Use case 1: selecting bursts¶
Notebook setup¶
[1]:
%matplotlib inline
%load_ext autoreload
%autoreload 2
[2]:
import sys
sys.path.append('../..')
[3]:
import numpy as np
import matplotlib.pyplot as plt
[4]:
import s1etad
from s1etad import Sentinel1Etad, ECorrectionType
Open the dataset¶
[5]:
filename = '../../sample-products/S1B_IW_ETA__AXDV_20190805T162509_20190805T162536_017453_020D3A_____.SAFE'
[6]:
eta = Sentinel1Etad(filename)
[7]:
eta
[7]:
Sentinel1Etad("../../sample-products/S1B_IW_ETA__AXDV_20190805T162509_20190805T162536_017453_020D3A_____.SAFE") # 0x7f8ccbe732d0
Sentinel-1 products list:
S1B_IW_SLC__1ADV_20190805T162509_20190805T162536_017453_020D3A_A857.SAFE
Number of swaths: 3
Swath list: IW1, IW2, IW3
Grid sampling:
x: 8.081406101630269e-07
y: 0.028777788199999974
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
Selecting the burst by filtering in time¶
The availability of the burst catalogue, allows to perform queries and filter the burst by performing time selection using the first_time and last_time keywords of the query_burst method.
If no time is provided then all the burst are selected:
[8]:
df = eta.query_burst()
print(f"Number of bursts: {len(df)}")
df.head()
Number of bursts: 27
[8]:
| bIndex | pIndex | sIndex | productID | swathID | azimuthTimeMin | azimuthTimeMax | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:09.836779 | 2019-08-05 16:25:13.002336 |
| 9 | 2 | 1 | 2 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW2 | 2019-08-05 16:25:10.757668 | 2019-08-05 16:25:13.952003 |
| 18 | 3 | 1 | 3 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW3 | 2019-08-05 16:25:11.736113 | 2019-08-05 16:25:14.930448 |
| 1 | 4 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:12.570669 | 2019-08-05 16:25:15.736226 |
| 10 | 5 | 1 | 2 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW2 | 2019-08-05 16:25:13.520336 | 2019-08-05 16:25:16.714671 |
It is possible to reduce the selection by start time in this case the stop time is the last available burst:
[9]:
import dateutil
first_time = dateutil.parser.parse('2019-08-05T16:25:30.117898')
df = eta.query_burst(first_time=first_time)
print(f"Number of bursts: {len(df)}")
df.head()
Number of bursts: 4
[9]:
| bIndex | pIndex | sIndex | productID | swathID | azimuthTimeMin | azimuthTimeMax | |
|---|---|---|---|---|---|---|---|
| 25 | 24 | 1 | 3 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW3 | 2019-08-05 16:25:31.017231 | 2019-08-05 16:25:34.240344 |
| 8 | 25 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:31.880565 | 2019-08-05 16:25:35.046122 |
| 17 | 26 | 1 | 2 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW2 | 2019-08-05 16:25:32.830232 | 2019-08-05 16:25:36.024566 |
| 26 | 27 | 1 | 3 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW3 | 2019-08-05 16:25:33.779899 | 2019-08-05 16:25:36.974233 |
It is possible to reduce the selection by the stop time in this case the start time is the first available burst:
[10]:
last_time = dateutil.parser.parse('2019-08-05T16:25:20.117899')
df = eta.query_burst(last_time=last_time)
print(f"Number of bursts: {len(df)}")
df.head()
Number of bursts: 8
[10]:
| bIndex | pIndex | sIndex | productID | swathID | azimuthTimeMin | azimuthTimeMax | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:09.836779 | 2019-08-05 16:25:13.002336 |
| 9 | 2 | 1 | 2 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW2 | 2019-08-05 16:25:10.757668 | 2019-08-05 16:25:13.952003 |
| 18 | 3 | 1 | 3 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW3 | 2019-08-05 16:25:11.736113 | 2019-08-05 16:25:14.930448 |
| 1 | 4 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:12.570669 | 2019-08-05 16:25:15.736226 |
| 10 | 5 | 1 | 2 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW2 | 2019-08-05 16:25:13.520336 | 2019-08-05 16:25:16.714671 |
It is possible to reduce the selection by the start and stop time:
[11]:
first_time = dateutil.parser.parse('2019-08-05T16:25:25.117898')
last_time = dateutil.parser.parse('2019-08-05T16:25:29.117899')
# query the catalogues for of all the swaths
df = eta.query_burst(first_time=first_time, last_time=last_time)
print(f"Number of bursts: {len(df)}")
df.head()
Number of bursts: 1
[11]:
| bIndex | pIndex | sIndex | productID | swathID | azimuthTimeMin | azimuthTimeMax | |
|---|---|---|---|---|---|---|---|
| 23 | 18 | 1 | 3 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW3 | 2019-08-05 16:25:25.520674 | 2019-08-05 16:25:28.715008 |
Selecting by swath (and time)¶
The time selection can be combined with a selection by swath using the swath keyword. If not used all the swath are used
[12]:
first_time = dateutil.parser.parse('2019-08-05T16:25:00.117898')
last_time = dateutil.parser.parse('2019-08-05T16:25:40.117899')
# query the catalogue for a subset of the swaths
df = eta.query_burst(first_time=first_time, last_time=last_time, swath='IW1')
print(f"Number of bursts: {len(df)}")
df
Number of bursts: 9
[12]:
| bIndex | pIndex | sIndex | productID | swathID | azimuthTimeMin | azimuthTimeMax | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:09.836779 | 2019-08-05 16:25:13.002336 |
| 1 | 4 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:12.570669 | 2019-08-05 16:25:15.736226 |
| 2 | 7 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:15.333337 | 2019-08-05 16:25:18.498893 |
| 3 | 10 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:18.096004 | 2019-08-05 16:25:21.261561 |
| 4 | 13 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:20.858672 | 2019-08-05 16:25:24.024229 |
| 5 | 16 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:23.621340 | 2019-08-05 16:25:26.786896 |
| 6 | 19 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:26.355230 | 2019-08-05 16:25:29.549564 |
| 7 | 22 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:29.117897 | 2019-08-05 16:25:32.283454 |
| 8 | 25 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:31.880565 | 2019-08-05 16:25:35.046122 |
Query the catalogue for a subset of the swaths:
[13]:
first_time = dateutil.parser.parse('2019-08-05T16:25:00.117898')
last_time = dateutil.parser.parse('2019-08-05T16:25:40.117899')
df = eta.query_burst(first_time=first_time, last_time=last_time, swath=['IW1', 'IW2'])
print(f"Number of bursts: {len(df)}")
df.head()
Number of bursts: 18
[13]:
| bIndex | pIndex | sIndex | productID | swathID | azimuthTimeMin | azimuthTimeMax | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:09.836779 | 2019-08-05 16:25:13.002336 |
| 9 | 2 | 1 | 2 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW2 | 2019-08-05 16:25:10.757668 | 2019-08-05 16:25:13.952003 |
| 1 | 4 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:12.570669 | 2019-08-05 16:25:15.736226 |
| 10 | 5 | 1 | 2 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW2 | 2019-08-05 16:25:13.520336 | 2019-08-05 16:25:16.714671 |
| 2 | 7 | 1 | 1 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW1 | 2019-08-05 16:25:15.333337 | 2019-08-05 16:25:18.498893 |
Selecting by Sentinel-1 product name (swath and time)¶
The time selection can be combined with a selection by swath using the product_name keyword.
[14]:
first_time = dateutil.parser.parse('2019-08-05T16:25:00.117898')
last_time = dateutil.parser.parse('2019-08-05T16:25:40.117899')
product_name='S1B_IW_SLC__1SDV_20190805T162509_20190805T162536_017453_020D3A_AAAA.SAFE'
df = eta.query_burst(first_time=first_time, last_time=last_time, product_name=product_name, swath=['IW3'])
print(f"Number of bursts: {len(df)}")
df.head()
Number of bursts: 9
[14]:
| bIndex | pIndex | sIndex | productID | swathID | azimuthTimeMin | azimuthTimeMax | |
|---|---|---|---|---|---|---|---|
| 18 | 3 | 1 | 3 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW3 | 2019-08-05 16:25:11.736113 | 2019-08-05 16:25:14.930448 |
| 19 | 6 | 1 | 3 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW3 | 2019-08-05 16:25:14.470003 | 2019-08-05 16:25:17.693115 |
| 20 | 9 | 1 | 3 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW3 | 2019-08-05 16:25:17.232671 | 2019-08-05 16:25:20.427005 |
| 21 | 12 | 1 | 3 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW3 | 2019-08-05 16:25:19.995338 | 2019-08-05 16:25:23.189673 |
| 22 | 15 | 1 | 3 | S1B_IW_SLC__1ADV_20190805T162509_20190805T1625... | IW3 | 2019-08-05 16:25:22.758006 | 2019-08-05 16:25:25.952341 |