dropbox/projects/FORESIGHT/packages/goblin_lite/.venv/lib/python3.10/site-packages/goblin_cbm_runner/cbm/data_processing/default_processing/SC_disturbances.py:145: UserWarning: Adjusted area for species 'Sitka' and yield class 'YC24_30' is 0 after adjustment.
warnings.warn(f"Adjusted area for species '{species}' and yield class '{yield_class}' is 0 after adjustment.")
/home/colmduff/Dropbox/projects/FORESIGHT/packages/goblin_lite/.venv/lib/python3.10/site-packages/goblin_cbm_runner/cbm/data_processing/default_processing/SC_disturbances.py:145: UserWarning: Adjusted area for species 'SGB' and yield class 'YC6' is 0 after adjustment.
warnings.warn(f"Adjusted area for species '{species}' and yield class '{yield_class}' is 0 after adjustment.")
/home/colmduff/Dropbox/projects/FORESIGHT/packages/goblin_lite/.venv/lib/python3.10/site-packages/goblin_cbm_runner/cbm/data_processing/default_processing/SC_disturbances.py:154: UserWarning: FOR SCENARIO 3: Total adjustment applied 0.0 does not match the intended area adjustment 6000.
warnings.warn(f"FOR SCENARIO {scenario}: Total adjustment applied {total_adjustment_applied} does not match the intended area adjustment {area_adjustment}.")
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🚀 Starting AF Simulation...
✅ AF Simulation Complete.
🚀 Starting Scenario 0 Simulation...
✅ Scenario 0 Simulation Complete.
🚀 Starting Scenario 1 Simulation...
✅ Scenario 1 Simulation Complete.
🚀 Starting Scenario 2 Simulation...
✅ Scenario 2 Simulation Complete.
🚀 Starting Scenario 3 Simulation...
✅ Scenario 3 Simulation Complete.
Running Forest Flux scenario -1
Running Forest Flux scenario 0
🚀 Starting Scenario 0 Simulation...
✅ Scenario 0 Simulation Complete.
Running Forest Flux scenario 1
🚀 Starting Scenario 1 Simulation...
✅ Scenario 1 Simulation Complete.
Running Forest Flux scenario 2
🚀 Starting Scenario 2 Simulation...
✅ Scenario 2 Simulation Complete.
Running Forest Flux scenario 3
🚀 Starting Scenario 3 Simulation...
✅ Scenario 3 Simulation Complete.
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
2 5.339220e+08 2.867735e+07
3 5.339220e+08 2.867735e+07
#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 1597.660312 25480.887284
0 345.156857 11.978671 -42419.597283 -29580.857430
1 345.254639 11.960627 -42555.037282 -29718.341292
2 150.043936 6.082288 -59365.973151 -53552.936750
3 163.716836 4.877576 5099.580183 10976.209297
#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
2 21.863310 0.142774 1.919709
3 21.863310 0.142774 1.919709
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 2.169335
1 0.396043 2.015091 0.334278 2.153109
2 0.042917 0.218468 0.036202 0.246916
3 0.042917 0.218468 0.036202 0.246916
N_indirect_fertiliser soils_CO2 soil_organic_N_direct \
index
-1 1.239013 508.885373 5.548316
0 0.400493 437.103111 2.410223
1 0.397497 436.821734 2.411134
2 0.045585 403.764999 0.261384
3 0.045585 403.764999 0.261384
soil_organic_N_indirect soil_inorganic_N_direct \
index
-1 0.765050 7.109264
0 0.334145 2.169335
1 0.334278 2.153109
2 0.036202 0.246916
3 0.036202 0.246916
soil_inorganic_N_indirect soil_histosol_N_direct crop_residue_direct \
index
-1 1.239013 1.203983 3.529468
0 0.400493 1.203983 3.529468
1 0.397497 1.203983 3.529468
2 0.045585 1.203983 3.529468
3 0.045585 0.000000 3.529468
soil_N_direct soil_N_indirect soils_N2O
index
-1 17.391033 2.004063 19.395095
0 9.313009 0.734637 10.047647
1 9.297695 0.731775 10.029470
2 5.241752 0.081786 5.323538
3 4.037769 0.081786 4.119555
#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 -826.301767 1.926434 0.512809 -636.467356
-1 wetland 2020 853.805160 81.496830 0.101991 3162.744029
-1 total 2020 1088.774939 126.264594 0.616311 4787.505968
0 cropland 2050 0.000000 0.000154 0.000004 0.005380
0 grassland 2050 2104.978992 42.838444 0.001258 3304.788820
0 forest 2050 -45815.484546 1.926434 0.512809 -45625.650135
0 wetland 2050 853.805160 81.496830 0.101991 3162.744029
0 total 2050 -42856.700393 126.261863 0.616062 -39158.111906
1 cropland 2050 0.000000 0.000154 0.000004 0.005380
1 grassland 2050 2108.032382 42.838436 0.001257 3307.841793
1 forest 2050 -45953.696558 1.926434 0.512809 -45763.862147
1 wetland 2050 853.805160 81.496830 0.101991 3162.744029
1 total 2050 -42991.859016 126.261855 0.616061 -39293.270946
2 cropland 2050 0.000000 0.000154 0.000004 0.005380
2 grassland 2050 2466.750641 42.837497 0.001172 3666.511060
2 forest 2050 -63090.293951 1.926434 0.512809 -62900.459540
2 wetland 2050 853.805160 81.496830 0.101991 3162.744029
2 total 2050 -59769.738150 126.260916 0.615975 -56071.199071
3 cropland 2050 0.000000 0.000154 0.000004 0.005380
3 grassland 2050 1100.700955 56.510398 0.000444 2683.109690
3 forest 2050 2741.309068 1.926434 0.512809 2931.143479
3 wetland 2050 853.805160 81.496830 0.101991 3162.744029
3 total 2050 4695.815184 139.933817 0.615247 8777.002577
# 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
2 3.529468 0.491902 0.090813
3 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
2 6.166289 4.112183
3 6.166289 4.112183
#Forest carbon flux
forest_flux_df = fetcher_class.get_forest_flux()
print(forest_flux_df)
# baseline is index -1
Year Scenario AGB BGB Deadwood \
index
0 2016 -1 -1.483167e+06 -292623.377894 450706.684939
1 2017 -1 -1.329146e+06 -259552.888450 390128.918183
2 2018 -1 -1.165888e+06 -231130.326643 340176.462016
3 2019 -1 -1.023885e+06 -193607.219187 284864.486511
4 2020 -1 -6.726993e+05 -119159.178839 224499.288330
... ... ... ... ... ...
399 2095 3 4.901180e+05 89949.303922 85245.890580
400 2096 3 7.148366e+05 139877.639149 55759.615771
401 2097 3 5.702518e+05 108988.138568 80799.793765
402 2098 3 3.918862e+05 77297.387112 98189.199641
403 2099 3 9.966661e+04 2682.274740 147647.468417
Litter Soil Harvest Total Ecosystem
index
0 360862.688404 -19354.592411 -6.111390e+05 -9.835760e+05
1 207662.538899 -22846.299798 -7.172140e+05 -1.013754e+06
2 60605.361161 -25956.277505 -8.012059e+05 -1.022192e+06
3 7386.388437 -26552.562870 -9.276150e+05 -9.517938e+05
4 -124837.441652 -28757.059873 -1.159521e+06 -7.209537e+05
... ... ... ... ...
399 19322.923837 -4450.889527 -1.294814e+06 6.801852e+05
400 -14785.382408 -3440.998235 -1.444073e+06 8.922475e+05
401 63632.838246 -1023.372511 -1.346598e+06 8.226492e+05
402 100723.434618 189.564358 -1.224614e+06 6.682858e+05
403 174258.884484 1010.008227 -1.043631e+06 4.252652e+05
[404 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