fluvialgen 1.0.0

A Python package for generating synthetic river networks and datasets

FluvialGen

A Python package for generating synthetic river networks and datasets.

Installation

You can install FluvialGen using pip:

pip install fluvialgen

Or install from source:

git clone https://github.com/joseenriqueruiznavarro/FluvialGen.git
cd FluvialGen
pip install -e .

Requirements

• Python >= 3.8
• NumPy
• Pandas
• SciPy
• Matplotlib
• GeoPandas
• Shapely
• Rasterio
• tqdm

Integration with River Models

MovingWindowBatcher

This class provides a way to process data in overlapping windows with batching support:

from river import compose, linear_model, preprocessing, optim, metrics
from fluvialgen.movingwindow_generator import MovingWindowBatcher
from river import datasets

# Create a River pipeline
model = compose.Select('clouds', 'humidity', 'pressure', 'temperature', 'wind')
model |= preprocessing.StandardScaler()
model |= linear_model.LinearRegression(optimizer=optim.SGD(0.001))

# Initialize metrics
metric = metrics.MAE()

# Create the dataset and batcher
dataset = datasets.Bikes()
batcher = MovingWindowBatcher(
    dataset=dataset,
    instance_size=2,  # Size of each window
    batch_size=2,     # Number of instances per batch
    n_instances=1000
)

# Train the model
try:
    # Process batches and train the model
    for X, y in batcher:
        # Train on each instance in the batch
        for i in range(len(X)):
            x = X.iloc[i]
            target = y.iloc[i]
            model.learn_one(x, target)
            
        # Make predictions and update metrics
        for i in range(len(X)):
            x = X.iloc[i]
            target = y.iloc[i]
            y_pred = model.predict_one(x)
            metric.update(target, y_pred)
            
    print(f"Final MAE: {metric}")

finally:
    # Clean up
    batcher.stop()

PastForecastBatcher

This class provides a way to process data with past data and forecast values:

from river import compose, linear_model, preprocessing, optim, metrics
from fluvialgen.past_forecast_batcher import PastForecastBatcher
from river import datasets

# Create a River pipeline
model = compose.Select('clouds', 'humidity', 'pressure', 'temperature', 'wind')
model |= preprocessing.StandardScaler()
model |= linear_model.LinearRegression(optimizer=optim.SGD(0.001))

# Initialize metrics
metric = metrics.MAE()

# Create the dataset and batcher
dataset = datasets.Bikes()
batcher = PastForecastBatcher(
    dataset=dataset,
    past_size=3,      # Number of past instances to include
    forecast_size=1,  # Use data 1 position ahead of past window
    n_instances=1000
)

# Train the model
try:
    # Process instances and train the model
    for X_past, y_forecast in batcher:
        # Train on past data
        for i in range(len(X_past)):
            x = X_past.iloc[i]
            # Note: y_forecast is a single value, not a Series
            # You would need to use your own past y values or another data source
            
        # Make prediction for the forecast position
        forecast_features = X_past.iloc[-1]  # Use last feature vector for prediction
        y_pred = model.predict_one(forecast_features)
        metric.update(y_forecast, y_pred)
            
    print(f"Final MAE: {metric}")

finally:
    # Clean up
    batcher.stop()

Data Structure

MovingWindowBatcher

For each batch, MovingWindowBatcher returns:

• X: DataFrame with all instances in the batch
• y: Series with all targets in the batch

For example, with instance_size=2 and batch_size=2:

• First batch:
  • X = DataFrame with [x1,x2,x2,x3]
  • y = Series with [y1,y2,y2,y3]
• Second batch:
  • X = DataFrame with [x2,x3,x3,x4]
  • y = Series with [y2,y3,y3,y4]

PastForecastBatcher

For each instance, PastForecastBatcher returns:

• X_past: DataFrame with past feature data
• y_forecast: Single value representing the target at the forecast position

For example, with past_size=3 and forecast_size=0:

• First instance:
  • X_past = DataFrame with [x1,x2,x3]
  • y_forecast = y4 (value at past_size + forecast_size position)
• Second instance:
  • X_past = DataFrame with [x2,x3,x4]
  • y_forecast = y5 (value at past_size + forecast_size position)

With past_size=3 and forecast_size=1:

• First instance:
  • X_past = DataFrame with [x1,x2,x3]
  • y_forecast = y5 (value at past_size + forecast_size position)
• Second instance:
  • X_past = DataFrame with [x2,x3,x4]
  • y_forecast = y6 (value at past_size + forecast_size position)