Ex 4 - Datacube¶

Once you have build the datacube, there is an initial learning curve to get around the datacube to fetch relevant values and metadata. We have created a class that can help you to easily read, process and manage the ICEcubes, called Datacube.

In [2]:

from pathlib import Path
import os
import icecube
from icecube.bin.datacube import Datacube
from icecube.bin.generate_cube import IceyeProcessGenerateCube

Please configure paths below as per need

In [3]:

icecube_abspath = str(Path(icecube.__file__).parent.parent)
resource_dir = os.path.join(str(Path(icecube.__file__).parent.parent), "tests/resources")
grd_raster_dir = os.path.join(resource_dir, "grd_stack")

# to show example with vectors
vector_labels_fpath = os.path.join(resource_dir, "labels/dummy_vector_labels.json")
# Example of the "labels/dummy_vector_labels.json"
#[
#    {
#        "product_file": "ICEYE_GRD_SLED_54549_20210427T215124_hollow_10x10pixels_fake_0.tif",
#        "labels": {
#            "objects": [
#                {
#                    "class": "rand-b",
#                    "bbox": {
#                        "xmin": 2,
#                        "ymin": 5,
#                        "xmax": 9,
#                        "ymax": 7
#                    }
#                },


# cube configuration fpath
cube_config_fpath = os.path.join(resource_dir, "json_config/config_use_case5.json")
# config_use_case5.json 
#{
#    "start_date": 20210425,
#    "end_date" : 20210430,
#    "min_incidence_angle" : 20,
#    "max_incidence_angle" : 34,
#    "temporal_resolution" : 1,
#    "temporal_overlap" : 1
#}

Below we will showcase an example of using the Datacube class for netCDF4 file format built with GRDs and vector labels.

Let's build the cube first with our sample dataset. It is worth mentioning that any icecube dataset can be easily attached to Datacube using Datacube.set_xrdataset(xr.Dataset) or if we have a netCDF4 file, it can easily be read using Datacube.read_cube(cube_fpath).

In [4]:

dc = IceyeProcessGenerateCube.create_cube(grd_raster_dir, cube_config_fpath, vector_labels_fpath)

09/07/2021 04:25:08 PM - sar_datacube_metadata.py - [INFO] - Building the metadata from the folder /mnt/xor/ICEYE_PACKAGES/icecube/tests/resources/grd_stack using GRD
processing rasters for cubes: 100%|██████████| 6/6 [00:00<00:00, 172.03it/s]
09/07/2021 04:25:08 PM - common_utils.py - [INFO] - create running time is 0.0593 seconds
09/07/2021 04:25:08 PM - sar_datacube_metadata.py - [INFO] - Building the metadata from the folder /mnt/xor/ICEYE_PACKAGES/icecube/tests/resources/grd_stack using GRD
processing rasters for labels cube: 100%|██████████| 6/6 [00:00<00:00, 3962.50it/s]
09/07/2021 04:25:08 PM - common_utils.py - [INFO] - create running time is 0.0168 seconds

datacube_inst is an instance of class Datacube so it encapsulates all the useful attributes and methods that we can use to process the datacube. The below cells demonstrate this with some examples:

In [5]:

type(dc)

Out[5]:

icecube.bin.datacube.Datacube

In [6]:

# Here is how our dataset looks like, referred to `xds`
xds = dc.xrdataset
xds

Out[6]:

<xarray.Dataset>
Dimensions:    (Azimuth: 10, Band: 6, Range: 10)
Coordinates:
  * Band       (Band) datetime64[ns] 2021-04-25 2021-04-26 ... 2021-04-30
  * Azimuth    (Azimuth) int64 0 1 2 3 4 5 6 7 8 9
  * Range      (Range) int64 0 1 2 3 4 5 6 7 8 9
Data variables:
    Intensity  (Band, Azimuth, Range) uint16 dask.array<chunksize=(1, 10, 10), meta=np.ndarray>
    Labels     (Band) |S1540 b'\x80\x04\x95\x1c\x00\x00\x00\x00\x00\x00\x00}\...

In [7]:

# Fetch all the data variables of the cube. We can see that "Intensity" and "Labels" are two data 
# variables found in our datacube
all_dvs = dc.get_data_variables()
all_dvs

Out[7]:

['Intensity', 'Labels']

In [8]:

# We can fetch all the images present in the datacube w.r.t a particular variable. 
# Please note that "None" denote that product file is missing because of the temporal gap.
dc.get_all_products(dc.get_xrarray(all_dvs[0]))

Out[8]:

['None',
 'None',
 'ICEYE_GRD_54549_20210427T215124_hollow_10x10pixels_fake_1.tif',
 'ICEYE_GRD_54549_20210427T215124_hollow_10x10pixels_fake_0.tif',
 'None',
 'None']

In [9]:

# We can pass the data variable to get the underlying xr.DataArray. We can see that the data cube has 
# 6 bands as per our user configuration. Also please notice that our metadata is contained in xr.DataArray.attrs
# as lists of keys, indexed by multi-temporal stack 
dc.get_xrarray(all_dvs[0])

Out[9]:

<xarray.DataArray 'Intensity' (Band: 6, Azimuth: 10, Range: 10)>
dask.array<concatenate, shape=(6, 10, 10), dtype=uint16, chunksize=(1, 10, 10), chunktype=numpy.ndarray>
Coordinates:
  * Band     (Band) datetime64[ns] 2021-04-25 2021-04-26 ... 2021-04-30
  * Azimuth  (Azimuth) int64 0 1 2 3 4 5 6 7 8 9
  * Range    (Range) int64 0 1 2 3 4 5 6 7 8 9
Attributes: (12/85)
    acquisition_start_utc:                ['None', 'None', '2021-04-27T21:51:...
    posX:                                 ['None', 'None', '[5474808.16271857...
    azimuth_spacing:                      ['None', 'None', '0.5', '0.5', 'Non...
    azimuth_time_interval:                ['None', 'None', '7.076784388926729...
    doppler_rate_poly_order:              ['None', 'None', '3', '3', 'None', ...
    orbit_repeat_cycle:                   ['None', 'None', '99999', '99999', ...
    ...                                   ...
    orbit_absolute_number:                ['None', 'None', '9915', '9915', 'N...
    range_spacing:                        ['None', 'None', '0.5', '0.5', 'Non...
    slant_range_to_first_pixel:           ['None', 'None', '621685.2427500114...
    dc_estimate_coeffs:                   ['None', 'None', '[[-2259.75195312 ...
    incidence_far:                        ['None', 'None', '32.17288399584505...
    azimuth_look_overlap:                 ['None', 'None', '1042.449568072064...

Values¶

In [10]:

# similarly we can see the relevant xr.DataArray associated with variable "Labels". 
# Plese note that label objects are serialized and then stored. So in order to read them back, they must be
# un-serialized.
dc.get_xrarray(all_dvs[1]).head(1)

Out[10]:

<xarray.DataArray 'Labels' (Band: 1)>
array([b'\x80\x04\x95\x1c\x00\x00\x00\x00\x00\x00\x00}\x94\x8c\x06labels\x94}\x94\x8c\x07objects\x94]\x94ss.'],
      dtype='|S1540')
Coordinates:
  * Band     (Band) datetime64[ns] 2021-04-25
Attributes:
    product_file:  ['None', 'None', 'ICEYE_GRD_54549_20210427T215124_hollow_1...

As we work with image files, it is easier to use them as reference to get values or metadata inside data arrays. Datacube.get_product_values(**args) provides convenient way to fetch labels for the images.

In [11]:

# One can specify the product-file and data variable to fetch the values for a raster
product_file = "ICEYE_GRD_54549_20210427T215124_hollow_10x10pixels_fake_1.tif"
dc.get_product_values(product_file, dc.get_xrarray(all_dvs[0]))

Out[11]:

array([[329, 389, 217, 418,  48,  67,  98, 423, 317, 525],
       [434, 508, 348, 198, 323, 436, 286, 320, 550, 407],
       [ 27, 265, 533, 416, 492,  87, 476, 559,  21, 363],
       [ 65,  77, 231, 319, 287,  17, 388, 594,  13, 245],
       [ 42, 360, 184, 164, 491, 253, 491,  59,  34,  75],
       [104, 100, 551, 504, 107,  31, 524, 376, 121, 264],
       [245, 321,  70, 441, 276, 573, 455, 417, 389, 251],
       [528, 323,  83, 333, 514, 229,  58, 202, 342, 351],
       [ 33, 246, 452, 307,   7, 300, 334, 248, 397,   1],
       [268, 325,  26, 227,  74, 482, 122, 136, 237, 483]], dtype=uint16)

In [12]:

# Similarly we can do it for the Labels as well. Please note that below a different xr.DataArray was passed
product_file = "ICEYE_GRD_54549_20210427T215124_hollow_10x10pixels_fake_1.tif"
dc.get_product_values(product_file, dc.get_xrarray(all_dvs[1]))

Out[12]:

{'objects': [{'class': 'rand-b',
   'bbox': {'xmin': 1, 'ymin': 0, 'xmax': 5, 'ymax': 9}},
  {'class': 'rand-b', 'bbox': {'xmin': 8, 'ymin': 9, 'xmax': 8, 'ymax': 9}},
  {'class': 'rand-b', 'bbox': {'xmin': 6, 'ymin': 9, 'xmax': 8, 'ymax': 9}},
  {'class': 'rand-b', 'bbox': {'xmin': 9, 'ymin': 0, 'xmax': 9, 'ymax': 3}},
  {'class': 'rand-b', 'bbox': {'xmin': 5, 'ymin': 7, 'xmax': 9, 'ymax': 8}},
  {'class': 'rand-c', 'bbox': {'xmin': 9, 'ymin': 7, 'xmax': 9, 'ymax': 8}},
  {'class': 'rand-a', 'bbox': {'xmin': 6, 'ymin': 5, 'xmax': 6, 'ymax': 5}},
  {'class': 'rand-c', 'bbox': {'xmin': 2, 'ymin': 9, 'xmax': 2, 'ymax': 9}},
  {'class': 'rand-b', 'bbox': {'xmin': 2, 'ymin': 7, 'xmax': 6, 'ymax': 7}},
  {'class': 'rand-a', 'bbox': {'xmin': 5, 'ymin': 5, 'xmax': 8, 'ymax': 8}},
  {'class': 'rand-c', 'bbox': {'xmin': 5, 'ymin': 3, 'xmax': 5, 'ymax': 4}},
  {'class': 'rand-a', 'bbox': {'xmin': 9, 'ymin': 5, 'xmax': 9, 'ymax': 5}},
  {'class': 'rand-a', 'bbox': {'xmin': 0, 'ymin': 5, 'xmax': 7, 'ymax': 8}},
  {'class': 'rand-c', 'bbox': {'xmin': 8, 'ymin': 4, 'xmax': 8, 'ymax': 8}},
  {'class': 'rand-c', 'bbox': {'xmin': 3, 'ymin': 2, 'xmax': 5, 'ymax': 9}},
  {'class': 'rand-b', 'bbox': {'xmin': 7, 'ymin': 2, 'xmax': 7, 'ymax': 6}},
  {'class': 'rand-b', 'bbox': {'xmin': 9, 'ymin': 2, 'xmax': 9, 'ymax': 9}},
  {'class': 'rand-a', 'bbox': {'xmin': 2, 'ymin': 1, 'xmax': 6, 'ymax': 2}},
  {'class': 'rand-b', 'bbox': {'xmin': 7, 'ymin': 5, 'xmax': 8, 'ymax': 9}},
  {'class': 'rand-a', 'bbox': {'xmin': 9, 'ymin': 2, 'xmax': 9, 'ymax': 9}},
  {'class': 'rand-b', 'bbox': {'xmin': 6, 'ymin': 6, 'xmax': 9, 'ymax': 7}},
  {'class': 'rand-c', 'bbox': {'xmin': 6, 'ymin': 3, 'xmax': 6, 'ymax': 9}},
  {'class': 'rand-b', 'bbox': {'xmin': 8, 'ymin': 3, 'xmax': 8, 'ymax': 3}},
  {'class': 'rand-a', 'bbox': {'xmin': 8, 'ymin': 3, 'xmax': 8, 'ymax': 8}},
  {'class': 'rand-a', 'bbox': {'xmin': 4, 'ymin': 7, 'xmax': 7, 'ymax': 8}},
  {'class': 'rand-a', 'bbox': {'xmin': 2, 'ymin': 1, 'xmax': 2, 'ymax': 7}},
  {'class': 'rand-b', 'bbox': {'xmin': 3, 'ymin': 7, 'xmax': 6, 'ymax': 7}},
  {'class': 'rand-b', 'bbox': {'xmin': 1, 'ymin': 4, 'xmax': 3, 'ymax': 4}},
  {'class': 'rand-c', 'bbox': {'xmin': 6, 'ymin': 1, 'xmax': 7, 'ymax': 6}},
  {'class': 'rand-b', 'bbox': {'xmin': 8, 'ymin': 4, 'xmax': 9, 'ymax': 9}},
  {'class': 'rand-a', 'bbox': {'xmin': 4, 'ymin': 3, 'xmax': 7, 'ymax': 9}},
  {'class': 'rand-a', 'bbox': {'xmin': 9, 'ymin': 3, 'xmax': 9, 'ymax': 6}},
  {'class': 'rand-a', 'bbox': {'xmin': 4, 'ymin': 4, 'xmax': 5, 'ymax': 4}},
  {'class': 'rand-a', 'bbox': {'xmin': 6, 'ymin': 7, 'xmax': 6, 'ymax': 7}},
  {'class': 'rand-b', 'bbox': {'xmin': 4, 'ymin': 3, 'xmax': 5, 'ymax': 8}},
  {'class': 'rand-c', 'bbox': {'xmin': 5, 'ymin': 7, 'xmax': 9, 'ymax': 8}},
  {'class': 'rand-c', 'bbox': {'xmin': 7, 'ymin': 9, 'xmax': 9, 'ymax': 9}},
  {'class': 'rand-b', 'bbox': {'xmin': 9, 'ymin': 7, 'xmax': 9, 'ymax': 9}},
  {'class': 'rand-a', 'bbox': {'xmin': 2, 'ymin': 2, 'xmax': 8, 'ymax': 7}},
  {'class': 'rand-b', 'bbox': {'xmin': 7, 'ymin': 8, 'xmax': 7, 'ymax': 8}}]}

Metadata¶

One can easily fetch the metadata associated with xr.Dataset, xr.DataArray, or on a product level using Datacube

In [13]:

# fetch metadata associated with xr.Dataset. One can see that the metadata is empty as intended on dataset level.
dc.get_xrdataset_metadata()

Out[13]:

{}

Let's see if we have metadata associated with our xr.DataArrays. We can check with the first data variable, that is "Intensity". In the output cell below, we can see that it returns a dictionary object with a list of values. Metadata/attribute keys are a superset of all attributes found in all the images of the stack. A list is maintained against each key and values are appended against each image in the corresponding index of the stack. For missing keys, "None" is appended as value.

In [14]:

# Let's see if we have metadata associated with our xr.DataArrays
# let's check with the first data variable
metadata = dc.get_xrarray_metadata(all_dvs[0])
# show only the first 5 keys
{k: metadata[k] for k in list(metadata)[:5]}

Out[14]:

{'acquisition_start_utc': ['None',
  'None',
  '2021-04-27T21:51:24.929476',
  '2021-04-27T21:51:24.929476',
  'None',
  'None'],
 'posX': ['None',
  'None',
  '[5474808.16271857 5474340.81737572 5473873.4037893  5473405.92419404\n 5472938.37636674 5472470.76254275 5472003.08161336 5471535.33246921\n 5471067.51734663 5470599.6340208  5470131.6847287  5469663.66836081\n 5469195.58380697 5468727.43330512 5468259.21462887 5467790.93001677\n 5467322.57835854 5466854.15854321 5466385.67281031 5465917.11893186\n 5465448.49914802 5464979.81234768 5464511.05741911 5464042.23660344\n 5463573.34767109 5463104.39286383 5462635.37106976 5462166.28117635\n 5461697.12542633 5461227.90158851 5460758.61190628 5460289.25526693\n 5459819.83055714 5459350.34002124 5458880.78142647 5458411.1570178\n 5457941.46568175 5457471.70630418 5457001.88113104 5456531.98792796\n 5456062.02894151 5455592.00305741 5455121.90916075 5454651.74949905\n 5454181.52183638 5453711.2284209  5453240.86813754 5452770.43987059\n 5452299.94586918 5451829.38389577 5451358.75620012 5450888.06166637\n 5450417.29917802 5449946.47098578 5449475.57485055 5449004.61302369\n 5448533.58438853 5448062.48782779 5447591.32559378 5447120.09544579\n 5446648.79963678 5446177.4370493  5445706.00656526 5445234.51043858\n 5444762.94642696 5444291.31678496 5443819.62039435 5443347.85613624\n 5442876.02626614 5442404.12854017 5441932.16521447 5441460.13517001\n 5440988.03728712 5440515.87382291 5440043.64253192 5439571.34567188\n 5439098.98212298 5438626.55076475 5438154.0538559  5437681.48914937\n 5437208.85890448]',
  '[5474808.16271857 5474340.81737572 5473873.4037893  5473405.92419404\n 5472938.37636674 5472470.76254275 5472003.08161336 5471535.33246921\n 5471067.51734663 5470599.6340208  5470131.6847287  5469663.66836081\n 5469195.58380697 5468727.43330512 5468259.21462887 5467790.93001677\n 5467322.57835854 5466854.15854321 5466385.67281031 5465917.11893186\n 5465448.49914802 5464979.81234768 5464511.05741911 5464042.23660344\n 5463573.34767109 5463104.39286383 5462635.37106976 5462166.28117635\n 5461697.12542633 5461227.90158851 5460758.61190628 5460289.25526693\n 5459819.83055714 5459350.34002124 5458880.78142647 5458411.1570178\n 5457941.46568175 5457471.70630418 5457001.88113104 5456531.98792796\n 5456062.02894151 5455592.00305741 5455121.90916075 5454651.74949905\n 5454181.52183638 5453711.2284209  5453240.86813754 5452770.43987059\n 5452299.94586918 5451829.38389577 5451358.75620012 5450888.06166637\n 5450417.29917802 5449946.47098578 5449475.57485055 5449004.61302369\n 5448533.58438853 5448062.48782779 5447591.32559378 5447120.09544579\n 5446648.79963678 5446177.4370493  5445706.00656526 5445234.51043858\n 5444762.94642696 5444291.31678496 5443819.62039435 5443347.85613624\n 5442876.02626614 5442404.12854017 5441932.16521447 5441460.13517001\n 5440988.03728712 5440515.87382291 5440043.64253192 5439571.34567188\n 5439098.98212298 5438626.55076475 5438154.0538559  5437681.48914937\n 5437208.85890448]',
  'None',
  'None'],
 'azimuth_spacing': ['None', 'None', '0.5', '0.5', 'None', 'None'],
 'azimuth_time_interval': ['None',
  'None',
  '7.076784388926729e-05',
  '7.076784388926729e-05',
  'None',
  'None'],
 'doppler_rate_poly_order': ['None', 'None', '3', '3', 'None', 'None']}

In [15]:

# Similarly we can show for second data variable, the associated metadata. As shown in output cell, 
# it's simply a dictionary of product-files.
dc.get_xrarray_metadata(all_dvs[1])

Out[15]:

{'product_file': ['None',
  'None',
  'ICEYE_GRD_54549_20210427T215124_hollow_10x10pixels_fake_1.tif',
  'ICEYE_GRD_54549_20210427T215124_hollow_10x10pixels_fake_0.tif',
  'None',
  'None']}

In [16]:

# Let's try to fetch metadata associated with a single product in xr.DataArray
product_file = "ICEYE_GRD_54549_20210427T215124_hollow_10x10pixels_fake_0.tif"
dc.get_metadata_by_product(product_file, dc.get_xrarray(all_dvs[0]))

Out[16]:

{'acquisition_start_utc': '2021-04-27T21:51:24.929476',
 'posX': '[5474808.16271857 5474340.81737572 5473873.4037893  5473405.92419404\n 5472938.37636674 5472470.76254275 5472003.08161336 5471535.33246921\n 5471067.51734663 5470599.6340208  5470131.6847287  5469663.66836081\n 5469195.58380697 5468727.43330512 5468259.21462887 5467790.93001677\n 5467322.57835854 5466854.15854321 5466385.67281031 5465917.11893186\n 5465448.49914802 5464979.81234768 5464511.05741911 5464042.23660344\n 5463573.34767109 5463104.39286383 5462635.37106976 5462166.28117635\n 5461697.12542633 5461227.90158851 5460758.61190628 5460289.25526693\n 5459819.83055714 5459350.34002124 5458880.78142647 5458411.1570178\n 5457941.46568175 5457471.70630418 5457001.88113104 5456531.98792796\n 5456062.02894151 5455592.00305741 5455121.90916075 5454651.74949905\n 5454181.52183638 5453711.2284209  5453240.86813754 5452770.43987059\n 5452299.94586918 5451829.38389577 5451358.75620012 5450888.06166637\n 5450417.29917802 5449946.47098578 5449475.57485055 5449004.61302369\n 5448533.58438853 5448062.48782779 5447591.32559378 5447120.09544579\n 5446648.79963678 5446177.4370493  5445706.00656526 5445234.51043858\n 5444762.94642696 5444291.31678496 5443819.62039435 5443347.85613624\n 5442876.02626614 5442404.12854017 5441932.16521447 5441460.13517001\n 5440988.03728712 5440515.87382291 5440043.64253192 5439571.34567188\n 5439098.98212298 5438626.55076475 5438154.0538559  5437681.48914937\n 5437208.85890448]',
 'azimuth_spacing': '0.5',
 'azimuth_time_interval': '7.076784388926729e-05',
 'doppler_rate_poly_order': '3',
 'orbit_repeat_cycle': '99999',
 'orbit_direction': 'ASCENDING',
 'carrier_frequency': '9650000000.0',
 'applied_processing': "{'library_version': '0.1.0', 'processing': {}}",
 'acquisition_end_utc': '2021-04-28T21:51:30.025535',
 'acquisition_mode': 'spotlight',
 'zerodoppler_end_utc': '2021-04-27T21:51:27.856415',
 'window_function_range': 'TAYLOR_20_4',
 'zerodoppler_start_utc': '2021-04-27T21:51:27.093679',
 'product_name': 'ICEYE_GRD_54549_20210427T215124_hollow_10x10pixels_fake_0',
 'incidence_angle_zero_doppler_time': '2021-04-27T21:51:27.475116',
 'azimuth_looks': '3',
 'coord_first_far': '[ 1.17480000e+04  1.00000000e+00  3.74264571e+01 -6.21675354e+00]',
 'posZ': '[4109487.20309768 4110089.38320021 4110691.51494008 4111293.59543885\n 4111895.62756019 4112497.60842612 4113099.5394648  4113701.42210389\n 4114303.25346613 4114905.03641401 4115506.76807074 4116108.44986388\n 4116710.0832205  4117311.66526454 4117913.19885731 4118514.68112321\n 4119116.11348921 4119717.4973818  4120318.82992609 4120920.1139822\n 4121521.34667574 4122122.52943307 4122723.66368008 4123324.7465431\n 4123925.78088107 4124526.76382077 4125127.69678798 4125728.58120801\n 4126329.41420836 4126930.19864679 4127530.93165127 4128131.61464698\n 4128732.24905865 4129332.83201498 4129933.36637252 4130533.84926045\n 4131134.28210339 4131734.66632543 4132334.99905648 4132935.2831519\n 4133535.51574207 4134135.69825099 4134735.83210218 4135335.91442675\n 4135935.94807886 4136535.9301901  4137135.86218387 4137735.74548311\n 4138335.5772201  4138935.36024782 4139535.09169906 4140134.77299661\n 4140734.40556282 4141333.98653118 4141933.51875348 4142532.9993637\n 4143132.42978405 4143731.81143628 4144331.14145507 4144930.42269102\n 4145529.6522793  4146128.83164154 4146727.96219887 4147327.0410872\n 4147926.07115592 4148525.04954141 4149123.9776647  4149722.85694634\n 4150321.68452341 4150920.46324413 4151519.19024607 4152117.86694965\n 4152716.49477483 4153315.07085991 4153913.5980519  4154512.07348958\n 4155110.49859279 4155708.87478087 4156307.19919332 4156905.47467597\n 4157503.69836878]',
 'total_processed_bandwidth_azimuth': '26363.724672587723',
 'range_look_overlap': '0.0',
 'coord_center': '[5875.         5389.           37.44561785   -6.25418761]',
 'state_vector_time_utc': "['2021-04-27T21:51:24.000000' '2021-04-27T21:51:24.100000'\n '2021-04-27T21:51:24.200000' '2021-04-27T21:51:24.300000'\n '2021-04-27T21:51:24.400000' '2021-04-27T21:51:24.500000'\n '2021-04-27T21:51:24.600000' '2021-04-27T21:51:24.700000'\n '2021-04-27T21:51:24.800000' '2021-04-27T21:51:24.900000'\n '2021-04-27T21:51:25.000000' '2021-04-27T21:51:25.100000'\n '2021-04-27T21:51:25.200000' '2021-04-27T21:51:25.300000'\n '2021-04-27T21:51:25.400000' '2021-04-27T21:51:25.500000'\n '2021-04-27T21:51:25.600000' '2021-04-27T21:51:25.700000'\n '2021-04-27T21:51:25.800000' '2021-04-27T21:51:25.900000'\n '2021-04-27T21:51:26.000000' '2021-04-27T21:51:26.100000'\n '2021-04-27T21:51:26.200000' '2021-04-27T21:51:26.300000'\n '2021-04-27T21:51:26.400000' '2021-04-27T21:51:26.500000'\n '2021-04-27T21:51:26.600000' '2021-04-27T21:51:26.700000'\n '2021-04-27T21:51:26.800000' '2021-04-27T21:51:26.900000'\n '2021-04-27T21:51:27.000000' '2021-04-27T21:51:27.100000'\n '2021-04-27T21:51:27.200000' '2021-04-27T21:51:27.300000'\n '2021-04-27T21:51:27.400000' '2021-04-27T21:51:27.500000'\n '2021-04-27T21:51:27.600000' '2021-04-27T21:51:27.700000'\n '2021-04-27T21:51:27.800000' '2021-04-27T21:51:27.900000'\n '2021-04-27T21:51:28.000000' '2021-04-27T21:51:28.100000'\n '2021-04-27T21:51:28.200000' '2021-04-27T21:51:28.300000'\n '2021-04-27T21:51:28.400000' '2021-04-27T21:51:28.500000'\n '2021-04-27T21:51:28.600000' '2021-04-27T21:51:28.700000'\n '2021-04-27T21:51:28.800000' '2021-04-27T21:51:28.900000'\n '2021-04-27T21:51:29.000000' '2021-04-27T21:51:29.100000'\n '2021-04-27T21:51:29.200000' '2021-04-27T21:51:29.300000'\n '2021-04-27T21:51:29.400000' '2021-04-27T21:51:29.500000'\n '2021-04-27T21:51:29.600000' '2021-04-27T21:51:29.700000'\n '2021-04-27T21:51:29.800000' '2021-04-27T21:51:29.900000'\n '2021-04-27T21:51:30.000000' '2021-04-27T21:51:30.100000'\n '2021-04-27T21:51:30.200000' '2021-04-27T21:51:30.300000'\n '2021-04-27T21:51:30.400000' '2021-04-27T21:51:30.500000'\n '2021-04-27T21:51:30.600000' '2021-04-27T21:51:30.700000'\n '2021-04-27T21:51:30.800000' '2021-04-27T21:51:30.900000'\n '2021-04-27T21:51:31.000000' '2021-04-27T21:51:31.100000'\n '2021-04-27T21:51:31.200000' '2021-04-27T21:51:31.300000'\n '2021-04-27T21:51:31.400000' '2021-04-27T21:51:31.500000'\n '2021-04-27T21:51:31.600000' '2021-04-27T21:51:31.700000'\n '2021-04-27T21:51:31.800000' '2021-04-27T21:51:31.900000'\n '2021-04-27T21:51:32.000000']",
 'number_of_state_vectors': '81',
 'dc_estimate_time_utc': "['2021-04-27T21:51:27.093640' '2021-04-27T21:51:27.178412'\n '2021-04-27T21:51:27.263185' '2021-04-27T21:51:27.347958'\n '2021-04-27T21:51:27.432730' '2021-04-27T21:51:27.517503'\n '2021-04-27T21:51:27.602275' '2021-04-27T21:51:27.687048'\n '2021-04-27T21:51:27.771820' '2021-04-27T21:51:27.856593']",
 'polarization': 'VV',
 'processing_prf': '36909.21454162281',
 'velX': '[-4673.12223293 -4673.79355399 -4674.46481748 -4675.13602017\n -4675.80716526 -4676.47824955 -4677.14927463 -4677.82024208\n -4678.49114871 -4679.16199769 -4679.83278583 -4680.50351471\n -4681.17418593 -4681.84479628 -4682.51534894 -4683.18584072\n -4683.85627319 -4684.52664797 -4685.19696183 -4685.86721796\n -4686.53741317 -4687.20754904 -4687.87762716 -4688.54764432\n -4689.21760372 -4689.88750215 -4690.5573412  -4691.22712246\n -4691.89684272 -4692.56650517 -4693.23610662 -4693.90564864\n -4694.57513283 -4695.24455598 -4695.91392128 -4696.58322553\n -4697.25247031 -4697.92165722 -4698.59078306 -4699.259851\n -4699.92885785 -4700.59780519 -4701.26669461 -4701.93552292\n -4702.60429329 -4703.27300253 -4703.94165222 -4704.61024395\n -4705.27877453 -4705.94724712 -4706.61565854 -4707.28401038\n -4707.95230421 -4708.62053684 -4709.28871145 -4709.95682485\n -4710.62487861 -4711.29287433 -4711.96080881 -4712.62868523\n -4713.2965004  -4713.96425589 -4714.6319533  -4715.29958942\n -4715.96716744 -4716.63468417 -4717.30214118 -4717.96954006\n -4718.63687762 -4719.30415703 -4719.97137511 -4720.63853343\n -4721.30563359 -4721.97267238 -4722.63965298 -4723.3065722\n -4723.97343163 -4724.64023284 -4725.30697265 -4725.97365423\n -4726.64027439]',
 'area_or_point': 'Area',
 'satellite_name': "('ICEYE-XY',)",
 'range_looks': '1',
 'ant_elev_corr_flag': '1',
 'dc_estimate_poly_order': '3',
 'tropo_range_delay': '2.8238412754811586',
 'processing_time': '2021-04-28T04:58:23',
 'grsr_poly_order': '4',
 'range_look_bandwidth': '299486104.61513484',
 'incidence_angle_ground_range_origin': '0.0',
 'incidence_angle_coefficients': '[ 3.16617271e+01  8.74389044e-05 -7.00297508e-11  1.37137269e-18\n  9.45921276e-23]',
 'orbit_relative_number': '9915',
 'orbit_processing_level': 'precise',
 'spec_version': '2.2',
 'product_level': 'GRD',
 'product_file': 'ICEYE_GRD_54549_20210427T215124_hollow_10x10pixels_fake_0.tif',
 'grsr_zero_doppler_time': '2021-04-27T21:51:27.475116',
 'look_side': 'right',
 'chirp_bandwidth': '300000000.0',
 'coord_first_near': '[ 1.          1.         37.41700285 -6.2818332 ]',
 'product_type': 'SpotlightExtendedDwell',
 'azimuth_look_bandwidth': '9478.957370916767',
 'sample_precision': 'uint16',
 'doppler_rate_coeffs': '[-5.58027093e+03  1.34212242e+06 -3.22798340e+08  7.76370140e+10]',
 'range_spread_comp_flag': '1',
 'incidence_center': '30.5',
 'satellite_look_angle': '30',
 'mean_orbit_altitude': '536675.3876030303',
 'range_sampling_rate': '358148331.2923262',
 'posY': '[-921374.0111596  -921461.46392007 -921548.89898784 -921636.31594396\n -921723.71520326 -921811.09634695 -921898.45958141 -921985.80511285\n -922073.13252274 -922160.44222547 -922247.73380269 -922335.00746057\n -922422.26340509 -922509.50121817 -922596.72131376 -922683.92327395\n -922771.10730469 -922858.27361175 -922945.42177747 -923032.55221539\n -923119.66450801 -923206.75886107 -923293.83548014 -923380.89394799\n -923467.93467772 -923554.95725228 -923641.96187719 -923728.94875781\n -923815.91747734 -923902.86844845 -923989.80125452 -924076.71610086\n -924163.61319261 -924250.49211339 -924337.35327547 -924424.19626264\n -924511.02128001 -924597.82853249 -924684.61760415 -924771.38890681\n -924858.1420247  -924944.8771627  -925031.59452554 -925118.2936977\n -925204.97509058 -925291.63828885 -925378.28349717 -925464.91092006\n -925551.52014242 -925638.11157524 -925724.6848036  -925811.24003196\n -925897.77746461 -925984.2966869  -926070.79810938 -926157.28131758\n -926243.74651573 -926330.19390792 -926416.62307992 -926503.03444186\n -926589.42757968 -926675.80269742 -926762.15999894 -926848.49907045\n -926934.82032164 -927021.1233389  -927107.40832605 -927193.67548674\n -927279.92440761 -927366.15549791 -927452.36834447 -927538.56315089\n -927624.7401206  -927710.89884068 -927797.03971997 -927883.16234572\n -927969.26692131 -928055.35364997 -928141.4221192  -928227.47273742\n -928313.50509229]',
 'coord_last_far': '[ 1.17480000e+04  1.07790000e+04  3.74741096e+01 -6.22731201e+00]',
 'velZ': '[6022.05575618 6021.5578102  6021.05979033 6020.56169896 6020.06353371\n 6019.56529696 6019.06698754 6018.56860427 6018.07014951 6017.57162092\n 6017.07302086 6016.57434815 6016.07560162 6015.57678364 6015.07789185\n 6014.57892863 6014.07989279 6013.58078316 6013.08160211 6012.58234728\n 6012.08302105 6011.58362224 6011.08414966 6010.58460569 6010.08498798\n 6009.58529889 6009.08553725 6008.58570188 6008.08579515 6007.5858147\n 6007.08576292 6006.58563861 6006.08544059 6005.58517126 6005.08482823\n 6004.5844139  6004.08392707 6003.58336657 6003.08273479 6002.58202933\n 6002.08125261 6001.58040342 6001.07948059 6000.5784865  6000.07741878\n 5999.57627981 5999.07506842 5998.57378341 5998.07242718 5997.57099734\n 5997.06949629 5996.56792284 5996.06627581 5995.56455758 5995.06276578\n 5994.5609028  5994.05896746 5993.55695855 5993.05487849 5992.55272489\n 5992.05050013 5991.54820304 5991.04583243 5990.54339068 5990.04087542\n 5989.53828905 5989.03563037 5988.53289819 5988.03009492 5987.52721816\n 5987.02427032 5986.52125021 5986.01815663 5985.51499198 5985.01175388\n 5984.50844473 5984.00506334 5983.50160851 5982.99808264 5982.49448335\n 5981.99081305]',
 'calibration_factor': '3.939204325311276e-08',
 'mean_earth_radius': '6370576.1554293325',
 'incidence_near': '31.661727086845776',
 'geo_ref_system': 'WGS84',
 'number_of_range_samples': '10',
 'grsr_ground_range_origin': '0.0',
 'avg_scene_height': '110.74176',
 'coord_last_near': '[ 1.00000000e+00  1.07790000e+04  3.74646332e+01 -6.29258576e+00]',
 'heading': '349.91295192092355',
 'number_of_dc_estimations': '10',
 'window_function_azimuth': 'TAYLOR_20_4',
 'incidence_angle_poly_order': '4',
 'chirp_duration': '3.397714285714286e-05',
 'number_of_azimuth_samples': '10',
 'processor_version': 'ICEYE_P_1.31',
 'grsr_coefficients': '[ 6.21685243e+05  5.24903202e-01  6.49477815e-07 -5.50559950e-13\n  1.30562747e-19]',
 'velY': '[-874.61792057 -874.43891918 -874.25989712 -874.08085523 -873.90179267\n -873.7227103  -873.54360769 -873.36448441 -873.18534133 -873.0061776\n -872.82699406 -872.6477903  -872.4685659  -872.2893217  -872.11005687\n -871.93077225 -871.75146742 -871.57214196 -871.39279673 -871.21343086\n -871.03404523 -870.85463941 -870.67521297 -870.49576677 -870.31629996\n -870.1368134  -869.95730666 -869.77777932 -869.59823223 -869.41866455\n -869.23907713 -869.05946955 -868.87984138 -868.70019348 -868.520525\n -868.3408368  -868.16112845 -867.98139953 -867.8016509  -867.6218817\n -867.44209279 -867.26228376 -867.08245416 -866.90260487 -866.72273502\n -866.54284549 -866.36293584 -866.18300565 -866.00305577 -865.82308536\n -865.64309527 -865.46308508 -865.28305437 -865.10300399 -864.92293308\n -864.74284252 -864.56273187 -864.38260071 -864.2024499  -864.02227858\n -863.84208762 -863.66187659 -863.48164506 -863.30139389 -863.12112223\n -862.94083095 -862.76051961 -862.58018779 -862.39983634 -862.21946442\n -862.03907289 -861.85866132 -861.67822928 -861.49777764 -861.31730553\n -861.13681383 -860.9563021  -860.77576992 -860.59521816 -860.41464594\n -860.23405415]',
 'acquisition_prf': '5886.672581404736',
 'gcp_terrain_model': 'DEM',
 'orbit_absolute_number': '9915',
 'range_spacing': '0.5',
 'slant_range_to_first_pixel': '621685.2427500114',
 'dc_estimate_coeffs': '[[-2259.75195312     0.             0.             0.        ]\n [-1743.7265625      0.             0.             0.        ]\n [-1227.69921875     0.             0.             0.        ]\n [ -711.671875       0.             0.             0.        ]\n [ -195.64453125     0.             0.             0.        ]\n [  320.38085938     0.             0.             0.        ]\n [  836.40625        0.             0.             0.        ]\n [ 1352.43359375     0.             0.             0.        ]\n [ 1868.4609375      0.             0.             0.        ]\n [ 2384.48828125     0.             0.             0.        ]]',
 'incidence_far': '32.172883995845055',
 'azimuth_look_overlap': '1042.4495680720643'}

In [17]:

# Let's try to get the metadata associated with our Labels now, which should be just the product name itself.
product_file = "ICEYE_GRD_54549_20210427T215124_hollow_10x10pixels_fake_0.tif"
dc.get_metadata_by_product(product_file, dc.get_xrarray(all_dvs[1]))

Out[17]:

{'product_file': 'ICEYE_GRD_54549_20210427T215124_hollow_10x10pixels_fake_0.tif'}

In [18]:

# finally we can save our datacube to required destination if needed as well
cube_save_path = os.path.join(icecube_abspath, "icecube/dataset/temp/my_awesome_datacube.nc")
Path(str(Path(cube_save_path).parent)).mkdir(parents=True, exist_ok=True)
dc.to_file(cube_save_path, format="netCDF4")

Hauskaa koodailua!