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CYGNSS Library is a Python package for working with CYGNSS data. The package can be used for downloading SRTM data. Please see How to use the package for more details.
you can install it using pip
.
pip install -U cygnsslib
or you can clone the repository and install it using the following command
pip install .
To use it with anaconda, install the environment as follows:
conda create -n cygnss
source activate cygnss
conda install -c conda-forge --file requiremnts.txt
You can then remove the local copy if you wish. Alternatively, if you wish to be able to make changes to your local copy without having to reinstall the package for the changes to take effect (e.g., for development purposes), you can use the following instead:
pip uninstall cygnsslib
Here the required Python packages:
gdal geographiclib lxml matplotlib netcdf4 numpy openpyxl pandas pysftp requests tqdm paramiko
Note: gdal
can be installed using conda
conda install -c conda-forge gdal
or using this method in Ubuntu
Also, you need the modified data-subscriber package from https://github.com/MiXIL/data-subscriber
The cygnsslib has several modules:
For most of the library, you need to set the environment variable CYGNSS_L1_PATH
to the path of the CYGNSS L1 data. i.e.
CYGNSS_L1_PATH=/cygnss_data/L1/v3.2
Note the folder needs to be named as the version (e.g. v3.2
)name in PODAAC, and the parent folder need to be L1
You can set the environment variable for the session in python as follows:
import os
os.environ['CYGNSS_L1_PATH'] = '/cygnss_data/L1/v3.2'
For SRTM you can specify the main path by specifying the environment variable SRTM_PATH
. The files will be saved in SRTM_PATH\hgt
There are several ways to find the DDM within a region.
Below an example of the usage:
import cygnsslib
import numpy as np
import datetime as dt
# this will make the strip get the path from os.environ["CYGNSS_L1_PATH"]
cygnss_l1_path = None
year = 2019
days_list = np.arange(1, 100)
ref_pos = (37.1906, -105.9921) # (lat,long)
radius = 10e3 # [m]
thesh_ddm_snr = 3.0 # split DDMs into above thesh_ddm_snr and below thesh_ddm_snr. Above this value the DDM image will be saved
thesh_noise = 1 # noise threshold, each pixel in the DDM below this value will be replaced by this value
kml_out_tag = f'my_data' # tag for all the output files, ex: my_data_above_thresh.kml
save_podaac_pass = True # if True and download_cygnss_data is True the PO DAAC username/password will be saved
# The default options are:
options = {'save_cvs': False,
'save_ddm_img': True,
'plt_tag': '',
'title_img_inc_ddm_time': False,
'img_save_type': ['png'],
'sheet_type': 'xls'}
out_folder_path = 'my_ddms'
cygnsslib.write_sp_within_radius(cygnss_l1_path, year, days_list, ref_pos, radius,
out_folder_path, kml_out_tag, thesh_ddm_snr, thesh_noise,
download_cygnss_data=True, out_options=options)
start_date = dt.date(year=2019, month=1, day=1)
end_date = dt.date(year=2019, month=6, day=10) # the end date included in the search
cygnsslib.write_sp_within_radius_between_dates(cygnss_l1_path, start_date, end_date, ref_pos=ref_pos,
radius=radius, out_folder_path=f'', out_file_tag=kml_out_tag, thresh_ddm_snr=thesh_ddm_snr,
plt_thresh_noise=thesh_noise, download_cygnss_data=True,
out_options=options, save_podaac_pass=save_podaac_pass)
in_kml = f'my_poly.kml'
cygnsslib.write_sp_from_kml(cygnss_l1_path, year, days_list, in_kml=in_kml, out_folder_path=f'', out_file_tag=kml_out_tag,
thresh_ddm_snr=thesh_ddm_snr, plt_thresh_noise=thesh_noise, download_cygnss_data=True,
out_options=options, save_podaac_pass=save_podaac_pass)
cygnsslib.write_sp_from_kml_between_dates(cygnss_l1_path, start_date, end_date, in_kml=in_kml, out_folder_path=f'', out_file_tag=kml_out_tag,
thresh_ddm_snr=thesh_ddm_snr, plt_thresh_noise=thesh_noise, download_cygnss_data=True,
out_options=options, save_podaac_pass=save_podaac_pass)
This will group DDMs within specific distance in one group. The kml file need to be the output of write_sp_from_kml()
or write_sp_within_radius_between_dates()
or write_sp_from_kml()
or write_sp_from_kml_between_dates()
This function need to be used after identifying the DDMs within a specific region
import cygnsslib
in_kml = f'my_data_above_thresh.kml'
max_dist = 1e3
out_kml = f'{in_kml[:-4]}_grp{max_dist:d}'
cygnsslib.group_sp_within_distance(in_kml, out_kml, max_dist, save_csv=True, sheet_type=f'xls')
This function will download cygnss antenna patterns
import cygnsslib
antenna_patterns_folder_path = f'/data/cygnss_antenna_patterns'
cygnsslib.download_cyg_antenna_patterns(antenna_patterns_folder_path)
The CygDdmId
class is basically work as an ID for the DDMs, with some features.
There are multiple static functions, which are
get_land_prod_info_from_ocean_prod()
find_cygnss_file()
get_sample_id_from_time_rltv_tcs()
cyg_id_list_from_kml()
The function cyg_id_list_from_kml()
generate a list of objects of CygDdmId
from a kml file. The kml file from The kml file need to be the output of write_sp_from_kml()
or write_sp_within_radius_between_dates()
or write_sp_from_kml()
or write_sp_from_kml_between_dates()
THe input parameters are:
file_name
[req]: None
or the file name. It's better to set it to None
.year
[req]: data yearday
[req]: day of the yearsc_num
[req]: spacecraft id, (1-8)ch_id
[req]: channel id (1-4)samp_id
[req]: sample id (zero-based)land_samp_id
[optional]: sample id of the land product (zero-based)sample_time_sec
[optional]: time (in seconds) of the selected DDM (sample) from the beginning of the dayland_file_name
[optional]: land product file nameddm_tag
[optional]: DDM tagThe current implemented methods are:
set_land_sample_id()
: set land_samp_id
set_ddm_time()
: set sample_time_sec
set_land_file_name()
: set land_file_name
fill_file_name()
: fill file name automaticallyfill_land_parameters()
: fill land product parameters, which are land_file_name
, land_samp_id
and sample_time_sec
get_utc_time()
: return DDM timeThere are many usage for this class here few
from cygnsslib import CygDdmId
year = 2019
day = 123
sc_num = 2
ch_id = 3
sample_id = 440
cyg_ddm_id = CygDdmId.CygDdmId(None, year, day, sc_num, ch_id, sample_id)
from cygnsslib import CygDdmId
in_kml = f'my_data_above_thresh.kml'
cyg_ddmid_list = CygDdmId.cyg_id_list_from_kml(in_kml)
for cyg_ddmid in cyg_ddmid_list:
cyg_ddmid.fill_file_name()
cyg_ddmid.fill_land_parameters(l1_land_folder_name=f'v3Land')
You can download both the standard DDMs and the rawif data. You can choose to download data between two dates or a list of days in the year.
If a file exists, the code will not re-download it.
Note if you select check_md5_exist_file=True
, then the code will do an md5 check for existing files. This will ensure existing files are good. However, this will make it very slow.
Below an example of downloading DDMs and the rawif files. The files will be organized as they're in PODAAC.
from cygnsslib import cygnss_download
import datetime as dt
import numpy as np
# Download data in the same year and range of days
days_list = np.arange(5, 10)
data_year = 2020
# list_sc_num = [3]
list_sc_num = None # Will download all the 8 spacecrafts
cyg_data_ver = f'v3.2'
cygnss_download.download_cyg_files(data_year, days_list, list_sc_num, cyg_data_ver, cyg_data_lvl=f'L1', checksum_exist_file=False)
# Downloading data between two dates (including end date)
st_date = dt.date(year=2019, month=1, day=12)
end_date = dt.date(year=2020, month=1, day=3)
cygnss_download.download_cyg_files_between_date(st_date, end_date, list_sc_num, cyg_data_ver=cyg_data_ver, cyg_data_lvl=f'L1',
checksum_exist_file=False)
# Downloading rawif data
download_l1_data = True # This will download the corresponding L1 data
cygnss_download.download_cyg_rawif_files(data_year, days_list, list_sc_num, save_podaac_pass=True,
download_l1_data=download_l1_data)
cygnss_download.download_rawif_cyg_files_between_date(st_date, end_date, list_sc_num, save_podaac_pass=True,
download_l1_data=download_l1_data)
You can get the PO.DAAC Drive API Credentials from https://podaac-tools.jpl.nasa.gov/drive/
If you entered the wrong PODAAC user/pass, you'll be asked to re-enter them again. However, you need to re-start the code after that so the saved user/pass are loaded again.
You can reset your PODAAC user/pass and enter new user/pass using the following code. Note when you run the code, you'll be asked to enter the new user/pass
Note: if you want to remove the current user/pass, set save_pass=False
from cygnsslib import cygnss_download
cygnss_download.get_podaac_cred(save_pass=True, reset_pass=True)
The files are in this URL: https://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.003/2000.02.11/
The username/password can be obtained from https://urs.earthdata.nasa.gov/home
from cygnsslib import srtm_download
import os
os.environ['SRTM_PATH'] = '/data/srtm_data' # The files will be downloaded into SRTM_PATH/hgt path
files_lat = [21, 22, 20]
files_lon = [29, 30]
srtm_download.download_srtm_ght_files(files_lat, files_lon, save_pass=True, unzipfile=False, remove_zippedfile=True)
You can reset your EarthData user/pass and enter new user/pass using the following code. Note when you run the code, you'll be asked to enter the new user/pass
from cygnsslib import srtm_download
srtm_download.get_earthdata_cred()
Create a circle or a square on the ground and export it to kml file
import cygnsslib
ref_pos = [37.1906, -105.9921] # [lat,long]
radius = 10e3 # [m]
out_kml = f'my_circle.kml'
cygnsslib.create_centered_polygon(ref_pos, radius, out_kml, shape=f"circle")
Create a kml file from a list of positions
import cygnsslib
import numpy as np
lyr_name = f"my_pos"
point_names = ["Y1", "Y2", "Y3"]
loc_list = np.array([[-34.0, 145.0], [-34.0, 146.0], [-34.0, 148]])
out_kml = f"my_pos.kml"
cygnsslib.create_kml_from_list_points(loc_list, loc_names=point_names, out_kml=out_kml, lyr_name=lyr_name)
Get DDMs info from the kml file. The kml file need to be the output of write_sp_from_kml()
or write_sp_within_radius_between_dates()
or write_sp_from_kml()
or write_sp_from_kml_between_dates()
The main goal of this function is extract the DDMs' parameters from the kml file. See CygDdmId
Class for more functionality
import cygnsslib
ddm_list = cygnsslib.get_list_ddm_info_from_kml(in_kml=f'my_data_above_thresh.kml')
Find CYGNSS Land product (experimental) from the standard ocean product. This function is still in developing.
import cygnsslib
xls_in = f'my_xls.xlsx'
cygnsslib.find_land_prod_sample_id_from_excel(xls_in, xls_out=None, start_col=1, st_row=1)
For more info, please contact:
Amer Melebari
Microwave Systems, Sensors and Imaging Lab (MiXiL)
University of Southern California
amelebar[AT]usc.edu
FAQs
Toolset for working with CYGNSS data and downloading CYGNSS data from PODAAC
We found that cygnsslib demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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