CustomProduct
CustomProduct¶
- class CustomProduct(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, **kwargs)[source]¶
Bases:
eoreader.products.product.Product
Custom products
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])Get the stack path for each asked band
get_date
([as_date])Get the product's acquisition date.
get_datetime
([as_datetime])Set product real name from metadata
Get default band: the first one of the stack
get_default_band_path
(**kwargs)Get default band path: the stack path.
Get the stack path.
Get the bands of the stack.
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.
stack
(bands[, resolution, size, stack_path, ...])Stack bands and index of a products.
to_repr
()Returns a representation of the product as a list
Attributes
Get UTM projection of stack.
Get UTM extent of stack.
Get UTM footprint of the products (without nodata, in french == emprise utile)
Output directory of the product, to write orthorectified data for example.
Sun mean angles (azimuth)
Sun mean angles (zenith)
- __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, **kwargs) None [source]¶
- 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 [source]¶
Get the stack path for each asked band
- 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) str [source]¶
Set product real name from metadata
- Returns
True name of the product (from metadata)
- Return type
- get_default_band() eoreader.bands.bands.BandNames [source]¶
Get default band: the first one of the stack
- Returns
Default band
- Return type
- get_default_band_path(**kwargs) Union[cloudpathlib.cloudpath.CloudPath, pathlib.Path] [source]¶
Get default band path: the stack path.
- Parameters
kwargs – Additional arguments
- Returns
Default band path
- Return type
Union[CloudPath, Path]
- get_existing_band_paths() dict [source]¶
Get the stack path.
- Returns
Dictionary containing the path of every orthorectified bands
- Return type
- get_existing_bands() list [source]¶
Get the bands of the stack.
- 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()¶
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, 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
orrioxarray.to_raster()
(such ascompress
)
- Returns
Stack as a DataArray
- Return type
xr.DataArray
- to_repr() list ¶
Returns a representation of the product as a list
- Returns
Representation of the product
- Return type
- 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 as03
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 stack.
- 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 stack.
- 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 -9999 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
- 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
forSentinel-2
- sensor_type¶
Sensor type, SAR or optical.
- split_name¶
Split name, to retrieve every information from its true name (dates, tile, product type…).
- sun_az¶
Sun mean angles (azimuth)
- sun_zen¶
Sun mean angles (zenith)
- tile_name¶
Tile if possible (for data that can be piled, for example S2 and Landsats).