Working with Signals¶
Signals are the core building blocks of meteaudata. They represent a single measured parameter (like temperature or pH) with its data and metadata.
Creating a Signal¶
import numpy as np
import pandas as pd
from meteaudata import Signal, DataProvenance
from meteaudata import resample, linear_interpolation, subset, replace_ranges
# Set random seed for reproducible examples
np.random.seed(42)
# Create a standard provenance for examples
provenance = DataProvenance(
source_repository="Example System",
project="Documentation Example",
location="Demo Location",
equipment="Temperature Sensor v2.1",
parameter="Temperature",
purpose="Documentation example",
metadata_id="doc_example_001"
)
# Create simple time series data
timestamps = pd.date_range('2024-01-01', periods=100, freq='h')
data = pd.Series(np.random.randn(100) * 10 + 20, index=timestamps, name="RAW")
# Create a simple signal
signal = Signal(
input_data=data,
name="Temperature",
provenance=provenance,
units="°C"
)
print(f"Created signal: {signal.name}")
print(f"Units: {signal.units}")
print(f"Time series count: {len(signal.time_series)}")
print(f"Time series names: {signal.all_time_series}")
Created signal: Temperature#1
Units: °C
Time series count: 1
Time series names: ['Temperature#1_RAW#1']
Adding Processing Steps¶
# Apply linear interpolation
signal.process(["Temperature#1_RAW#1"], linear_interpolation)
print(f"After processing: {len(signal.time_series)} time series")
print(f"Available time series: {list(signal.time_series.keys())}")
After processing: 2 time series
Available time series: ['Temperature#1_RAW#1', 'Temperature#1_LIN-INT#1']
Accessing Time Series Data¶
# Get the processed time series
processed_ts = signal.time_series["Temperature#1_LIN-INT#1"]
print(f"Processed series name: {processed_ts.series.name}")
print(f"Processing steps: {len(processed_ts.processing_steps)}")
print(f"Last processing step: {processed_ts.processing_steps[-1].type}")
Processed series name: Temperature#1_LIN-INT#1
Processing steps: 1
Last processing step: ProcessingType.GAP_FILLING
# Access the actual data
data = processed_ts.series
print(f"Data shape: {data.shape}")
print(f"Sample values: {data.head(3).values}")
Signal Attributes¶
# Explore signal metadata
print(f"Signal name: {signal.name}")
print(f"Units: {signal.units}")
print(f"Created on: {signal.created_on}")
print(f"Provenance: {signal.provenance.parameter}")
print(f"Equipment: {signal.provenance.equipment}")
Signal name: Temperature#1
Units: °C
Created on: 2025-12-10 19:11:08.449000
Provenance: Temperature
Equipment: Temperature Sensor v2.1
See Also¶
- Managing Datasets - Combining multiple signals
- Time Series Processing - Working with individual time series
- Processing Steps - Available processing functions