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• Current release: 1.0.8
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• 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, VWP, and gridded horizontal wind volumes
• improved multi-radar compositing and reference-style CR plotting
• read_auto and read_PA now correctly recognize and build supported PA files
• plot_ppi, plot_ppi_map, and the legacy Graph / GraphMap plotting paths now auto-register pycwr colormap names such as CN_ref and CN_vel
• the PA reader implementation is cleaner internally, with explicit helpers for moment decoding, field padding, and range construction

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python -m pip install pycwr

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python -m pip install "pycwr[full]"

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

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

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• pycwr 1.0.8 requires Python >=3.9
• python -m pip install pycwr is the recommended path for normal users
• 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 1.0.8 for release stability
• source install is still useful when you are developing locally or rebuilding the Cython extension

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python setup.py build_ext --inplace

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python -m build

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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])

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

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from pycwr.draw import plot_ppi plot_ppi(radar, field="dBZ", sweep=0, show=True)

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from pycwr.draw import plot_ppi_map plot_ppi_map(radar, field="dBZ", sweep=0, show=True)

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from pycwr.draw import plot_section plot_section(radar, start=(-50, 0), end=(50, 0), field="dBZ", show=True)

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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)

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• 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

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• 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)

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• aligned: historical shared grid used by old processing chains
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• range_mode="aligned" for historical compatibility
• range_mode="native" when full low-level reflectivity coverage matters

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

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

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• dBZ uses the reflectivity palette such as CN_ref
• V uses the velocity palette such as CN_vel
• HCL uses a discrete hydrometeor classification palette
• unknown fields fall back to viridis

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from pycwr.draw import plot_ppi, plot_ppi_map plot_ppi(radar, field="dBZ", sweep=0, show=True) plot_ppi_map(radar, field="dBZ", sweep=0, show=True)

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plot_ppi(radar, field="dBZ", sweep=0, cmap="CN_ref", cmap_bins=16, show=True) plot_ppi_map(radar, field="V", sweep=0, cmap="CN_vel", min_max=(-27.0, 27.0), show=True)

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from pycwr.draw.RadarPlot import GraphMap from cartopy import crs as ccrs display = GraphMap(radar, ccrs.PlateCarree()) display.plot_ppi_map(ax, 0, "dBZ", cmap="CN_ref")

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• CN_ref, CN_vel, and the other pycwr colormap names are registered automatically when plotting starts. You no longer need to "warm up" the plotting module first.
• If a field is missing in the radar file, plotting raises a field-not-found error. For example, some files may contain dBZ but not V.
• If you need the raw colormap registry for custom matplotlib work, you can explicitly import pycwr.draw.colormap.

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• 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

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

from pycwr.qc import apply_dualpol_qc qc_radar = apply_dualpol_qc(radar, inplace=False, clear_air_mode="mask")

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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", )

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• 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
• retrieve_wind_volume_xy / retrieve_wind_volume_lonlat: fixed-height gridded horizontal wind volume built from multiple VVP sweeps

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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) wind = radar.retrieve_wind_volume_xy( XRange=np.arange(-20_000.0, 20_001.0, 10_000.0), YRange=np.arange(-20_000.0, 20_001.0, 10_000.0), level_heights=np.array([500.0, 1000.0, 1500.0]), sweeps=[0, 1, 2], max_range_km=30.0, )

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

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radar.add_product_WIND_VOLUME_xy( XRange=np.arange(-20_000.0, 20_001.0, 10_000.0), YRange=np.arange(-20_000.0, 20_001.0, 10_000.0), level_heights=np.array([500.0, 1000.0, 1500.0]), sweeps=[0, 1, 2], )

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• gridded u/v wind on multiple height levels
• one wind profile for each horizontal grid point
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• a full dynamic u/v/w retrieval
• a dual-Doppler or multi-radar variational wind analysis
• a substitute for vertical motion retrieval in convective dynamics studies

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1. For each requested sweep, pycwr selects the velocity field, preferring corrected velocity when available.
2. A local VVP is solved on each sweep with a moving azimuth-range window.
3. Each sweep is summarized and, when sweeps=None or sweeps="auto", the strongest sweeps are selected automatically.
4. The valid sweep-level VVP samples are interpolated horizontally onto the target grid with inverse-distance weighting.
5. Fixed-height wind values are reconstructed from the selected sweeps with local vertical aggregation or short-gap linear interpolation.
6. Diagnostics such as fit residual, support counts, and heuristic quality score are stored with the result.

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• radar.retrieve_wind_volume_xy(...) for Cartesian x/y/z
• radar.retrieve_wind_volume_lonlat(...) for regular lon/lat grids
• radar.add_product_WIND_VOLUME_xy(...) or radar.add_product_WIND_VOLUME_lonlat(...) to store the result in radar.product

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• use sweeps=None to enable auto sweep selection
• use sweeps="all" only when you explicitly want every available sweep
• set a realistic max_range_km instead of using the whole radar volume
• increase workers on multi-core machines when the target grid is not tiny

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wind = radar.retrieve_wind_volume_xy( XRange=np.arange(-60_000.0, 60_001.0, 5_000.0), YRange=np.arange(-60_000.0, 60_001.0, 5_000.0), level_heights=np.arange(500.0, 3_500.0, 500.0), sweeps=None, max_range_km=60.0, az_num=31, bin_num=5, azimuth_step=12, range_step=6, horizontal_radius_m=8_000.0, max_horizontal_radius_m=14_000.0, horizontal_min_neighbors=3, vertical_tolerance_m=400.0, max_vertical_gap_m=1_200.0, workers=4, )

• XRange, YRange, level_heights: target output grid in meters
• XLon, YLat: target output grid in degrees for lon/lat mode
• sweeps: None or "auto" enables auto selection; "all" keeps all sweeps; a list such as [0, 1, 2] forces explicit sweeps
• max_range_km: strongest first-order control for retrieval stability
• az_num, bin_num: local VVP window size
• azimuth_step, range_step: output thinning of the local sweep VVP
• horizontal_radius_m: horizontal radius used when mapping sweep VVP samples to the target grid
• max_horizontal_radius_m: optional expanded search radius when local support is sparse
• vertical_tolerance_m: local height matching tolerance
• max_vertical_gap_m: maximum allowed vertical interpolation gap
• workers: process-based parallelism for sweep-level and grid-level work

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• use a wider az_num when velocity is sparse or noisy
• keep bin_num modest; too small is noisy and too large oversmooths
• do not push max_range_km farther than the range where velocity remains reasonably continuous
• on sparse cases, it is better to slightly increase search radius than to force too many poor sweeps

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• u, v: eastward and northward wind components
• wind_speed, wind_direction
• fit_rmse: local retrieval residual
• valid_count: effective sample count
• source_sweep_count: number of sweeps contributing to the voxel
• neighbor_count: horizontal support count
• effective_vertical_support: number of vertically supporting sweep samples
• sweep_spread_m: vertical spread of supporting sweeps
• quality_score: heuristic score from 0 to 100
• quality_flag: 0/1/2/3 for invalid, low-confidence, usable, and high-confidence voxels

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• selection_mode
• requested_sweeps
• selected_sweeps
• rejected_sweeps
• rejected_sweep_reasons
• workers_used
• parallel_mode

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• no precipitation or very weak echo, so there is no valid Doppler velocity
• poor azimuth coverage in the local window
• sweep geometry that does not support the requested height well
• too aggressive a range limit or too small a horizontal search radius

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• quality_score and quality_flag
• fit_rmse
• valid_count
• source_sweep_count
• selected_sweeps and rejected_sweep_reasons

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• small grids may see little benefit
• moderate and large grids usually benefit from workers=2 or workers=4
• very large worker counts may stop scaling once process overhead dominates

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

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from pycwr.interp import run_radar_network_3d

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from pycwr.GraphicalInterface import create_app, launch

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• Short Personal Blog Examples: runnable examples by feature
• Letter Of Intent To Purchase Car: plotting-focused entry points
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• Web viewer: launch it locally with python scripts/LaunchGUI.py

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• 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, including gridded horizontal wind volumes
• multi-radar compositing has a documented high-level workflow
• packaging is split into base and full dependency sets for lower installation friction
• PA samples matching the supported format markers are routed through read_PA instead of being misidentified as WSR98D
• default plotting now resolves pycwr colormap names consistently across both the easy plotting API and legacy class-style plotting