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#!/usr/bin/env python3 import numpy as np import matplotlib.pyplot as plt import scipy import pandas as pd import glob import os import sys import argparse from osgeo import gdal from scipy import stats from pathlib import Path class OpenTif: """ a Class that stores the band array and metadata of a Gtiff file.""" def __init__(self, filename): self.ds = gdal.Open(filename) self.basename = os.path.splitext(os.path.basename(filename))[0] self.band = self.ds.GetRasterBand(1) self.data = self.band.ReadAsArray() self.xsize = self.ds.RasterXSize self.ysize = self.ds.RasterYSize self.left = self.ds.GetGeoTransform()[0] self.top = self.ds.GetGeoTransform()[3] self.xres = self.ds.GetGeoTransform()[1] self.yres = self.ds.GetGeoTransform()[5] self.right = self.left + self.xsize * self.xres self.bottom = self.top + self.ysize * self.yres self.projection = self.ds.GetProjection() pix_lin, pix_col = np.indices((self.ds.RasterYSize, self.ds.RasterXSize)) self.lat, self.lon = self.top + self.yres*pix_lin, self.left+self.xres*pix_col if __name__ == "__main__": # parse frame name as argument parser = argparse.ArgumentParser(description="Detect coregistration error") parser.add_argument("frame", help="frame name") args = parser.parse_args() frame = args.frame track = str(int(frame[0:3])) input_dir = "/gws/ssde/j25a/nceo_geohazards/vol1/public/LiCSAR_products/{}/{}/interferograms/".format(track, frame) output_dir = frame Path(frame).mkdir(parents=True, exist_ok=True) Path(frame+"/png").mkdir(parents=True, exist_ok=True) stats_file = "{}/{}_middle_column_stats.txt".format(frame, frame) if os.path.exists(stats_file): os.remove(stats_file) with open(stats_file, "a") as f: f.write("Frame Pair Slope R_squares Abs_slope\n") for unw in glob.glob(input_dir+"*/*.geo.unw.tif"): tif=OpenTif(unw) pair = unw.split('/')[-1][:17] print(pair, frame) fig, ax=plt.subplots(1,2, figsize=(6, 3)) ax[0].imshow(tif.data, vmin = np.nanpercentile(tif.data, 0.5), vmax = np.nanpercentile(tif.data, 99.5)) ax[0].plot([tif.xsize // 2,tif.xsize // 2], [0, tif.ysize+1]) ax[0].set_title(pair) middle_column = tif.data[:, tif.xsize // 2] middle_column_latitudes = tif.lat[:, tif.xsize // 2] ax[1].plot(middle_column_latitudes, middle_column) non_nan_mask = ~np.isnan(middle_column) slope, intercept, r_value, p_value, std_err = stats.linregress(middle_column_latitudes[non_nan_mask], middle_column[non_nan_mask]) ax[1].annotate("Phase = {:.2f} latitude + {:.2f} \n$R^2$ = {:.3f}".format(slope, intercept, r_value**2), xy=(0.05, 0.965), xycoords='axes fraction', ha='left', va='top', zorder=10) ax[1].plot(middle_column_latitudes, middle_column_latitudes*slope+intercept) ax[1].set_xlabel("Latitude") ax[1].set_ylabel("Phase / mm") ax[1].set_title("Middle Column") plt.tight_layout() plt.savefig("{}/png/{}_middle_column_{}.png".format(frame, frame, pair), dpi=500) plt.close() del tif, middle_column, middle_column_latitudes, non_nan_mask f.write("{} {} {} {} {}\n".format(frame, pair, slope, r_value**2, abs(slope))) f.close() # read text file into pandas DataFrame df = pd.read_csv("{}/{}_middle_column_stats.txt".format(frame, frame), sep=" ") plt.hist2d(df["Abs_slope"], df["R_squares"], bins=20, cmin=1) plt.title(frame) plt.xlabel("|slope|") plt.ylabel("R^2") plt.xlim(0,100) plt.ylim(0,1) plt.savefig("{}/{}_middle_column_hist2d.png".format(frame, frame)) plt.close() df['Epoch1'] = df['Pair'].str[:8] df['date1'] = pd.to_datetime(df['Epoch1'], format='%Y%m%d') fig, axes = plt.subplots(2) df.plot.scatter(x='date1', y='Abs_slope', c='R_squares', colormap='viridis', ax=axes[0]) df.plot.scatter(x='date1', y='R_squares', c='Abs_slope', colormap='viridis', ax=axes[1]) axes[0].set_ylim(0, 100) axes[1].set_ylim(0, 1) axes[0].set_xlabel("") plt.suptitle(frame) plt.savefig("{}/{}_scatter.png".format(frame, frame)) plt.close()