Statistics computation
Notebook setup
[1]:
# %matplotlib notebook # does not work in JupyterLab
%matplotlib inline
%load_ext autoreload
%autoreload 2
[2]:
import sys
sys.path.append("../..")
[3]:
import numpy as np
from matplotlib import pyplot as plt
[4]:
import s1etad
Product introspection and navigation
[5]:
filename = (
"data/"
"S1B_IW_ETA__AXDV_20200124T221416_20200124T221444_019964_025C43_0A63.SAFE"
)
[6]:
product = s1etad.Sentinel1Etad(filename)
[7]:
product
[7]:
Sentinel1Etad("data/S1B_IW_ETA__AXDV_20200124T221416_20200124T221444_019964_025C43_0A63.SAFE") # 0x75406c222ba0
Number of Sentinel-1 slices: 1
Sentinel-1 products list:
S1B_IW_SLC__1ADV_20200124T221416_20200124T221444_019964_025C43_95FB.SAFE
Number of swaths: 3
Swath list: IW1, IW2, IW3
Azimuth time:
min: 2020-01-24 22:14:16.480938
max: 2020-01-24 22:14:44.428152
Range time:
min: 0.005328684957372668
max: 0.006383362874313361
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
ionosphericDelayCorrection: True
solidEarthTideCorrection: True
bistaticAzimuthCorrection: True
dopplerShiftRangeCorrection: True
FMMismatchAzimuthCorrection: True
[8]:
swath = product["IW1"]
[9]:
burst = swath[1]
[10]:
t, tau = burst.get_burst_grid()
print("t.shape", t.shape)
print("tau.shape", tau.shape)
t.shape (108,)
tau.shape (402,)
[11]:
correction = burst.get_correction(s1etad.ECorrectionType.SUM, meter=True)
[12]:
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)
[13]:
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"]))
[13]:
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
[14]:
import pandas as pd
Initialize dataframes
[15]:
x_corrections_names = [
"tropospheric",
"ionospheric",
"geodetic",
"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", "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
[16]:
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
[17]:
xstats_df.head()
[17]:
| tropospheric | ionospheric | ... | geodetic | 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 | 2.991868 | 3.125494 | 0.051849 | 3.247490 | 0.287995 | 0.296082 | 0.004669 | 0.304159 | ... | 0.002253 | 0.130591 | -0.392571 | -0.000340 | 0.223336 | 0.393251 | 3.035098 | 3.557500 | 0.228950 | 4.043201 |
| 1 | 4.0 | 4.325513 | 2.968199 | 3.121315 | 0.058543 | 3.239469 | 0.287955 | 0.296054 | 0.004664 | 0.304149 | ... | 0.002250 | 0.130647 | -0.391074 | -0.000983 | 0.222059 | 0.388200 | 3.019752 | 3.552707 | 0.234305 | 4.047338 |
| 2 | 7.0 | 7.082111 | 2.953548 | 3.106764 | 0.066205 | 3.239229 | 0.287941 | 0.296030 | 0.004660 | 0.304127 | ... | 0.002246 | 0.130691 | -0.390572 | -0.000135 | 0.222517 | 0.390753 | 2.998921 | 3.539033 | 0.236990 | 4.031289 |
| 3 | 10.0 | 9.838710 | 2.951492 | 3.097788 | 0.059333 | 3.234565 | 0.287908 | 0.295998 | 0.004667 | 0.304083 | ... | 0.002248 | 0.130748 | -0.390581 | 0.000236 | 0.222655 | 0.391716 | 2.995706 | 3.530451 | 0.234525 | 4.023035 |
| 4 | 13.0 | 12.595308 | 2.946268 | 3.086150 | 0.063749 | 3.230967 | 0.287858 | 0.295965 | 0.004664 | 0.304046 | ... | 0.002246 | 0.130800 | -0.390005 | 0.000818 | 0.222826 | 0.392551 | 2.996350 | 3.519417 | 0.235051 | 4.016111 |
5 rows × 22 columns
Maximum value of corrections in range direction
[18]:
xstats_df.abs().max()
[18]:
bIndex 28.000000
t 26.378299
tropospheric min 3.394855
mean 3.554976
std 0.081620
max 3.705028
ionospheric min 0.321480
mean 0.330572
std 0.005515
max 0.339645
geodetic min 0.123262
mean 0.127102
std 0.002358
max 0.131108
doppler min 0.470995
mean 0.003383
std 0.269357
max 0.475423
sum min 3.431760
mean 4.005704
std 0.286521
max 4.595461
dtype: float64
Correction statistics in azimuth direction
[19]:
ystats_df.head()
[19]:
| geodetic | bistatic | fmrate | sum | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bIndex | t | min | mean | std | max | min | mean | std | max | min | mean | std | max | min | mean | std | max | |
| 0 | 1.0 | 1.568915 | 0.000086 | 0.000235 | 0.000078 | 0.000377 | -3.432131 | -2.875080 | 0.322415 | -2.318029 | -0.231449 | -0.000281 | 0.044394 | 0.172403 | -3.914210 | -3.214748 | 0.331061 | -2.558306 |
| 1 | 4.0 | 4.325513 | 0.000103 | 0.000252 | 0.000078 | 0.000394 | -3.432070 | -2.875029 | 0.322409 | -2.317988 | -0.216776 | 0.002960 | 0.036497 | 0.189662 | -3.872442 | -3.211433 | 0.323444 | -2.546432 |
| 2 | 7.0 | 7.082111 | 0.000121 | 0.000270 | 0.000078 | 0.000411 | -3.432010 | -2.874978 | 0.322403 | -2.317947 | -0.182971 | 0.002239 | 0.042057 | 0.203343 | -3.916885 | -3.212079 | 0.329247 | -2.462662 |
| 3 | 10.0 | 9.838710 | 0.000139 | 0.000287 | 0.000079 | 0.000430 | -3.431949 | -2.874927 | 0.322397 | -2.317906 | -0.216336 | 0.001052 | 0.046356 | 0.267493 | -3.921901 | -3.213192 | 0.320566 | -2.477026 |
| 4 | 13.0 | 12.595308 | 0.000156 | 0.000305 | 0.000079 | 0.000447 | -3.431887 | -2.874876 | 0.322392 | -2.317865 | -0.233642 | -0.002473 | 0.040933 | 0.156730 | -3.964584 | -3.216642 | 0.326327 | -2.504485 |
Maximum value of corrections in azimuth direction
[20]:
ystats_df.abs().max()
[20]:
bIndex 28.000000
t 26.378299
geodetic min 0.000768
mean 0.000906
std 0.000082
max 0.001040
bistatic min 3.432131
mean 2.875080
std 0.381417
max 2.318029
fmrate min 0.294991
mean 0.013260
std 0.064609
max 0.278755
sum min 3.999128
mean 3.227354
std 0.391009
max 2.642278
dtype: float64
Plot
[21]:
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>
[22]:
xstats_df.iloc[xstats_df["sum", "max"].abs().argmax()]
[22]:
bIndex 6.000000
t 6.246334
tropospheric min 3.379352
mean 3.554976
std 0.073661
max 3.701563
ionospheric min 0.321467
mean 0.330548
std 0.005226
max 0.339589
geodetic min 0.108477
mean 0.111889
std 0.001997
max 0.115449
doppler min -0.470775
mean -0.001597
std 0.266933
max 0.467533
sum min 3.364260
mean 4.005494
std 0.283930
max 4.595461
Name: 20, dtype: float64
[23]:
ystats_df.iloc[ystats_df["sum", "max"].abs().argmax()]
[23]:
bIndex 28.000000
t 26.378299
geodetic min 0.000242
mean 0.000392
std 0.000079
max 0.000534
bistatic min -3.431576
mean -2.874615
std 0.322362
max -2.317654
fmrate min -0.221405
mean -0.004832
std 0.030318
max 0.177377
sum min -3.989237
mean -3.218622
std 0.329532
max -2.642278
Name: 9, dtype: float64
[24]:
xstats_df.loc[xstats_df[""]["bIndex"] == 7]["sum"]
[24]:
| min | mean | std | max | |
|---|---|---|---|---|
| 2 | 2.998921 | 3.539033 | 0.23699 | 4.031289 |
[25]:
ystats_df.loc[ystats_df[""]["bIndex"] == 4]["sum"]
[25]:
| min | mean | std | max | |
|---|---|---|---|---|
| 1 | -3.872442 | -3.211433 | 0.323444 | -2.546432 |