diff --git a/tricco/compute.py b/tricco/compute.py
index e33c8030a74e224bcc8b49ae06c031167bd8a40d..bd362644ff7f06e3953ab1ea84b78191b1f70c3e 100644
--- a/tricco/compute.py
+++ b/tricco/compute.py
@@ -3,19 +3,21 @@ import numpy as np
from .grid_functions import get_edges_of_cell
from .cubulation_functions import EnhancedTri, cubing_next_round, shift_coordinates, compute_connected_components, compute_list_of_components
-# compute_cubulation
-# Assign to each triangle a 3d-coordinate called cube coordinate.
-# INPUT:
-# start_triangle int the triangle index where the cubulatiion algorithm starts
-# radius int the number of "outward" steps
-# print_progress Boolean print progress of iteration (default False)
-# OUTPUT:
-# Returns a list of arrays of the following form:
-# np.array([<triangle index>, <cube array>])
-# where <cube array> is an array with three entries that encode the cube coordinate of the triangle with <triangle index>.
def compute_cubulation(start_triangle, radius, print_progress=False):
-
+ """"
+ Assign to each triangle a 3d-coordinate called cube coordinate.
+
+ Input:
+ start_triangle int the triangle index where the cubulatiion algorithm starts
+ radius int the number of "outward" steps
+ print_progress Boolean print progress of iteration (default False)
+
+ Output:
+ Returns a list of arrays of the following form:
+ np.array([<triangle index>, <cube array>])
+ where <cube array> is an array with three entries that encode the cube coordinate of the triangle with <triangle index>.
+ """
# initialization
init = EnhancedTri(start_triangle, np.array([0,0,0])) # assign coordinates (0,0,0) to start_triangle
cube_coordinates = [np.array([init.triangle, init.cube], dtype=object)]
@@ -37,19 +39,19 @@ def compute_cubulation(start_triangle, radius, print_progress=False):
return shift_coordinates(cube_coordinates, radius)
-# compute_connected_2d_components:
-# From given cubuluation, compute connected components and output them as a list of lists
-# INPUT:
-# cubulation list of arrays output of compute_cubulation
-# field_cube numpy array input field in cubulated 3d-coordinates
-# connectivity string use 'vertex' (default) or 'edge' connectivity
-# OUTPUT:
-# - Returns a list of lists.
-# - Each inner list corresponds to a connected component and contains
-# all triangle indices that belong to this connected component
-
def compute_connected_components_2d(cubulation, field_cube, connectivity = 'vertex'):
-
+ """
+ From given cubuluation, compute connected components and output them as a list of lists
+
+ Input:
+ cubulation list of arrays output of compute_cubulation
+ field_cube numpy array input field in cubulated 3d-coordinates
+ connectivity string use 'vertex' (default) or 'edge' connectivity
+ Output:
+ - Returns a list of lists.
+ - Each inner list corresponds to a connected component and contains
+ all triangle indices that belong to this connected component
+ """
# compute connected components on field_cube
# components_cube is a 3-dimensional array that contains
# the connected component index of a triangle with cube coordinates (x,y,z) at position component_array[x][y][z]
@@ -62,19 +64,20 @@ def compute_connected_components_2d(cubulation, field_cube, connectivity = 'vert
return components
-# compute_connected_3d_components:
-# From given cubuluation, compute connected components and output them as a list of lists
-# INPUT:
-# cubulation list of arrays output of compute_cubulation
-# field_cube numpy array input field in dimensions of (lev x cubulated coordinates)
-# connectivity string use 'vertex' (default) or 'edge' connectivity
-# OUTPUT:
-# - Returns a list of lists.
-# - Each inner list corresponds to a connected component and contains all tuples of (level, triangle index) that
-# belong to this connected component
-
def compute_connected_components_3d(cubulation, field_cube, connectivity = 'vertex'):
-
+ """
+ From given cubuluation, compute connected components and output them as a list of lists
+
+ Input:
+ cubulation list of arrays output of compute_cubulation
+ field_cube numpy array input field in dimensions of (lev x cubulated coordinates)
+ connectivity string use 'vertex' (default) or 'edge' connectivity
+
+ Output:
+ - Returns a list of lists.
+ - Each inner list corresponds to a connected component and contains all tuples of (level, triangle index) that
+ belong to this connected component
+ """
nlev = np.shape(field_cube)[0] # number of vertical levels
# Step 1: compute connected components for each level separately,
diff --git a/tricco/cubulation_functions.py b/tricco/cubulation_functions.py
index 344cd65a2d85599deb85c94496c30637b446e272..737556269672650f0af3fe6b259ac525b1cb4fca 100644
--- a/tricco/cubulation_functions.py
+++ b/tricco/cubulation_functions.py
@@ -3,9 +3,12 @@ import cc3d # external library for connected component labeling on cubic grids
from .grid_functions import get_vertices_of_edge, get_edges_of_cell, get_neighbors_of_cell
-# class to enhance a triangle with its cube coordinate
-# for a given triangle create an object consisting of the triangle index and the triangle's cube coordinate
+
class EnhancedTri:
+ """
+ class to enhance a triangle with its cube coordinate
+ for a given triangle create an object consisting of the triangle index and the triangle's cube coordinate
+ """
def __init__(self, triangle, cube):
self.triangle = triangle
self.cube = cube
@@ -13,19 +16,21 @@ class EnhancedTri:
print("Triangle index:", self.triangle, " Cube coordinate:", self.cube)
-# colouring edges of a triangle
-# situation: the colours of two edges are already known,
-# the third edge must be coloured
-# called from colour_new_edges in this special situation
-# INPUT:
-# first_known_edge int index of a already coloured edge
-# second_known_edge int index of a already coloured edge
-# new_edge int index of the not yet coloured edge
-# edge_colours dict dictionary storing to each edge index the colour
-# OUTPUT:
-# None, but updates edge_colours
def colour_exactly_one_new_edge(first_known_edge, second_known_edge, new_edge, edge_colours):
-
+ """
+ Colouring edges of a triangle
+ Situation: the colours of two edges are already known,
+ the third edge must be coloured
+ called from colour_new_edges in this special situation
+
+ Input:
+ first_known_edge int index of a already coloured edge
+ second_known_edge int index of a already coloured edge
+ new_edge int index of the not yet coloured edge
+ edge_colours dict dictionary storing to each edge index the colour
+ Output:
+ none, but updates edge_colours
+ """
# get the colours of the already coloured edges
first_colour = edge_colours[first_known_edge]
second_colour = edge_colours[second_known_edge]
@@ -37,17 +42,19 @@ def colour_exactly_one_new_edge(first_known_edge, second_known_edge, new_edge, e
return
-# colouring edges of a triangle
-# from old_triangle with already coloured edges determine the edge_colours of the adjacent new_triangle
-# INPUT:
-# old_triangle_edges set contains the edge indices of old_triangle's edges
-# new_triangle_edges set contains the edge indices of new_triangle's edges
-# joint_edge int index of the shared edge from old_triangle and new_triangle
-# edge_colours dict dictionary storing to each edge index the colour
-# OUTPUT:
-# None, but updates edge_colours
def colour_new_edges(old_triangle_edges, new_triangle_edges, joint_edge, edge_colours):
-
+ """
+ Colouring edges of a triangle
+ From old_triangle with already coloured edges determine the edge_colours of the adjacent new_triangle
+
+ Input:
+ old_triangle_edges set contains the edge indices of old_triangle's edges
+ new_triangle_edges set contains the edge indices of new_triangle's edges
+ joint_edge int index of the shared edge from old_triangle and new_triangle
+ edge_colours dict dictionary storing to each edge index the colour
+ Output:
+ none, but updates edge_colours
+ """
# determine the edges of new_triangle that are not shared with old_triangle
new_edges = new_triangle_edges.difference( old_triangle_edges )
first_edge = new_edges.pop()
@@ -95,17 +102,20 @@ def colour_new_edges(old_triangle_edges, new_triangle_edges, joint_edge, edge_co
edge_colours[ second_edge ] = edge_colours[ first_comparison_edge ]
-# compute cube coordinate of a new triangle
-# given the old_triangle's cube_coordinate derive the cube coordinate of the adjacent new_triangle
-# by using the colour of their joint_edge
-# INPUT:
-# old EnhancedTri the old EnhancedTri with known cube_coordinates
-# joint_edge int index of the shared edge from old_triangle and new_triangle
-# edge_colours dict dictionary storing to each edge index the colour
-# OUTPUT:
-# new_cube_coordinate, the cube_coordinate of new_triangle
def determine_cube_coordinates (old, old_triangle_edges, new_triangle_edges, joint_edge, edge_colours):
-
+ """
+ Compute cube coordinate of a new triangle
+ Given the old_triangle's cube_coordinate derive the cube coordinate of the adjacent new_triangle
+ by using the colour of their joint_edge
+
+ Input:
+ old EnhancedTri the old EnhancedTri with known cube_coordinates
+ joint_edge int index of the shared edge from old_triangle and new_triangle
+ edge_colours dict dictionary storing to each edge index the colour
+
+ Output:
+ new_cube_coordinate, the cube_coordinate of new_triangle
+ """
# get colour of the joint_edge and call it direction
direction = edge_colours[joint_edge]
# the direction encodes the parallel class of the joint edge
@@ -130,16 +140,18 @@ def determine_cube_coordinates (old, old_triangle_edges, new_triangle_edges, joi
return new_cube_coordinate
-# iterate over the outmost triangles and compute the cube_coordinates of their adjacent triangles
-# INPUT:
-# cube_coordinates array EnhancedTri's of the triangles whose cube_coordinates are already computed
-# visited_triangles list indices of the triangles whose cube_coordinates are already computed
-# outmost list EnhancedTri's of the triangles considered in the round before
-# edge_colours dict dictionary storing to each edge index the colour
-# OUTPUT:
-# updated outmost, EnhancedTri's of the new triangles considered this round
def cubing_next_round(cube_coordinates, visited_triangles, outmost, edge_colours):
+ """
+ Iterate over the outmost triangles and compute the cube_coordinates of their adjacent triangles
+ Input:
+ cube_coordinates array EnhancedTri's of the triangles whose cube_coordinates are already computed
+ visited_triangles list indices of the triangles whose cube_coordinates are already computed
+ outmost list EnhancedTri's of the triangles considered in the round before
+ edge_colours dict dictionary storing to each edge index the colour
+ Output:
+ updated outmost, EnhancedTri's of the new triangles considered this round
+ """
# list for triangles that will be outmost in next iteration
new_outmost = []
@@ -169,36 +181,38 @@ def cubing_next_round(cube_coordinates, visited_triangles, outmost, edge_colours
return new_outmost
-# shift cube coordinates such that they are all positive
-# INPUT:
-# cube_coordinates array stores triangle - cube_coordinate pairs
-# radius int same as in compute_cube_coordiantes
-# OUTPUT:
-# shifted cube_coordinates
def shift_coordinates (cube_coordinates, radius):
+ """
+ Shift cube coordinates such that they are all positive
+
+ Input:
+ cube_coordinates array stores triangle - cube_coordinate pairs
+ radius int same as in compute_cube_coordiantes
+
+ Output:
+ shifted cube_coordinates
+ """
shift = int(radius/2)
-
# update all coordinates
for entry in cube_coordinates:
cube = entry[1] # get cube_coordinate
-
# consider all three directions
for direction in (0,1,2):
cube[direction] += shift # shift coordinate
-
return cube_coordinates
-# compute connected components
-# INPUT:
-# field_cube array obtained as part of field prepaation
-# connectivity string use 'vertex' (default) or 'edge' connectivity
-# OUTPUT:
-# component_cube, 3d array containing the component indices
def compute_connected_components(field_cube, connectivity = 'vertex'):
+ """"
+ Compute connected components
+ Input:
+ field_cube array obtained as part of field prepaation
+ connectivity string use 'vertex' (default) or 'edge' connectivity
+ Output:
+ component_cube, 3d array containing the component indices
+ """"
import sys
-
# translate connectivity - string into input value for 3d connected component labeling
if connectivity == 'edge':
connectivity_value = 6
@@ -206,23 +220,24 @@ def compute_connected_components(field_cube, connectivity = 'vertex'):
connectivity_value = 26
if connectivity != 'vertex': # use default 'vertex' connectivity for invalid input
sys.exit("Invalid input: only 'vertex' or 'edge' connectivity are allowed.")
-
# calling external 3d connected component labeling
component_cube = cc3d.connected_components(field_cube, connectivity = connectivity_value)
-
return component_cube
-# which triangle belongs to which component?
-# INPUT:
-# cubulation array output of compute_cube_coordinates
-# component_cube 3d arry output of compute_connected_components
-# OUTPUT:
-# Returns a list of lists.
-# Each inner list corresponds to one connected component and contains the indices
-# of all triangles that belong to it
def compute_list_of_components(cubulation, component_cube):
-
+ """
+ Which triangle belongs to which component?
+
+ Input:
+ cubulation array output of compute_cube_coordinates
+ component_cube 3d arry output of compute_connected_components
+
+ Output:
+ Returns a list of lists.
+ Each inner list corresponds to one connected component and contains the indices
+ of all triangles that belong to it.
+ """
# number of found components on cubical grid
ncomp = component_cube.max()
diff --git a/tricco/grid_functions.py b/tricco/grid_functions.py
index e9cc8439458de4d5926077bca0e5ae0a433eab9d..9a2b9ebfb7a3f865fb705fd4e66d93e68404d650 100644
--- a/tricco/grid_functions.py
+++ b/tricco/grid_functions.py
@@ -1,17 +1,21 @@
# xarray dataset that holds the grid
grid = None
-# get_neighbors_of_cell:
-# returns edge neighbors of a given triangle
def get_neighbors_of_cell(triangle):
+ """
+ Returns edge neighbors of a given triangle
+ """
return grid.neighbor_cell_index[:,triangle].values
-# get_edges_of_cell:
-# returns the three edges of a given triangle
def get_edges_of_cell(triangle):
+ """
+ Returns the three edges of a given triangle
+ """
return grid.edge_of_cell[:, triangle].values
-# get_vertices_of_edge:
-# returns the two vertices of a given edge
+
def get_vertices_of_edge(edge):
+ """
+ Returns the two vertices of a given edge
+ """
return grid.edge_vertices[:, edge].values
diff --git a/tricco/prepare.py b/tricco/prepare.py
index 2f8a1c1eb908750a851b0cb1580b0fdbab1a8305..f04ac73b5e28835a3d8b3814aa220a64cbf20e4a 100644
--- a/tricco/prepare.py
+++ b/tricco/prepare.py
@@ -1,26 +1,30 @@
import numpy as np
import xarray as xr
-# path_input:
-# create globale variables to open the xArray dataset from gridfile and datafile
-# INPUT:
-# path string location where gridfile and datafile are stored
-# gridfile string name of the file containing the grid
-# datafile string name of the file containing the field
-
-# we only need the following information about the horizontal triangular grid:
-# - the edge neighbors of a given triangle
-# - the three edges of a given triangle
-# - the two vertices of a given edge
def prepare_grid(model, path, file):
+ """
+ Returns grid as xarray dataset.
+
+ We only need the following information about the horizontal triangular grid:
+ - the edge neighbors of a given triangle
+ - the three edges of a given triangle
+ - the two vertices of a given edge
+
+ Input:
+ model string name of considered model
+ path string location where gridfile is stored
+ file string name of the file containing the grid
+
+ Output:
+ grid as xarray dataset
+ """
if model == 'ICON':
_ds_grid = _grid_icon(path+file)
return _ds_grid
# implementation for ICON model
-
def _grid_icon(_gridfile):
ds_grid = xr.open_dataset(_gridfile)
@@ -40,22 +44,41 @@ def _grid_icon(_gridfile):
return ds_grid
-# prepare_field
-# returns the data field as a numpy array, also applies threshold-based mask
-# INPUT:
-# file: file containing the data field
-# var: variabe name
-# threshold: set field to one if field>=threshold, and to zero otherwise
-# OUTPUT:
-# field both on the triangular grid as well as on the cubulated grid
-
def prepare_field(model, path, file, var, threshold, cubulation):
+ """
+ Returns the one-level data field as a numpy array, also applies threshold-based mask.
+
+ Input:
+ model : name of considered model
+ path : path to file
+ file : file containing the data field
+ var : variabe name
+ threshold : set field to one if field>=threshold, and to zero otherwise
+ cubulation: cubulation of horizontal grid
+
+ Output:
+ field both on the triangular grid as well as on the cubulated grid
+ """
if model == 'ICON':
_field, _field_cube = _field_icon(path, file, var, threshold, cubulation)
return _field, _field_cube
def prepare_field_lev(model, path, file, var, threshold, cubulation):
+ """
+ Returns the many-level data field as a numpy array, also applies threshold-based mask.
+
+ Input:
+ model : name of considered model
+ path : path to file
+ file : file containing the data field
+ var : variabe name
+ threshold : set field to one if field>=threshold, and to zero otherwise
+ cubulation: cubulation of horizontal grid
+
+ Output:
+ field both on the triangular grid as well as on the cubulated grid
+ """
if model == 'ICON':
_field, _field_cube = _field_icon_lev(path, file, var, threshold, cubulation)
return _field, _field_cube