deepsensor 0.1.3

A Python package for modelling xarray and pandas data with neural processes.

DeepSensor: A Python package for modelling environmental data with neural processes

DeepSensor is an open source project and Python package for modelling xarray and pandas data with neural processes (NPs).

NOTE: This package is currently undergoing very active development. If you are interested in using DeepSensor, please get in touch first ([e-mail]).

Why neural processes?

NPs are a highly flexible class of probabilistic models that can:

• ingest multiple context sets (i.e. data streams) containing gridded or pointwise observations
• handle multiple gridded resolutions
• predict at arbitrary target locations
• quantify prediction uncertainty

These capabilities make NPs well suited to modelling spatio-temporal data, such as satellite observations, climate model output, and in-situ measurements. NPs have been used for range of environmental applications, including:

• spatial interpolation (sensor placement)
• downscaling (i.e. super-resolution)
• forecasting

Why DeepSensor?

DeepSensor aims to faithfully match the flexibility of NPs with a simple and intuitive interface. DeepSensor wraps around the powerful neuralprocessess package for the core modelling functionality, while allowing users to stay in the familiar xarray and pandas world and avoid the murky depths of tensors!

Backend agnosticism

DeepSensor leverages the backends package to be compatible with either PyTorch or TensorFlow deep learning libraries. Simply import deepsensor.torch or import deepsensor.tensorflow to choose your backend!

Quick start

Here we will demonstrate a simple example of training a convolutional conditional neural process (ConvCNP) to spatially interpolate ERA5 data. First, pip install the package. In this case we will use the TensorFlow backend.

pip install deepsensor
pip install tensorflow
pip install tensorflow-probability

We can go from imports to predictions with a trained model in <30 lines of code!

import deepsensor.tensorflow
from deepsensor.data.loader import TaskLoader
from deepsensor.data.processor import DataProcessor
from deepsensor.model.models import ConvNP
from deepsensor.train.train import train_epoch

import xarray as xr
import pandas as pd
import numpy as np

# Load raw data
ds_raw = xr.tutorial.open_dataset("air_temperature")

# Normalise data
data_processor = DataProcessor(x1_name="lat", x1_map=(15, 75), x2_name="lon", x2_map=(200, 330))
ds = data_processor(ds_raw)

# Set up task loader
task_loader = TaskLoader(context=ds, target=ds)

# Set up model
model = ConvNP(data_processor, task_loader)

# Generate training tasks with up to 10% of grid cells passed as context and all grid cells
# passed as targets
train_tasks = []
for date in pd.date_range("2013-01-01", "2014-11-30"):
    task = task_loader(date, context_sampling=np.random.uniform(0.0, 0.1), target_sampling="all")
    train_tasks.append(task)
    
# Train model
for epoch in range(100):
    train_epoch(model, train_tasks, progress_bar=True)
    
# Predict on new task with 10% of context data
test_task = task_loader("2014-12-31", 0.1)
mean_ds, std_ds = model.predict(test_task, X_t=ds_raw)

After training, the model can predict directly to xarray in your data's original units and coordinate system:

>>> mean_ds
<xarray.Dataset>
Dimensions:  (time: 1, lat: 25, lon: 53)
Coordinates:
  * time     (time) datetime64[ns] 2014-12-31
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
Data variables:
    air      (time, lat, lon) float32 246.7 244.4 245.5 ... 290.2 289.8 289.4

Learn more

• Documentation: TODO
• Issue tracker: https://github.com/tom-andersson/deepsensor/issues
• Source code: https://github.com/tom-andersson/deepsensor

Talks

• Environmental Sensor Placement with ConvGNPs: https://youtu.be/v0pmqh09u1Y

Papers

• TODO