Example data sets

Datasets

skgstat.data.pancake(N=500, band=0, seed=42, as_dataframe=False)[source]

Sample of the pancake_field. By default, the Red band is sampled at 500 random location without replacement.

Parameters

  • N (int) – Number of sample points to use.

  • band (int) – can be 0 (Red), 1 (Green), 2 (Blue) or 'mean', which will average all three RGB bands

  • seed (int) – By default a seed is set to always return the same sample for same N and band input

  • as_dataframe (bool) – If True, the data is returned as pandas.Dataframe. Default is False.

Returns

result – Dictionary of the sample and a citation information. The sample is a tuple of two numpy arrays.

Return type

dict

See also

get_sample pancake_field

Notes

The image originates from a photograph of an actual pancake. The image was cropped to an 500x500 pixel extent keeping the center of the original photograph. If you use this example somewhere else, please cite SciKit-GStat [501]_, as it is distributed with the library.

References

501

Mirko Mälicke, Helge David Schneider, Sebastian Müller, & Egil Möller. (2021, April 20). mmaelicke/scikit-gstat: A scipy flavoured geostatistical variogram analysis toolbox (Version v0.5.0). Zenodo. http://doi.org/10.5281/zenodo.4704356

skgstat.data.pancake_field(band=0)[source]

Image of a pancake with apparent spatial structure. The pankcake has three RGB bands.

Parameters

band (int) – can be 0 (Red), 1 (Green), 2 (Blue) or 'mean', which will average all three RGB bands

Returns

result – Dictionary of the sample and a citation information. The sample is 2D numpy array of the field.

Return type

dict

See also

skgstat.data._loader.field(), skgstat.data.pancake()

Notes

The image originates from a photograph of an actual pancake. The image was cropped to an 500x500 pixel extent keeping the center of the original photograph. If you use this example somewhere else, please cite SciKit-GStat [501]_, as it is distributed with the library.

References

501

Mirko Mälicke, Helge David Schneider, Sebastian Müller, & Egil Möller. (2021, April 20). mmaelicke/scikit-gstat: A scipy flavoured geostatistical variogram analysis toolbox (Version v0.5.0). Zenodo. http://doi.org/10.5281/zenodo.4704356

skgstat.data.aniso(N=500, seed=42, as_dataframe=False)[source]

Sample of the ansio_field. By default the greyscale image is sampled at 500 random locations.

Parameters

  • N (int) – Number of sample points to use.

  • seed (int) – By default a seed is set to always return the same sample for same N and band input

  • as_dataframe (bool) – If True, the data is returned as pandas.Dataframe. Default is False.

Returns

result – Dictionary of the sample and a citation information. The sample is a tuple of two numpy arrays.

Return type

dict

See also

skgstat.data._loader.field()

field loader

aniso_field()

Return the full field

Notes

This image was created using gstools.SRF. The spatial random field was created using a Gaussian model and has a size of 500x500 pixel. The created field was normalized and rescaled to the value range of a uint8. The spatial model includes a small nugget (~ 1/25 of the value range). If you use this example somewhere else, please cite SciKit-GStat [501]_, as it is distributed with the library.

References

501

Mirko Mälicke, Helge David Schneider, Sebastian Müller, & Egil Möller. (2021, April 20). mmaelicke/scikit-gstat: A scipy flavoured geostatistical variogram analysis toolbox (Version v0.5.0). Zenodo. http://doi.org/10.5281/zenodo.4704356

skgstat.data.aniso_field()[source]

Image of a greyscale image with geometric anisotropy. The anisotropy has a North-Easth orientation and has a approx. 3 times larger correlation length than in the perpendicular orientation.

Returns

result – Dictionary of the sample and a citation information. The sample a numpy array repesenting the image.

Return type

dict

See also

skgstat.data._loader.field()

field loader

aniso()

Return a sample

Notes

This image was created using gstools.SRF. The spatial random field was created using a Gaussian model and has a size of 500x500 pixel. The created field was normalized and rescaled to the value range of a uint8. The spatial model includes a small nugget (~ 1/25 of the value range). If you use this example somewhere else, please cite SciKit-GStat [501]_, as it is distributed with the library.

References

501

Mirko Mälicke, Helge David Schneider, Sebastian Müller, & Egil Möller. (2021, April 20). mmaelicke/scikit-gstat: A scipy flavoured geostatistical variogram analysis toolbox (Version v0.5.0). Zenodo. http://doi.org/10.5281/zenodo.4704356

Utility Functions

..automodule:: skgstat.data._loader
members

field, get_sample

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MetricSpace - Coordinate representation