pycwr 1.0.5

pycwr 1.0.5: high-compatibility Chinese weather radar IO, geometry, QC, plotting, and compositing

pycwr

pycwr is a Python toolkit for operational Chinese weather radar workflows. It covers radar base-data reading, geometry, plotting, quality control, hydrometeor classification, single-radar wind retrieval, multi-radar compositing, and export.

• Current release: 1.0.5
• 中文说明
• API reference
• Test guide
• Draw quickstart
• Web viewer quickstart
• Contributors

Why 1.0.5 matters

1.0.5 continues the first stable release line intended to be usable for production-style usage and GitHub distribution. This refresh also includes the Wc variable update in the public release line.

Highlights:

• compatibility-focused readers for common Chinese weather radar formats
• clearer PRD data model for sweep inspection and downstream processing
• aligned and native reflectivity access kept side by side when low-level reflectivity range is longer than Doppler range
• lighter default dependencies with plotting, QC, web viewer, and interop isolated into the optional full stack
• stronger geometry and regression coverage
• built-in single-radar wind retrieval workflows: VAD, VVP, and VWP
• improved multi-radar compositing and reference-style CR plotting

Installation

Base install:

python -m pip install -r requirements-core.txt
python -m pip install .

Full install:

python -m pip install -r requirements-full.txt
python -m pip install ".[full]"

Notes:

• pycwr 1.0.5 requires Python >=3.9
• base install is enough for readers, PRD, geometry, interpolation, and NetCDF-style export
• full install is recommended for plotting, map plotting, QC, Py-ART/xradar interop, and the web viewer
• upstream arm_pyart and xradar currently require Python >=3.10, so on Python 3.9 the full install still covers plotting, QC, and the web viewer, but not those two optional interop stacks
• pandas is pinned to <3 in 1.0.5 for release stability

Rebuild the extension after editing pycwr/core/RadarGridC.pyx:

python setup.py build_ext --inplace

Build release artifacts:

python -m build

5-minute quickstart

Read one radar file and inspect the returned volume:

from pycwr.io import read_auto

radar = read_auto("Z_RADR_I_Z9046_20260317065928_O_DOR_SAD_CAP_FMT.bin.bz2")
print(radar.summary())
print(radar.available_fields())
print(radar.sweep_summary()[0])

Get one field from one sweep:

dBZ0 = radar.get_sweep_field(0, "dBZ")
velocity0 = radar.get_sweep_field(0, "V")

Plot a PPI:

from pycwr.draw import plot_ppi

plot_ppi(radar, field="dBZ", sweep=0, show=True)

Extract a vertical section:

from pycwr.draw import plot_section

plot_section(radar, start=(-50, 0), end=(50, 0), field="dBZ", show=True)

Generate a simple product:

import numpy as np

x = np.arange(-150_000.0, 150_001.0, 1_000.0)
y = np.arange(-150_000.0, 150_001.0, 1_000.0)
radar.add_product_CR_xy(x, y)
print(radar.product)

API map

Module	What it is for	Recommended starting points
pycwr.io	Read and write radar base data	read_auto, read_WSR98D, read_SAB, read_CC, read_SC, read_PA
pycwr.core	Central volume object, geometry, export helpers	PRD, radar.summary(), radar.get_sweep_field()
pycwr.draw	Plotting and quick-look figures	plot_ppi, plot_ppi_map, plot_rhi, plot_section, plot_vvp, plot_wind_profile
pycwr.qc	Dual-pol QC	apply_dualpol_qc, run_dualpol_qc
pycwr.retrieve	Hydrometeor and wind retrieval	classify_hydrometeors, retrieve_vad, retrieve_vvp, retrieve_vwp
pycwr.interp	Multi-radar compositing	run_radar_network_3d
pycwr.GraphicalInterface	Local web viewer	create_app, launch

Core object model

All readers return pycwr.core.NRadar.PRD.

The most important parts of PRD are:

• fields: one xarray.Dataset per sweep
• scan_info: site and scan metadata
• extended_fields: native sidecar fields when aligned and native ranges differ
• product: computed product dataset

Useful inspection helpers:

• summary(): compact full-volume summary
• available_fields(sweep=None, range_mode="aligned")
• sweep_summary()
• get_sweep_field(sweep, field_name, range_mode="aligned", sort_by_azimuth=False)
• get_native_sweep_field(sweep, field_name)
• ordered_az(inplace=False)

Aligned vs native reflectivity

For some low sweeps, reflectivity may exist in two forms:

• aligned: historical shared grid used by old processing chains
• native: original reflectivity range before Doppler-driven truncation

Use:

• range_mode="aligned" for historical compatibility
• range_mode="native" when full low-level reflectivity coverage matters

Example:

aligned = radar.get_sweep_field(0, "dBZ", range_mode="aligned")
native = radar.get_sweep_field(0, "dBZ", range_mode="native")

Common workflows

Plotting

Recommended public plotting APIs:

from pycwr.draw import (
    plot,
    plot_ppi,
    plot_ppi_map,
    plot_rhi,
    plot_section,
    plot_section_lonlat,
    plot_vvp,
    plot_wind_profile,
)

These functions return an EasyPlotResult with fig, ax, and artist.

Products

Common PRD product methods:

• add_product_CR_xy
• add_product_CAPPI_xy
• add_product_CAPPI_3d_xy
• add_product_VIL_xy
• add_product_ET_xy
• add_product_CR_lonlat
• add_product_CAPPI_lonlat
• add_product_VIL_lonlat
• add_product_ET_lonlat
• add_product_VWP

Example:

radar.add_product_CAPPI_xy(x, y, 3000.0)
radar.add_product_VIL_xy(x, y, [1000.0, 2000.0, 3000.0])

Quality control

from pycwr.qc import apply_dualpol_qc

qc_radar = apply_dualpol_qc(radar, inplace=False, clear_air_mode="mask")

Common corrected fields include Zc, ZDRc, PhiDPc, KDPc, and mask fields such as QC_MASK and CLEAR_AIR_MASK when enabled.

Hydrometeor classification

hcl_radar = radar.classify_hydrometeors(
    inplace=False,
    band="C",
    profile_height=[0.0, 2000.0, 4000.0, 8000.0],
    profile_temperature=[24.0, 12.0, 2.0, -16.0],
    confidence_field="HCL_CONF",
)

You can also classify directly from arrays with pycwr.retrieve.classify_hydrometeors(...).

Wind retrieval

pycwr ships three single-radar wind workflows:

• retrieve_vad: ring-wise harmonic fit on one or more sweeps
• retrieve_vvp: local least-squares horizontal wind retrieval on one sweep
• retrieve_vwp: vertical wind profile built from multiple VAD layers

Example:

vad = radar.retrieve_vad(sweeps=[0, 1, 2], max_range_km=40.0, gate_step=4)
vvp = radar.retrieve_vvp(0, max_range_km=20.0, az_num=91, bin_num=5)
vwp = radar.retrieve_vwp(sweeps=[0, 1, 2], max_range_km=40.0, height_step=500.0)

The stored profile product path is:

radar.add_product_VWP(sweeps=[0, 1, 2], max_range_km=40.0, height_step=500.0)

Export and interop

Common export helpers:

• radar.to_wsr98d(...)
• radar.to_nexrad_level2_msg31(...)
• radar.to_nexrad_level2_msg1(...)
• radar.to_pyart_radar(...)
• radar.to_xradar(...)
• radar.to_cfgridded_netcdf(...)

Use these when you need to move pycwr data into Py-ART, xradar, or other NetCDF-style workflows.

Multi-radar compositing

from pycwr.interp import run_radar_network_3d

This entry point builds 3D network products on a regular lon/lat grid and can write NetCDF directly. It is the recommended high-level interface for network CR and CAPPI workflows.

Web viewer

from pycwr.GraphicalInterface import create_app, launch

Or use the script:

python scripts/LaunchGUI.py

The viewer is local-only by design and requires a token for API access.

What to read next

• API reference: detailed public interface notes
• Test guide: runnable examples by feature
• Draw quickstart: plotting-focused entry points
• Web viewer quickstart: local viewer setup

Release notes for users

For 1.0.5, the most important user-visible behavior rules are:

• radar reading returns one stable PRD object across supported formats
• low-level reflectivity can now be queried explicitly in aligned or native mode
• QC and hydrometeor classification can write corrected fields back into PRD
• single-radar wind retrieval is now part of the public API
• multi-radar compositing has a documented high-level workflow
• packaging is split into base and full dependency sets for lower installation friction