### YAML Configuration File Example

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/config_files/cli.md

This is an example of the expected YAML configuration file output. It contains a 'DEFAULT' section with baseline parameters and subsequent sections for each unique command area identifier found in the input dataset.

```yaml
DEFAULT:
  use_default: true
  planting_date: "2025-07-30"
  crop_type: Wheat
  soil_coef: 0.6
  distribution_unif: 0.8

Teesta Canal A:
  use_default: false
  planting_date: "2025-07-30"
  crop_type: Wheat
  soil_coef: 0.6
  distribution_unif: 0.8

T1T:
  use_default: false
  planting_date: '2025-07-07'
  crop_type: Wheat
  soil_coef: 0.5
  distribution_unif: 1.0
...
```

--------------------------------

### Install and Initialize sDRIPS Project

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Installs the sDRIPS package using pip and initializes a new project directory with the necessary folder structure and configuration templates. It also includes options for forcing an update of existing configurations and verifying the installation.

```bash
# Install sDRIPS
pip install sdrips

# Initialize project (creates Data/, Shapefiles/, config_files/ directories)
sdrips init --dir /path/to/my_irrigation_project

# Force update existing configurations
sdrips init --dir . --force

# Verify installation
sdrips test --dir ./tests
```

--------------------------------

### sDRIPS Configuration Example (High-Level Controls)

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/full_guide/cli.md

Example of high-level module controls within the `sdrips_config.yaml` file for generating advisories without in-situ sensor integration. This section dictates the types of advisories to be generated.

```yaml
# -------------------------------
# HIGH-LEVEL MODULE CONTROLS
# -------------------------------
Command_Area_Net_Water_Requirement: true  # Generate advisory on how much water should be allotted to each command area
Canal_water_allotment: false          # Generate advisory on how much water should be allotted to each canal (From primary to tertiary canals)
Insitu_Sensor_integration: false  # Toggle to integrate in-situ sensor data with satellite data for bias correction
Weather_station_integration: false  # Toggle to integrate local weather station data to improve accuracy
```

--------------------------------

### CLI Help Command

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/config_files/cli.md

This command displays the help information for the `sdrips.cmd_config` module, outlining all available options and their descriptions.

```bash
python -m sdrips.cmd_config -h
```

--------------------------------

### Initialize sDRIPS Project

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/full_guide/cli.md

Initializes a new sDRIPS project in the current directory. This command fetches the latest configuration files required for sDRIPS operation. Ensure your conda environment is activated before running.

```bash
sdrips init -d .
```

--------------------------------

### Clone sDRIPS Repository

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/Development/Developer_Version.md

This command clones the sDRIPS repository to your local machine. Ensure you have Git installed.

```bash
git clone https://github.com/UW-SASWE/sDRIPS.git .
```

--------------------------------

### Main sDRIPS Configuration Setup (YAML)

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Sets up the main sDRIPS execution parameters using a YAML configuration file. This includes enabling/disabling various modules, specifying input data paths (shapefiles, GEE assets), defining the analysis period, setting data storage locations, and configuring processing parameters like the number of parallel cores.

```yaml
# config_files/sdrips_config.yaml

# Module activation flags
Command_Area_Net_Water_Requirement: true  # Calculate water needs per command area
Canal_water_allotment: false  # Distribute water through canal network
Insitu_Sensor_integration: false  # Integrate ground sensors for bias correction
Weather_station_integration: false  # Use local weather station data

# Command area shapefile
Irrigation_cmd_area_shapefile:
  path: Shapefiles/my_command_areas.shp
  feature_name: AREA_NAME  # Unique ID column
  numeric_id_name: ID  # Numeric ID for sorting
  area_column_name: Irrigable_Area  # Area in square meters

# Google Earth Engine asset (optional)
GEE_Asset_ID:
  id: "users/myaccount/my_command_areas"  # GEE asset path
  shp: Shapefiles/my_command_areas.shp  # Fallback local shapefile

# Analysis period
Date_Running:
  start_date: "2024-06-15"
  default_run_week: false
  run_week: ["currentweek"]  # Options: currentweek, lastweek, or both

# Data storage
Save_Data_Location:
  save_data_loc: Data/

# Command area configuration
Cmd_Area_Config:
  path: config_files/ca_config.yaml

# Crop coefficients
Crop_Config:
  path: config_files/crop_config.yaml

# Processing
Multiprocessing:
  cores: 7  # Parallel processing workers

```

--------------------------------

### Check Help for Command Area Creation CLI

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/cmd_area_creation/cli.md

This command displays the help information for the `sdrips.cmd_area_creation` CLI tool. It outlines all available arguments and options for generating command area polygons.

```bash
python -m sdrips.cmd_area_creation -h
```

--------------------------------

### Define Crop Coefficients (Kc) in YAML

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/Configuration/crop_config.md

This YAML snippet demonstrates the structure for defining crop coefficients (Kc) across different growth stages. It includes examples of constant Kc values and linear progressions of Kc over a specified duration, essential for estimating crop evapotranspiration.

```yaml
# -------------------------------
# Crop configuration file
# -------------------------------

Rice:
  "0-20": 1.1
  "21-45": [linear, 1.1, 1.4, 25]
  "46-105": 1.4
  "106-115": [linear, 1.4, 1.0, 10]
  "116-140": 1.0

Wheat:
  "0-28": 0.5
  "29-56": [linear, 0.5, 1.15, 28]
  "57-112": 1.15
  "113-119": [linear, 1.15, 0.7, 7]
  "120-140": 0.7
  # Ref: https://www.researchgate.net/publication/4875041_Actual_evapotranspiration_and_crop_coefficients_of_wheat_Triticum_aestivum_under_varying_moisture_levels_of_humid_tropical_canal_command_area/figures?lo=1

```

--------------------------------

### Weather Station Data CSV Format Example

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Example format for weather station data CSV files. Specifies STATION_ID, Date, and meteorological variables such as Temp_C, Wind_ms, and Precip_mm.

```csv
STATION_ID,Date,Temp_C,Wind_ms,Precip_mm
STN_001,2024-06-15,29.3,2.5,5.2
STN_002,2024-06-15,28.7,3.1,4.8
```

--------------------------------

### Secrets Configuration File Example (YAML)

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/Configuration/secrets.md

This YAML snippet demonstrates the structure for storing Google Earth Engine and IMERG account credentials in the secrets.yaml file. It requires placeholders for usernames, passwords, and key file paths.

```yaml
GEE_Account:
  username: <GEE_USERNAME> # Google Earth Engine service account
  key_file: <GEE_KEY_FILE> # Path to the key file

IMERG_Account:
  username: <IMERG_USERNAME> # IMERG username
  password: <IMERG_PASSWORD> # IMERG password
```

--------------------------------

### Sensor Data CSV Format Example

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Example format for sensor data CSV files used by sDRIPS. Includes Timestamp and meteorological variables like Temperature, Water Content, and Wind, along with spatial coordinates.

```csv
Timestamp,Temperature,Water Content,Wind,Latitude,Longitude
2024-06-15 08:00:00,28.5,0.32,2.1,23.45,89.67
2024-06-15 08:00:00,29.1,0.28,1.8,23.48,89.72
```

--------------------------------

### Run sDRIPS Execution

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/full_guide/cli.md

Executes the sDRIPS run with the specified `sdrips_config.yaml` file. This process can take time depending on the size of the shapefile as assets are exported to Google Earth Engine. Progress bars indicate the current step and estimated completion time.

```bash
sdrips run -c config_files/sdrips_config.yaml
```

--------------------------------

### Generate Command Area Config CLI

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/config_files/cli.md

This command generates a YAML configuration file for command areas. It takes the path to a vector dataset, the name of the attribute column containing command area identifiers, a default planting date, a default crop type, and an output file path as input. Shapefile format is recommended for easy integration with Google Earth Engine.

```bash
python -m sdrips.cmd_config -s "../../Shapefiles/cmd_area_wgs/Teesta_Command_Areas.shp" -c "CNLNM" -d 2025-07-07 -cc Rice -o '../Data/cmd_config.yaml'
```

--------------------------------

### Create Command Area Configuration

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/full_guide/cli.md

Generates the `ca_config.yaml` file required for sDRIPS command area processing. It requires the path to the command area shapefile, the column name containing unique IDs, the planting date, and the crop type. For help with input parameters, use the -h flag.

```python
python -m sdrips.cmd_config -s "Shapefiles/TBP_Command_Area/commandarea_teesta_ph1_simplified_modified_Irrigable_Area_Final.shp" -c CNLNM_ID -d 2023-01-15 -cc Rice
```

--------------------------------

### Initialize Earth Engine

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Initializes the Google Earth Engine (GEE) environment using a service account and key file. This is a prerequisite for using GEE functionalities within the project. Requires service account credentials and a key file path.

```python
from sdrips.utils.ee_utils import initialize_earth_engine
import ee

initialize_earth_engine(
    service_account='my-account@project.iam.gserviceaccount.com',
    key_file='gee-key.json'
)
```

--------------------------------

### Execute Full sDRIPS Workflow (CLI)

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Initiate the complete sDRIPS irrigation advisory workflow from the command line. This command requires a path to the main configuration file.

```bash
# Command-line execution
sdrips run --config config_files/sdrips_config.yaml
```

--------------------------------

### Get Growth Kc Value

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Retrieves the Kc (crop coefficient) value for a specific growth day based on provided crop coefficients. It handles both constant stages and linear interpolation between defined values. Takes the coefficients and the number of growth days as input.

```python
from sdrips.utils.utils import get_growth_kc

# Example for interpolated value
# Assuming 'coefficients' is loaded from read_crop_coefficients
c_value = get_growth_kc(coefficients, num_days=35)

# Example for constant stage
c_value_early = get_growth_kc(coefficients, num_days=15)
```

--------------------------------

### Add New Crop Configuration in YAML

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/Configuration/crop_config.md

This YAML snippet illustrates the syntax for adding a new crop to the configuration file. It specifies how to define Kc values using either a constant rate or a linear progression over a defined number of days, allowing for custom crop entries.

```yaml
CropName:
"startDay-endDay": value                # Constant value
"startDay-endDay": [linear, startVal, endVal, duration]  # Linear progression over 'duration' days
- startVal: value at the beginning of the stage
- endVal: value at the end of the stage
- durationDays: number of days over which the value increases/decreases linearly

Example of adding a new crop:

Crop_XXXX:
  "0-28": 0.51
  "29-56": [linear, 0.51, 1.29, 28]
  "57-112": 1.29
  "113-119": [linear, 1.29, 0.52, 7]
  "120-140": 0.52

```

--------------------------------

### Authenticate Google Earth Engine (Python)

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Perform interactive authentication for Google Earth Engine (GEE) using OAuth. This method opens a browser window for user authorization. After authentication, the GEE client is initialized with the specified project ID.

```python
import ee
ee.Authenticate()  # Opens browser for OAuth
ee.Initialize(project='my-gee-project')
```

--------------------------------

### Generate Command Area Polygons with CLI

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/cmd_area_creation/cli.md

This command generates command-area polygons around a canal/line network. It uses an iterative buffer process to match each polygon to a target area. Requires input shapefile, target area column name, and an output shapefile path.

```bash
python -m sdrips.cmd_area_creation -s '../../Shapefiles/canal_network/Irrigation_Canal_Networks.shp' -c "CMD_Ar_m2" -o '../Data/created_cmd_area/created_cmd_area.shp'
```

--------------------------------

### Print Generated Configuration File Path

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/config_files/Creating_CMD_Config.ipynb

Prints the path to the generated command area configuration YAML file. This confirms the location where the output file has been saved.

```python
print(ca_path)
```

--------------------------------

### Commit and Push Changes

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/Development/Developer_Version.md

Stages changes, commits them with a descriptive message, and pushes the feature branch to the remote repository. Replace '<changed-or-new-script>' with the actual file names.

```bash
git add <changed-or-new-script>
git commit -m "Adding XXXXX functionality"
git push origin feature/my-new-module
```

--------------------------------

### Check run_cmd_config Function Signature and Docstring

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/config_files/Creating_CMD_Config.ipynb

Displays the signature and docstring of the 'run_cmd_config' function, which outlines its parameters, their types, default values, and a detailed explanation of its functionality, inputs, and outputs. This helps in understanding how to use the function effectively.

```python
run_cmd_config?
```

--------------------------------

### Generate Command Area Configuration from Vector Data

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Generates a YAML configuration file for command areas using Python. It takes a vector data file (Shapefile, GeoJSON, GeoPackage) as input and allows specifying default crop, planting date, soil, and distribution uniformity parameters. A CLI equivalent is also provided.

```python
from sdrips.cmd_config import run_cmd_config
from pathlib import Path

# Generate config from shapefile
output_path = run_cmd_config(
    shp_path='Shapefiles/command_areas.shp',
    column_name='AREA_ID',  # Column with unique command area identifiers
    default_planting_date='2024-04-15',  # ISO format YYYY-MM-DD
    default_crop_type='Rice',  # Options: Rice, Wheat, Corn, Sugarcane, Potato, Lentils, Eggplant
    default_soil_coef=0.5,  # Soil coefficient (0.3-0.7)
    default_distribution_unif=0.85,  # Irrigation efficiency (0.7-1.0)
    output_path='config_files/ca_config.yaml'
)
print(f"Configuration created at: {output_path}")

# CLI equivalent
# python -m sdrips.cmd_config -s Shapefiles/command_areas.shp -c AREA_ID \
#   -d 2024-04-15 -cc Rice -sc 0.5 -du 0.85 -o config_files/ca_config.yaml
```

--------------------------------

### Execute Full sDRIPS Workflow (Python)

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Programmatically run the entire sDRIPS irrigation advisory workflow using the `run_sdrips` function. This function takes the path to the configuration file as an argument.

```python
# Programmatic execution
from sdrips.run_sdrips import run_sdrips

# Execute full workflow
run_sdrips('config_files/sdrips_config.yaml')
```

--------------------------------

### Run sDRIPS Test Suite Locally

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/Development/Developer_Version.md

Executes the sDRIPS test suite to ensure that your changes do not introduce regressions. This command requires the test directory to be specified.

```bash
python src/sdrips/cli/sdrips_test.py -d ./tests/
```

--------------------------------

### Inspect create_cmd_area Function Signature and Docstring (Python)

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/cmd_area_creation/Creating_CMD_Config.ipynb

This snippet shows how to inspect the `create_cmd_area` function to understand its input parameters, their types, and the function's purpose. The docstring provides detailed information on the function's behavior, parameters, and limitations, emphasizing that the output command areas are estimations and should be used with caution.

```python
create_cmd_area?
```

--------------------------------

### Integrate Weather Stations for Forcings (YAML)

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Configuration file for integrating a network of local weather stations. Specifies which meteorological variables to use from stations, their names, shapefile location, and interpolation methods.

```yaml
Weather_station_integration: true

Weather_station_integration_config:
  air_temperature_station: true
  air_temperature_variable: 'Temp_C'
  wind_speed_station: true
  wind_speed_variable: 'Wind_ms'
  precipitation_station: true
  precipitation_variable: 'Precip_mm'
  weather_station_shp_file: Shapefiles/weather_stations.shp
  weather_station_feature_name: STATION_ID
  EPSG_code: 'EPSG:4326'
  weather_station_data_path: WeatherData/stations.csv
  interpolation_method: idw
```

--------------------------------

### Process Command Areas in Parallel for ET Estimation

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Fetches Landsat imagery, applies SEBAL and Penman-Monteith algorithms to estimate actual and reference ET, calculates net irrigation, and exports results. It requires a configuration file and utilizes Google Earth Engine and GFS meteorological data.

```python
from sdrips.processing import process_cmd_area_parallel
from sdrips.logging_utils import setup_logger
from multiprocessing import Queue

log_queue = Queue()
logger = setup_logger(log_queue=log_queue, log_level='INFO')

process_cmd_area_parallel(
    config_path='config_files/sdrips_config.yaml',
    main_logger=logger,
    log_queue=log_queue,
    cores=4  # Number of parallel workers
)
```

--------------------------------

### Load and Inspect Canal Network Data (Python)

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/cmd_area_creation/Creating_CMD_Config.ipynb

Loads a canal network shapefile using GeoPandas and displays the first few rows of the dataframe. This step is crucial for understanding the input data, including geometry types (e.g., LINESTRING) and attribute columns like 'CMD_Ar_m2', which will be used to determine the target area for buffering.

```python
shp_path = '../../Shapefiles/canal_network/Irrigation_Canal_Networks.shp'
f = gpd.read_file(shp_path)
f.head()
```

--------------------------------

### Visualize Canal Network Data (Python)

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/cmd_area_creation/Creating_CMD_Config.ipynb

Visualizes the loaded canal network data using GeoPandas' `explore` method, which integrates with Folium to create interactive maps. This helps in geographically understanding the canal network before generating command areas.

```python
f.explore()
```

--------------------------------

### Integrate Weather Stations for Forcings (Python)

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Python script to execute the weather station integration process within sDRIPS. This function uses the configuration to ingest weather station data, interpolate it, and improve meteorological forcings for ET calculations.

```python
from sdrips.stn_corr_evapotranspiration import process_cmd_area_stn_parallel

# Run ET with weather station integration
process_cmd_area_stn_parallel(
    config_path='config_files/sdrips_config.yaml',
    main_logger=logger,
    log_queue=log_queue,
    cores=4
)
```

--------------------------------

### Configure GEE and IMERG Credentials (YAML)

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Store sensitive credentials for Google Earth Engine (GEE) service accounts and IMERG precipitation data access. The GEE configuration requires the service account email and the path to the private key file. IMERG credentials include username and password.

```yaml
# config_files/secrets.yaml

GEE_Service_Account:
  service_account: "my-service-account@project.iam.gserviceaccount.com"
  key_path: "path/to/gee-service-account-key.json"

IMERG_Credentials:
  username: "my_imerg_username"
  password: "my_imerg_password"
```

--------------------------------

### YAML Configuration for IMERG Precipitation Links

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/Configuration/config_links.md

This snippet defines the structure for managing base URLs and file naming patterns for IMERG precipitation data. It includes base URL categories (before, year, after) and file name components (prefix, suffix) used by the pipeline to access data.

```yaml
precipitation: 
  base_urls:
    before: "https://jsimpsonhttps.pps.eosdis.nasa.gov/imerg/gis/early/"
    year:   "https://jsimpsonhttps.pps.eosdis.nasa.gov/imerg/gis/"
    after:  "https://jsimpsonhttps.pps.eosdis.nasa.gov/imerg/gis/early/"

  file_pattern:
    prefix: "3B-HHR-E.MS.MRG.3IMERG."
    suffix: "-S233000-E235959.1410.V07B.1day.tif"

```

--------------------------------

### Generate Command Area Configuration YAML

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/config_files/Creating_CMD_Config.ipynb

Executes the 'run_cmd_config' function to generate a YAML configuration file. It specifies the input Shapefile path, the column containing command area names, default planting date, default crop type, and the desired output path for the YAML file. The function returns the path to the generated file.

```python
ca_path = run_cmd_config(shp_path = '../../Shapefiles/cmd_area_wgs/Teesta_Command_Areas.shp',column_name = 'CNLNM', default_planting_date = '2025-07-07', default_crop_type='Wheat', output_path='../Data/cmd_config.yaml')
```

--------------------------------

### Load YAML Configuration

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Loads YAML configuration files programmatically. This function is essential for reading settings and parameters used throughout the SDrips project. It takes a file path as input and returns a dictionary representing the configuration.

```python
from sdrips.utils.utils import load_yaml_config

config = load_yaml_config('config_files/sdrips_config.yaml')
print(config['Date_Running']['start_date'])
```

--------------------------------

### Evapotranspiration Estimation (Python)

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Initiate the parallel processing of command areas to estimate Penman-Monteith and SEBAL evapotranspiration (ET). This function requires a logging queue for inter-process communication and a logger instance.

```python
from sdrips.evapotranspiration import process_cmd_area_parallel
import logging
from multiprocessing import Queue

# Setup logging queue for multiprocessing
log_queue = Queue()
logger = logging.getLogger(__name__)

```

--------------------------------

### Configure sDRIPS Module Toggles

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Enable or disable various modules within the sDRIPS workflow. This includes ET estimation, precipitation consideration, weather forecasts, percolation analysis, and region statistics. A 'Clean_Directory' option is also available to clear previous outputs.

```yaml
Run_ET_Estimation:
  et_estimation: true

Precipitation_Config:
  consider_preciptation: true
  imerg_user: myusername  # From secrets.yaml
  imerg_pass: mypassword  # From secrets.yaml

Weather_Config:
  consider_forecasted_weather: true
  forecast_hours: 168  # 7-day forecast

Percolation_Config:
  consider_percolation: true
  field_capacity_threshold: 0.3

Region_stats:
  estimate_region_stats: true

Clean_Directory:
  clear_directory_condition: false  # Clear previous outputs before running
```

--------------------------------

### Read Crop Coefficients

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Reads crop coefficients from a YAML configuration file for a specified crop. It can return specific values or ranges, and supports interpolation for growth stages. Requires a crop configuration file and the crop name.

```python
from sdrips.utils.utils import read_crop_coefficients

coefficients = read_crop_coefficients(
    config_path='config_files/crop_config.yaml',
    crop='Rice'
)
```

--------------------------------

### YAML Configuration for Command Area Settings

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/Configuration/command_area_config.md

Defines default and specific command area parameters like planting date, crop type, soil coefficient, and water distribution efficiency. The 'use_default' flag controls whether individual command area settings are applied or if default values are used.

```yaml
DEFAULT:
  use_default: true
  planting_date: '2023-01-15'
  crop_type: Rice
  soil_coef: 0.5
  distribution_unif: 1.0
Teesta_Canal_A_1:
  use_default: false
  planting_date: '2023-01-15'
  crop_type: Rice
  soil_coef: 0.5
  distribution_unif: 1.0
T1T_2:
  use_default: false
  planting_date: '2023-01-15'
  crop_type: Rice
  soil_coef: 0.5
  distribution_unif: 1.0
```

--------------------------------

### Configure Crop Coefficients (YAML)

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Define crop-specific growth stage coefficients for evapotranspiration (ET) estimation. Supports constant values or linear interpolation between defined stages. The format is 'stage_days': coefficient or [interpolation_type, start_coefficient, end_coefficient, duration_days].

```yaml
# config_files/crop_config.yaml

Rice:
  "0-20": 1.1  # Constant Kc for days 0-20
  "21-45": [linear, 1.1, 1.4, 25]  # Linear from 1.1 to 1.4 over 25 days
  "46-105": 1.4  # Peak growth stage
  "106-115": [linear, 1.4, 1.0, 10]  # Senescence
  "116-140": 1.0  # Late season

Wheat:
  "0-28": 0.5
  "29-56": [linear, 0.5, 1.15, 28]
  "57-112": 1.15
  "113-119": [linear, 1.15, 0.7, 7]
  "120-140": 0.7

Corn:
  "0-18": 0.4
  "19-54": [linear, 0.4, 1.1, 36]
  "55-96": 1.1
  "97-120": [linear, 1.1, 0.6, 24]

Potato:
  "0-20": [linear, 0.4, 0.7, 20]
  "21-60": [linear, 0.7, 1.15, 40]
  "61-80": 1.15
  "81-120": [linear, 1.15, 0.6, 40]

Sugarcane:
  "0-30": 0.4
  "31-90": [linear, 0.4, 1.25, 60]
  "91-270": 1.25
  "271-365": [linear, 1.25, 0.75, 95]
```

--------------------------------

### Upload Shapefile to Earth Engine

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Uploads a shapefile to Google Earth Engine as an asset. This allows for geospatial data to be processed within GEE. Requires the path to the shapefile, an asset folder, asset name, and GEE authentication details.

```python
from sdrips.utils.ee_utils import upload_shapefile_to_ee

asset_id = upload_shapefile_to_ee(
    shp_path='Shapefiles/command_areas.shp',
    asset_folder='users/myaccount',
    asset_name='my_command_areas',
    service_account='my-account@project.iam.gserviceaccount.com',
    key_path='gee-key.json'
)
print(f"Uploaded to: {asset_id}")
```

--------------------------------

### Download GFS Weather Forecast Data

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Downloads GFS weather forecast data, supporting both recent data from NOAA servers and historical data from Google Earth Engine. It allows specifying date ranges, geographical bounds, forecast hours, and desired meteorological variables.

```python
from sdrips.gfs_processing import gfsdata, gfsdata_noaa, gfsdata_ee
from datetime import datetime
import ee

ee.Initialize()

# Automatic source selection based on date
gfsdata('config_files/sdrips_config.yaml')

# Recent data from NOAA server (last 10 days)
gfsdata_noaa(
    start_date='2024-06-15',
    leftlon=88.5,
    rightlon=92.5,
    toplat=26.5,
    bottomlat=22.0,
    forecast_hours=168,  # 7-day forecast
    save_data_loc='Data/',
    variable='precipitable_water_entire_atmosphere'
)

# Historical data from Google Earth Engine (>10 days old)
gfsdata_ee(
    start_date='2024-05-01',
    leftlon=88.5,
    rightlon=92.5,
    toplat=26.5,
    bottomlat=22.0,
    forecast_hours=168,
    save_data_loc='Data/',
    variable='total_precipitation_surface',
    service_account='myaccount@project.iam.gserviceaccount.com',
    key_path='gee-key.json'
)
```

--------------------------------

### Integrate In-situ Sensors for Bias Correction (YAML)

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Configuration file for integrating ground-based soil moisture and weather sensors. Enables specific sensors, maps them to variables, specifies data sources, and defines interpolation methods for bias correction.

```yaml
# config_files/sdrips_config.yaml

Insitu_Sensor_integration: true

Insitu_Sensor_integration_config:
  air_temperature_sensor: true
  air_temperature_variable: 'Temperature'
  soil_moisture_sensor: true
  soil_moisture_variable: 'Water Content'
  wind_speed_sensor: true
  wind_speed_variable: 'Wind'
  specific_humidity_sensor: false
  specific_humidity_variable: 'Specific Humidity'
  hub_url: "https://my-sensor-hub.com/data/"
  date_regex: "(\d{4}_\d{2}_\d{2})"
  date_format: "%Y_%m_%d"
  sensor_data_path: "SensorData/"
  interpolation_method: kriging  # Options: best, average, idw, kriging, nearest_neighbor
```

--------------------------------

### Download and Process IMERG Precipitation Data

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Downloads daily IMERG precipitation GeoTIFFs, clips them to specified bounds, resamples to a desired resolution, and aggregates daily data into weekly cumulative totals. It handles authentication with NASA GESDISC credentials and can process data for a specific day or download a range.

```python
from sdrips.precipitation import imergprecip, process_day, compute_weekly_cummulative_precip
from datetime import date, timedelta
from pathlib import Path

# Download 7 days of IMERG data
imergprecip('config_files/sdrips_config.yaml')

# Manual single-day processing
success = process_day(
    date=date(2024, 6, 15),
    bounds=(88.5, 22.0, 92.5, 26.5),  # (left, bottom, right, top) in EPSG:4326
    save_data_loc='Data/',
    imerg_username='myusername',
    imerg_password='mypassword',
    precip_links_base_url={
        'before': 'https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3IMERGDF.07/',
        'year': 'https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3IMERGDF.07/',
        'after': 'https://gpm1.gesdisc.osa.gov/data/GPM_L3/GPM_3IMERGDF.07/'
    },
    suffix='.tif',
    prefix='3B-DAY.MS.MRG.3IMERG.'
)

# Aggregate weekly cumulative precipitation
file_paths = [
    Path('Data/precip/global.precip.imerg.20240615.tif'),
    Path('Data/precip/global.precip.imerg.20240616.tif')
    # ... 7 days total
]
compute_weekly_cummulative_precip(
    file_paths=file_paths,
    output_path=Path('Data/precip/precip.currentweek.tif')
)
```

--------------------------------

### Integrate In-situ Sensors for Bias Correction (Python)

Source: https://context7.com/uw-saswe/sdrips/llms.txt

Python script to execute the sensor integration process within sDRIPS. This function utilizes the configuration file to process sensor data, perform bias correction, and recalculate ET.

```python
from sdrips.sen_corr_evapotranspiration import process_cmd_area_sensor_parallel

# Run ET with sensor correction
process_cmd_area_sensor_parallel(
    config_path='config_files/sdrips_config.yaml',
    main_logger=logger,
    log_queue=log_queue,
    cores=4
)
```

--------------------------------

### Import Command Config Module in Python

Source: https://github.com/uw-saswe/sdrips/blob/main/docs/tutorials/config_files/Creating_CMD_Config.ipynb

Imports the 'run_cmd_config' function from the 'sdrips.cmd_config' module. This function is used to generate YAML configuration files for command areas.

```python
from sdrips.cmd_config import run_cmd_config
```