Spot6Product¶

class Spot6Product(product_path: Union[str, cloudpathlib.cloudpath.CloudPath, pathlib.Path], archive_path: Optional[Union[str, cloudpathlib.cloudpath.CloudPath, pathlib.Path]] = None, output_path: Optional[Union[str, cloudpathlib.cloudpath.CloudPath, pathlib.Path]] = None, remove_tmp: bool = False)[source]¶

Bases: eoreader.products.optical.dimap_product.DimapProduct

Class of SPOT-6 products. See here for more information.

Methods

`__init__`(product_path[, archive_path, ...])
`clean_tmp`()	Clean the temporary directory of the current product
`clear`()	Clear this product's cache
`default_transform`(**kwargs)	Returns default transform data of the default band (UTM), as the `rasterio.warp.calculate_default_transform` does: - transform - width - height - CRS
`get_band_paths`(band_list[, resolution])	Return the paths of required bands.
`get_date`([as_date])	Get the product's acquisition date.
`get_datetime`([as_datetime])	Get the product's acquisition datetime, with format `YYYYMMDDTHHMMSS` <-> `%Y%m%dT%H%M%S`
`get_default_band`()	Get default band: `GREEN` for optical data as every optical satellite has a GREEN band.
`get_default_band_path`(**kwargs)	Get default band (`GREEN` for optical data) path.
`get_existing_band_paths`()	Return the existing band paths.
`get_existing_bands`()	Return the existing band paths.
`has_band`(band)	Does this products has the specified band ?
`has_bands`(bands)	Does this products has the specified bands ?
`load`(bands[, resolution, size])	Open the bands and compute the wanted index.
`open_mask`(mask_str, **kwargs)	Open DIMAP V2 mask (GML files stored in MASKS) as `gpd.GeoDataFrame`.
`read_mtd`()	Read metadata and outputs the metadata XML root and its namespaces as a dict most of the time, except from L8-collection 1 data which outputs a `pandas.DataFrame`
`stack`(bands[, resolution, size, stack_path, ...])	Stack bands and index of a products.

Attributes

`crs`	Get UTM projection of the tile
`extent`	Get real footprint in UTM of the products (without nodata, in french == emprise utile)
`footprint`	Get real footprint in UTM of the products (without nodata, in french == emprise utile)
`get_mean_sun_angles`
`output`	Output directory of the product, to write orthorectified data for example.

__init__(product_path: Union[str, cloudpathlib.cloudpath.CloudPath, pathlib.Path], archive_path: Optional[Union[str, cloudpathlib.cloudpath.CloudPath, pathlib.Path]] = None, output_path: Optional[Union[str, cloudpathlib.cloudpath.CloudPath, pathlib.Path]] = None, remove_tmp: bool = False) → None¶

clean_tmp()¶: Clean the temporary directory of the current product

clear()¶: Clear this product’s cache

default_transform(**kwargs) -> (<class 'affine.Affine'>, <class 'int'>, <class 'int'>, <class 'rasterio.crs.CRS'>)¶

Returns default transform data of the default band (UTM), as the rasterio.warp.calculate_default_transform does: - transform - width - height - CRS

Overload in order not to reproject WGS84 data

Parameters: kwargs – Additional arguments
Returns: transform, width, height
Return type: Affine, int, int

get_band_paths(band_list: list, resolution: Optional[float] = None, **kwargs) → dict¶

Return the paths of required bands.

>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"IMG_PHR1B_PMS_001"
>>> prod = Reader().open(path)
>>> prod.get_band_paths([GREEN, RED])
{
    <OpticalBandNames.GREEN: 'GREEN'>:
    'IMG_PHR1A_PMS_001/DIM_PHR1A_PMS_202005110231585_ORT_5547047101.XML',
    <OpticalBandNames.RED: 'RED'>:
    'IMG_PHR1A_PMS_001/DIM_PHR1A_PMS_202005110231585_ORT_5547047101.XML'
}

Parameters

band_list (list) – List of the wanted bands
resolution (float) – Band resolution
kwargs – Other arguments used to load bands

Returns

Dictionary containing the path of each queried band

Return type

dict

get_date(as_date: bool = False) → Union[str, datetime.date]¶

Get the product’s acquisition date.

>>> from eoreader.reader import Reader
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_date(as_date=True)
datetime.datetime(2020, 8, 24, 0, 0)
>>> prod.get_date(as_date=False)
'20200824'

Parameters: as_date (bool) – Return the date as a datetime.date. If false, returns a string.
Returns: Its acquisition date
Return type: str

get_datetime(as_datetime: bool = False) → Union[str, datetime.datetime]¶

Get the product’s acquisition datetime, with format YYYYMMDDTHHMMSS <-> %Y%m%dT%H%M%S

>>> from eoreader.reader import Reader
>>> path = r"IMG_PHR1B_PMS_001"
>>> prod = Reader().open(path)
>>> prod.get_datetime(as_datetime=True)
datetime.datetime(2020, 5, 11, 2, 31, 58)
>>> prod.get_datetime(as_datetime=False)
'20200511T023158'

Parameters: as_datetime (bool) – Return the date as a datetime.datetime. If false, returns a string.
Returns: Its acquisition datetime
Return type: Union[str, datetime.datetime]

get_default_band() → eoreader.bands.bands.BandNames¶

Get default band: GREEN for optical data as every optical satellite has a GREEN band.

>>> from eoreader.reader import Reader
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_default_band()
<OpticalBandNames.GREEN: 'GREEN'>

Returns: Default band
Return type: str

get_default_band_path(**kwargs) → Union[cloudpathlib.cloudpath.CloudPath, pathlib.Path]¶

Get default band (GREEN for optical data) path.

>>> from eoreader.reader import Reader
>>> path = r"IMG_PHR1B_PMS_001"
>>> prod = Reader().open(path)
>>> prod.get_default_band_path()
'IMG_PHR1A_PMS_001/DIM_PHR1A_PMS_202005110231585_ORT_5547047101.XML'

Parameters: kwargs – Additional arguments
Returns: Default band path
Return type: Union[CloudPath, Path]

get_existing_band_paths() → dict¶

Return the existing band paths.

>>> from eoreader.reader import Reader
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_existing_band_paths()
{
    <OpticalBandNames.CA: 'COASTAL_AEROSOL'>: 'zip+file://S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip!/S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE/GRANULE/L1C_T30TTK_A027018_20200824T111345/IMG_DATA/T30TTK_20200824T110631_B01.jp2',
    ...,
    <OpticalBandNames.SWIR_2: 'SWIR_2'>: 'zip+file://S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip!/S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE/GRANULE/L1C_T30TTK_A027018_20200824T111345/IMG_DATA/T30TTK_20200824T110631_B12.jp2'
}

Returns: Dictionary containing the path of each queried band
Return type: dict

get_existing_bands() → list¶

Return the existing band paths.

>>> from eoreader.reader import Reader
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_existing_bands()
[<OpticalBandNames.CA: 'COASTAL_AEROSOL'>,
<OpticalBandNames.BLUE: 'BLUE'>,
<OpticalBandNames.GREEN: 'GREEN'>,
<OpticalBandNames.RED: 'RED'>,
<OpticalBandNames.VRE_1: 'VEGETATION_RED_EDGE_1'>,
<OpticalBandNames.VRE_2: 'VEGETATION_RED_EDGE_2'>,
<OpticalBandNames.VRE_3: 'VEGETATION_RED_EDGE_3'>,
<OpticalBandNames.NIR: 'NIR'>,
<OpticalBandNames.NNIR: 'NARROW_NIR'>,
<OpticalBandNames.WV: 'WATER_VAPOUR'>,
<OpticalBandNames.CIRRUS: 'CIRRUS'>,
<OpticalBandNames.SWIR_1: 'SWIR_1'>,
<OpticalBandNames.SWIR_2: 'SWIR_2'>]

Returns: List of existing bands in the products
Return type: list

has_band(band: Union[eoreader.bands.bands.BandNames, Callable]) → bool¶

Does this products has the specified band ?

By band, we mean:

satellite band
index
DEM band
cloud band

>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.has_band(GREEN)
True
>>> prod.has_band(TIR_2)
False
>>> prod.has_band(NDVI)
True
>>> prod.has_band(SHADOWS)
False
>>> prod.has_band(HILLSHADE)
True

Parameters: band (Union[BandNames, Callable]) – EOReader band (optical, SAR, clouds, DEM)
Returns: True if the products has the specified band
Return type: bool

has_bands(bands: Union[list, eoreader.bands.bands.BandNames, Callable]) → bool¶

Does this products has the specified bands ?

By band, we mean:

satellite band
index
DEM band
cloud band

See has_bands for a code example.

Parameters: bands (Union[list, BandNames, Callable]) – EOReader bands (optical, SAR, clouds, DEM)
Returns: True if the products has the specified band
Return type: bool

load(bands: Union[list, eoreader.bands.bands.BandNames, Callable], resolution: Optional[float] = None, size: Optional[Union[list, tuple]] = None, **kwargs) → dict¶

Open the bands and compute the wanted index.

The bands will be purged of nodata and invalid pixels, the nodata will be set to 0 and the bands will be masked arrays in float.

Bands that come out this function at the same time are collocated and therefore have the same shapes. This can be broken if you load data separately. Its is best to always load DEM data with some real bands.

>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> bands = prod.load([GREEN, NDVI], resolution=20)
>>> bands
{
    <function NDVI at 0x000001EFFFF5DD08>: <xarray.DataArray 'NDVI' (band: 1, y: 5490, x: 5490)>
    array([[[0.949506  , 0.92181516, 0.9279379 , ..., 1.8002278 ,
             1.5424857 , 1.6747767 ],
            [0.95369846, 0.91685396, 0.8957871 , ..., 1.5847116 ,
             1.5248713 , 1.5011379 ],
            [2.9928885 , 1.3031474 , 1.0076253 , ..., 1.5969834 ,
             1.5590671 , 1.5018653 ],
            ...,
            [1.4245619 , 1.6115025 , 1.6201663 , ..., 1.2387121 ,
             1.4025431 , 1.800678  ],
            [1.5627214 , 1.822388  , 1.7245892 , ..., 1.1694248 ,
             1.2573677 , 1.5767351 ],
            [1.653781  , 1.6424649 , 1.5923225 , ..., 1.3072611 ,
             1.2181134 , 1.2478763 ]]], dtype=float32)
    Coordinates:
      * band         (band) int32 1
      * y            (y) float64 4.5e+06 4.5e+06 4.5e+06 ... 4.39e+06 4.39e+06
      * x            (x) float64 2e+05 2e+05 2e+05 ... 3.097e+05 3.098e+05 3.098e+05
        spatial_ref  int32 0,
    <OpticalBandNames.GREEN: 'GREEN'>: <xarray.DataArray (band: 1, y: 5490, x: 5490)>
    array([[[0.0615  , 0.061625, 0.061   , ..., 0.12085 , 0.120225,
             0.113575],
            [0.061075, 0.06045 , 0.06025 , ..., 0.114625, 0.119625,
             0.117625],
            [0.06475 , 0.06145 , 0.060925, ..., 0.111475, 0.114925,
             0.115175],
            ...,
            [0.1516  , 0.14195 , 0.1391  , ..., 0.159975, 0.14145 ,
             0.127075],
            [0.140325, 0.125975, 0.131875, ..., 0.18245 , 0.1565  ,
             0.13015 ],
            [0.133475, 0.1341  , 0.13345 , ..., 0.15565 , 0.170675,
             0.16405 ]]], dtype=float32)
    Coordinates:
      * band         (band) int32 1
      * y            (y) float64 4.5e+06 4.5e+06 4.5e+06 ... 4.39e+06 4.39e+06
      * x            (x) float64 2e+05 2e+05 2e+05 ... 3.097e+05 3.098e+05 3.098e+05
        spatial_ref  int32 0
}

Parameters

bands (Union[list, BandNames, Callable]) – Band list
resolution (float) – Resolution of the band, in meters
size (Union[tuple, list]) – Size of the array (width, height). Not used if resolution is provided.
kwargs – Other arguments used to load bands

Returns

{band_name, band xarray}

Return type

dict

open_mask(mask_str: str, **kwargs) → geopandas.geodataframe.GeoDataFrame¶

Open DIMAP V2 mask (GML files stored in MASKS) as gpd.GeoDataFrame.

Masks than can be called that way are:

CLD: Cloud vector mask
DET: Out of order detectors vector mask
QTE: Synthetic technical quality vector mask
ROI: Region of Interest vector mask
SLT: Straylight vector mask
SNW: Snow vector mask
VIS: Hidden area vector mask (optional)

>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"IMG_PHR1A_PMS_001"
>>> prod.open_mask("ROI")
                                             gml_id  ...                                           geometry
0  source_image_footprint-DS_PHR1A_20200511023124...  ...  POLYGON ((118.86239 -2.81569, 118.86255 -2.815...
[1 rows x 3 columns]

Parameters: mask_str (str) – Mask name, such as CLD, DET, ROI…
Returns: Mask as a vector
Return type: gpd.GeoDataFrame

read_mtd() → Any¶

Read metadata and outputs the metadata XML root and its namespaces as a dict most of the time, except from L8-collection 1 data which outputs a pandas.DataFrame

>>> from eoreader.reader import Reader
>>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip"
>>> prod = Reader().open(path)
>>> prod.read_mtd()
(<Element product at 0x1832895d788>, '')

Returns: Metadata XML root and its namespace or pd.DataFrame
Return type: Any

stack(bands: list, resolution: Optional[float] = None, size: Optional[Union[list, tuple]] = None, stack_path: Optional[Union[str, cloudpathlib.cloudpath.CloudPath, pathlib.Path]] = None, save_as_int: bool = False, **kwargs) → xarray.core.dataarray.DataArray¶

Stack bands and index of a products.

>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> stack = prod.stack([NDVI, MNDWI, GREEN], resolution=20)  # In meters
>>> stack
<xarray.DataArray 'NDVI_MNDWI_GREEN' (z: 3, y: 5490, x: 5490)>
array([[[ 0.949506  ,  0.92181516,  0.9279379 , ...,  1.8002278 ,
          1.5424857 ,  1.6747767 ],
        [ 0.95369846,  0.91685396,  0.8957871 , ...,  1.5847116 ,
          1.5248713 ,  1.5011379 ],
        [ 2.9928885 ,  1.3031474 ,  1.0076253 , ...,  1.5969834 ,
          1.5590671 ,  1.5018653 ],
        ...,
        [ 1.4245619 ,  1.6115025 ,  1.6201663 , ...,  1.2387121 ,
          1.4025431 ,  1.800678  ],
        [ 1.5627214 ,  1.822388  ,  1.7245892 , ...,  1.1694248 ,
          1.2573677 ,  1.5767351 ],
        [ 1.653781  ,  1.6424649 ,  1.5923225 , ...,  1.3072611 ,
          1.2181134 ,  1.2478763 ]],
       [[ 0.27066118,  0.23466069,  0.18792598, ..., -0.4611526 ,
         -0.49751845, -0.4865216 ],
        [ 0.22425456,  0.28004232,  0.27851456, ..., -0.5032771 ,
         -0.501796  , -0.502669  ],
        [-0.07466951,  0.06360884,  0.1207174 , ..., -0.50617427,
         -0.50219285, -0.5034222 ],
        [-0.47076276, -0.4705828 , -0.4747971 , ..., -0.32138503,
         -0.36619243, -0.37428448],
        [-0.4826967 , -0.5032287 , -0.48544118, ..., -0.278925  ,
         -0.31404778, -0.36052078],
        [-0.488381  , -0.48253912, -0.4697526 , ..., -0.38105175,
         -0.30813277, -0.27739233]],
       [[ 0.0615    ,  0.061625  ,  0.061     , ...,  0.12085   ,
          0.120225  ,  0.113575  ],
        [ 0.061075  ,  0.06045   ,  0.06025   , ...,  0.114625  ,
          0.119625  ,  0.117625  ],
        [ 0.06475   ,  0.06145   ,  0.060925  , ...,  0.111475  ,
          0.114925  ,  0.115175  ],
        ...,
        [ 0.1516    ,  0.14195   ,  0.1391    , ...,  0.159975  ,
          0.14145   ,  0.127075  ],
        [ 0.140325  ,  0.125975  ,  0.131875  , ...,  0.18245   ,
          0.1565    ,  0.13015   ],
        [ 0.133475  ,  0.1341    ,  0.13345   , ...,  0.15565   ,
          0.170675  ,  0.16405   ]]], dtype=float32)
Coordinates:
  * y            (y) float64 4.5e+06 4.5e+06 4.5e+06 ... 4.39e+06 4.39e+06
  * x            (x) float64 2e+05 2e+05 2e+05 ... 3.097e+05 3.098e+05 3.098e+05
    spatial_ref  int32 0
  * z            (z) MultiIndex
  - variable     (z) object 'NDVI' 'MNDWI' 'GREEN'
  - band         (z) int64 1 1 1
-Attributes:
    long_name:  ['NDVI', 'MNDWI', 'GREEN']

Parameters

bands (list) – Bands and index combination
resolution (float) – Stack resolution. . If not specified, use the product resolution.
size (Union[tuple, list]) – Size of the array (width, height). Not used if resolution is provided.
stack_path (Union[str, CloudPath, Path]) – Stack path
save_as_int (bool) – Convert stack to uint16 to save disk space (and therefore multiply the values by 10.000)
**kwargs – Other arguments passed to load or rioxarray.to_raster() (such as compress)

Returns

Stack as a DataArray

Return type

xr.DataArray

archive_path¶: Archive path, same as the product path if not specified. Useful when you want to know where both the extracted and archived version of your product are stored.

band_names¶: Band mapping between band wrapping names such as GREEN and band real number such as 03 for Sentinel-2.

condensed_name¶: Condensed name, the filename with only useful data to keep the name unique (ie. 20191215T110441_S2_30TXP_L2A_122756). Used to shorten names and paths.

corresponding_ref¶: The corresponding reference products to the current one (if the product is not a reference but has a reference data corresponding to it). A list because of multiple ref in case of non-stackable products (S3, S1…)

property crs: rasterio.crs.CRS¶

Get UTM projection of the tile

>>> from eoreader.reader import Reader
>>> path = r"IMG_PHR1B_PMS_001"
>>> prod = Reader().open(path)
>>> prod.crs
CRS.from_epsg(32618)

Returns: CRS object
Return type: rasterio.crs.CRS

date¶: Acquisition date.

datetime¶: Acquisition datetime.

property extent: geopandas.geodataframe.GeoDataFrame¶

Get real footprint in UTM of the products (without nodata, in french == emprise utile)

>>> from eoreader.reader import Reader
>>> path = r"IMG_PHR1B_PMS_001"
>>> prod = Reader().open(path)
>>> prod.footprint
                                             gml_id  ...                                           geometry
0  source_image_footprint-DS_PHR1A_20200511023124...  ...  POLYGON ((707025.261 9688613.833, 707043.276 9...
[1 rows x 3 columns]

Returns: Footprint as a GeoDataFrame
Return type: gpd.GeoDataFrame

filename¶: Product filename

property footprint: geopandas.geodataframe.GeoDataFrame¶

Get real footprint in UTM of the products (without nodata, in french == emprise utile)

>>> from eoreader.reader import Reader
>>> path = r"IMG_PHR1B_PMS_001"
>>> prod = Reader().open(path)
>>> prod.footprint
                                             gml_id  ...                                           geometry
0  source_image_footprint-DS_PHR1A_20200511023124...  ...  POLYGON ((707025.261 9688613.833, 707043.276 9...
[1 rows x 3 columns]

Returns: Footprint as a GeoDataFrame
Return type: gpd.GeoDataFrame

get_mean_sun_angles = <methodtools._LruCacheWire object>¶

is_archived¶: Is the archived product is processed (a products is considered as archived if its products path is a directory).

is_reference¶: If the product is a reference, used for algorithms that need pre and post data, such as fire detection.

name¶: Product true name (as specified in the metadata)

needs_extraction¶: Does this products needs to be extracted to be processed ? (True by default).

nodata¶: Product nodata, set to -9999 by default

ortho_path¶: Orthorectified path. Can be set to use manually orthorectified or pansharpened data, especially useful for VHR data on steep terrain.

property output: Union[cloudpathlib.cloudpath.CloudPath, pathlib.Path]¶: Output directory of the product, to write orthorectified data for example.

path¶: Usable path to the product, either extracted or archived path, according to the satellite.

platform¶: Product platform, such as Sentinel-2

product_type¶: Product type, satellite-related field, such as L1C or L2A for Sentinel-2 data.

resolution¶: Default resolution in meters of the current product. For SAR product, we use Ground Range resolution as we will automatically orthorectify the tiles.

sat_id¶: Satellite ID, i.e. S2 for Sentinel-2

sensor_type¶: Sensor type, SAR or optical.

split_name¶: Split name, to retrieve every information from its true name (dates, tile, product type…).

tile_name¶: Tile if possible (for data that can be piled, for example S2 and Landsats).