USGS Study Package From Primitives¶
This notebook demonstrates the composable primitives in ras_commander.usgs for USGS gauge study assembly.
What You'll Learn:
- UsgsObservations — metadata, datasets, peak flow, summaries, gap analysis
- UsgsDrainageAreaComparison — multi-source area comparison with agreement status
- How to assemble a study report from primitives (notebook-local workflow)
Key Principle: Keep study packaging, readiness assessment, and report assembly in notebook or project code. The library exposes only composable primitives.
Python
# Uncomment to install/upgrade ras-commander from pip
#!pip install --upgrade ras-commander
#Import the ras-commander package
from ras_commander import *
Development Mode¶
To test with a local development copy instead of the pip-installed package, convert this cell to code and convert the cell above to markdown:
Python
import sys
from pathlib import Path
# Add repo root to path
notebook_dir = Path.cwd()
repo_root = notebook_dir.parent if notebook_dir.name == 'examples' else notebook_dir
if str(repo_root) not in sys.path:
sys.path.insert(0, str(repo_root))
from ras_commander import *
Python
import json
from pathlib import Path
from ras_commander.usgs import (
UsgsObservations,
UsgsDrainageAreaComparison,
)
Gauge Metadata and Continuous Observations¶
Retrieve metadata and observation datasets for USGS gauge 05577500 (Sangamon River at Springfield, IL).
Python
site_id = "05577500"
start_datetime = "2024-01-01"
end_datetime = "2024-12-31"
metadata = UsgsObservations.get_gauge_metadata(site_id)
print(f"Gauge: {metadata.get('station_name', 'N/A')}")
print(f"Site ID: {site_id}")
print(f"Drainage area: {metadata.get('drainage_area_sqmi', 'N/A')} sq mi")
print(f"Latitude: {metadata.get('latitude', 'N/A')}")
print(f"Longitude: {metadata.get('longitude', 'N/A')}")
assert isinstance(metadata, dict), "Metadata should be a dict"
assert 'drainage_area_sqmi' in metadata, "Should have drainage area"
print(f"\nMetadata keys: {sorted(metadata.keys())}")
Text Only
Gauge: SPRING CREEK AT SPRINGFIELD, IL
Site ID: 05577500
Drainage area: 107.0 sq mi
Latitude: 39.81541667
Longitude: -89.6994167
Metadata keys: ['available_parameters', 'county', 'drainage_area_sqmi', 'gage_datum_ft', 'huc_cd', 'latitude', 'longitude', 'site_id', 'site_type', 'state', 'station_name']
Python
continuous_flow = UsgsObservations.get_dataset(
site_id=site_id,
dataset_id="continuous_flow",
start_datetime=start_datetime,
end_datetime=end_datetime,
)
daily_flow = UsgsObservations.get_dataset(
site_id=site_id,
dataset_id="daily_flow",
start_datetime=start_datetime,
end_datetime=end_datetime,
)
print(f"Continuous flow: {len(continuous_flow)} records")
print(f"Daily flow: {len(daily_flow)} records")
assert len(continuous_flow) > 0 or len(daily_flow) > 0, "Should have flow data"
if not continuous_flow.empty:
print(f"\nContinuous flow columns: {list(continuous_flow.columns)}")
print(f"Date range: {continuous_flow.index.min()} to {continuous_flow.index.max()}")
if not daily_flow.empty:
print(f"\nDaily flow columns: {list(daily_flow.columns)}")
print(f"Date range: {daily_flow.index.min()} to {daily_flow.index.max()}")
Text Only
Continuous flow: 32881 records
Daily flow: 366 records
Continuous flow columns: ['datetime', 'value']
Date range: 0 to 32880
Daily flow columns: ['datetime', 'value']
Date range: 0 to 365
Dataset Summary and Gap Analysis¶
Use summarize_dataset and analyze_gaps to build report-ready metadata for each dataset.
Python
continuous_flow_summary = UsgsObservations.summarize_dataset(
dataset_id="continuous_flow",
frame=continuous_flow,
status="ok" if not continuous_flow.empty else "empty",
)
continuous_flow_gaps = UsgsObservations.analyze_gaps(
dataset_id="continuous_flow",
frame=continuous_flow,
)
print("=== Continuous Flow Summary ===")
for key, val in continuous_flow_summary.items():
print(f" {key}: {val}")
print("\n=== Continuous Flow Gap Analysis ===")
for key, val in continuous_flow_gaps.items():
print(f" {key}: {val}")
assert isinstance(continuous_flow_summary, dict), "Summary should be dict"
assert isinstance(continuous_flow_gaps, dict), "Gaps should be dict"
Text Only
=== Continuous Flow Summary ===
dataset_id: continuous_flow
observation_type: continuous
parameter: flow
status: ok
units: cfs
record_count: 32881
file_name: continuous_flow.csv
columns: ['datetime', 'value']
start_datetime: 2024-01-01T06:00:00+00:00
end_datetime: 2025-01-01T05:45:00+00:00
error: None
=== Continuous Flow Gap Analysis ===
has_gaps: True
gap_count: 358
gap_locations: [(np.datetime64('2024-01-12T18:30:00.000000'), np.datetime64('2024-01-12T22:30:00.000000'), 15), (np.datetime64('2024-01-12T22:30:00.000000'), np.datetime64('2024-01-13T02:30:00.000000'), 15), (np.datetime64('2024-01-13T02:30:00.000000'), np.datetime64('2024-01-13T06:30:00.000000'), 15), (np.datetime64('2024-01-13T06:30:00.000000'), np.datetime64('2024-01-13T10:30:00.000000'), 15), (np.datetime64('2024-01-13T10:30:00.000000'), np.datetime64('2024-01-13T14:30:00.000000'), 15), (np.datetime64('2024-01-13T14:30:00.000000'), np.datetime64('2024-01-13T18:30:00.000000'), 15), (np.datetime64('2024-01-13T18:30:00.000000'), np.datetime64('2024-01-13T22:30:00.000000'), 15), (np.datetime64('2024-01-13T22:30:00.000000'), np.datetime64('2024-01-14T02:30:00.000000'), 15), (np.datetime64('2024-01-14T02:30:00.000000'), np.datetime64('2024-01-14T06:30:00.000000'), 15), (np.datetime64('2024-01-14T06:30:00.000000'), np.datetime64('2024-01-14T10:30:00.000000'), 15), 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(np.datetime64('2024-12-11T17:15:00.000000'), np.datetime64('2024-12-11T17:45:00.000000'), 1), (np.datetime64('2024-12-13T05:45:00.000000'), np.datetime64('2024-12-13T17:45:00.000000'), 47), (np.datetime64('2024-12-13T17:45:00.000000'), np.datetime64('2024-12-14T05:45:00.000000'), 47), (np.datetime64('2024-12-14T05:45:00.000000'), np.datetime64('2024-12-14T16:00:00.000000'), 40), (np.datetime64('2024-12-20T07:00:00.000000'), np.datetime64('2024-12-20T07:30:00.000000'), 1), (np.datetime64('2024-12-20T12:45:00.000000'), np.datetime64('2024-12-20T13:15:00.000000'), 1), (np.datetime64('2024-12-20T13:30:00.000000'), np.datetime64('2024-12-20T14:00:00.000000'), 1), (np.datetime64('2024-12-20T14:15:00.000000'), np.datetime64('2024-12-20T14:45:00.000000'), 1), (np.datetime64('2024-12-20T14:45:00.000000'), np.datetime64('2024-12-20T15:15:00.000000'), 1), (np.datetime64('2024-12-20T15:30:00.000000'), np.datetime64('2024-12-20T16:00:00.000000'), 1), (np.datetime64('2024-12-20T16:15:00.000000'), np.datetime64('2024-12-20T16:45:00.000000'), 1), (np.datetime64('2024-12-21T09:45:00.000000'), np.datetime64('2024-12-21T10:15:00.000000'), 1), (np.datetime64('2024-12-22T17:00:00.000000'), np.datetime64('2024-12-22T17:30:00.000000'), 1), (np.datetime64('2024-12-25T10:00:00.000000'), np.datetime64('2024-12-25T11:15:00.000000'), 4), (np.datetime64('2024-12-25T14:30:00.000000'), np.datetime64('2024-12-25T15:45:00.000000'), 4), (np.datetime64('2024-12-27T12:45:00.000000'), np.datetime64('2024-12-27T14:00:00.000000'), 4), (np.datetime64('2024-12-27T15:15:00.000000'), np.datetime64('2024-12-27T15:45:00.000000'), 1), (np.datetime64('2024-12-27T20:00:00.000000'), np.datetime64('2024-12-27T20:45:00.000000'), 2), (np.datetime64('2024-12-27T20:45:00.000000'), np.datetime64('2024-12-27T21:15:00.000000'), 1), (np.datetime64('2024-12-27T23:15:00.000000'), np.datetime64('2024-12-27T23:45:00.000000'), 1), (np.datetime64('2024-12-28T01:45:00.000000'), np.datetime64('2024-12-28T02:15:00.000000'), 1), (np.datetime64('2024-12-29T08:30:00.000000'), np.datetime64('2024-12-29T09:00:00.000000'), 1), (np.datetime64('2024-12-29T09:00:00.000000'), np.datetime64('2024-12-29T09:30:00.000000'), 1), (np.datetime64('2024-12-29T09:45:00.000000'), np.datetime64('2024-12-29T10:15:00.000000'), 1), (np.datetime64('2024-12-29T10:15:00.000000'), np.datetime64('2024-12-29T10:45:00.000000'), 1), (np.datetime64('2024-12-30T17:45:00.000000'), np.datetime64('2024-12-30T18:15:00.000000'), 1), (np.datetime64('2024-12-30T18:45:00.000000'), np.datetime64('2024-12-30T19:15:00.000000'), 1), (np.datetime64('2024-12-30T20:15:00.000000'), np.datetime64('2024-12-30T20:45:00.000000'), 1), (np.datetime64('2024-12-30T20:45:00.000000'), np.datetime64('2024-12-30T21:15:00.000000'), 1), (np.datetime64('2024-12-30T22:00:00.000000'), np.datetime64('2024-12-30T22:30:00.000000'), 1), (np.datetime64('2024-12-30T23:00:00.000000'), np.datetime64('2024-12-30T23:30:00.000000'), 1), (np.datetime64('2024-12-31T01:00:00.000000'), np.datetime64('2024-12-31T01:30:00.000000'), 1), (np.datetime64('2024-12-31T02:00:00.000000'), np.datetime64('2024-12-31T02:30:00.000000'), 1), (np.datetime64('2024-12-31T03:45:00.000000'), np.datetime64('2024-12-31T05:00:00.000000'), 4), (np.datetime64('2024-12-31T05:30:00.000000'), np.datetime64('2024-12-31T06:00:00.000000'), 1), (np.datetime64('2024-12-31T06:15:00.000000'), np.datetime64('2024-12-31T06:45:00.000000'), 1), (np.datetime64('2024-12-31T14:00:00.000000'), np.datetime64('2024-12-31T14:30:00.000000'), 1)]
max_gap_size: 47
data_coverage: 0.935820810564663
minor_gaps: 235
major_gaps: 123
dataset_id: continuous_flow
status: ok
expected_interval: 15MIN
Peak Flow Data¶
Retrieve annual peak flow records — essential for flood frequency analysis and model calibration.
Python
peak_flow = UsgsObservations.get_peak_flow_data(site_id)
print(f"Peak flow records: {len(peak_flow)}")
assert len(peak_flow) > 0, "Should have peak flow records for a major gauge"
print(f"Columns: {list(peak_flow.columns)}")
print(f"\nFirst 5 records:")
peak_flow.head()
Text Only
Peak flow records: 78
Columns: ['datetime', 'value', 'gage_height_ft', 'agency_cd', 'site_no', 'peak_dt', 'peak_tm', 'peak_va', 'peak_cd', 'gage_ht', 'gage_ht_cd', 'year_last_pk', 'ag_dt', 'ag_tm', 'ag_gage_ht', 'ag_gage_ht_cd']
First 5 records:
| datetime | value | gage_height_ft | agency_cd | site_no | peak_dt | peak_tm | peak_va | peak_cd | gage_ht | gage_ht_cd | year_last_pk | ag_dt | ag_tm | ag_gage_ht | ag_gage_ht_cd | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1948-07-26 | 1050 | 9.05 | USGS | 05577500 | 1948-07-26 | NaN | 1050 | NaN | 9.05 | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 1949-01-27 | 639 | 8.14 | USGS | 05577500 | 1949-01-27 | NaN | 639 | NaN | 8.14 | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 1950-01-14 | 855 | 8.85 | USGS | 05577500 | 1950-01-14 | NaN | 855 | NaN | 8.85 | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 1951-06-28 | 3030 | 12.16 | USGS | 05577500 | 1951-06-28 | NaN | 3030 | NaN | 12.16 | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 1952-07-02 | 729 | 8.45 | USGS | 05577500 | 1952-07-02 | NaN | 729 | NaN | 8.45 | NaN | NaN | NaN | NaN | NaN | NaN |
Drainage Area Comparison¶
Compare up to 4 drainage area sources. Agreement status: - close (<=5%): Areas agree well - moderate (<=10%): Minor discrepancies - review (>10%): Needs investigation
Python
drainage_area_review = UsgsDrainageAreaComparison.compare_areas(
gauge_area_sqmi=metadata.get("drainage_area_sqmi"),
official_basin_area_sqmi=125.8,
taudem_area_sqmi=124.9,
model_area_sqmi=126.4,
)
print(f"Reference: {drainage_area_review['reference_label']} "
f"({drainage_area_review['reference_area_sqmi']} sq mi)")
print(f"Agreement status: {drainage_area_review['agreement_status']}")
print(f"Max percent difference: {drainage_area_review['max_abs_percent_difference']:.2f}%")
assert isinstance(drainage_area_review, dict), "Should return dict"
assert 'agreement_status' in drainage_area_review, "Should have agreement_status"
assert drainage_area_review['provided_area_count'] >= 2, "Should compare multiple areas"
print("\n=== Per-Source Comparisons ===")
for key, comp in drainage_area_review['comparisons'].items():
ref_tag = " (REFERENCE)" if comp['is_reference'] else ""
pct = comp['difference_from_reference_percent']
pct_str = f"{pct:+.2f}%" if pct is not None else "--"
print(f" {comp['label']}: {comp['area_sqmi']:.1f} sq mi [{pct_str}]{ref_tag}")
Text Only
Reference: Gauge (107.0 sq mi)
Agreement status: review
Max percent difference: 18.13%
=== Per-Source Comparisons ===
Gauge: 107.0 sq mi [--] (REFERENCE)
Official Basin: 125.8 sq mi [+17.57%]
TauDEM: 124.9 sq mi [+16.73%]
Model: 126.4 sq mi [+18.13%]
Notebook-Local Report Assembly¶
This cell demonstrates how to assemble a complete study report from the primitives above. This is intentionally notebook-local workflow code — not part of the library's public API.
Python
report = {
"study": {
"site_id": site_id,
"start_datetime": start_datetime,
"end_datetime": end_datetime,
},
"gauge_metadata": metadata,
"datasets": {
"continuous_flow": continuous_flow_summary,
"daily_flow": UsgsObservations.summarize_dataset(
dataset_id="daily_flow",
frame=daily_flow,
status="ok" if not daily_flow.empty else "empty",
),
"peak_flow": {"record_count": len(peak_flow), "status": "ok" if not peak_flow.empty else "empty"},
},
"data_gaps": {
"continuous_flow": continuous_flow_gaps,
"daily_flow": UsgsObservations.analyze_gaps(
dataset_id="daily_flow",
frame=daily_flow,
),
},
"drainage_area_review": drainage_area_review,
}
def _to_json_safe(value):
"""Recursively convert non-serializable types for JSON output."""
if isinstance(value, dict):
return {key: _to_json_safe(val) for key, val in value.items()}
if isinstance(value, (list, tuple)):
return [_to_json_safe(item) for item in value]
if hasattr(value, "tolist"):
try:
return _to_json_safe(value.tolist())
except Exception:
pass
if hasattr(value, "item"):
try:
return value.item()
except Exception:
pass
if hasattr(value, "isoformat"):
try:
return value.isoformat()
except Exception:
pass
return value
output_dir = Path("out") / f"USGS_{site_id}_study_package"
output_dir.mkdir(parents=True, exist_ok=True)
report_path = output_dir / "report.json"
report_path.write_text(json.dumps(_to_json_safe(report), indent=2), encoding="utf-8")
print(f"Report written to: {report_path}")
print(f"Report size: {report_path.stat().st_size:,} bytes")
print(f"\nDatasets covered: {list(report['datasets'].keys())}")
print(f"Drainage area agreement: {report['drainage_area_review']['agreement_status']}")
assert report_path.exists(), "Report file should exist"
roundtrip = json.loads(report_path.read_text(encoding='utf-8'))
assert roundtrip['study']['site_id'] == site_id, "Report roundtrip should preserve site_id"
print("\nAll assertions passed.")
Text Only
Report written to: out\USGS_05577500_study_package\report.json
Report size: 43,005 bytes
Datasets covered: ['continuous_flow', 'daily_flow', 'peak_flow']
Drainage area agreement: review
All assertions passed.