Release v0.3.0

.gitignore

@@ -240,5 +240,6 @@ Sessionx.vim
 tags
 # Persistent undo
 [._]*.un~
+
 # Data
-ale
+ale/umbra*

CHANGELOG.md

@@ -6,6 +6,17 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [PEP 440](https://www.python.org/dev/peps/pep-0440/)
 and uses [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.3.0]
+
+### Changed
+* Changed PFA workflow to use a sublclass of the SicdSlc class
+
+### Added
+* Cal/Val scripts for performing absolute geolocation error (ALE) assessment
+
+### Fixed
+* Property assignment issues that caused bugs in the SicdRzdSlc class
+
 ## [0.2.0]
 
 ### Added

ale/ale.py

@@ -0,0 +1,314 @@
+from argparse import ArgumentParser
+from datetime import datetime
+from pathlib import Path
+
+import isce3
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import requests
+from lmfit import Model
+from osgeo import gdal, osr
+from pyproj import Transformer
+from shapely import box
+from shapely.geometry import Point
+from shapely.ops import transform
+
+
+gdal.UseExceptions()
+
+
+def gaussfit(x, y, A, x0, y0, sigma_x, sigma_y, theta):
+    theta = np.radians(theta)
+    sigx2 = sigma_x**2
+    sigy2 = sigma_y**2
+    a = np.cos(theta) ** 2 / (2 * sigx2) + np.sin(theta) ** 2 / (2 * sigy2)
+    b = np.sin(theta) ** 2 / (2 * sigx2) + np.cos(theta) ** 2 / (2 * sigy2)
+    c = np.sin(2 * theta) / (4 * sigx2) - np.sin(2 * theta) / (4 * sigy2)
+
+    expo = -a * (x - x0) ** 2 - b * (y - y0) ** 2 - 2 * c * (x - x0) * (y - y0)
+    return A * np.exp(expo)
+
+
+def get_cr_df(bounds, epsg, date, outdir):
+    rosamond_bounds = [-124.409591, 32.534156, -114.131211, 42.009518]
+    rosamond_bounds = box(*rosamond_bounds)
+    transformer = Transformer.from_crs('EPSG:4326', f'EPSG:{epsg}', always_xy=True)
+    rosamond_bounds = transform(transformer.transform, rosamond_bounds)
+    assert bounds.intersects(rosamond_bounds), f'Images does not intersect with Rosamond bounds {rosamond_bounds}.'
+    date_str = date.strftime('%Y-%m-%d+%H\u0021%M')
+
+    crdata = outdir / f'{date_str.split("+")[0]}_crdata.csv'
+    if not crdata.exists():
+        res = requests.get(
+            f'https://uavsar.jpl.nasa.gov/cgi-bin/corner-reflectors.pl?date={str(date_str)}&project=rosamond_plate_location'
+        )
+        crdata.write_bytes(res.content)
+    cr_df = pd.read_csv(crdata)
+    new_cols = {
+        '   "Corner ID"': 'ID',
+        'Latitude (deg)': 'lat',
+        'Longitude (deg)': 'lon',
+        'Azimuth (deg)': 'azm',
+        'Height Above Ellipsoid (m)': 'hgt',
+        'Side Length (m)': 'slen',
+    }
+    cr_df.rename(columns=new_cols, inplace=True)
+    cr_df.drop(columns=cr_df.keys()[-1], inplace=True)
+    return cr_df
+
+
+def add_image_location(cr_df, epsg, x_start, y_start, x_spacing, y_spacing, bounds):
+    blank = [np.nan] * cr_df.shape[0]
+    blank_bool = [False] * cr_df.shape[0]
+    cr_df = cr_df.assign(
+        UTMx=blank,
+        UTMy=blank,
+        xloc=blank,
+        yloc=blank,
+        xloc_floats=blank,
+        yloc_floats=blank,
+        inPoly=blank_bool,
+    )
+    transformer = Transformer.from_crs('EPSG:4326', f'EPSG:{epsg}', always_xy=True)
+    for idx, row in cr_df.iterrows():
+        row['UTMx'], row['UTMy'] = transformer.transform(row['lon'], row['lat'])
+        row['xloc_floats'] = (row['UTMx'] - x_start) / x_spacing
+        row['xloc'] = int(round(row['xloc_floats']))
+        row['yloc_floats'] = (row['UTMy'] - y_start) / y_spacing
+        row['yloc'] = int(round(row['yloc_floats']))
+        row['inPoly'] = bounds.intersects(Point(row['UTMx'], row['UTMy']))
+        cr_df.iloc[idx] = row
+
+    cr_df = cr_df[cr_df['inPoly']]
+    cr_df.drop('inPoly', axis=1, inplace=True)
+    cr_df = cr_df.reset_index(drop=True)
+    return cr_df
+
+
+def filter_valid_data(cr_df, data):
+    cr_df = cr_df.assign(has_data=False)
+    for idx, row in cr_df.iterrows():
+        xloc = int(row['xloc'])
+        yloc = int(row['yloc'])
+        local_data = data[yloc - 2 : yloc + 2, xloc - 3 : xloc + 3]
+        row['has_data'] = bool(~np.all(np.isnan(local_data)))
+        cr_df.iloc[idx] = row
+    cr_df = cr_df[cr_df['has_data']]
+    cr_df.drop('has_data', axis=1, inplace=True)
+    cr_df = cr_df.reset_index(drop=True)
+    cr_df = cr_df.loc[cr_df['slen'] > 0.8].reset_index(drop=True)  # excluding SWOT CRs (0.7 m as a side length)
+    return cr_df
+
+
+def filter_orientation(cr_df, azimuth_angle):
+    looking_east = azimuth_angle < 180
+    if looking_east:
+        cr_df = cr_df[(cr_df['azm'] < 200) & (cr_df['azm'] > 20)].reset_index(drop=True)
+    else:
+        cr_df = cr_df[cr_df['azm'] > 340].reset_index(drop=True)
+    return cr_df
+
+
+def plot_crs_on_image(cr_df, data, outdir, fileprefix):
+    buffer = 50
+    min_x = cr_df['xloc'].min() - buffer
+    max_x = cr_df['xloc'].max() + buffer
+    min_y = cr_df['yloc'].min() - buffer
+    max_y = cr_df['yloc'].max() + buffer
+
+    fig, ax = plt.subplots(figsize=(15, 7))
+    ax.imshow(data, cmap='gray', interpolation='bilinear', vmin=0.3, vmax=1.7, origin='upper')
+    ax.set_xlim(min_x, max_x)
+    ax.set_ylim(min_y, max_y)
+    ax.axis('off')
+
+    for sl in pd.unique(cr_df.slen):
+        xx = cr_df.loc[cr_df['slen'] == sl]['xloc']
+        yy = cr_df.loc[cr_df['slen'] == sl]['yloc']
+        id = cr_df.loc[cr_df['slen'] == sl]['ID']
+        color = {2.4384: 'blue', 4.8: 'red', 2.8: 'yellow'}.get(sl, 'green')
+        ax.scatter(xx, yy, color=color, marker='o', facecolor='none', lw=1)
+        for id_ann, xx_ann, yy_ann in zip(id, xx, yy):
+            id_ann = f'{int(id_ann)} ({sl:.1f} m)'
+            ax.annotate(id_ann, (xx_ann, yy_ann), fontsize=10, color='grey')
+
+    ax.set_aspect(1)
+    ax.set_title('Corner Reflector Locations')
+    plt.gca().invert_yaxis()
+    fig.savefig(outdir / f'{fileprefix}_CR_locations.png', dpi=300, bbox_inches='tight')
+
+
+def calculate_ale_for_cr(point, data, outdir, fileprefix, search_window=100, oversample_factor=4):
+    ybuff = search_window // 2
+    xbuff = search_window // 2
+    yrange = np.s_[int(point['yloc'] - ybuff) : int(point['yloc'] + ybuff)]
+    xrange = np.s_[int(point['xloc'] - xbuff) : int(point['xloc'] + xbuff)]
+    cropped_data = data[yrange, xrange]
+    yind, xind = np.unravel_index(np.argmax(cropped_data, axis=None), cropped_data.shape)
+
+    center_buff = 32
+    yind_full = int(point['yloc'] - ybuff) + yind
+    xind_full = int(point['xloc'] - xbuff) + xind
+    ycenter = np.s_[int(yind_full - center_buff) : int(yind_full + center_buff)]
+    xcenter = np.s_[int(xind_full - center_buff) : int(xind_full + center_buff)]
+    centered_data = data[ycenter, xcenter]
+
+    data_ovs = isce3.cal.point_target_info.oversample(centered_data, oversample_factor, baseband=True)
+
+    yoff2 = int(data_ovs.shape[0] / 2)
+    xoff2 = int(data_ovs.shape[1] / 2)
+    numpix = 8
+    zoom_half_size = numpix * oversample_factor
+    data_ovs_zoom = data_ovs[
+        yoff2 - zoom_half_size : yoff2 + zoom_half_size, xoff2 - zoom_half_size : xoff2 + zoom_half_size
+    ]
+
+    N = numpix * 2 * oversample_factor
+    x = np.linspace(0, numpix * 2 * oversample_factor - 1, N)
+    y = np.linspace(0, numpix * 2 * oversample_factor - 1, N)
+    Xg, Yg = np.meshgrid(x, y)
+    fmodel = Model(gaussfit, independent_vars=('x', 'y'))
+    theta = 0.1  # deg
+    x0 = numpix * oversample_factor
+    y0 = numpix * oversample_factor
+    sigx = 2
+    sigy = 5
+    A = np.max(data_ovs_zoom)
+    result = fmodel.fit(data_ovs_zoom, x=Xg, y=Yg, A=A, x0=x0, y0=y0, sigma_x=sigx, sigma_y=sigy, theta=theta)
+    fit = fmodel.func(Xg, Yg, **result.best_values)
+
+    ypeak_ovs = result.best_values['y0'] + yoff2 - zoom_half_size
+    ypeak_centered = ypeak_ovs / oversample_factor
+    ypeak = ypeak_centered + yind_full - center_buff
+    point['yloc_cr'] = ypeak
+
+    xpeak_ovs = result.best_values['x0'] + xoff2 - zoom_half_size
+    xpeak_centered = xpeak_ovs / oversample_factor
+    xpeak = xpeak_centered + xind_full - center_buff
+    point['xloc_cr'] = xpeak
+
+    xreal_centered = point['xloc'] - xind_full + center_buff
+    xreal_ovs = xreal_centered * oversample_factor
+
+    yreal_centered = point['yloc'] - yind_full + center_buff
+    yreal_ovs = yreal_centered * oversample_factor
+
+    plt.rcParams.update({'font.size': 14})
+    fig, ax = plt.subplots(1, 3, figsize=(15, 7))
+    ax[0].imshow(centered_data, cmap='gray', interpolation=None, origin='upper')
+    ax[0].plot(xpeak_centered, ypeak_centered, 'r+', label='Return Peak')
+    ax[0].plot(xreal_centered, yreal_centered, 'b+', label='CR Location')
+    ax[0].legend()
+    ax[0].set_title(f'Corner Reflector ID: {int(point["ID"])}')
+    ax[1].imshow(data_ovs, cmap='gray', interpolation=None, origin='upper')
+    ax[1].plot(xpeak_ovs, ypeak_ovs, 'r+')
+    ax[1].plot(xreal_ovs, yreal_ovs, 'b+')
+    ax[1].set_title(f'Oversampled Corner Reflector ID: {point["ID"]}')
+    ax[2].imshow(fit, cmap='gray', interpolation=None, origin='upper')
+    ax[2].plot(result.best_values['x0'], result.best_values['y0'], 'r+')
+    ax[2].set_title(f'Gaussian Fit Corner Reflector ID: {int(point["ID"])}')
+    [axi.axis('off') for axi in ax]
+    fig.tight_layout()
+    fig.savefig(outdir / f'{fileprefix}_CR_{int(point["ID"])}.png', dpi=300, bbox_inches='tight')
+
+    return point
+
+
+def cr_mean(data):
+    return np.round(np.nanmean(data), 3)
+
+
+def cr_spread(data):
+    return np.round(np.nanstd(data) / np.sqrt(np.size(data)), 3)
+
+
+def plot_ale(cr_df, azmangle, outdir, fileprefix):
+    east_ale = cr_df['easting_ale']
+    north_ale = cr_df['northing_ale']
+    ale = cr_df['ale']
+    los = np.deg2rad(90 - azmangle)
+    fig, ax = plt.subplots(figsize=(8, 8))
+    ax.scatter(east_ale, north_ale, s=20, c='k', alpha=0.6, marker='o')
+    ax.annotate(
+        'LOS',
+        xytext=(np.cos(los) * 10, np.sin(los) * 10),
+        xy=(0, 0),
+        arrowprops=dict(edgecolor='darkblue', arrowstyle='<-'),
+        color='darkblue',
+    )
+    ax.grid(True)
+    ax.set_xlim(-15.25, 15.25)
+    ax.set_ylim(-15.25, 15.25)
+    ax.axhline(0, color='black')
+    ax.axvline(0, color='black')
+    east_metric = f'Easting: {cr_mean(east_ale)} +/- {cr_spread(east_ale)} m'
+    north_metric = f'Northing: {cr_mean(north_ale)} +/- {cr_spread(north_ale)} m'
+    overall_metric = f'Overall: {cr_mean(ale)} +/- {cr_spread(ale)} m'
+    ax.set_title(f'{east_metric}, {north_metric}, {overall_metric}', fontsize=10)
+    ax.set_xlabel('Easting Error (m)')
+    ax.set_ylabel('Northing Error (m)')
+    fig.suptitle('Absolute Location Error')
+    plt.savefig(outdir / f'{fileprefix}_ale.png', dpi=300, bbox_inches='tight', transparent=True)
+
+
+def ale(filepath, date, azmangle, outdir, fileprefix):
+    outdir.mkdir(parents=True, exist_ok=True)
+    ds = gdal.Open(str(filepath))
+    data = ds.GetRasterBand(1).ReadAsArray()
+    geotransform = ds.GetGeoTransform()
+    x_start = geotransform[0] + 0.5 * geotransform[1]
+    y_start = geotransform[3] + 0.5 * geotransform[5]
+    x_end = x_start + geotransform[1] * ds.RasterXSize
+    y_end = y_start + geotransform[5] * ds.RasterYSize
+    bounds = (x_start, y_start, x_end, y_end)
+    bounds = box(*bounds)
+    x_spacing = geotransform[1]
+    y_spacing = geotransform[5]
+
+    srs = osr.SpatialReference()
+    srs.ImportFromWkt(ds.GetProjectionRef())
+    epsg = int(srs.GetAuthorityCode(None))
+    cr_df = get_cr_df(bounds, epsg, date, outdir)
+    cr_df = add_image_location(cr_df, epsg, x_start, y_start, x_spacing, y_spacing, bounds)
+    cr_df = filter_valid_data(cr_df, data)
+    cr_df = filter_orientation(cr_df, azmangle)
+    cr_df = cr_df.assign(yloc_cr=np.nan, xloc_cr=np.nan)
+    plot_crs_on_image(cr_df, data, outdir, fileprefix)
+    for idx, cr in cr_df.iterrows():
+        cr = calculate_ale_for_cr(cr, data, outdir, fileprefix)
+        cr_df.iloc[idx] = cr
+
+    cr_df['easting_ale'] = (cr_df['xloc_cr'] - cr_df['xloc_floats']) * x_spacing
+    cr_df['northing_ale'] = (cr_df['yloc_cr'] - cr_df['yloc_floats']) * y_spacing
+    cr_df['ale'] = np.sqrt(cr_df['northing_ale'] ** 2 + cr_df['easting_ale'] ** 2)
+    cr_df.to_csv(outdir / (fileprefix + '_ale.csv'), index=False)
+    plot_ale(cr_df, azmangle, outdir, fileprefix)
+
+
+def main():
+    """Example:
+    python ale.py rtc.tif 2024-12-03 DESC out --fileprefix rtc
+    """
+    parser = ArgumentParser(description='Absolute Location Error Estimation.')
+    parser.add_argument('filepath', type=str, help='Path to the file to be processed')
+    parser.add_argument('date', type=str, help='Date of the image collection (YYYY-MM-DD)')
+    parser.add_argument('azmangle', type=int, help='Azimuth angle of the image (clockwise from North in degrees)')
+    parser.add_argument('outdir', type=str, help='Directory to save the results')
+    parser.add_argument(
+        '--fileprefix', type=str, help='Prefix for the output filenames (default: input filename)', default=None
+    )
+    args = parser.parse_args()
+    args.filepath = Path(args.filepath)
+    args.date = datetime.strptime(args.date, '%Y-%m-%d')
+    assert 0 <= args.azmangle <= 360, f'Azimuth angle {args.azmangle} is out of range [0, 360].'
+    args.outdir = Path(args.outdir)
+    if args.fileprefix is None:
+        args.fileprefix = Path(args.filepath).stem
+    assert args.filepath.exists(), f'File {args.filepath} does not exist.'
+
+    ale(args.filepath, args.date, args.azmangle, args.outdir, args.fileprefix)
+
+
+if __name__ == '__main__':
+    main()

ale/environment.yml

@@ -0,0 +1,20 @@
+name: multirtc-acc
+channels:
+  - conda-forge
+  - nodefaults
+dependencies:
+  # For running
+  - python
+  - pip
+  - gdal>=3.0
+  - numpy>=1.20
+  - isce3
+  - requests
+  - lxml
+  - shapely
+  - pyproj
+  - pandas
+  - matplotlib
+  - lmfit
+  # For static analysis
+  - ruff