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scarputils.py
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scarputils.py
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"""
Utilities for post-processing and spatial analysis of scarp template matching
results
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.ndimage import zoom
from osgeo import gdal, osr
from copy import copy
def calculate_local_swath_orientation(data):
first = data[0]
idx = np.where(~np.isnan(first))
x0 = (idx.max() + idx.min()) / 2.
y0 = 0
v0 = np.array([x0, y0])
last = data[-1]
idx = np.where(~np.isnan(last))
x1 = (idx.max() + idx.min()) / 2.
y1 = -(data.shape[0] - 1)
v1 = np.array([x1, y1])
trend = v1 - v0 / np.norm(v1 - v0)
ex = np.array([1, 0])
theta = np.acos(trend.dot(ex))
return -(np.pi / 2 - theta)
def extract_swath_profile(data, nrow, ncol, length=1000, de=1, alpha=None):
if not alpha:
alpha = calculate_local_swath_orientation(data) + np.pi / 2
lx = (length * de / 2) * np.cos(alpha)
ly = (length * de / 2) * np.sin(alpha)
x = np.linspace(ncol - lx, ncol + lx)
y = np.linspace(nrow - ly, nrow + ly)
profile = data[y, x]
def load_masked_results(data_filename, snr_filename):
snr = gdal.Open(snr_filename)
snr = snr.GetRasterBand(1).ReadAsArray()
mask = snr > 100
del snr
data = gdal.Open(data_filename)
data = data.GetRasterBand(1).ReadAsArray()
data[data == 0] = np.nan
data[~mask] = np.nan
return data
def plot_polar_scatterplot(theta, data):
colors = data
ax = plt.subplot(111, projection='polar')
c = ax.scatter(theta, data, c=colors, cmap='viridis', alpha=0.75)
def plot_polar_histogram(theta, data, nbins=20):
#radii =
colors = radii
ax = plt.subplot(111, projection='polar')
c = ax.bars(theta, radii, c=colors, cmap='viridis', alpha=0.75)
def plot_violinplot(data):
ax = plt.subplot(111)
c = ax.violinplot(data, showmeans=true, showmedians=true)
def plot_distribution_ns(data, smoothing_length=None, de=1):
nrows = data.shape[0]
mean = np.zeros((nrows,))
sd = np.zeros((nrows,))
med = np.zeros((nrows,))
for i, row in enumerate(data):
mean[i] = np.nanmean(row)
sd[i] = np.nanstd(row)
med[i] = np.nanmedian(row)
for param in mean, sd, med:
param[np.isnan(param)] = 0
if smoothing_length:
n = float(smoothing_length / de)
kern = (1 / n) * np.ones((int(n),))
mean = np.convolve(mean, kern, mode='same')
sd = np.convolve(sd, kern, mode='same')
med = np.convolve(sd, kern, mode='same')
fig = plt.figure()
#ax = fig.add_subplot(211)
#imdata = zoom(data, 0.25, order=0)
#imdata[imdata == 9999] = np.nan
#im = ax.imshow(np.flipud(np.rot90(np.rot90(imdata)).T), cmap='viridis', aspect='auto')
#ax.tick_params(labelbottom='off', labelleft='off')
#cbar = plt.colorbar(im, shrink=0.5, orientation='horizontal')
#cbar.ax.set_xlabel('Amplitude [m]')
ax = fig.add_subplot(212)
x = de * np.arange(len(mean))
ax.fill_between(x, med - sd, med + sd, color=[0.5, 0.5, 0.5], alpha=0.5)
ax.plot(x, med, color=[1, 0, 0], alpha=0.75)
ax.set_xlabel('Along-swath distance [m]')
ax.set_ylabel('log$_{10}$($\kappa t$) [m$^2$]')
ymax = (med + sd).max()
#ax.set_ylim(0, ymax + 10)
ax.set_xlim(0, x.max())
def mask_results(results, ang_average, ang_tol=20*(np.pi/180), amp_thresh=0.1, age_thresh=10):
ang_mask = np.abs(results[2,:,:] - ang_average) > ang_tol
amp_mask = np.abs(results[0,:,:]) <= amp_thresh
age_mask = results[1,:,:] < age_thresh
results[:, ang_mask] = np.nan
results[:, amp_mask] = np.nan
results[:, age_mask] = np.nan
snr_thresh = np.median(results[3,:,:])
snr_mask = results[3,:,:] <= snr_thresh
def calculate_alpha_band(results, snr_min, snr_max=1000):
snr = copy(results[3])
if snr_max < np.nanmax(snr):
snr[snr > snr_max] = snr_max
alpha = (snr - snr_min) / (snr_max - snr_min)
return alpha
def write_tiff(filename, array, alpha, data_file):
nbands = 2
nrows, ncols = array.shape
inraster = gdal.Open(data_file)
transform = inraster.GetGeoTransform()
driver = gdal.GetDriverByName('GTiff')
outraster = driver.Create(filename, ncols, nrows, nbands, gdal.GDT_Float32)
outraster.SetGeoTransform(transform)
out_band = outraster.GetRasterBand(1)
out_band.WriteArray(array)
out_band.SetNoDataValue(np.nan)
out_band.FlushCache()
out_band = outraster.GetRasterBand(2)
out_band.WriteArray(alpha)
out_band.SetNoDataValue(np.nan)
out_band.FlushCache()
srs = osr.SpatialReference()
srs.ImportFromWkt(inraster.GetProjectionRef())
outraster.SetProjection(srs.ExportToWkt())