Jupyter Notebook Interface of sDRIPS¶

This page shows the notebook-friendly version of the sDRIPS command workflows. Use these functions when you want to run one step at a time, inspect intermediate files, or combine sDRIPS with your own analysis code.

Workflow Map¶

Task	Import	Function
Initialize a project	sdrips.cli.sdrips_init	initialize_project()
Run verification tests	sdrips.cli.sdrips_test	run_tests()
Run sDRIPS	sdrips.run_sdrips	run_sdrips()
Build command-area config	sdrips.cmd_config	run_cmd_config()
Estimate command-area polygons	sdrips.cmd_area_creation	create_cmd_area()

Recommended Imports¶

from pathlib import Path

from sdrips.cli.sdrips_init import initialize_project
from sdrips.cli.sdrips_test import run_tests
from sdrips.run_sdrips import run_sdrips
from sdrips.cmd_config import run_cmd_config
from sdrips.cmd_area_creation import create_cmd_area

Initialize a Project¶

Create the standard project folders and default configuration files:

initialize_project(project_dir=".", force=False)

The function creates:

Data/
Shapefiles/
config_files/

and downloads:

config_links.yaml
crop_config.yaml
sdrips_config.yaml
secrets.yaml

Create Command-Area Polygons¶

Use this step only when command-area boundaries are not already available. The function reads a canal or line network and creates estimated command-area polygons using the target area column.

created_path = create_cmd_area(
    shape_path="Shapefiles/canal_network/Irrigation_Canal_Networks.shp",
    column_name="CMD_Ar_m2",
    output_path="Shapefiles/created_cmd_area/created_cmd_area.shp",
)

created_path

Parameter	Description
shape_path	Input canal or line-network vector file.
column_name	Column containing target area values, usually in square meters.
output_path	Output shapefile path.

Generate ca_config.yaml¶

Use run_cmd_config() to create command-area crop settings from a vector file.

ca_config_path = run_cmd_config(
    shp_path="Shapefiles/cmd_area/Teesta_Command_Areas.shp",
    column_name="CNLNM",
    default_planting_date="2025-07-07",
    default_crop_type="Rice",
    default_soil_coef=0.5,
    default_distribution_unif=1.0,
    output_path="config_files/ca_config.yaml",
)

ca_config_path

Parameter	Description
shp_path	Command-area vector file. Shapefile format is recommended for Google Earth Engine integration.
column_name	Column containing unique command-area IDs.
default_planting_date	Default planting date in YYYY-MM-DD format.
default_crop_type	Crop type available in crop_config.yaml, such as Rice, Wheat, or Corn.
default_soil_coef	Soil coefficient used for each generated command area unless edited later.
default_distribution_unif	Distribution uniformity used for each generated command area unless edited later.
output_path	Output YAML path. Default behavior writes to config_files/ca_config.yaml.

Preview the generated file:

print(Path(ca_config_path).read_text())

Inspect Configuration Files¶

Two utility loaders are useful in notebooks:

from sdrips.utils.initialize import load_config
from sdrips.utils.utils import load_yaml_config

Use load_config() when you want attribute-style access:

config = load_config("config_files/sdrips_config.yaml")

config.Save_Data_Location.save_data_loc
config.Date_Running.run_week
config.Multiprocessing.cores

Use load_yaml_config() when you want a standard dictionary:

config_dict = load_yaml_config("config_files/sdrips_config.yaml")

config_dict["Save_Data_Location"]["save_data_loc"]

Check Crop Coefficients¶

You can inspect the crop coefficient values that sDRIPS will use for a crop.

from sdrips.utils.utils import read_crop_coefficients, get_growth_kc

coefficients = read_crop_coefficients("config_files/crop_config.yaml", crop="Rice")
kc_day_35 = get_growth_kc(coefficients, num_days=35)

kc_day_35

This is useful when checking whether a planting date and crop type are producing reasonable growth-stage values.

Earth Engine Utilities¶

Most users can let the main sDRIPS workflow initialize Earth Engine automatically. For notebooks, you can also initialize Earth Engine explicitly.

from sdrips.utils.ee_utils import initialize_earth_engine

initialize_earth_engine(
    service_account="your-service-account@project.iam.gserviceaccount.com",
    key_file="config_files/path_to_key.json",
)

Run Verification Tests¶

Run the standard verification workflow:

run_tests(test_dir=Path("./tests"), sensor_test=False)

Run the sensor-corrected test case:

run_tests(test_dir=Path("./tests"), sensor_test=True)

The test workflow downloads expected files, prepares test configuration, runs sDRIPS, and compares selected raster and CSV outputs.

Run sDRIPS¶

After editing sdrips_config.yaml, run:

run_sdrips("config_files/sdrips_config.yaml")

The function uses the module controls in sdrips_config.yaml, including ET estimation, precipitation, weather, percolation, command-area statistics, sensor correction, weather-station correction, and canal water allotment.

Typical outputs are written under the configured Save_Data_Location:

Data/
  Landsat/
  percolation/
  logs/
  Landsat_Command_Area_Stats.csv

Notebook Help¶

Jupyter can show function signatures and docstrings directly:

run_cmd_config?
create_cmd_area?
run_sdrips?

For a fuller walkthrough, see the notebook tutorials:

Tutorial	Use it when
Creating command areas	You need to estimate command-area polygons from canal data.
Creating command-area config files	You need to generate ca_config.yaml interactively.