constants.py

# Place all your constants here
import os

# Note: constants should be UPPER_CASE
constants_path = os.path.realpath(__file__)
SRC_PATH = os.path.dirname(constants_path)
PROJECT_PATH = os.path.dirname(SRC_PATH)
SERVER_PATH = os.path.join('/Users', 'romero61', '..', '..', 'capstone', 'pyforest')

# The larger study area to use for earth engine this study uses the western region of paraguay
STUDY_BOUNDARY_PATH = os.path.join(SERVER_PATH,'ml_data', 'study_boundary', 'study_boundary.shp')


# RAW DATA
# raw earth engine data
HANSEN_TREECOVER_FILEPATH = [
    os.path.join(SERVER_PATH, 'ml_data', 'raw_hansen', 'treecover2000.tif')
]
# raw earth engine data 
HANSEN_LOSSYEAR_FILEPATHS = [
    os.path.join(SERVER_PATH, 'ml_data', 'raw_hansen', 'lossyear.tiff')
]


# raw earth engine data 
PRECIPITATION_FILEPATH = os.path.join(SERVER_PATH, 'ml_data', 'raw_CHIRPS', 'average_annual_precipitation.tif')

# raw Nelson data of distance to cities and ports
PORTS_FILEPATH = os.path.join(SERVER_PATH, 'ml_data', 'raw_nelson', 'travel_time_to_ports_5.tif')

CITIES_FILEPATH = os.path.join(SERVER_PATH, 'ml_data', 'raw_nelson', 'travel_time_to_cities_12.tif')

# loss year in binary for year of deforestation 2011-2020
DEFORESTATION_1120_PATH = os.path.join(SERVER_PATH, 'ml_data', 'output', 'deforestation-cumulative_0110', 'deforestation11_20.tif')


# treecover in 2010
TREECOVER_2010 = os.path.join(SERVER_PATH, 'ml_data', 'output', 'deforestation-cumulative_0110', 'treecover2010.tif')

# INFONA DATA
PROPERTIES_SHAPEFILE_PATHS = [
    os.path.join(SERVER_PATH, 'ml_data', 'active_inactive_subsets', f'active_inactive_{i}.gpkg')
    for i in range(11, 22)
]

# INFONA DATA
LUP_YEAR = [
    os.path.join(SERVER_PATH, 'ml_data', 'lup_subsets',  'lup_10.gpkg')
]

# River Data 
ROAD_PATH = [
    os.path.join(SERVER_PATH, 'ml_data', 'features', 'dissolved_road', 'dissolved_road.gpkg')
]

# Road Data
RIVER_PATH = [
    os.path.join(SERVER_PATH, 'ml_data', 'features', 'river_buffer', 'river_buffer.gpkg')
]

# Soil Data
SOIL_RASTER = os.path.join(SERVER_PATH, 'ml_data','features', 'soil',  'merged_soil.tif')

# Simulated  land use plans
SIMULATED_LUP_PATH =  os.path.join (SERVER_PATH, 'lup_simulation_data', 'simulation_results', 'optimal_mock_properties')

# Where to save outputs
OUTPUT_PATH = [
    os.path.join(SERVER_PATH, 'ml_data', 'output')
]


# PROCESSED DATA 1
# Rasters Not yet masked and cropped w/ nodata value of -1
LUP_LUT_RASTER = os.path.join(SERVER_PATH, 'ml_data','output', 'processed_rasters', 'land_use_type', 'lup_10_land_use_type_raster.tif')

ROAD_DISTANCE_RASTER =  os.path.join(SERVER_PATH, 'ml_data','output', 'processed_rasters', 'road_raster', 'road_raster.tif')

RIVER_DISTANCE_RASTER =  os.path.join(SERVER_PATH, 'ml_data','output', 'processed_rasters', 'river_raster', 'river_raster.tif')

PORT_DISTANCE_RASTER =  os.path.join(SERVER_PATH, 'ml_data','output', 'processed_rasters', 'nelson', 'ports_masked.tif')

CITIES_DISTANCE_RASTER =  os.path.join(SERVER_PATH, 'ml_data','output', 'processed_rasters', 'nelson', 'cities_masked.tif')

# Rasters of simulated land use plans
SIM_LUP_RASTER_PATH = [os.path.join(SERVER_PATH, 'ml_data','output', 'processed_rasters','land_type')]


# PROCESSED DATA 2 
# The folder masked rasters contains the required files for the machine learning model. 
# deforestation11_20_masked.tif is alway the 'y' target variable
#  Masked with LUP still need to mask with treecover2010
MASKED_RASTERS_DIR = [
    os.path.join(OUTPUT_PATH[0], 'masked_rasters')
    ]

# Feature Files
# Training Model
FEATURES_DIR = [
    os.path.join(OUTPUT_PATH[0], 'tree_masked_rasters')
    ]
# Simulated Land use Plans to Predict
SIMULATION_FEATURES_DIR = [
    os.path.join(OUTPUT_PATH[0], 'sim_lup_features')
]

MASKED_RASTERS_SIM_DIR = [
    os.path.join(OUTPUT_PATH[0], 'masked_rasters_sims')
    ]