swmm-utils 0.2.2

Utilities for interpreting EPA SWMM input (.inp) and report (.rpt) files.

SWMM Utils

Utilities for interpreting EPA SWMM input (.inp) and report (.rpt) files.

Overview

This project provides a comprehensive toolkit for working with EPA SWMM (Storm Water Management Model) files. It enables:

Input Files (.inp)

• Simple, intuitive API: Load, modify, and save SWMM models with typed properties
• Multi-format support: Work with .inp, JSON, or Parquet formats
• Context manager support: Clean resource management with with statements
• Typed properties: Access model components (junctions, conduits, etc.) with autocomplete
• Round-trip conversion: Load → Modify → Save without data loss

Report Files (.rpt)

• Comprehensive parsing: Extract all major sections from SWMM simulation reports
• Structured data access: Access simulation results through typed properties
• Easy analysis: Programmatically analyze hydraulic and hydrologic results
• Multiple result types: Node depths, link flows, pumping, storage, LID performance, water quality, and more

Quick Start

Installation

# Clone the repository
git clone https://github.com/neeraip/swmm-utils.git
cd swmm-utils

# Install the package
pip install -e .

Basic Usage - Input Files

from swmm_utils import SwmmInput

# Load, modify, and save with context manager
with SwmmInput("model.inp") as inp:
    # Modify using typed properties
    inp.title = "My Modified Model"
    
    # Access model components with autocomplete
    print(f"Junctions: {len(inp.junctions)}")
    print(f"Conduits: {len(inp.conduits)}")
    
    # Modify model data
    for junction in inp.junctions:
        junction['elevation'] = float(junction.get('elevation', 0)) + 10
    
    # Update options
    inp.options['REPORT_STEP'] = '00:15:00'
    
    # Save to different formats
    inp.to_inp("modified.inp")
    inp.to_json("model.json")
    inp.to_parquet("model.parquet", single_file=True)

# Load from JSON or Parquet
with SwmmInput("model.json") as inp:
    print(f"Title: {inp.title}")
    inp.to_inp("from_json.inp")

Basic Usage - Report Files

from swmm_utils import SwmmReport

# Load and analyze simulation results
with SwmmReport("simulation.rpt") as report:
    # Access simulation metadata
    print(f"SWMM Version: {report.header['version']}")
    print(f"Flow Units: {report.analysis_options['flow_units']}")
    
    # Analyze node results
    for node in report.node_depth:
        if node['maximum_depth'] > 10:
            print(f"Deep node: {node['name']} - {node['maximum_depth']:.2f} ft")
    
    # Check pump performance
    for pump in report.pumping_summary:
        print(f"Pump {pump['pump_name']}: {pump['percent_utilized']:.1f}% utilized")
    
    # Analyze subcatchment runoff
    for sub in report.subcatchment_runoff:
        print(f"{sub['name']}: {sub['peak_runoff']:.2f} CFS peak")
    
    # Check for surcharged nodes
    if report.node_surcharge:
        print(f"Warning: {len(report.node_surcharge)} nodes surcharged!")
    
    # LID performance (if applicable)
    for lid in report.lid_performance:
        print(f"{lid['subcatchment']}/{lid['lid_control']}: "
              f"{lid['infil_loss']:.2f} in infiltrated")

Advanced Usage - Lower-Level API

For more control over input files, you can use the decoder/encoder directly:

from swmm_utils import SwmmInputDecoder, SwmmInputEncoder

# Decode a SWMM .inp file into a Python dict
decoder = SwmmInputDecoder()
model = decoder.decode_file("model.inp")

# Access structured data
for junction in model['junctions']:
    print(f"{junction['name']}: elevation={junction['elevation']}")

# Modify the model
model['junctions'][0]['elevation'] = '100.5'
model['title'] = 'Modified SWMM Model'

# Encode to different formats
encoder = SwmmInputEncoder()

# Write back to .inp format
encoder.encode_to_inp_file(model, "modified.inp")

# Encode to JSON
encoder.encode_to_json(model, "model.json", pretty=True)

# Encode to Parquet (multi-file: one file per section)
encoder.encode_to_parquet(model, "model_parquet/", single_file=False)

# Encode to Parquet (single-file: all sections in one file)
encoder.encode_to_parquet(model, "model.parquet", single_file=True)

# Decode from JSON or Parquet
json_model = decoder.decode_json("model.json")
parquet_model = decoder.decode_parquet("model.parquet")

API Overview

SwmmInput (Recommended for Input Files)

The high-level interface with typed properties and context manager support:

• Constructor: SwmmInput(filepath=None) - Load from .inp, .json, or .parquet file (optional)
• Context Manager: Use with with statement for clean resource management
• Typed Properties: Access sections like input.title, input.junctions, input.conduits, etc.
• Output Methods:
  • to_inp(filepath) - Save to .inp format
  • to_json(filepath, pretty=True) - Save to JSON format
  • to_parquet(filepath, single_file=False) - Save to Parquet format

Available Typed Properties:

• title (str)
• options (dict)
• junctions, outfalls, storage (lists)
• conduits, pumps, orifices, weirs (lists)
• subcatchments, raingages (lists)
• curves, timeseries, controls, pollutants, landuses (lists)

SwmmReport (For Report Files)

The high-level interface for reading SWMM simulation results:

• Constructor: SwmmReport(filepath=None) - Load from .rpt file (optional)
• Context Manager: Use with with statement for clean resource management
• Typed Properties: Access results like report.node_depth, report.link_flow, etc.

Available Report Sections:

• header - Version, build, title
• element_count - Count of model elements
• analysis_options - Simulation settings
• continuity - Mass balance (runoff, routing, quality)
• subcatchment_runoff - Runoff summary by subcatchment
• node_depth - Node depth statistics
• node_inflow - Node inflow statistics
• node_flooding - Flooded nodes
• node_surcharge - Surcharged nodes
• storage_volume - Storage unit performance
• outfall_loading - Outfall loading statistics
• link_flow - Link flow statistics
• flow_classification - Flow regime classification
• conduit_surcharge - Surcharged conduits
• pumping_summary - Pump performance
• lid_performance - LID control performance
• groundwater_summary - Groundwater continuity
• quality_routing_continuity - Water quality mass balance
• subcatchment_washoff - Pollutant washoff
• link_pollutant_load - Pollutant loads in links
• analysis_time - Simulation timing

SwmmInputDecoder & SwmmInputEncoder

Lower-level API for more control over input files:

• Decoder Methods:

  • decode_file(filepath) - Decode .inp file to dict
  • decode_json(filepath) - Decode JSON file to dict
  • decode_parquet(path) - Decode Parquet file/directory to dict

• Encoder Methods:

  • encode_to_inp_file(data, filepath) - Encode dict to .inp file
  • encode_to_json(data, filepath, pretty=True) - Encode dict to JSON
  • encode_to_parquet(data, path, single_file=False) - Encode dict to Parquet

SwmmReportDecoder

Lower-level API for report file parsing:

• Decoder Methods:
  • decode_file(filepath) - Decode .rpt file to dict

Architecture

Input Files:
.inp file → SwmmInput → Modify Properties → Save (.inp/JSON/Parquet)
               ↓
          Typed Properties
       (title, junctions, etc.)

Report Files:
.rpt file → SwmmReport → Access Results
               ↓
          Typed Properties
       (node_depth, link_flow, etc.)

The architecture uses Python dictionaries as the in-memory data model:

1. SwmmInput/SwmmReport: High-level interfaces with typed properties and context managers
2. Decoders: Read .inp/.rpt/JSON/Parquet files into Python dict structures
3. Encoders: Write dict objects to .inp/JSON/Parquet formats (input files only)
4. Dict Model: Simple Python dictionaries - easy to inspect, modify, and manipulate

Features

Input File Features

• ✅ Simple, intuitive API with typed properties
• ✅ Context manager support for clean resource management
• ✅ Decode all SWMM 5.2.4 input file sections (60+ sections)
• ✅ Encode to .inp, JSON, and Parquet formats
• ✅ Decode from .inp, JSON, and Parquet formats
• ✅ Configurable Parquet output (single-file or multi-file modes)
• ✅ Round-trip conversion (load → modify → save) without data loss
• ✅ Full support for comments, whitespace, and formatting

Report File Features

• ✅ Comprehensive parsing of SWMM 5.2 report files
• ✅ Extract 20+ report sections (hydraulics, hydrology, water quality)
• ✅ Node results: depth, inflow, flooding, surcharge
• ✅ Link results: flow, velocity, classification
• ✅ Pump and storage performance metrics
• ✅ LID (Low Impact Development) performance analysis
• ✅ Water quality: pollutant loads, washoff, continuity
• ✅ Groundwater and RDII tracking
• ✅ Easy result lookup by element name

Testing

• ✅ Comprehensive test suite (40 tests passing)
• ✅ Input file tests (28 tests)
• ✅ Report file tests (12 tests)

Supported SWMM Sections

Project Configuration

• [TITLE] - Project title and description
• [OPTIONS] - Simulation options (34 parameters)
• [REPORT] - Output reporting options
• [FILES] - External file references
• [MAP] - Map extent and units
• [BACKDROP] - Background image settings
• [PROFILES] - Longitudinal profile definitions

Hydrology

• [RAINGAGES] - Rain gage definitions
• [EVAPORATION] - Evaporation data
• [SUBCATCHMENTS] - Subcatchment properties
• [SUBAREAS] - Subcatchment surface areas
• [INFILTRATION] - Infiltration parameters
• [AQUIFERS] - Groundwater aquifer properties
• [GROUNDWATER] - Subcatchment groundwater
• [GWF] - Groundwater flow equations
• [SNOWPACKS] - Snow pack parameters
• [TEMPERATURE] - Temperature data
• [ADJUSTMENTS] - Climate adjustments

Hydraulic Network - Nodes

• [JUNCTIONS] - Junction nodes
• [OUTFALLS] - Outfall nodes
• [STORAGE] - Storage unit nodes
• [DIVIDERS] - Flow divider nodes

Hydraulic Network - Links

• [CONDUITS] - Conduit links
• [PUMPS] - Pump links
• [ORIFICES] - Orifice links
• [WEIRS] - Weir links
• [OUTLETS] - Outlet links

Cross-Sections

• [XSECTIONS] - Link cross-section geometry
• [LOSSES] - Minor losses
• [TRANSECTS] - Irregular cross-section data

Water Quality

• [POLLUTANTS] - Pollutant properties
• [LANDUSES] - Land use categories
• [COVERAGES] - Subcatchment land use coverage
• [BUILDUP] - Pollutant buildup functions
• [WASHOFF] - Pollutant washoff functions
• [TREATMENT] - Treatment equations
• [INFLOWS] - External inflows
• [DWF] - Dry weather inflows
• [RDII] - RDII inflow parameters
• [HYDROGRAPHS] - Unit hydrograph data
• [LOADING] - Initial pollutant loads

LID Controls (Low Impact Development)

• [LID_CONTROLS] - LID control definitions
• [LID_USAGE] - LID usage in subcatchments

Street/Inlet Modeling (SWMM 5.2+)

• [STREETS] - Street cross-section properties
• [INLETS] - Inlet design parameters
• [INLET_USAGE] - Inlet usage on streets

Curves & Time Series

• [TIMESERIES] - Time series data
• [PATTERNS] - Time patterns
• [CURVES] - Curve data

Operational Controls

• [CONTROLS] - Rule-based controls

Visualization

• [COORDINATES] - Node coordinates
• [VERTICES] - Link vertices
• [POLYGONS] - Subcatchment polygons
• [SYMBOLS] - Rain gage symbols
• [LABELS] - Map labels
• [TAGS] - Object tags

Examples

Example 1: Input File - Decode and Analyze

from swmm_utils import SwmmInputDecoder

decoder = SwmmInputDecoder()
model = decoder.decode_file("large_network.inp")

# Count elements
print(f"Junctions: {len(model.get('junctions', []))}")
print(f"Conduits: {len(model.get('conduits', []))}")
print(f"Subcatchments: {len(model.get('subcatchments', []))}")

# Find high-elevation junctions
for junc in model.get('junctions', []):
    if float(junc['elevation']) > 100:
        print(f"High junction: {junc['name']} at {junc['elevation']}m")

Example 2: Report File - Analyze Simulation Results

from swmm_utils import SwmmReport

with SwmmReport("results.rpt") as report:
    # Check for critical conditions
    print(f"Analysis: {report.header['title']}")
    print(f"Flow Units: {report.analysis_options.get('flow_units', 'N/A')}")
    
    # Find nodes with excessive depth
    critical_nodes = [
        node for node in report.node_depth 
        if node['maximum_depth'] > 10
    ]
    print(f"\n{len(critical_nodes)} nodes exceeded 10 ft depth")
    
    # Analyze pump efficiency
    if report.pumping_summary:
        for pump in report.pumping_summary:
            if pump['percent_utilized'] < 20:
                print(f"Pump {pump['pump_name']} underutilized: "
                      f"{pump['percent_utilized']:.1f}%")
    
    # Check system continuity
    continuity = report.continuity.get('flow_routing', {})
    error = continuity.get('continuity_error')
    if error and abs(error) > 1.0:
        print(f"Warning: Continuity error {error:.2f}%")

Example 3: Convert Input Files for Analytics

from swmm_utils import SwmmInputDecoder, SwmmInputEncoder

# Decode SWMM model
decoder = SwmmInputDecoder()
model = decoder.decode_file("network.inp")

# Export to Parquet for analysis in pandas/R/SQL
encoder = SwmmInputEncoder()
encoder.encode_to_parquet(model, "network_parquet/", single_file=False)

# Now analyze with pandas
import pandas as pd
junctions = pd.read_parquet("network_parquet/junctions.parquet")
conduits = pd.read_parquet("network_parquet/conduits.parquet")

print(junctions.describe())
print(f"Average pipe length: {conduits['length'].astype(float).mean():.2f}")

Example 4: Complete Workflow - Simulate and Analyze

import subprocess
from swmm_utils import SwmmInput, SwmmReport

# Step 1: Modify input file
with SwmmInput("model.inp") as inp:
    # Increase all pipe roughness by 10%
    for conduit in inp.conduits:
        roughness = float(conduit.get('roughness', 0.01))
        conduit['roughness'] = str(roughness * 1.1)
    
    inp.to_inp("modified.inp")

# Step 2: Run SWMM simulation
subprocess.run([
    "./bin/runswmm", 
    "modified.inp", 
    "modified.rpt", 
    "modified.out"
])

# Step 3: Analyze results
with SwmmReport("modified.rpt") as report:
    print(f"Simulation complete!")
    print(f"Total runtime: {report.analysis_time.get('elapsed', 'N/A')}")
    
    # Compare peak flows
    for link in report.link_flow[:10]:
        print(f"{link['name']}: {link['maximum_flow']:.2f} CFS")

Example 5: Batch Processing

from pathlib import Path
from swmm_utils import SwmmInputDecoder, SwmmInputEncoder

decoder = SwmmInputDecoder()
encoder = SwmmInputEncoder()

# Convert all .inp files in a directory to JSON
for inp_file in Path("models/").glob("*.inp"):
    model = decoder.decode_file(str(inp_file))
    json_file = inp_file.with_suffix('.json')
    encoder.encode_to_json(model, str(json_file), pretty=True)
    print(f"Converted {inp_file.name} → {json_file.name}")

Example 6: LID Performance Analysis

from swmm_utils import SwmmReport

with SwmmReport("lid_scenario.rpt") as report:
    # Analyze LID performance
    if report.lid_performance:
        # Group by subcatchment
        from collections import defaultdict
        by_subcatchment = defaultdict(list)
        
        for lid in report.lid_performance:
            by_subcatchment[lid['subcatchment']].append(lid)
        
        # Calculate total infiltration per subcatchment
        for sub, lids in by_subcatchment.items():
            total_infil = sum(lid['infil_loss'] for lid in lids)
            total_inflow = sum(lid['total_inflow'] for lid in lids)
            reduction = (total_infil / total_inflow * 100) if total_inflow > 0 else 0
            
            print(f"{sub}: {reduction:.1f}% runoff reduction via infiltration")

Example 7: Batch Processing

### Example 7: Round-Trip Conversion

```python
from swmm_utils import SwmmInputDecoder, SwmmInputEncoder

decoder = SwmmInputDecoder()
encoder = SwmmInputEncoder()

# Decode from .inp
model = decoder.decode_file("original.inp")

# Encode to JSON
encoder.encode_to_json(model, "model.json", pretty=True)

# Decode from JSON
json_model = decoder.decode_json("model.json")

# Encode to Parquet (single file)
encoder.encode_to_parquet(json_model, "model.parquet", single_file=True)

# Decode from Parquet
parquet_model = decoder.decode_parquet("model.parquet")

# Encode back to .inp
encoder.encode_to_inp_file(parquet_model, "final.inp")

# All data preserved throughout the round-trip!

Testing

# Run all tests
pytest -q

# Run with coverage
pytest --cov=swmm_utils --cov-report=html

# Run specific test file
pytest tests/test_rpt.py -v

All 40 tests pass, including comprehensive format conversion, round-trip tests, and report parsing.

Running Examples

Before running these examples, make sure you have the built SWMM binary executable runswmm in the /bin directory.

# Example 1: Basic input file operations
python examples/example1/example1.py

# Example 2: Report parsing with water quality
python examples/example2/example2.py

Project Structure

swmm-utils/
├── src/
│   └── swmm_utils/              # Main package
│       ├── __init__.py          # Package exports
│       ├── inp.py               # High-level input file interface
│       ├── inp_decoder.py       # Decode .inp/JSON/Parquet → dict
│       ├── inp_encoder.py       # Encode dict → .inp/JSON/Parquet
│       ├── rpt.py               # High-level report file interface
│       └── rpt_decoder.py       # Decode .rpt → dict
├── examples/
│   ├── example1/                # Basic input file example
│   └── example2/                # Report parsing example
├── tests/
│   ├── test_inp.py              # Input file interface tests
│   ├── test_inp_decoder_encoder.py  # Core parsing tests
│   ├── test_inp_formats.py      # Format conversion tests
│   └── test_rpt.py              # Report parser tests
├── data/                        # Location for sample SWMM files
├── bin/                         # Location for swmm binary executable
├── docs/
│   └── SWMM_INPUT_FILE.md       # Complete SWMM input file reference
├── setup.py                     # Package configuration
├── requirements.txt             # Core dependencies
├── requirements-dev.txt         # Development dependencies
└── README.md                    # Project information

Performance

Input Files

Tested on various SWMM models:

• Decode .inp: ~0.05 seconds (240 junctions)
• Encode to JSON: 873 KB (240 junctions)
• Encode to Parquet (multi-file): 18 files, ~110 KB total
• Encode to Parquet (single-file): 1 file, ~109 KB
• Round-trip (.inp → JSON → Parquet → .inp): All data preserved

Report Files

Tested on diverse simulation results:

• Parse .rpt: ~0.02 seconds (small models) to ~0.5 seconds (large models)
• Large model support: Successfully parsed 809 KB report with 2,227 nodes
• Memory efficient: Processes reports on-demand without loading entire file

Documentation

• README.md - This file (overview and quick start)
• examples/ - Working examples with real SWMM models
• docs/SWMM_INPUT_FILE.md - Complete SWMM format reference

Dependencies

Required

• Python 3.8+
• pyarrow >= 10.0.0 (for Parquet support)

Development

• pytest >= 7.0.0
• pytest-cov >= 4.0.0

Known Limitations

Input Files

1. Round-trip Formatting: Some cosmetic differences

   • Comments may not be preserved in exact original positions
   • Whitespace normalized to SWMM standard format
   • All data and structure fully preserved

2. Complex Sections: Some sections have simplified handling

   • [CONTROLS] - Stored as text (complex rule syntax)
   • [TRANSECTS] - Multi-line format preserved

Report Files

1. Read-only: Report files are parsed for reading only (no modification/encoding)
2. Section Availability: Not all sections appear in every report (depends on simulation settings)
3. Format Variations: Minor format differences across SWMM versions handled gracefully

Contributing

Contributions welcome! Areas of interest:

• Enhanced validation logic for input files
• Additional report sections or metrics
• Model manipulation utilities
• Performance optimization
• Additional output formats (e.g., GeoJSON)
• Documentation improvements
• More example scripts

License

MIT LICENSE

Acknowledgments

• EPA SWMM development team for the excellent documentation
• Apache Arrow/Parquet for columnar analytics support
• Contributors and users who provided feedback and testing

Recent Updates

January 2026 - Report Parser

• ✅ Added comprehensive SWMM report (.rpt) file parsing
• ✅ Support for 20+ report sections including:
  • Hydraulic results (nodes, links, pumps, storage)
  • Hydrologic results (subcatchments, runoff)
  • Water quality (pollutants, washoff, continuity)
  • LID performance analysis
  • System diagnostics (surcharge, flooding, flow classification)
• ✅ High-level SwmmReport interface with typed properties
• ✅ Context manager support
• ✅ 12 new tests (40 total tests passing)

Previous - Input File Parser

• ✅ Refactored to encode/decode pattern
• ✅ Added high-level SwmmInput interface
• ✅ Multi-format support (JSON, Parquet)
• ✅ Comprehensive test suite

Contact

For questions or issues, please open a GitHub issue.

---

Status: ✅ Production-ready

• Input Files: Full encode/decode support for SWMM 5.2.4
• Report Files: Comprehensive parsing for SWMM 5.2+ reports