Add Lake Champlain USGS Water level data provider for coastal model t…

extern/Coastal_model_waterlevel_data_providers/Lake_Champlain_USGS_Waterlevel_BMI/README.md

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+# Testing BMI Python modules.
+ - model.py: This file is the "model" it takes inputs and gives an output
+ - bmi_model.py: This is the Basic Model Interface that talks with the model. This is what we are testing.
+ - run_bmi_model.py: This is a file that mimics the framework, in the sense that it initializes the model with the BMI function. Then it runs the model with the BMI Update function, etc.
+ - run_bmi_unit_test.py: This is a file that runs each BMI unit test to make sure that the BMI is complete and functioning as expected.
+ - config.yml: This is a configuration file that the BMI reads to set inital_time (initial value of current_model_time) and time_step_seconds (time_step_size).
+ - environment.yml: Environment file with the required Python libraries needed to run the model with BMI. Create the environment with this command: `conda env create -f environment.yml`, then activate it with `conda activate bmi_test`
+
+# About
+This is an implementation of a Python-based model that fulfills the Python language BMI interface and can be used in the Framework. This Python BMI interface servers as a USGS water level observation station data provider to force NextGen coastal models (SCHISM, DFlowFM) with a single offshore water level boundary upstream of the Lake Champlain domain.
+
+# Implementation Details
+The USGS data provider BMI here serves to provide USGS station water level data to coastal models all the up until 2023-01-01 00:00:00. Any time beyond the last observation time of the USGS station data will serve as a constant value over future forecast times for a given coastal model application for the Lake Champlain domain. We will be updating the USGS observation csv dataset in the near future with close to realtime values.
+## Test the complete BMI functionality
+`python run_bmi_unit_test.py`
+
+## Run the model
+`python run_bmi_model.py`
+
+## Sample output
+model time, ETA2_bnd
+3600.00, 29.84
+7200.00, 30.45
+10800.00, 30.89

extern/Coastal_model_waterlevel_data_providers/Lake_Champlain_USGS_Waterlevel_BMI/__init__.py

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+from .bmi_model import bmi_model

extern/Coastal_model_waterlevel_data_providers/Lake_Champlain_USGS_Waterlevel_BMI/__main__.py

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+from run_bmi_model import execute
+
+# TODO: maybe add something for running tests also
+if __name__ == '__main__':
+    execute()

extern/Coastal_model_waterlevel_data_providers/Lake_Champlain_USGS_Waterlevel_BMI/bmi_grid.py

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+"""bmi_grid.py
+Module for supporting BMI grid meta data and functionality
+
+@author Nels Frazier
+@version 0.1
+"""
+
+from enum import Enum
+from functools import reduce
+from typing import TYPE_CHECKING
+import numpy as np
+
+if TYPE_CHECKING:
+    from typing import Tuple
+    from numpy.typing import NDArray
+
+_error_on_grid_type: bool = False
+
+def error_on_grid_type(flag: bool = False) -> None:
+    """Set the behavior of the module to throw an error on grid functions that are not applicable.
+
+    Args:
+        flag (bool): 
+            True will enable exceptions to be thrown for access to grid functions that are not applicable to the grid type.
+
+            False will attempt to provide reasonable default semantics for returning information from those functions.
+
+    Returns:
+        None
+    """
+    global _error_on_grid_type
+    _error_on_grid_type = flag
+
+class GridTypeAccessError(TypeError):
+    """Grid meta data not accessible for grid type"""
+
+class GridType(str, Enum):
+    """
+        Enumeration of supported BMI grid types (https://bmi.readthedocs.io/en/stable/#get-grid-type)
+    """
+    scalar = "scalar", #0 dim
+    points = "points", #1 dim
+    vector = "vector", #1 dim
+    unstructured = "unstructured", #1-N
+    structured_quadrilateral = "structured_quadrilaterl", #2 dim
+    rectilinear = "rectilinear", #2 dim dx != dy
+    uniform_rectilinear = "uniform_rectilinear" #2 dim -- dx = dy
+
+class GridUnits(Enum):
+    """
+        Enumeration of supported Grid Units to facilitate framework interactions
+    """
+    none = -1
+    m = 0
+    km = 1
+
+class Grid():
+    """
+        Structure for holding required BMI meta data for any grid intended to be used via BMI
+    """
+    def __init__(self, id: int, rank: int , type: GridType, units: GridUnits = GridUnits.none) :
+        """_summary_
+
+        Args:
+            id (int): User defined identifier for this grid
+            rank (int): The number of dimensions of the grid
+            type (GridType): The type of BMI grid this meta data represents
+        """
+        self._id: int = id
+        self._rank: int = rank
+        self._size: int = 0
+        self._type: GridType = type #FIXME validate type/rank?
+        self._shape: 'NDArray[np.int32]' = None #array of size rank
+        self._spacing: 'NDArray[np.float64]' = None #array of size rank
+        self._origin: 'NDArray[np.float64]' = None #array of size rank
+        self._grid_x: 'NDArray[np.float64]' = None #array of size rank
+        self._grid_y: 'NDArray[np.float64]' = None #array of size rank
+        self._grid_z: 'NDArray[np.float64]' = None #array of size rank
+        self._units: 'NDArray[np.int16]' = None #array of size rank
+
+        if( rank == 0 ):
+            # We have to use a 1 dim representation for a scalar cause numpy initialization is weird
+            # np.zeros( [1] ) gives you an array([0.])
+            # np.zeros( [0] ) gives you an emptty array([])
+            # np.zeros( () ) gives a scalar wrapped in an array array(0.)
+            # This latter is really what we want, but then it is hard to communicate the actual size
+            # (as a numerical value...)
+            self._shape = np.zeros( (), np.int32 ) #note, int32 is important here -- assumed by ngen
+            self._spacing = np.zeros( (), np.float64 )
+            self._origin = np.zeros( (), np.float64 )
+            self._units = np.ones( (), dtype=np.int16)
+            self._units[()] = units.value
+            #self._shape[...] = 1
+        else:
+            self._shape = np.zeros( rank, np.int32) #set the shape rank, with 0 allocated values
+            self._spacing = np.zeros( rank, np.float64 )
+            self._origin = np.zeros( rank, np.float64 )
+            self._units = np.array( [units.value]*rank, dtype=np.int16)
+        #Make the array "immutable", can only modify via setting
+        self._shape.flags.writeable = False
+
+    # TODO consider restricting resetting of grid values after they have been initialized
+
+    @property
+    def units(self) -> 'NDArray[np.int16]':
+        """The grid units, specifically the units of grid spacing in each rank
+
+        Raises:
+            GridTypeAccessError: When error_on_grid_type is toggled on, this exeption is raised when called on a scalar grid.
+
+        Returns:
+            NDArray[np.int16]: array of GridUnit enum VALUES denoting the grid unit for each rank.
+        """
+        if _error_on_grid_type and self.type == GridType.scalar:
+            raise GridTypeAccessError("Scalar has no grid units")
+        return self._units
+
+    @units.setter
+    def units(self, units: 'NDArray[np.int16]'):
+        """Set the grid spacing units for each dimension.
+
+        Args:
+            units (NDArray[np.int16]): the GridUnit enum VALUES denoting the grid spacing units in each dimension.
+        """
+
+        if self.rank > 0:
+            self._units = np.array(units, dtype=np.int16)
+            self._units.flags.writeable = False
+        #noop for scalar or grids with rank < 1
+
+    @property
+    def id(self) -> int:
+        """The unique grid identifer.
+
+        Returns:
+            int: grid identifier
+        """
+        return self._id
+
+    @property
+    def rank(self) -> int:
+        """The dimensionality of the grid.
+
+        Returns:
+            int: Number of dimensions of the grid.
+        """
+        return self._rank
+
+    @property
+    def size(self) -> int:
+        """The total number of elements in the grid
+
+        Returns:
+            int: number of grid elements
+        """
+        return self._size
+        #if _error_on_grid_type and self.type == GridType.scalar:
+        #    raise GridTypeAccessError("Scalar has no grid size")
+
+        #if self.shape is None or self.shape.ndim == 0: #it is None or np.array( () )
+        #    return 0
+        #else:
+        #    #multiply the shape of each dimension together
+        #    return reduce( lambda x, y: x*y, self._shape)
+
+    @property
+    def type(self) -> GridType:
+        """The type of BMI grid.
+
+        Returns:
+            GridType: bmi grid type
+        """
+        return self._type
+
+    @property
+    def shape(self) -> 'NDArray[np.int32]':
+        """The shape of the grid (the size of each dimension)
+
+        Returns:
+            NDArray[np.int32]: size of each dimension
+        """
+        """
+            From the BMI docs:
+            The grid shape is the number of rows and columns of nodes,
+            as opposed to other types of element (such as cells or faces). 
+            It is possible for grid values to be associated with the nodes or with the cells.
+        """
+        if _error_on_grid_type and self.type == GridType.scalar:
+            raise GridTypeAccessError("Scalar has no shape")
+        return self._shape
+
+    @shape.setter
+    def shape(self, shape: 'NDArray[np.int32]') -> None:
+        """Set the shape of the grid to the provided shape
+
+        Args:
+            shape (NDArray[np.int32]): the size of each dimension of the grid
+        """
+        #Create a new shape array and replace the old one, make it immutable
+        if self.rank > 0:
+            self._shape = np.array(shape, dtype=np.int32)
+            self._shape.flags.writeable = False
+        #noop for scalar or grids with rank < 1
+
+    @property
+    def spacing(self) -> 'NDArray[np.float64]':
+        """The spacing of the grid
+
+        Returns:
+            NDArray[np.float64]: Tuple of size rank with the spacing in each of rank dimensions
+        """
+        if _error_on_grid_type and self.type == GridType.scalar:
+            raise GridTypeAccessError("Scalar has no grid spacing")
+        return self._spacing
+
+    @spacing.setter
+    def spacing(self, spacing: 'NDArray[np.float64]') -> None:
+        """Set the spacing of each grid dimension.
+
+        Args:
+            spacing (NDArray[np.float64]): Tuple of size rank with the spacing for each dimension
+        """
+        if self.rank > 0:
+            self._spacing = np.array(spacing, dtype=np.float64)
+            self._spacing.flags.writeable = False
+        #noop for scalar or grids with rank < 1
+
+    @property
+    def origin(self) -> 'NDArray[np.float64]':
+        """The origin point of the grid
+
+        Returns:
+            NDArray[np.float64]: Tuple of size rank with the coordinates of the the grid origin
+        """
+        if _error_on_grid_type and self.type == GridType.scalar:
+            raise GridTypeAccessError("Scalar has no grid origin")
+        return self._origin
+
+    @origin.setter
+    def origin(self, origin: 'NDArray[np.float64]') -> None:
+        """Set the grid origin location
+
+        Args:
+            origin (NDArray[np.float64]): Tuple of size rank with grid origin coordinates.
+        """
+        if self.rank > 0:
+            self._origin = np.array(origin, dtype=np.float64)
+            self._origin.flags.writeable = False
+        #noop for scalar or grids with rank < 1
+
+    @property
+    def grid_x(self) -> 'NDArray[np.float64]':
+        """Coordinates of the x components of the grid
+
+        Returns:
+            NDArray[np.float64]: array of cooridnate values in the x direction
+        """
+        if _error_on_grid_type and self.type == GridType.scalar:
+            raise GridTypeAccessError("Scalar has no grid x value")
+        else:
+            return self._grid_x
+        #TODO refactor this -- not generic to grid, this works for structured/quads, not for unstructured
+        #if (self.type == GridType.rectilinear or self.type == GridType.uniform_rectilinear) and len(self.shape) > 0:
+        #    # https://bmi.readthedocs.io/en/stable/model_grids.html#model-grids
+        #    # bmi states dimension info in `ij` form (last dimension indexed first...) in the shape meta
+        #    # so x would at index rank, y at rank-1, z at rank-2 ect...
+        #    idx = self.rank-1 #index is 0 based, rank is 1 based, adjust...
+        #    return np.array( [ self.origin[idx] + self.spacing[idx]*x for x in range(self.shape[idx]) ], dtype=np.float64 )
+        #else:    
+        #    #TODO should this raise an error or return an empty array?
+        #    #raise RuntimeError(f"Cannot get x coordinates of grid with shape {self.shape}")
+        #    return np.array((), dtype=np.float64)
+
+    @property
+    def grid_y(self) -> 'NDArray[np.float64]':
+        """Coordinates of the y components of the grid
+
+        Returns:
+            NDArray[np.float64]: array of coordinate values in the y direction
+        """
+        if _error_on_grid_type and self.type == GridType.scalar:
+            raise GridTypeAccessError("Scalar has no grid y value")
+        else:
+            return self._grid_y
+        #if (self.type == GridType.rectilinear or self.type == GridType.uniform_rectilinear) and len(self.shape) > 1:
+        #    idx = self.rank-2 #index is 0 based, rank is 1 based, adjust...
+        #    return np.array( [ self.origin[idx] + self.spacing[idx]*y for y in range(self.shape[idx]) ], dtype=np.float64 )
+        #else:    
+        #    #TODO should this raise an error or return an empty array?
+        #    #raise RuntimeError(f"Cannot get y coordinates of grid with shape {self.shape}")
+        #    return np.array((), dtype=np.float64)
+
+    @property
+    def grid_z(self) -> 'NDArray[np.float64]':
+        """Coordinates of the z components of the grid
+
+        Returns:
+            NDArray[np.float64]: array of coordinate values in the z direction
+        """
+        if _error_on_grid_type and self.type == GridType.scalar:
+            raise GridTypeAccessError("Scalar has no grid z value")
+        else:
+            return self._grid_z
+        #if (self.type == GridType.rectilinear or self.type == GridType.uniform_rectilinear) and len(self.shape) > 2:
+        #    idx = self.rank-3 #index is 0 based, rank is 1 based, adjust...
+        #    return np.array( [ self.origin[idx] + self.spacing[idx]*z for z in range(self.shape[idx]) ], dtype=np.float64 )
+        #else:    
+        #    #TODO should this raise an error or return an empty array?
+        #    #raise RuntimeError(f"Cannot get z coordinates of grid with shape {self.shape}")
+        #    return np.array((), dtype=np.float64)