OperatorsDH

Operators (D to H)

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Definition

Operators in the GAML language are used to compose complex expressions. An operator performs a function on one, two, or n operands (which are other expressions and thus may be themselves composed of operators) and returns the result of this function.

Most of them use a classical prefixed functional syntax (i.e. operator_name(operand1, operand2, operand3), see below), with the exception of arithmetic (e.g. +, /), logical (and, or), comparison (e.g. >, <), access (., [..]) and pair (::) operators, which require an infixed notation (i.e. operand1 operator_symbol operand1).

The ternary functional if-else operator, ? :, uses a special infixed syntax composed with two symbols (e.g. operand1 ? operand2 : operand3). Two unary operators (- and !) use a traditional prefixed syntax that does not require parentheses unless the operand is itself a complex expression (e.g. - 10, ! (operand1 or operand2)).

Finally, special constructor operators ({...} for constructing points, [...] for constructing lists and maps) will require their operands to be placed between their two symbols (e.g. {1,2,3}, [operand1, operand2, ..., operandn] or [key1::value1, key2::value2... keyn::valuen]).

With the exception of these special cases above, the following rules apply to the syntax of operators:

if they only have one operand, the functional prefixed syntax is mandatory (e.g. operator_name(operand1))
if they have two arguments, either the functional prefixed syntax (e.g. operator_name(operand1, operand2)) or the infixed syntax (e.g. operand1 operator_name operand2) can be used.
if they have more than two arguments, either the functional prefixed syntax (e.g. operator_name(operand1, operand2, ..., operand)) or a special infixed syntax with the first operand on the left-hand side of the operator name (e.g. operand1 operator_name(operand2, ..., operand)) can be used.

All of these alternative syntaxes are completely equivalent.

Operators in GAML are purely functional, i.e. they are guaranteed to not have any side effects on their operands. For instance, the shuffle operator, which randomizes the positions of elements in a list, does not modify its list operand but returns a new shuffled list.

Priority between operators

The priority of operators determines, in the case of complex expressions composed of several operators, which one(s) will be evaluated first.

GAML follows in general the traditional priorities attributed to arithmetic, boolean, comparison operators, with some twists. Namely:

the constructor operators, like ::, used to compose pairs of operands, have the lowest priority of all operators (e.g. a > b :: b > c will return a pair of boolean values, which means that the two comparisons are evaluated before the operator applies. Similarly, [a > 10, b > 5] will return a list of boolean values.
it is followed by the ?: operator, the functional if-else (e.g. a > b ? a + 10 : a - 10 will return the result of the if-else).
next are the logical operators, and and or (e.g. a > b or b > c will return the value of the test)
next are the comparison operators (i.e. >, <, <=, >=, =, !=)
next the arithmetic operators in their logical order (multiplicative operators have a higher priority than additive operators)
next the unary operators - and !
next the access operators . and [] (e.g. {1,2,3}.x > 20 + {4,5,6}.y will return the result of the comparison between the x and y ordinates of the two points)
and finally the functional operators, which have the highest priority of all.

Using actions as operators

Actions defined in species can be used as operators, provided they are called on the correct agent. The syntax is that of normal functional operators, but the agent that will perform the action must be added as the first operand.

For instance, if the following species is defined:

species spec1 {
        int min(int x, int y) {
                return x > y ? x : y;
        }
}

Any agent instance of spec1 can use min as an operator (if the action conflicts with an existing operator, a warning will be emitted). For instance, in the same model, the following line is perfectly acceptable:

global {
        init {
                create spec1;
                spec1 my_agent <- spec1[0];
                int the_min <- my_agent min(10,20); // or min(my_agent, 10, 20);
        }
}

If the action doesn't have any operands, the syntax to use is my_agent the_action(). Finally, if it does not return a value, it might still be used but is considering as returning a value of type unknown (e.g. unknown result <- my_agent the_action(op1, op2);).

Note that due to the fact that actions are written by modelers, the general functional contract is not respected in that case: actions might perfectly have side effects on their operands (including the agent).

Operators by categories

Dates

Displays

horizontal, stack, vertical,

edge

edge_between, strahler,

EDP-related operators

diff, diff2,

Files-related operators

agent_file, copy_file, crs, csv_file, delete_file, dxf_file, evaluate_sub_model, file_exists, folder, folder_exists, gaml_file, geojson_file, get, gif_file, gml_file, graph6_file, graphdimacs_file, graphdot_file, graphgexf_file, graphgml_file, graphml_file, graphtsplib_file, grid_file, image_file, is_agent, is_csv, is_dxf, is_gaml, is_geojson, is_gif, is_gml, is_graph6, is_graphdimacs, is_graphdot, is_graphgexf, is_graphgml, is_graphml, is_graphtsplib, is_grid, is_image, is_json, is_obj, is_osm, is_pgm, is_property, is_shape, is_simulation, is_svg, is_text, is_threeds, is_xml, json_file, new_folder, obj_file, osm_file, pgm_file, property_file, read, rename_file, shape_file, simulation_file, step_sub_model, svg_file, text_file, threeds_file, unzip, writable, xml_file, zip,

GamaMetaType

type_of,

GamaSVGFile

image,

Graphs-related operators

add_edge, add_node, adjacency, agent_from_geometry, all_pairs_shortest_path, alpha_index, as_distance_graph, as_edge_graph, as_intersection_graph, as_path, as_spatial_graph, beta_index, betweenness_centrality, biggest_cliques_of, connected_components_of, connectivity_index, contains_edge, contains_vertex, degree_of, directed, edge, edge_between, edge_betweenness, edges, gamma_index, generate_barabasi_albert, generate_complete_graph, generate_random_graph, generate_watts_strogatz, girvan_newman_clustering, grid_cells_to_graph, in_degree_of, in_edges_of, k_spanning_tree_clustering, label_propagation_clustering, layout_circle, layout_force, layout_force_FR, layout_force_FR_indexed, layout_grid, load_shortest_paths, main_connected_component, max_flow_between, maximal_cliques_of, nb_cycles, neighbors_of, node, nodes, out_degree_of, out_edges_of, path_between, paths_between, predecessors_of, remove_node_from, rewire_n, source_of, spatial_graph, strahler, successors_of, sum, target_of, undirected, use_cache, weight_of, with_k_shortest_path_algorithm, with_shortest_path_algorithm, with_weights,

Grid-related operators

as_4_grid, as_grid, as_hexagonal_grid, cell_at, cells_in, cells_overlapping, field, grid_at, neighbors_of, path_between, points_in, values_in,

ImageOperators

*, antialiased, blend, blurred, brighter, clipped_with, darker, grayscale, horizontal_flip, image, matrix, rotated_by, sharpened, snapshot, tinted_with, vertical_flip, with_height, with_size, with_width,

Iterator operators

accumulate, all_match, as_map, collect, count, create_map, first_with, frequency_of, group_by, index_by, last_with, max_of, mean_of, min_of, none_matches, one_matches, product_of, sort_by, sum_of, variance_of, where, where, where, with_max_of, with_min_of,

List-related operators

all_indexes_of, copy_between, index_of, last_index_of,

Logical operators

:, !, ?, add_3Dmodel, add_geometry, add_icon, and, or, xor,

Map comparaison operators

fuzzy_kappa, fuzzy_kappa_sim, kappa, kappa_sim, percent_absolute_deviation,

Map-related operators

as_map, create_map, index_of, last_index_of,

Matrix-related operators

-, /, ., *, +, append_horizontally, append_vertically, column_at, columns_list, determinant, eigenvalues, flatten, index_of, inverse, last_index_of, row_at, rows_list, shuffle, trace, transpose,

multicriteria operators

electre_DM, evidence_theory_DM, fuzzy_choquet_DM, promethee_DM, weighted_means_DM,

Path-related operators

agent_from_geometry, all_pairs_shortest_path, as_path, load_shortest_paths, max_flow_between, path_between, path_to, paths_between, use_cache,

Pedestrian

generate_pedestrian_network,

Points-related operators

-, /, *, +, <, <=, >, >=, add_point, angle_between, any_location_in, centroid, closest_points_with, farthest_point_to, grid_at, norm, points_along, points_at, points_on,

Random operators

binomial, exp_density, exp_rnd, flip, gamma_density, gamma_rnd, gamma_trunc_rnd, gauss, generate_terrain, lognormal_density, lognormal_rnd, lognormal_trunc_rnd, poisson, rnd, rnd_choice, sample, shuffle, skew_gauss, truncated_gauss, weibull_density, weibull_rnd, weibull_trunc_rnd,

Shape

arc, box, circle, cone, cone3D, cross, cube, curve, cylinder, ellipse, elliptical_arc, envelope, geometry_collection, hexagon, line, link, plan, polygon, polyhedron, pyramid, rectangle, sphere, square, squircle, teapot, triangle,

Spatial operators

-, *, +, add_point, agent_closest_to, agent_farthest_to, agents_at_distance, agents_covering, agents_crossing, agents_inside, agents_overlapping, agents_partially_overlapping, agents_touching, angle_between, any_location_in, arc, around, as_4_grid, as_driving_graph, as_grid, as_hexagonal_grid, at_distance, at_location, box, centroid, circle, clean, clean_network, closest_points_with, closest_to, cone, cone3D, convex_hull, covering, covers, cross, crosses, crossing, crs, CRS_transform, cube, curve, cylinder, direction_between, disjoint_from, distance_between, distance_to, ellipse, elliptical_arc, envelope, farthest_point_to, farthest_to, geometry_collection, gini, hexagon, hierarchical_clustering, IDW, inside, inter, intersects, inverse_rotation, k_nearest_neighbors, line, link, masked_by, moran, neighbors_at, neighbors_of, normalized_rotation, overlapping, overlaps, partially_overlapping, partially_overlaps, path_between, path_to, plan, points_along, points_at, points_on, polygon, polyhedron, pyramid, rectangle, rotated_by, rotation_composition, round, scaled_to, set_z, simple_clustering_by_distance, simplification, skeletonize, smooth, sphere, split_at, split_geometry, split_lines, square, squircle, teapot, to_GAMA_CRS, to_rectangles, to_segments, to_squares, to_sub_geometries, touches, touching, towards, transformed_by, translated_by, triangle, triangulate, union, using, voronoi, with_precision, without_holes,

Spatial properties operators

covers, crosses, intersects, partially_overlaps, touches,

Spatial queries operators

agent_closest_to, agent_farthest_to, agents_at_distance, agents_covering, agents_crossing, agents_inside, agents_overlapping, agents_partially_overlapping, agents_touching, at_distance, closest_to, covering, crossing, farthest_to, inside, neighbors_at, neighbors_of, overlapping, partially_overlapping, touching,

Spatial relations operators

direction_between, distance_between, distance_to, path_between, path_to, towards,

Spatial statistical operators

hierarchical_clustering, k_nearest_neighbors, simple_clustering_by_distance,

Spatial transformations operators

-, *, +, as_4_grid, as_grid, as_hexagonal_grid, at_location, clean, clean_network, convex_hull, CRS_transform, inverse_rotation, normalized_rotation, rotated_by, rotation_composition, scaled_to, simplification, skeletonize, smooth, split_geometry, split_lines, to_GAMA_CRS, to_rectangles, to_segments, to_squares, to_sub_geometries, transformed_by, translated_by, triangulate, voronoi, with_precision, without_holes,

Species-related operators

index_of, last_index_of, of_generic_species, of_species,

Statistical operators

auto_correlation, beta, binomial_coeff, binomial_complemented, binomial_sum, build, chi_square, chi_square_complemented, correlation, covariance, dbscan, distribution_of, distribution2d_of, dtw, durbin_watson, frequency_of, gamma, gamma_distribution, gamma_distribution_complemented, geometric_mean, gini, harmonic_mean, hierarchical_clustering, incomplete_beta, incomplete_gamma, incomplete_gamma_complement, k_nearest_neighbors, kmeans, kurtosis, log_gamma, max, mean, mean_deviation, median, min, moment, moran, morrisAnalysis, mul, normal_area, normal_density, normal_inverse, predict, pValue_for_fStat, pValue_for_tStat, quantile, quantile_inverse, rank_interpolated, residuals, rms, rSquare, simple_clustering_by_distance, skewness, sobolAnalysis, split, split_in, split_using, standard_deviation, student_area, student_t_inverse, sum, t_test, variance,

Strings-related operators

+, <, <=, >, >=, at, capitalize, char, compress, contains, contains_all, contains_any, copy_between, date, empty, first, in, indented_by, index_of, is_number, last, last_index_of, length, lower_case, regex_matches, replace, replace_regex, reverse, sample, shuffle, split_with, string, uncompress, upper_case,

SubModel

load_sub_model,

System

., choose, command, copy, copy_from_clipboard, copy_to_clipboard, copy_to_clipboard, dead, enter, every, from_gaml, is_error, is_reachable, is_warning, play_sound, user_confirm, user_input_dialog, wizard, wizard_page,

Time-related operators

date, string,

Types-related operators

action, agent, BDIPlan, bool, container, conversation, directory, emotion, file, float, gaml_type, geometry, graph, int, kml, list, map, matrix, mental_state, message, Norm, pair, path, point, predicate, regression, rgb, Sanction, skill, social_link, species, topology, unknown,

User control operators

choose, enter, user_confirm, user_input_dialog, wizard, wizard_page,

Operators

`darker`

Possible uses:

darker (image) ---> image
image darker float ---> image
darker (image , float) ---> image

Result:

Used to return an image darker by a percentage (between 0 - no change - and 1 - 100% darker). If the percentage is below zero or abovde 1, returns the image untouched Used to return an image 10% darker. This operation can be applied multiple times in a row if greater than 10% changes in brightness are desired.

`date`

Possible uses:

string date string ---> date
date (string , string) ---> date
date (string, string, string) ---> date

Result:

converts a string to a date following a custom pattern. The pattern can use "%Y %M %N %D %E %h %m %s %z" for outputting years, months, name of month, days, name of days, hours, minutes, seconds and the time-zone. A null or empty pattern will parse the date using one of the ISO date & time formats (similar to date('...') in that case). The pattern can also follow the pattern definition found here, which gives much more control over what will be parsed: https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html#patterns. Different patterns are available by default as constant: #iso_local, #iso_simple, #iso_offset, #iso_zoned and #custom, which can be changed in the preferences

Special cases:

In addition to the date and pattern string operands, a specific locale (e.g. 'fr', 'en'...) can be added.

date d <- date("1999-january-30", 'yyyy-MMMM-dd', 'en');

Examples:

date den <- date("1999-12-30", 'yyyy-MM-dd');

`dbscan`

Possible uses:

dbscan (list, float, int) ---> list<list>

Result:

returns the list of clusters (list of instance indices) computed with the dbscan (density-based spatial clustering of applications with noise) algorithm from the first operand data according to the maximum radius of the neighborhood to be considered (eps) and the minimum number of points needed for a cluster (minPts). Usage: dbscan(data,eps,minPoints)

Special cases:

if the lengths of two vectors in the right-hand aren't equal, returns 0

Examples:

list<list> var0 <- dbscan ([[2,4,5], [3,8,2], [1,1,3], [4,3,4]],10,2); // var0 equals [[0,1,2,3]]

`dead`

Possible uses:

dead (agent) ---> bool

Result:

true if the agent is dead (or null), false otherwise.

Examples:

bool var0 <- dead(agent_A); // var0 equals true or false

`decompress`

Same signification as uncompress

`degree_of`

Possible uses:

graph degree_of unknown ---> int
degree_of (graph , unknown) ---> int

Result:

returns the degree (in+out) of a vertex (right-hand operand) in the graph given as left-hand operand.

Examples:

int var1 <- graphFromMap degree_of (node(3)); // var1 equals 3

See also: in_degree_of, out_degree_of,

`delete_file`

Possible uses:

delete_file (string) ---> bool

Result:

delete a file or a folder

Examples:

bool delete_file_ok <- delete_file(["../includes/my_folder"];

`deserialize`

Possible uses:

deserialize (string) ---> unknown

Result:

Deserializes an object precedently serialized using serialize or to_binary.It is safer to deserialize agents or simulations with the 'restore' or 'create' statements rather than with this operator.

See also: from_gaml, from_json,

`det`

Same signification as determinant

`determinant`

Possible uses:

determinant (matrix) ---> float

Result:

The determinant of the given matrix

Examples:

float var0 <- determinant(matrix([[1,2],[3,4]])); // var0 equals -2

`diff`

Possible uses:

float diff float ---> float
diff (float , float) ---> float

Result:

A placeholder function for expressing equations

`diff2`

Possible uses:

float diff2 float ---> float
diff2 (float , float) ---> float

Result:

A placeholder function for expressing equations

`directed`

Possible uses:

directed (graph) ---> graph

Result:

the operand graph becomes a directed graph.

Comment:

WARNING / side effect: this operator modifies the operand and does not create a new graph.

See also: undirected,

`direction_between`

Possible uses:

topology direction_between container<unknown,geometry> ---> float
direction_between (topology , container<unknown,geometry>) ---> float

Result:

A direction (in degree) between a list of two geometries (geometries, agents, points) considering a topology.

Examples:

float var0 <- my_topology direction_between [ag1, ag2]; // var0 equals the direction between ag1 and ag2 considering the topology my_topology

See also: towards, direction_to, distance_to, distance_between, path_between, path_to,

`direction_to`

Same signification as towards

`directory`

Possible uses:

directory (any) ---> directory

Result:

casts the operand in a directory object.

`disjoint_from`

Possible uses:

geometry disjoint_from geometry ---> bool
disjoint_from (geometry , geometry) ---> bool

Result:

A boolean, equal to true if the left-geometry (or agent/point) is disjoints from the right-geometry (or agent/point).

Special cases:

if one of the operand is null, returns true.
if one operand is a point, returns false if the point is included in the geometry.

Examples:

bool var0 <- polyline([{10,10},{20,20}]) disjoint_from polyline([{15,15},{25,25}]); // var0 equals false 
bool var1 <- polygon([{10,10},{10,20},{20,20},{20,10}]) disjoint_from polygon([{15,15},{15,25},{25,25},{25,15}]); // var1 equals false 
bool var2 <- polygon([{10,10},{10,20},{20,20},{20,10}]) disjoint_from {25,25}; // var2 equals true 
bool var3 <- polygon([{10,10},{10,20},{20,20},{20,10}]) disjoint_from polygon([{35,35},{35,45},{45,45},{45,35}]); // var3 equals true

See also: intersects, crosses, overlaps, partially_overlaps, touches,

`distance_between`

Possible uses:

topology distance_between container<unknown,geometry> ---> float
distance_between (topology , container<unknown,geometry>) ---> float

Result:

A distance between a list of geometries (geometries, agents, points) considering a topology.

Examples:

float var0 <- my_topology distance_between [ag1, ag2, ag3]; // var0 equals the distance between ag1, ag2 and ag3 considering the topology my_topology

See also: towards, direction_to, distance_to, direction_between, path_between, path_to,

`distance_to`

Possible uses:

geometry distance_to geometry ---> float
distance_to (geometry , geometry) ---> float
point distance_to point ---> float
distance_to (point , point) ---> float

Result:

A distance between two geometries (geometries, agents or points) considering the topology of the agent applying the operator.

Examples:

float var0 <- ag1 distance_to ag2; // var0 equals the distance between ag1 and ag2 considering the topology of the agent applying the operator

See also: towards, direction_to, distance_between, direction_between, path_between, path_to,

`distinct`

Same signification as remove_duplicates

`distribution_of`

Possible uses:

distribution_of (container) ---> map
container distribution_of int ---> map
distribution_of (container , int) ---> map
distribution_of (container, int, float, float) ---> map

Result:

Discretize a list of values into n bins (computes the bins from a numerical variable into n (default 10) bins. Returns a distribution map with the values (values key), the interval legends (legend key), the distribution parameters (params keys, for cumulative charts). Parameters can be (list), (list, nbbins) or (list,nbbins,valmin,valmax)

Examples:

map var0 <- distribution_of([1,1,2,12.5],10); // var0 equals map(['values'::[2,1,0,0,0,0,1,0,0,0],'legend'::['[0.0:2.0]','[2.0:4.0]','[4.0:6.0]','[6.0:8.0]','[8.0:10.0]','[10.0:12.0]','[12.0:14.0]','[14.0:16.0]','[16.0:18.0]','[18.0:20.0]'],'parlist'::[1,0]]) 
map var1 <- distribution_of([1,1,2,12.5]); // var1 equals map(['values'::[2,1,0,0,0,0,1,0,0,0],'legend'::['[0.0:2.0]','[2.0:4.0]','[4.0:6.0]','[6.0:8.0]','[8.0:10.0]','[10.0:12.0]','[12.0:14.0]','[14.0:16.0]','[16.0:18.0]','[18.0:20.0]'],'parlist'::[1,0]]) 
map var2 <- distribution_of([1,1,2,12.5]); // var2 equals map(['values'::[2,1,0,0,0,0,1,0,0,0],'legend'::['[0.0:2.0]','[2.0:4.0]','[4.0:6.0]','[6.0:8.0]','[8.0:10.0]','[10.0:12.0]','[12.0:14.0]','[14.0:16.0]','[16.0:18.0]','[18.0:20.0]'],'parlist'::[1,0]])

See also: as_map,

`distribution2d_of`

Possible uses:

container distribution2d_of container ---> map
distribution2d_of (container , container) ---> map
distribution2d_of (container, container, int, int) ---> map
distribution2d_of (container, container, int, float, float, int, float, float) ---> map

Result:

Discretize two lists of values into n bins (computes the bins from a numerical variable into n (default 10) bins. Returns a distribution map with the values (values key), the interval legends (legend key), the distribution parameters (params keys, for cumulative charts). Parameters can be (list), (list, nbbins) or (list,nbbins,valmin,valmax)

Examples:

map var0 <- distribution2d_of([1,1,2,12.5]); // var0 equals map(['values'::[2,1,0,0,0,0,1,0,0,0],'legend'::['[0.0:2.0]','[2.0:4.0]','[4.0:6.0]','[6.0:8.0]','[8.0:10.0]','[10.0:12.0]','[12.0:14.0]','[14.0:16.0]','[16.0:18.0]','[18.0:20.0]'],'parlist'::[1,0]]) 
map var1 <- distribution2d_of([1,1,2,12.5],10); // var1 equals map(['values'::[2,1,0,0,0,0,1,0,0,0],'legend'::['[0.0:2.0]','[2.0:4.0]','[4.0:6.0]','[6.0:8.0]','[8.0:10.0]','[10.0:12.0]','[12.0:14.0]','[14.0:16.0]','[16.0:18.0]','[18.0:20.0]'],'parlist'::[1,0]]) 
map var2 <- distribution2d_of([1,1,2,12.5],10); // var2 equals map(['values'::[2,1,0,0,0,0,1,0,0,0],'legend'::['[0.0:2.0]','[2.0:4.0]','[4.0:6.0]','[6.0:8.0]','[8.0:10.0]','[10.0:12.0]','[12.0:14.0]','[14.0:16.0]','[16.0:18.0]','[18.0:20.0]'],'parlist'::[1,0]])

See also: as_map,

`div`

Possible uses:

float div int ---> int
div (float , int) ---> int
int div float ---> int
div (int , float) ---> int
float div float ---> int
div (float , float) ---> int
int div int ---> int
div (int , int) ---> int

Result:

Returns the truncation of the division of the left-hand operand by the right-hand operand.

Special cases:

if the right-hand operand is equal to zero, raises an exception.

Examples:

int var0 <- 40.5 div 3; // var0 equals 13 
int var1 <- 40 div 4.1; // var1 equals 9 
int var2 <- 40.1 div 4.5; // var2 equals 8 
int var3 <- 40 div 3; // var3 equals 13

See also: mod,

`dnorm`

Same signification as normal_density

`dtw`

Possible uses:

list dtw list ---> float
dtw (list , list) ---> float
dtw (list, list, int) ---> float

Result:

returns the dynamic time warping between the two series of values (step pattern used: symetric1) returns the dynamic time warping between the two series of values (step pattern used: symetric1) with Sakoe-Chiba band (radius: the window width of Sakoe-Chiba band)

Examples:

float var0 <- dtw([32.0,5.0,1.0,3.0],[1.0,10.0,5.0,1.0]); // var0 equals 38.0 
float var1 <- dtw([10.0,5.0,1.0, 3.0],[1.0,10.0,5.0,1.0], 2); // var1 equals 11.0

`durbin_watson`

Possible uses:

durbin_watson (container) ---> float

Result:

Durbin-Watson computation

Examples:

float var0 <- durbin_watson([13,2,1,4,1,2]) with_precision(4); // var0 equals 0.7231

`dxf_file`

Possible uses:

dxf_file (string) ---> file
string dxf_file float ---> file
dxf_file (string , float) ---> file

Result:

Constructs a file of type dxf. Allowed extensions are limited to dxf

Special cases:

dxf_file(string): This file constructor allows to read a dxf (.dxf) file

file f <- dxf_file("file.dxf");

dxf_file(string,float): This file constructor allows to read a dxf (.dxf) file and specify the unit (meter by default)

file f <- dxf_file("file.dxf",#m);

See also: is_dxf,

`edge`

Possible uses:

edge (pair) ---> unknown
edge (unknown) ---> unknown
unknown edge float ---> unknown
edge (unknown , float) ---> unknown
pair edge float ---> unknown
edge (pair , float) ---> unknown
pair edge int ---> unknown
edge (pair , int) ---> unknown
unknown edge int ---> unknown
edge (unknown , int) ---> unknown
unknown edge unknown ---> unknown
edge (unknown , unknown) ---> unknown
edge (unknown, unknown, unknown) ---> unknown
edge (unknown, unknown, int) ---> unknown
edge (pair, unknown, int) ---> unknown
edge (unknown, unknown, float) ---> unknown
edge (pair, unknown, float) ---> unknown
edge (unknown, unknown, unknown, int) ---> unknown
edge (unknown, unknown, unknown, float) ---> unknown

Result:

Allows to create a wrapper (of type unknown) that wraps two objects and indicates they should be considered as the source and the target of a new edge of a graph. The third (omissible) parameter indicates which weight this edge should have in the graph

Comment:

Useful only in graph-related operations (addition, removal of edges, creation of graphs)

`edge_between`

Possible uses:

graph edge_between pair ---> unknown
edge_between (graph , pair) ---> unknown

Result:

returns the edge linking two nodes

Examples:

unknown var0 <- graphFromMap edge_between node1::node2; // var0 equals edge1

See also: out_edges_of, in_edges_of,

`edge_betweenness`

Possible uses:

edge_betweenness (graph) ---> map

Result:

returns a map containing for each edge (key), its betweenness centrality (value): number of shortest paths passing through each edge

Examples:

graph graphEpidemio <- graph([]); 
map var1 <- edge_betweenness(graphEpidemio); // var1 equals the edge betweenness index of the graph

`edges`

Possible uses:

edges (container) ---> container

Result:

Allows to create a wrapper (of type list) that wraps a list of objects and indicates they should be considered as edges of a graph

`eigenvalues`

Possible uses:

eigenvalues (matrix) ---> list<float>

Result:

The list of the eigen values of the given matrix

Examples:

list<float> var0 <- eigenvalues(matrix([[5,-3],[6,-4]])); // var0 equals [2.0000000000000004,-0.9999999999999998]

`electre_DM`

Possible uses:

electre_DM (list<list>, list<map<string,unknown>>, float) ---> int

Result:

The index of the best candidate according to a method based on the ELECTRE methods. The principle of the ELECTRE methods is to compare the possible candidates by pair. These methods analyses the possible outranking relation existing between two candidates. A candidate outranks another if this one is at least as good as the other one. The ELECTRE methods are based on two concepts: the concordance and the discordance. The concordance characterizes the fact that, for an outranking relation to be validated, a sufficient majority of criteria should be in favor of this assertion. The discordance characterizes the fact that, for an outranking relation to be validated, none of the criteria in the minority should oppose too strongly this assertion. These two conditions must be true for validating the outranking assertion. More information about the ELECTRE methods can be found in Figueira, J., Mousseau, V., Roy, B.: ELECTRE Methods. In: Figueira, J., Greco, S., and Ehrgott, M., (Eds.), Multiple Criteria Decision Analysis: State of the Art Surveys, Springer, New York, 133--162 (2005). The first operand is the list of candidates (a candidate is a list of criterion values); the second operand the list of criterion: A criterion is a map that contains fives elements: a name, a weight, a preference value (p), an indifference value (q) and a veto value (v). The preference value represents the threshold from which the difference between two criterion values allows to prefer one vector of values over another. The indifference value represents the threshold from which the difference between two criterion values is considered significant. The veto value represents the threshold from which the difference between two criterion values disqualifies the candidate that obtained the smaller value; the last operand is the fuzzy cut.

Special cases:

returns -1 is the list of candidates is nil or empty

Examples:

int var0 <- electre_DM([[1.0, 7.0],[4.0,2.0],[3.0, 3.0]], [["name"::"utility", "weight" :: 2.0,"p"::0.5, "q"::0.0, "s"::1.0, "maximize" :: true],["name"::"price", "weight" :: 1.0,"p"::0.5, "q"::0.0, "s"::1.0, "maximize" :: false]],0.7); // var0 equals 0

See also: weighted_means_DM, promethee_DM, evidence_theory_DM,

`ellipse`

Possible uses:

float ellipse float ---> geometry
ellipse (float , float) ---> geometry

Result:

An ellipse geometry which x-radius is equal to the first operand and y-radius is equal to the second operand

Comment:

the center of the ellipse is by default the location of the current agent in which has been called this operator.

Special cases:

returns a point if both operands are lower or equal to 0, a line if only one is.

Examples:

geometry var0 <- ellipse(10, 10); // var0 equals a geometry as an ellipse of width 10 and height 10.

See also: around, cone, line, link, norm, point, polygon, polyline, rectangle, square, circle, squircle, triangle,

`elliptical_arc`

Possible uses:

elliptical_arc (point, point, float, int) ---> geometry

Result:

An elliptical arc from the first operand (point) to the second operand (point), which radius is equal to the third operand, and a int giving the number of points to use as a last operand

Examples:

geometry var0 <- elliptical_arc({0,0},{10,10},5.0, 20); // var0 equals a geometry from {0,0} to {10,10} considering a radius of 5.0 built using 20 points

See also: arc, around, cone, line, link, norm, point, polygon, polyline, super_ellipse, rectangle, square, circle, ellipse, triangle,

`emotion`

Possible uses:

emotion (any) ---> emotion

Result:

casts the operand in a emotion object.

`empty`

Possible uses:

empty (container<KeyType,ValueType>) ---> bool
empty (string) ---> bool

Result:

true if the operand is empty, false otherwise.

Comment:

the empty operator behavior depends on the nature of the operand

Special cases:

if it is a map, empty returns true if the map contains no key-value mappings, and false otherwise
if it is a file, empty returns true if the content of the file (that is also a container) is empty, and false otherwise
if it is a population, empty returns true if there is no agent in the population, and false otherwise
if it is a graph, empty returns true if it contains no vertex and no edge, and false otherwise
if it is a matrix of int, float or object, it will return true if all elements are respectively 0, 0.0 or null, and false otherwise
if it is a matrix of geometry, it will return true if the matrix contains no cell, and false otherwise
if it is a list, empty returns true if there is no element in the list, and false otherwise

bool var0 <- empty([]); // var0 equals true

if it is a string, empty returns true if the string does not contain any character, and false otherwise

bool var1 <- empty ('abced'); // var1 equals false

`enlarged_by`

Same signification as +

`enter`

Possible uses:

string enter int ---> unknown
enter (string , int) ---> unknown
string enter float ---> unknown
enter (string , float) ---> unknown
string enter any GAML type ---> unknown
enter (string , any GAML type) ---> unknown
string enter string ---> unknown
enter (string , string) ---> unknown
string enter bool ---> unknown
enter (string , bool) ---> unknown
string enter unknown ---> unknown
enter (string , unknown) ---> unknown
enter (string, any GAML type, unknown) ---> unknown
enter (string, float, float, float) ---> unknown
enter (string, int, int, int) ---> unknown
enter (string, float, float, float, float) ---> unknown
enter (string, int, int, int, int) ---> unknown

Result:

Allows the user to enter a string by specifying a title and an initial value

Special cases:

When the second operand is the boolean type or a boolean value, the GUI is then a switch

map<string,unknown> m <- user_input(enter("Title",true)); 
map<string,unknown> m2 <- user_input(enter("Title",bool));

The GUI is then a slider when an init value, a min (int or float), a max (int or float) (and eventually a step (int or float) ) operands.

map resMinMax <- user_input([enter("Title",5,0)]) 
map resMinMax <- user_input([enter("Title",5,0,10)]) 
map resMMStepFF <- user_input([enter("Title",5,0.1,10.1,0.5)]);

`envelope`

Possible uses:

envelope (unknown) ---> geometry

Result:

A 3D geometry that represents the box that surrounds the geometries or the surface described by the arguments. More general than geometry(arguments).envelope, as it allows to pass int, double, point, image files, shape files, asc files, or any list combining these arguments, in which case the envelope will be correctly expanded. If an envelope cannot be determined from the arguments, a default one of dimensions (0,100, 0, 100, 0, 100) is returned

Special cases:

This operator is often used to define the environment of simulation

Examples:

file road_shapefile <- file("../includes/roads.shp"); 
geometry shape <- envelope(road_shapefile); 
// shape is the system variable of  the environment 
geometry var3 <- polygon([{0,0}, {20,0}, {10,10}, {10,0}]); // var3 equals create a polygon to get the envolpe 
float var4 <- envelope(polygon([{0,0}, {20,0}, {10,10}, {10,0}])).area; // var4 equals 200.0

`eval_gaml`

Same signification as from_gaml

`eval_when`

Possible uses:

eval_when (BDIPlan) ---> bool

Result:

evaluate the facet when of a given plan

Examples:

eval_when(plan1)

`evaluate_sub_model`

Possible uses:

agent evaluate_sub_model string ---> unknown
evaluate_sub_model (agent , string) ---> unknown

Result:

Load a submodel

Comment:

loaded submodel

`even`

Possible uses:

even (int) ---> bool

Result:

Returns true if the operand is even and false if it is odd.

Special cases:

if the operand is equal to 0, it returns true.
if the operand is a float, it is truncated before

Examples:

bool var0 <- even (3); // var0 equals false 
bool var1 <- even(-12); // var1 equals true

`every`

Possible uses:

every (any expression) ---> bool
every (int) ---> bool
unknown every int ---> unknown
every (unknown , int) ---> unknown
bool every int ---> bool
every (bool , int) ---> bool
list every any expression ---> list<date>
every (list , any expression) ---> list<date>
int every int ---> int
every (int , int) ---> int
float every int ---> float
every (float , int) ---> float
list every int ---> list
every (list , int) ---> list

Result:

returns the first operand every 2nd operand * cycle, nil otherwise returns the first bool operand every 2nd operand * cycle, false otherwise expects a frequency (expressed in seconds of simulated time) as argument. Will return true every time the current_date matches with this frequency applies a step to an interval of dates defined by 'date1 to date2'. Beware that using every with #month or #year will produce odd results,as these pseudo-constants are not constant; only the first value will be used to compute the intervals, so, for instance, if current_date is set to February#month will only represent 28 or 29 days. returns the first integer operand every 2nd operand * cycle, 0 otherwise returns the first float operand every 2nd operand * cycle, 0.0 otherwise true every operand * cycle, false otherwise Retrieves elements from the first argument every step (second argument) elements. Raises an error if the step is negative or equal to zero

Comment:

the value of the every operator depends on the cycle. It can be used to return a value every x cycle. object every(10#cycle) is strictly equivalent to every(10#cycle) ? object : nilthe value of the every operator depends on the cycle. It can be used to return a value every x cycle. object every(10#cycle) is strictly equivalent to every(10#cycle) ? object : falseUsed to do something at regular intervals of time. Can be used in conjunction with 'since', 'after', 'before', 'until' or 'between', so that this computation only takes place in the temporal segment defined by these operators. In all cases, the starting_date of the model is used as a reference starting pointthe value of the every operator depends on the cycle. It can be used to return a value every x cycle. 1000 every(10#cycle) is strictly equivalent to every(10#cycle) ? 1000 : 0the value of the every operator depends on the cycle. It can be used to return a value every x cycle. 1000.0 every(10#cycle) is strictly equivalent to every(10#cycle) ? 1000.0 : 0.0the value of the every operator depends on the cycle. It can be used to do something every x cycle.

Examples:

if ({2000,2000} every(2#cycle) != nil) {write "this is a point";} 
	     else {write "this is nil";} 
if (true every(2#cycle) != false) {write "this is true";} 
	     else {write "this is false";} 
reflex when: every(2#days) since date('2000-01-01') { .. } 
state a { transition to: b when: every(2#mn);} state b { transition to: a when: every(30#s);} // This oscillatory behavior will use the starting_date of the model as its starting point in time 
(date('2000-01-01') to date('2010-01-01')) every (#day) // builds an interval between these two dates which contains all the days starting from the beginning of the interval 
if (1000 every(2#cycle) != 0) {write "this is a value";} 
	     else {write "this is 0";} 
if (1000.0 every(2#cycle) != 0) {write "this is a value";} 
	     else {write "this is 0.0";} 
if every(2#cycle) {write "the cycle number is even";} 
	     else {write "the cycle number is odd";}

See also: since, after, to,

`every_cycle`

Same signification as every

`evidence_theory_DM`

Possible uses:

list<list> evidence_theory_DM list<map<string,unknown>> ---> int
evidence_theory_DM (list<list> , list<map<string,unknown>>) ---> int
evidence_theory_DM (list<list>, list<map<string,unknown>>, bool) ---> int

Result:

The index of the best candidate according to a method based on the Evidence theory. This theory, which was proposed by Shafer (Shafer G (1976) A mathematical theory of evidence, Princeton University Press), is based on the work of Dempster (Dempster A (1967) Upper and lower probabilities induced by multivalued mapping. Annals of Mathematical Statistics, vol. 38, pp. 325--339) on lower and upper probability distributions. The first operand is the list of candidates (a candidate is a list of criterion values); the second operand the list of criterion: A criterion is a map that contains seven elements: a name, a first threshold s1, a second threshold s2, a value for the assertion "this candidate is the best" at threshold s1 (v1p), a value for the assertion "this candidate is the best" at threshold s2 (v2p), a value for the assertion "this candidate is not the best" at threshold s1 (v1c), a value for the assertion "this candidate is not the best" at threshold s2 (v2c). v1p, v2p, v1c and v2c have to been defined in order that: v1p + v1c <= 1.0; v2p + v2c <= 1.0.; the last operand allows to use a simple version of this multi-criteria decision making method (simple if true)

Special cases:

returns -1 is the list of candidates is nil or empty
if the operator is used with only 2 operands (the candidates and the criteria), the last parameter (use simple method) is set to true

Examples:

int var0 <- evidence_theory_DM([[1.0, 7.0],[4.0,2.0],[3.0, 3.0]], [["name"::"utility", "s1" :: 0.0,"s2"::1.0, "v1p"::0.0, "v2p"::1.0, "v1c"::0.0, "v2c"::0.0, "maximize" :: true],["name"::"price",  "s1" :: 0.0,"s2"::1.0, "v1p"::0.0, "v2p"::1.0, "v1c"::0.0, "v2c"::0.0, "maximize" :: true]], false); // var0 equals 0 
int var1 <- evidence_theory_DM([[1.0, 7.0],[4.0,2.0],[3.0, 3.0]], [["name"::"utility", "s1" :: 0.0,"s2"::1.0, "v1p"::0.0, "v2p"::1.0, "v1c"::0.0, "v2c"::0.0, "maximize" :: true],["name"::"price",  "s1" :: 0.0,"s2"::1.0, "v1p"::0.0, "v2p"::1.0, "v1c"::0.0, "v2c"::0.0, "maximize" :: true]]); // var1 equals 0

See also: weighted_means_DM, electre_DM,

`exp`

Possible uses:

exp (float) ---> float
exp (int) ---> float

Result:

Returns Euler's number e raised to the power of the operand.

Special cases:

the operand is casted to a float before being evaluated.

Examples:

float var0 <- exp (0.0); // var0 equals 1.0

See also: ln,

`exp_density`

Possible uses:

float exp_density float ---> float
exp_density (float , float) ---> float

Result:

returns the probability density function (PDF) at the specified point x of the exponential distribution with the given rate.

Examples:

float var0 <- exp_density(5,3) ; // var0 equals 0.731

See also: binomial, gamma_rnd, gauss_rnd, lognormal_rnd, poisson, rnd, skew_gauss, lognormal_density, gamma_density,

`exp_rnd`

Possible uses:

exp_rnd (float) ---> float

Result:

returns a random value from a exponential distribution with specified values of the rate (lambda) parameters. See https://mathworld.wolfram.com/ExponentialDistribution.html for more details ).

Examples:

float var0 <- exp_rnd(5) ; // var0 equals 0.731

See also: binomial, gamma_rnd, gauss_rnd, lognormal_rnd, poisson, rnd, skew_gauss, truncated_gauss, weibull_trunc_rnd,

`fact`

Possible uses:

fact (int) ---> float

Result:

Returns the factorial of the operand.

Special cases:

if the operand is less than 0, fact returns 0.

Examples:

float var0 <- fact(4); // var0 equals 24

`farthest_point_to`

Possible uses:

geometry farthest_point_to point ---> point
farthest_point_to (geometry , point) ---> point

Result:

the farthest point of the left-operand to the left-point.

Examples:

point var0 <- geom farthest_point_to(pt); // var0 equals the farthest point of geom to pt

See also: any_location_in, any_point_in, closest_points_with, points_at,

`farthest_to`

Possible uses:

container<unknown,geometry> farthest_to geometry ---> geometry
farthest_to (container<unknown,geometry> , geometry) ---> geometry

Result:

An agent or a geometry among the left-operand list of agents, species or meta-population (addition of species), the farthest to the operand (casted as a geometry).

Comment:

the distance is computed in the topology of the calling agent (the agent in which this operator is used), with the distance algorithm specific to the topology.

Examples:

geometry var0 <- [ag1, ag2, ag3] closest_to(self); // var0 equals return the farthest agent among ag1, ag2 and ag3 to the agent applying the operator. 
(species1 + species2) closest_to self

See also: neighbors_at, neighbors_of, inside, overlapping, agents_overlapping, agents_inside, agent_closest_to, closest_to, agent_farthest_to,

`field`

Possible uses:

unknown field float ---> field
field (unknown , float) ---> field
int field int ---> field
field (int , int) ---> field
field (int, int, float) ---> field
field (int, int, float, float) ---> field

`field_with`

Possible uses:

point field_with any expression ---> field
field_with (point , any expression) ---> field

Result:

creates a field with a size provided by the first operand, and filled by the evaluation of the second operand for each cell

Comment:

Note that both components of the right operand point should be positive, otherwise an exception is raised.

See also: matrix, as_matrix,

`file`

Possible uses:

file (any) ---> file

Result:

casts the operand in a file object.

`file_exists`

Possible uses:

file_exists (string) ---> bool

Result:

Test whether the parameter is the path to an existing file. False if it does not exist of if it is a folder

Examples:

string file_name <-"../includes/buildings.shp"; 
if file_exists(file_name){ 
	write "File exists in the computer"; 
}

`first`

Possible uses:

first (string) ---> string
first (container<KeyType,ValueType>) ---> ValueType
int first container ---> list
first (int , container) ---> list

Result:

the first value of the operand

Comment:

the first operator behavior depends on the nature of the operand

Special cases:

if it is a map, first returns the first value of the first pair (in insertion order)
if it is a file, first returns the first element of the content of the file (that is also a container)
if it is a population, first returns the first agent of the population
if it is a graph, first returns the first edge (in creation order)
if it is a matrix, first returns the element at {0,0} in the matrix
for a matrix of int or float, it will return 0 if the matrix is empty
for a matrix of object or geometry, it will return nil if the matrix is empty
if it is a string, first returns a string composed of its first character

string var0 <- first ('abce'); // var0 equals 'a'

if it is a list, first returns the first element of the list, or nil if the list is empty

int var1 <- first ([1, 2, 3]); // var1 equals 1

See also: last,

`first_of`

Same signification as first

`first_with`

Possible uses:

container first_with any expression ---> unknown
first_with (container , any expression) ---> unknown

Result:

the first element of the left-hand operand that makes the right-hand operand evaluate to true.

Comment:

in the right-hand operand, the keyword each can be used to represent, in turn, each of the right-hand operand elements.

Special cases:

if the left-hand operand is nil, first_with throws an error. If there is no element that satisfies the condition, it returns nil
if the left-operand is a map, the keyword each will contain each value

int var4 <- [1::2, 3::4, 5::6] first_with (each >= 4); // var4 equals 4 
pair var5 <- [1::2, 3::4, 5::6].pairs first_with (each.value >= 4); // var5 equals (3::4)

Examples:

unknown var0 <- [1,2,3,4,5,6,7,8] first_with (each > 3); // var0 equals 4 
unknown var2 <- g2 first_with (length(g2 out_edges_of each) = 0); // var2 equals node9 
unknown var3 <- (list(node) first_with (round(node(each).location.x) > 32); // var3 equals node2

See also: group_by, last_with, where,

`flatten`

Possible uses:

flatten (field) ---> field
field flatten unknown ---> field
flatten (field , unknown) ---> field

`flip`

Possible uses:

flip (float) ---> bool

Result:

true or false given the probability represented by the operand

Special cases:

flip 0 always returns false, flip 1 true

Examples:

bool var0 <- flip (0.66666); // var0 equals 2/3 chances to return true.

See also: rnd,

`float`

Possible uses:

float (any) ---> float

Result:

casts the operand in a float object.

`floor`

Possible uses:

floor (float) ---> int

Result:

Maps the operand to the largest previous following integer, i.e. the largest integer not greater than x.

Examples:

int var0 <- floor(3); // var0 equals 3 
int var1 <- floor(3.5); // var1 equals 3 
int var2 <- floor(-4.7); // var2 equals -5

See also: ceil, round,

`folder`

Possible uses:

folder (string) ---> file

Result:

opens an existing repository

Special cases:

If the specified string does not refer to an existing repository, an exception is risen.

Examples:

file dirT <- folder("../includes/"); 
				// dirT represents the repository "../includes/" 
				// dirT.contents here contains the list of the names of included files

See also: file, new_folder,

`folder_exists`

Possible uses:

folder_exists (string) ---> bool

Result:

Test whether the parameter is the path to an existing folder. False if it doesnt exist or if it is a file

Examples:

string file_name <-"../includes/"; 
if folder_exists(file_name){ 
	write "Folder exists in the computer"; 
}

`font`

Possible uses:

string font int ---> font
font (string , int) ---> font
font (string, int, int) ---> font

Result:

Creates a new font, by specifying its name (either a font face name like 'Lucida Grande Bold' or 'Helvetica', or a logical name like 'Dialog', 'SansSerif', 'Serif', etc.), a size in points and a style, either #bold, #italic or #plain or a combination (addition) of them.

Examples:

font var0 <- font ('Helvetica Neue',12, #bold + #italic); // var0 equals a bold and italic face of the Helvetica Neue family

`frequency_of`

Possible uses:

container frequency_of any expression ---> map
frequency_of (container , any expression) ---> map

Result:

Returns a map with keys equal to the application of the right-hand argument (like collect) and values equal to the frequency of this key (i.e. how many times it has been obtained)

Examples:

map var0 <- [1, 2, 3, 3, 4, 4, 5, 3, 3, 4] frequency_of each; // var0 equals map([1::1,2::1,3::4,4::3,5::1])

`from`

Same signification as since

`from_binary`

Same signification as deserialize

`from_gaml`

Possible uses:

from_gaml (string) ---> unknown

Result:

Evaluates/deserialises the given GAML string into a value.

Examples:

unknown var0 <- eval_gaml("2+3"); // var0 equals 5

`from_json`

Possible uses:

from_json (string) ---> unknown

Result:

Deserializes an object precedently serialized using 'to_json' (or an arbitrary json string obtained elsewhere). Agents and populations are not supported yet (i.e. they will return maps)

See also: from_gaml, from_binary,

`fuzzy_choquet_DM`

Possible uses:

fuzzy_choquet_DM (list<list>, list<string>, map) ---> int

Result:

The index of the candidate that maximizes the Fuzzy Choquet Integral value. The first operand is the list of candidates (a candidate is a list of criterion values); the second operand the list of criterion (list of string); the third operand the weights of each sub-set of criteria (map with list for key and float for value)

Special cases:

returns -1 is the list of candidates is nil or empty

Examples:

int var0 <- fuzzy_choquet_DM([[1.0, 7.0],[4.0,2.0],[3.0, 3.0]], ["utility", "price", "size"],[["utility"]::0.5,["size"]::0.1,["price"]::0.4,["utility", "price"]::0.55]); // var0 equals 0

See also: promethee_DM, electre_DM, evidence_theory_DM,

`fuzzy_kappa`

Possible uses:

fuzzy_kappa (list<agent>, list<unknown>, list<unknown>, list<float>, list<unknown>, matrix<float>, float) ---> float
fuzzy_kappa (list<agent>, list<unknown>, list<unknown>, list<float>, list<unknown>, matrix<float>, float, list<unknown>) ---> float

Result:

fuzzy kappa indicator for 2 map comparisons: fuzzy_kappa(agents_list,list_vals1,list_vals2, output_similarity_per_agents,categories,fuzzy_categories_matrix, fuzzy_distance, weights). Reference: Visser, H., and T. de Nijs, 2006. The map comparison kit, Environmental Modelling & Software, 21 fuzzy kappa indicator for 2 map comparisons: fuzzy_kappa(agents_list,list_vals1,list_vals2, output_similarity_per_agents,categories,fuzzy_categories_matrix, fuzzy_distance). Reference: Visser, H., and T. de Nijs, 2006. The map comparison kit, Environmental Modelling & Software, 21

Examples:

fuzzy_kappa([ag1, ag2, ag3, ag4, ag5],[cat1,cat1,cat2,cat3,cat2],[cat2,cat1,cat2,cat1,cat2], similarity_per_agents,[cat1,cat2,cat3],[[1,0,0],[0,1,0],[0,0,1]], 2, [1.0,3.0,2.0,2.0,4.0]) 
fuzzy_kappa([ag1, ag2, ag3, ag4, ag5],[cat1,cat1,cat2,cat3,cat2],[cat2,cat1,cat2,cat1,cat2], similarity_per_agents,[cat1,cat2,cat3],[[1,0,0],[0,1,0],[0,0,1]], 2)

`fuzzy_kappa_sim`

Possible uses:

fuzzy_kappa_sim (list<agent>, list<unknown>, list<unknown>, list<unknown>, list<float>, list<unknown>, matrix<float>, float) ---> float
fuzzy_kappa_sim (list<agent>, list<unknown>, list<unknown>, list<unknown>, list<float>, list<unknown>, matrix<float>, float, list<unknown>) ---> float

Result:

fuzzy kappa simulation indicator for 2 map comparisons: fuzzy_kappa_sim(agents_list,list_vals1,list_vals2, output_similarity_per_agents,fuzzy_transitions_matrix, fuzzy_distance). Reference: Jasper van Vliet, Alex Hagen-Zanker, Jelle Hurkens, Hedwig van Delden, A fuzzy set approach to assess the predictive accuracy of land use simulations, Ecological Modelling, 24 July 2013, Pages 32-42, ISSN 0304-3800, fuzzy kappa simulation indicator for 2 map comparisons: fuzzy_kappa_sim(agents_list,list_vals1,list_vals2, output_similarity_per_agents,fuzzy_transitions_matrix, fuzzy_distance, weights). Reference: Jasper van Vliet, Alex Hagen-Zanker, Jelle Hurkens, Hedwig van Delden, A fuzzy set approach to assess the predictive accuracy of land use simulations, Ecological Modelling, 24 July 2013, Pages 32-42, ISSN 0304-3800,

Examples:

fuzzy_kappa_sim([ag1, ag2, ag3, ag4, ag5], [cat1,cat1,cat2,cat3,cat2],[cat2,cat1,cat2,cat1,cat2], similarity_per_agents,[cat1,cat2,cat3],[[1,0,0,0,0,0,0,0,0],[0,1,0,0,0,0,0,0,0],[0,0,1,0,0,0,0,0,0],[0,0,0,1,0,0,0,0,0],[0,0,0,0,1,0,0,0,0],[0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,1,0,0],[0,0,0,0,0,0,0,1,0],[0,0,0,0,0,0,0,0,1]], 2) 
fuzzy_kappa_sim([ag1, ag2, ag3, ag4, ag5], [cat1,cat1,cat2,cat3,cat2],[cat2,cat1,cat2,cat1,cat2], similarity_per_agents,[cat1,cat2,cat3],[[1,0,0,0,0,0,0,0,0],[0,1,0,0,0,0,0,0,0],[0,0,1,0,0,0,0,0,0],[0,0,0,1,0,0,0,0,0],[0,0,0,0,1,0,0,0,0],[0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,1,0,0],[0,0,0,0,0,0,0,1,0],[0,0,0,0,0,0,0,0,1]], 2,[1.0,3.0,2.0,2.0,4.0])

`gaml_file`

Possible uses:

gaml_file (string) ---> file

Result:

Constructs a file of type gaml. Allowed extensions are limited to gaml, experiment

Special cases:

gaml_file(string): This file constructor allows to read a gaml file (.gaml)

file f <- gaml_file("file.gaml");

See also: is_gaml,

`gaml_type`

Possible uses:

gaml_type (any) ---> gaml_type

Result:

casts the operand in a gaml_type object.

`gamma`

Possible uses:

gamma (float) ---> float

Result:

Returns the value of the Gamma function at x.

Examples:

float var0 <- gamma(5); // var0 equals 24.0

`gamma_density`

Possible uses:

gamma_density (float, float, float) ---> float

Result:

gamma_density(x,shape,scale) returns the probability density function (PDF) at the specified point x of the Gamma distribution with the given shape and scale.

Examples:

float var0 <- gamma_density(1,9,0.5); // var0 equals 0.731

See also: binomial, gauss_rnd, lognormal_rnd, poisson, rnd, skew_gauss, truncated_gauss, weibull_rnd, weibull_density, lognormal_density,

`gamma_distribution`

Possible uses:

gamma_distribution (float, float, float) ---> float

Result:

Returns the integral from zero to x of the gamma probability density function.

Comment:

incomplete_gamma(a,x) is equal to pgamma(a,1,x).

Examples:

float var0 <- gamma_distribution(2,3,0.9) with_precision(3); // var0 equals 0.269

`gamma_distribution_complemented`

Possible uses:

gamma_distribution_complemented (float, float, float) ---> float

Result:

Returns the integral from x to infinity of the gamma probability density function.

Examples:

float var0 <- gamma_distribution_complemented(2,3,0.9) with_precision(3); // var0 equals 0.731

`gamma_index`

Possible uses:

gamma_index (graph) ---> float

Result:

returns the gamma index of the graph (A measure of connectivity that considers the relationship between the number of observed links and the number of possible links: gamma = e/(3 * (v - 2)) - for planar graph.

Examples:

graph graphEpidemio <- graph([]); 
float var1 <- gamma_index(graphEpidemio); // var1 equals the gamma index of the graph

See also: alpha_index, beta_index, nb_cycles, connectivity_index,

`gamma_rnd`

Possible uses:

float gamma_rnd float ---> float
gamma_rnd (float , float) ---> float

Result:

returns a random value from a gamma distribution with specified values of the shape and scale parameters

Examples:

float var0 <- gamma_rnd(9,0.5); // var0 equals 0.731

See also: binomial, gauss_rnd, lognormal_rnd, poisson, rnd, skew_gauss, truncated_gauss, weibull_rnd, gamma_trunc_rnd,

`gamma_trunc_rnd`

Possible uses:

gamma_trunc_rnd (float, float, float, float) ---> float
gamma_trunc_rnd (float, float, float, bool) ---> float

Result:

returns a random value from a truncated gamma distribution (in a range or given only one boundary) with specified values of the shape and scale parameters.

Special cases:

when 2 float operands are specified, they are taken as mininimum and maximum values for the result

gamma_trunc_rnd(2,3,0,5)

when 1 float and a boolean (isMax) operands are specified, the float value represents the single boundary (max if the boolean is true, min otherwise),

gamma_trunc_rnd(2,3,5,true)

See also: gamma_rnd, weibull_trunc_rnd, lognormal_trunc_rnd, truncated_gauss,

`gauss`

Possible uses:

gauss (point) ---> float
float gauss float ---> float
gauss (float , float) ---> float

Result:

A value from a normally distributed random variable with expected value (mean as first operand) and variance (standardDeviation as second operand). The probability density function of such a variable is a Gaussian. The operator can be used with an operand of type point {meand,standardDeviation}.

Special cases:

when standardDeviation value is 0.0, it always returns the mean value
when the operand is a point, it is read as {mean, standardDeviation}

Examples:

float var0 <- gauss(0,0.3); // var0 equals 0.22354 
float var1 <- gauss({0,0.3}); // var1 equals 0.22354

See also: binomial, gamma_rnd, lognormal_rnd, poisson, rnd, skew_gauss, truncated_gauss, weibull_rnd,

`gauss_rnd`

Same signification as gauss

`generate_barabasi_albert`

Possible uses:

generate_barabasi_albert (container, int, int, bool) ---> graph
generate_barabasi_albert (int, int, int, bool) ---> graph
generate_barabasi_albert (int, int, int, bool, species) ---> graph
generate_barabasi_albert (int, int, int, bool, species, species) ---> graph

Result:

returns a random scale-free network (following Barabasi-Albert (BA) model).

Comment:

The Barabasi-Albert (BA) model is an algorithm for generating random scale-free networks using a preferential attachment mechanism. A scale-free network is a network whose degree distribution follows a power law, at least asymptotically.Such networks are widely observed in natural and human-made systems, including the Internet, the world wide web, citation networks, and some social networks. [From Wikipedia article]The map operand should includes following elements:The Barabasi-Albert (BA) model is an algorithm for generating random scale-free networks using a preferential attachment mechanism. A scale-free network is a network whose degree distribution follows a power law, at least asymptotically.Such networks are widely observed in natural and human-made systems, including the Internet, the world wide web, citation networks, and some social networks. [From Wikipedia article]The map operand should includes following elements:The Barabasi-Albert (BA) model is an algorithm for generating random scale-free networks using a preferential attachment mechanism. A scale-free network is a network whose degree distribution follows a power law, at least asymptotically.Such networks are widely observed in natural and human-made systems, including the Internet, the world wide web, citation networks, and some social networks. [From Wikipedia article]The map operand should includes following elements:The Barabasi-Albert (BA) model is an algorithm for generating random scale-free networks using a preferential attachment mechanism. A scale-free network is a network whose degree distribution follows a power law, at least asymptotically.Such networks are widely observed in natural and human-made systems, including the Internet, the world wide web, citation networks, and some social networks. [From Wikipedia article]The map operand should includes following elements:

Special cases:

"nbInitNodes": number of initial nodes; "nbEdgesAdded": number of edges of each new node added during the network growth; "nbNodes": final number of nodes; "directed": is the graph directed or not; "node_species": the species of vertices; "edges_species": the species of edges

graph<myVertexSpecy,myEdgeSpecy> myGraph <- generate_watts_strogatz( 
			60, 
			1, 
			100, 
		true, 
			myVertexSpecies);

"nbInitNodes": number of initial nodes; "nodes": list of existing nodes to connect (agents or geometries); "nbEdgesAdded": number of edges of each new node added during the network growth; "directed": is the graph directed or not;

graph myGraph <- generate_watts_strogatz(people, 10,1,false);

"nbInitNodes": number of initial nodes; "nbEdgesAdded": number of edges of each new node added during the network growth; "nbNodes": final number of nodes; "directed": is the graph directed or not; "node_species": the species of vertices; "edges_species": the species of edges

graph<myVertexSpecy,myEdgeSpecy> myGraph <- generate_watts_strogatz( 
			60, 
			1, 
			100, 
		true, 
			myVertexSpecies, 
			myEdgeSpecies);

"nbInitNodes": number of initial nodes; "nbEdgesAdded": number of edges of each new node added during the network growth; "nbNodes": final number of nodes; "directed": is the graph directed or not;

graph<myVertexSpecy,myEdgeSpecy> myGraph <- generate_watts_strogatz( 
			60, 
			1, 
			100, 
		true);

See also: generate_watts_strogatz,

`generate_complete_graph`

Possible uses:

bool generate_complete_graph list ---> graph
generate_complete_graph (bool , list) ---> graph
int generate_complete_graph bool ---> graph
generate_complete_graph (int , bool) ---> graph
generate_complete_graph (int, bool, species) ---> graph
generate_complete_graph (bool, list, species) ---> graph
generate_complete_graph (int, bool, species, species) ---> graph

Result:

returns a fully connected graph.

Special cases:

"directed": is the graph has to be directed or not;"nodes": the list of existing nodes

graph<myVertexSpecy,myEdgeSpecy> myGraph <- generate_complete_graph( 
			true, 
			nodes);

nbNodes: number of nodes to create;directed: is the graph directed or not;node_species: the species of nodes

graph myGraph <- generate_complete_graph( 
			100, 
			true, 
			node_species);

"directed": is the graph has to be directed or not;"nodes": the list of existing nodes; "edges_species": the species of edges

graph<myVertexSpecy,myEdgeSpecy> myGraph <- generate_complete_graph( 
true, 
nodes, 
edge_species);

nbNodes: number of nodes to create;directed: is the graph directed or not;node_species: the species of nodes; edges_species: the species of edges

graph<myVertexSpecy,myEdgeSpecy> myGraph <- generate_complete_graph( 
100, 
true, 
node_species, 
edge_species);

nbNodes: number of nodes to create;directed: is the graph directed or not

graph myGraph <- generate_complete_graph( 
			100, 
			true);

See also: generate_barabasi_albert, generate_watts_strogatz,

`generate_pedestrian_network`

Possible uses:

generate_pedestrian_network (list<container<unknown,geometry>>, container<unknown,geometry>, bool, bool, float, float, bool, float, float, float, float) ---> list<geometry>
generate_pedestrian_network (list<container<unknown,geometry>>, container<unknown,geometry>, bool, bool, float, float, bool, float, float, float, float, float) ---> list<geometry>
generate_pedestrian_network (list<container<unknown,geometry>>, container<unknown,geometry>, container<unknown,geometry>, bool, bool, float, float, bool, float, float, float, float) ---> list<geometry>
generate_pedestrian_network (list<container<unknown,geometry>>, container<unknown,geometry>, container<unknown,geometry>, bool, bool, float, float, bool, float, float, float, float, float) ---> list<geometry>

Result:

The method allows to build a network of corridors to be used by pedestrian while traveling around a space made of obstacles and other users. It makes it possible to avoide collision with other agents (e.g. buildings) including other pedestrians and in the same time managing a path to a destination in a complex environment (e.g. a city). The method is highly customizable, with many parameters listed as below:

obstacles : a list containing the lists of geometries or agents that are obstacles for pedestrians (e.g. walls, cars).
bounds : a list of geometries that represent the spatial boundary of the network (i.e. the enclosing space of the network).
open : a boolean expression that will add nodes in the network within open areas. More precisely, new invisible points are added to improve triangulation in areas with very few obstacles.
randomDist : a boolean expression, related to the previous 'open' parameter, that allows to switch between a random (true) spatial distribution or a distribution (false) that build upon a equidistant repartition of points all around the area.
open area : a float in meters representing the minimum distance for an area to be considered as an open area (i.e. euclidian distance between centroid and farest obstacle)
density point : a float representing the density of points per meter within open areas.
clean network : a boolean expression that allows to enhance the network (true) or living as it is generated (false). Enhancement includes filling very small gaps between edges and nodes.
cliping : tolerance for the cliping in triangulation (float; distance) - see skeletonize operator
tolerance : tolerance for the triangulation (float)
min dist obstacle : minimal distance to obstacles to keep a path (float; if 0.0, no filtering)
simplification : simplification distance for the final geometries
square size : size of squares for decomposition (optimization)

Special cases:

The method allows to build a network of corridors to be used by pedestrian while traveling around a space made of obstacles and other users. It makes it possible to avoide collision with other agents (e.g. buildings) including other pedestrians and in the same time managing a path to a destination in a complex environment (e.g. a city). The method is highly customizable, with many parameters listed as below:
1. obstacles : a list containing the lists of geometries or agents that are obstacles for pedestrians (e.g. walls, cars).
2. bounds : a list of geometries that represent the spatial boundary of the network (i.e. the enclosing space of the network).
3. open : a boolean expression that will add nodes in the network within open areas. More precisely, new invisible points are added to improve triangulation in areas with very few obstacles.
4. randomDist : a boolean expression, related to the previous 'open' parameter, that allows to switch between a random (true) spatial distribution or a distribution (false) that build upon a equidistant repartition of points all around the area.
5. open area : a float in meters representing the minimum distance for an area to be considered as an open area (i.e. euclidian distance between centroid and farest obstacle)
6. density point : a float representing the density of points per meter within open areas.
7. clean network : a boolean expression that allows to enhance the network (true) or living as it is generated (false). Enhancement includes filling very small gaps between edges and nodes.
8. cliping : tolerance for the cliping in triangulation (float; distance) - see skeletonize operator
9. tolerance : tolerance for the triangulation (float)
10. min dist obstacle : minimal distance to obstacles to keep a path (float; if 0.0, no filtering)
11. simplification : simplification distance for the final geometries
The method allows to build a network of corridors to be used by pedestrian while traveling around a space made of obstacles and other users. It makes it possible to avoide collision with other agents (e.g. buildings) including other pedestrians and in the same time managing a path to a destination in a complex environment (e.g. a city). The method is highly customizable, with many parameters listed as below:
1. obstacles : a list containing the lists of geometries or agents that are obstacles for pedestrians (e.g. walls, cars).
2. bounds : a list of geometries that represent the spatial boundary of the network (i.e. the enclosing space of the network).
3. regular network : allows to combine the generated network with a simplified car user oriented network. More specifically, the network generated will combine enhance pedestrian oriented generated network with the given network: The property of the latter does not allows pedestrian to avoid collision (1D) when using its edges (while moving in 2D space and avoiding collision in the former).
4. open : a boolean expression that will add nodes in the network within open areas. More precisely, new invisible points are added to improve triangulation in areas with very few obstacles.
5. randomDist : a boolean expression, related to the previous 'open' parameter, that allows to switch between a random (true) spatial distribution or a distribution (false) that build upon a equidistant repartition of points all around the area.
6. open area : a float in meters representing the minimum distance for an area to be considered as an open area (i.e. euclidian distance between centroid and farest obstacle)
7. density point : a float representing the density of points per meter within open areas.
8. clean network : a boolean expression that allows to enhance the network (true) or living as it is generated (false). Enhancement includes filling very small gaps between edges and nodes.
9. cliping : tolerance for the cliping in triangulation (float; distance) - see skeletonize operator
10. tolerance : tolerance for the triangulation (float)
11. min dist obstacle : minimal distance to obstacles to keep a path (float; if 0.0, no filtering)
12. simplification : simplification distance for the final geometries
The method allows to build a network of corridors to be used by pedestrian while traveling around a space made of obstacles and other users. It makes it possible to avoide collision with other agents (e.g. buildings) including other pedestrians and in the same time managing a path to a destination in a complex environment (e.g. a city). The method is highly customizable, with many parameters listed as below:
1. obstacles : a list containing the lists of geometries or agents that are obstacles for pedestrians (e.g. walls, cars).
2. bounds : a list of geometries that represent the spatial boundary of the network (i.e. the enclosing space of the network).
3. regular network : allows to combine the generated network with a simplified car user oriented network. More specifically, the network generated will combine enhance pedestrian oriented generated network with the given network: The property of the latter does not allows pedestrian to avoid collision (1D) when using its edges (while moving in 2D space and avoiding collision in the former).
4. open : a boolean expression that will add nodes in the network within open areas. More precisely, new invisible points are added to improve triangulation in areas with very few obstacles.
5. randomDist : a boolean expression, related to the previous 'open' parameter, that allows to switch between a random (true) spatial distribution or a distribution (false) that build upon a equidistant repartition of points all around the area.
6. open area : a float in meters representing the minimum distance for an area to be considered as an open area (i.e. euclidian distance between centroid and farest obstacle)
7. density point : a float representing the density of points per meter within open areas.
8. clean network : a boolean expression that allows to enhance the network (true) or living as it is generated (false). Enhancement includes filling very small gaps between edges and nodes.
9. cliping : tolerance for the cliping in triangulation (float; distance) - see skeletonize operator
10. tolerance : tolerance for the triangulation (float)
11. min dist obstacle : minimal distance to obstacles to keep a path (float; if 0.0, no filtering)

Examples:

list<geometry> var0 <- generate_pedestrian_network([wall], [world],true,false,3.0,0.1, true,0.1,0.0,0.0,0.0,0.0); // var0 equals a list of polylines corresponding to the pedestrian paths 
list<geometry> var1 <- generate_pedestrian_network([wall], [world], [road], true,false,3.0,0.1, true,0.1,0.0,0.0,0.0,50.0); // var1 equals a list of polylines corresponding to the pedestrian paths 
list<geometry> var2 <- generate_pedestrian_network([wall], [world],true,false,3.0,0.1, true,0.1,0.0,0.0,0.0,50.0); // var2 equals a list of polylines corresponding to the pedestrian paths 
list<geometry> var3 <- generate_pedestrian_network([wall], [world], [road], true,false,3.0,0.1, true,0.1,0.0,0.0,0.0); // var3 equals a list of polylines corresponding to the pedestrian paths

`generate_random_graph`

Possible uses:

generate_random_graph (int, int, bool) ---> graph
generate_random_graph (int, int, bool, species) ---> graph
generate_random_graph (int, int, bool, species, species) ---> graph

Result:

returns a random graph.

Special cases:

nbNodes: number of nodes to create;nbEdges: number of edges to create;directed: is the graph directed or not

graph myGraph <- generate_random_graph( 
50, 
100, 
true);

nbNodes: number of nodes to create;nbEdges: number of edges to create;directed: is the graph directed or not;node_species: the species of nodes

graph myGraph <- generate_random_graph( 
50, 
100, 
true, 
node_species);

nbNodes: number of nodes to be created; nbEdges: number of edges to be created; directed: is the graph has to be directed or not;node_species: the species of nodes; edges_species: the species of edges

graph<node_species,edge_species> myGraph <- generate_random_graph( 
50, 
100, 
true, 
node_species, 
edge_species);

See also: generate_barabasi_albert, generate_watts_strogatz,

`generate_terrain`

Possible uses:

generate_terrain (int, int, int, float, float, float) ---> field

Result:

This operator allows to generate a pseudo-terrain using a simplex noise generator. Its usage is kept simple: it takes first a seed (random or not), then the dimensions (width and height) of the field to generate, then a level (between 0 and 1) of details (which actually determines the number of passes to make), then the value (between 0 and 1) of smoothess, with 0 being completely rought and 1 super smooth, and finally the value (between 0 and 1) of scattering, with 0 building maps in 'one piece' and 1 completely scattered ones.

`generate_watts_strogatz`

Possible uses:

generate_watts_strogatz (int, float, int, bool) ---> graph
generate_watts_strogatz (container, float, int, bool) ---> graph
generate_watts_strogatz (int, float, int, bool, species) ---> graph
generate_watts_strogatz (int, float, int, bool, species, species) ---> graph

Result:

returns a random small-world network (following Watts-Strogatz model).

Comment:

The Watts-Strogatz model is a random graph generation model that produces graphs with small-world properties, including short average path lengths and high clustering.A small-world network is a type of graph in which most nodes are not neighbors of one another, but most nodes can be reached from every other by a small number of hops or steps. [From Wikipedia article]The map operand should includes following elements:The Watts-Strogatz model is a random graph generation model that produces graphs with small-world properties, including short average path lengths and high clustering.A small-world network is a type of graph in which most nodes are not neighbors of one another, but most nodes can be reached from every other by a small number of hops or steps. [From Wikipedia article]The map operand should includes following elements:The Watts-Strogatz model is a random graph generation model that produces graphs with small-world properties, including short average path lengths and high clustering.A small-world network is a type of graph in which most nodes are not neighbors of one another, but most nodes can be reached from every other by a small number of hops or steps. [From Wikipedia article]The map operand should includes following elements:The Watts-Strogatz model is a random graph generation model that produces graphs with small-world properties, including short average path lengths and high clustering.A small-world network is a type of graph in which most nodes are not neighbors of one another, but most nodes can be reached from every other by a small number of hops or steps. [From Wikipedia article]The map operand should includes following elements:

Special cases:

"nbNodes": the graph will contain (size + 1) nodes (size must be greater than k); "p": probability to "rewire" an edge (so it must be between 0 and 1, the parameter is often called beta in the literature); "k": the base degree of each node (k must be greater than 2 and even); "directed": is the graph directed or not

graph<myVertexSpecy,myEdgeSpecy> myGraph <- generate_watts_strogatz( 
			100, 
			0.3, 
			5, 
		true);

"nbNodes": the graph will contain (size + 1) nodes (size must be greater than k); "p": probability to "rewire" an edge (so it must be between 0 and 1, the parameter is often called beta in the literature); "k": the base degree of each node (k must be greater than 2 and even); "directed": is the graph directed or not; "node_species": the species of vertices; "edges_species": the species of edges

graph<myVertexSpecy,myEdgeSpecy> myGraph <- generate_watts_strogatz( 
			100, 
			0.3, 
			5, 
		true, 
			myVertexSpecies, 
			myEdgeSpecies);

"nbNodes": the graph will contain (size + 1) nodes (size must be greater than k); "p": probability to "rewire" an edge (so it must be between 0 and 1, the parameter is often called beta in the literature); "k": the base degree of each node (k must be greater than 2 and even); "directed": is the graph directed or not; "node_species": the species of vertices

graph<myVertexSpecy,myEdgeSpecy> myGraph <- generate_watts_strogatz( 
			100, 
			0.3, 
			5, 
		true, 
			myVertexSpecies);

"nodes": the list of nodes to connect; "p": probability to "rewire" an edge (so it must be between 0 and 1, the parameter is often called beta in the literature); "k": the base degree of each node (k must be greater than 2 and even); "directed": is the graph directed or not

graph<myVertexSpecy,myEdgeSpecy> myGraph <- generate_watts_strogatz( 
			people, 
			0.3, 
			5, 
		true);

See also: generate_barabasi_albert,

`geojson_file`

Possible uses:

geojson_file (string) ---> file
string geojson_file int ---> file
geojson_file (string , int) ---> file
string geojson_file string ---> file
geojson_file (string , string) ---> file
string geojson_file bool ---> file
geojson_file (string , bool) ---> file
geojson_file (string, int, bool) ---> file
geojson_file (string, string, bool) ---> file

Result:

Constructs a file of type geojson. Allowed extensions are limited to json, geojson, geo.json

Special cases:

geojson_file(string): This file constructor allows to read a geojson file (https://geojson.org/)

file f <- geojson_file("file.json");

geojson_file(string,int): This file constructor allows to read a geojson file and specifying the coordinates system code, as an int

file f <- geojson_file("file.json", 32648);

geojson_file(string,string): This file constructor allows to read a geojson file and specifying the coordinates system code (epg,...,), as a string

file f <- geojson_file("file.json", "EPSG:32648");

geojson_file(string,bool): This file constructor allows to read a geojson file and take a potential z value (not taken in account by default)

file f <- geojson_file("file.json", true);

geojson_file(string,int,bool): This file constructor allows to read a geojson file, specifying the coordinates system code, as an int and take a potential z value (not taken in account by default)

file f <- geojson_file("file.json",32648, true);

geojson_file(string,string,bool): This file constructor allows to read a geojson file, specifying the coordinates system code (epg,...,), as a string and take a potential z value (not taken in account by default

file f <- geojson_file("file.json", "EPSG:32648",true);

See also: is_geojson,

`geometric_mean`

Possible uses:

geometric_mean (container) ---> float

Result:

the geometric mean of the elements of the operand. See Geometric_mean for more details.

Comment:

The operator casts all the numerical element of the list into float. The elements that are not numerical are discarded.

Examples:

float var0 <- geometric_mean ([4.5, 3.5, 5.5, 7.0]); // var0 equals 4.962326343467649

See also: mean, median, harmonic_mean,

`geometry`

Possible uses:

geometry (any) ---> geometry

Result:

casts the operand in a geometry object.

`geometry_collection`

Possible uses:

geometry_collection (container<unknown,geometry>) ---> geometry

Result:

A geometry collection (multi-geometry) composed of the given list of geometries.

Special cases:

if the operand is nil, returns the point geometry {0,0}
if the operand is composed of a single geometry, returns a copy of the geometry.

Examples:

geometry var0 <- geometry_collection([{0,0}, {0,10}, {10,10}, {10,0}]); // var0 equals a geometry composed of the 4 points (multi-point).

See also: around, circle, cone, link, norm, point, polygone, rectangle, square, triangle, line,

`get`

Possible uses:

agent get string ---> unknown
get (agent , string) ---> unknown
geometry get string ---> unknown
get (geometry , string) ---> unknown

Result:

Reads an attribute of the specified agent (or geometry) (left operand). The attribute name is specified by the right operand.

Special cases:

Reading the attribute of another agent

string agent_name <- an_agent get('name');     // reads then 'name' attribute of an_agent then assigns the returned value to the agent_name variable

Reading the attribute of a geometry

string geom_area <- a_geometry get('area');     // reads then 'area' attribute of 'a_geometry' variable then assigns the returned value to the geom_area variable

`get_about`

Possible uses:

get_about (emotion) ---> predicate

Result:

get the about value of the given emotion

Examples:

get_about(emotion)

`get_agent`

Possible uses:

get_agent (social_link) ---> agent

Result:

get the agent value of the given social link

Examples:

get_agent(social_link1)

`get_agent_cause`

Possible uses:

get_agent_cause (emotion) ---> agent
get_agent_cause (predicate) ---> agent

Result:

get the agent cause value of the given emotion evaluate the agent_cause value of a predicate

Examples:

get_agent_cause(emotion) 
get_agent_cause(pred1)

`get_belief_op`

Possible uses:

agent get_belief_op predicate ---> mental_state
get_belief_op (agent , predicate) ---> mental_state

Result:

get the belief in the belief base with the given predicate.

Examples:

mental_state var0 <- get_belief_op(self,predicate("has_water")); // var0 equals nil

`get_belief_with_name_op`

Possible uses:

agent get_belief_with_name_op string ---> mental_state
get_belief_with_name_op (agent , string) ---> mental_state

Result:

get the belief in the belief base with the given name.

Examples:

mental_state var0 <- get_belief_with_name_op(self,"has_water"); // var0 equals nil

`get_beliefs_op`

Possible uses:

agent get_beliefs_op predicate ---> list<mental_state>
get_beliefs_op (agent , predicate) ---> list<mental_state>

Result:

get the beliefs in the belief base with the given predicate.

Examples:

get_beliefs_op(self,predicate("has_water"))

`get_beliefs_with_name_op`

Possible uses:

agent get_beliefs_with_name_op string ---> list<mental_state>
get_beliefs_with_name_op (agent , string) ---> list<mental_state>

Result:

get the list of beliefs in the belief base which predicate has the given name.

Examples:

get_beliefs_with_name_op(self,"has_water")

`get_current_intention_op`

Possible uses:

get_current_intention_op (agent) ---> mental_state

Result:

get the current intention.

Examples:

mental_state var0 <- get_current_intention_op(self); // var0 equals nil

`get_decay`

Possible uses:

get_decay (emotion) ---> float

Result:

get the decay value of the given emotion

Examples:

get_decay(emotion)

`get_desire_op`

Possible uses:

agent get_desire_op predicate ---> mental_state
get_desire_op (agent , predicate) ---> mental_state

Result:

get the desire in the desire base with the given predicate.

Examples:

mental_state var0 <- get_belief_op(self,predicate("has_water")); // var0 equals nil

`get_desire_with_name_op`

Possible uses:

agent get_desire_with_name_op string ---> mental_state
get_desire_with_name_op (agent , string) ---> mental_state

Result:

get the desire in the desire base with the given name.

Examples:

mental_state var0 <- get_desire_with_name_op(self,"has_water"); // var0 equals nil

`get_desires_op`

Possible uses:

agent get_desires_op predicate ---> list<mental_state>
get_desires_op (agent , predicate) ---> list<mental_state>

Result:

get the desires in the desire base with the given predicate.

Examples:

get_desires_op(self,predicate("has_water"))

`get_desires_with_name_op`

Possible uses:

agent get_desires_with_name_op string ---> list<mental_state>
get_desires_with_name_op (agent , string) ---> list<mental_state>

Result:

get the list of desires in the desire base which predicate has the given name.

Examples:

get_desires_with_name_op(self,"has_water")

`get_dominance`

Possible uses:

get_dominance (social_link) ---> float

Result:

get the dominance value of the given social link

Examples:

get_dominance(social_link1)

`get_familiarity`

Possible uses:

get_familiarity (social_link) ---> float

Result:

get the familiarity value of the given social link

Examples:

get_familiarity(social_link1)

`get_ideal_op`

Possible uses:

agent get_ideal_op predicate ---> mental_state
get_ideal_op (agent , predicate) ---> mental_state

Result:

get the ideal in the ideal base with the given name.

Examples:

mental_state var0 <- get_ideal_op(self,predicate("has_water")); // var0 equals nil

`get_ideal_with_name_op`

Possible uses:

agent get_ideal_with_name_op string ---> mental_state
get_ideal_with_name_op (agent , string) ---> mental_state

Result:

get the ideal in the ideal base with the given name.

Examples:

mental_state var0 <- get_ideal_with_name_op(self,"has_water"); // var0 equals nil

`get_ideals_op`

Possible uses:

agent get_ideals_op predicate ---> list<mental_state>
get_ideals_op (agent , predicate) ---> list<mental_state>

Result:

get the ideal in the ideal base with the given name.

Examples:

get_ideals_op(self,predicate("has_water"))

`get_ideals_with_name_op`

Possible uses:

agent get_ideals_with_name_op string ---> list<mental_state>
get_ideals_with_name_op (agent , string) ---> list<mental_state>

Result:

get the list of ideals in the ideal base which predicate has the given name.

Examples:

get_ideals_with_name_op(self,"has_water")

`get_intensity`

Possible uses:

get_intensity (emotion) ---> float

Result:

get the intensity value of the given emotion

Examples:

get_intensity(emo1)

`get_intention_op`

Possible uses:

agent get_intention_op predicate ---> mental_state
get_intention_op (agent , predicate) ---> mental_state

Result:

get the intention in the intention base with the given predicate.

Examples:

get_intention_op(self,predicate("has_water"))

`get_intention_with_name_op`

Possible uses:

agent get_intention_with_name_op string ---> mental_state
get_intention_with_name_op (agent , string) ---> mental_state

Result:

get the intention in the intention base with the given name.

Examples:

get_intention_with_name_op(self,"has_water")

`get_intentions_op`

Possible uses:

agent get_intentions_op predicate ---> list<mental_state>
get_intentions_op (agent , predicate) ---> list<mental_state>

Result:

get the intentions in the intention base with the given predicate.

Examples:

get_intentions_op(self,predicate("has_water"))

`get_intentions_with_name_op`

Possible uses:

agent get_intentions_with_name_op string ---> list<mental_state>
get_intentions_with_name_op (agent , string) ---> list<mental_state>

Result:

get the list of intentions in the intention base which predicate has the given name.

Examples:

get_intentions_with_name_op(self,"has_water")

`get_lifetime`

Possible uses:

get_lifetime (mental_state) ---> int

Result:

get the lifetime value of the given mental state

Examples:

get_lifetime(mental_state1)

`get_liking`

Possible uses:

get_liking (social_link) ---> float

Result:

get the liking value of the given social link

Examples:

get_liking(social_link1)

`get_modality`

Possible uses:

get_modality (mental_state) ---> string

Result:

get the modality value of the given mental state

Examples:

get_modality(mental_state1)

`get_obligation_op`

Possible uses:

agent get_obligation_op predicate ---> mental_state
get_obligation_op (agent , predicate) ---> mental_state

Result:

get the obligation in the obligation base with the given predicate.

Examples:

mental_state var0 <- get_obligation_op(self,predicate("has_water")); // var0 equals nil

`get_obligation_with_name_op`

Possible uses:

agent get_obligation_with_name_op string ---> mental_state
get_obligation_with_name_op (agent , string) ---> mental_state

Result:

get the obligation in the obligation base with the given name.

Examples:

mental_state var0 <- get_obligation_with_name_op(self,"has_water"); // var0 equals nil

`get_obligations_op`

Possible uses:

agent get_obligations_op predicate ---> list<mental_state>
get_obligations_op (agent , predicate) ---> list<mental_state>

Result:

get the obligations in the obligation base with the given predicate.

Examples:

get_obligations_op(self,predicate("has_water"))

`get_obligations_with_name_op`

Possible uses:

agent get_obligations_with_name_op string ---> list<mental_state>
get_obligations_with_name_op (agent , string) ---> list<mental_state>

Result:

get the list of obligations in the obligation base which predicate has the given name.

Examples:

get_obligations_with_name_op(self,"has_water")

`get_plan_name`

Possible uses:

get_plan_name (BDIPlan) ---> string

Result:

get the name of a given plan

Examples:

get_plan_name(agent.current_plan)

`get_predicate`

Possible uses:

get_predicate (mental_state) ---> predicate

Result:

get the predicate value of the given mental state

Examples:

get_predicate(mental_state1)

`get_solidarity`

Possible uses:

get_solidarity (social_link) ---> float

Result:

get the solidarity value of the given social link

Examples:

get_solidarity(social_link1)

`get_strength`

Possible uses:

get_strength (mental_state) ---> float

Result:

get the strength value of the given mental state

Examples:

get_strength(mental_state1)

`get_super_intention`

Possible uses:

get_super_intention (predicate) ---> mental_state

Result:

get the super intention linked to a mental state

Examples:

get_super_intention(get_belief(pred1))

`get_trust`

Possible uses:

get_trust (social_link) ---> float

Result:

get the familiarity value of the given social link

Examples:

get_familiarity(social_link1)

`get_truth`

Possible uses:

get_truth (predicate) ---> bool

Result:

evaluate the truth value of a predicate

Examples:

get_truth(pred1)

`get_uncertainties_op`

Possible uses:

agent get_uncertainties_op predicate ---> list<mental_state>
get_uncertainties_op (agent , predicate) ---> list<mental_state>

Result:

get the uncertainties in the uncertainty base with the given predicate.

Examples:

get_uncertainties_op(self,predicate("has_water"))

`get_uncertainties_with_name_op`

Possible uses:

agent get_uncertainties_with_name_op string ---> list<mental_state>
get_uncertainties_with_name_op (agent , string) ---> list<mental_state>

Result:

get the list of uncertainties in the uncertainty base which predicate has the given name.

Examples:

get_uncertainties_with_name_op(self,"has_water")

`get_uncertainty_op`

Possible uses:

agent get_uncertainty_op predicate ---> mental_state
get_uncertainty_op (agent , predicate) ---> mental_state

Result:

get the uncertainty in the uncertainty base with the given predicate.

Examples:

mental_state var0 <- get_uncertainty_op(self,predicate("has_water")); // var0 equals nil

`get_uncertainty_with_name_op`

Possible uses:

agent get_uncertainty_with_name_op string ---> mental_state
get_uncertainty_with_name_op (agent , string) ---> mental_state

Result:

get the uncertainty in the uncertainty base with the given name.

Examples:

mental_state var0 <- get_uncertainty_with_name_op(self,"has_water"); // var0 equals nil

`get_values`

Possible uses:

get_values (predicate) ---> map<string,unknown>

Result:

return the map values of a predicate

Examples:

get_values(pred1)

`gif_file`

Possible uses:

gif_file (string) ---> file
string gif_file matrix<int> ---> file
gif_file (string , matrix<int>) ---> file

Result:

Constructs a file of type gif. Allowed extensions are limited to gif

Special cases:

gif_file(string): This file constructor allows to read a gif file

gif_file f <- gif_file("file.gif");

gif_file(string,matrix): This file constructor allows to store a matrix in a gif file (it does not save it - just store it in memory)

gif_file f <- gif_file("file.gif",matrix([10,10],[10,10]));

See also: is_gif,

`gini`

Possible uses:

gini (list<float>) ---> float

Special cases:

return the Gini Index of the given list of values (list of floats)

float var0 <- gini([1.0, 0.5, 2.0]); // var0 equals the gini index computed i.e. 0.2857143

`girvan_newman_clustering`

Possible uses:

graph girvan_newman_clustering int ---> list
girvan_newman_clustering (graph , int) ---> list

Result:

The Girvan�Newman algorithm is a hierarchical method used to detect communities. It detects communities by progressively removing edges from the original network.It returns a list of list of vertices and takes as operand the graph and the number of clusters

`gml_file`

Possible uses:

gml_file (string) ---> file
string gml_file int ---> file
gml_file (string , int) ---> file
string gml_file string ---> file
gml_file (string , string) ---> file
string gml_file bool ---> file
gml_file (string , bool) ---> file
gml_file (string, int, bool) ---> file
gml_file (string, string, bool) ---> file

Result:

Constructs a file of type gml. Allowed extensions are limited to gml

Special cases:

gml_file(string): This file constructor allows to read a gml file

file f <- gml_file("file.gml");

gml_file(string,int): This file constructor allows to read a gml file and specifying the coordinates system code, as an int (epsg code)

file f <- gml_file("file.gml", 32648);

gml_file(string,string): This file constructor allows to read a gml file and specifying the coordinates system code (epg,...,), as a string

file f <- gml_file("file.gml", "EPSG:32648");

gml_file(string,bool): This file constructor allows to read a gml file and take a potential z value (not taken in account by default)

file f <- gml_file("file.gml", true);

gml_file(string,int,bool): This file constructor allows to read a gml file, specifying the coordinates system code, as an int (epsg code) and take a potential z value (not taken in account by default)

file f <- gml_file("file.gml", 32648, true);

gml_file(string,string,bool): This file constructor allows to read a gml file, specifying the coordinates system code (epg,...,), as a string and take a potential z value (not taken in account by default

file f <- gml_file("file.gml", "EPSG:32648",true);

See also: is_gml,

`gradient`

Possible uses:

gradient (list<rgb>) ---> map<rgb,float>
gradient (map<rgb,float>) ---> map<rgb,float>
rgb gradient rgb ---> map<rgb,float>
gradient (rgb , rgb) ---> map<rgb,float>
gradient (rgb, rgb, float) ---> map<rgb,float>

Result:

returns the definition of a linear gradient between two colors, represented internally as a color map [start::0.0,stop::1.0] returns the definition of a linear gradient between n colors, represented internally as a color map [c1::0,c2::1/n-1, ... cn::n-1/n-1] returns the definition of a linear gradient between two colors, with a ratio (between 0 and 1, otherwise clamped) represented internally as a color map [start::0.0,(startr+stop(1-r))::r, stop::1.0] returns the definition of a linear gradient between n colors provided with their positions on a scale between 0 and 1. A similar color map is returned, in the same color order, with all the positions normalized (so that they are shifted and scaled to fit between 0 and 1). Throws an error if the number of colors is less than 2 or if the positions are not strictly ordered

`graph`

Possible uses:

graph (any) ---> graph

Result:

casts the operand in a graph object.

`graph6_file`

Possible uses:

graph6_file (string) ---> file
string graph6_file species ---> file
graph6_file (string , species) ---> file
graph6_file (string, species, species) ---> file

Result:

Constructs a file of type graph6. Allowed extensions are limited to graph6

Special cases:

graph6_file(string): References a graph6 file by its filename
graph6_file(string,species): References a graph6 file by its filename and the species to use to instantiate the nodes
graph6_file(string,species,species): References a graph6 file by its filename and the species to use to instantiate the nodes and the edges

See also: is_graph6,

`graphdimacs_file`

Possible uses:

graphdimacs_file (string) ---> file
string graphdimacs_file species ---> file
graphdimacs_file (string , species) ---> file
graphdimacs_file (string, species, species) ---> file

Result:

Constructs a file of type graphdimacs. Allowed extensions are limited to dimacs

Special cases:

graphdimacs_file(string): References a dimacs file by its filename
graphdimacs_file(string,species): References a dimacs file by its filename and the species to use to instantiate the nodes
graphdimacs_file(string,species,species): References a dimacs file by its filename and the species to use to instantiate the nodes and the edges

See also: is_graphdimacs,

`graphdot_file`

Possible uses:

graphdot_file (string) ---> file
string graphdot_file species ---> file
graphdot_file (string , species) ---> file
graphdot_file (string, species, species) ---> file

Result:

Constructs a file of type graphdot. Allowed extensions are limited to dot

Special cases:

graphdot_file(string): References a dot graph file by its filename
graphdot_file(string,species): References a dot graph file by its filename and the species to use to instantiate the nodes
graphdot_file(string,species,species): References a dot graph file by its filename and the 2 species to use to instantiate the nodes and the edges

See also: is_graphdot,

`graphgexf_file`

Possible uses:

graphgexf_file (string) ---> file
string graphgexf_file species ---> file
graphgexf_file (string , species) ---> file
graphgexf_file (string, species, species) ---> file

Result:

Constructs a file of type graphgexf. Allowed extensions are limited to gexf

Special cases:

graphgexf_file(string): References a gexf graph file by its filename
graphgexf_file(string,species): References a gexf graph file by its filename and the species to use to instantiate the nodes
graphgexf_file(string,species,species): References a gexf graph file by its filename and the 2 species to use to instantiate the nodes and the edges

See also: is_graphgexf,

`graphgml_file`

Possible uses:

graphgml_file (string) ---> file
string graphgml_file species ---> file
graphgml_file (string , species) ---> file
graphgml_file (string, species, species) ---> file

Result:

Constructs a file of type graphgml. Allowed extensions are limited to gml

Special cases:

graphgml_file(string): References a gml graph file by its filename
graphgml_file(string,species): References a gml graph file by its filename and the species to use to instantiate the nodes
graphgml_file(string,species,species): References a gml graph file by its filename and the 2 species to use to instantiate the nodes and the edges

See also: is_graphgml,

`graphml_file`

Possible uses:

graphml_file (string) ---> file
string graphml_file species ---> file
graphml_file (string , species) ---> file
graphml_file (string, species, species) ---> file
graphml_file (string, species, species, string, string) ---> file

Result:

Constructs a file of type graphml. Allowed extensions are limited to graphml

Special cases:

graphml_file(string): References a graphml graph file by its filename
graphml_file(string,species): References a graphml graph file by its filename and the species to use to instantiate the nodes
graphml_file(string,species,species): References a graphml graph file by its filename and the 2 species to use to instantiate the nodes and the edges
graphml_file(string,species,species,string,string): References a graphml graph file by its filename and the 2 species to use to instantiate the nodes and the edges

See also: is_graphml,

`graphtsplib_file`

Possible uses:

graphtsplib_file (string) ---> file
string graphtsplib_file species ---> file
graphtsplib_file (string , species) ---> file
graphtsplib_file (string, species, species) ---> file

Result:

Constructs a file of type graphtsplib. Allowed extensions are limited to tsplib

Special cases:

graphtsplib_file(string): References a tsplib graph file by its filename
graphtsplib_file(string,species): References a tsplib graph file by its filename and the species to use to instantiate the nodes
graphtsplib_file(string,species,species): References a tsplib graph file by its filename and the 2 species to use to instantiate the nodes and the edges

See also: is_graphtsplib,

`grayscale`

Possible uses:

grayscale (rgb) ---> rgb

Result:

Converts rgb color to grayscale value

Comment:

r=red, g=green, b=blue. Between 0 and 255 and gray = 0.299 * red + 0.587 * green + 0.114 * blue (Photoshop value)

Examples:

rgb var0 <- grayscale (rgb(255,0,0)); // var0 equals to a dark grey

See also: rgb, hsb,

`grayscale`

Possible uses:

grayscale (image) ---> image

Result:

Used to convert any image to a grayscale color palette and return it. The original image is left untouched

`grid_at`

Possible uses:

species grid_at point ---> agent
grid_at (species , point) ---> agent

Result:

returns the cell of the grid (right-hand operand) at the position given by the right-hand operand

Comment:

If the left-hand operand is a point of floats, it is used as a point of ints.

Special cases:

if the left-hand operand is not a grid cell species, returns nil

Examples:

agent var0 <- grid_cell grid_at {1,2}; // var0 equals the agent grid_cell with grid_x=1 and grid_y = 2

`grid_cells_to_graph`

Possible uses:

grid_cells_to_graph (container) ---> graph
container grid_cells_to_graph species ---> graph
grid_cells_to_graph (container , species) ---> graph

Result:

creates a graph from a list of cells (operand). An edge is created between neighbors.

Examples:

my_cell_graph <- grid_cells_to_graph(cells_list);

See also: as_intersection_graph, as_edge_graph,

`grid_file`

Possible uses:

grid_file (string) ---> file
string grid_file bool ---> file
grid_file (string , bool) ---> file
string grid_file int ---> file
grid_file (string , int) ---> file
string grid_file string ---> file
grid_file (string , string) ---> file
string grid_file field ---> file
grid_file (string , field) ---> file

Result:

Constructs a file of type grid. Allowed extensions are limited to asc, tif

Special cases:

grid_file(string): This file constructor allows to read a asc file or a tif (geotif) file

file f <- grid_file("file.asc");

grid_file(string,bool): This file constructor allows to read a asc file or a tif (geotif) file, but without converting it into shapes. Only a matrix of float values is created

file f <- grid_file("file.asc", false);

grid_file(string,int): This file constructor allows to read a asc file or a tif (geotif) file specifying the coordinates system code, as an int (epsg code)

file f <- grid_file("file.asc", 32648);

grid_file(string,string): This file constructor allows to read a asc file or a tif (geotif) file specifying the coordinates system code (epg,...,), as a string

file f <- grid_file("file.asc","EPSG:32648");

grid_file(string,field): This allows to build a writable grid file from the values of a field

file f <- grid_file("file.tif",my_field); save f;

See also: is_grid,

`group_by`

Possible uses:

container group_by any expression ---> map
group_by (container , any expression) ---> map

Result:

Returns a map, where the keys take the possible values of the right-hand operand and the map values are the list of elements of the left-hand operand associated to the key value

Comment:

in the right-hand operand, the keyword each can be used to represent, in turn, each of the right-hand operand elements.

Special cases:

if the left-hand operand is nil, group_by throws an error

Examples:

map var0 <- [1,2,3,4,5,6,7,8] group_by (each > 3); // var0 equals [false::[1, 2, 3], true::[4, 5, 6, 7, 8]] 
map var1 <- g2 group_by (length(g2 out_edges_of each) ); // var1 equals [ 0::[node9, node7, node10, node8, node11], 1::[node6], 2::[node5], 3::[node4]] 
map var2 <- (list(node) group_by (round(node(each).location.x)); // var2 equals [32::[node5], 21::[node1], 4::[node0], 66::[node2], 96::[node3]] 
map<bool,list> var3 <- [1::2, 3::4, 5::6] group_by (each > 4); // var3 equals [false::[2, 4], true::[6]]

See also: first_with, last_with, where,

`harmonic_mean`

Possible uses:

harmonic_mean (container) ---> float

Result:

the harmonic mean of the elements of the operand. See Harmonic_mean for more details.

Comment:

The operator casts all the numerical element of the list into float. The elements that are not numerical are discarded.

Examples:

float var0 <- harmonic_mean ([4.5, 3.5, 5.5, 7.0]); // var0 equals 4.804159445407279

See also: mean, median, geometric_mean,

`has_belief_op`

Possible uses:

agent has_belief_op predicate ---> bool
has_belief_op (agent , predicate) ---> bool

Result:

indicates if there already is a belief about the given predicate.

Examples:

bool var0 <- has_belief_op(self,predicate("has_water")); // var0 equals false

`has_belief_with_name_op`

Possible uses:

agent has_belief_with_name_op string ---> bool
has_belief_with_name_op (agent , string) ---> bool

Result:

indicates if there already is a belief about the given name.

Examples:

bool var0 <- has_belief_with_name_op(self,"has_water"); // var0 equals false

`has_desire_op`

Possible uses:

agent has_desire_op predicate ---> bool
has_desire_op (agent , predicate) ---> bool

Result:

indicates if there already is a desire about the given predicate.

Examples:

bool var0 <- has_desire_op(self,predicate("has_water")); // var0 equals false

`has_desire_with_name_op`

Possible uses:

agent has_desire_with_name_op string ---> bool
has_desire_with_name_op (agent , string) ---> bool

Result:

indicates if there already is a desire about the given name.

Examples:

bool var0 <- has_desire_with_name_op(self,"has_water"); // var0 equals false

`has_ideal_op`

Possible uses:

agent has_ideal_op predicate ---> bool
has_ideal_op (agent , predicate) ---> bool

Result:

indicates if there already is an ideal about the given predicate.

Examples:

bool var0 <- has_ideal_op(self,predicate("has_water")); // var0 equals false

`has_ideal_with_name_op`

Possible uses:

agent has_ideal_with_name_op string ---> bool
has_ideal_with_name_op (agent , string) ---> bool

Result:

indicates if there already is an ideal about the given name.

Examples:

bool var0 <- has_ideal_with_name_op(self,"has_water"); // var0 equals false

`has_intention_op`

Possible uses:

agent has_intention_op predicate ---> bool
has_intention_op (agent , predicate) ---> bool

Result:

indicates if there already is an intention about the given predicate.

Examples:

bool var0 <- has_intention_op(self,predicate("has_water")); // var0 equals false

`has_intention_with_name_op`

Possible uses:

agent has_intention_with_name_op string ---> bool
has_intention_with_name_op (agent , string) ---> bool

Result:

indicates if there already is an intention about the given name.

Examples:

bool var0 <- has_intention_with_name_op(self,"has_water"); // var0 equals false

`has_obligation_op`

Possible uses:

agent has_obligation_op predicate ---> bool
has_obligation_op (agent , predicate) ---> bool

Result:

indicates if there already is an obligation about the given predicate.

Examples:

bool var0 <- has_obligation_op(self,predicate("has_water")); // var0 equals false

`has_obligation_with_name_op`

Possible uses:

agent has_obligation_with_name_op string ---> bool
has_obligation_with_name_op (agent , string) ---> bool

Result:

indicates if there already is an obligation about the given name.

Examples:

bool var0 <- has_obligation_with_name_op(self,"has_water"); // var0 equals false

`has_uncertainty_op`

Possible uses:

agent has_uncertainty_op predicate ---> bool
has_uncertainty_op (agent , predicate) ---> bool

Result:

indicates if there already is an uncertainty about the given predicate.

Examples:

bool var0 <- has_uncertainty_op(self,predicate("has_water")); // var0 equals false

`has_uncertainty_with_name_op`

Possible uses:

agent has_uncertainty_with_name_op string ---> bool
has_uncertainty_with_name_op (agent , string) ---> bool

Result:

indicates if there already is an uncertainty about the given name.

Examples:

bool var0 <- has_uncertainty_with_name_op(self,"has_water"); // var0 equals false

`hexagon`

Possible uses:

hexagon (point) ---> geometry
hexagon (float) ---> geometry
float hexagon float ---> geometry
hexagon (float , float) ---> geometry

Result:

A hexagon geometry which the given with and height

Comment:

the center of the hexagon is by default the location of the current agent in which has been called this operator.

Special cases:

returns nil if the operand is nil.

Examples:

geometry var0 <- hexagon(10,5); // var0 equals a geometry as a hexagon of width of 10 and height of 5. 
geometry var1 <- hexagon({10,5}); // var1 equals a geometry as a hexagon of width of 10 and height of 5. 
geometry var2 <- hexagon(10); // var2 equals a geometry as a hexagon of width of 10 and height of 10.

See also: around, circle, cone, line, link, norm, point, polygon, polyline, rectangle, triangle,

`hierarchical_clustering`

Possible uses:

container<unknown,agent> hierarchical_clustering float ---> list
hierarchical_clustering (container<unknown,agent> , float) ---> list

Result:

A tree (list of list) contained groups of agents clustered by distance considering a distance min between two groups.

Comment:

use of hierarchical clustering with Minimum for linkage criterion between two groups of agents.

Examples:

list var0 <- [ag1, ag2, ag3, ag4, ag5] hierarchical_clustering 20.0; // var0 equals for example, can return [[[ag1],[ag3]], [ag2], [[[ag4],[ag5]],[ag6]]

See also: simple_clustering_by_distance,

`horizontal`

Possible uses:

horizontal (map<unknown,int>) ---> unknown<string>

Result:

Creates a horizontal layout node (a sash). Sashes can contain any number (> 1) of other elements: stacks, horizontal or vertical sashes, or display indices. Each element is represented by a pair in the map, where the key is the element and the value its weight within the sash

`horizontal_flip`

Possible uses:

horizontal_flip (image) ---> image

Result:

Returns an image flipped horizontally by reflecting the original image around the y axis. The original image is left untouched

`hsb`

Possible uses:

hsb (float, float, float) ---> rgb
hsb (float, float, float, int) ---> rgb
hsb (float, float, float, float) ---> rgb

Result:

Converts hsb (h=hue, s=saturation, b=brightness) value to Gama color

Comment:

h,s and b components should be floating-point values between 0.0 and 1.0 and when used alpha should be an integer (between 0 and 255) or a float (between 0 and 1) . Examples: Red=(0.0,1.0,1.0), Yellow=(0.16,1.0,1.0), Green=(0.33,1.0,1.0), Cyan=(0.5,1.0,1.0), Blue=(0.66,1.0,1.0), Magenta=(0.83,1.0,1.0)

Examples:

rgb var0 <- hsb (0.5,1.0,1.0,0.0); // var0 equals rgb("cyan",0) 
rgb var1 <- hsb (0.0,1.0,1.0); // var1 equals rgb("red")

See also: rgb,

`hypot`

Possible uses:

hypot (float, float, float, float) ---> float

Result:

Returns sqrt(x2 +y2) without intermediate overflow or underflow.

Special cases:

If either argument is infinite, then the result is positive infinity. If either argument is NaN and neither argument is infinite, then the result is NaN.

Examples:

float var0 <- hypot(0,1,0,1); // var0 equals sqrt(2)

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OperatorsDH

Operators (D to H)

Definition

Priority between operators

Using actions as operators

Table of Contents

Operators by categories

3D

Arithmetic operators

BDI

Casting operators

Color-related operators

Comparison operators

Containers-related operators

Date-related operators

Dates

Displays

edge

EDP-related operators

Files-related operators

GamaMetaType

GamaSVGFile

Graphs-related operators

Grid-related operators

ImageOperators

Iterator operators

List-related operators

Logical operators

Map comparaison operators

Map-related operators

Matrix-related operators

multicriteria operators

Path-related operators

Pedestrian

Points-related operators

Random operators

Shape

Spatial operators

Spatial properties operators

Spatial queries operators

Spatial relations operators

Spatial statistical operators

Spatial transformations operators

Species-related operators

Statistical operators

Strings-related operators

SubModel

System

Time-related operators

Types-related operators

User control operators

Operators

darker

Possible uses:

Result:

date

Possible uses:

Result:

Special cases:

Examples:

dbscan

Possible uses:

Result:

Special cases:

Examples:

dead

Possible uses:

Result:

Examples:

decompress

degree_of

Possible uses:

Result:

Examples:

delete_file

Possible uses:

Result:

Examples:

deserialize

Possible uses:

`darker`

`date`

`dbscan`

`dead`

`decompress`

`degree_of`

`delete_file`

`deserialize`

`det`

`determinant`

`diff`

`diff2`

`directed`

`direction_between`

`direction_to`

`directory`

`disjoint_from`

`distance_between`

`distance_to`

`distinct`

`distribution_of`

`distribution2d_of`

`div`

`dnorm`

`dtw`

`durbin_watson`

`dxf_file`

`edge`