add_manual_bounded_constraints(x, data)

## S4 method for signature 'ConservationProblem,data.frame'
add_manual_bounded_constraints(x, data)

## S4 method for signature 'ConservationProblem,tbl_df'
add_manual_bounded_constraints(x, data)

# set seed for reproducibility
set.seed(500)

# load data
data(sim_pu_polygons, sim_features, sim_pu_zones_polygons,
     sim_features_zones)

# create minimal problem
p1 <- problem(sim_pu_polygons, sim_features, "cost") %>%
      add_min_set_objective() %>%
      add_relative_targets(0.2) %>%
      add_binary_decisions() %>%
      add_default_solver(verbose = FALSE)

# create problem with locked in constraints using add_locked_constraints
p2 <- p1 %>% add_locked_in_constraints("locked_in")

# create identical problem using add_manual_bounded_constraints
bounds_data <- data.frame(pu = which(sim_pu_polygons$locked_in),
                          lower = 1, upper = 1)

p3 <- p1 %>% add_manual_bounded_constraints(bounds_data)
## Not run: 
# solve problems
s1 <- solve(p1)
s2 <- solve(p2)
s3 <- solve(p3)

# plot solutions
par(mfrow = c(1,3), mar = c(0, 0, 4.1, 0))
plot(s1, main = "none locked in")
plot(s1[s1$solution_1 == 1, ], col = "darkgreen", add = TRUE)

plot(s2, main = "add_locked_in_constraints")
plot(s2[s2$solution_1 == 1, ], col = "darkgreen", add = TRUE)

plot(s3, main = "add_bounds_constraints")
plot(s3[s3$solution_1 == 1, ], col = "darkgreen", add = TRUE)

## End(Not run)
# create minimal problem with multiple zones
p4 <- problem(sim_pu_zones_polygons, sim_features_zones,
              c("cost_1", "cost_2", "cost_3")) %>%
      add_min_set_objective() %>%
      add_relative_targets(matrix(runif(15, 0.1, 0.2), nrow = 5,
                                  ncol = 3)) %>%
      add_binary_decisions() %>%
      add_default_solver(verbose = FALSE)

# create data.frame with the following constraints:
# planning units 1, 2, and 3 must be allocated to zone 1 in the solution
# planning units 4, and 5 must be allocated to zone 2 in the solution
# planning units 8 and 9 must not be allocated to zone 3 in the solution
bounds_data2 <- data.frame(pu = c(1, 2, 3, 4, 5, 8, 9),
                           zone = c(rep("zone_1", 3), rep("zone_2", 2),
                                    rep("zone_3", 2)),
                           lower = c(rep(1, 5), rep(0, 2)),
                           upper = c(rep(1, 5), rep(0, 2)))

# print bounds data
print(bounds_data2)

# create problem with added constraints
p5 <- p4 %>% add_manual_bounded_constraints(bounds_data2)
## Not run: 
# solve problem
s4 <- solve(p4)
s5 <- solve(p5)

# create two new columns representing the zone id that each planning unit
# was allocated to in the two solutions
s4$solution <- category_vector(s4@data[, c("solution_1_zone_1",
                                           "solution_1_zone_2",
                                           "solution_1_zone_3")])
s4$solution <- factor(s4$solution)

s4$solution_bounded <- category_vector(s5@data[, c("solution_1_zone_1",
                                                   "solution_1_zone_2",
                                                   "solution_1_zone_3")])
s4$solution_bounded <- factor(s4$solution_bounded)

# plot solutions
spplot(s4, zcol = c("solution", "solution_bounded"), axes = FALSE,
       box = FALSE)

## End(Not run)