RasterStats#

class RasterStats[source]#

Bases: object

Band-wise means and standard deviations.

Attributes

vars

Channel variances, if self.stds is set.

__init__(means: Optional[Sequence[float]] = None, stds: Optional[Sequence[float]] = None, counts: Optional[Sequence[float]] = None)[source]#

Constructor.

Parameters

means (Optional[Sequence[float]]) – Band means. Defaults to None.
stds (Optional[Sequence[float]]) – Band standard deviations. Defaults to None.
counts (Optional[Sequence[float]]) – Band pixel counts (used to compute the specified means and stds). Defaults to None.

Methods

`__init__`([means, stds, counts])	Constructor.
`compute`(raster_sources[, sample_prob, ...])	Compute the mean and stds over all the raster_sources.
`compute_from_chips`(chips[, running_mean, ...])	Compute running mean and var from chips in stream.
`compute_from_pixels`(pixels[, running_mean, ...])	Update running mean and var from pixel values.
`load`(stats_uri)	Load stats from file.
`save`(stats_uri)	Save stats to file.
`to_dict`()

__init__(means: Optional[Sequence[float]] = None, stds: Optional[Sequence[float]] = None, counts: Optional[Sequence[float]] = None)[source]#

Constructor.

Parameters

means (Optional[Sequence[float]]) – Band means. Defaults to None.
stds (Optional[Sequence[float]]) – Band standard deviations. Defaults to None.
counts (Optional[Sequence[float]]) – Band pixel counts (used to compute the specified means and stds). Defaults to None.

compute(raster_sources: Sequence[RasterSource], sample_prob: float | None = None, chip_sz: int = 300, stride: int | None = None, nodata_value: float | None = 0) → None[source]#

Compute the mean and stds over all the raster_sources.

This ignores NODATA values if nodata_value is not None.

If sample_prob is set, then a subset of each scene is used to compute stats which speeds up the computation. Roughly speaking, if sample_prob=0.5, then half the pixels in the scene will be used. More precisely, the number of chips is equal to sample_prob * (width * height / 300^2), or 1, whichever is greater. Each chip is uniformly sampled from the scene with replacement. Otherwise, it uses a sliding window over the entire scene to compute stats.

Parameters

raster_sources (Sequence[RasterSource]) – List of RasterSources.
sample_prob (float | None) – Pixel sampling probability. See notes above. Defaults to None.
nodata_value (float | None) – NODATA value. If set, these pixels will be ignored when computing stats.
chip_sz (int) –
stride (int | None) –

Return type

None

compute_from_chips(chips: Iterable[ndarray], running_mean: numpy.ndarray | None = None, running_var: numpy.ndarray | None = None, running_count: numpy.ndarray | None = None) → tuple[None, None, None] | tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray][source]#

Compute running mean and var from chips in stream.

Parameters

chips (Iterable[ndarray]) –
running_mean (numpy.ndarray | None) –
running_var (numpy.ndarray | None) –
running_count (numpy.ndarray | None) –

Return type

tuple[None, None, None] | tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]

compute_from_pixels(pixels: ndarray, running_mean: numpy.ndarray | None = None, running_var: numpy.ndarray | None = None, running_count: numpy.ndarray | None = None) → tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray][source]#

Update running mean and var from pixel values.

Parameters

pixels (ndarray) –
running_mean (numpy.ndarray | None) –
running_var (numpy.ndarray | None) –
running_count (numpy.ndarray | None) –

Return type

tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]

classmethod load(stats_uri: str) → Self[source]#

Load stats from file.

Parameters: stats_uri (str) –
Return type: Self

save(stats_uri: str) → None[source]#

Save stats to file.

Parameters: stats_uri (str) –
Return type: None

to_dict() → dict[source]#

Return type: dict

property vars: numpy.ndarray | None#: Channel variances, if self.stds is set.