pymt_topography package

Submodules

pymt_topography.bmi module

class pymt_topography.bmi.Topography

Bases: Bmi

BMI-mediated access to NASA SRTM land elevation data.

METADATA = '/home/docs/checkouts/readthedocs.org/user_builds/pymt-topography/checkouts/latest/pymt_topography/data/Topography'
finalize() → None

Perform tear-down tasks for the model.

Perform all tasks that take place after exiting the model’s time loop. This typically includes deallocating memory, closing files and printing reports.

get_component_name() → str

Name of the component.

Returns:

The name of the component.

Return type:

str

get_current_time() → float

Current time of the model.

Returns:

The current model time.

Return type:

float

get_end_time() → float

End time of the model.

Returns:

The maximum model time.

Return type:

float

get_grid_edge_count(grid: int) → int

Get the number of edges in the grid.

Parameters:

grid (int) – A grid identifier.

Returns:

The total number of grid edges.

Return type:

int

get_grid_edge_nodes(grid: int, edge_nodes: ndarray) → ndarray

Get the edge-node connectivity.

Parameters:

  • grid (int) – A grid identifier.

  • edge_nodes (ndarray of int, shape (2 x nnodes,)) – A numpy array to place the edge-node connectivity. For each edge, connectivity is given as node at edge tail, followed by node at edge head.

Returns:

The input numpy array that holds the edge-node connectivity.

Return type:

ndarray of int

get_grid_face_count(grid: int) → int

Get the number of faces in the grid.

Parameters:

grid (int) – A grid identifier.

Returns:

The total number of grid faces.

Return type:

int

get_grid_face_edges(grid: int, face_edges: ndarray) → ndarray

Get the face-edge connectivity.

Parameters:

  • grid (int) – A grid identifier.

  • face_edges (ndarray of int) – A numpy array to place the face-edge connectivity.

Returns:

The input numpy array that holds the face-edge connectivity.

Return type:

ndarray of int

get_grid_face_nodes(grid: int, face_nodes: ndarray) → ndarray

Get the face-node connectivity.

Parameters:

  • grid (int) – A grid identifier.

  • face_nodes (ndarray of int) – A numpy array to place the face-node connectivity. For each face, the nodes (listed in a counter-clockwise direction) that form the boundary of the face.

Returns:

The input numpy array that holds the face-node connectivity.

Return type:

ndarray of int

get_grid_node_count(grid: int) → int

Get the number of nodes in the grid.

Parameters:

grid (int) – A grid identifier.

Returns:

The total number of grid nodes.

Return type:

int

get_grid_nodes_per_face(grid: int, nodes_per_face: ndarray) → ndarray

Get the number of nodes for each face.

Parameters:

  • grid (int) – A grid identifier.

  • nodes_per_face (ndarray of int, shape (nfaces,)) – A numpy array to place the number of edges per face.

Returns:

The input numpy array that holds the number of nodes per edge.

Return type:

ndarray of int

get_grid_origin(grid: int, origin: ndarray) → ndarray

Get coordinates for the lower-left corner of the computational grid.

Parameters:

  • grid (int) – A grid identifier.

  • origin (ndarray of float, shape (ndim,)) – A numpy array to hold the coordinates of the lower-left corner of the grid.

Returns:

The input numpy array that holds the coordinates of the grid’s lower-left corner.

Return type:

ndarray of float

get_grid_rank(grid: int) → int

Get number of dimensions of the computational grid.

Parameters:

grid (int) – A grid identifier.

Returns:

Rank of the grid.

Return type:

int

get_grid_shape(grid: int, shape: ndarray) → ndarray

Get dimensions of the computational grid.

Parameters:

  • grid (int) – A grid identifier.

  • shape (ndarray of int, shape (ndim,)) – A numpy array into which to place the shape of the grid.

Returns:

The input numpy array that holds the grid’s shape.

Return type:

ndarray of int

get_grid_size(grid: int) → int

Get the total number of elements in the computational grid.

Parameters:

grid (int) – A grid identifier.

Returns:

Size of the grid.

Return type:

int

get_grid_spacing(grid: int, spacing: ndarray) → ndarray

Get distance between nodes of the computational grid.

Parameters:

  • grid (int) – A grid identifier.

  • spacing (ndarray of float, shape (ndim,)) – A numpy array to hold the spacing between grid rows and columns.

Returns:

The input numpy array that holds the grid’s spacing.

Return type:

ndarray of float

get_grid_type(grid: int) → str

Get the grid type as a string.

Parameters:

grid (int) – A grid identifier.

Returns:

Type of grid as a string.

Return type:

str

get_grid_x(grid: int, x: ndarray) → ndarray

Get coordinates of grid nodes in the x direction.

Parameters:

  • grid (int) – A grid identifier.

  • x (ndarray of float, shape (nrows,)) – A numpy array to hold the x-coordinates of the grid node columns.

Returns:

The input numpy array that holds the grid’s column x-coordinates.

Return type:

ndarray of float

get_grid_y(grid: int, y: ndarray) → ndarray

Get coordinates of grid nodes in the y direction.

Parameters:

  • grid (int) – A grid identifier.

  • y (ndarray of float, shape (ncols,)) – A numpy array to hold the y-coordinates of the grid node rows.

Returns:

The input numpy array that holds the grid’s row y-coordinates.

Return type:

ndarray of float

get_grid_z(grid: int, z: ndarray) → ndarray

Get coordinates of grid nodes in the z direction.

Parameters:

  • grid (int) – A grid identifier.

  • z (ndarray of float, shape (nlayers,)) – A numpy array to hold the z-coordinates of the grid nodes layers.

Returns:

The input numpy array that holds the grid’s layer z-coordinates.

Return type:

ndarray of float

get_input_item_count() → int

Count of a model’s input variables.

Returns:

The number of input variables.

Return type:

int

get_input_var_names() → Tuple[str]

List of a model’s input variables.

Input variable names must be CSDMS Standard Names, also known as long variable names.

Returns:

The input variables for the model.

Return type:

list of str

Notes

Standard Names enable the CSDMS framework to determine whether an input variable in one model is equivalent to, or compatible with, an output variable in another model. This allows the framework to automatically connect components.

Standard Names do not have to be used within the model.

get_output_item_count() → int

Count of a model’s output variables.

Returns:

The number of output variables.

Return type:

int

get_output_var_names() → Tuple[str]

List of a model’s output variables.

Output variable names must be CSDMS Standard Names, also known as long variable names.

Returns:

The output variables for the model.

Return type:

list of str

get_start_time() → float

Start time of the model.

Model times should be of type float.

Returns:

The model start time.

Return type:

float

get_time_step() → float

Current time step of the model.

The model time step should be of type float.

Returns:

The time step used in model.

Return type:

float

get_time_units() → str

Time units of the model.

Returns:

The model time unit; e.g., days or s.

Return type:

float

Notes

CSDMS uses the UDUNITS standard from Unidata.

get_value(name: str, dest: ndarray) → ndarray

Get a copy of values of the given variable.

This is a getter for the model, used to access the model’s current state. It returns a copy of a model variable, with the return type, size and rank dependent on the variable.

Parameters:

  • name (str) – An input or output variable name, a CSDMS Standard Name.

  • dest (ndarray) – A numpy array into which to place the values.

Returns:

The same numpy array that was passed as an input buffer.

Return type:

ndarray

get_value_at_indices(name: str, dest: ndarray, inds: ndarray) → ndarray

Get values at particular indices.

Parameters:

  • name (str) – An input or output variable name, a CSDMS Standard Name.

  • dest (ndarray) – A numpy array into which to place the values.

  • indices (array_like) – The indices into the variable array.

Returns:

Value of the model variable at the given location.

Return type:

array_like

get_value_ptr(name: str) → ndarray

Get a reference to values of the given variable.

This is a getter for the model, used to access the model’s current state. It returns a reference to a model variable, with the return type, size and rank dependent on the variable.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

A reference to a model variable.

Return type:

array_like

get_var_grid(name: str) → int

Get grid identifier for the given variable.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

The grid identifier.

Return type:

int

get_var_itemsize(name: str) → int

Get memory use for each array element in bytes.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

Item size in bytes.

Return type:

int

get_var_location(name: str) → str

Get the grid element type that the a given variable is defined on.

The grid topology can be composed of nodes, edges, and faces.

node

A point that has a coordinate pair or triplet: the most basic element of the topology.

edge

A line or curve bounded by two nodes.

face

A plane or surface enclosed by a set of edges. In a 2D horizontal application one may consider the word “polygon”, but in the hierarchy of elements the word “face” is most common.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

The grid location on which the variable is defined. Must be one of “node”, “edge”, or “face”.

Return type:

str

Notes

CSDMS uses the ugrid conventions to define unstructured grids.

get_var_nbytes(name: str) → int

Get size, in bytes, of the given variable.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

The size of the variable, counted in bytes.

Return type:

int

get_var_type(name: str) → str

Get data type of the given variable.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

The Python variable type; e.g., str, int, float.

Return type:

str

get_var_units(name: str) → str

Get units of the given variable.

Standard unit names, in lower case, should be used, such as meters or seconds. Standard abbreviations, like m for meters, are also supported. For variables with compound units, each unit name is separated by a single space, with exponents other than 1 placed immediately after the name, as in m s-1 for velocity, W m-2 for an energy flux, or km2 for an area.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

The variable units.

Return type:

str

Notes

CSDMS uses the UDUNITS standard from Unidata.

initialize(config_file: str) → None

Perform startup tasks for the model.

Perform all tasks that take place before entering the model’s time loop, including opening files and initializing the model state. Model inputs are read from a text-based configuration file, specified by filename.

Parameters:

config_file (str, optional) – The path to the model configuration file.

Notes

Models should be refactored, if necessary, to use a configuration file. CSDMS does not impose any constraint on how configuration files are formatted, although YAML is recommended. A template of a model’s configuration file with placeholder values is used by the BMI.

set_value(name: str, values: ndarray) → None

Specify a new value for a model variable.

This is the setter for the model, used to change the model’s current state. It accepts, through src, a new value for a model variable, with the type, size and rank of src dependent on the variable.

Parameters:

  • var_name (str) – An input or output variable name, a CSDMS Standard Name.

  • src (array_like) – The new value for the specified variable.

set_value_at_indices(name: str, inds: ndarray, src: ndarray) → None

Specify a new value for a model variable at particular indices.

Parameters:

  • var_name (str) – An input or output variable name, a CSDMS Standard Name.

  • indices (array_like) – The indices into the variable array.

  • src (array_like) – The new value for the specified variable.

update() → None

Advance model state by one time step.

Perform all tasks that take place within one pass through the model’s time loop. This typically includes incrementing all of the model’s state variables. If the model’s state variables don’t change in time, then they can be computed by the initialize() method and this method can return with no action.

update_until(time: float) → None

Advance model state until the given time.

Parameters:

time (float) – A model time later than the current model time.

Module contents

class pymt_topography.Topography

Bases: Bmi

BMI-mediated access to NASA SRTM land elevation data.

METADATA = '/home/docs/checkouts/readthedocs.org/user_builds/pymt-topography/checkouts/latest/pymt_topography/data/Topography'
finalize() → None

Perform tear-down tasks for the model.

Perform all tasks that take place after exiting the model’s time loop. This typically includes deallocating memory, closing files and printing reports.

get_component_name() → str

Name of the component.

Returns:

The name of the component.

Return type:

str

get_current_time() → float

Current time of the model.

Returns:

The current model time.

Return type:

float

get_end_time() → float

End time of the model.

Returns:

The maximum model time.

Return type:

float

get_grid_edge_count(grid: int) → int

Get the number of edges in the grid.

Parameters:

grid (int) – A grid identifier.

Returns:

The total number of grid edges.

Return type:

int

get_grid_edge_nodes(grid: int, edge_nodes: ndarray) → ndarray

Get the edge-node connectivity.

Parameters:

  • grid (int) – A grid identifier.

  • edge_nodes (ndarray of int, shape (2 x nnodes,)) – A numpy array to place the edge-node connectivity. For each edge, connectivity is given as node at edge tail, followed by node at edge head.

Returns:

The input numpy array that holds the edge-node connectivity.

Return type:

ndarray of int

get_grid_face_count(grid: int) → int

Get the number of faces in the grid.

Parameters:

grid (int) – A grid identifier.

Returns:

The total number of grid faces.

Return type:

int

get_grid_face_edges(grid: int, face_edges: ndarray) → ndarray

Get the face-edge connectivity.

Parameters:

  • grid (int) – A grid identifier.

  • face_edges (ndarray of int) – A numpy array to place the face-edge connectivity.

Returns:

The input numpy array that holds the face-edge connectivity.

Return type:

ndarray of int

get_grid_face_nodes(grid: int, face_nodes: ndarray) → ndarray

Get the face-node connectivity.

Parameters:

  • grid (int) – A grid identifier.

  • face_nodes (ndarray of int) – A numpy array to place the face-node connectivity. For each face, the nodes (listed in a counter-clockwise direction) that form the boundary of the face.

Returns:

The input numpy array that holds the face-node connectivity.

Return type:

ndarray of int

get_grid_node_count(grid: int) → int

Get the number of nodes in the grid.

Parameters:

grid (int) – A grid identifier.

Returns:

The total number of grid nodes.

Return type:

int

get_grid_nodes_per_face(grid: int, nodes_per_face: ndarray) → ndarray

Get the number of nodes for each face.

Parameters:

  • grid (int) – A grid identifier.

  • nodes_per_face (ndarray of int, shape (nfaces,)) – A numpy array to place the number of edges per face.

Returns:

The input numpy array that holds the number of nodes per edge.

Return type:

ndarray of int

get_grid_origin(grid: int, origin: ndarray) → ndarray

Get coordinates for the lower-left corner of the computational grid.

Parameters:

  • grid (int) – A grid identifier.

  • origin (ndarray of float, shape (ndim,)) – A numpy array to hold the coordinates of the lower-left corner of the grid.

Returns:

The input numpy array that holds the coordinates of the grid’s lower-left corner.

Return type:

ndarray of float

get_grid_rank(grid: int) → int

Get number of dimensions of the computational grid.

Parameters:

grid (int) – A grid identifier.

Returns:

Rank of the grid.

Return type:

int

get_grid_shape(grid: int, shape: ndarray) → ndarray

Get dimensions of the computational grid.

Parameters:

  • grid (int) – A grid identifier.

  • shape (ndarray of int, shape (ndim,)) – A numpy array into which to place the shape of the grid.

Returns:

The input numpy array that holds the grid’s shape.

Return type:

ndarray of int

get_grid_size(grid: int) → int

Get the total number of elements in the computational grid.

Parameters:

grid (int) – A grid identifier.

Returns:

Size of the grid.

Return type:

int

get_grid_spacing(grid: int, spacing: ndarray) → ndarray

Get distance between nodes of the computational grid.

Parameters:

  • grid (int) – A grid identifier.

  • spacing (ndarray of float, shape (ndim,)) – A numpy array to hold the spacing between grid rows and columns.

Returns:

The input numpy array that holds the grid’s spacing.

Return type:

ndarray of float

get_grid_type(grid: int) → str

Get the grid type as a string.

Parameters:

grid (int) – A grid identifier.

Returns:

Type of grid as a string.

Return type:

str

get_grid_x(grid: int, x: ndarray) → ndarray

Get coordinates of grid nodes in the x direction.

Parameters:

  • grid (int) – A grid identifier.

  • x (ndarray of float, shape (nrows,)) – A numpy array to hold the x-coordinates of the grid node columns.

Returns:

The input numpy array that holds the grid’s column x-coordinates.

Return type:

ndarray of float

get_grid_y(grid: int, y: ndarray) → ndarray

Get coordinates of grid nodes in the y direction.

Parameters:

  • grid (int) – A grid identifier.

  • y (ndarray of float, shape (ncols,)) – A numpy array to hold the y-coordinates of the grid node rows.

Returns:

The input numpy array that holds the grid’s row y-coordinates.

Return type:

ndarray of float

get_grid_z(grid: int, z: ndarray) → ndarray

Get coordinates of grid nodes in the z direction.

Parameters:

  • grid (int) – A grid identifier.

  • z (ndarray of float, shape (nlayers,)) – A numpy array to hold the z-coordinates of the grid nodes layers.

Returns:

The input numpy array that holds the grid’s layer z-coordinates.

Return type:

ndarray of float

get_input_item_count() → int

Count of a model’s input variables.

Returns:

The number of input variables.

Return type:

int

get_input_var_names() → Tuple[str]

List of a model’s input variables.

Input variable names must be CSDMS Standard Names, also known as long variable names.

Returns:

The input variables for the model.

Return type:

list of str

Notes

Standard Names enable the CSDMS framework to determine whether an input variable in one model is equivalent to, or compatible with, an output variable in another model. This allows the framework to automatically connect components.

Standard Names do not have to be used within the model.

get_output_item_count() → int

Count of a model’s output variables.

Returns:

The number of output variables.

Return type:

int

get_output_var_names() → Tuple[str]

List of a model’s output variables.

Output variable names must be CSDMS Standard Names, also known as long variable names.

Returns:

The output variables for the model.

Return type:

list of str

get_start_time() → float

Start time of the model.

Model times should be of type float.

Returns:

The model start time.

Return type:

float

get_time_step() → float

Current time step of the model.

The model time step should be of type float.

Returns:

The time step used in model.

Return type:

float

get_time_units() → str

Time units of the model.

Returns:

The model time unit; e.g., days or s.

Return type:

float

Notes

CSDMS uses the UDUNITS standard from Unidata.

get_value(name: str, dest: ndarray) → ndarray

Get a copy of values of the given variable.

This is a getter for the model, used to access the model’s current state. It returns a copy of a model variable, with the return type, size and rank dependent on the variable.

Parameters:

  • name (str) – An input or output variable name, a CSDMS Standard Name.

  • dest (ndarray) – A numpy array into which to place the values.

Returns:

The same numpy array that was passed as an input buffer.

Return type:

ndarray

get_value_at_indices(name: str, dest: ndarray, inds: ndarray) → ndarray

Get values at particular indices.

Parameters:

  • name (str) – An input or output variable name, a CSDMS Standard Name.

  • dest (ndarray) – A numpy array into which to place the values.

  • indices (array_like) – The indices into the variable array.

Returns:

Value of the model variable at the given location.

Return type:

array_like

get_value_ptr(name: str) → ndarray

Get a reference to values of the given variable.

This is a getter for the model, used to access the model’s current state. It returns a reference to a model variable, with the return type, size and rank dependent on the variable.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

A reference to a model variable.

Return type:

array_like

get_var_grid(name: str) → int

Get grid identifier for the given variable.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

The grid identifier.

Return type:

int

get_var_itemsize(name: str) → int

Get memory use for each array element in bytes.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

Item size in bytes.

Return type:

int

get_var_location(name: str) → str

Get the grid element type that the a given variable is defined on.

The grid topology can be composed of nodes, edges, and faces.

node

A point that has a coordinate pair or triplet: the most basic element of the topology.

edge

A line or curve bounded by two nodes.

face

A plane or surface enclosed by a set of edges. In a 2D horizontal application one may consider the word “polygon”, but in the hierarchy of elements the word “face” is most common.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

The grid location on which the variable is defined. Must be one of “node”, “edge”, or “face”.

Return type:

str

Notes

CSDMS uses the ugrid conventions to define unstructured grids.

get_var_nbytes(name: str) → int

Get size, in bytes, of the given variable.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

The size of the variable, counted in bytes.

Return type:

int

get_var_type(name: str) → str

Get data type of the given variable.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

The Python variable type; e.g., str, int, float.

Return type:

str

get_var_units(name: str) → str

Get units of the given variable.

Standard unit names, in lower case, should be used, such as meters or seconds. Standard abbreviations, like m for meters, are also supported. For variables with compound units, each unit name is separated by a single space, with exponents other than 1 placed immediately after the name, as in m s-1 for velocity, W m-2 for an energy flux, or km2 for an area.

Parameters:

name (str) – An input or output variable name, a CSDMS Standard Name.

Returns:

The variable units.

Return type:

str

Notes

CSDMS uses the UDUNITS standard from Unidata.

initialize(config_file: str) → None

Perform startup tasks for the model.

Perform all tasks that take place before entering the model’s time loop, including opening files and initializing the model state. Model inputs are read from a text-based configuration file, specified by filename.

Parameters:

config_file (str, optional) – The path to the model configuration file.

Notes

Models should be refactored, if necessary, to use a configuration file. CSDMS does not impose any constraint on how configuration files are formatted, although YAML is recommended. A template of a model’s configuration file with placeholder values is used by the BMI.

set_value(name: str, values: ndarray) → None

Specify a new value for a model variable.

This is the setter for the model, used to change the model’s current state. It accepts, through src, a new value for a model variable, with the type, size and rank of src dependent on the variable.

Parameters:

  • var_name (str) – An input or output variable name, a CSDMS Standard Name.

  • src (array_like) – The new value for the specified variable.

set_value_at_indices(name: str, inds: ndarray, src: ndarray) → None

Specify a new value for a model variable at particular indices.

Parameters:

  • var_name (str) – An input or output variable name, a CSDMS Standard Name.

  • indices (array_like) – The indices into the variable array.

  • src (array_like) – The new value for the specified variable.

update() → None

Advance model state by one time step.

Perform all tasks that take place within one pass through the model’s time loop. This typically includes incrementing all of the model’s state variables. If the model’s state variables don’t change in time, then they can be computed by the initialize() method and this method can return with no action.

update_until(time: float) → None

Advance model state until the given time.

Parameters:

time (float) – A model time later than the current model time.