Simply read a single PixC netcdf

from pixcdust.readers.netcdf import NcSimpleReader
import glob

swot_nc_files = glob.glob('/tmp/pixc'+'/*/*nc')

path = swot_nc_files[0]

# You can specify conditions on variables to filter data
conditions= {"sig0":{'operator': "gt", 'threshold': 20},  # sig0 > 20
             "classification":{'operator': "ge", 'threshold': 3},  # classification >= 3
            }

ncsimple = NcSimpleReader(path, variables=['height', 'sig0', 'classification'], conditions=conditions)

ncsimple.read()
ncsimple.data

<xarray.Dataset> Size: 4MB
Dimensions:         (points: 109059)
Coordinates:
    latitude        (points) float64 872kB 43.77 43.77 43.86 ... 43.27 43.28
    longitude       (points) float64 872kB 0.7553 0.7639 1.23 ... 1.243 1.346
  * points          (points) object 872kB POINT (0.7552957317809046 43.770648...
Data variables:
    height          (points) float32 436kB 237.4 238.4 156.3 ... 362.6 259.3
    sig0            (points) float32 436kB 71.78 503.4 56.08 ... 21.96 34.85
    classification  (points) float32 436kB 3.0 6.0 3.0 3.0 ... 6.0 6.0 6.0 3.0
Indexes:
    points   GeometryIndex (crs=EPSG:4326)
Attributes:
    description:                 cloud of geolocated interferogram pixels
    interferogram_size_azimuth:  3244
    interferogram_size_range:    4856
    looks_to_efflooks:           1.5337356015127774
    num_azimuth_looks:           7.0
    azimuth_offset:              6

xarray.Dataset

• Dimensions:
  • points: 109059
• Coordinates: (3)
  • latitude
    (points)
    float64
    43.77 43.77 43.86 ... 43.27 43.28

    long_name :

    latitude (positive N, negative S)

    standard_name :

    latitude

    units :

    degrees_north

    quality_flag :

    geolocation_qual

    valid_min :

    -80.0

    valid_max :

    80.0

    comment :

    Geodetic latitude [-80,80] (degrees north of equator) of the pixel.

    array([43.77064851, 43.77224769, 43.85757066, ..., 43.26596377,
           43.26601752, 43.2848009 ])

  • longitude
    (points)
    float64
    0.7553 0.7639 1.23 ... 1.243 1.346

    long_name :

    longitude (degrees East)

    standard_name :

    longitude

    units :

    degrees_east

    quality_flag :

    geolocation_qual

    valid_min :

    -180.0

    valid_max :

    180.0

    comment :

    Longitude [-180,180) (east of the Greenwich meridian) of the pixel.

    array([0.75529573, 0.76391317, 1.22956286, ..., 1.24252561, 1.24282121,
           1.34637966])

  • points
    (points)
    object
    POINT (0.7552957317809046 43.770...

    crs :

    EPSG:4326

    array([<POINT (0.755 43.771)>, <POINT (0.764 43.772)>, <POINT (1.23 43.858)>,
           ..., <POINT (1.243 43.266)>, <POINT (1.243 43.266)>,
           <POINT (1.346 43.285)>], dtype=object)

• Data variables: (3)
  • height
    (points)
    float32
    237.4 238.4 156.3 ... 362.6 259.3

    long_name :

    height above reference ellipsoid

    units :

    m

    quality_flag :

    geolocation_qual

    valid_min :

    -1500.0

    valid_max :

    15000.0

    comment :

    Height of the pixel above the reference ellipsoid.

    array([237.40173, 238.39923, 156.33269, ..., 362.552  , 362.6449 ,
           259.34995], dtype=float32)

  • sig0
    (points)
    float32
    71.78 503.4 56.08 ... 21.96 34.85

    long_name :

    sigma0

    units :

    1

    quality_flag :

    sig0_qual

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.

    array([ 71.78494 , 503.38083 ,  56.08387 , ...,  83.2498  ,  21.960453,
            34.848278], dtype=float32)

  • classification
    (points)
    float32
    3.0 6.0 3.0 3.0 ... 6.0 6.0 6.0 3.0

    long_name :

    classification

    quality_flag :

    classification_qual

    flag_meanings :

    land land_near_water water_near_land open_water dark_water low_coh_water_near_land open_low_coh_water

    flag_values :

    [1 2 3 4 5 6 7]

    valid_min :

    1

    valid_max :

    7

    comment :

    Flags indicating water detection results.

    array([3., 6., 3., ..., 6., 6., 3.], dtype=float32)

• Indexes: (1)
  • points
    GeometryIndex (crs=EPSG:4326)

    GeometryIndex(
        [<POINT (0.755 43.771)>
         <POINT (0.764 43.772)>
         <POINT (1.23 43.858)>
         <POINT (1.242 43.86)>
         ...
         <POINT (1.243 43.266)>
         <POINT (1.243 43.266)>
         <POINT (1.243 43.266)>
         <POINT (1.346 43.285)>],
        crs=EPSG:4326)

• Attributes: (6)

  description :

  cloud of geolocated interferogram pixels

  interferogram_size_azimuth :

  3244

  interferogram_size_range :

  4856

  looks_to_efflooks :

  1.5337356015127774

  num_azimuth_looks :

  7.0

  azimuth_offset :

  6

# show some information
str_time_start, dt_time_start, cycle_number, pass_number, tile_number, swath_size = (
    ncsimple.extract_info_from_nc_attrs(path)
)
print(dt_time_start, cycle_number, pass_number, tile_number)

2023-04-07 09:36:56 483 16 78

use data with xarray

ncsimple.to_xarray() # same thing as ncsimple.data

<xarray.Dataset> Size: 4MB
Dimensions:         (points: 109059)
Coordinates:
    latitude        (points) float64 872kB 43.77 43.77 43.86 ... 43.27 43.28
    longitude       (points) float64 872kB 0.7553 0.7639 1.23 ... 1.243 1.346
  * points          (points) object 872kB POINT (0.7552957317809046 43.770648...
Data variables:
    height          (points) float32 436kB 237.4 238.4 156.3 ... 362.6 259.3
    sig0            (points) float32 436kB 71.78 503.4 56.08 ... 21.96 34.85
    classification  (points) float32 436kB 3.0 6.0 3.0 3.0 ... 6.0 6.0 6.0 3.0
Indexes:
    points   GeometryIndex (crs=EPSG:4326)
Attributes:
    description:                 cloud of geolocated interferogram pixels
    interferogram_size_azimuth:  3244
    interferogram_size_range:    4856
    looks_to_efflooks:           1.5337356015127774
    num_azimuth_looks:           7.0
    azimuth_offset:              6

xarray.Dataset

• Dimensions:
  • points: 109059
• Coordinates: (3)
  • latitude
    (points)
    float64
    43.77 43.77 43.86 ... 43.27 43.28

    long_name :

    latitude (positive N, negative S)

    standard_name :

    latitude

    units :

    degrees_north

    quality_flag :

    geolocation_qual

    valid_min :

    -80.0

    valid_max :

    80.0

    comment :

    Geodetic latitude [-80,80] (degrees north of equator) of the pixel.

    array([43.77064851, 43.77224769, 43.85757066, ..., 43.26596377,
           43.26601752, 43.2848009 ])

  • longitude
    (points)
    float64
    0.7553 0.7639 1.23 ... 1.243 1.346

    long_name :

    longitude (degrees East)

    standard_name :

    longitude

    units :

    degrees_east

    quality_flag :

    geolocation_qual

    valid_min :

    -180.0

    valid_max :

    180.0

    comment :

    Longitude [-180,180) (east of the Greenwich meridian) of the pixel.

    array([0.75529573, 0.76391317, 1.22956286, ..., 1.24252561, 1.24282121,
           1.34637966])

  • points
    (points)
    object
    POINT (0.7552957317809046 43.770...

    crs :

    EPSG:4326

    array([<POINT (0.755 43.771)>, <POINT (0.764 43.772)>, <POINT (1.23 43.858)>,
           ..., <POINT (1.243 43.266)>, <POINT (1.243 43.266)>,
           <POINT (1.346 43.285)>], dtype=object)

• Data variables: (3)
  • height
    (points)
    float32
    237.4 238.4 156.3 ... 362.6 259.3

    long_name :

    height above reference ellipsoid

    units :

    m

    quality_flag :

    geolocation_qual

    valid_min :

    -1500.0

    valid_max :

    15000.0

    comment :

    Height of the pixel above the reference ellipsoid.

    array([237.40173, 238.39923, 156.33269, ..., 362.552  , 362.6449 ,
           259.34995], dtype=float32)

  • sig0
    (points)
    float32
    71.78 503.4 56.08 ... 21.96 34.85

    long_name :

    sigma0

    units :

    1

    quality_flag :

    sig0_qual

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.

    array([ 71.78494 , 503.38083 ,  56.08387 , ...,  83.2498  ,  21.960453,
            34.848278], dtype=float32)

  • classification
    (points)
    float32
    3.0 6.0 3.0 3.0 ... 6.0 6.0 6.0 3.0

    long_name :

    classification

    quality_flag :

    classification_qual

    flag_meanings :

    land land_near_water water_near_land open_water dark_water low_coh_water_near_land open_low_coh_water

    flag_values :

    [1 2 3 4 5 6 7]

    valid_min :

    1

    valid_max :

    7

    comment :

    Flags indicating water detection results.

    array([3., 6., 3., ..., 6., 6., 3.], dtype=float32)

• Indexes: (1)
  • points
    GeometryIndex (crs=EPSG:4326)

    GeometryIndex(
        [<POINT (0.755 43.771)>
         <POINT (0.764 43.772)>
         <POINT (1.23 43.858)>
         <POINT (1.242 43.86)>
         ...
         <POINT (1.243 43.266)>
         <POINT (1.243 43.266)>
         <POINT (1.243 43.266)>
         <POINT (1.346 43.285)>],
        crs=EPSG:4326)

• Attributes: (6)

  description :

  cloud of geolocated interferogram pixels

  interferogram_size_azimuth :

  3244

  interferogram_size_range :

  4856

  looks_to_efflooks :

  1.5337356015127774

  num_azimuth_looks :

  7.0

  azimuth_offset :

  6

use data with pandas.dataframe

ncsimple.to_dataframe()

	height	sig0	classification	latitude	longitude
points
POINT (0.7552957317809046 43.77064850723897)	237.401733	71.784943	3.0	43.770649	0.755296
POINT (0.7639131748832142 43.772247691826806)	238.399231	503.380829	6.0	43.772248	0.763913
POINT (1.2295628619697254 43.85757065547359)	156.332687	56.083870	3.0	43.857571	1.229563
POINT (1.2415500542200562 43.85973888474052)	138.921036	63.636265	3.0	43.859739	1.241550
POINT (1.2417066520121693 43.85976720035634)	138.882965	108.496094	4.0	43.859767	1.241707
...	...	...	...	...	...
POINT (1.1943494760700446 43.257191227213)	265.892639	120.980354	3.0	43.257191	1.194349
POINT (1.2433065548072477 43.26610576917034)	366.280731	26.830383	6.0	43.266106	1.243307
POINT (1.2425256128721571 43.26596377126247)	362.552002	83.249802	6.0	43.265964	1.242526
POINT (1.2428212090849797 43.26601752324333)	362.644897	21.960453	6.0	43.266018	1.242821
POINT (1.3463796618983679 43.28480090306965)	259.349945	34.848278	3.0	43.284801	1.346380

109059 rows × 5 columns

use data with geopandas.geodataframe

gdf = ncsimple.to_geodataframe()
gdf

	points	height	sig0	classification	latitude	longitude
0	POINT (0.75530 43.77065)	237.401733	71.784943	3.0	43.770649	0.755296
1	POINT (0.76391 43.77225)	238.399231	503.380829	6.0	43.772248	0.763913
2	POINT (1.22956 43.85757)	156.332687	56.083870	3.0	43.857571	1.229563
3	POINT (1.24155 43.85974)	138.921036	63.636265	3.0	43.859739	1.241550
4	POINT (1.24171 43.85977)	138.882965	108.496094	4.0	43.859767	1.241707
...	...	...	...	...	...	...
109054	POINT (1.19435 43.25719)	265.892639	120.980354	3.0	43.257191	1.194349
109055	POINT (1.24331 43.26611)	366.280731	26.830383	6.0	43.266106	1.243307
109056	POINT (1.24253 43.26596)	362.552002	83.249802	6.0	43.265964	1.242526
109057	POINT (1.24282 43.26602)	362.644897	21.960453	6.0	43.266018	1.242821
109058	POINT (1.34638 43.28480)	259.349945	34.848278	3.0	43.284801	1.346380

109059 rows × 6 columns