One Band Raster Dataset

In [35]:

# NBVAL_IGNORE_OUTPUT
from pyramids.dataset import Dataset

path = r"../../../examples/data/geotiff/south-america-mswep_1979010100.tif"

# NBVAL_IGNORE_OUTPUT from pyramids.dataset import Dataset path = r"../../../examples/data/geotiff/south-america-mswep_1979010100.tif"

In [36]:

dataset = Dataset.read_file(path)
print(dataset)

dataset = Dataset.read_file(path) print(dataset)

            Top Left Corner: (-110.0, 18.1)
            Cell size: 0.1
            Dimension: 780 * 850
            EPSG: 4326
            Number of Bands: 1
            Band names: ['Band_1']
            Band colors: {0: 'gray_index'}
            Band units: ['']
            Scale: [1.0]
            Offset: [0]
            Mask: -9999.0
            Data type: float32
            File: ../../examples/data/geotiff/south-america-mswep_1979010100.tif
        

In [37]:

dataset.plot()

dataset.plot()

Out[37]:

<cleopatra.array_glyph.ArrayGlyph at 0x12de88bf610>

Raster Dimension¶

In [38]:

print(dataset.cell_size)

print(dataset.cell_size)

0.1

In [39]:

print(f"Rows , Columns = {dataset.rows}, {dataset.columns}")

print(f"Rows , Columns = {dataset.rows}, {dataset.columns}")

Rows , Columns = 780, 850

In [40]:

print(dataset.shape)

print(dataset.shape)

(1, 780, 850)

In [41]:

print(dataset.band_count)

print(dataset.band_count)

1

Band Information¶

In [42]:

print(dataset.band_names)

print(dataset.band_names)

['Band_1']

In [43]:

print(dataset.dtype)

print(dataset.dtype)

['float32']

In [44]:

print(dataset.band_units)

print(dataset.band_units)

['']

In [45]:

print(dataset.scale)

print(dataset.scale)

[1.0]

In [46]:

print(dataset.offset)

print(dataset.offset)

[0]

Geospatial Information¶

In [47]:

print(dataset.geotransform)
print(dataset.top_left_corner)

print(dataset.geotransform) print(dataset.top_left_corner)

(-110.0, 0.1, 0.0, 18.1, 0.0, -0.1)
(-110.0, 18.1)

In [48]:

print(dataset.bounds)

print(dataset.bounds)

                                            geometry
0  POLYGON ((-110 18.1, -110 -59.9, -25 -59.9, -2...

In [49]:

print(dataset.bbox)

print(dataset.bbox)

[-110.0, -59.9, -25.0, 18.1]

In [50]:

print(dataset.epsg)
print(dataset.crs)

print(dataset.epsg) print(dataset.crs)

4326
GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

In [51]:

print(dataset.no_data_value)

print(dataset.no_data_value)

[-9999.0]

In [52]:

print(dataset.lon)

print(dataset.lon)

[-109.95 -109.85 -109.75 -109.65 -109.55 -109.45 -109.35 -109.25 -109.15
 -109.05 -108.95 -108.85 -108.75 -108.65 -108.55 -108.45 -108.35 -108.25
 -108.15 -108.05 -107.95 -107.85 -107.75 -107.65 -107.55 -107.45 -107.35
 -107.25 -107.15 -107.05 -106.95 -106.85 -106.75 -106.65 -106.55 -106.45
 -106.35 -106.25 -106.15 -106.05 -105.95 -105.85 -105.75 -105.65 -105.55
 -105.45 -105.35 -105.25 -105.15 -105.05 -104.95 -104.85 -104.75 -104.65
 -104.55 -104.45 -104.35 -104.25 -104.15 -104.05 -103.95 -103.85 -103.75
 -103.65 -103.55 -103.45 -103.35 -103.25 -103.15 -103.05 -102.95 -102.85
 -102.75 -102.65 -102.55 -102.45 -102.35 -102.25 -102.15 -102.05 -101.95
 -101.85 -101.75 -101.65 -101.55 -101.45 -101.35 -101.25 -101.15 -101.05
 -100.95 -100.85 -100.75 -100.65 -100.55 -100.45 -100.35 -100.25 -100.15
 -100.05  -99.95  -99.85  -99.75  -99.65  -99.55  -99.45  -99.35  -99.25
  -99.15  -99.05  -98.95  -98.85  -98.75  -98.65  -98.55  -98.45  -98.35
  -98.25  -98.15  -98.05  -97.95  -97.85  -97.75  -97.65  -97.55  -97.45
  -97.35  -97.25  -97.15  -97.05  -96.95  -96.85  -96.75  -96.65  -96.55
  -96.45  -96.35  -96.25  -96.15  -96.05  -95.95  -95.85  -95.75  -95.65
  -95.55  -95.45  -95.35  -95.25  -95.15  -95.05  -94.95  -94.85  -94.75
  -94.65  -94.55  -94.45  -94.35  -94.25  -94.15  -94.05  -93.95  -93.85
  -93.75  -93.65  -93.55  -93.45  -93.35  -93.25  -93.15  -93.05  -92.95
  -92.85  -92.75  -92.65  -92.55  -92.45  -92.35  -92.25  -92.15  -92.05
  -91.95  -91.85  -91.75  -91.65  -91.55  -91.45  -91.35  -91.25  -91.15
  -91.05  -90.95  -90.85  -90.75  -90.65  -90.55  -90.45  -90.35  -90.25
  -90.15  -90.05  -89.95  -89.85  -89.75  -89.65  -89.55  -89.45  -89.35
  -89.25  -89.15  -89.05  -88.95  -88.85  -88.75  -88.65  -88.55  -88.45
  -88.35  -88.25  -88.15  -88.05  -87.95  -87.85  -87.75  -87.65  -87.55
  -87.45  -87.35  -87.25  -87.15  -87.05  -86.95  -86.85  -86.75  -86.65
  -86.55  -86.45  -86.35  -86.25  -86.15  -86.05  -85.95  -85.85  -85.75
  -85.65  -85.55  -85.45  -85.35  -85.25  -85.15  -85.05  -84.95  -84.85
  -84.75  -84.65  -84.55  -84.45  -84.35  -84.25  -84.15  -84.05  -83.95
  -83.85  -83.75  -83.65  -83.55  -83.45  -83.35  -83.25  -83.15  -83.05
  -82.95  -82.85  -82.75  -82.65  -82.55  -82.45  -82.35  -82.25  -82.15
  -82.05  -81.95  -81.85  -81.75  -81.65  -81.55  -81.45  -81.35  -81.25
  -81.15  -81.05  -80.95  -80.85  -80.75  -80.65  -80.55  -80.45  -80.35
  -80.25  -80.15  -80.05  -79.95  -79.85  -79.75  -79.65  -79.55  -79.45
  -79.35  -79.25  -79.15  -79.05  -78.95  -78.85  -78.75  -78.65  -78.55
  -78.45  -78.35  -78.25  -78.15  -78.05  -77.95  -77.85  -77.75  -77.65
  -77.55  -77.45  -77.35  -77.25  -77.15  -77.05  -76.95  -76.85  -76.75
  -76.65  -76.55  -76.45  -76.35  -76.25  -76.15  -76.05  -75.95  -75.85
  -75.75  -75.65  -75.55  -75.45  -75.35  -75.25  -75.15  -75.05  -74.95
  -74.85  -74.75  -74.65  -74.55  -74.45  -74.35  -74.25  -74.15  -74.05
  -73.95  -73.85  -73.75  -73.65  -73.55  -73.45  -73.35  -73.25  -73.15
  -73.05  -72.95  -72.85  -72.75  -72.65  -72.55  -72.45  -72.35  -72.25
  -72.15  -72.05  -71.95  -71.85  -71.75  -71.65  -71.55  -71.45  -71.35
  -71.25  -71.15  -71.05  -70.95  -70.85  -70.75  -70.65  -70.55  -70.45
  -70.35  -70.25  -70.15  -70.05  -69.95  -69.85  -69.75  -69.65  -69.55
  -69.45  -69.35  -69.25  -69.15  -69.05  -68.95  -68.85  -68.75  -68.65
  -68.55  -68.45  -68.35  -68.25  -68.15  -68.05  -67.95  -67.85  -67.75
  -67.65  -67.55  -67.45  -67.35  -67.25  -67.15  -67.05  -66.95  -66.85
  -66.75  -66.65  -66.55  -66.45  -66.35  -66.25  -66.15  -66.05  -65.95
  -65.85  -65.75  -65.65  -65.55  -65.45  -65.35  -65.25  -65.15  -65.05
  -64.95  -64.85  -64.75  -64.65  -64.55  -64.45  -64.35  -64.25  -64.15
  -64.05  -63.95  -63.85  -63.75  -63.65  -63.55  -63.45  -63.35  -63.25
  -63.15  -63.05  -62.95  -62.85  -62.75  -62.65  -62.55  -62.45  -62.35
  -62.25  -62.15  -62.05  -61.95  -61.85  -61.75  -61.65  -61.55  -61.45
  -61.35  -61.25  -61.15  -61.05  -60.95  -60.85  -60.75  -60.65  -60.55
  -60.45  -60.35  -60.25  -60.15  -60.05  -59.95  -59.85  -59.75  -59.65
  -59.55  -59.45  -59.35  -59.25  -59.15  -59.05  -58.95  -58.85  -58.75
  -58.65  -58.55  -58.45  -58.35  -58.25  -58.15  -58.05  -57.95  -57.85
  -57.75  -57.65  -57.55  -57.45  -57.35  -57.25  -57.15  -57.05  -56.95
  -56.85  -56.75  -56.65  -56.55  -56.45  -56.35  -56.25  -56.15  -56.05
  -55.95  -55.85  -55.75  -55.65  -55.55  -55.45  -55.35  -55.25  -55.15
  -55.05  -54.95  -54.85  -54.75  -54.65  -54.55  -54.45  -54.35  -54.25
  -54.15  -54.05  -53.95  -53.85  -53.75  -53.65  -53.55  -53.45  -53.35
  -53.25  -53.15  -53.05  -52.95  -52.85  -52.75  -52.65  -52.55  -52.45
  -52.35  -52.25  -52.15  -52.05  -51.95  -51.85  -51.75  -51.65  -51.55
  -51.45  -51.35  -51.25  -51.15  -51.05  -50.95  -50.85  -50.75  -50.65
  -50.55  -50.45  -50.35  -50.25  -50.15  -50.05  -49.95  -49.85  -49.75
  -49.65  -49.55  -49.45  -49.35  -49.25  -49.15  -49.05  -48.95  -48.85
  -48.75  -48.65  -48.55  -48.45  -48.35  -48.25  -48.15  -48.05  -47.95
  -47.85  -47.75  -47.65  -47.55  -47.45  -47.35  -47.25  -47.15  -47.05
  -46.95  -46.85  -46.75  -46.65  -46.55  -46.45  -46.35  -46.25  -46.15
  -46.05  -45.95  -45.85  -45.75  -45.65  -45.55  -45.45  -45.35  -45.25
  -45.15  -45.05  -44.95  -44.85  -44.75  -44.65  -44.55  -44.45  -44.35
  -44.25  -44.15  -44.05  -43.95  -43.85  -43.75  -43.65  -43.55  -43.45
  -43.35  -43.25  -43.15  -43.05  -42.95  -42.85  -42.75  -42.65  -42.55
  -42.45  -42.35  -42.25  -42.15  -42.05  -41.95  -41.85  -41.75  -41.65
  -41.55  -41.45  -41.35  -41.25  -41.15  -41.05  -40.95  -40.85  -40.75
  -40.65  -40.55  -40.45  -40.35  -40.25  -40.15  -40.05  -39.95  -39.85
  -39.75  -39.65  -39.55  -39.45  -39.35  -39.25  -39.15  -39.05  -38.95
  -38.85  -38.75  -38.65  -38.55  -38.45  -38.35  -38.25  -38.15  -38.05
  -37.95  -37.85  -37.75  -37.65  -37.55  -37.45  -37.35  -37.25  -37.15
  -37.05  -36.95  -36.85  -36.75  -36.65  -36.55  -36.45  -36.35  -36.25
  -36.15  -36.05  -35.95  -35.85  -35.75  -35.65  -35.55  -35.45  -35.35
  -35.25  -35.15  -35.05  -34.95  -34.85  -34.75  -34.65  -34.55  -34.45
  -34.35  -34.25  -34.15  -34.05  -33.95  -33.85  -33.75  -33.65  -33.55
  -33.45  -33.35  -33.25  -33.15  -33.05  -32.95  -32.85  -32.75  -32.65
  -32.55  -32.45  -32.35  -32.25  -32.15  -32.05  -31.95  -31.85  -31.75
  -31.65  -31.55  -31.45  -31.35  -31.25  -31.15  -31.05  -30.95  -30.85
  -30.75  -30.65  -30.55  -30.45  -30.35  -30.25  -30.15  -30.05  -29.95
  -29.85  -29.75  -29.65  -29.55  -29.45  -29.35  -29.25  -29.15  -29.05
  -28.95  -28.85  -28.75  -28.65  -28.55  -28.45  -28.35  -28.25  -28.15
  -28.05  -27.95  -27.85  -27.75  -27.65  -27.55  -27.45  -27.35  -27.25
  -27.15  -27.05  -26.95  -26.85  -26.75  -26.65  -26.55  -26.45  -26.35
  -26.25  -26.15  -26.05  -25.95  -25.85  -25.75  -25.65  -25.55  -25.45
  -25.35  -25.25  -25.15  -25.05]

In [53]:

print(dataset.lat)

print(dataset.lat)

[ 1.805e+01  1.795e+01  1.785e+01  1.775e+01  1.765e+01  1.755e+01
  1.745e+01  1.735e+01  1.725e+01  1.715e+01  1.705e+01  1.695e+01
  1.685e+01  1.675e+01  1.665e+01  1.655e+01  1.645e+01  1.635e+01
  1.625e+01  1.615e+01  1.605e+01  1.595e+01  1.585e+01  1.575e+01
  1.565e+01  1.555e+01  1.545e+01  1.535e+01  1.525e+01  1.515e+01
  1.505e+01  1.495e+01  1.485e+01  1.475e+01  1.465e+01  1.455e+01
  1.445e+01  1.435e+01  1.425e+01  1.415e+01  1.405e+01  1.395e+01
  1.385e+01  1.375e+01  1.365e+01  1.355e+01  1.345e+01  1.335e+01
  1.325e+01  1.315e+01  1.305e+01  1.295e+01  1.285e+01  1.275e+01
  1.265e+01  1.255e+01  1.245e+01  1.235e+01  1.225e+01  1.215e+01
  1.205e+01  1.195e+01  1.185e+01  1.175e+01  1.165e+01  1.155e+01
  1.145e+01  1.135e+01  1.125e+01  1.115e+01  1.105e+01  1.095e+01
  1.085e+01  1.075e+01  1.065e+01  1.055e+01  1.045e+01  1.035e+01
  1.025e+01  1.015e+01  1.005e+01  9.950e+00  9.850e+00  9.750e+00
  9.650e+00  9.550e+00  9.450e+00  9.350e+00  9.250e+00  9.150e+00
  9.050e+00  8.950e+00  8.850e+00  8.750e+00  8.650e+00  8.550e+00
  8.450e+00  8.350e+00  8.250e+00  8.150e+00  8.050e+00  7.950e+00
  7.850e+00  7.750e+00  7.650e+00  7.550e+00  7.450e+00  7.350e+00
  7.250e+00  7.150e+00  7.050e+00  6.950e+00  6.850e+00  6.750e+00
  6.650e+00  6.550e+00  6.450e+00  6.350e+00  6.250e+00  6.150e+00
  6.050e+00  5.950e+00  5.850e+00  5.750e+00  5.650e+00  5.550e+00
  5.450e+00  5.350e+00  5.250e+00  5.150e+00  5.050e+00  4.950e+00
  4.850e+00  4.750e+00  4.650e+00  4.550e+00  4.450e+00  4.350e+00
  4.250e+00  4.150e+00  4.050e+00  3.950e+00  3.850e+00  3.750e+00
  3.650e+00  3.550e+00  3.450e+00  3.350e+00  3.250e+00  3.150e+00
  3.050e+00  2.950e+00  2.850e+00  2.750e+00  2.650e+00  2.550e+00
  2.450e+00  2.350e+00  2.250e+00  2.150e+00  2.050e+00  1.950e+00
  1.850e+00  1.750e+00  1.650e+00  1.550e+00  1.450e+00  1.350e+00
  1.250e+00  1.150e+00  1.050e+00  9.500e-01  8.500e-01  7.500e-01
  6.500e-01  5.500e-01  4.500e-01  3.500e-01  2.500e-01  1.500e-01
  5.000e-02 -5.000e-02 -1.500e-01 -2.500e-01 -3.500e-01 -4.500e-01
 -5.500e-01 -6.500e-01 -7.500e-01 -8.500e-01 -9.500e-01 -1.050e+00
 -1.150e+00 -1.250e+00 -1.350e+00 -1.450e+00 -1.550e+00 -1.650e+00
 -1.750e+00 -1.850e+00 -1.950e+00 -2.050e+00 -2.150e+00 -2.250e+00
 -2.350e+00 -2.450e+00 -2.550e+00 -2.650e+00 -2.750e+00 -2.850e+00
 -2.950e+00 -3.050e+00 -3.150e+00 -3.250e+00 -3.350e+00 -3.450e+00
 -3.550e+00 -3.650e+00 -3.750e+00 -3.850e+00 -3.950e+00 -4.050e+00
 -4.150e+00 -4.250e+00 -4.350e+00 -4.450e+00 -4.550e+00 -4.650e+00
 -4.750e+00 -4.850e+00 -4.950e+00 -5.050e+00 -5.150e+00 -5.250e+00
 -5.350e+00 -5.450e+00 -5.550e+00 -5.650e+00 -5.750e+00 -5.850e+00
 -5.950e+00 -6.050e+00 -6.150e+00 -6.250e+00 -6.350e+00 -6.450e+00
 -6.550e+00 -6.650e+00 -6.750e+00 -6.850e+00 -6.950e+00 -7.050e+00
 -7.150e+00 -7.250e+00 -7.350e+00 -7.450e+00 -7.550e+00 -7.650e+00
 -7.750e+00 -7.850e+00 -7.950e+00 -8.050e+00 -8.150e+00 -8.250e+00
 -8.350e+00 -8.450e+00 -8.550e+00 -8.650e+00 -8.750e+00 -8.850e+00
 -8.950e+00 -9.050e+00 -9.150e+00 -9.250e+00 -9.350e+00 -9.450e+00
 -9.550e+00 -9.650e+00 -9.750e+00 -9.850e+00 -9.950e+00 -1.005e+01
 -1.015e+01 -1.025e+01 -1.035e+01 -1.045e+01 -1.055e+01 -1.065e+01
 -1.075e+01 -1.085e+01 -1.095e+01 -1.105e+01 -1.115e+01 -1.125e+01
 -1.135e+01 -1.145e+01 -1.155e+01 -1.165e+01 -1.175e+01 -1.185e+01
 -1.195e+01 -1.205e+01 -1.215e+01 -1.225e+01 -1.235e+01 -1.245e+01
 -1.255e+01 -1.265e+01 -1.275e+01 -1.285e+01 -1.295e+01 -1.305e+01
 -1.315e+01 -1.325e+01 -1.335e+01 -1.345e+01 -1.355e+01 -1.365e+01
 -1.375e+01 -1.385e+01 -1.395e+01 -1.405e+01 -1.415e+01 -1.425e+01
 -1.435e+01 -1.445e+01 -1.455e+01 -1.465e+01 -1.475e+01 -1.485e+01
 -1.495e+01 -1.505e+01 -1.515e+01 -1.525e+01 -1.535e+01 -1.545e+01
 -1.555e+01 -1.565e+01 -1.575e+01 -1.585e+01 -1.595e+01 -1.605e+01
 -1.615e+01 -1.625e+01 -1.635e+01 -1.645e+01 -1.655e+01 -1.665e+01
 -1.675e+01 -1.685e+01 -1.695e+01 -1.705e+01 -1.715e+01 -1.725e+01
 -1.735e+01 -1.745e+01 -1.755e+01 -1.765e+01 -1.775e+01 -1.785e+01
 -1.795e+01 -1.805e+01 -1.815e+01 -1.825e+01 -1.835e+01 -1.845e+01
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 -5.935e+01 -5.945e+01 -5.955e+01 -5.965e+01 -5.975e+01 -5.985e+01]

In [54]:

print(dataset.x)

print(dataset.x)

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  -25.35  -25.25  -25.15  -25.05]

In [55]:

print(dataset.y)

print(dataset.y)

[ 1.805e+01  1.795e+01  1.785e+01  1.775e+01  1.765e+01  1.755e+01
  1.745e+01  1.735e+01  1.725e+01  1.715e+01  1.705e+01  1.695e+01
  1.685e+01  1.675e+01  1.665e+01  1.655e+01  1.645e+01  1.635e+01
  1.625e+01  1.615e+01  1.605e+01  1.595e+01  1.585e+01  1.575e+01
  1.565e+01  1.555e+01  1.545e+01  1.535e+01  1.525e+01  1.515e+01
  1.505e+01  1.495e+01  1.485e+01  1.475e+01  1.465e+01  1.455e+01
  1.445e+01  1.435e+01  1.425e+01  1.415e+01  1.405e+01  1.395e+01
  1.385e+01  1.375e+01  1.365e+01  1.355e+01  1.345e+01  1.335e+01
  1.325e+01  1.315e+01  1.305e+01  1.295e+01  1.285e+01  1.275e+01
  1.265e+01  1.255e+01  1.245e+01  1.235e+01  1.225e+01  1.215e+01
  1.205e+01  1.195e+01  1.185e+01  1.175e+01  1.165e+01  1.155e+01
  1.145e+01  1.135e+01  1.125e+01  1.115e+01  1.105e+01  1.095e+01
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Driver information and metadata¶

In [56]:

print(dataset.meta_data)

print(dataset.meta_data)

{'AREA_OR_POINT': 'Area'}

In [57]:

print(dataset.block_size)

print(dataset.block_size)

[[850, 2]]

In [58]:

print(dataset.file_name)

print(dataset.file_name)

../../examples/data/geotiff/south-america-mswep_1979010100.tif

In [59]:

print(dataset.driver_type)

print(dataset.driver_type)

geotiff

Create a copy of the dataset¶

In [60]:

dataset_copy = dataset.copy()
print(dataset_copy)

dataset_copy = dataset.copy() print(dataset_copy)

            Top Left Corner: (-110.0, 18.1)
            Cell size: 0.1
            Dimension: 780 * 850
            EPSG: 4326
            Number of Bands: 1
            Band names: ['Band_1']
            Band colors: {0: 'gray_index'}
            Band units: ['']
            Scale: [1.0]
            Offset: [0]
            Mask: -9999.0
            Data type: float32
            File: 
        

Add a new band¶

In [61]:

import numpy as np

new_band = np.random.rand(dataset.rows, dataset.columns)
new_dataset = dataset.add_band(new_band, unit="mm")
print(new_dataset)

import numpy as np new_band = np.random.rand(dataset.rows, dataset.columns) new_dataset = dataset.add_band(new_band, unit="mm") print(new_dataset)

            Top Left Corner: (-110.0, 18.1)
            Cell size: 0.1
            Dimension: 780 * 850
            EPSG: 4326
            Number of Bands: 2
            Band names: ['Band_1', 'Band_2']
            Band colors: {0: 'gray_index', 1: 'undefined'}
            Band units: ['', 'mm']
            Scale: [1.0, 1.0]
            Offset: [0, 0]
            Mask: -9999.0
            Data type: float32
            File: 
        

Read band values¶

In [62]:

arr = dataset.read_array()
print(arr.shape)

arr = dataset.read_array() print(arr.shape)

(780, 850)

In [63]:

arr

arr

Out[63]:

array([[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       ...,
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],
      shape=(780, 850), dtype=float32)

In [64]:

block = dataset.read_array(band=0, window=(0, 0, 100, 100))
print(block.shape)
print(block)

block = dataset.read_array(band=0, window=(0, 0, 100, 100)) print(block.shape) print(block)

(100, 100)
[[-9999. -9999. -9999. ... -9999. -9999. -9999.]
 [-9999. -9999. -9999. ... -9999. -9999. -9999.]
 [-9999. -9999. -9999. ... -9999. -9999. -9999.]
 ...
 [-9999. -9999. -9999. ... -9999. -9999. -9999.]
 [-9999. -9999. -9999. ... -9999. -9999. -9999.]
 [-9999. -9999. -9999. ... -9999. -9999. -9999.]]

In [65]:

dataset.get_block_arrangement(x_block_size=100, y_block_size=100)

dataset.get_block_arrangement(x_block_size=100, y_block_size=100)

Out[65]:

	x_offset	y_offset	window_xsize	window_ysize
0	0	0	100	100
1	100	0	100	100
2	200	0	100	100
3	300	0	100	100
4	400	0	100	100
...	...	...	...	...
67	400	700	100	80
68	500	700	100	80
69	600	700	100	80
70	700	700	100	80
71	800	700	50	80

72 rows × 4 columns

— Band statistics¶

In [66]:

stats = dataset.stats(band=0)
stats

stats = dataset.stats(band=0) stats

Out[66]:

	min	max	mean	std
Band_1	-9998.992188	65.326111	-1949.050171	3413.121826

Attribute Table¶

— read band attribute table¶

In [67]:

df = dataset.get_attribute_table(band=0)
print(df)

df = dataset.get_attribute_table(band=0) print(df)

  Precipitation Range (mm)   Category              Description
0                     0-50        Low   Very low precipitation
1                   51-100   Moderate   Moderate precipitation
2                  101-200       High       High precipitation
3                  201-500  Very High  Very high precipitation
4                     >500    Extreme    Extreme precipitation

— Set attribute table¶

In [68]:

import pandas as pd

attribute_table = {
    'Precipitation Range (mm)': ['0-50', '51-100', '101-200', '201-500', '>500'],
    'Category': ['Low', 'Moderate', 'High', 'Very High', 'Extreme'],
    'Description': [
        'Very low precipitation',
        'Moderate precipitation',
        'High precipitation',
        'Very high precipitation',
        'Extreme precipitation',
    ],
}
df = pd.DataFrame(attribute_table)
dataset.set_attribute_table(df, band=0)

import pandas as pd attribute_table = { 'Precipitation Range (mm)': ['0-50', '51-100', '101-200', '201-500', '>500'], 'Category': ['Low', 'Moderate', 'High', 'Very High', 'Extreme'], 'Description': [ 'Very low precipitation', 'Moderate precipitation', 'High precipitation', 'Very high precipitation', 'Extreme precipitation', ], } df = pd.DataFrame(attribute_table) dataset.set_attribute_table(df, band=0)