Statistics computation
Notebook setup
[33]:
# %matplotlib notebook # does not work in JupyterLab
%matplotlib inline
%load_ext autoreload
%autoreload 2
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
[34]:
import sys
sys.path.append("../..")
[35]:
import numpy as np
from matplotlib import pyplot as plt
[36]:
import s1etad
Product introspection and navigation
[37]:
filename = (
"data/"
"S1A_IW_ETA__AXDV_20230806T211729_20230806T211757_049760_05FBCB_9DD6.SAFE"
)
[38]:
product = s1etad.Sentinel1Etad(filename)
[39]:
product
[39]:
Sentinel1Etad("data/S1A_IW_ETA__AXDV_20230806T211729_20230806T211757_049760_05FBCB_9DD6.SAFE") # 0x7e31b4418b90
Number of Sentinel-1 slices: 1
Sentinel-1 products list:
S1A_IW_SLC__1SDV_20230806T211729_20230806T211757_049760_05FBCB_BC56.SAFE
Number of swaths: 3
Swath list: IW1, IW2, IW3
Azimuth time:
min: 2023-08-06 21:17:29.208211
max: 2023-08-06 21:17:57.184751
Range time:
min: 0.0053335639608434815
max: 0.006389868212274445
Grid sampling:
x: 8.131672451354599e-07
y: 0.02932551319648094
unit: s
Grid spacing:
x: 200.0
y: 200.0
unit: m
Processing settings:
troposphericDelayCorrection: True
troposphericDelayCorrectionGradient: True
ionosphericDelayCorrection: True
solidEarthTideCorrection: True
oceanTidalLoadingCorrection: True
bistaticAzimuthCorrection: True
dopplerShiftRangeCorrection: True
FMMismatchAzimuthCorrection: True
[40]:
swath = product["IW1"]
[41]:
burst = swath[1]
[42]:
t, tau = burst.get_burst_grid()
print("t.shape", t.shape)
print("tau.shape", tau.shape)
t.shape (108,)
tau.shape (402,)
[43]:
correction = burst.get_correction(s1etad.ECorrectionType.SUM, meter=True)
[44]:
rg_correction = correction["x"]
az_correction = correction["y"]
print("rg_correction.shape", rg_correction.shape)
print("az_correction.shape", az_correction.shape)
rg_correction.shape (108, 402)
az_correction.shape (108, 402)
[45]:
plt.figure()
plt.imshow(
correction["x"],
extent=[tau[0] * 1e6, tau[-1] * 1e6, t[0], t[-1]],
aspect="auto",
)
plt.xlabel(r"$\tau\ [\mu s]$")
plt.ylabel("t [s]")
plt.colorbar().set_label("[{}]".format(correction["unit"]))
plt.title("{} correction in range direction".format(correction["name"]))
[45]:
Text(0.5, 1.0, 'sum correction in range direction')
Collect data statistics
NOTE: statistics are also available in the XML annotation file included in the S1-ETAD product
[46]:
import pandas as pd
Initialize dataframes
[47]:
x_corrections_names = [
"tropospheric",
"ionospheric",
"geodetic",
"otl",
"doppler",
"sum",
]
xcols = [("", "bIndex"), ("", "t")] + [
(cname, name)
for cname in x_corrections_names
for name in ("min", "mean", "std", "max")
]
xcols = pd.MultiIndex.from_tuples(xcols)
xstats_df = pd.DataFrame(columns=xcols, dtype=np.float64)
y_corrections_names = ["geodetic", "otl", "bistatic", "fmrate", "sum"]
ycols = [("", "bIndex"), ("", "t")] + [
(cname, name)
for cname in y_corrections_names
for name in ("min", "mean", "std", "max")
]
ycols = pd.MultiIndex.from_tuples(ycols)
ystats_df = pd.DataFrame(columns=ycols, dtype=np.float64)
Collect statistics
[48]:
xrows = []
yrows = []
for swath in product:
for burst in swath:
az, _ = burst.get_burst_grid()
t = np.mean(az[[0, -1]])
# range
row = {("", "bIndex"): burst.burst_index, ("", "t"): t}
for name in x_corrections_names:
data = burst.get_correction(name, meter=True)
row[(name, "min")] = data["x"].min()
row[(name, "mean")] = data["x"].mean()
row[(name, "std")] = data["x"].std()
row[(name, "max")] = data["x"].max()
xrows.append(row)
# azimuth
row = {("", "bIndex"): int(burst.burst_index), ("", "t"): t}
for name in y_corrections_names:
# NOTE: meter is False in this case due to a limitation of the
# current implementation
data = burst.get_correction(name, meter=True)
row[(name, "min")] = data["y"].min()
row[(name, "mean")] = data["y"].mean()
row[(name, "std")] = data["y"].std()
row[(name, "max")] = data["y"].max()
yrows.append(row)
xstats_df = pd.DataFrame(xrows, columns=xcols, dtype=np.float64)
ystats_df = pd.DataFrame(yrows, columns=ycols, dtype=np.float64)
Inspect results
Correction statistics in range direction
[49]:
xstats_df.head()
[49]:
| tropospheric | ionospheric | ... | otl | doppler | sum | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bIndex | t | min | mean | std | max | min | mean | std | max | ... | std | max | min | mean | std | max | min | mean | std | max | |
| 0 | 1.0 | 1.568915 | 3.008053 | 3.234029 | 0.057668 | 3.361616 | 0.298202 | 0.306018 | 0.004509 | 0.314064 | ... | 0.000585 | -0.006255 | -0.387818 | 0.001916 | 0.220776 | 0.392171 | 3.057412 | 3.586854 | 0.221667 | 4.075176 |
| 1 | 4.0 | 4.340176 | 2.771171 | 3.224370 | 0.072341 | 3.361880 | 0.298284 | 0.306125 | 0.004521 | 0.314301 | ... | 0.000518 | -0.006242 | -0.401401 | -0.003059 | 0.225790 | 0.393946 | 3.037196 | 3.572150 | 0.249083 | 4.094163 |
| 2 | 7.0 | 7.096774 | 2.945506 | 3.182714 | 0.056279 | 3.315454 | 0.298378 | 0.306257 | 0.004547 | 0.314288 | ... | 0.000399 | -0.006260 | -0.398034 | -0.004197 | 0.223368 | 0.385729 | 3.009343 | 3.529350 | 0.241088 | 4.048191 |
| 3 | 10.0 | 9.853372 | 2.641564 | 3.144260 | 0.074011 | 3.286178 | 0.298469 | 0.306383 | 0.004561 | 0.314512 | ... | 0.000280 | -0.006294 | -0.391642 | -0.002417 | 0.222298 | 0.385544 | 2.667863 | 3.492359 | 0.259266 | 4.017747 |
| 4 | 13.0 | 12.609971 | 2.602527 | 3.092772 | 0.132585 | 3.268511 | 0.298562 | 0.306522 | 0.004626 | 0.314640 | ... | 0.000256 | -0.006388 | -0.390746 | -0.000781 | 0.223230 | 0.393230 | 2.815424 | 3.441967 | 0.238379 | 3.947523 |
5 rows × 26 columns
Maximum value of corrections in range direction
[50]:
xstats_df.abs().max()
[50]:
bIndex 28.000000
t 26.392962
tropospheric min 3.378197
mean 3.687771
std 0.268407
max 3.821249
ionospheric min 0.331433
mean 0.340329
std 0.005276
max 0.349176
geodetic min 0.066308
mean 0.063517
std 0.001449
max 0.060773
otl min 0.013393
mean 0.012385
std 0.002101
max 0.009958
doppler min 0.475958
mean 0.008562
std 0.268146
max 0.468848
sum min 3.482343
mean 4.072243
std 0.400115
max 4.669038
dtype: float64
Correction statistics in azimuth direction
[51]:
ystats_df.head()
[51]:
| geodetic | otl | ... | bistatic | fmrate | sum | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bIndex | t | min | mean | std | max | min | mean | std | max | ... | std | max | min | mean | std | max | min | mean | std | max | |
| 0 | 1.0 | 1.568915 | 0.033256 | 0.033581 | 0.000147 | 0.033895 | -0.004123 | -0.003900 | 0.000074 | -0.003679 | ... | 0.320736 | -2.338625 | -0.228979 | 0.047914 | 0.068379 | 0.664657 | -3.566790 | -2.772001 | 0.329576 | -2.032584 |
| 1 | 4.0 | 4.340176 | 0.033115 | 0.033442 | 0.000147 | 0.033757 | -0.004002 | -0.003786 | 0.000092 | -0.003583 | ... | 0.320746 | -2.338695 | -0.337562 | 0.069707 | 0.089394 | 0.719700 | -3.689494 | -2.750318 | 0.320929 | -2.096363 |
| 2 | 7.0 | 7.096774 | 0.032976 | 0.033302 | 0.000146 | 0.033618 | -0.003974 | -0.003812 | 0.000070 | -0.003649 | ... | 0.320756 | -2.338765 | -0.270434 | 0.072256 | 0.124514 | 0.547098 | -3.537063 | -2.748021 | 0.330033 | -1.969231 |
| 3 | 10.0 | 9.853372 | 0.032835 | 0.033162 | 0.000146 | 0.033477 | -0.004009 | -0.003881 | 0.000049 | -0.003746 | ... | 0.320765 | -2.338835 | -0.246629 | 0.087287 | 0.175363 | 1.343980 | -3.549836 | -2.733285 | 0.330746 | -1.809654 |
| 4 | 13.0 | 12.609971 | 0.032693 | 0.033020 | 0.000146 | 0.033335 | -0.004021 | -0.003935 | 0.000032 | -0.003879 | ... | 0.320775 | -2.338905 | -0.969977 | 0.029867 | 0.217065 | 0.795282 | -4.123705 | -2.790985 | 0.408657 | -1.822057 |
5 rows × 22 columns
Maximum value of corrections in azimuth direction
[52]:
ystats_df.abs().max()
[52]:
bIndex 28.000000
t 26.392962
geodetic min 0.033960
mean 0.034267
std 0.000153
max 0.034568
otl min 0.005545
mean 0.004978
std 0.000334
max 0.004679
bistatic min 3.447857
mean 2.893556
std 0.380738
max 2.339255
fmrate min 1.127943
mean 0.131468
std 0.280620
max 1.484044
sum min 4.231225
mean 2.792112
std 0.469678
max 2.108454
dtype: float64
Plot
[53]:
iw3 = product["IW3"]
iw3_df = ystats_df[ystats_df[("", "bIndex")].isin(iw3.burst_list)]
t = iw3_df[("", "t")]
fmrate_df = iw3_df.loc[:, "fmrate"]
fmrate_df.insert(0, "t", t)
fmrate_df = fmrate_df.sort_values(by="t")
plt.figure()
fmrate_df.plot(x="t", style="o-")
plt.ylabel("t [s]")
plt.ylabel("[m]")
plt.title("IW3 - FM Rate correction (azimuth)")
plt.grid()
<Figure size 640x480 with 0 Axes>
[54]:
xstats_df.iloc[xstats_df["sum", "max"].abs().argmax()]
[54]:
bIndex 24.000000
t 22.800587
tropospheric min 3.378197
mean 3.676702
std 0.090626
max 3.821066
ionospheric min 0.331312
mean 0.340210
std 0.005075
max 0.349042
geodetic min -0.056184
mean -0.053722
std 0.001248
max -0.051373
otl min -0.012863
mean -0.011922
std 0.001018
max -0.009846
doppler min -0.473726
mean -0.000140
std 0.268146
max 0.468038
sum min 3.435790
mean 4.062204
std 0.278705
max 4.669038
Name: 26, dtype: float64
[55]:
ystats_df.iloc[ystats_df["sum", "max"].abs().argmax()]
[55]:
bIndex 28.000000
t 26.392962
geodetic min 0.031968
mean 0.032294
std 0.000145
max 0.032610
otl min -0.004078
mean -0.003980
std 0.000048
max -0.003893
bistatic min -3.447857
mean -2.893556
std 0.320823
max -2.339255
fmrate min -0.433052
mean 0.062571
std 0.082976
max 0.763479
sum min -3.809156
mean -2.759478
std 0.325230
max -2.108454
Name: 9, dtype: float64
[56]:
xstats_df.loc[xstats_df[""]["bIndex"] == 7]["sum"]
[56]:
| min | mean | std | max | |
|---|---|---|---|---|
| 2 | 3.009343 | 3.52935 | 0.241088 | 4.048191 |
[57]:
ystats_df.loc[ystats_df[""]["bIndex"] == 4]["sum"]
[57]:
| min | mean | std | max | |
|---|---|---|---|---|
| 1 | -3.689494 | -2.750318 | 0.320929 | -2.096363 |