I/O

read_array(*args, **kwargs)	Overloaded function.
write_array(*args, **kwargs)	Overloaded function.
from_gdal(name, *[, partition_shape])	Create new array, filled with values read using GDAL
to_gdal(*args, **kwargs)	Overloaded function.

lue.framework.read_array(*args, **kwargs)

Overloaded function.

1. read_array(array_pathname: str, *, partition_shape: Optional[tuple] = None) -> object

   Create new array, filled with values read from dataset

   param str array_pathname:

   Pathname to array in dataset

   param tuple partition_shape:

   Shape of the array partitions. When not passed in, a default shape will be used which might not result in the best performance and scalability.

   rtype:

   PartitionedArray specialization

   The pathname must be formatted as: <dataset pathname>/<phenomenon name>/<property-set name>/<property name>

   It is assumed that the array does not vary through time. Otherwise, use the overload that accepts a time step index.

2. read_array(array_pathname: str, center_cell: tuple, subset_shape: tuple, *, partition_shape: Optional[tuple] = None) -> object

   Create new array, filled with values read from dataset

   param str array_pathname:

   Pathname to array in dataset

   param tuple center_cell:

   Indices of center cell of array subset

   param tuple subset_shape:

   Shape of array subset

   param tuple partition_shape:

   Shape of the array partitions. When not passed in, a default shape will be used which might not result in the best performance and scalability.

   rtype:

   PartitionedArray specialization

   The pathname must be formatted as: <dataset pathname>/<phenomenon name>/<property-set name>/<property name>

   It is assumed that the array does not vary through time. Otherwise, use the overload that accepts a time step index.

3. read_array(array_pathname: str, time_step_idx: int, *, partition_shape: Optional[tuple] = None) -> object

   Create new array, filled with values read from dataset

   param str array_pathname:

   Pathname to array in dataset

   param int time_step_idx:

   Index of the time step to read [0, nr_time_steps)

   param tuple partition_shape:

   Shape of the array partitions. When not passed in, a default shape will be used which might not result in the best performance and scalability.

   rtype:

   PartitionedArray specialization

   The pathname must be formatted as: <dataset pathname>/<phenomenon name>/<property-set name>/<property name>

   It is assumed that the array varies through time. Otherwise, use the overload without a time step index.

lue.framework.write_array(*args, **kwargs)

Overloaded function.

1. write_array(arg0: lue.lue_py.framework.PartitionedArray_uint8_2, arg1: str) -> lue.lue_py.framework.future<void>

2. write_array(arg0: lue.lue_py.framework.PartitionedArray_uint32_2, arg1: str) -> lue.lue_py.framework.future<void>

3. write_array(arg0: lue.lue_py.framework.PartitionedArray_uint64_2, arg1: str) -> lue.lue_py.framework.future<void>

4. write_array(arg0: lue.lue_py.framework.PartitionedArray_int32_2, arg1: str) -> lue.lue_py.framework.future<void>

5. write_array(arg0: lue.lue_py.framework.PartitionedArray_int64_2, arg1: str) -> lue.lue_py.framework.future<void>

6. write_array(arg0: lue.lue_py.framework.PartitionedArray_float32_2, arg1: str) -> lue.lue_py.framework.future<void>

7. write_array(arg0: lue.lue_py.framework.PartitionedArray_float64_2, arg1: str) -> lue.lue_py.framework.future<void>

8. write_array(arg0: lue.lue_py.framework.PartitionedArray_uint8_2, arg1: int, arg2: str) -> lue.lue_py.framework.future<void>

9. write_array(arg0: lue.lue_py.framework.PartitionedArray_uint32_2, arg1: int, arg2: str) -> lue.lue_py.framework.future<void>

10. write_array(arg0: lue.lue_py.framework.PartitionedArray_uint64_2, arg1: int, arg2: str) -> lue.lue_py.framework.future<void>

11. write_array(arg0: lue.lue_py.framework.PartitionedArray_int32_2, arg1: int, arg2: str) -> lue.lue_py.framework.future<void>

12. write_array(arg0: lue.lue_py.framework.PartitionedArray_int64_2, arg1: int, arg2: str) -> lue.lue_py.framework.future<void>

13. write_array(arg0: lue.lue_py.framework.PartitionedArray_float32_2, arg1: int, arg2: str) -> lue.lue_py.framework.future<void>

14. write_array(arg0: lue.lue_py.framework.PartitionedArray_float64_2, arg1: int, arg2: str) -> lue.lue_py.framework.future<void>

lue.framework.from_gdal(name: str, *, partition_shape: tuple | None = None) → object

Create new array, filled with values read using GDAL

Parameters:

• name (str) – Name of dataset to read

• partition_shape (tuple) – Shape of the array partitions. When not passed in, a default shape will be used which might not result in the best performance and scalability.

Return type:

PartitionedArray specialization

lue.framework.to_gdal(*args, **kwargs)

Overloaded function.

1. to_gdal(array: lue.lue_py.framework.PartitionedArray_uint8_2, name: str, clone_name: str = ‘’) -> lue.lue_py.framework.future<void>

2. to_gdal(array: lue.lue_py.framework.PartitionedArray_uint32_2, name: str, clone_name: str = ‘’) -> lue.lue_py.framework.future<void>

3. to_gdal(array: lue.lue_py.framework.PartitionedArray_int32_2, name: str, clone_name: str = ‘’) -> lue.lue_py.framework.future<void>

4. to_gdal(array: lue.lue_py.framework.PartitionedArray_float32_2, name: str, clone_name: str = ‘’) -> lue.lue_py.framework.future<void>

5. to_gdal(array: lue.lue_py.framework.PartitionedArray_float64_2, name: str, clone_name: str = ‘’) -> lue.lue_py.framework.future<void>