DimapV2Product#

class DimapV2Product(product_path: Union[str, CloudPath, Path], archive_path: Optional[Union[str, CloudPath, Path]] = None, output_path: Optional[Union[str, CloudPath, Path]] = None, remove_tmp: bool = False, **kwargs)[source]#

Bases: VhrProduct

Super Class of DIMAP V2 products. See here for more information.

__init__(product_path: Union[str, CloudPath, Path], archive_path: Optional[Union[str, CloudPath, Path]] = None, output_path: Optional[Union[str, CloudPath, Path]] = None, remove_tmp: bool = False, **kwargs) None[source]#
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, CRS

Return type

Affine, int, int, CRS

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])
{
    <SpectralBandNames.GREEN: 'GREEN'>:
    'IMG_PHR1A_PMS_001/DIM_PHR1A_PMS_202005110231585_ORT_5547047101.XML',
    <SpectralBandNames.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, 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][source]#

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]

get_default_band() 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()
<SpectralBandNames.GREEN: 'GREEN'>
Returns

Default band

Return type

str

get_default_band_path(**kwargs) Union[CloudPath, 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 (orthorectified if needed).

>>> 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()
{
    <SpectralBandNames.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',
    ...,
    <SpectralBandNames.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.

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

List of existing bands in the products

Return type

list

get_quicklook_path() str[source]#

Get quicklook path if existing.

Returns

Quicklook path

Return type

str

get_raw_band_paths(**kwargs) dict#

Return the raw band paths.

Parameters

kwargs – Additional arguments

Returns

Dictionary containing the path of each queried band

Return type

dict

has_band(band: Union[BandNames, str]) bool#

Does this product 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, str]) – EOReader band (optical, SAR, clouds, DEM)

Returns

True if the products has the specified band

Return type

bool

has_bands(bands: Union[list, BandNames, str]) bool#

Does this product 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, str]) – EOReader bands (optical, SAR, clouds, DEM)

Returns

True if the products has the specified band

Return type

bool

load(bands: Union[list, BandNames, str], resolution: Optional[float] = None, size: Optional[Union[list, tuple]] = None, **kwargs) dict#

Open the bands and compute the wanted index.

  • For Optical data:

    The bands will be purged of nodata and invalid pixels (if specified with the CLEAN_OPTICAL keyword), the nodata will be set to -9999 and the bands will be DataArrays in float32.

  • For SAR data:

    The bands will be purged of nodata (not over the sea), the nodata will be set to 0 to respect SNAP’s behavior and the bands will be DataArray in float32.

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)
Parameters
  • bands (Union[list, BandNames, str]) – 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) GeoDataFrame[source]#

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

plot() None#

Plot the quicklook if existing

read_mtd()#

Read metadata and outputs the metadata XML root and its namespaces as a dict.

>>> 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

Return type

(etree._Element, dict)

stack(bands: list, resolution: Optional[float] = None, size: Optional[Union[list, tuple]] = None, stack_path: Optional[Union[str, CloudPath, Path]] = None, save_as_int: bool = False, **kwargs) 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
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_combi#

Band combination, i.e. PAN, PMS, MS…

bands#

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.

constellation#

Product constellation, such as Sentinel-2

constellation_id#

Constellation ID, i.e. S2 for Sentinel-2

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…)

crs = <methodtools._LruCacheWire object>[source]#
date#

Acquisition date.

datetime#

Acquisition datetime.

extent = <methodtools._LruCacheWire object>[source]#
filename#

Product filename

footprint = <methodtools._LruCacheWire object>[source]#
get_cloud_cover = <methodtools._LruCacheWire object>[source]#
get_mean_sun_angles = <methodtools._LruCacheWire object>[source]#
get_mean_viewing_angles = <methodtools._LruCacheWire object>[source]#
get_orbit_direction = <methodtools._LruCacheWire object>#
instrument#

Product instrument, such as MSI for Sentinel-2 data.

is_archived#

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

is_ortho#

True if the images are orthorectified and the footprint is retrieved easily.

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 product 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, 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.

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.

sensor_type#

Sensor type, SAR or optical.

split_name#

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

property stac: StacItem#
tile_name#

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