CosmoProduct¶
- class CosmoProduct(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.sar.sar_product.SarProduct
Class for COSMO-SkyMed (both generations) Products More info here.
Methods
__init__
(product_path[, archive_path, ...])Clean the temporary directory of the current product
clear
()Clear this product's cache
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: The first existing one between VV and HH for SAR data.
get_default_band_path
(**kwargs)Get default band path (the first existing one between VV and HH for SAR data), ready to use (orthorectified)
Return the existing orthorectified band paths (including despeckle bands).
Return the existing orthorectified bands (including despeckle bands).
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.
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
Get UTM projection
Get UTM extent of the tile
Get UTM footprint of the products (without nodata, in french == emprise utile)
Output directory of the product, to write orthorectified data for example.
Get the WGS84 extent of the file before any reprojection.
- clean_tmp()¶
Clean the temporary directory of the current product
- clear()¶
Clear this product’s cache
- get_band_paths(band_list: list, resolution: Optional[float] = None, **kwargs) dict ¶
Return the paths of required bands.
Warning
This functions orthorectifies and despeckles SAR bands if not existing !
>>> from eoreader.reader import Reader >>> from eoreader.bands import * >>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip" >>> prod = Reader().open(path) >>> prod.get_band_paths([VV, HH]) { <SarBandNames.VV: 'VV'>: '20191215T060906_S1_IW_GRD/20191215T060906_S1_IW_GRD_VV.tif' } >>> # HH doesn't exist
- 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'
- get_datetime(as_datetime: bool = False) Union[str, datetime.datetime] [source]¶
Get the product’s acquisition datetime, with format YYYYMMDDTHHMMSS <-> %Y%m%dT%H%M%S
>>> from eoreader.reader import Reader >>> path = r"1011117-766193" >>> prod = Reader().open(path) >>> prod.get_datetime(as_datetime=True) datetime.datetime(2020, 10, 28, 22, 46, 25) >>> prod.get_datetime(as_datetime=False) '20201028T224625'
- 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: The first existing one between VV and HH for SAR data.
>>> from eoreader.reader import Reader >>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip" >>> prod = Reader().open(path) >>> prod.get_default_band() <SarBandNames.VV: 'VV'>
- Returns
Default band
- Return type
- get_default_band_path(**kwargs) Union[cloudpathlib.cloudpath.CloudPath, pathlib.Path] ¶
Get default band path (the first existing one between VV and HH for SAR data), ready to use (orthorectified)
Warning
This functions orthorectifies SAR bands if not existing !
>>> from eoreader.reader import Reader >>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip" >>> prod = Reader().open(path) >>> prod.get_default_band_path() Executing processing graph ....10%....20%....30%....40%....50%....60%....70%....80%....90% done. '20191215T060906_S1_IW_GRD/20191215T060906_S1_IW_GRD_VV.tif'
- Parameters
kwargs – Additional arguments
- Returns
Default band path
- Return type
Union[CloudPath, Path]
- get_existing_band_paths() dict ¶
Return the existing orthorectified band paths (including despeckle bands).
Warning
This functions orthorectifies SAR bands if not existing !
Warning
This functions despeckles SAR bands if not existing !
>>> from eoreader.reader import Reader >>> from eoreader.bands import * >>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip" >>> prod = Reader().open(path) >>> prod.get_existing_band_paths() Executing processing graph ....10%....20%....30%....40%....50%....60%....70%....80%....90% done. Executing processing graph ....10%....20%....30%....40%....50%....60%....70%....80%....90% done. { <SarBandNames.VV: 'VV'>: '20191215T060906_S1_IW_GRD/20191215T060906_S1_IW_GRD_VV.tif', <SarBandNames.VH: 'VH'>: '20191215T060906_S1_IW_GRD/20191215T060906_S1_IW_GRD_VH.tif', <SarBandNames.VV_DSPK: 'VV_DSPK'>: '20191215T060906_S1_IW_GRD/20191215T060906_S1_IW_GRD_VV_DSPK.tif', <SarBandNames.VH_DSPK: 'VH_DSPK'>: '20191215T060906_S1_IW_GRD/20191215T060906_S1_IW_GRD_VH_DSPK.tif' }
- Returns
Dictionary containing the path of every orthorectified bands
- Return type
- get_existing_bands() list ¶
Return the existing orthorectified bands (including despeckle bands).
>>> from eoreader.reader import Reader >>> from eoreader.bands import * >>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip" >>> prod = Reader().open(path) >>> prod.get_existing_bands() [<SarBandNames.VV: 'VV'>, <SarBandNames.VH: 'VH'>, <SarBandNames.VV_DSPK: 'VV_DSPK'>, <SarBandNames.VH_DSPK: 'VH_DSPK'>]
- Returns
List of existing bands in the products
- Return type
- 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
- 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.
- 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
- Returns
{band_name, band xarray}
- Return type
- 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
>>> from eoreader.reader import Reader >>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip" >>> prod = Reader().open(path) >>> prod.utm_crs() CRS.from_epsg(32630)
- Returns
CRS object
- Return type
crs.CRS
- date¶
Acquisition date.
- datetime¶
Acquisition datetime.
- default_transform = <methodtools._LruCacheWire object>¶
- property extent: geopandas.geodataframe.GeoDataFrame¶
Get UTM extent of the tile
>>> from eoreader.reader import Reader >>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip" >>> prod = Reader().open(path) >>> prod.utm_extent() Name ... geometry 0 Sentinel-1 Image Overlay ... POLYGON ((817914.501 4684349.823, 555708.624 4... [1 rows x 12 columns]
- Returns
Footprint in UTM
- Return type
gpd.GeoDataFrame
- filename¶
Product filename
- property footprint: geopandas.geodataframe.GeoDataFrame¶
Get UTM footprint of the products (without nodata, in french == emprise utile)
>>> from eoreader.reader import Reader >>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip" >>> prod = Reader().open(path) >>> prod.footprint index geometry 0 0 POLYGON ((199980.000 4500000.000, 199980.000 4...
- Returns
Footprint as a GeoDataFrame
- Return type
gpd.GeoDataFrame
- 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 -999 by default
- 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
- pol_channels¶
Polarization Channels stored in the current product
- 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.
- sar_prod_type¶
SAR product type, either Single Look Complex or Ground Range
- sat_id¶
Satellite ID, i.e. S2 for Sentinel-2
- sensor_mode¶
Sensor Mode of the current product
- 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).
- property wgs84_extent: geopandas.geodataframe.GeoDataFrame¶
Get the WGS84 extent of the file before any reprojection. This is useful when the SAR pre-process has not been done yet.
>>> from eoreader.reader import Reader >>> path = r"1011117-766193" >>> prod = Reader().open(path) >>> prod.wgs84_extent geometry 0 POLYGON ((108.09797 15.61011, 108.48224 15.678...
- Returns
WGS84 extent as a gpd.GeoDataFrame
- Return type
gpd.GeoDataFrame