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estimate_ss_cor <- function(ref_pcs, ref_genotypes, link_function) {
X_mat <- as.matrix(cbind(1, ref_pcs))
W_mat <- diag(x=1, nrow=nrow(ref_pcs), ncol=nrow(ref_pcs))
P_mat <- W_mat - X_mat %*% solve(t(X_mat) %*% X_mat) %*% t(X_mat)
est_cor <- matrix(data=NA, nrow=ncol(ref_genotypes), ncol=ncol(ref_genotypes))
denominators <- rep(NA, ncol(ref_genotypes))
for (i in 1:ncol(ref_genotypes))
{
temp_G <- ref_genotypes[,i]
denominators[i] <- sqrt(t(temp_G) %*% P_mat %*% temp_G)
}
for (temp_row in 2:ncol(ref_genotypes))
{
for (temp_col in 1:(temp_row-1))
{
est_cor[temp_row, temp_col] <- t(ref_genotypes[,temp_row]) %*% P_mat %*% ref_genotypes[,temp_col] /
(denominators[temp_row] * denominators[temp_col])
est_cor[temp_col, temp_row] <- est_cor[temp_row, temp_col]
}
}
return (est_cor)
} |
NULL
.networkfirewall$associate_firewall_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), FirewallPolicyArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$associate_firewall_policy_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), FirewallPolicyArn = structure(logical(0), tags = list(type = "string")), UpdateToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$associate_subnets_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), SubnetMappings = structure(list(structure(list(SubnetId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$associate_subnets_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), SubnetMappings = structure(list(structure(list(SubnetId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), UpdateToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$create_firewall_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallName = structure(logical(0), tags = list(type = "string")), FirewallPolicyArn = structure(logical(0), tags = list(type = "string")), VpcId = structure(logical(0), tags = list(type = "string")), SubnetMappings = structure(list(structure(list(SubnetId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), DeleteProtection = structure(logical(0), tags = list(type = "boolean")), SubnetChangeProtection = structure(logical(0), tags = list(type = "boolean")), FirewallPolicyChangeProtection = structure(logical(0), tags = list(type = "boolean")), Description = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$create_firewall_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Firewall = structure(list(FirewallName = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallPolicyArn = structure(logical(0), tags = list(type = "string")), VpcId = structure(logical(0), tags = list(type = "string")), SubnetMappings = structure(list(structure(list(SubnetId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), DeleteProtection = structure(logical(0), tags = list(type = "boolean")), SubnetChangeProtection = structure(logical(0), tags = list(type = "boolean")), FirewallPolicyChangeProtection = structure(logical(0), tags = list(type = "boolean")), Description = structure(logical(0), tags = list(type = "string")), FirewallId = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), FirewallStatus = structure(list(Status = structure(logical(0), tags = list(type = "string")), ConfigurationSyncStateSummary = structure(logical(0), tags = list(type = "string")), SyncStates = structure(list(structure(list(Attachment = structure(list(SubnetId = structure(logical(0), tags = list(type = "string")), EndpointId = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), Config = structure(list(structure(list(SyncStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$create_firewall_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallPolicyName = structure(logical(0), tags = list(type = "string")), FirewallPolicy = structure(list(StatelessRuleGroupReferences = structure(list(structure(list(ResourceArn = structure(logical(0), tags = list(type = "string")), Priority = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))), tags = list(type = "list")), StatelessDefaultActions = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), StatelessFragmentDefaultActions = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), StatelessCustomActions = structure(list(structure(list(ActionName = structure(logical(0), tags = list(type = "string")), ActionDefinition = structure(list(PublishMetricAction = structure(list(Dimensions = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), StatefulRuleGroupReferences = structure(list(structure(list(ResourceArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), Description = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), DryRun = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$create_firewall_policy_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), FirewallPolicyResponse = structure(list(FirewallPolicyName = structure(logical(0), tags = list(type = "string")), FirewallPolicyArn = structure(logical(0), tags = list(type = "string")), FirewallPolicyId = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), FirewallPolicyStatus = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$create_rule_group_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(RuleGroupName = structure(logical(0), tags = list(type = "string")), RuleGroup = structure(list(RuleVariables = structure(list(IPSets = structure(list(structure(list(Definition = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "map")), PortSets = structure(list(structure(list(Definition = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure")), RulesSource = structure(list(RulesString = structure(logical(0), tags = list(type = "string")), RulesSourceList = structure(list(Targets = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), TargetTypes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), GeneratedRulesType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), StatefulRules = structure(list(structure(list(Action = structure(logical(0), tags = list(type = "string")), Header = structure(list(Protocol = structure(logical(0), tags = list(type = "string")), Source = structure(logical(0), tags = list(type = "string")), SourcePort = structure(logical(0), tags = list(type = "string")), Direction = structure(logical(0), tags = list(type = "string")), Destination = structure(logical(0), tags = list(type = "string")), DestinationPort = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), RuleOptions = structure(list(structure(list(Keyword = structure(logical(0), tags = list(type = "string")), Settings = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list")), StatelessRulesAndCustomActions = structure(list(StatelessRules = structure(list(structure(list(RuleDefinition = structure(list(MatchAttributes = structure(list(Sources = structure(list(structure(list(AddressDefinition = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Destinations = structure(list(structure(list(AddressDefinition = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), SourcePorts = structure(list(structure(list(FromPort = structure(logical(0), tags = list(type = "integer")), ToPort = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))), tags = list(type = "list")), DestinationPorts = structure(list(structure(list(FromPort = structure(logical(0), tags = list(type = "integer")), ToPort = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))), tags = list(type = "list")), Protocols = structure(list(structure(logical(0), tags = list(type = "integer"))), tags = list(type = "list")), TCPFlags = structure(list(structure(list(Flags = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), Masks = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), Actions = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), Priority = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))), tags = list(type = "list")), CustomActions = structure(list(structure(list(ActionName = structure(logical(0), tags = list(type = "string")), ActionDefinition = structure(list(PublishMetricAction = structure(list(Dimensions = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure")), Rules = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), Capacity = structure(logical(0), tags = list(type = "integer")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), DryRun = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$create_rule_group_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), RuleGroupResponse = structure(list(RuleGroupArn = structure(logical(0), tags = list(type = "string")), RuleGroupName = structure(logical(0), tags = list(type = "string")), RuleGroupId = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string")), Capacity = structure(logical(0), tags = list(type = "integer")), RuleGroupStatus = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$delete_firewall_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallName = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$delete_firewall_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Firewall = structure(list(FirewallName = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallPolicyArn = structure(logical(0), tags = list(type = "string")), VpcId = structure(logical(0), tags = list(type = "string")), SubnetMappings = structure(list(structure(list(SubnetId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), DeleteProtection = structure(logical(0), tags = list(type = "boolean")), SubnetChangeProtection = structure(logical(0), tags = list(type = "boolean")), FirewallPolicyChangeProtection = structure(logical(0), tags = list(type = "boolean")), Description = structure(logical(0), tags = list(type = "string")), FirewallId = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), FirewallStatus = structure(list(Status = structure(logical(0), tags = list(type = "string")), ConfigurationSyncStateSummary = structure(logical(0), tags = list(type = "string")), SyncStates = structure(list(structure(list(Attachment = structure(list(SubnetId = structure(logical(0), tags = list(type = "string")), EndpointId = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), Config = structure(list(structure(list(SyncStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$delete_firewall_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallPolicyName = structure(logical(0), tags = list(type = "string")), FirewallPolicyArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$delete_firewall_policy_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallPolicyResponse = structure(list(FirewallPolicyName = structure(logical(0), tags = list(type = "string")), FirewallPolicyArn = structure(logical(0), tags = list(type = "string")), FirewallPolicyId = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), FirewallPolicyStatus = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$delete_resource_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$delete_resource_policy_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$delete_rule_group_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(RuleGroupName = structure(logical(0), tags = list(type = "string")), RuleGroupArn = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$delete_rule_group_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(RuleGroupResponse = structure(list(RuleGroupArn = structure(logical(0), tags = list(type = "string")), RuleGroupName = structure(logical(0), tags = list(type = "string")), RuleGroupId = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string")), Capacity = structure(logical(0), tags = list(type = "integer")), RuleGroupStatus = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$describe_firewall_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallName = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$describe_firewall_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), Firewall = structure(list(FirewallName = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallPolicyArn = structure(logical(0), tags = list(type = "string")), VpcId = structure(logical(0), tags = list(type = "string")), SubnetMappings = structure(list(structure(list(SubnetId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), DeleteProtection = structure(logical(0), tags = list(type = "boolean")), SubnetChangeProtection = structure(logical(0), tags = list(type = "boolean")), FirewallPolicyChangeProtection = structure(logical(0), tags = list(type = "boolean")), Description = structure(logical(0), tags = list(type = "string")), FirewallId = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), FirewallStatus = structure(list(Status = structure(logical(0), tags = list(type = "string")), ConfigurationSyncStateSummary = structure(logical(0), tags = list(type = "string")), SyncStates = structure(list(structure(list(Attachment = structure(list(SubnetId = structure(logical(0), tags = list(type = "string")), EndpointId = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), Config = structure(list(structure(list(SyncStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$describe_firewall_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallPolicyName = structure(logical(0), tags = list(type = "string")), FirewallPolicyArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$describe_firewall_policy_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), FirewallPolicyResponse = structure(list(FirewallPolicyName = structure(logical(0), tags = list(type = "string")), FirewallPolicyArn = structure(logical(0), tags = list(type = "string")), FirewallPolicyId = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), FirewallPolicyStatus = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), FirewallPolicy = structure(list(StatelessRuleGroupReferences = structure(list(structure(list(ResourceArn = structure(logical(0), tags = list(type = "string")), Priority = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))), tags = list(type = "list")), StatelessDefaultActions = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), StatelessFragmentDefaultActions = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), StatelessCustomActions = structure(list(structure(list(ActionName = structure(logical(0), tags = list(type = "string")), ActionDefinition = structure(list(PublishMetricAction = structure(list(Dimensions = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), StatefulRuleGroupReferences = structure(list(structure(list(ResourceArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$describe_logging_configuration_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$describe_logging_configuration_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallArn = structure(logical(0), tags = list(type = "string")), LoggingConfiguration = structure(list(LogDestinationConfigs = structure(list(structure(list(LogType = structure(logical(0), tags = list(type = "string")), LogDestinationType = structure(logical(0), tags = list(type = "string")), LogDestination = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$describe_resource_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$describe_resource_policy_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Policy = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$describe_rule_group_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(RuleGroupName = structure(logical(0), tags = list(type = "string")), RuleGroupArn = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$describe_rule_group_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), RuleGroup = structure(list(RuleVariables = structure(list(IPSets = structure(list(structure(list(Definition = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "map")), PortSets = structure(list(structure(list(Definition = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure")), RulesSource = structure(list(RulesString = structure(logical(0), tags = list(type = "string")), RulesSourceList = structure(list(Targets = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), TargetTypes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), GeneratedRulesType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), StatefulRules = structure(list(structure(list(Action = structure(logical(0), tags = list(type = "string")), Header = structure(list(Protocol = structure(logical(0), tags = list(type = "string")), Source = structure(logical(0), tags = list(type = "string")), SourcePort = structure(logical(0), tags = list(type = "string")), Direction = structure(logical(0), tags = list(type = "string")), Destination = structure(logical(0), tags = list(type = "string")), DestinationPort = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), RuleOptions = structure(list(structure(list(Keyword = structure(logical(0), tags = list(type = "string")), Settings = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list")), StatelessRulesAndCustomActions = structure(list(StatelessRules = structure(list(structure(list(RuleDefinition = structure(list(MatchAttributes = structure(list(Sources = structure(list(structure(list(AddressDefinition = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Destinations = structure(list(structure(list(AddressDefinition = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), SourcePorts = structure(list(structure(list(FromPort = structure(logical(0), tags = list(type = "integer")), ToPort = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))), tags = list(type = "list")), DestinationPorts = structure(list(structure(list(FromPort = structure(logical(0), tags = list(type = "integer")), ToPort = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))), tags = list(type = "list")), Protocols = structure(list(structure(logical(0), tags = list(type = "integer"))), tags = list(type = "list")), TCPFlags = structure(list(structure(list(Flags = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), Masks = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), Actions = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), Priority = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))), tags = list(type = "list")), CustomActions = structure(list(structure(list(ActionName = structure(logical(0), tags = list(type = "string")), ActionDefinition = structure(list(PublishMetricAction = structure(list(Dimensions = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure")), RuleGroupResponse = structure(list(RuleGroupArn = structure(logical(0), tags = list(type = "string")), RuleGroupName = structure(logical(0), tags = list(type = "string")), RuleGroupId = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string")), Capacity = structure(logical(0), tags = list(type = "integer")), RuleGroupStatus = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$disassociate_subnets_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), SubnetIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$disassociate_subnets_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), SubnetMappings = structure(list(structure(list(SubnetId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), UpdateToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$list_firewall_policies_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$list_firewall_policies_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NextToken = structure(logical(0), tags = list(type = "string")), FirewallPolicies = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$list_firewalls_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NextToken = structure(logical(0), tags = list(type = "string")), VpcIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), MaxResults = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$list_firewalls_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NextToken = structure(logical(0), tags = list(type = "string")), Firewalls = structure(list(structure(list(FirewallName = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$list_rule_groups_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$list_rule_groups_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NextToken = structure(logical(0), tags = list(type = "string")), RuleGroups = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$list_tags_for_resource_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer")), ResourceArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$list_tags_for_resource_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NextToken = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$put_resource_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceArn = structure(logical(0), tags = list(type = "string")), Policy = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$put_resource_policy_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$tag_resource_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceArn = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$tag_resource_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$untag_resource_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceArn = structure(logical(0), tags = list(type = "string")), TagKeys = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$untag_resource_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_firewall_delete_protection_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), DeleteProtection = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_firewall_delete_protection_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), DeleteProtection = structure(logical(0), tags = list(type = "boolean")), UpdateToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_firewall_description_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_firewall_description_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), UpdateToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_firewall_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), FirewallPolicyArn = structure(logical(0), tags = list(type = "string")), FirewallPolicyName = structure(logical(0), tags = list(type = "string")), FirewallPolicy = structure(list(StatelessRuleGroupReferences = structure(list(structure(list(ResourceArn = structure(logical(0), tags = list(type = "string")), Priority = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))), tags = list(type = "list")), StatelessDefaultActions = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), StatelessFragmentDefaultActions = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), StatelessCustomActions = structure(list(structure(list(ActionName = structure(logical(0), tags = list(type = "string")), ActionDefinition = structure(list(PublishMetricAction = structure(list(Dimensions = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), StatefulRuleGroupReferences = structure(list(structure(list(ResourceArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), Description = structure(logical(0), tags = list(type = "string")), DryRun = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_firewall_policy_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), FirewallPolicyResponse = structure(list(FirewallPolicyName = structure(logical(0), tags = list(type = "string")), FirewallPolicyArn = structure(logical(0), tags = list(type = "string")), FirewallPolicyId = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), FirewallPolicyStatus = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_firewall_policy_change_protection_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), FirewallPolicyChangeProtection = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_firewall_policy_change_protection_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), FirewallPolicyChangeProtection = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_logging_configuration_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), LoggingConfiguration = structure(list(LogDestinationConfigs = structure(list(structure(list(LogType = structure(logical(0), tags = list(type = "string")), LogDestinationType = structure(logical(0), tags = list(type = "string")), LogDestination = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_logging_configuration_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), LoggingConfiguration = structure(list(LogDestinationConfigs = structure(list(structure(list(LogType = structure(logical(0), tags = list(type = "string")), LogDestinationType = structure(logical(0), tags = list(type = "string")), LogDestination = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_rule_group_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), RuleGroupArn = structure(logical(0), tags = list(type = "string")), RuleGroupName = structure(logical(0), tags = list(type = "string")), RuleGroup = structure(list(RuleVariables = structure(list(IPSets = structure(list(structure(list(Definition = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "map")), PortSets = structure(list(structure(list(Definition = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure")), RulesSource = structure(list(RulesString = structure(logical(0), tags = list(type = "string")), RulesSourceList = structure(list(Targets = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), TargetTypes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), GeneratedRulesType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), StatefulRules = structure(list(structure(list(Action = structure(logical(0), tags = list(type = "string")), Header = structure(list(Protocol = structure(logical(0), tags = list(type = "string")), Source = structure(logical(0), tags = list(type = "string")), SourcePort = structure(logical(0), tags = list(type = "string")), Direction = structure(logical(0), tags = list(type = "string")), Destination = structure(logical(0), tags = list(type = "string")), DestinationPort = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), RuleOptions = structure(list(structure(list(Keyword = structure(logical(0), tags = list(type = "string")), Settings = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list")), StatelessRulesAndCustomActions = structure(list(StatelessRules = structure(list(structure(list(RuleDefinition = structure(list(MatchAttributes = structure(list(Sources = structure(list(structure(list(AddressDefinition = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Destinations = structure(list(structure(list(AddressDefinition = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), SourcePorts = structure(list(structure(list(FromPort = structure(logical(0), tags = list(type = "integer")), ToPort = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))), tags = list(type = "list")), DestinationPorts = structure(list(structure(list(FromPort = structure(logical(0), tags = list(type = "integer")), ToPort = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))), tags = list(type = "list")), Protocols = structure(list(structure(logical(0), tags = list(type = "integer"))), tags = list(type = "list")), TCPFlags = structure(list(structure(list(Flags = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), Masks = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), Actions = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), Priority = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))), tags = list(type = "list")), CustomActions = structure(list(structure(list(ActionName = structure(logical(0), tags = list(type = "string")), ActionDefinition = structure(list(PublishMetricAction = structure(list(Dimensions = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure")), Rules = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), DryRun = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_rule_group_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), RuleGroupResponse = structure(list(RuleGroupArn = structure(logical(0), tags = list(type = "string")), RuleGroupName = structure(logical(0), tags = list(type = "string")), RuleGroupId = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string")), Capacity = structure(logical(0), tags = list(type = "integer")), RuleGroupStatus = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_subnet_change_protection_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), SubnetChangeProtection = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.networkfirewall$update_subnet_change_protection_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UpdateToken = structure(logical(0), tags = list(type = "string")), FirewallArn = structure(logical(0), tags = list(type = "string")), FirewallName = structure(logical(0), tags = list(type = "string")), SubnetChangeProtection = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
return(populate(args, shape))
} |
check_param_list <- function(pm, type = "string", check_paths = FALSE, correct = TRUE) {
. <- valid_s2tiles <- reference_path <- NULL
if (is(pm, "character")) {
if (file.exists(pm)) {
pm <- jsonlite::fromJSON(pm)
} else {
print_message(
type = "error",
"The file ",pm," does not exist."
)
}
} else if (!is(pm, "list")) {
print_message(
type = "error",
"\"",deparse(substitute(pm)),"\"",
"must be a list or a path of a JSON parameter file."
)
}
if (length(nn(pm$list_prods)) == 0) {pm$list_prods <- NA}
if (length(nn(pm$list_rgb)) == 0) {pm$list_rgb <- NA}
if (length(nn(pm$list_indices)) == 0) {pm$list_indices <- NA}
pm_def <- formals(sen2r::sen2r)
pm_def <- sapply(pm_def[!names(pm_def) %in% c("param_list","gui","use_python","tmpdir","rmtmp")], eval)
for (sel_par in names(pm_def)) {
if (length(nn(pm[[sel_par]])) == 0) {
print_message(
type = if (type == "error") {"warning"} else {type},
paste0("Parameter \"",sel_par,"\" was not specified; ",
"setting it to the default ('",pm_def[[sel_par]],"').")
)
pm[[sel_par]] <- pm_def[[sel_par]]
}
}
pm_length1 <- c(
"preprocess", "online", "order_lta", "downloader",
"overwrite_safe", "rm_safe", "step_atmcorr", "sen2cor_use_dem",
"max_cloud_safe", "timeperiod", "extent_name", "index_source",
"mask_type", "max_mask", "mask_smooth", "mask_buffer", "clip_on_extent",
"extent_as_mask", "reference_path", "res_s2", "unit", "proj", "resampling",
"resampling_scl", "outformat", "rgb_outformat", "index_datatype",
"compression", "rgb_compression", "overwrite", "path_l1c", "path_l2a",
"path_tiles", "path_merged", "path_out", "path_rgb", "path_indices",
"path_subdirs", "thumbnails", "parallel", "processing_order"
)
for (sel_par in pm_length1) {
if (length(nn(pm[[sel_par]])) > 1) {
print_message(
type = type,
paste0("Parameter \"",sel_par,"\" must be of length 1; ",
"only the first element ('",pm[[sel_par]][1],"') is used.")
)
pm[[sel_par]] <- pm[[sel_par]][1]
}
}
pm_logical <- c(
"preprocess", "online", "overwrite_safe", "clip_on_extent",
"extent_as_mask", "path_subdirs", "thumbnails", "overwrite"
)
for (sel_par in pm_logical) {
if (any(!is(pm[[sel_par]], "logical"), !pm[[sel_par]] %in% c(TRUE,FALSE))) {
print_message(
type = type,
paste0("Parameter \"",sel_par,"\" must be TRUE or FALSE; ",
"setting it to the default (",pm_def[[sel_par]],").")
)
pm[[sel_par]] <- pm_def[[sel_par]]
}
}
if (all(!pm$sel_sensor %in% c("s2a", "s2b"))) {
print_message(
type = type,
"Parameter \"sel_sensor\" must be 's2a', 's2b' or both (setting to the default)."
)
pm$sel_sensor <- pm_def$sel_sensor
}
if (all(!pm$server %in% c("scihub", "gcloud"))) {
print_message(
type = type,
"Parameter \"server\" must be 'scihub', 'gcloud' or both (setting to the default)."
)
pm$server <- pm_def$server
}
if (!pm$downloader %in% c("builtin", "aria2")) {
print_message(
type = type,
"Parameter \"downloader\" must be 'builtin' or 'aria2' (setting to the default)."
)
pm$downloader <- pm_def$downloader
}
if (pm$rm_safe == TRUE) {
pm$rm_safe <- "yes"
} else if (pm$rm_safe == FALSE) {
pm$rm_safe <- "no"
} else if (!pm$rm_safe %in% c("yes", "all", "no", "l1c")) {
print_message(
type = type,
"Parameter \"rm_safe\" must be one among 'yes' (or 'all'), ",
"'no' and 'l1c' (setting to the default)."
)
pm$rm_safe <- pm_def$rm_safe
}
if (!is(pm$max_cloud_safe, "numeric")) {
if (is.na(suppressWarnings(as.numeric(as.character(pm$max_cloud_safe))))) {
print_message(
type = type,
"Parameter \"max_cloud_safe\" must be numeric (setting it to the default)."
)
pm$max_cloud_safe <- pm_def$max_cloud_safe
} else {
pm$max_cloud_safe <- as.numeric(as.character(pm$max_cloud_safe))
}
}
if (pm$max_cloud_safe < 0) {
print_message(
type = if (type == "error") {"warning"} else {type},
"Minimum allowed cloud cover value is 0; ",
"setting parameter \"max_cloud_safe\" to 0."
)
pm$max_cloud_safe <- 0
} else if (pm$max_cloud_safe > 100) {
print_message(
type = if (type == "error") {"warning"} else {type},
"Maximum allowed cloud cover value is 100; ",
"setting parameter \"max_cloud_safe\" to 100."
)
pm$max_cloud_safe <- 0
}
if (!anyNA(pm$timewindow)) {
if (length(pm$timewindow)==1) {
if (is(pm$timewindow, "numeric") | is(pm$timewindow, "difftime")) {
pm$timewindow <- c(Sys.Date() - pm$timewindow, Sys.Date())
} else {
pm$timewindow <- rep(pm$timewindow, 2)
}
} else if (length(pm$timewindow)>2) {
print_message(
type = type,
"Parameter 'timewindow' must be of length 1 or 2."
)
}
if (is(pm$timewindow, "character")) {
tryCatch(pm$timewindow <- as.Date(pm$timewindow), error = print)
} else if (is(pm$timewindow, "POSIXt")) {
pm$timewindow <- as.Date(pm$timewindow)
}
if (!is(pm$timewindow, "Date")) {
print_message(
type = type,
"Parameter 'timewindow' must be a Date object."
)
}
} else if (pm$online == TRUE) {
pm$timewindow <- c(Sys.Date() - 90, Sys.Date())
}
if (!pm$timeperiod %in% c("full", "seasonal")) {
print_message(
type = type,
"Parameter \"timeperiod\" must be one among 'full' and 'seasonal' (setting to the default)."
)
pm$timeperiod <- pm_def$timeperiod
}
if (inherits(pm$extent, "character") | inherits(pm$extent, "geojson")) {
tryCatch(
pm$extent <- st_read(pm$extent, quiet=TRUE),
error = function(e) {
print_message(
type = type,
"Extent can not be read from the specified file or string."
)
}
)
} else if (inherits(pm$extent, "Spatial")) {
pm$extent <- st_as_sf(pm$extent)
}
if (inherits(pm$extent, c("sfc", "sf"))) {
if (any(!st_is_valid(pm$extent))) {
pm$extent <- st_make_valid(pm$extent)
}
}
pm$s2tiles_selected <- toupper(pm$s2tiles_selected)
invalid_s2tiles <- pm$s2tiles_selected[
!is.na(pm$s2tiles_selected) &
!grepl("^[0-9]{2}[A-Z]{3}$", pm$s2tiles_selected)
]
if (length(nn(invalid_s2tiles)) > 0) {
print_message(
type = type,
"Values '",
paste(invalid_s2tiles, collapse = "', '"),
"' are not valid tiles ID and will be removed."
)
valid_s2orbits <- pm$s2tiles_selected[!pm$s2tiles_selected %in% invalid_s2orbits]
pm$s2tiles_selected <- if (length(nn(valid_s2tiles)) == 0) {NA} else {valid_s2tiles}
}
if (all(
pm$online,
all(is.na(pm$extent)) || length(nn(pm$extent))==0,
all(is.na(pm$s2tiles_selected)) || length(nn(pm$s2tiles_selected))==0
)) {
print_message(
type = type,
"In online mode, at least one parameter among 'extent' and ",
"'s2tiles_selected' must be provided."
)
}
if (is(pm$s2orbits_selected, "numeric")) {
pm$s2orbits_selected <- str_pad2(pm$s2orbits_selected, 3, "left", "0")
}
invalid_s2orbits <- pm$s2orbits_selected[
!is.na(pm$s2orbits_selected) &
(is.na(suppressWarnings(as.numeric(pm$s2orbits_selected))) |
as.numeric(pm$s2orbits_selected) < 0 |
as.numeric(pm$s2orbits_selected) > 143)
]
if (length(nn(invalid_s2orbits)) > 0) {
print_message(
type = type,
"Values '",
paste(invalid_s2orbits, collapse = "', '"),
"' are not valid orbit ID and will be removed."
)
valid_s2orbits <- pm$s2orbits_selected[!pm$s2orbits_selected %in% invalid_s2orbits]
pm$s2orbits_selected <- if (length(nn(valid_s2orbits)) == 0) {NA} else {valid_s2orbits}
}
invalid_prods <- pm$list_prods[
!is.na(pm$list_prods) &
!pm$list_prods %in% c("BOA","TOA","SCL","TCI","WVP","AOT","CLD","SNW")
]
if (length(nn(invalid_prods)) > 0) {
print_message(
type = type,
"Values '",
paste(invalid_prods, collapse = "', '"),
"' are not valid products and will be removed."
)
}
pm$list_prods <- pm$list_prods[!is.na(pm$list_prods) & !pm$list_prods %in% invalid_prods]
if (length(nn(pm$list_prods)) == 0) {pm$list_prods <- NA}
invalid_indices <- pm$list_indices[!is.na(pm$list_indices) & !pm$list_indices %in% list_indices(all=TRUE)$name]
if (length(nn(invalid_indices)) > 0) {
print_message(
type = type,
"Values '",
paste(invalid_indices, collapse = "', '"),
"' are not valid index names and will be removed."
)
}
pm$list_indices <- pm$list_indices[!is.na(pm$list_indices) & !pm$list_indices %in% invalid_indices]
if (length(nn(pm$list_indices)) == 0) {pm$list_indices <- NA}
invalid_rgb <- pm$list_rgb[!is.na(pm$list_rgb) & !grepl("^RGB[0-9a-f]{3}[BT]$", pm$list_rgb)]
if (length(nn(invalid_rgb)) > 0) {
print_message(
type = type,
"Values '",
paste(invalid_rgb, collapse = "', '"),
"' are not valid RGB names and will be removed."
)
}
pm$list_rgb <- pm$list_rgb[!is.na(pm$list_rgb) & !pm$list_rgb %in% invalid_rgb]
if (length(nn(pm$list_rgb))>0) {
rgb_bands <- lapply(
strsplit(gsub("^RGB([0-9a-f]{3})([BT])$","\\1",pm$list_rgb),""),
function(x) {strtoi(paste0("0x",x))}
)
rgb_sources <- gsub("^RGB([0-9a-f]{3})([BT])$","\\2OA",pm$list_rgb)
rgb_list <- foreach(i = seq_along(pm$list_rgb), .combine=c) %do% {
if (any(
rgb_bands[[i]]<1 |
rgb_bands[[i]]>12 |
rgb_bands[[i]]==10 & rgb_sources[i]=="BOA"
)) {
print_message(
type = type,
"RGB ",pm$list_rgb[i]," can not be computed (bands out of range)."
)
character(0)
} else {
pm$list_rgb[i]
}
}
pm$list_rgb <- rgb_list
} else {pm$list_rgb <- NA}
if (all(is.array(pm$rgb_ranges), length(dim(pm$rgb_ranges)) > 2)) {
pm$rgb_ranges <- split(pm$rgb_ranges, nrow(pm$rgb_ranges))
}
if (is.matrix(pm$rgb_ranges)) {
pm$rgb_ranges <- list(pm$rgb_ranges)
}
if (all(is.na(pm$list_rgb), length(nn(pm$rgb_ranges))==0)) {
pm$rgb_ranges <- NA
}
if (!all(is.na(pm$rgb_ranges)) & length(pm$rgb_ranges) != length(pm$list_rgb)) {
print_message(
type = type,
"\"rgb_ranges\" and \"list_rgb\" must be of the same length."
)
pm$rgb_ranges <- pm$list_rgb <- NA
}
if (!pm$index_source %in% c("BOA", "TOA")) {
print_message(
type = type,
"Parameter \"index_source\" must be one among 'BOA' and 'TOA' (setting to the default)."
)
pm$index_source <- pm_def$index_source
}
if (!pm$mask_type %in% c(NA, "nodata", "cloud_high_proba", "cloud_medium_proba",
"cloud_low_proba", "cloud_and_shadow", "clear_sky", "land") &
!grepl("^scl_[\\_0-9]+$", pm$mask_type)
) {
print_message(
type = type,
"Parameter \"mask_type\" is not accepted (setting to the default)."
)
pm$mask_type <- pm_def$mask_type
}
if (
!is.na(pm$mask_type) &
all(is.na(nn(pm$list_indices))) &
all(is.na(nn(pm$list_prods[pm$list_prods!="SCL"])))
) {
pm$mask_type <- NA
pm$max_mask <- 100
}
if (!is(pm$max_mask, "numeric")) {
if (is.na(suppressWarnings(as.numeric(as.character(pm$max_mask))))) {
print_message(
type = type,
"Parameter \"max_mask\" must be numeric (setting it to the default)."
)
pm$max_mask <- pm_def$max_mask
} else {
pm$max_mask <- as.numeric(as.character(pm$max_mask))
}
}
if (pm$max_mask < 0) {
print_message(
type = if (type == "error") {"warning"} else {type},
"Minimum allowed cloud cover value is 0; ",
"setting parameter \"max_mask\" to 0."
)
pm$max_mask <- 0
} else if (pm$max_mask > 100) {
print_message(
type = if (type == "error") {"warning"} else {type},
"Maximum allowed cloud cover value is 100; ",
"setting parameter \"max_mask\" to 100."
)
pm$max_mask <- 0
}
if (all(pm$max_mask < 100, is.na(pm$mask_type))) {
print_message(
type = if (type == "error") {"warning"} else {type},
"\"max_mask\" was set to ",pm$max_mask,", but no masks were defined: ",
"set argument \"mask_type\" properly in order to use a cloud mask."
)
}
if (!is(pm$mask_smooth, "numeric")) {
if (is.na(suppressWarnings(as.numeric(as.character(pm$mask_smooth))))) {
print_message(
type = type,
"Parameter \"mask_smooth\" must be numeric (setting it to the default)."
)
pm$mask_smooth <- pm_def$mask_smooth
} else {
pm$mask_smooth <- as.numeric(as.character(pm$mask_smooth))
}
}
if (pm$mask_smooth < 0) {
print_message(
type = type,
"Parameter \"mask_smooth\" must be positive (setting it to the default)."
)
pm$mask_smooth <- pm_def$mask_smooth
}
if (!is(pm$mask_buffer, "numeric")) {
if (is.na(suppressWarnings(as.numeric(as.character(pm$mask_buffer))))) {
print_message(
type = type,
"Parameter \"mask_buffer\" must be numeric (setting it to the default)."
)
pm$mask_buffer <- pm_def$mask_buffer
} else {
pm$mask_buffer <- as.numeric(as.character(pm$mask_buffer))
}
}
if (is.na(pm$extent_name) || length(nn(pm$extent_name))==0 || pm$extent_name=="") {
print_message(
type = type,
"The extent name (parameter \"extent_name\" ) can not be empty."
)
}
if (grepl("[ \\.\\_]", pm$extent_name)) {
print_message(
type = type,
"The extent name (parameter \"extent_name\" ) can not contain ",
"spaces, points nor underscores."
)
}
if (grepl("^[0-9]{2}[A-Z]{3}$", pm$extent_name)) {
print_message(
type = type,
"The extent name (parameter \"extent_name\" ) can not cannot be ",
"a five-length string with the same structure of a tile ID",
"(two numeric and three uppercase character values)."
)
}
if (all(!is.na(pm$reference_path), pm$reference_path != "")) {
if(!file.exists(pm$reference_path)) {
print_message(
type = type,
"File \"",pm$reference_path,"\" does not exist ",
"(replacing parameter \"",reference_path,"\" with default value)."
)
pm$reference_path <- pm_def$reference_path
}
}
if (all(!is.na(pm$res), !is(pm$res, "numeric"))) {
if (anyNA(suppressWarnings(as.numeric(as.character(pm$res))))) {
print_message(
type = type,
"Parameter \"res\" must be numeric (setting it to the default)."
)
pm$res <- pm_def$res
} else {
pm$res <- as.numeric(as.character(pm$res))
}
}
if (!anyNA(pm$res) & any(pm$res <= 0)) {
print_message(
type = type,
"Output custom resolution (parameter \"res\" ) must be positive."
)
}
if (length(pm$res) == 1) {
pm$res <- rep(pm$res, 2)
}
if ((!anyNA(pm$res) & !is.null(pm$res)) & (!anyNA(pm$res_s2) & !is.null(pm$res_s2))) {
print_message(
type = if (type == "error") {"warning"} else {type},
"Both native and custom resolution were provided; ",
"only custom one (\"res\") will be used."
)
pm$res_s2 <- NA
}
if (!anyNA(pm$res_s2) & any(!pm$res_s2 %in% c("10m", "20m", "60m"))) {
print_message(
type = type,
"Output native resolution (parameter \"res_s2\" ) is invalid ",
"(accepted values are '10m', '20m' and '60m'); setting it to default."
)
if (!any(!pm$res_s2 %in% c("10m","20m","60m"))) {
pm$res_s2 <- pm_def$res_s2
}
}
if (any(!pm$res_s2 %in% c("10m","20m","60m"))) {
pm$res_s2 <- if (as.integer(mean(pm$res)) >= 60) {"60m"} else if (as.integer(mean(pm$res)) >= 20) {"20m"} else {"10m"}
}
if (pm$unit != "Meter") {
print_message(
type = if (type == "error") {"warning"} else {type},
"Only \"unit\" == 'meter' is accepted."
)
pm$unit <- "Meter"
}
if (inherits(try(st_crs2(pm$proj), silent = TRUE), "try-error")) {
print_message(
type = type,
"Output projection (parameter \"proj\" ) is not recognised; ",
"setting it to default."
)
pm$proj <- pm_def$proj
}
if (!pm$resampling %in% c("near", "mode", "bilinear", "cubic",
"cubicspline", "lanczos", "average", "mode")) {
print_message(
type = type,
"Parameter \"resampling\" is not accepted (setting to the default)."
)
pm$resampling <- pm_def$resampling
}
if (!pm$resampling_scl %in% c("near", "mode")) {
print_message(
type = type,
"Parameter \"resampling_scl\" is not accepted (setting to the default)."
)
pm$resampling_scl <- pm_def$resampling_scl
}
gdal_formats <- fromJSON(
system.file("extdata/settings/gdal_formats.json",package="sen2r")
)$drivers
if (!pm$outformat %in% c(gdal_formats$name, "BigTIFF")) {
print_message(
type = type,
"Parameter \"outformat\" is not accepted (setting to the default)."
)
pm$outformat <- pm_def$outformat
}
if (anyNA(pm$rgb_outformat)) {pm$rgb_outformat <- pm$outformat}
if (!pm$rgb_outformat %in% gdal_formats$name) {
print_message(
type = type,
"Parameter \"rgb_outformat\" is not accepted (setting to the default)."
)
pm$rgb_outformat <- pm_def$rgb_outformat
}
if (!pm$index_datatype %in% c("Byte", "UInt16", "Int16", "UInt32", "Int32", "Float32", "Float64")) {
print_message(
type = type,
"Parameter \"index_datatype\" is not accepted (setting to the default)."
)
pm$index_datatype <- pm_def$index_datatype
}
if (!as.character(pm$compression) %in% c(NA, "NONE", "LZW", "DEFLATE", "PACKBITS", "JPEG", 1:100)) {
print_message(
type = type,
"Parameter \"compression\" is not accepted (setting to the default)."
)
pm$compression <- pm_def$compression
}
if (anyNA(pm$rgb_compression)) {pm$rgb_compression <- pm$compression}
if (!as.character(pm$rgb_compression) %in% c(NA, "NONE", "LZW", "DEFLATE", "PACKBITS", "JPEG", 1:100)) {
print_message(
type = type,
"Parameter \"rgb_compression\" is not accepted (setting to the default)."
)
pm$rgb_compression <- pm_def$rgb_compression
}
l1c_prods <- c("TOA")
l2a_prods <- c("BOA","SCL","TCI")
if (pm$preprocess==TRUE) {
list_prods <- if (!is.na(pm$mask_type)) {
unique(c(pm$list_prods, "SCL"))
} else {
pm$list_prods
}
if (any(!is.na(pm$list_rgb))) {
list_prods <- unique(c(
list_prods,
paste0(unique(substr(pm$list_rgb,7,7)),"OA")
))
}
if (any(!is.na(pm$list_indices))) {
list_prods <- unique(c(list_prods, pm$index_source))
}
list_prods <- list_prods[!is.na(list_prods)]
pm$s2_levels <- if (length(list_prods) > 0) {
c(
if (any(list_prods %in% l1c_prods)) {"l1c"},
if (any(list_prods %in% l2a_prods)) {"l2a"}
)
} else {
pm_def$s2_levels
}
}
if (all(!pm$s2_levels %in% c("l1c", "l2a"))) {
print_message(
type = type,
"Parameter \"s2_levels\" must be 'l1c', 'l2a' or both (setting to the default)."
)
pm$s2_levels <- pm_def$s2_levels
}
if (pm$step_atmcorr == "no") {
print_message(
type = if (type == "error") {"warning"} else {type},
"Value \"no\" for parameter \"step_atmcorr\" is deprecated ",
"(\"l2a\" will be used)."
)
pm$step_atmcorr <- "l2a"
} else if (!pm$step_atmcorr %in% c("auto", "scihub", "l2a")) {
print_message(
type = type,
"Parameter \"step_atmcorr\" must be one among 'auto', 'scihub' and 'l2a' ",
"(setting to the default)."
)
pm$step_atmcorr <- pm_def$step_atmcorr
}
if (!is(pm$sen2cor_use_dem, "logical")) {
print_message(
type = type,
paste0("Parameter sen2cor_use_dem must be TRUE or FALSE; ",
"setting it to the default (NA).")
)
pm$sen2cor_use_dem <- NA
}
if (all(!is.na(pm$path_l1c), pm$path_l1c != "")) {
if(!dir.exists(pm$path_l1c)) {
if(!dir.exists(dirname(pm$path_l1c))) {
print_message(
type = type,
"Directory \"",dirname(pm$path_l1c),"\" does not exist ",
"(it must be created before continuing)."
)
}
}
} else if (all("l1c" %in% pm$s2_levels, check_paths)) {
print_message(
type = type,
"Parameter \"path_l1c\" was not specified; ",
"please provide the path of an existing directory."
)
}
if (all(!is.na(pm$path_l2a), pm$path_l2a != "")) {
if(!dir.exists(pm$path_l2a)) {
if(!dir.exists(dirname(pm$path_l2a))) {
print_message(
type = type,
"Directory \"",dirname(pm$path_l2a),"\" does not exist ",
"(it must be created before continuing)."
)
}
}
} else if (all("l2a" %in% pm$s2_levels, check_paths)) {
print_message(
type = type,
"Parameter \"path_l2a\" was not specified; ",
"please provide the path of an existing directory."
)
}
if (all(!is.na(pm$path_tiles), pm$path_tiles != "", pm$preprocess == TRUE)) {
if(!dir.exists(pm$path_tiles)) {
if(!dir.exists(dirname(pm$path_tiles))) {
print_message(
type = type,
"Directory \"",dirname(pm$path_tiles),"\" does not exist ",
"(it must be created before continuing)."
)
}
}
}
if (all(!is.na(pm$path_merged), pm$path_merged != "", pm$preprocess == TRUE)) {
if(!dir.exists(pm$path_merged)) {
if(!dir.exists(dirname(pm$path_merged))) {
print_message(
type = type,
"Directory \"",dirname(pm$path_merged),"\" does not exist ",
"(it must be created before continuing)."
)
}
}
}
if (pm$preprocess == TRUE) {
if (sum(!is.na(pm$list_rgb))==0) {
pm$path_rgb <- NA
} else if (is.na(pm$path_rgb) | pm$path_rgb=="") {
pm$path_rgb <- pm$path_out
}
if (all(!is.na(pm$path_rgb), pm$path_rgb != "")) {
if(!dir.exists(pm$path_rgb)) {
if(!dir.exists(dirname(pm$path_rgb))) {
print_message(
type = type,
"Directory \"",dirname(pm$path_rgb),"\" does not exist ",
"(it must be created before continuing)."
)
}
}
} else if (all(length(nn(pm$list_rgb[!is.na(pm$list_rgb)])) > 0, check_paths)) {
print_message(
type = type,
"Neither parameter \"path_rgb\" nor \"path_out\" were specified; ",
"please provide the path of an existing directory for at least one of the two."
)
}
}
if (pm$preprocess == TRUE) {
if (sum(!is.na(pm$list_indices))==0) {
pm$path_indices <- NA
} else if (is.na(pm$path_indices) | pm$path_indices=="") {
pm$path_indices <- pm$path_out
}
if (all(!is.na(pm$path_indices), pm$path_indices != "")) {
if(!dir.exists(pm$path_indices)) {
if(!dir.exists(dirname(pm$path_indices))) {
print_message(
type = type,
"Directory \"",dirname(pm$path_indices),"\" does not exist ",
"(it must be created before continuing)."
)
}
}
} else if (all(length(nn(pm$list_indices[!is.na(pm$list_indices)])) > 0, check_paths)) {
print_message(
type = type,
"Neither parameter \"path_indices\" nor \"path_out\" were specified; ",
"please provide the path of an existing directory for at least one of the two."
)
}
}
if (pm$preprocess == TRUE) {
if (sum(!is.na(nn(pm$list_prods)))==0) {
pm$path_out <- NA
}
if (all(!is.na(pm$path_out), pm$path_out != "")) {
if(!dir.exists(pm$path_out)) {
if(!dir.exists(dirname(pm$path_out))) {
print_message(
type = type,
"Directory \"",dirname(pm$path_out),"\" does not exist ",
"(it must be created before continuing)."
)
}
}
} else if (all(length(nn(pm$list_prods[!is.na(pm$list_prods)])) > 0, check_paths)) {
print_message(
type = type,
"Parameter \"path_out\" was not specified; ",
"please provide the path of an existing directory."
)
}
}
if (length(pm$log) > 2) {
print_message(
type = type,
paste0("Parameter \"log\" must be of length 1 or 2; ",
"only the first two elements are used.")
)
pm$log <- pm$log[1:2]
}
for (i in 1:2) {
if (all(!is.na(pm$log[i]), pm$log[i] != "")) {
if(!dir.exists(dirname(pm$log[i]))) {
print_message(
type = type,
"Directory \"",dirname(pm$log[i]),"\" does not exist ",
"(it must be created before continuing)."
)
}
}
}
if (all(
!is(pm$parallel, "logical"), !pm$parallel %in% c(TRUE,FALSE),
!is(pm$parallel, "numeric"), pm$parallel > 0
)) {
print_message(
type = type,
"Parameter \"",sel_par,"\" must be TRUE or FALSE, ",
"or a positive integer value; ",
"setting it to the default (",pm_def$parallel,")."
)
pm$parallel <- pm_def$parallel
}
if (all(
is(pm$parallel, "numeric"),
!is(pm$parallel, "integer")
)) {
pm$parallel <- floor(pm$parallel)
}
if (length(nn(pm$processing_order) > 0)) {
if (pm$preprocess == FALSE) {
pm$processing_order <- "by_step"
}
}
if (!as.character(pm$processing_order) %in% c("by_step", "mixed", "by_date", "by_groups", 1:4)) {
print_message(
type = type,
"Parameter \"processing_order\" must be one among 'by_step', 'mixed' and ",
"'by_date', 'by_groups' (setting to the default)."
)
pm$processing_order <- pm_def$processing_order
}
if (correct==TRUE) {
return(pm)
} else {
return(invisible(NULL))
}
} |
categorical_pal <- function(n) {
stopifnot(n > 0)
x <- c("
"
if (n > length(x)) warning("Cannot make ", n, " categorical colors")
n <- min(n, length(x))
x[seq_len(n)]
}
sequential_pal <- function(n) {
stopifnot(n >= 0)
x <- list(
"
c("
c("
c("
c("
c("
c("
"
c("
"
c("
"
)
if (n > length(x)) warning("Cannot make ", n, " sequential colors")
n <- min(n, length(x))
if (n == 0) character() else x[[n]]
}
diverging_pal <- function(n) {
stopifnot(n > 0)
x <- list(
"
c("
c("
c("
c("
c("
c("
"
c("
"
c("
"
c("
"
c("
"
)
if (n > length(x)) warning("Cannot make ", n, " divergent colors")
n <- min(n, length(x))
if (n == 0) character() else x[[n]]
}
r_pal <- function(n) {
x <- palette()
if (n > length(x)) warning("Cannot make ", n, " divergent colors")
n <- min(n, length(x))
if (n == 0) character() else x[[n]]
} |
insertPmet <- function(vals, nc2, var2, dim2, units2 = NA, conv = NULL,
missval = -6999, verbose = FALSE, ...) {
vals[vals == -6999 | vals == -9999] <- NA
if (!is.null(conv)) {
vals <- lapply(vals, conv)
}
var <- ncdf4::ncvar_def(name = var2, units = units2, dim = dim2, missval = missval, verbose = verbose)
nc2 <- ncdf4::ncvar_add(nc = nc2, v = var, verbose = verbose)
ncdf4::ncvar_put(nc = nc2, varid = var2, vals = vals)
}
met2CF.PalEONregional <- function(in.path, in.prefix, outfolder, start_date, end_date, overwrite = FALSE,
verbose = FALSE, ...) {
start_year <- lubridate::year(start_date)
end_year <- lubridate::year(end_date)
if (!file.exists(outfolder)) {
dir.create(outfolder)
}
by.folder <- list.dirs(in.path, recursive = FALSE, full.names = FALSE)
if (length(by.folder) == 0) {
PEcAn.logger::logger.severe("met2CF.PalEON, could not detect input folders", in.path)
}
rows <- end_year - start_year + 1
results <- data.frame(file = character(rows),
host = character(rows),
mimetype = character(rows),
formatname = character(rows),
startdate = character(rows),
enddate = character(rows),
dbfile.name = in.prefix,
stringsAsFactors = FALSE)
for (year in start_year:end_year) {
my.prefix <- in.prefix
if (nchar(my.prefix) > 0) {
my.prefix <- paste0(my.prefix, ".")
}
new.file <- file.path(outfolder, sprintf("%s%04d.nc", my.prefix, year))
row <- year - start_year + 1
results$file[row] <- new.file
results$host[row] <- PEcAn.remote::fqdn()
results$startdate[row] <- paste0(year, "-01-01 00:00:00")
results$enddate[row] <- paste0(year, "-12-31 23:59:59")
results$mimetype[row] <- "application/x-netcdf"
results$formatname[row] <- "CF"
if (file.exists(new.file) && !overwrite) {
PEcAn.logger::logger.debug("File '", new.file, "' already exists, skipping to next file.")
next
}
met <- list()
for (i in seq_along(by.folder)) {
met[[i]] <- NA
}
names(met) <- by.folder
met[["time"]] <- NA
for (v in by.folder) {
fnames <- dir(file.path(in.path, v), full.names = TRUE)
for (m in 1:12) {
stub <- paste0(year, "_", formatC(m, width = 2, format = "d", flag = "0"))
sel <- grep(stub, fnames)
if (length(sel) == 0) {
PEcAn.logger::logger.severe("missing file", v, stub)
}
old.file <- fnames[sel]
nc1 <- ncdf4::nc_open(old.file, write = FALSE)
if (length(met[[v]]) <= 1) {
met[[v]] <- aperm(ncdf4::ncvar_get(nc = nc1, varid = v),c(2,1,3))
} else {
tmp <- aperm(ncdf4::ncvar_get(nc = nc1, varid = v),c(2,1,3))
met[[v]] <- abind::abind(met[[v]], tmp)
}
if (v == by.folder[1]) {
if (length(met[["time"]]) <= 1) {
met[["time"]] <- nc1$dim[["time"]]$vals
} else {
tmp <- nc1$dim[["time"]]$vals
met[["time"]] <- abind::abind(met[["time"]], tmp)
}
}
ncdf4::nc_close(nc1)
}
}
nc1 <- ncdf4::nc_open(old.file)
tdim <- nc1$dim[["time"]]
met[["time"]] <- udunits2::ud.convert(met[["time"]],"days","seconds")
tdim$units <- paste0("seconds since ",year,"-01-01 00:00:00")
tdim$vals <- met[["time"]]
tdim$len <- length(tdim$vals)
lat <- ncdf4::ncdim_def(name = "latitude", units = "degrees", vals = nc1$dim[["lat"]]$vals, create_dimvar = TRUE)
lon <- ncdf4::ncdim_def(name = "longitude", units = "degrees", vals = nc1$dim[["lon"]]$vals, create_dimvar = TRUE)
time <- ncdf4::ncdim_def(name = "time", units = tdim$units, vals = tdim$vals,
create_dimvar = TRUE, unlim = TRUE)
dim <- list(lat, lon, time)
cp.global.atts <- ncdf4::ncatt_get(nc = nc1, varid = 0)
ncdf4::nc_close(nc1)
print(year)
var <- ncdf4::ncvar_def(name = "air_temperature", units = "K", dim = dim,
missval = as.numeric(-9999))
nc2 <- ncdf4::nc_create(filename = new.file, vars = var, verbose = verbose)
ncdf4::ncvar_put(nc = nc2, varid = "air_temperature", vals = met[["tair"]])
insertPmet(met[["psurf"]], nc2 = nc2, var2 = "air_pressure", units2 = "Pa", dim2 = dim,
verbose = verbose)
insertPmet(met[["qair"]], nc2 = nc2, var2 = "specific_humidity", units2 = "kg/kg", dim2 = dim,
verbose = verbose)
insertPmet(met[["swdown"]], nc2 = nc2, var2 = "surface_downwelling_shortwave_flux_in_air",
units2 = "W m-2", dim2 = dim, verbose = verbose)
insertPmet(met[["lwdown"]], nc2 = nc2, var2 = "surface_downwelling_longwave_flux_in_air",
units2 = "W m-2", dim2 = dim, verbose = verbose)
insertPmet(met[["wind"]], nc2 = nc2, var2 = "wind_speed", units2 = "m s-1", dim2 = dim, verbose = verbose)
insertPmet(met[["precipf"]], nc2 = nc2, var2 = "precipitation_flux", units2 = "kg/m^2/s",
dim2 = dim, verbose = verbose)
for (j in seq_along(cp.global.atts)) {
ncdf4::ncatt_put(nc = nc2, varid = 0, attname = names(cp.global.atts)[j], attval = cp.global.atts[[j]])
}
ncdf4::nc_close(nc2)
}
return(invisible(results))
}
met2CF.PalEON <- function(in.path, in.prefix, outfolder, start_date, end_date, lat, lon, overwrite = FALSE,
verbose = FALSE, ...) {
start_year <- lubridate::year(start_date)
end_year <- lubridate::year(end_date)
if (!file.exists(outfolder)) {
dir.create(outfolder)
}
by.folder <- list.dirs(in.path, recursive = FALSE, full.names = FALSE)
if (length(by.folder) == 0) {
PEcAn.logger::logger.severe("met2CF.PalEON, could not detect input folders", in.path)
}
rows <- end_year - start_year + 1
results <- data.frame(file = character(rows),
host = character(rows),
mimetype = character(rows),
formatname = character(rows),
startdate = character(rows),
enddate = character(rows),
dbfile.name = in.prefix,
stringsAsFactors = FALSE)
for (year in start_year:end_year) {
my.prefix <- in.prefix
if (nchar(my.prefix) > 0) {
my.prefix <- paste0(my.prefix, ".")
}
new.file <- file.path(outfolder, sprintf("%s%04d.nc", my.prefix, year))
row <- year - start_year + 1
results$file[row] <- new.file
results$host[row] <- PEcAn.remote::fqdn()
results$startdate[row] <- paste0(year, "-01-01 00:00:00")
results$enddate[row] <- paste0(year, "-12-31 23:59:59")
results$mimetype[row] <- "application/x-netcdf"
results$formatname[row] <- "CF"
if (file.exists(new.file) && !overwrite) {
PEcAn.logger::logger.debug("File '", new.file, "' already exists, skipping to next file.")
next
}
met <- list()
for (i in seq_along(by.folder)) {
met[[i]] <- NA
}
names(met) <- by.folder
met[["time"]] <- NA
if(FALSE){
for (v in by.folder) {
fnames <- dir(file.path(in.path, v), full.names = TRUE)
for (m in 1:12) {
stub <- paste0(formatC(m, width = 2, format = "d", flag = "0"), ".",
formatC(year,width = 4,format = 'd',flag = '0'))
sel <- grep(stub, fnames)
if (length(sel) == 0) {
PEcAn.logger::logger.severe("missing file", v, stub)
}
old.file <- fnames[sel]
nc1 <- ncdf4::nc_open(old.file, write = FALSE)
if (length(met[[v]]) <= 1) {
met[[v]] <- ncdf4::ncvar_get(nc = nc1, varid = v)
} else {
tmp <- ncdf4::ncvar_get(nc = nc1, varid = v)
met[[v]] <- abind::abind(met[[v]], tmp)
}
if (v == by.folder[1]) {
if (length(met[["time"]]) <= 1) {
met[["time"]] <- nc1$dim[["time"]]$vals
} else {
tmp <- nc1$dim[["time"]]$vals
met[["time"]] <- abind::abind(met[["time"]], tmp)
}
}
ncdf4::nc_close(nc1)
}
}
}
fnames <- dir(file.path(in.path, by.folder[1]), full.names = TRUE)
stub <- paste0(formatC(1, width = 2, format = "d", flag = "0"), ".",
formatC(year,width = 4,format = 'd',flag = '0'))
sel <- grep(stub, fnames)
if (length(sel) == 0) {
PEcAn.logger::logger.severe("missing file", by.folder[1], stub)
}
old.file <- fnames[sel]
var.ids <- c('air_temperature','precipitation_flux',
'surface_downwelling_shortwave_flux_in_air',
'surface_downwelling_longwave_flux_in_air',
'air_pressure','specific_humidity',
'wind_speed')
for(v in var.ids){
met[[v]] <- ncdf4::ncvar_get(nc = nc1, varid = v)
}
nc1 <- ncdf4::nc_open(old.file)
tdim <- nc1$dim[["time"]]
met[["time"]] <- met[["time"]] + (850 - 1700)
tdim$units <- "days since 1700-01-01 00:00:00"
tdim$vals <- met[["time"]]
tdim$len <- length(tdim$vals)
latlon <- lat
latlon[2] <- lon
lat <- ncdf4::ncdim_def(name = "latitude", units = "", vals = 1:1, create_dimvar = FALSE)
lon <- ncdf4::ncdim_def(name = "longitude", units = "", vals = 1:1, create_dimvar = FALSE)
time <- ncdf4::ncdim_def(name = "time", units = tdim$units, vals = tdim$vals,
create_dimvar = TRUE, unlim = TRUE)
dim <- list(lat, lon, time)
cp.global.atts <- ncdf4::ncatt_get(nc = nc1, varid = 0)
ncdf4::nc_close(nc1)
print(c(latlon, year))
var <- ncdf4::ncvar_def(name = "latitude", units = "degree_north", dim = (list(lat, lon, time)),
missval = as.numeric(-9999))
nc2 <- ncdf4::nc_create(filename = new.file, vars = var, verbose = verbose)
ncdf4::ncvar_put(nc = nc2, varid = "latitude", vals = rep(latlon[1], tdim$len))
var <- ncdf4::ncvar_def(name = "longitude", units = "degree_east", dim = (list(lat, lon, time)),
missval = as.numeric(-9999))
nc2 <- ncdf4::ncvar_add(nc = nc2, v = var, verbose = verbose)
ncdf4::ncvar_put(nc = nc2, varid = "longitude", vals = rep(latlon[2], tdim$len))
insertPmet(met[["air_temperature"]], nc2 = nc2, var2 = "air_temperature", units2 = "K", dim2 = dim,
verbose = verbose)
insertPmet(met[["air_pressure"]], nc2 = nc2, var2 = "air_pressure", units2 = "Pa", dim2 = dim,
verbose = verbose)
insertPmet(met[["specific_humidity"]], nc2 = nc2, var2 = "specific_humidity", units2 = "kg/kg", dim2 = dim,
verbose = verbose)
insertPmet(met[["surface_downwelling_shortwave_flux_in_air"]], nc2 = nc2, var2 = "surface_downwelling_shortwave_flux_in_air",
units2 = "W m-2", dim2 = dim, verbose = verbose)
insertPmet(met[["surface_downwelling_longwave_flux_in_air"]], nc2 = nc2, var2 = "surface_downwelling_longwave_flux_in_air",
units2 = "W m-2", dim2 = dim, verbose = verbose)
insertPmet(met[["wind_speed"]], nc2 = nc2, var2 = "wind_speed", units2 = "m s-1", dim2 = dim, verbose = verbose)
insertPmet(met[["precipitation_flux"]], nc2 = nc2, var2 = "precipitation_flux", units2 = "kg/m^2/s",
dim2 = dim, verbose = verbose)
for (j in seq_along(cp.global.atts)) {
ncdf4::ncatt_put(nc = nc2, varid = 0, attname = names(cp.global.atts)[j], attval = cp.global.atts[[j]])
}
ncdf4::nc_close(nc2)
}
return(invisible(results))
}
met2CF.ALMA <- function(in.path, in.prefix, outfolder, start_date, end_date, overwrite = FALSE, verbose = FALSE) {
start_year <- lubridate::year(start_date)
end_year <- lubridate::year(end_date)
if (!file.exists(outfolder)) {
dir.create(outfolder)
}
by.file <- dir(in.path, pattern = ".nc")
if (length(by.file) == 0) {
by.file <- FALSE
by.folder <- list.dirs(in.path, recursive = FALSE, full.names = FALSE)
if (length(by.folder) == 0) {
PEcAn.logger::logger.severe("met2CF.ALMA, could not detect input file or folders", in.path)
}
} else {
by.file <- TRUE
}
rows <- end_year - start_year + 1
results <- data.frame(file = character(rows),
host = character(rows),
mimetype = character(rows),
formatname = character(rows),
startdate = character(rows),
enddate = character(rows),
dbfile.name = in.prefix,
stringsAsFactors = FALSE)
for (year in start_year:end_year) {
new.file <- file.path(outfolder, paste(in.prefix, year, "nc", sep = "."))
row <- year - start_year + 1
results$file[row] <- new.file
results$host[row] <- PEcAn.remote::fqdn()
results$startdate[row] <- paste0(year, "-01-01 00:00:00")
results$enddate[row] <- paste0(year, "-12-31 23:59:59")
results$mimetype[row] <- "application/x-netcdf"
results$formatname[row] <- "CF"
if (file.exists(new.file) && !overwrite) {
PEcAn.logger::logger.debug("File '", new.file, "' already exists, skipping to next file.")
next
}
if (by.file) {
old.file <- file.path(in.path, paste(in.prefix, year, "nc", sep = "."))
nc1 <- ncdf4::nc_open(old.file, write = TRUE)
tdim <- nc1$dim[["DTIME"]]
ncdf4::nc_close(nc1)
} else {
met <- list()
for (i in seq_along(by.folder)) {
met[[i]] <- NA
}
names(met) <- by.folder
met[["time"]] <- NA
for (v in by.folder) {
fnames <- dir(file.path(in.path, v), full.names = TRUE)
for (m in 1:12) {
sel <- grep(paste0(year, "_", formatC(m, width = 2, format = "d", flag = "0")), fnames)
old.file <- fnames[sel]
nc1 <- ncdf4::nc_open(old.file, write = FALSE)
if (length(met[[v]]) <= 1) {
met[[v]] <- ncdf4::ncvar_get(nc = nc1, varid = v)
} else {
tmp <- ncdf4::ncvar_get(nc = nc1, varid = v)
met[[v]] <- abind::abind(met[[v]], tmp)
}
if (v == by.folder[1]) {
if (length(met[["time"]]) <= 1) {
met[["time"]] <- nc1$dim[["time"]]$vals
} else {
tmp <- nc1$dim[["time"]]$vals
met[["time"]] <- abind::abind(met[["time"]], tmp)
}
}
ncdf4::nc_close(nc1)
}
}
}
nc1 <- ncdf4::nc_open(old.file)
tdim <- nc1$dim[["time"]]
latlon <- nc1$dim$lat$vals
latlon[2] <- nc1$dim$lon$vals
lat <- ncdf4::ncdim_def(name = "latitude", units = "", vals = 1:1, create_dimvar = FALSE)
lon <- ncdf4::ncdim_def(name = "longitude", units = "", vals = 1:1, create_dimvar = FALSE)
time <- ncdf4::ncdim_def(name = "time", units = tdim$units, vals = met[["time"]],
create_dimvar = TRUE, unlim = TRUE)
dim <- list(lat, lon, time)
print(latlon)
var <- ncdf4::ncvar_def(name = "latitude", units = "degree_north", dim = (list(lat, lon, time)),
missval = as.numeric(-9999))
nc2 <- ncdf4::nc_create(filename = new.file, vars = var, verbose = verbose)
ncdf4::ncvar_put(nc = nc2, varid = "latitude", vals = rep(latlon[1], tdim$len))
var <- ncdf4::ncvar_def(name = "longitude", units = "degree_east", dim = (list(lat, lon, time)),
missval = as.numeric(-9999))
nc2 <- ncdf4::ncvar_add(nc = nc2, v = var, verbose = verbose)
ncdf4::ncvar_put(nc = nc2, varid = "longitude", vals = rep(latlon[2], tdim$len))
copyvals(nc1 = nc1,
var1 = "TA",
nc2 = nc2,
var2 = "air_temperature", units2 = "K",
dim2 = dim,
conv = function(x) { udunits2::ud.convert(x, "degC", "K") },
verbose = verbose)
copyvals(nc1 = nc1,
var1 = "PRESS",
nc2 = nc2,
var2 = "air_pressure", units2 = "Pa",
dim2 = dim,
conv = function(x) { udunits2::ud.convert(x, 'kPa', 'Pa') },
verbose = verbose)
copyvals(nc1 = nc1,
var1 = "CO2",
nc2 = nc2,
var2 = "mole_fraction_of_carbon_dioxide_in_air", units2 = "mole/mole",
dim2 = dim, conv = function(x) { udunits2::ud.convert(x, "mol/mol", "ppm") },
verbose = verbose)
copyvals(nc1 = nc1,
var1 = "TS1",
nc2 = nc2,
var2 = "soil_temperature", units2 = "K",
dim2 = dim,
conv = function(x) { udunits2::ud.convert(x, "degC", "K") },
verbose = verbose)
copyvals(nc1 = nc1,
var1 = "RH",
nc2 = nc2,
var2 = "relative_humidity", dim2 = dim,
verbose = verbose)
rh <- ncdf4::ncvar_get(nc = nc1, varid = "RH")
rh[rh == -6999 | rh == -9999] <- NA
rh <- rh/100
ta <- ncdf4::ncvar_get(nc = nc1, varid = "TA")
ta[ta == -6999 | ta == -9999] <- NA
ta <- udunits2::ud.convert(ta, "degC", "K")
sh <- rh2qair(rh = rh, T = ta)
var <- ncdf4::ncvar_def(name = "specific_humidity", units = "kg/kg", dim = dim, missval = -6999,
verbose = verbose)
nc2 <- ncdf4::ncvar_add(nc = nc2, v = var, verbose = verbose)
ncdf4::ncvar_put(nc = nc2, varid = "specific_humidity", vals = sh)
copyvals(nc1 = nc1,
var1 = "VPD",
nc2 = nc2,
var2 = "water_vapor_saturation_deficit", units2 = "mol m-2 s-1",
dim2 = dim,
conv = function(x) { ifelse(x < 0, NA, x * 1000) },
verbose = verbose)
copyvals(nc1 = nc1,
var1 = "Rg",
nc2 = nc2,
var2 = "surface_downwelling_shortwave_flux_in_air", dim2 = dim,
verbose = verbose)
copyvals(nc1 = nc1,
var1 = "Rgl",
nc2 = nc2,
var2 = "surface_downwelling_longwave_flux_in_air", dim2 = dim,
verbose = verbose)
copyvals(nc1 = nc1,
var1 = "PAR",
nc2 = nc2,
var2 = "surface_downwelling_photosynthetic_photon_flux_in_air", units2 = "mol m-2 s-1",
dim2 = dim,
conv = function(x) { udunits2::ud.convert(x, "umol m-2 s-1", "mol m-2 s-1") },
verbose = verbose)
copyvals(nc1 = nc1,
var1 = "WD",
nc2 = nc2,
var2 = "wind_direction", dim2 = dim,
verbose = verbose)
copyvals(nc1 = nc1,
var1 = "WS",
nc2 = nc2,
var2 = "wind_speed", dim2 = dim,
verbose = verbose)
t <- tdim$vals
min <- 0.02083 / 30
timestep <- round(x = mean(diff(t)) / min, digits = 1)
copyvals(nc1 = nc1,
var1 = "PREC",
nc2 = nc2,
var2 = "precipitation_flux", units2 = "kg/m^2/s",
dim2 = dim, conv = function(x) { x / timestep / 60 },
verbose = verbose)
wd <- ncdf4::ncvar_get(nc = nc1, varid = "WD")
wd[wd == -6999 | wd == -9999] <- NA
ws <- ncdf4::ncvar_get(nc = nc1, varid = "WS")
ws[ws == -6999 | ws == -9999] <- NA
ew <- ws * cos(wd * (pi / 180))
nw <- ws * sin(wd * (pi / 180))
max <- ncdf4::ncatt_get(nc = nc1, varid = "WS", "valid_max")$value
var <- ncdf4::ncvar_def(name = "eastward_wind", units = "m/s", dim = dim, missval = -6999, verbose = verbose)
nc2 <- ncdf4::ncvar_add(nc = nc2, v = var, verbose = verbose)
ncdf4::ncvar_put(nc = nc2, varid = "eastward_wind", vals = ew)
ncdf4::ncatt_put(nc = nc2, varid = "eastward_wind", attname = "valid_min", attval = -max)
ncdf4::ncatt_put(nc = nc2, varid = "eastward_wind", attname = "valid_max", attval = max)
var <- ncdf4::ncvar_def(name = "northward_wind", units = "m/s", dim = dim, missval = -6999, verbose = verbose)
nc2 <- ncdf4::ncvar_add(nc = nc2, v = var, verbose = verbose)
ncdf4::ncvar_put(nc = nc2, varid = "northward_wind", vals = nw)
ncdf4::ncatt_put(nc = nc2, varid = "northward_wind", attname = "valid_min", attval = -max)
ncdf4::ncatt_put(nc = nc2, varid = "northward_wind", attname = "valid_max", attval = max)
cp.global.atts <- ncdf4::ncatt_get(nc = nc1, varid = 0)
for (j in seq_along(cp.global.atts)) {
ncdf4::ncatt_put(nc = nc2, varid = 0, attname = names(cp.global.atts)[j], attval = cp.global.atts[[j]])
}
ncdf4::nc_close(nc1)
ncdf4::nc_close(nc2)
}
return(invisible(results))
} |
LD.thin <- function(x, threshold, max.dist = 250e3, beg = 1, end = ncol(x), which.snps,
dist.unit = c("bases", "indices", "cM"), extract = TRUE, keep = c("left", "right", "random")) {
if(missing(which.snps)) which.snps <- rep(TRUE, end-beg+1)
if(!is.logical(which.snps) | length(which.snps) != end-beg+1)
stop("which.snps must be a Logical vector of length end - beg + 1")
if(is.null(x@mu) | is.null(x@sigma))
stop("LD.thin needs mu and sigma to be set for LD computation (use set.stats)")
which.snps <- which.snps & (x@snps$callrate > 0) & (x@snps$maf > 0)
dist.unit <- match.arg(dist.unit)
if(dist.unit == "bases") {
pos = as.integer(x@snps$pos)
} else if(dist.unit == "indices") {
pos = seq_len(ncol(x))
} else {
if(max.dist > 100)
warning("max.dist value seems very high for dist.unit = \"cM\"")
pos = as.integer( x@snps$dist * 1e4 )
max.dist = as.integer(max.dist*1e4)
}
if( all(x@snps$pos == x@snps$pos[1]) )
stop("Position of SNPs must be available")
keep <- match.arg(keep)
if(keep == "left") {
w <- .Call("gg_ld_thin_left", x@bed, x@mu, x@sigma, threshold, pos,
as.integer(x@snps$chr), as.integer(max.dist), as.integer(beg)-1L, as.integer(end)-1L,
which.snps)
} else if (keep == "right"){
w <- .Call("gg_ld_thin_right", x@bed, x@mu, x@sigma, threshold, pos,
as.integer(x@snps$chr), as.integer(max.dist), as.integer(beg)-1L, as.integer(end)-1L,
which.snps)
} else {
w <- .Call("gg_ld_thin_random", x@bed, x@mu, x@sigma, threshold, pos,
as.integer(x@snps$chr), as.integer(max.dist), as.integer(beg)-1L, as.integer(end)-1L,
which.snps)
}
if(!extract) return(w)
x[ , seq(beg,end)[w] ]
} |
meucci.ranking <- function(R, p, order){
R = coredata(R)
J = nrow( R )
N = ncol( R )
k = length( order )
Aeq = matrix(rep(1, J), ncol=J)
beq = matrix(1, 1)
V = R[ , order[1:(k-1)] ] - R[ , order[2:k] ]
A = t( V )
b = matrix(rep(0, nrow(A)), ncol=1)
p_ = EntropyProg( p , A , b , Aeq , beq )$p_
out <- meucci.moments(R, p_)
return( out )
} |
print.bammdata = function(x, ...)
{
print.phylo(as.phylo.bammdata(x));
nsamples <- length(x$eventData);
cat(paste("\nPosterior samples:", nsamples,"\n\n"));
cat("List elements:\n");
cat("\t",names(x)[1:10]);
cat("\t",names(x)[11:length(x)]);
cat('\n');
} |
library(readr)
library(dplyr)
library(stringr)
library(usethis)
mig_recodes <- read_csv(
"data-raw/mig-flow-recode.csv",
col_types = cols(
characteristic = col_character(),
code = col_double(),
desc = col_character(),
ordered = col_logical()
)
) %>%
mutate(code = as.character(code)) %>%
mutate(code = str_pad(code, 2, pad = "0"))
use_data(mig_recodes, overwrite = TRUE) |
context("fctr.labelled")
no_lab = rep(1:2,3)
vec_with_lab = no_lab
var_lab(vec_with_lab) = "Fruits"
val_lab(vec_with_lab) = c(Apple=1,Bananas=2)
expect_identical(fctr(unlab(vec_with_lab)),factor(no_lab))
expect_identical(fctr(unvr(vec_with_lab)),factor(no_lab,levels = 1:2,labels= c("Apple","Bananas")))
expect_identical(fctr(unvr(vec_with_lab)),fctr(vec_with_lab, prepend_var_lab = FALSE))
a = letters[1:3]
var_lab(a) = "letters"
expect_identical(fctr(a),
factor(unlab(a),levels = c("a", "b", "c"),labels= c("letters|a", "letters|b", "letters|c")))
a = letters[1:3]
var_lab(a) = "letters"
expect_identical(fctr(unvr(a)),
factor(unlab(a)))
expect_identical(fctr(vec_with_lab),
factor(no_lab,levels = 1:2,labels= c("Fruits|Apple","Fruits|Bananas")))
expect_identical(fctr(vec_with_lab, ordered = TRUE),
factor(no_lab,levels = 1:2,labels= c("Fruits|Apple","Fruits|Bananas"), ordered = TRUE))
expect_identical(fctr(no_lab,levels = 1:2,labels= c("Fruits|Apple","Fruits|Bananas"), ordered = TRUE),
factor(no_lab,levels = 1:2,labels= c("Fruits|Apple","Fruits|Bananas"), ordered = TRUE))
expect_identical(fctr(unvl(vec_with_lab)),factor(no_lab,levels = 1:2,labels= c("Fruits|1","Fruits|2")))
vec_with_lab2 = add_val_lab(vec_with_lab, c("Ananas" = 42))
expect_identical(fctr(vec_with_lab2),
factor(no_lab,levels = c(1:2, 42),
labels= c("Fruits|Apple","Fruits|Bananas","Fruits|Ananas")))
expect_identical(fctr(vec_with_lab2, drop_unused_labels = TRUE),
factor(no_lab,levels = c(1:2),
labels= c("Fruits|Apple","Fruits|Bananas")))
a = factor(c("a", "b", "c"), levels = rev(c("a", "b", "c", "d", "e")))
expect_identical(fctr(a, drop_unused_labels = TRUE), factor(a))
expect_identical(fctr(a, drop_unused_labels = FALSE), a)
var_lab(a) = "My 'a' with labels"
expect_identical(fctr(a, drop_unused_labels = FALSE, prepend_var_lab = FALSE), unvr(a))
b = a
levels(b) = paste0(var_lab(b),"|", levels(b))
expect_identical(fctr(a, drop_unused_labels = FALSE, prepend_var_lab = TRUE), unvr(b))
expect_identical(fctr(a, drop_unused_labels = TRUE, prepend_var_lab = FALSE), factor(a))
test_ds = data.frame(total = 1, s2b = sample(2:3,100,replace = TRUE))
test_ds = unlab(test_ds)
val_lab(test_ds$s2b) = c('18 - 26' = 2, '27 - 35' = 3)
expect_identical(levels(fctr(test_ds$s2b)), names(val_lab(test_ds$s2b)))
context("fctr.labelled - some values without labels")
no_lab = c(no_lab,5:6)
vec_with_lab = c(vec_with_lab,5:6)
expect_identical(fctr(unlab(vec_with_lab)),
factor(no_lab))
expect_identical(fctr(unvr(vec_with_lab)),
factor(no_lab,levels = c(1:2,5:6),labels= c("Apple","Bananas","5","6")))
expect_identical(fctr(vec_with_lab),
factor(no_lab,levels = c(1:2,5:6),labels= c("Fruits|Apple","Fruits|Bananas","Fruits|5","Fruits|6")))
expect_identical(fctr(unvl(vec_with_lab)),
factor(no_lab,levels = c(1:2,5:6),labels= c("Fruits|1","Fruits|2","Fruits|5","Fruits|6")))
context( "fctr - errors and warnings")
a = 1
class(a) = "labelled"
attr(a, "labels") = c(a = 1, b = 1)
expect_error(fctr(a))
attr(a, "labels") = c(a = 1, a = 2)
expect_identical(suppressWarnings(fctr(a)), factor(1, levels = 1:2, labels = c("a","a|")))
attr(a, "labels") = c(a = 1, a = 2, a = 3)
expect_identical(suppressWarnings(fctr(a)), factor(1, levels = 1:3, labels = c("a","a|","a||")))
a = 1:3
val_lab(a) = c("1" = 3)
suppressWarnings(expect_identical(fctr(a), factor(c("1", "2", "1|"), levels = c("1", "2", "1|")))) |
inlf <- function(x, table) {
if(inherits(x, "lfactor")) {
m1 <- match(x=as.character(x), table=as.character(table), nomatch=0, incomparables=NULL)
m2 <- match(x=as.character(switchllevels(x)), table=as.character(table), nomatch=0, incomparables=NULL)
} else {
m1 <- match(x=as.character(x), table=as.character(table), nomatch=0, incomparables=NULL)
m2 <- match(x=as.character(x), table=as.character(switchllevels(table)), nomatch=0, incomparables=NULL)
}
((m1 > 0) | (m2 > 0))
}
methods::setGeneric("%in%")
methods::setMethod("%in%", methods::signature(x="lfactor"), inlf)
methods::setMethod("%in%", methods::signature(table="lfactor"), inlf)
methods::setMethod("%in%", methods::signature(x="lfactor", table="lfactor"), inlf) |
dist_betw_matrices <-
function(x,y, distance=c("rmsd", "mad", "propdiff"), align_cols=TRUE, cores=1)
{
distance <- match.arg(distance)
if(!is.matrix(x)) x <- as.matrix(x)
if(!is.matrix(y)) y <- as.matrix(y)
if(align_cols) {
aligned <- align_matrix_cols(x, y)
x <- aligned$x
y <- aligned$y
if(ncol(x) < 2) {
stop("In trying to align columns, we omitted all but 1 column")
}
}
if(ncol(x) != ncol(y))
stop("x and y should have the same number of columns")
x <- t(x)
y <- t(y)
cores <- setup_cluster(cores)
if(distance=="rmsd") {
func <- rmsd_betw_matrices
} else if(distance=="mad") {
func <- mad_betw_matrices
} else {
func <- propdiff_betw_matrices
}
if(n_cores(cores)==1) {
result <- func(x, y)
}
else {
func_by_xcol <- function(i) func(x[,i,drop=FALSE], y)
result <- cluster_lapply(cores, 1:ncol(x), func_by_xcol)
result <- matrix(unlist(result), ncol=ncol(y), byrow=TRUE)
}
dimnames(result) <- list(colnames(x), colnames(y))
result
} |
getQuantile <- function(Ftheta, mu, sigma, dist,
par.location = 0, par.scale = 1, par.shape = 1, dist.par = NULL) {
switch(dist,
Uniform = {
a <- 0
b <- 1
if(!is.null(dist.par)){
a <- dist.par[1]
b <- dist.par[2]
}
EX <- (a+b)/2
VarX <- (b-a)^2/12
q <- qunif(Ftheta,min=a,max=b)
},
Normal = {
a <- par.location
b <- par.scale
if(!is.null(dist.par)){
a <- dist.par[1]
b <- dist.par[2]
}
EX <- a
VarX <- b^2
q <- qnorm(Ftheta, mean = a, sd = b)
},
Normal2 = {
a <- par.location
b <- par.scale
if(!is.null(dist.par)){
a <- dist.par[1]
b <- dist.par[2]
}
EX <- a^2 + b^2
VarX <- 4 * a^2 * b^2 + 2 * b^4
q <- qchisq(Ftheta, 1)
},
DoubleExp = {
a <- par.location
b <- par.scale
if(!is.null(dist.par)){
a <- dist.par[1]
b <- dist.par[2]
}
EX <- a
VarX <- 2 * b^2
q <- a + (-1)^(Ftheta > 0.5) * b * log(2*(1-Ftheta + (-1+2*Ftheta)*(Ftheta<=0.5)))
},
DoubleExp2 = {
a <- par.location
b <- par.scale
if(!is.null(dist.par)){
a <- dist.par[1]
b <- dist.par[2]
}
EX <- 2 * b^2 + a^2
EY3 <- 6 * b^3 + 6 * a * b^2 + 5 * a^3
EY4 <- 24 * b^4 + 4 * a * EY3 - 6 * a^2 * (2 * b^2 + a^2) + 5 * a^4
VarX <- EY4 - EX^2
stop("Result not available for this distribution.")
},
LogNormal = {
a <- par.location
b <- par.scale
if(!is.null(dist.par)){
a <- dist.par[1]
b <- dist.par[2]
}
EX <- exp(a + b^2 / 2)
VarX <- exp(2 * (a + b^2)) - exp(2 * a + b^2)
q <- qlnorm(Ftheta)
},
Gamma = {
k <- par.scale
o <- par.shape
if(!is.null(dist.par)){
k <- dist.par[1]
o <- dist.par[2]
}
EX <- k * o
VarX <- o * k^2
q <- qgamma(Ftheta, shape = o, scale = k)
},
Weibull = {
k <- par.shape
l <- par.scale
if(!is.null(dist.par)){
k <- dist.par[1]
l <- dist.par[2]
}
EX <- l * gamma(1 + 1 / k)
VarX <- l^2 * (gamma(1 + 2 / k) - (gamma(1 + 1 / k))^2)
q <- qweibull(Ftheta,shape = k, scale = l)
},
t = {
v <- par.shape
if(!is.null(dist.par)){
v <- dist.par[1]
}
EX <- 0
VarX <- v/(v-2)
q <- qt(Ftheta, v)
},
{
a <- par.location
b <- par.scale
if(!is.null(dist.par)){
a <- dist.par[1]
b <- dist.par[2]
}
EX <- a
VarX <- b^2
q <- qnorm(Ftheta, mean = a, sd = b)
}
)
theta <- (q - EX)/sqrt(VarX) * (sigma) + mu
return(theta)
} |
context("Test API response columns are renamed correctly")
unnamed_data <- read.table(system.file("extdata", "example_api_data_unnamed.txt", package = "scrobbler"), header = TRUE)
test_renamed_data <- rename_api_response(unnamed_data)
verified_renamed_data <- read.table(system.file("extdata", "example_api_data_renamed.txt", package = "scrobbler"), header = TRUE)
test_that("Rename API table is successful", {
expect_equal(test_renamed_data, verified_renamed_data)
}) |
rasterise_grob <- function(grob, vp = NULL) {
dim_inch <- dev.size("in")
dim_pix <- dev.size("px")
res <- dim_pix[1] / dim_inch[1]
vp_size <- deviceDim(unit(1, 'npc'), unit(1, 'npc'))
vp_loc <- deviceLoc(unit(0, 'npc'), unit(0, 'npc'))
raster_loc <- unit.c(-1 * vp_loc$x, -1 * vp_loc$y)
if (is.null(vp) && is_reference_grob(grob)) {
return(list(
raster = get_layer(grob$id),
location = raster_loc,
dimension = unit(dim_inch, 'inch')
))
}
if (is.null(vp)) vp <- viewport()
vp_parent <- viewport(vp_loc$x, vp_loc$y, vp_size$w, vp_size$h,
just = c('left', 'bottom'), clip = 'off')
cur <- dev.cur()
cap <- agg_capture(
width = dim_inch[1], height = dim_inch[2], units = 'in',
background = NA, res = res, scaling = getOption("ggfx.scaling", 1)
)
on.exit({
dev.off()
dev.set(cur)
}, add = TRUE)
pushViewport(vp)
pushViewport(vp_parent)
grid.draw(grob)
list(
raster = cap(native = TRUE),
location = raster_loc,
dimension = unit(dim_inch, 'inch')
)
}
groberize_raster <- function(raster, loc, dim, id, include) {
if (!is.null(id)) {
store_raster(raster, id)
}
if (!include) {
return(nullGrob())
}
rasterGrob(raster, x = loc[1], y = loc[2], width = dim[1], height = dim[2],
just = c('left', 'bottom'))
}
NULL |
.snormFit <-
function(x, mean = 0, sd = 1, xi = 1.5,
scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE,
title = NULL, description = NULL, ...)
{
dist = dsnorm
model = "SNORM Parameter Estimation"
scale = "not used"
x = x.orig = as.vector(x)
obj = function(x, y = x, trace) {
f = try(-sum(log(dist(y, x[1], x[2], x[3]))), silent = TRUE)
if (is.na(f) | class(f) == "try-error") return(1e9)
if (trace) {
cat("\n Objective Function Value: ", -f)
cat("\n Parameter Estimates: ", x, "\n")
}
f }
r = nlminb(
start = c(mean = 0, sd = 1, xi = 1.5),
objective = obj,
lower = c(-Inf, 0, 0),
upper = c( Inf, Inf, Inf),
y = x,
trace = trace)
names(r$par) <- c("mean", "sd", "xi")
if (is.null(title)) title = model
if (is.null(description)) description = description()
fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence)
if (doplot) {
x = as.vector(x.orig)
if (span == "auto") span = seq(min(x), max(x), length = 501)
z = density(x, n = 100, ...)
x = z$x[z$y > 0]
y = z$y[z$y > 0]
y.points = dist(span, r$par[1], r$par[2], r$par[3])
ylim = log(c(min(y.points), max(y.points)))
if (add) {
lines(x = span, y = log(y.points), col = "steelblue")
} else {
plot(x, log(y), xlim = c(span[1], span[length(span)]),
ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...)
title(main = model)
lines(x = span, y = log(y.points), col = "steelblue")
}
}
new("fDISTFIT",
call = match.call(),
model = model,
data = as.data.frame(x.orig),
fit = fit,
title = title,
description = description() )
}
snormFit <-
function(x, ...)
{
start = c(mean = mean(x), sd = sqrt(var(x)), xi = 1)
loglik = function(x, y = x){
f = -sum(log(dsnorm(y, x[1], x[2], x[3])))
f }
fit = nlminb(
start = start,
objective = loglik,
lower = c(-Inf, 0, 0),
upper = c( Inf, Inf, Inf),
y = x, ...)
names(fit$par) = c("mean", "sd", "xi")
fit
} |
setMethod(
f = "show",
signature = signature(
object = "SamplingPattern"
),
definition = function(object) {
sampleSize <- getSampleSize(object)
cat("Object of class", dQuote(class(object)), "\n")
cat("sample size:", sampleSize, "\n")
}
) |
ll_flexrsurv_alpha0alpha_bh<-function(alpha0alpha, beta0, beta, gamma0,
Y, X0, X, Z,
expected_rate,
weights=NULL,
step, Nstep,
intTD=intTD_NC, intweightsfunc=intweights_CAV_SIM,
nT0basis,
Spline_t0=BSplineBasis(knots=NULL, degree=3, keep.duplicates=TRUE), Intercept_t0=TRUE,
ialpha0, nX0,
nX,
ialpha,
nTbasis,
Spline_t =BSplineBasis(knots=NULL, degree=3, keep.duplicates=TRUE),
Intercept_t_NPH=rep(TRUE, nX),
debug=FALSE, ...){
if(is.null(Z)){
nZ <- 0
} else {
nZ <- Z@nZ
}
if(Intercept_t0){
tmpgamma0 <- gamma0
}
else {
tmpgamma0 <- c(0, gamma0)
}
YT0Gamma0 <- predictSpline(Spline_t0*tmpgamma0, Y[,1], intercept=Intercept_t0)
if( nX0){
PHterm <-exp(X0 %*% alpha0alpha[ialpha0])
}
else PHterm <- 1
if(nZ) {
Zalphabeta <- Z@DM %*%( diag(alpha0alpha[ialpha]) %*% Z@signature %*% t(ExpandAllCoefBasis(beta, ncol=nZ, value=1)) )
if(nX) {
Zalphabeta <- Zalphabeta + X %*% t(ExpandCoefBasis(beta0,
ncol=nX,
splinebasis=Spline_t,
expand=!Intercept_t_NPH,
value=0))
}
}
else {
if(nX) {
Zalphabeta <- X %*% t(ExpandCoefBasis(beta0,
ncol=nX,
splinebasis=Spline_t,
expand=!Intercept_t_NPH,
value=0))
}
else {
Zalphabeta <- NULL
}
}
if(nX + nZ) {
NPHterm <- intTD(rateTD_bh_alphabeta, intTo=Y[,1], intToStatus=Y[,2],
step, Nstep,
intweightsfunc=intweightsfunc,
gamma0=gamma0, Zalphabeta=Zalphabeta,
Spline_t0=Spline_t0*tmpgamma0, Intercept_t0=Intercept_t0,
Spline_t = Spline_t, Intercept_t=TRUE)
}
else {
NPHterm <- predict(integrate(Spline_t0*tmpgamma0), Y[,1], intercep=Intercept_t0)
}
YT0 <- evaluate(Spline_t0, Y[,1], intercept=Intercept_t0)
if(nX + nZ){
YT <- evaluate(Spline_t, Y[,1], intercept=TRUE)
eventterm <- ifelse(Y[,2] ,
log( PHterm * (YT0Gamma0) * exp(apply(YT * Zalphabeta, 1, sum)) + expected_rate ),
0)
}
else {
eventterm <- ifelse(Y[,2] ,
log( PHterm * (YT0Gamma0) + expected_rate ),
0)
}
if (!is.null(weights)) {
ret <- crossprod(eventterm - PHterm * NPHterm , weights)
}
else {
ret <- sum( eventterm - PHterm * NPHterm )
}
ret
} |
context("taxa functions")
test_that("Test read.names",{
names<-c(
"1\t|\tall\t|\t\t|\tsynonym\t|",
"1\t|\troot\t|\t\t|\tscientific name\t|",
"2\t|\tBacteria\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"2\t|\tMonera\t|\tMonera <Bacteria>\t|\tin-part\t|",
"2\t|\tProcaryotae\t|\tProcaryotae <Bacteria>\t|\tin-part\t|"
)
out<-data.table::data.table('id'=1:2,'name'=c('root','Bacteria'),key='id')
data.table::setindex(out,'name')
expect_warning(expect_equal(read.names(textConnection(names)),out))
out<-data.table::data.table('id'=rep(1:2,2:3),'name'=c('all','root','Bacteria','Monera','Procaryotae'),key='id')
data.table::setindex(out,'name')
expect_warning(expect_equal(read.names(textConnection(names),FALSE),out))
expect_warning(read.names(textConnection(names)),'SQLite')
})
test_that("Test read.nodes",{
nodes<-c(
"1\t|\t1\t|\tno rank\t|\t\t|\t8\t|\t0\t|\t1\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"2\t|\t131567\t|\tsuperkingdom\t|\t\t|\t0\t|\t0\t|\t11\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"6\t|\t335928\t|\tgenus\t|\t\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t0\t|\t0\t|\t\t|",
"7\t|\t6\t|\tspecies\t|\tAC\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t1\t|\t0\t|\t\t|",
"9\t|\t32199\t|\tspecies\t|\tBA\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t1\t|\t0\t|\t\t|"
)
out<-data.table::data.table('id'=c(1:2,6:7,9),'rank'=c('no rank','superkingdom','genus','species','species'),'parent'=c(1,131567,335928,6,32199),key='id')
expect_warning(expect_equal(read.nodes(textConnection(nodes)),out))
expect_warning(read.nodes(textConnection(nodes)),'SQLite')
})
test_that("Test read.names.sql",{
names<-c(
"1\t|\tall\t|\t\t|\tsynonym\t|",
"1\t|\troot\t|\t\t|\tscientific name\t|",
"2\t|\tBacteria\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"2\t|\tMonera\t|\tMonera <Bacteria>\t|\tin-part\t|",
"2\t|\tProcaryotae\t|\tProcaryotae <Bacteria>\t|\tin-part\t|"
)
tmp<-tempfile()
out<-data.frame('id'=1:2,'name'=c('root','Bacteria'),scientific=c(1,1),stringsAsFactors=FALSE)
out2<-data.frame('id'=rep(1:2,2:3),'name'=c('all','root','Bacteria','Monera','Procaryotae'),'scientific'=c(0,1,1,0,0),stringsAsFactors=FALSE)
expect_warning(expect_error(read.names.sql('____NOT_A_REAL____.FILE'),'cannot open'),'cannot open')
expect_equal(read.names.sql(textConnection(names),tmp),tmp)
expect_true(file.exists(tmp))
expect_message(read.names.sql(textConnection(names),tmp),'contains')
expect_error(db <- RSQLite::dbConnect(RSQLite::SQLite(), dbname=tmp),NA)
expect_equal(RSQLite::dbGetQuery(db,"SELECT * FROM names WHERE scientific"),out)
expect_equal(RSQLite::dbGetQuery(db,"SELECT * FROM names"),out2)
RSQLite::dbDisconnect(db)
expect_equal(read.names.sql(textConnection(names[-length(names)]),tmp,overwrite=TRUE),tmp)
expect_error(db <- RSQLite::dbConnect(RSQLite::SQLite(), dbname=tmp),NA)
expect_equal(RSQLite::dbGetQuery(db,"SELECT * FROM names"),out2[-length(names),])
RSQLite::dbDisconnect(db)
})
test_that("Test read.nodes.sql",{
nodes<-c(
"1\t|\t1\t|\tno rank\t|\t\t|\t8\t|\t0\t|\t1\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"2\t|\t131567\t|\tsuperkingdom\t|\t\t|\t0\t|\t0\t|\t11\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"6\t|\t335928\t|\tgenus\t|\t\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t0\t|\t0\t|\t\t|",
"7\t|\t6\t|\tspecies\t|\tAC\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t1\t|\t0\t|\t\t|",
"9\t|\t32199\t|\tspecies\t|\tBA\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t1\t|\t0\t|\t\t|"
)
tmp<-tempfile()
out<-data.frame('id'=c(1:2,6:7,9),'rank'=c('no rank','superkingdom','genus','species','species'),'parent'=c(1,131567,335928,6,32199),stringsAsFactors=FALSE)
expect_equal(read.nodes.sql(textConnection(nodes),tmp),tmp)
expect_true(file.exists(tmp))
expect_message(read.nodes.sql(textConnection(nodes),tmp),'contains')
expect_equal(read.nodes.sql(textConnection(nodes),tmp),tmp)
expect_error(db <- RSQLite::dbConnect(RSQLite::SQLite(), dbname=tmp),NA)
expect_equal(RSQLite::dbGetQuery(db,"SELECT * FROM nodes"),out)
RSQLite::dbDisconnect(db)
expect_equal(read.nodes.sql(textConnection(nodes[-length(nodes)]),tmp,overwrite=TRUE),tmp)
expect_error(db <- RSQLite::dbConnect(RSQLite::SQLite(), dbname=tmp),NA)
expect_equal(RSQLite::dbGetQuery(db,"SELECT * FROM nodes"),out[-length(nodes),])
RSQLite::dbDisconnect(db)
})
test_that("Test lastNotNa",{
expect_equal(lastNotNa(1:100),100)
expect_equal(lastNotNa(c(1:100,rep(NA,50))),100)
expect_equal(lastNotNa(c(rep(NA,50),1:100,rep(NA,50))),100)
expect_equal(lastNotNa(rep(NA,100),'z'),'z')
expect_equal(lastNotNa(rep(NA,100),-999),-999)
expect_equal(lastNotNa(rep(NA,100),NA),NA)
expect_equal(lastNotNa(c(),999),999)
})
test_that("Test streamingRead",{
expect_equal(streamingRead(textConnection(letters),2,paste,collapse=''),unname(as.list(tapply(letters,rep(1:13,each=2),function(x)paste(x,collapse='')))))
expect_output(streamingRead(textConnection(letters),2,vocal=TRUE),'.............')
temp<-tempfile()
writeLines(letters,temp)
expect_equal(streamingRead(temp,2,paste,collapse=''),unname(as.list(tapply(letters,rep(1:13,each=2),function(x)paste(x,collapse='')))))
expect_equal(streamingRead(temp,100,paste,collapse=''),list(paste(letters,collapse='')))
expect_equal(streamingRead(temp,0),list())
expect_output(streamingRead(temp,2,vocal=TRUE),'.............')
gz<-gzfile(temp,'w')
writeLines(letters,gz)
close(gz)
expect_equal(streamingRead(temp,2,paste,collapse=''),unname(as.list(tapply(letters,rep(1:13,each=2),function(x)paste(x,collapse='')))))
expect_output(streamingRead(temp,2,vocal=TRUE),'.............')
handle<-file(temp)
expect_equal(streamingRead(handle,2,paste,collapse=''),unname(as.list(tapply(letters,rep(1:13,each=2),function(x)paste(x,collapse='')))))
handle<-file(temp,'w')
expect_error(streamingRead(handle,2,paste,collapse=''),'read.*connection')
})
test_that("Test trimTaxa",{
expect_error(taxonomizr:::trimTaxa('NotARealFile','test'),'file')
expect_error(.C('taxaTrim',c('NotARealFile','test'),PACKAGE='taxonomizr'),'file')
tmp<-tempfile()
out<-c(
'head\t1\t2\t3',
'a\t2\t3\t4',
'b\t3\t4\t5',
'c\t4\t5\t6'
)
writeLines(out,tmp)
tmp2<-tempfile()
dir.create(tmp2)
expect_error(.C('taxaTrim',c(tmp,tmp2),PACKAGE='taxonomizr'),'file')
tmp2<-tempfile()
expect_error(taxonomizr:::trimTaxa(tmp,tmp2),NA)
expect_equal(readLines(tmp2),c('2\t3','3\t4','4\t5'))
writeLines(c(out,'1\t2\t3\t4\t5'),tmp)
expect_error(taxonomizr:::trimTaxa(tmp,tmp2),"line")
file.remove(tmp2);tmp2<-tempfile()
gzHandle<-gzfile(tmp)
writeLines(out,gzHandle)
close(gzHandle)
expect_error(taxonomizr:::trimTaxa(tmp,tmp2),NA)
expect_equal(readLines(tmp2),c('2\t3','3\t4','4\t5'))
file.remove(tmp2);tmp2<-tempfile()
expect_error(taxonomizr:::trimTaxa(tmp,tmp2,2),NA)
expect_equal(readLines(tmp2),c('2','3','4'))
file.remove(tmp2);tmp2<-tempfile()
expect_error(taxonomizr:::trimTaxa(tmp,tmp2,c(2,4)),NA)
expect_equal(readLines(tmp2),c('2\t4','3\t5','4\t6'))
expect_error(taxonomizr:::trimTaxa(tmp,tmp2,c(2,4)),NA)
expect_equal(readLines(tmp2),rep(c('2\t4','3\t5','4\t6'),2))
with_mock(`R.utils::gunzip`=function(...){},expect_error(taxonomizr:::trimTaxa(tmp,tmp2),'unzip'))
})
test_that("Test read.accession2taxid",{
taxa<-c(
"accession\taccession.version\ttaxid\tgi",
"Z17427\tZ17427.1\t3702\t16569",
"Z17428\tZ17428.1\t3702\t16570",
"Z17429\tZ17429.1\t3702\t16571",
"Z17430\tZ17430.1\t3702\t16572"
)
outFile<-tempfile()
outFile2<-tempfile()
inFile<-tempfile()
writeLines(taxa,inFile)
file.create(outFile)
expect_error(read.accession2taxid(inFile,outFile),NA)
file.remove(outFile)
expect_error(read.accession2taxid(inFile,outFile),NA)
expect_message(read.accession2taxid(inFile,outFile),'contains')
expect_error(read.accession2taxid(inFile,outFile,overwrite=TRUE),NA)
db<-RSQLite::dbConnect(RSQLite::SQLite(),dbname=outFile)
result<-data.frame('base'=c('Z17427','Z17428','Z17429','Z17430'),'accession'=c('Z17427.1','Z17428.1','Z17429.1','Z17430.1'),taxa=3702,stringsAsFactors=FALSE)
expect_true(file.exists(outFile))
expect_equal(RSQLite::dbGetQuery(db,'SELECT * FROM accessionTaxa'),result)
file.remove(outFile)
expect_error(read.accession2taxid(inFile,outFile,extraSqlCommand='pragma temp_store = 2;'),NA)
file.remove(outFile)
expect_error(read.accession2taxid(inFile,outFile,indexTaxa=TRUE),NA)
expect_equal(RSQLite::dbGetQuery(db,'SELECT * FROM accessionTaxa'),result)
RSQLite::dbDisconnect(db)
file.remove(outFile)
if(.Platform$OS.type == "unix"){
expect_error(read.accession2taxid(inFile,outFile,extraSqlCommand='DROP TABLE NOTEXISTXYZ;'),'NOTEXISTXYZ')
expect_true(file.exists(outFile))
}
})
test_that("Test getTaxonomy and getRawTaxonomy",{
namesText<-c(
"1\t|\tall\t|\t\t|\tsynonym\t|",
"1\t|\troot\t|\t\t|\tscientific name\t|",
"2\t|\tBacteria\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"2\t|\tMonera\t|\tMonera <Bacteria>\t|\tin-part\t|",
"2\t|\tProcaryotae\t|\tProcaryotae <Bacteria>\t|\tin-part\t|",
"9606\t|\tHomo sapiens\t|\t\t|\tscientific name", "9605\t|\tHomo\t|\t\t|\tscientific name",
"207598\t|\tHomininae\t|\t\t|\tscientific name", "9604\t|\tHominidae\t|\t\t|\tscientific name",
"314295\t|\tHominoidea\t|\t\t|\tscientific name", "9526\t|\tCatarrhini\t|\t\t|\tscientific name",
"314293\t|\tSimiiformes\t|\t\t|\tscientific name", "376913\t|\tHaplorrhini\t|\t\t|\tscientific name",
"9443\t|\tPrimates\t|\t\t|\tscientific name", "314146\t|\tEuarchontoglires\t|\t\t|\tscientific name",
"1437010\t|\tBoreoeutheria\t|\t\t|\tscientific name", "9347\t|\tEutheria\t|\t\t|\tscientific name",
"32525\t|\tTheria\t|\t\t|\tscientific name", "40674\t|\tMammalia\t|\t\t|\tscientific name",
"32524\t|\tAmniota\t|\t\t|\tscientific name", "32523\t|\tTetrapoda\t|\t\t|\tscientific name",
"1338369\t|\tDipnotetrapodomorpha\t|\t\t|\tscientific name",
"8287\t|\tSarcopterygii\t|\t\t|\tscientific name", "117571\t|\tEuteleostomi\t|\t\t|\tscientific name",
"117570\t|\tTeleostomi\t|\t\t|\tscientific name", "7776\t|\tGnathostomata\t|\t\t|\tscientific name",
"7742\t|\tVertebrata\t|\t\t|\tscientific name", "89593\t|\tCraniata\t|\t\t|\tscientific name",
"7711\t|\tChordata\t|\t\t|\tscientific name", "33511\t|\tDeuterostomia\t|\t\t|\tscientific name",
"33213\t|\tBilateria\t|\t\t|\tscientific name", "6072\t|\tEumetazoa\t|\t\t|\tscientific name",
"33208\t|\tMetazoa\t|\t\t|\tscientific name", "33154\t|\tOpisthokonta\t|\t\t|\tscientific name",
"2759\t|\tEukaryota\t|\t\t|\tscientific name", "131567\t|\tcellular organisms\t|\t\t|\tscientific name"
)
nodesText<-c(
"1\t|\t1\t|\tno rank\t|\t\t|\t8\t|\t0\t|\t1\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"2\t|\t131567\t|\tsuperkingdom\t|\t\t|\t0\t|\t0\t|\t11\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"6\t|\t335928\t|\tgenus\t|\t\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t0\t|\t0\t|\t\t|",
"7\t|\t6\t|\tspecies\t|\tAC\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t1\t|\t0\t|\t\t|",
"9\t|\t32199\t|\tspecies\t|\tBA\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t1\t|\t0\t|\t\t|",
"9606\t|\t9605\t|\tspecies", "9605\t|\t207598\t|\tgenus", "207598\t|\t9604\t|\tsubfamily",
"9604\t|\t314295\t|\tfamily", "314295\t|\t9526\t|\tsuperfamily",
"9526\t|\t314293\t|\tparvorder", "314293\t|\t376913\t|\tinfraorder",
"376913\t|\t9443\t|\tsuborder", "9443\t|\t314146\t|\torder",
"314146\t|\t1437010\t|\tsuperorder", "1437010\t|\t9347\t|\tno rank",
"9347\t|\t32525\t|\tno rank", "32525\t|\t40674\t|\tno rank",
"40674\t|\t32524\t|\tclass", "32524\t|\t32523\t|\tno rank", "32523\t|\t1338369\t|\tno rank",
"1338369\t|\t8287\t|\tno rank", "8287\t|\t117571\t|\tno rank",
"117571\t|\t117570\t|\tno rank", "117570\t|\t7776\t|\tno rank",
"7776\t|\t7742\t|\tno rank", "7742\t|\t89593\t|\tno rank", "89593\t|\t7711\t|\tsubphylum",
"7711\t|\t33511\t|\tphylum", "33511\t|\t33213\t|\tno rank", "33213\t|\t6072\t|\tno rank",
"6072\t|\t33208\t|\tno rank", "33208\t|\t33154\t|\tkingdom",
"33154\t|\t2759\t|\tno rank", "2759\t|\t131567\t|\tsuperkingdom",
"131567\t|\t1\t|\tno rank"
)
tmp<-tempfile()
read.names.sql(textConnection(namesText),tmp)
read.nodes.sql(textConnection(nodesText),tmp)
desiredTaxa<-c('superkingdom','phylum','class','order','family','genus','species')
out<-matrix(c(
"Eukaryota","Chordata","Mammalia","Primates","Hominidae","Homo","Homo sapiens",
"Eukaryota","Chordata","Mammalia","Primates","Hominidae","Homo",NA
),byrow=TRUE,dimnames=list(c('9606','9605'),desiredTaxa),nrow=2)
expect_equal(getTaxonomy(c(9606,9605),tmp,desiredTaxa=desiredTaxa),out)
expect_equal(getTaxonomy(c(9605,9606,9605),tmp,desiredTaxa=desiredTaxa),out[c(2,1,2),])
expect_equal(getTaxonomy(c(9605,9606,9605),tmp,desiredTaxa=desiredTaxa[3:1]),out[c(2,1,2),3:1])
expect_equal(getTaxonomy(9606,tmp,desiredTaxa='NOTREAL'),matrix(as.character(NA),dimnames=list(9606,'NOTREAL')))
expect_equal(getTaxonomy(9999999,tmp,desiredTaxa='class'),matrix(as.character(NA),dimnames=list(9999999,'class')))
expect_equal(getTaxonomy(c(9999999,9606),tmp,desiredTaxa='class'),matrix(c(NA,'Mammalia'),dimnames=list(c('9999999',' 9606'),'class'),nrow=2))
expect_equal(getTaxonomy(c(),tmp,desiredTaxa=desiredTaxa),NULL)
desiredRaw<-list(`9606` = c(species = "Homo sapiens", genus = "Homo", subfamily = "Homininae", family = "Hominidae", superfamily = "Hominoidea", parvorder = "Catarrhini", infraorder = "Simiiformes", suborder = "Haplorrhini", order = "Primates", superorder = "Euarchontoglires", `no rank` = "Boreoeutheria", `no rank.1` = "Eutheria", `no rank.2` = "Theria", class = "Mammalia", `no rank.3` = "Amniota", `no rank.4` = "Tetrapoda", `no rank.5` = "Dipnotetrapodomorpha", `no rank.6` = "Sarcopterygii", `no rank.7` = "Euteleostomi", `no rank.8` = "Teleostomi", `no rank.9` = "Gnathostomata", `no rank.10` = "Vertebrata", subphylum = "Craniata", phylum = "Chordata", `no rank.11` = "Deuterostomia", `no rank.12` = "Bilateria", `no rank.13` = "Eumetazoa", kingdom = "Metazoa", `no rank.14` = "Opisthokonta", superkingdom = "Eukaryota", `no rank.15` = "cellular organisms"), `9605` = c(genus = "Homo", subfamily = "Homininae", family = "Hominidae", superfamily = "Hominoidea", parvorder = "Catarrhini", infraorder = "Simiiformes", suborder = "Haplorrhini", order = "Primates", superorder = "Euarchontoglires", `no rank` = "Boreoeutheria", `no rank.1` = "Eutheria", `no rank.2` = "Theria", class = "Mammalia", `no rank.3` = "Amniota", `no rank.4` = "Tetrapoda", `no rank.5` = "Dipnotetrapodomorpha", `no rank.6` = "Sarcopterygii", `no rank.7` = "Euteleostomi", `no rank.8` = "Teleostomi", `no rank.9` = "Gnathostomata", `no rank.10` = "Vertebrata", subphylum = "Craniata", phylum = "Chordata", `no rank.11` = "Deuterostomia", `no rank.12` = "Bilateria", `no rank.13` = "Eumetazoa", kingdom = "Metazoa", `no rank.14` = "Opisthokonta", superkingdom = "Eukaryota", `no rank.15` = "cellular organisms"))
expect_equal(getRawTaxonomy(c(9606,9605),tmp),desiredRaw)
expect_equal(getRawTaxonomy(c(9606,9605,9605,9606),tmp),desiredRaw[c(1:2,2:1)])
expect_equal(getRawTaxonomy(c(),tmp),NULL)
expect_equal(getRawTaxonomy(c(1),tmp),list('1'=NULL))
expect_equal(getRawTaxonomy(c(1,0),tmp),list('1'=NULL,'0'=structure(as.character(NA),.Names=NA)))
expect_equal(getRawTaxonomy(c(NA),tmp),list('NA'=NULL))
expect_equal(getRawTaxonomy(c(NA,NA),tmp),list('NA'=NULL,'NA'=NULL))
expect_equal(getRawTaxonomy(c(NA,9606),tmp),c(list(' NA'=NULL),desiredRaw[1]))
naDf<-out
naDf[,]<-NA
rownames(naDf)<-c('NA','NA')
expect_equal(getTaxonomy(c(NA,NA),tmp),naDf)
suppressWarnings(expect_equal(getTaxonomy(c(NA,9605,NA,'9604,9605'),tmp),rbind(' NA'=naDf[1,],'9605'=out[2,],' NA'=naDf[1,],' NA'=naDf[1,])))
expect_equal(getTaxonomy('9605',tmp),getTaxonomy(9605,tmp))
expect_warning(getTaxonomy('9605,123',tmp),'coercion')
expect_warning(getRawTaxonomy('9605,123',tmp),'coercion')
cycle<-c(
"9606\t|\t9605\t|\tno rank\t|\t\t|\t8\t|\t0\t|\t1\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"9605\t|\t9606\t|\tsuperkingdom\t|\t\t|\t0\t|\t0\t|\t11\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|"
)
tmp2<-tempfile()
read.names.sql(textConnection(namesText),tmp2)
read.nodes.sql(textConnection(cycle),tmp2)
expect_error(getTaxonomy(9606,tmp2),'cycle')
expect_error(getRawTaxonomy(9606,tmp2),'cycle')
})
test_that("Test getTaxonomy and getRawTaxonomy with duplicated taxa ranks",{
namesText<-c(
"1\t|\tall\t|\t\t|\tsynonym\t|",
"1\t|\troot\t|\t\t|\tscientific name\t|",
"2\t|\tBacteria\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"2\t|\tMonera\t|\tMonera <Bacteria>\t|\tin-part\t|",
"2\t|\tProcaryotae\t|\tProcaryotae <Bacteria>\t|\tin-part\t|",
"3\t|\tClade A\t|\t\t|\tscientific name\t|",
"4\t|\tClade B\t|\t\t|\tscientific name\t|",
"5\t|\tClade C\t|\t\t|\tscientific name\t|",
"6\t|\tClade D\t|\t\t|\tscientific name\t|",
"7\t|\tClade E\t|\t\t|\tscientific name\t|"
)
nodesText<-c(
"1\t|\t1\t|\tno rank\t|\t\t|\t8\t|\t0\t|\t1\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"2\t|\t1\t|\tsuperkingdom\t|\t\t|\t0\t|\t0\t|\t11\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"3\t|\t2\t|\tclade",
"4\t|\t3\t|\tclade",
"5\t|\t4\t|\tclade",
"6\t|\t5\t|\tclade",
"7\t|\t6\t|\tclade"
)
tmp<-tempfile()
read.names.sql(textConnection(namesText),tmp)
read.nodes.sql(textConnection(nodesText),tmp)
desiredTaxa<-c('superkingdom','clade')
out<-matrix(c(
"Bacteria","Clade A",
"Bacteria","Clade A"
),byrow=TRUE,dimnames=list(c('7','6'),desiredTaxa),nrow=2)
expect_equal(getTaxonomy(c(7,6),tmp,desiredTaxa=desiredTaxa),out)
expect_equal(getRawTaxonomy(c(7,6,3),tmp),list('7'=c('clade'='Clade E','clade.1'='Clade D','clade.2'='Clade C','clade.3'='Clade B','clade.4'='Clade A','superkingdom'='Bacteria'),'6'=c('clade'='Clade D','clade.1'='Clade C','clade.2'='Clade B','clade.3'='Clade A','superkingdom'='Bacteria'),'3'=c('clade'='Clade A','superkingdom'='Bacteria')))
expect_equal(getRawTaxonomy(c(7),tmp),list('7'=c('clade'='Clade E','clade.1'='Clade D','clade.2'='Clade C','clade.3'='Clade B','clade.4'='Clade A','superkingdom'='Bacteria')))
})
test_that("Test getTaxonomy with deprecated data.tables",{
namesText<-c(
"1\t|\tall\t|\t\t|\tsynonym\t|",
"1\t|\troot\t|\t\t|\tscientific name\t|",
"2\t|\tBacteria\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"2\t|\tMonera\t|\tMonera <Bacteria>\t|\tin-part\t|",
"2\t|\tProcaryotae\t|\tProcaryotae <Bacteria>\t|\tin-part\t|",
"9606\t|\tHomo sapiens\t|\t\t|\tscientific name", "9605\t|\tHomo\t|\t\t|\tscientific name",
"207598\t|\tHomininae\t|\t\t|\tscientific name", "9604\t|\tHominidae\t|\t\t|\tscientific name",
"314295\t|\tHominoidea\t|\t\t|\tscientific name", "9526\t|\tCatarrhini\t|\t\t|\tscientific name",
"314293\t|\tSimiiformes\t|\t\t|\tscientific name", "376913\t|\tHaplorrhini\t|\t\t|\tscientific name",
"9443\t|\tPrimates\t|\t\t|\tscientific name", "314146\t|\tEuarchontoglires\t|\t\t|\tscientific name",
"1437010\t|\tBoreoeutheria\t|\t\t|\tscientific name", "9347\t|\tEutheria\t|\t\t|\tscientific name",
"32525\t|\tTheria\t|\t\t|\tscientific name", "40674\t|\tMammalia\t|\t\t|\tscientific name",
"32524\t|\tAmniota\t|\t\t|\tscientific name", "32523\t|\tTetrapoda\t|\t\t|\tscientific name",
"1338369\t|\tDipnotetrapodomorpha\t|\t\t|\tscientific name",
"8287\t|\tSarcopterygii\t|\t\t|\tscientific name", "117571\t|\tEuteleostomi\t|\t\t|\tscientific name",
"117570\t|\tTeleostomi\t|\t\t|\tscientific name", "7776\t|\tGnathostomata\t|\t\t|\tscientific name",
"7742\t|\tVertebrata\t|\t\t|\tscientific name", "89593\t|\tCraniata\t|\t\t|\tscientific name",
"7711\t|\tChordata\t|\t\t|\tscientific name", "33511\t|\tDeuterostomia\t|\t\t|\tscientific name",
"33213\t|\tBilateria\t|\t\t|\tscientific name", "6072\t|\tEumetazoa\t|\t\t|\tscientific name",
"33208\t|\tMetazoa\t|\t\t|\tscientific name", "33154\t|\tOpisthokonta\t|\t\t|\tscientific name",
"2759\t|\tEukaryota\t|\t\t|\tscientific name", "131567\t|\tcellular organisms\t|\t\t|\tscientific name"
)
taxaNames<-expect_warning(read.names(textConnection(namesText)))
nodesText<-c(
"1\t|\t1\t|\tno rank\t|\t\t|\t8\t|\t0\t|\t1\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"2\t|\t131567\t|\tsuperkingdom\t|\t\t|\t0\t|\t0\t|\t11\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"6\t|\t335928\t|\tgenus\t|\t\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t0\t|\t0\t|\t\t|",
"7\t|\t6\t|\tspecies\t|\tAC\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t1\t|\t0\t|\t\t|",
"9\t|\t32199\t|\tspecies\t|\tBA\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t1\t|\t0\t|\t\t|",
"9606\t|\t9605\t|\tspecies", "9605\t|\t207598\t|\tgenus", "207598\t|\t9604\t|\tsubfamily",
"9604\t|\t314295\t|\tfamily", "314295\t|\t9526\t|\tsuperfamily",
"9526\t|\t314293\t|\tparvorder", "314293\t|\t376913\t|\tinfraorder",
"376913\t|\t9443\t|\tsuborder", "9443\t|\t314146\t|\torder",
"314146\t|\t1437010\t|\tsuperorder", "1437010\t|\t9347\t|\tno rank",
"9347\t|\t32525\t|\tno rank", "32525\t|\t40674\t|\tno rank",
"40674\t|\t32524\t|\tclass", "32524\t|\t32523\t|\tno rank", "32523\t|\t1338369\t|\tno rank",
"1338369\t|\t8287\t|\tno rank", "8287\t|\t117571\t|\tno rank",
"117571\t|\t117570\t|\tno rank", "117570\t|\t7776\t|\tno rank",
"7776\t|\t7742\t|\tno rank", "7742\t|\t89593\t|\tno rank", "89593\t|\t7711\t|\tsubphylum",
"7711\t|\t33511\t|\tphylum", "33511\t|\t33213\t|\tno rank", "33213\t|\t6072\t|\tno rank",
"6072\t|\t33208\t|\tno rank", "33208\t|\t33154\t|\tkingdom",
"33154\t|\t2759\t|\tno rank", "2759\t|\t131567\t|\tsuperkingdom",
"131567\t|\t1\t|\tno rank"
)
taxaNodes<-expect_warning(read.nodes(textConnection(nodesText)))
desiredTaxa<-c('superkingdom','phylum','class','order','family','genus','species')
out<-matrix(c(
"Eukaryota","Chordata","Mammalia","Primates","Hominidae","Homo","Homo sapiens",
"Eukaryota","Chordata","Mammalia","Primates","Hominidae","Homo",NA
),byrow=TRUE,dimnames=list(c('9606','9605'),desiredTaxa),nrow=2)
expect_warning(expect_equal(getTaxonomy(c(9606,9605),taxaNodes,taxaNames,mc.cores=1,desiredTaxa=desiredTaxa),out))
expect_warning(expect_equal(getTaxonomy(c(9605,9606,9605),taxaNodes,taxaNames,mc.cores=1,desiredTaxa=desiredTaxa),out[c(2,1,2),]))
expect_warning(expect_equal(getTaxonomy(c(9605,9606,9605),taxaNodes,taxaNames,mc.cores=1,desiredTaxa=desiredTaxa[3:1]),out[c(2,1,2),3:1]))
expect_warning(expect_output(getTaxonomy(9606,taxaNodes,taxaNames,mc.cores=1,debug=TRUE),'\\\\t'))
expect_warning(expect_equal(getTaxonomy(9606,taxaNodes,taxaNames,mc.cores=1,desiredTaxa='NOTREAL'),matrix(as.character(NA),dimnames=list(9606,'NOTREAL'))))
expect_warning(expect_equal(getTaxonomy(9999999,taxaNodes,taxaNames,mc.cores=1,desiredTaxa='class'),matrix(as.character(NA),dimnames=list(9999999,'class'))))
if(.Platform$OS.type == "unix")expect_warning(expect_equal(getTaxonomy(c(9605,9606,9605),taxaNodes,taxaNames,mc.cores=2,desiredTaxa=desiredTaxa),out[c(2,1,2),]))
expect_warning(expect_equal(getTaxonomy(c(),taxaNodes,taxaNames,mc.cores=1,desiredTaxa=desiredTaxa),NULL))
naDf<-out
naDf[,]<-NA
rownames(naDf)<-c('NA','NA')
expect_warning(expect_equal(getTaxonomy(c(NA,NA),taxaNodes,taxaNames),naDf))
expect_warning(expect_equal(getTaxonomy(c(NA,9605,NA,'9604,9605'),taxaNodes,taxaNames),rbind(' NA'=naDf[1,],'9605'=out[2,],' NA'=naDf[1,],' NA'=naDf[1,])))
expect_warning(expect_equal(getTaxonomy('9605',taxaNodes,taxaNames),getTaxonomy(9605,taxaNodes,taxaNames)))
expect_warning(getTaxonomy('9605,123',taxaNodes,taxaNames),'coercion')
expect_warning(getTaxonomy(9999999,taxaNodes,taxaNames),'SQLite')
})
test_that("Test getTaxonomy2",{
namesText<-c(
"1\t|\tall\t|\t\t|\tsynonym\t|",
"1\t|\troot\t|\t\t|\tscientific name\t|",
"2\t|\tBacteria\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"2\t|\tMonera\t|\tMonera <Bacteria>\t|\tin-part\t|",
"2\t|\tProcaryotae\t|\tProcaryotae <Bacteria>\t|\tin-part\t|",
"9606\t|\tHomo sapiens\t|\t\t|\tscientific name", "9605\t|\tHomo\t|\t\t|\tscientific name",
"207598\t|\tHomininae\t|\t\t|\tscientific name", "9604\t|\tHominidae\t|\t\t|\tscientific name",
"314295\t|\tHominoidea\t|\t\t|\tscientific name", "9526\t|\tCatarrhini\t|\t\t|\tscientific name",
"314293\t|\tSimiiformes\t|\t\t|\tscientific name", "376913\t|\tHaplorrhini\t|\t\t|\tscientific name",
"9443\t|\tPrimates\t|\t\t|\tscientific name", "314146\t|\tEuarchontoglires\t|\t\t|\tscientific name",
"1437010\t|\tBoreoeutheria\t|\t\t|\tscientific name", "9347\t|\tEutheria\t|\t\t|\tscientific name",
"32525\t|\tTheria\t|\t\t|\tscientific name", "40674\t|\tMammalia\t|\t\t|\tscientific name",
"32524\t|\tAmniota\t|\t\t|\tscientific name", "32523\t|\tTetrapoda\t|\t\t|\tscientific name",
"1338369\t|\tDipnotetrapodomorpha\t|\t\t|\tscientific name",
"8287\t|\tSarcopterygii\t|\t\t|\tscientific name", "117571\t|\tEuteleostomi\t|\t\t|\tscientific name",
"117570\t|\tTeleostomi\t|\t\t|\tscientific name", "7776\t|\tGnathostomata\t|\t\t|\tscientific name",
"7742\t|\tVertebrata\t|\t\t|\tscientific name", "89593\t|\tCraniata\t|\t\t|\tscientific name",
"7711\t|\tChordata\t|\t\t|\tscientific name", "33511\t|\tDeuterostomia\t|\t\t|\tscientific name",
"33213\t|\tBilateria\t|\t\t|\tscientific name", "6072\t|\tEumetazoa\t|\t\t|\tscientific name",
"33208\t|\tMetazoa\t|\t\t|\tscientific name", "33154\t|\tOpisthokonta\t|\t\t|\tscientific name",
"2759\t|\tEukaryota\t|\t\t|\tscientific name", "131567\t|\tcellular organisms\t|\t\t|\tscientific name"
)
taxaNames<-expect_warning(read.names(textConnection(namesText)))
nodesText<-c(
"1\t|\t1\t|\tno rank\t|\t\t|\t8\t|\t0\t|\t1\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"2\t|\t131567\t|\tsuperkingdom\t|\t\t|\t0\t|\t0\t|\t11\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"6\t|\t335928\t|\tgenus\t|\t\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t0\t|\t0\t|\t\t|",
"7\t|\t6\t|\tspecies\t|\tAC\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t1\t|\t0\t|\t\t|",
"9\t|\t32199\t|\tspecies\t|\tBA\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t1\t|\t0\t|\t\t|",
"9606\t|\t9605\t|\tspecies", "9605\t|\t207598\t|\tgenus", "207598\t|\t9604\t|\tsubfamily",
"9604\t|\t314295\t|\tfamily", "314295\t|\t9526\t|\tsuperfamily",
"9526\t|\t314293\t|\tparvorder", "314293\t|\t376913\t|\tinfraorder",
"376913\t|\t9443\t|\tsuborder", "9443\t|\t314146\t|\torder",
"314146\t|\t1437010\t|\tsuperorder", "1437010\t|\t9347\t|\tno rank",
"9347\t|\t32525\t|\tno rank", "32525\t|\t40674\t|\tno rank",
"40674\t|\t32524\t|\tclass", "32524\t|\t32523\t|\tno rank", "32523\t|\t1338369\t|\tno rank",
"1338369\t|\t8287\t|\tno rank", "8287\t|\t117571\t|\tno rank",
"117571\t|\t117570\t|\tno rank", "117570\t|\t7776\t|\tno rank",
"7776\t|\t7742\t|\tno rank", "7742\t|\t89593\t|\tno rank", "89593\t|\t7711\t|\tsubphylum",
"7711\t|\t33511\t|\tphylum", "33511\t|\t33213\t|\tno rank", "33213\t|\t6072\t|\tno rank",
"6072\t|\t33208\t|\tno rank", "33208\t|\t33154\t|\tkingdom",
"33154\t|\t2759\t|\tno rank", "2759\t|\t131567\t|\tsuperkingdom",
"131567\t|\t1\t|\tno rank"
)
taxaNodes<-expect_warning(read.nodes(textConnection(nodesText)))
desiredTaxa<-c('superkingdom','phylum','class','order','family','genus','species')
out<-matrix(c(
"Eukaryota","Chordata","Mammalia","Primates","Hominidae","Homo","Homo sapiens",
"Eukaryota","Chordata","Mammalia","Primates","Hominidae","Homo",NA
),byrow=TRUE,dimnames=list(c('9606','9605'),desiredTaxa),nrow=2)
expect_warning(expect_equal(getTaxonomy2(c(9606,9605),taxaNodes,taxaNames,mc.cores=1,desiredTaxa=desiredTaxa),out))
expect_warning(expect_equal(getTaxonomy2(c(9605,9606,9605),taxaNodes,taxaNames,mc.cores=1,desiredTaxa=desiredTaxa),out[c(2,1,2),]))
expect_warning(expect_equal(getTaxonomy2(c(9605,9606,9605),taxaNodes,taxaNames,mc.cores=1,desiredTaxa=desiredTaxa[3:1]),out[c(2,1,2),3:1]))
expect_warning(expect_output(getTaxonomy2(9606,taxaNodes,taxaNames,mc.cores=1,debug=TRUE),'\\\\t'))
expect_warning(expect_equal(getTaxonomy2(9606,taxaNodes,taxaNames,mc.cores=1,desiredTaxa='NOTREAL'),matrix(as.character(NA),dimnames=list(9606,'NOTREAL'))))
expect_warning(expect_equal(getTaxonomy2(9999999,taxaNodes,taxaNames,mc.cores=1,desiredTaxa='class'),matrix(as.character(NA),dimnames=list(9999999,'class'))))
if(.Platform$OS.type == "unix")expect_warning(expect_equal(getTaxonomy2(c(9605,9606,9605),taxaNodes,taxaNames,mc.cores=2,desiredTaxa=desiredTaxa),out[c(2,1,2),]))
expect_warning(expect_equal(getTaxonomy2(c(),taxaNodes,taxaNames,mc.cores=1,desiredTaxa=desiredTaxa),NULL))
naDf<-out
naDf[,]<-NA
rownames(naDf)<-c('NA','NA')
expect_warning(expect_equal(getTaxonomy2(c(NA,NA),taxaNodes,taxaNames),naDf))
expect_warning(expect_equal(getTaxonomy2(c(NA,9605,NA,'9604,9605'),taxaNodes,taxaNames),rbind(' NA'=naDf[1,],'9605'=out[2,],' NA'=naDf[1,],' NA'=naDf[1,])))
expect_warning(expect_equal(getTaxonomy2('9605',taxaNodes,taxaNames),getTaxonomy2(9605,taxaNodes,taxaNames)))
expect_warning(getTaxonomy2('9605,123',taxaNodes,taxaNames),'coercion')
})
test_that("Test getParentNodes",{
nodesText<-c(
"1\t|\t1\t|\tno rank\t|\t\t|\t8\t|\t0\t|\t1\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"2\t|\t131567\t|\tsuperkingdom\t|\t\t|\t0\t|\t0\t|\t11\t|\t0\t|\t0\t|\t0\t|\t0\t|\t0\t|\t\t|",
"6\t|\t335928\t|\tgenus\t|\t\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t0\t|\t0\t|\t\t|",
"7\t|\t6\t|\tspecies\t|\tAC\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t1\t|\t0\t|\t\t|",
"9\t|\t32199\t|\tspecies\t|\tBA\t|\t0\t|\t1\t|\t11\t|\t1\t|\t0\t|\t1\t|\t1\t|\t0\t|\t\t|",
"9606\t|\t9605\t|\tspecies", "9605\t|\t207598\t|\tgenus", "207598\t|\t9604\t|\tsubfamily",
"9604\t|\t314295\t|\tfamily", "314295\t|\t9526\t|\tsuperfamily",
"9526\t|\t314293\t|\tparvorder", "314293\t|\t376913\t|\tinfraorder",
"376913\t|\t9443\t|\tsuborder", "9443\t|\t314146\t|\torder",
"314146\t|\t1437010\t|\tsuperorder", "1437010\t|\t9347\t|\tno rank",
"9347\t|\t32525\t|\tno rank", "32525\t|\t40674\t|\tno rank",
"40674\t|\t32524\t|\tclass", "32524\t|\t32523\t|\tno rank", "32523\t|\t1338369\t|\tno rank",
"1338369\t|\t8287\t|\tno rank", "8287\t|\t117571\t|\tno rank",
"117571\t|\t117570\t|\tno rank", "117570\t|\t7776\t|\tno rank",
"7776\t|\t7742\t|\tno rank", "7742\t|\t89593\t|\tno rank", "89593\t|\t7711\t|\tsubphylum",
"7711\t|\t33511\t|\tphylum", "33511\t|\t33213\t|\tno rank", "33213\t|\t6072\t|\tno rank",
"6072\t|\t33208\t|\tno rank", "33208\t|\t33154\t|\tkingdom",
"33154\t|\t2759\t|\tno rank", "2759\t|\t131567\t|\tsuperkingdom",
"131567\t|\t1\t|\tno rank"
)
namesText<-c(
"1\t|\tall\t|\t\t|\tsynonym\t|",
"1\t|\troot\t|\t\t|\tscientific name\t|",
"2\t|\tBacteria\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"2\t|\tMonera\t|\tMonera <Bacteria>\t|\tin-part\t|",
"2\t|\tProcaryotae\t|\tProcaryotae <Bacteria>\t|\tin-part\t|",
"9606\t|\tMan\t|\t\t|\tsynonym","9606\t|\thuman\t|\t\t|\tsynonym",
"9606\t|\tHomo sapiens\t|\t\t|\tscientific name", "9605\t|\tHomo\t|\t\t|\tscientific name",
"207598\t|\tHomininae\t|\t\t|\tscientific name", "9604\t|\tHominidae\t|\t\t|\tscientific name",
"314295\t|\tHominoidea\t|\t\t|\tscientific name", "9526\t|\tCatarrhini\t|\t\t|\tscientific name",
"314293\t|\tSimiiformes\t|\t\t|\tscientific name", "376913\t|\tHaplorrhini\t|\t\t|\tscientific name",
"9443\t|\tPrimates\t|\t\t|\tscientific name", "314146\t|\tEuarchontoglires\t|\t\t|\tscientific name",
"1437010\t|\tBoreoeutheria\t|\t\t|\tscientific name", "9347\t|\tEutheria\t|\t\t|\tscientific name",
"32525\t|\tTheria\t|\t\t|\tscientific name", "40674\t|\tMammalia\t|\t\t|\tscientific name",
"32524\t|\tAmniota\t|\t\t|\tscientific name", "32523\t|\tTetrapoda\t|\t\t|\tscientific name",
"1338369\t|\tDipnotetrapodomorpha\t|\t\t|\tscientific name",
"8287\t|\tSarcopterygii\t|\t\t|\tscientific name", "117571\t|\tEuteleostomi\t|\t\t|\tscientific name",
"117570\t|\tTeleostomi\t|\t\t|\tscientific name", "7776\t|\tGnathostomata\t|\t\t|\tscientific name",
"7742\t|\tVertebrata\t|\t\t|\tscientific name", "89593\t|\tCraniata\t|\t\t|\tscientific name",
"7711\t|\tChordata\t|\t\t|\tscientific name", "33511\t|\tDeuterostomia\t|\t\t|\tscientific name",
"33213\t|\tBilateria\t|\t\t|\tscientific name", "6072\t|\tEumetazoa\t|\t\t|\tscientific name",
"33208\t|\tMetazoa\t|\t\t|\tscientific name", "33154\t|\tOpisthokonta\t|\t\t|\tscientific name",
"2759\t|\tEukaryota\t|\t\t|\tscientific name", "131567\t|\tcellular organisms\t|\t\t|\tscientific name"
)
tmp<-tempfile()
read.nodes.sql(textConnection(nodesText),tmp)
read.names.sql(textConnection(namesText),tmp)
expect_equal(taxonomizr:::getParentNodes(c(9606,9605),tmp),data.frame('name'=c('Homo sapiens','Homo'),'parent'=c(9605,207598),'rank'=c('species','genus'),stringsAsFactors=FALSE))
expect_equal(taxonomizr:::getParentNodes(c('a'=9606,'b'=9605),tmp),data.frame('name'=c('Homo sapiens','Homo'),'parent'=c(9605,207598),'rank'=c('species','genus'),stringsAsFactors=FALSE))
expect_equal(taxonomizr:::getParentNodes(c(NA,9606,9999999,9606),tmp),data.frame('name'=c(NA,'Homo sapiens',NA,'Homo sapiens'),'parent'=c(NA,9605,NA,9605),'rank'=c(NA,'species',NA,'species'),stringsAsFactors=FALSE))
with_mock(`RSQLite::dbGetQuery`=function(...){data.frame('id'=c(9999))},expect_error(taxonomizr:::getParentNodes(c(9606,9605),tmp),'finding'))
with_mock(`RSQLite::dbGetQuery`=function(...){data.frame('id'=c(9605,9606))},expect_error(taxonomizr:::getParentNodes(c(9606,9605),tmp),'finding'))
})
test_that("Test checkDownloadMd5",{
tmp<-tempfile()
tmp2<-tempfile()
writeLines('THISISSOMEDATA',tmp)
writeLines('THISISSOMEOTHERDATA',tmp2)
md5<-tools::md5sum(tmp)
expect_true(checkDownloadMd5(sprintf('file://%s',tmp),tmp2))
expect_error(checkDownloadMd5(sprintf('file://%s',tmp),tmp2,TRUE),'md5')
writeLines(sprintf('%s EXTRASTUFF',md5),sprintf('%s.md5',tmp))
expect_false(checkDownloadMd5(sprintf('file://%s',tmp),tmp2))
file.copy(tmp,tmp2,TRUE)
expect_true(checkDownloadMd5(sprintf('file://%s',tmp),tmp2))
writeLines(sprintf('SOMEOTHERMD5 EXTRASTUFF',md5),sprintf('%s.md5',tmp))
expect_false(checkDownloadMd5(sprintf('file://%s',tmp),tmp2))
})
test_that("Test accessionToTaxa",{
taxa<-c(
"accession\taccession.version\ttaxid\tgi",
"Z17427\tZ17427.1\t3702\t16569",
"Z17428\tZ17428.1\t3702\t16570",
"Z17429\tZ17429.1\t3702\t16571",
"Z17430\tZ17430.1\t3702\t16572",
"X62402\tX62402.1\t9606\t30394"
)
inFile<-tempfile()
sqlFile<-tempfile()
expect_error(accessionToTaxa("Z17430.1",notARealVariable),"found")
expect_error(accessionToTaxa("Z17430.1",sqlFile),"exist")
expect_error(accessionToTaxa(c(),notARealVariable),"found")
expect_error(accessionToTaxa(c(),sqlFile),"exist")
writeLines(taxa,inFile)
read.accession2taxid(inFile,sqlFile)
expect_equal(accessionToTaxa(c("Z17430.1","Z17429.1","X62402.1"),sqlFile),c(3702,3702,9606))
expect_equal(accessionToTaxa(c('A'="Z17430.1",'B'="Z17429.1",'C'="X62402.1"),sqlFile),c(3702,3702,9606))
expect_equal(accessionToTaxa(c(),sqlFile),c())
expect_equal(accessionToTaxa(c("Z17430.1","NOTREAL","X62402.1","Z17429.1","X62402.1"),sqlFile),c(3702,NA,9606,3702,9606))
expect_error(accessionToTaxa("Z17430.1","NOTREAL"),"exist")
expect_equal(accessionToTaxa(c(),sqlFile),c())
expect_equal(accessionToTaxa(c("Z17430.1","Z17429.1","X62402.1"),sqlFile,'base'),as.integer(c(NA,NA,NA)))
expect_equal(accessionToTaxa(c("Z17430","NOTREAL","X62402","Z17429","X62402"),sqlFile,'base'),c(3702,NA,9606,3702,9606))
expect_equal(accessionToTaxa(c("Z17430","NOTREAL","X62402","Z17429","X62402"),sqlFile,'version'),as.integer(c(NA,NA,NA,NA,NA)))
with_mock(`RSQLite::dbGetQuery`=function(...){data.frame('accession'=c(9605,9606))},expect_error(accessionToTaxa(c("Z17430.1","X62402.1"),sqlFile),'mismatch'))
with_mock(`RSQLite::dbGetQuery`=function(...){data.frame('accession'=c("X62402.1","Z17430.1"))},expect_error(accessionToTaxa(c("Z17430.1","X62402.1"),sqlFile),'mismatch'))
with_mock(`RSQLite::dbGetQuery`=function(...){data.frame('accession'=c("NOTREAL","NOTREAL2","NOTREAL3"))},expect_error(accessionToTaxa(c("Z17430.1","X62402.1"),sqlFile),'mismatch'))
})
test_that("Test condenseTaxa",{
taxas<-matrix(c(
'a','b','c','e',
'a','b','d','e'
),nrow=2,byrow=TRUE)
expect_equal(condenseTaxa(taxas),matrix(c('a','b',NA,NA),nrow=1,dimnames=list('1',c('V1','V2','V3','V4'))))
expect_equal(condenseTaxa(taxas[c(1,1,1),]),matrix(c('a','b','c','e'),nrow=1,dimnames=list('1',c('V1','V2','V3','V4'))))
expect_equal(condenseTaxa(taxas[1,,drop=FALSE]),matrix(c('a','b','c','e'),nrow=1,dimnames=list('1',c('V1','V2','V3','V4'))))
expect_equal(condenseTaxa(taxas[,1,drop=FALSE]),matrix(c('a'),nrow=1,dimnames=list('1',c('V1'))))
expect_equal(condenseTaxa(taxas[1,1,drop=FALSE]),matrix(c('a'),nrow=1,dimnames=list('1',c('V1'))))
expect_equal(condenseTaxa(taxas[c(1,1,1,2),]),matrix(c('a','b',NA,NA),nrow=1,dimnames=list('1',c('V1','V2','V3','V4'))))
expect_equal(condenseTaxa(taxas[,3,drop=FALSE]),matrix(c(as.character(NA)),nrow=1,dimnames=list('1',c('V1'))))
expect_equal(condenseTaxa(taxas[,3,drop=FALSE],1:2),matrix(c('c','d'),nrow=2,dimnames=list(c('1','2'),c('V1'))))
expect_equal(condenseTaxa(taxas[0,]),NULL)
expect_equal(condenseTaxa(taxas[c(1:2,1),],c(1,1,2)),matrix(c('a','a','b','b',NA,'c',NA,'e'),nrow=2,dimnames=list(c('1','2'),c('V1','V2','V3','V4'))))
expect_equal(condenseTaxa(taxas[c(1:2,rep(1,10)),],c(1,1,rep(2,10))),matrix(c('a','a','b','b',NA,'c',NA,'e'),nrow=2,dimnames=list(c('1','2'),c('V1','V2','V3','V4'))))
taxas<-matrix(c(
'a','b',NA,'e',
'a','b','d','e'
),nrow=2,byrow=TRUE,dimnames=list(NULL,c('a','b','c','d')))
expect_equal(condenseTaxa(taxas),matrix(c('a','b',NA,NA),nrow=1,dimnames=list('1',c('a','b','c','d'))))
expect_equal(condenseTaxa(taxas[1,,drop=FALSE]),matrix(c('a','b',NA,'e'),nrow=1,dimnames=list('1',c('a','b','c','d'))))
expect_equal(condenseTaxa(taxas[c(1,1,1),,drop=FALSE]),matrix(c('a','b',NA,'e'),nrow=1,dimnames=list('1',c('a','b','c','d'))))
out<-matrix(c('a','b',NA,NA),byrow=TRUE,nrow=10,ncol=4,dimnames=list(as.character(1:10),c('a','b','c','d')))
expect_equal(condenseTaxa(taxas[rep(1:2,each=10),,drop=FALSE],rep(1:10,2)),out)
rownames(out)<-letters[1:10]
expect_equal(condenseTaxa(taxas[rep(1:2,each=10),,drop=FALSE],rep(letters[1:10],2)),out)
})
test_that("Test getNamesAndNodes",{
tmp<-tempfile()
dir.create(tmp)
testFile<-system.file('testdata/fakeNamesNodes.tar.gz',package='taxonomizr')
if(.Platform$OS.type == "windows"){
R.utils::gunzip(testFile,remove=FALSE,skip=TRUE)
testFile<-system.file('testdata/fakeNamesNodes.tar',package='taxonomizr')
}
fakeFile<-sprintf('file://%s',testFile)
expect_error(getNamesAndNodes(tmp,fakeFile),NA)
expect_equal(sort(list.files(tmp,'^(names|nodes).dmp$')),c('names.dmp','nodes.dmp'))
expect_message(getNamesAndNodes(tmp,fakeFile),'exist')
expect_equal(getNamesAndNodes(tmp,fakeFile),file.path(tmp,c('names.dmp','nodes.dmp')))
expect_error(getNamesAndNodes(tmp,fakeFile,'NOTREAL.FILE'),'finding|incomplete')
tmp<-tempfile()
with_mock(`file.copy`=function(...)TRUE,expect_error(getNamesAndNodes(tmp,fakeFile),'copying'))
if(.Platform$OS.type == "windows")file.remove('fakeNamesNodes.tar')
tmp<-tempfile()
dir.create(tmp)
newFake<-file.path(tmp,'fake')
download.file(fakeFile,newFake,mode='wb')
fakeMd5<-tools::md5sum(newFake)
writeLines(sprintf('%s EXTRATEXT',fakeMd5),sprintf('%s.md5',newFake))
expect_error(getNamesAndNodes(tmp,sprintf('file://%s',newFake)),NA)
tmp<-tempfile()
dir.create(tmp)
writeLines('NOTREALHASH EXTRATEXT',sprintf('%s.md5',newFake))
expect_error(getNamesAndNodes(tmp,sprintf('file://%s',newFake)),'match')
})
test_that("Test getAccession2taxid",{
tmp<-tempfile()
dir.create(tmp)
types<-c('XxXx','XyXyX')
targets<-sprintf('nucl_%s.accession2taxid.gz',types)
sapply(targets,function(xx)writeLines('TEST',file.path(tmp,xx),sep=''))
tmp2<-tempfile()
dir.create(tmp2)
expect_error(getAccession2taxid(tmp2,baseUrl=sprintf('file://%s',tmp),types=c('nucl_XxXx','nucl_XyXyX')),NA)
expect_equal(sort(list.files(tmp2,'accession2taxid.gz$')),sort(targets))
expect_message(getAccession2taxid(tmp2,baseUrl=sprintf('file://%s',tmp),types=c('nucl_XxXx','nucl_XyXyX')),'exist')
file.remove(list.files(tmp2,'^nucl_.*.gz$',full.names=TRUE))
writeLines('NOTREALHASH EXTRATEXT',sprintf('%s.md5',file.path(tmp,targets[1])))
expect_error(getAccession2taxid(tmp2,baseUrl=sprintf('file://%s',tmp),types=c('nucl_XxXx','nucl_XyXyX')),'match')
fakeMd5<-tools::md5sum(file.path(tmp,targets[1]))
file.remove(list.files(tmp2,'^nucl_.*.gz$',full.names=TRUE))
writeLines(sprintf('%s EXTRATEXT',fakeMd5),sprintf('%s.md5',file.path(tmp,targets[1])))
expect_error(getAccession2taxid(tmp2,baseUrl=sprintf('file://%s',tmp),types=c('nucl_XxXx','nucl_XyXyX')),NA)
fakeMd5<-tools::md5sum(file.path(tmp,targets[2]))
file.remove(list.files(tmp2,'^nucl_.*.gz$',full.names=TRUE))
writeLines('NOTREALHASH EXTRATEXT',sprintf('%s.md5',file.path(tmp,targets[2])))
expect_error(getAccession2taxid(tmp2,baseUrl=sprintf('file://%s',tmp),types=c('nucl_XxXx','nucl_XyXyX')),'match')
file.remove(list.files(tmp2,'^nucl_.*.gz$',full.names=TRUE))
writeLines(sprintf('%s EXTRATEXT',fakeMd5),sprintf('%s.md5',file.path(tmp,targets[2])))
expect_error(getAccession2taxid(tmp2,baseUrl=sprintf('file://%s',tmp),types=c('nucl_XxXx','nucl_XyXyX')),NA)
})
test_that("Test getId with deprecated data.table",{
namesText<-c(
"1\t|\troot\t|\t\t|\tscientific name\t|",
"4\t|\tMulti\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"3\t|\tMulti\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"2\t|\tBacteria\t|\tBacteria <prokaryotes>\t|\tscientific name\t|"
)
expect_warning(names<-read.names(textConnection(namesText)))
expect_warning(expect_equal(getId('Bacteria',names),'2'))
expect_warning(expect_equal(getId(c('Bacteria','root','Bacteria','NOTREAL'),names),c('2','1','2',NA)))
expect_warning(expect_equal(getId('Not a real name',names),as.character(NA)))
expect_warning(expect_equal(getId('Multi',names),'3,4'))
expect_warning(getId('Multi',names),'Multiple')
expect_warning(getId('Bacteria',names),'SQLite')
})
test_that("Test getId2",{
namesText<-c(
"1\t|\troot\t|\t\t|\tscientific name\t|",
"4\t|\tMulti1\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"3\t|\tMulti1\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"2\t|\tBacteria\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"5\t|\tMulti2\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"6\t|\tMulti2\t|\tBacteria <prokaryotes>\t|\tscientific name\t|"
)
expect_warning(names<-read.names(textConnection(namesText)))
expect_warning(expect_equal(getId2('Bacteria',names),'2'))
expect_warning(expect_equal(getId2(c('Bacteria','root','Bacteria','NOTREAL'),names),c('2','1','2',NA)))
expect_warning(expect_equal(getId2('Not a real name',names),as.character(NA)))
expect_warning(expect_equal(getId2(c('Bacteria','Multi1','NOTREAL'),names),c('2','3,4',NA)))
expect_warning(getId2(c('Multi1','Bacteria','Multi2'),names),'Multiple.*Multi1, Multi2')
expect_warning(getId2('Bacteria',names),'SQLite')
})
test_that("Test getId",{
namesText<-c(
"1\t|\troot\t|\t\t|\tscientific name\t|",
"4\t|\tMulti1\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"3\t|\tMulti1\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"2\t|\tBacteria\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"5\t|\tMulti2\t|\tBacteria <prokaryotes>\t|\tscientific name\t|",
"6\t|\tMulti2\t|\tBacteria <prokaryotes>\t|\tscientific name\t|"
)
tmp<-tempfile()
read.names.sql(textConnection(namesText),tmp)
expect_equal(getId('Bacteria',tmp),'2')
expect_equal(getId(c('Bacteria','root','Bacteria','NOTREAL'),tmp),c('2','1','2',NA))
expect_equal(getId('Not a real name',tmp),as.character(NA))
suppressWarnings(expect_equal(getId(c('Bacteria','Multi1','NOTREAL'),tmp),c('2','3,4',NA)))
expect_warning(getId(c('Multi1','Bacteria','Multi2'),tmp),'Multiple.*Multi1, Multi2')
})
test_that("Test getAccessions",{
taxa<-c(
"accession\taccession.version\ttaxid\tgi",
"Z17427\tZ17427.1\t3702\t16569",
"Z17428\tZ17428.1\t3702\t16570",
"Z17429\tZ17429.1\t3702\t16571",
"Z17430\tZ17430.1\t3702\t16572",
"X62402\tX62402.1\t9606\t30394"
)
inFile<-tempfile()
sqlFile<-tempfile()
expect_error(getAccessions("Z17430.1",notARealVariable),"found")
expect_error(getAccessions("Z17430.1",sqlFile),"exist")
expect_error(getAccessions(c(),notARealVariable),"found")
expect_error(getAccessions(c(),sqlFile),"exist")
writeLines(taxa,inFile)
read.accession2taxid(inFile,sqlFile)
expect_equal(getAccessions(3702,sqlFile),data.frame('taxa'=3702,'accession'=c("Z17427.1","Z17428.1","Z17429.1","Z17430.1"),stringsAsFactors=FALSE))
expect_equal(getAccessions(c(3702,9606),sqlFile),data.frame('taxa'=rep(c(3702,9606),c(4,1)),'accession'=c("Z17427.1","Z17428.1","Z17429.1","Z17430.1","X62402.1"),stringsAsFactors=FALSE))
expect_equal(getAccessions(c(3702,9606),sqlFile,'base'),data.frame('taxa'=rep(c(3702,9606),c(4,1)),'accession'=c("Z17427","Z17428","Z17429","Z17430","X62402"),stringsAsFactors=FALSE))
expect_equal(getAccessions(c(3702,9999),sqlFile),data.frame('taxa'=rep(c(3702,9999),c(4,1)),'accession'=c("Z17427.1","Z17428.1","Z17429.1","Z17430.1",NA),stringsAsFactors=FALSE))
expect_equal(getAccessions(c(3702,9999,'NOTREAL'),sqlFile),data.frame('taxa'=rep(c(3702,9999,'NOTREAL'),c(4,1,1)),'accession'=c("Z17427.1","Z17428.1","Z17429.1","Z17430.1",NA,NA),stringsAsFactors=FALSE))
expect_equal(getAccessions(c(3702,9999,'NOTREAL'),sqlFile,'base'),data.frame('taxa'=rep(c(3702,9999,'NOTREAL'),c(4,1,1)),'accession'=c("Z17427","Z17428","Z17429","Z17430",NA,NA),stringsAsFactors=FALSE))
expect_equal(getAccessions(c(3702,9999,'NOTREAL'),sqlFile,'version'),data.frame('taxa'=rep(c(3702,9999,'NOTREAL'),c(4,1,1)),'accession'=c("Z17427.1","Z17428.1","Z17429.1","Z17430.1",NA,NA),stringsAsFactors=FALSE))
expect_equal(getAccessions(c(3702,9606),sqlFile,limit=3),data.frame('taxa'=rep(c(3702,9606),c(3,0)),'accession'=c("Z17427.1","Z17428.1","Z17429.1"),stringsAsFactors=FALSE))
expect_equal(getAccessions(c(),sqlFile),NULL)
})
test_that("Test prepareDatabase",{
tmp<-tempfile()
tmpDir<-tempfile()
dir.create(tmpDir)
types<-c('XxXx','XyXyX')
taxa<-list(c(
"accession\taccession.version\ttaxid\tgi",
"Z17427\tZ17427.1\t3702\t16569",
"Z17428\tZ17428.1\t3702\t16570"
),c(
"accession\taccession.version\ttaxid\tgi",
"Z17429\tZ17429.1\t3702\t16571",
"Z17430\tZ17430.1\t3702\t16572"
))
targets<-sprintf('nucl_%s.accession2taxid.gz',types)
mapply(function(xx,yy)writeLines(xx,file.path(tmpDir,yy)),taxa,targets)
testFile<-system.file('testdata/fakeNamesNodes.tar.gz',package='taxonomizr')
if(.Platform$OS.type == "windows"){
R.utils::gunzip(testFile,remove=FALSE,skip=TRUE)
testFile<-system.file('testdata/fakeNamesNodes.tar',package='taxonomizr')
}
fakeFile<-sprintf('file://%s',testFile)
expect_error(prepareDatabase(tmp,tmpDir,url=fakeFile,baseUrl=sprintf('file://%s',tmpDir),types=c('nucl_XxXx','nucl_XyXyX')),NA)
db<-RSQLite::dbConnect(RSQLite::SQLite(),tmp)
expect_equal(sort(RSQLite::dbListTables(db)),c('accessionTaxa','names','nodes'))
expect_equal(colnames(RSQLite::dbGetQuery(db,'SELECT * FROM names LIMIT 1')),c('id','name','scientific'))
expect_equal(colnames(RSQLite::dbGetQuery(db,'SELECT * FROM nodes LIMIT 1')),c('id','rank','parent'))
expect_equal(colnames(RSQLite::dbGetQuery(db,'SELECT * FROM accessionTaxa LIMIT 1')),c('base','accession','taxa'))
expect_message(prepareDatabase(tmp,tmpDir,url=fakeFile,baseUrl=sprintf('file://%s',tmpDir),types=c('nucl_XxXx','nucl_XyXyX')),'exists')
expect_message(prepareDatabase(tmp,tmpDir,url='file://NOTAREALFILE',baseUrl='ALSONOTAREALFILE',types=c('NOTREAL','NOTREAL2')),'exists')
tmpDir2<-tempfile()
expect_error(prepareDatabase(tmp,tmpDir2,url=fakeFile,baseUrl=sprintf('file://%s',tmpDir),types=c('nucl_XxXx','nucl_XyXyX')),NA)
RSQLite::dbDisconnect(db)
if(.Platform$OS.type == "windows")file.remove('fakeNamesNodes.tar')
})
test_that("Test makeNewick",{
expect_equal(makeNewick(matrix('A')),'(A)')
expect_equal(makeNewick(matrix('')),'()')
expect_equal(makeNewick(matrix(NA),naSub='Missing'),'(Missing)')
expect_equal(makeNewick(matrix(LETTERS[1:6],nrow=1)),'((((((F)E)D)C)B)A)')
expect_equal(makeNewick(matrix(LETTERS[1:6],ncol=1)),'(A,B,C,D,E,F)')
expect_equal(makeNewick(rbind(LETTERS[1:6],LETTERS[1:6])),'((((((F)E)D)C)B)A)')
expect_equal(makeNewick(rbind(LETTERS[1:6],LETTERS[1:6],LETTERS[1:6])),'((((((F)E)D)C)B)A)')
expect_equal(makeNewick(rbind(LETTERS[1:6],LETTERS[1:6],LETTERS[7:12])),'((((((F)E)D)C)B)A,(((((L)K)J)I)H)G)')
expect_equal(makeNewick(matrix(c('A','B','D','A','C','D'),nrow=2,byrow=TRUE)),'(((D)B,(D)C)A)')
expect_equal(makeNewick(matrix(c('A',NA,'D','A','C','D'),nrow=2,byrow=TRUE),naSub='_'),'(((D)_,(D)C)A)')
expect_equal(makeNewick(matrix(c('A',NA,'D','A',NA,'D'),nrow=2,byrow=TRUE),naSub='_'),'(((D)_)A)')
expect_equal(makeNewick(matrix(c('A','B',NA,'A','C',NA),nrow=2,byrow=TRUE),naSub='xx'),'(((xx)B,(xx)C)A)')
expect_equal(makeNewick(matrix(c('Abc','B','D','Abc','C','D'),nrow=2,byrow=TRUE)),'(((D)B,(D)C)Abc)')
expect_equal(makeNewick(matrix(c('Abc','B','Abc','D','C','D'),nrow=3,byrow=TRUE)),'((B,D)Abc,(D)C)')
expect_equal(makeNewick(matrix(c('Abc','D','Abc','B','C','D'),nrow=3,byrow=TRUE)),'((D,B)Abc,(D)C)')
}) |
idle_time <- function(eventlog, level, append, append_column, units, ...) {
UseMethod("idle_time")
}
idle_time.eventlog <- function(eventlog,
level = c("log","case","trace","resource"),
append = FALSE,
append_column = NULL,
units = c("days","hours","mins","secs","weeks"),
sort = TRUE,
...) {
level <- match.arg(level)
level <- deprecated_level(level, ...)
units <- match.arg(units)
if(is.null(append_column)) {
append_column <- case_when(level == "case" ~ "idle_time",
level == "resource" ~ "idle_time",
T ~ "NA")
}
FUN <- switch(level,
log = idle_time_log,
case = idle_time_case,
trace = idle_time_trace,
resource = idle_time_resource)
output <- FUN(eventlog = eventlog, units = units)
if(sort && level %in% c("case","resource")) {
output %>%
arrange(-idle_time) -> output
}
return_metric(eventlog, output, level, append, append_column, "idle_time", 1, empty_label = T) -> t
attr(t, "units") <- units
t
}
idle_time.grouped_eventlog <- function(eventlog,
level = c("log","case","trace","resource"),
append = FALSE,
append_column = NULL,
units = c("days","hours","mins","secs","weeks"),
sort = TRUE,
...) {
level <- match.arg(level)
level <- deprecated_level(level, ...)
units <- match.arg(units)
if(is.null(append_column)) {
append_column <- case_when(level == "case" ~ "idle_time",
level == "resource" ~ "idle_time",
T ~ "NA")
}
FUN <- switch(level,
log = idle_time_log,
case = idle_time_case,
trace = idle_time_trace,
resource = idle_time_resource)
if(level != "log") {
grouped_metric(eventlog, FUN, units) -> output
}
else {
grouped_metric_raw_log(eventlog, FUN, units) -> output
}
if(sort && level %in% c("case","resource")) {
output %>%
arrange(-idle_time) -> output
}
return_metric(eventlog, output, level, append, append_column, "idle_time", 1, empty_label = T) -> t
attr(t, "units") <- units
t
} |
expected <- eval(parse(text="c(\"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"1\", \"0\", \"0\", \"0\", \"1\", \"1\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"1\", \"0\", \"0\", \"0\", \"0\", NA, \"0\", \"0\", \"0\", \"1\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"1\", \"0\", \"0\", \"0\", \"1\", \"0\", \"0\", \"0\", \"1\", \"1\", \"0\", \"0\", \"1\", \"0\", \"1\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", NA, \"0\", \"0\", \"0\", \"1\", \"1\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"1\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"1\", \"0\", \"0\", \"0\", NA, \"0\", \"0\", \"1\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"1\", \"0\", \"0\", \"0\", \"0\", \"1\", NA, \"0\", \"0\", \"0\", \"1\", \"0\", \"0\", \"0\", \"1\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\")"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, NA, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, NA, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c(\"0\", \"1\"), class = \"factor\"))"));
do.call(`as.character`, argv);
}, o=expected); |
multiLine_plot <-
function (bpstart=0, bpend=1000000000, dList, title=NULL, showmaxlr=3, kcut=NULL, pl='linear'
,ylim=c(-0.5,10),color=c('violet','green','red','blue'),markSNP=NULL,round=2,legend_cex=1)
{
summaryStats=do.call(rbind,dList[,'SummaryStats'])
if (is.null(kcut)){
kcut=dList[,'k_cutoff'][[1]]
}
kcutCol=which(dList[,'k_cutoff'][[1]]==min(kcut))
dframe_inRange=summaryStats[summaryStats$bp>=bpstart&summaryStats$bp<=bpend,]
if (pl=='logit'){
ylim=c(0,10); ylab='Odds Ratio';threshold=1
} else {
ylab='Beta';threshold=0
}
plot(0,0,main=title,xlab='Location (bp)',ylab=ylab, xlim=range(dframe_inRange$bp),ylim=ylim)
abline(h=threshold)
skipCol=which(names(dframe_inRange)=='lo_1')-1
for (i in 1:length(kcut)){
signColor=color[length(kcut)-i+1]
boundCol=(length(kcut)-i+1)*2+skipCol+c(-1,0)
lineColor=c('grey',signColor)[as.numeric(dframe_inRange[,(2*kcutCol-1+skipCol)]>threshold | dframe_inRange[,(2*kcutCol+skipCol)]<threshold)+1]
segments(dframe_inRange$bp,dframe_inRange[,boundCol[1]],dframe_inRange$bp,dframe_inRange[,boundCol[2]],col=lineColor)
}
signAtKcutoff = dframe_inRange[dframe_inRange[,(2*kcutCol-1+skipCol)]>threshold | dframe_inRange[,(2*kcutCol+skipCol)]<threshold,]
signAtKcutoff=signAtKcutoff[order(signAtKcutoff$maxlr,decreasing = T),]
points(signAtKcutoff$bp,signAtKcutoff$mle,pch='-')
num_of_hits = nrow(signAtKcutoff)
if (num_of_hits>0 & is.null(markSNP)){
text(signAtKcutoff$bp,signAtKcutoff[,ncol(signAtKcutoff)],signAtKcutoff$SNP,srt=90,pos=4,offset=0.3,cex=0.75)
} else if (!is.null(markSNP)) {
snpIndex=which(dframe_inRange$SNP %in% markSNP)
text(dframe_inRange$bp[snpIndex],dframe_inRange[snpIndex,ncol(dframe_inRange)],dframe_inRange$SNP[snpIndex],srt=90,pos=4,offset=0.3,cex=0.75)
}
legend_txt=c(paste0('> 1/',kcut[1],' LI'),paste0('1/',kcut,' LI'))
if (num_of_hits > 0 & showmaxlr > 0) {
if (num_of_hits < showmaxlr) { showmaxlr = num_of_hits }
legend.txt2=''
for (i in 1:showmaxlr) {
hit = paste ("max LR =", round(signAtKcutoff$maxlr[i],round), "at", signAtKcutoff$snp[i], "(", signAtKcutoff$bp[i] ,")")
legend.txt2 = c(legend.txt2, hit)
}
legend("topright",8,legend.txt2,col=par("col"),lty=0,bty="n",cex=legend_cex)
}
legend("topleft",legend_txt,col=c("grey",color),lty=c(1.5),bty="n",cex=legend_cex)
} |
context("nn-linear")
test_that("nn_linear", {
linear <- nn_linear(10, 1)
x <- torch_randn(10, 10)
y <- linear(x)
expect_tensor(y)
expect_length(as_array(y), 10)
})
test_that("initialization is identical to pytorch", {
torch_manual_seed(1)
expect_equal(
nn_linear(1,1)$weight$item(),
0.5152631998062134
)
}) |
modelOptimise <-
function (model, options, ...) {
funcName <- paste(model$type, "Optimise", sep="")
if ( exists(funcName, mode="function") ) {
func <- get(funcName, mode="function")
model <- func(model, options, ...)
} else {
if ( "optimiser" %in% names(options) ) {
funcName <- paste(options$optimiser, "optim", sep="")
} else {
funcName <- "CGoptim"
}
optFunc <- get(funcName, mode="function")
params <- modelExtractParam(model)
newParams <- optFunc(params, modelObjective, modelGradient, options, model)
model <- modelExpandParam(model, newParams$xmin)
model$llscore <- newParams$objective
}
return (model)
} |
data("Reit98")
KL <<- Reit98
nVar <<- 9
dMax <<- 2
yDeb <<- etat0 <- c(3.105053, 3.335297, -2.367497,
3.276356, 0.2985118, -2.195064,
-35.06073, -20.59253, -18.33939)
intgrMthod <<- "rk4"
nIterMin <<- 1
tEnd <<- 0.5
timeStep <<- 0.001
printIter <<- 10
lastIter <<- 50 |
context("criteria")
testCriterionL2loss <- function(testset, estset) {
sum((testset - mean(estset))^2)
}
testCriterionL1loss <- function(testset, estset) {
sum(abs(testset - mean(estset)))
}
testCriterionMod <- function(testset, estset) {
length(testset) / (length(testset) - 1) * sum((testset[-length(testset)] - mean(estset))^2)
}
test_that("criterionL2loss is working", {
testtestset <- rnorm(43)
testestset <- rnorm(34)
expect_equal(criterionL2loss(testset = testtestset, estset = testestset),
testCriterionL2loss(testset = testtestset, estset = testestset))
expect_equal(criterionL2loss(testset = testtestset, estset = testestset, value = NULL),
testCriterionL2loss(testset = testtestset, estset = testestset))
expect_equal(criterionL2loss(testset = testtestset, estset = testestset, test = "s"),
testCriterionL2loss(testset = testtestset, estset = testestset))
expect_identical(criterionL2loss(testset = rep(1, 10), estset = 0), 10)
expect_identical(criterionL2loss(testset = 1:10, estset = 1:10), sum((1:10 - 5.5)^2))
expect_identical(criterionL2loss(testset = rep(1, 10), estset = rnorm(10), value = 0), 10)
expect_identical(criterionL2loss(testset = 1:10, estset = rnorm(10), value = 12.37), sum((1:10 - 12.37)^2))
})
test_that("criterionL1loss is working", {
testtestset <- rnorm(43)
testestset <- rnorm(34)
expect_equal(criterionL1loss(testset = testtestset, estset = testestset),
testCriterionL1loss(testset = testtestset, estset = testestset))
expect_equal(criterionL1loss(testset = testtestset, estset = testestset, value = NULL),
testCriterionL1loss(testset = testtestset, estset = testestset))
expect_equal(criterionL1loss(testset = testtestset, estset = testestset, test = "s"),
testCriterionL1loss(testset = testtestset, estset = testestset))
expect_identical(criterionL1loss(testset = rep(1, 10), estset = 0), 10)
expect_identical(criterionL1loss(testset = 1:10, estset = 1:10), sum(abs(1:10 - 5.5)))
expect_identical(criterionL1loss(testset = rep(1, 10), estset = rnorm(10), value = 0), 10)
expect_identical(criterionL1loss(testset = 1:10, estset = rnorm(10), value = 12.37), sum(abs(1:10 - 12.37)))
})
test_that("criterionMod is working", {
testtestset <- rnorm(43)
testestset <- rnorm(34)
expect_identical(criterionMod(testset = testtestset, estset = testestset),
testCriterionMod(testset = testtestset, estset = testestset))
expect_identical(criterionMod(testset = 1, estset = 10), NaN)
expect_equal(criterionMod(testset = testtestset, estset = testestset, value = NULL),
testCriterionMod(testset = testtestset, estset = testestset))
expect_equal(criterionMod(testset = testtestset, estset = testestset, test = "s"),
testCriterionMod(testset = testtestset, estset = testestset))
expect_identical(criterionMod(testset = rep(1, 10), estset = 0), 10)
expect_identical(criterionMod(testset = 1:10, estset = 1:10), sum(10 / 9 * (1:9 - 5.5)^2))
expect_identical(criterionMod(testset = rep(1, 10), estset = rnorm(10), value = 0), 10)
expect_identical(criterionMod(testset = 1:10, estset = rnorm(10), value = 12.37), sum(10 / 9 * (1:9 - 12.37)^2))
}) |
rsf.main <- function(X,
ntree = 1000,
method = "mdms",
splitrule = "logrank",
importance = "random",
B = 1000,
ci = 90,
parallel = FALSE,
conf = NULL,
verbose = TRUE,
seed = NULL) {
if (!parallel) {
if (is.null(seed)) {
digits <- getOption("digits")
seed <- runif(n=B, min=1, max=2) * 10^(digits-2)
} else {
seed <- (0:(B-1)) + seed
}
rsf.obj <- rsf.main.signif(X=X,
ntree=ntree,
method=method,
splitrule=splitrule,
importance=importance,
B=B,
verbose=verbose,
seed=seed)
rsf.obs.boot <- rsf.obj$boot.obs
rsf.noise.boot <- rsf.obj$boot.noise
} else {
if (conf$type == "SOCKET") {
cl <- parallel::makeCluster(spec=conf$spec,
type="PSOCK",
homogeneous=conf$homo,
outfile=conf$outfile,
verbose=conf$verbose)
cpus <- length(conf$spec)
} else if (conf$type == "MPI") {
cl <- parallel::makeCluster(spec=conf$spec,
type="MPI",
homogeneous=conf$homo,
outfile=conf$outfile,
verbose=conf$verbose)
cpus <- conf$spec
} else {
stop("Unrecognized cluster type\n")
}
parallel::clusterSetRNGStream(cl=cl, iseed=seed)
parallel::clusterEvalQ(cl=cl, expr=library("randomForestSRC"))
parallel::clusterEvalQ(cl=cl, expr=library("survival"))
parallel::clusterExport(cl=cl,
varlist=c("rsf.main.signif"),
envir=.GlobalEnv)
rsf.cl <- parallel::clusterCall(cl=cl, fun=rsf.main.signif,
X=X,
ntree=ntree,
method=method,
splitrule=splitrule,
importance=importance,
B=ceiling(B/cpus),
verbose=verbose,
seed=NULL)
parallel::stopCluster(cl)
rsf.obs.boot <- matrix(data=NA, nrow=0, ncol=ncol(rsf.cl[[1]]$boot.obs))
rsf.noise.boot <- matrix(data=NA, nrow=0, ncol=ncol(rsf.cl[[1]]$boot.noise))
for (b in 1:cpus) {
rsf.obs.boot <- rbind(rsf.obs.boot, rsf.cl[[b]]$boot.obs)
rsf.noise.boot <- rbind(rsf.noise.boot, rsf.cl[[b]]$boot.noise)
}
}
theta <- (100-ci)/200
ranks.obs.mean <- apply(X=rsf.obs.boot, MARGIN=2, FUN=mean)
ranks.noise.mean <- apply(X=rsf.noise.boot, MARGIN=2, FUN=mean)
ranks.obs.se <- apply(X=rsf.obs.boot, MARGIN=2, FUN=sd)
ranks.noise.se <- apply(X=rsf.noise.boot, MARGIN=2, FUN=sd)
ranks.obs.bpci <- apply(X=rsf.obs.boot, MARGIN=2, FUN=function(x) quantile(x=x, probs=c(theta, 1-theta)))
ranks.noise.bpci <- apply(X=rsf.noise.boot, MARGIN=2, FUN=function(x) quantile(x=x, probs=c(theta, 1-theta)))
names(ranks.noise.mean) <- gsub(pattern=".noise", replacement="", x=names(ranks.noise.mean), ignore.case=F, fixed=F)
w <- pmatch(x=names(ranks.obs.mean), table=names(ranks.noise.mean))
ranks.noise.mean <- ranks.noise.mean[w]
ranks.noise.se <- ranks.noise.se[w]
ranks.noise.bpci <- ranks.noise.bpci[,w]
if (method == "mdms") {
mat.ranks <- data.frame("obs.mean"=ranks.obs.mean, "obs.se"=ranks.obs.se,
"obs.LBCI"=ranks.obs.bpci[1,], "obs.UBCI"=ranks.obs.bpci[2,],
"noise.mean"=ranks.noise.mean, "noise.se"=ranks.noise.se,
"noise.LBCI"=ranks.noise.bpci[1,], "noise.UBCI"=ranks.noise.bpci[2,],
"signif.1SE"=(ranks.obs.mean + ranks.obs.se < ranks.noise.mean),
"signif.CI"=(ranks.obs.bpci[2,] < ranks.noise.bpci[1,]))
ord <- order(mat.ranks[,"obs.mean"], decreasing=F)
} else {
stop("Unmatched method \n")
}
mat.ranks <- mat.ranks[ord,]
return(mat.ranks)
}
rsf.int <- function(X,
ntree = 1000,
method = "imdms",
splitrule = "logrank",
importance = "random",
B = 1000,
ci = 90,
parallel = FALSE,
conf = NULL,
verbose = TRUE,
seed = NULL) {
p <- ncol(X) - 2
if (!parallel) {
if (is.null(seed)) {
digits <- getOption("digits")
seed <- runif(n=B, min=1, max=2) * 10^(digits-2)
} else {
seed <- (0:(B-1)) + seed
}
rsf.obj <- rsf.int.signif(X=X,
ntree=ntree,
method=method,
splitrule=splitrule,
importance=importance,
B=B,
verbose=verbose,
seed=seed)
rsf.obs.boot <- rsf.obj$boot.obs
rsf.noise.boot <- rsf.obj$boot.noise
} else {
if (conf$type == "SOCKET") {
cl <- parallel::makeCluster(spec=conf$spec,
type="PSOCK",
homogeneous=conf$homo,
outfile=conf$outfile,
verbose=conf$verbose)
cpus <- length(conf$spec)
} else if (conf$type == "MPI") {
cl <- parallel::makeCluster(spec=conf$spec,
type="MPI",
homogeneous=conf$homo,
outfile=conf$outfile,
verbose=conf$verbose)
cpus <- conf$spec
} else {
stop("Unrecognized cluster type\n")
}
parallel::clusterSetRNGStream(cl=cl, iseed=seed)
parallel::clusterEvalQ(cl=cl, expr=library("randomForestSRC"))
parallel::clusterEvalQ(cl=cl, expr=library("survival"))
parallel::clusterExport(cl=cl,
varlist=c("rsf.int.signif"),
envir=.GlobalEnv)
rsf.cl <- parallel::clusterCall(cl=cl, fun=rsf.int.signif,
X=X,
ntree=ntree,
method=method,
splitrule=splitrule,
importance=importance,
B=ceiling(B/cpus),
verbose=verbose,
seed=NULL)
parallel::stopCluster(cl)
if (method == "imdms") {
rsf.obs.boot <- array(data=NA, dim=c(dim(rsf.cl[[1]]$boot.obs)[1], dim(rsf.cl[[1]]$boot.obs)[2], 0))
rsf.noise.boot <- array(data=NA, dim=c(dim(rsf.cl[[1]]$boot.noise)[1], dim(rsf.cl[[1]]$boot.noise)[2], 0))
for (b in 1:cpus) {
rsf.obs.boot <- abind::abind(rsf.obs.boot, rsf.cl[[b]]$boot.obs)
rsf.noise.boot <- abind::abind(rsf.noise.boot, rsf.cl[[b]]$boot.noise)
}
} else {
stop("Unmatched method \n")
}
}
theta <- (100-ci)/200
if (method == "imdms") {
int.obs.mean <- apply(X=rsf.obs.boot, MARGIN=1:2, FUN=mean)
int.noise.mean <- apply(X=rsf.noise.boot, MARGIN=1:2, FUN=mean)
int.obs.se <- apply(X=rsf.obs.boot, MARGIN=1:2, FUN=sd)
int.noise.se <- apply(X=rsf.noise.boot, MARGIN=1:2, FUN=sd)
int.obs.bpci <- apply(X=rsf.obs.boot, MARGIN=1:2, FUN=function(x) quantile(x=x, probs=c(theta, 1-theta)))
int.noise.bpci <- apply(X=rsf.noise.boot, MARGIN=1:2, FUN=function(x) quantile(x=x, probs=c(theta, 1-theta)))
int.obs.bpci <- aperm(a=int.obs.bpci, perm=c(2,3,1))
int.noise.bpci <- aperm(a=int.noise.bpci, perm=c(2,3,1))
vo.mean <- numeric(choose(n=p, k=2))
vn.mean <- numeric(choose(n=p, k=2))
vo.se <- numeric(choose(n=p, k=2))
vn.se <- numeric(choose(n=p, k=2))
mo.bpci <- matrix(data=NA, nrow=4, ncol=choose(n=p, k=2))
mn.bpci <- matrix(data=NA, nrow=4, ncol=choose(n=p, k=2))
k <- 1
for (i in 2:p) {
for (j in 1:(i-1)) {
vmean <- c(int.obs.mean[i,j], int.obs.mean[j,i])
vse <- c(int.obs.se[i,j], int.obs.se[j,i])
mbpci <- cbind(int.obs.bpci[i,j,], int.obs.bpci[j,i,])
vo.mean[k] <- vmean[which.min(vmean)]
vo.se[k] <- vse[which.min(vmean)]
mo.bpci[,k] <- mbpci[,which.min(vmean)]
names(vo.mean)[k] <- paste(colnames(int.obs.mean)[j], rownames(int.obs.mean)[i], sep=":")
vmean <- c(int.noise.mean[i,j], int.noise.mean[j,i])
vse <- c(int.noise.se[i,j], int.noise.se[j,i])
mbpci <- cbind(int.noise.bpci[i,j,], int.noise.bpci[j,i,])
vn.mean[k] <- vmean[which.min(vmean)]
vn.se[k] <- vse[which.min(vmean)]
mn.bpci[,k] <- mbpci[,which.min(vmean)]
names(vn.mean)[k] <- paste(colnames(int.noise.mean)[j], rownames(int.noise.mean)[i], sep=":")
k <- k+1
}
}
mat.int <- data.frame("obs.mean"=vo.mean, "obs.se"=vo.se,
"obs.LBCI"=mo.bpci[1,], "obs.UBCI"=mo.bpci[2,],
"noise.mean"=vn.mean, "noise.se"=vn.se,
"noise.LBCI"=mn.bpci[1,], "noise.UBCI"=mn.bpci[2,],
"signif.1SE"=(vo.mean + vo.se < vn.mean),
"signif.CI"=(mo.bpci[2,] < mn.bpci[1,]))
ord <- order(mat.int[,"obs.mean"], decreasing=F)
} else {
stop("Unmatched method \n")
}
mat.int <- mat.int[ord,]
return(mat.int)
}
cph.main <- function (X, main.term) {
p <- ncol(X) - 2
fmla.main <- as.formula(paste("survival::Surv(time=", colnames(X)[1], ", event=", colnames(X)[2], ", type=\"right\") ~ .", sep=""))
P.cph.main <- numeric(p)
names(P.cph.main) <- main.term
for (j in 1:p) {
Z <- X[,c(colnames(X)[1], colnames(X)[2], main.term[j])]
coxfit <- tryCatch({survival::coxph(fmla.main, data=Z, model=T, x=T, y=T)}, error=function(w){NULL}, warning=function(w){NULL})
if (is.null(coxfit)) {
P.cph.main[j] <- NA
} else {
P.cph.main[j] <- summary(coxfit)$coefficients[1,"Pr(>|z|)",drop=T]
}
}
P.cph.bh.main <- P.cph.main
nna <- !is.na(P.cph.main)
P.cph.bh.main[nna] <- p.adjust(p=P.cph.main[nna], method="BH")
P.cph.bh.main[!nna] <- NA
return(list("raw"=P.cph.main, "fdr"=P.cph.bh.main))
}
cph.int <- function (X, int.term) {
p <- ncol(X) - 2
int.term.list <- strsplit(x=int.term, split=":")
fmla.int <- as.formula(paste("survival::Surv(time=", colnames(X)[1], ", event=", colnames(X)[2], ", type=\"right\") ~ . + (.)^2", sep=""))
P.cph.int <- numeric(p*(p-1)/2)
names(P.cph.int) <- int.term
for (j in 1:(p*(p-1)/2)) {
Z <- X[,c(colnames(X)[1], colnames(X)[2], int.term.list[[j]])]
coxfit <- tryCatch({survival::coxph(fmla.int, data=Z, model=T, x=T, y=T)}, error=function(w){NULL}, warning=function(w){NULL})
if (is.null(coxfit)) {
P.cph.int[j] <- NA
} else {
P.cph.int[j] <- summary(coxfit)$coefficients[3,"Pr(>|z|)",drop=T]
}
}
P.cph.bh.int <- P.cph.int
nna <- !is.na(P.cph.int)
P.cph.bh.int[nna] <- p.adjust(p=P.cph.int[nna], method="BH")
P.cph.bh.int[!nna] <- NA
return(list("raw"=P.cph.int, "fdr"=P.cph.bh.int))
}
IRSF.news <- function(...) {
newsfile <- file.path(system.file(package="IRSF"), "NEWS")
file.show(newsfile)
} |
bayesCaret <- list(
type = c("Classification", "Regression"),
library = c(),
parameters = data.frame(
parameter = c(
"shiftAmount",
"retainMinValues",
"doEcdf",
"online",
"mode",
"numBuckets",
"sampleFromAllBuckets",
"regressor"
),
class = c(
"numeric",
"integer",
"boolean",
"integer",
"character",
"integer",
"boolean",
"function"
),
label = c(
"shiftAmount",
"retainMinValues",
"doEcdf",
"online",
"mode",
"numBuckets",
"sampleFromAllBuckets",
"regressor"
)
),
prob = NULL
)
bayesCaret$grid <- function(x = NULL, y = NULL, len = NULL, search = "grid") {
if (missing(y)) {
warning("y is missing; assuming you want to do regression then?")
}
isClassification <- (is.character(y) || is.factor(y))
numTrain <- NULL
cOnline <- c(0)
if (!missing(x)) {
numTrain <- if (is.data.frame(x)) nrow(x) else length(x)
cOnline <- c(cOnline, round(1.2 * numTrain))
} else if (!missing(y)) {
numTrain <- if (is.data.frame(y)) nrow(y) else length(y)
cOnline <- c(cOnline, round(1.2 * numTrain))
}
modes <- c("full", "simple")
if (isClassification) {
modes <- c(modes, "naive")
}
gridList <- list(
shiftAmount = c(0, 0.1),
doEcdf = c(TRUE, FALSE),
online = sort(c(cOnline, .Machine$integer.max)),
mode = modes,
regressor = NA
)
rmv <- NULL
rmvMin <- if (isClassification) 1 else 2
if (!is.null(numTrain)) {
rmv <- c(max(rmvMin, ceiling(numTrain * 0.01)), max(rmvMin, ceiling(numTrain * 0.05)))
}
if (isClassification) {
rmv <- c(rmv, 0, 1, 2, 11)
gridList[["numBuckets"]] <- NA
gridList[["sampleFromAllBuckets"]] <- FALSE
} else {
rmv <- c(rmv, 2, 11)
gridList[["numBuckets"]] <- c(5, 10, NA)
gridList[["sampleFromAllBuckets"]] <- c(TRUE, FALSE)
}
gridList[["retainMinValues"]] <- sort(unique(rmv))
return(expand.grid(gridList))
}
bayesCaret$fit <- function(x, y, wts, param, lev, last, weights, classProbs, ...) {
args <- list(...)
m <- args[["messages"]]
w <- args[["warnings"]]
p <- args[["parallel"]]
if (!is.null(m)) {
mmb::setMessages(is.logical(m) && m)
}
if (!is.null(w)) {
mmb::setWarnings(is.logical(w) && w)
}
theData <- data.frame(x)
theData$y <- y
return(list(
train = theData,
labelCol = "y",
param = param,
parallel = if (is.logical(p)) p else !foreach::getDoParRegistered()
))
}
bayesCaret$predict <- function(modelFit, newdata, submodels) {
classify <- modelFit$problemType == "Classification"
theFunc <- if (classify) mmb::bayesProbabilityAssign else mmb::bayesRegressAssign
default_classification <- list(
shiftAmount = 0.1,
retainMinValues = 1,
doEcdf = FALSE,
online = 0,
simple = FALSE,
naive = FALSE
)
default_regression <- list(
shiftAmount = 0.1,
retainMinValues = 2,
doEcdf = FALSE,
online = 0,
simple = FALSE,
numBuckets = ceiling(log2(nrow(modelFit$train))),
sampleFromAllBuckets = TRUE,
regressor = NA
)
dfParams <- if (classify) default_classification else default_regression
dfTune <- modelFit$tuneValue
dfTune$simple <- dfTune$mode == "simple"
dfTune$naive <- dfTune$mode == "naive"
dfTune$mode <- NULL
for (n in names(dfParams)) {
if (n %in% colnames(dfTune)) {
dfParams[[n]] <- dfTune[[n]]
}
}
dfParams[["dfTrain"]] <- modelFit$train
dfParams[["dfValid"]] <- data.frame(newdata)
dfParams[["targetCol"]] <- modelFit$labelCol
dfParams[["useParallel"]] <- modelFit$parallel
return(do.call(theFunc, dfParams))
} |
heart <- structure(list(id = 1:103,
status = c(0, 0, 1, 1, 0, 0, 2, 0, 0, 2, 1, 0, 2, 2, 0, 2, 0, 1, 0, 2, 2, 2,
2, 2, 0, -1, 0, 1, 0, 2, 0, 2, 0, 0, 0, 2, 2, 1, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 2, 0, 0,
0, 2, 0, 0, 2, 0, 2, 0, 0, 0, 1, 2, 0, 1, 2, 0, 2, 0, 0, 2, 1, 0, 0, 0, 0, 2, 0, 0, -1, 1, 2,
0, 0, 2, 0, 2, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, -1, -1, 0),
ftime = c(NA, NA, 15, 3, NA, NA, 624, NA, NA, 46, 127, NA, 64, 1350, NA, 280, NA, 23, NA, 10, 1024,
39, 730, 136, 1775, NA, NA, 1, NA, 836, NA, 60, 1536, 1549, NA, 54, 47, 0, 51, 1367, 1264, NA, NA, NA, 44,
994, 51, NA, 1106, 897, 253, NA, 147, NA, 51, 875, NA, 322, 838, 65, NA, NA, 815, 551, 66, NA, 228, 65, 660,
25, 589, 592, 63, 12, NA, 499, NA, 305, 29, 456, 439, NA, 48, 297, NA, 389, 50, 339, 68, 26, NA, 30, 237,
161, 14, 167, 110, 13, NA, 1, NA, NA, NA),
age = c(NA, NA, 19832, 14741, NA, NA, 18630, NA, NA, 15535, 17550, NA,
19949, 19764, NA, 18088, NA, 20783, NA, 20208, 15838, 15638,
21317, 19002, 12159, NA, NA, 19802, NA, 16419, NA, 23541,
17912, 14834, NA, 17915, 22481, 15151, 18453, 17748, 16604,
NA, NA, NA, 13216, 17756, 17223, NA, 13423, 16842, 17831,
NA, 17332, NA, 19168, 14217, NA, 17558, 15192, 17919, NA,
NA, 11955, 17862, 18746, NA, 7197, 16526, 17537, 19363,
17346, 9765, 20601, 10657, NA, 19069, NA, 17998, 19710,
16989, 19324, NA, 19502, 15639, NA, 17875, 16953, 19886,
18783, 19164, NA, 16739, 17468, 16016, 14714, 9746, 8646, 10552, NA, 12843, NA, NA, NA),
mismat = c(NA, NA, 1.11, 1.66, NA, NA, 1.32, NA, NA, 0.61, 0.36, NA, 1.89, 0.87, NA, 1.12, NA,
2.05, NA, 2.76, 1.13, 1.38, 0.96, 1.62, 1.06, NA, NA, 0.47, NA,
1.58, NA, 0.69, 0.91, 0.38, NA, 2.09, 0.87, 0.87, NA, 0.75, 0.98,
NA, NA, NA, 0, 0.81, 1.38, NA, 1.35, NA, 1.08, NA, NA, NA, 1.51,
0.98, NA, 1.82, 0.19, 0.66, NA, NA, 1.93, 0.12, 1.12, NA, 1.02,
1.68, 1.2, 1.68, 0.97, 1.46, 2.16, 0.61, NA, 1.7, NA, 0.81, 1.08,
1.41, 1.94, NA, 3.05, 0.6, NA, 1.44, 2.25, 0.68, 1.33, 0.82,
NA, 0.16, 0.33, 1.2, NA, 0.46, 1.78, 0.77, NA, 0.67, NA, NA, NA)),
.Names = c("id", "status", "ftime", "age", "mismat"), class = "data.frame", row.names = c(NA, -103))
heart2 <- na.omit(heart)
heart2 <- transform(heart2,age.std=scale(age),mismat.std=scale(mismat))
require(moc)
pgompertz <- function(x,la,ka) {1-exp(-la*(exp(ka*x)-1)/ka)}
dgompertz <- function(x,la,ka) {(exp(-la * (exp(ka * x) - 1)/ka) * la * exp(ka * x) )}
qgompertz <- function(prob,la,ka) {log(1-ka*log(1-prob)/la)/ka}
gompertz.mean <- function(la,ka) integrate(function(x) x*dgompertz(x,la,ka),0,Inf)$value
Gompertz.Surv <- function(x,la,ka,cens)
{
(x[,2]==0)*(1-pgompertz(x[,1],la[,1],ka[,1]))*cens[,2]+
(x[,2]<0)*pgompertz(x[,1],la[,1],ka[,1])*cens[,2]+
(x[,2]>0)*dgompertz(x[,1],la[,1],ka[,1])*cens[,2]
}
la.heart <- list(G1=function(p) {cbind(exp(p[1]+p[2]*heart2$age.std),0)},
G2=function(p) {cbind(exp(p[3]+p[4]*heart2$age.std),0)})
attr(la.heart,"parameters") <- c("lambda_1","age_1","lambda_2","age_2")
ka.heart <- list(G1=function(p) {cbind(p[1],0)},
G2=function(p) {cbind(p[2],0)})
attr(ka.heart,"parameters") <- c("kappa_1","kappa_2")
cens.heart <- list(G1=function(p) {cbind(0,c(1,1,0)[heart2$status+1])},
G2=function(p) {cbind(0,c(1,0,1)[heart2$status+1])})
mix.heart <- function(p) {t(apply(cbind(p[1]+p[2]*heart2$mismat.std+p[3]*heart2$age.std),1,inv.glogit))}
attr(mix.heart,"parameters") <- c("Cons","mismatch","age")
heart.expected2 <- list(
G1=function(p) {cbind(qgompertz(0.5,la.heart[[1]](p[1:4])[,1],
ka.heart[[1]](p[5:6])[,1]),1)},
G2=function(p) {cbind(qgompertz(0.5,la.heart[[2]](p[1:4])[,1],
ka.heart[[2]](p[5:6])[,1]),2)}
)
heart.moc <- moc(heart2[,c("ftime","status")],density=Gompertz.Surv,joint=TRUE,groups=2,
gmu=la.heart,gshape=ka.heart,gextra=cens.heart,gmixture=mix.heart,
pgmu=c(-6,1,-4,0.3),pgshape=c(-0.0015,-0.0055),pgmix=c(1.4,0.4,0.2),
gradtol=1e-6,iterlim=200)
heart.moc
cens.heart2 <- list(G1=function(p) {p1 <- inv.glogit(p[1]); cbind(0,c(1,p1)[heart2$status+1])},
G2=function(p) {p2 <- inv.glogit(p[2]); cbind(0,c(1,p2)[heart2$status+1])})
attr(cens.surv2,"parameters") <- c("OR_1","OR_2")
heart.moc2 <- moc(heart2[,c("ftime","status")],density=Gompertz.Surv,joint=TRUE,groups=2,
gmu=la.heart,gshape=ka.heart,gextra=cens.heart2,gmixture=mix.heart,
pgmu=heart.moc$coef[1:4],pgshape=heart.moc$coef[5:6],pgmix=heart.moc$coef[7:9],pgextra=c(0,0),
gradtol=1e-5,iterlim=200)
heart.moc2
Weibull.Surv <- function(x,la,ka,cens)
{
(x[,2]==0)*(1-pweibull(x[,1],shape=la[,1],scale=ka[,1]))*cens[,2]+
(x[,2]<0)*pweibull(x[,1],shape=la[,1],scale=ka[,1])*cens[,2]+
(x[,2]>0)*dweibull(x[,1],shape=la[,1],scale=ka[,1])*cens[,2]
}
weibull.mean <- function(a,b) {b*gamma(1+1/a)}
ka.wb.heart <- list(G1=function(p) {cbind(exp(p[1]+p[2]*heart2$age.std),0)},
G2=function(p) {cbind(exp(p[3]+p[4]*heart2$age.std),0)})
attr(ka.wb.heart,"parameters") <- c("log.beta_1","age_1","log.beta_2","age_2")
la.wb.heart <- list(G1=function(p) {cbind(exp(p[1]),0)},
G2=function(p) {cbind(exp(p[2]),0)})
attr(la.wb.heart,"parameters") <- c("log.alpha_1","log.alpha_2")
expected.wb.heart <- list(
G1=function(p) {cbind(weibull.mean(la.wb.heart[[1]](p[1:2])[,1],
ka.wb.heart[[1]](p[3:6])[,1]), heart2$status)},
G2=function(p) {cbind(weibull.mean(la.wb.heart[[2]](p[1:2])[,1],
ka.wb.heart[[2]](p[3:6])[,1]), heart2$status)}
)
heart2.tmp <- heart2
heart2.tmp$ftime[which(heart2.tmp$ftime==0)] <- 0.1
heart.moc.wb <- moc(heart2.tmp[,c("ftime","status")],density=Weibull.Surv,joint=TRUE,groups=2,
gmu=la.wb.heart,gshape=ka.wb.heart,gextra=cens.heart2,gmixture=mix.heart,
expected=expected.wb.heart,pgmu=c(-1,1),pgshape=c(6,0,8,0),pgmix=c(0,0,0),pgextra=c(0,0),
gradtol=1e-6,iterlim=200)
heart.moc.wb |
linfinity_depth <- function(dt){
if (is.data.frame(dt)) {
dt <- as.matrix(dt)
}
if (!is.array(dt) || !is.numeric(dt))
stop("Argument \"dt\" must be a numeric matrix or dataframe.")
if (any(!is.finite(dt))) {
stop("Missing or infinite values are not allowed in argument \"dt\"")
}
distances <- as.matrix(dist(dt, method = "maximum", diag = T, upper = T))
unname(1/(1+colMeans(distances)))
} |
call_sig_reg <- function(res, nr, threshold = 3.5, win = 10)
{
if(class(res)!="sharpr2")
{
stop('The first argument must be an object obtained from sharpr2.')
}
re <- res$score[[nr]]
sig_reg <- which(re$est_a - threshold*sqrt(re$var_nb) > 0)
sig_reg_l <- length(sig_reg)
if(sig_reg_l>1)
{
keep <- rep(TRUE, sig_reg_l)
p <- 1
count <- 1
for(cum in 2:sig_reg_l)
{
if(sig_reg[cum]==(sig_reg[cum-1]+1))
{
count <- count + 1
if(cum==sig_reg_l)
{
if(count<=win)
{
keep[p:cum] <- FALSE
}
}
}else{
if(count<=win)
{
keep[p:(cum-1)] <- FALSE
}
count <- 1
p <- cum
}
}
sig_reg <- sig_reg[keep]
if(length(sig_reg)<=win)
{
sig_reg <- NA
}
}else{
sig_reg <- NA
}
motif <- NA
if(!is.na(sig_reg[1]))
{
max_mean <- c()
for(bp in 1:length(sig_reg))
{
max_mean <- c(max_mean,mean(re$est_a[max(1,sig_reg[bp]-10):min(sig_reg[bp]+10,length(re$est_a)),1]))
}
max_bp <- which(max_mean==max(max_mean))
max_bp <- max_bp[ceiling(length(max_bp)/2)]
o_start <- as.numeric(strsplit(as.character(res$region[[nr]]),'-')[[1]][1])
motif <- c(o_start + sig_reg[max_bp] - 1 -10, o_start + sig_reg[max_bp] -1 +10)
sig_reg <- sig_reg + o_start - 1
}
res <- list(sig_reg=sig_reg, motif=motif)
return(res)
} |
test_that("normal deviance equals MSE", {
y <- c(0, 2)
pred <- c(1, 1)
expect_equal(mse(y, pred), deviance_normal(y, pred))
})
test_that("Poisson deviance only accepts positive predictions", {
y <- 0:3
pred <- y + 0.1
pred_bad <- y
expect_silent(deviance_poisson(y, pred))
expect_error(deviance_poisson(y, pred_bad))
})
test_that("Poisson deviance only accepts non-negative actual values", {
y <- 0:3
pred <- y + 0.1
y_bad <- y - 0.01
expect_silent(deviance_poisson(y, pred))
expect_error(deviance_poisson(y_bad, pred))
})
test_that("Gamma deviance only accepts positive predictions", {
y <- 1:4
pred <- y + 0.1
pred_bad <- y - 1
expect_silent(deviance_gamma(y, pred))
expect_error(deviance_gamma(y, pred_bad))
})
test_that("Gamma deviance only accepts positive actual values", {
y <- 1:4
pred <- y + 0.1
y_bad <- y - 1
expect_silent(deviance_gamma(y, pred))
expect_error(deviance_gamma(y_bad, pred))
})
test_that("Poisson deviance is 0 if actual = predicted", {
y <- 1:2
pred <- y
expect_equal(deviance_poisson(y, pred), 0)
})
test_that("Poisson deviance is 2 * pred for actual 0", {
y <- 0
pred <- 0.5
expect_equal(deviance_poisson(y, pred), 2 * pred)
})
test_that("Gamma deviance is 0 if actual = predicted", {
y <- 1:2
pred <- y
expect_equal(deviance_gamma(y, pred), 0)
})
test_that("Gamma deviance is 2 * (1 - log(2)) if predicted = actual / 2", {
y <- 2
pred <- y / 2
expect_equal(deviance_gamma(y, pred), 2 * (1 - log(2)))
})
test_that("normal deviance is Tweedie 0", {
y <- 1:3
pred <- y + 0.1
w <- 1:3
expect_equal(deviance_normal(y, pred, w),
deviance_tweedie(y, pred, w, tweedie_p = 0))
})
test_that("Poisson deviance is Tweedie 1", {
y <- 1:3
pred <- y + 0.1
w <- 1:3
expect_equal(deviance_poisson(y, pred, w),
deviance_tweedie(y, pred, w, tweedie_p = 1))
})
test_that("Gamma deviance is Tweedie 2", {
y <- 1:3
pred <- y + 0.1
w <- 1:3
expect_equal(deviance_gamma(y, pred, w),
deviance_tweedie(y, pred, w, tweedie_p = 2))
})
test_that("Tweedie deviance 0.5 gives error", {
y <- 1:3
pred <- y + 0.1
w <- 1:3
expect_error(deviance_tweedie(y, pred, w, tweedie_p = 0.5))
})
test_that("deviance with weight 1 gives same as unweighted", {
y <- c(0.1, 0.2, 1, 2)
pred <- y + 0.1
w <- rep(1, length(y))
expect_equal(deviance_poisson(y, pred, w), deviance_poisson(y, pred))
expect_equal(deviance_gamma(y, pred, w), deviance_gamma(y, pred))
expect_equal(deviance_normal(y, pred, w), deviance_normal(y, pred))
expect_equal(deviance_tweedie(y, pred, w, tweedie_p = 1.5),
deviance_tweedie(y, pred, tweedie_p = 1.5))
})
test_that("deviance with weight 2 gives same as weight 1", {
y <- c(0.1, 0.2, 1, 2)
pred <- y + 0.1
w1 <- rep(1, length(y))
w2 <- rep(2, length(y))
expect_equal(deviance_poisson(y, pred, w1),
deviance_poisson(y, pred, w2))
expect_equal(deviance_gamma(y, pred, w1),
deviance_gamma(y, pred, w2))
expect_equal(deviance_normal(y, pred, w1),
deviance_normal(y, pred, w2))
expect_equal(deviance_tweedie(y, pred, w = w1, tweedie_p = 1.5),
deviance_tweedie(y, pred, w = w2, tweedie_p = 1.5))
})
test_that("deviance with varying weights is different from unweighted", {
y <- c(0.1, 0.2, 1, 2)
pred <- y + 0.1
w <- 1:4
expect_false(deviance_poisson(y, pred, w) == deviance_poisson(y, pred))
expect_false(deviance_gamma(y, pred, w) == deviance_gamma(y, pred))
expect_false(deviance_normal(y, pred, w) == deviance_normal(y, pred))
expect_false(deviance_tweedie(y, pred, w, tweedie_p = 1.5) ==
deviance_tweedie(y, pred, tweedie_p = 1.5))
}) |
Rowz2Keep<-function(Ldat, EQ, G1, RESMAX)
{
keep1 = !is.na(G1$TT)
if(all(RESMAX>0))
{
residp = Ldat$sec - EQ$t - G1$TT
aresidp = abs(residp)
okayP = Ldat$phase=="P" & aresidp<RESMAX[1]
okayS = Ldat$phase=="S" & aresidp<RESMAX[2]
keep = keep1 & (okayP | okayS )
keepind=which(keep)
if(length( keepind )<4)
{
k1 = order(aresidp, decreasing = TRUE)
keepind = k1[1:4]
}
}
else
{
keepind=which(keep1)
}
return(keepind)
} |
as_prediction.PredictionDataSurv = function(x, check = TRUE, ...) {
invoke(PredictionSurv$new, check = check, .args = x)
}
check_prediction_data.PredictionDataSurv = function(pdata) {
n = length(assert_row_ids(pdata$row_id))
assert_surv(pdata$truth, "Surv", len = n, any.missing = TRUE, null.ok = TRUE)
assert_numeric(pdata$crank, len = n, any.missing = FALSE, null.ok = FALSE)
assert_numeric(pdata$response, len = n, any.missing = FALSE, null.ok = TRUE)
assert_numeric(pdata$lp, len = n, any.missing = FALSE, null.ok = TRUE)
if (inherits(pdata$distr, "VectorDistribution")) {
assert(nrow(pdata$distr$modelTable) == n)
} else {
assert_matrix(pdata$distr, nrows = n, any.missing = FALSE, null.ok = TRUE)
}
pdata
}
is_missing_prediction_data.PredictionDataSurv = function(pdata) {
miss = logical(length(pdata$row_id))
if (!is.null(pdata$crank)) {
miss = is.na(pdata$crank)
}
if (!is.null(pdata$lp)) {
miss = miss | is.na(pdata$lp)
}
if (!is.null(pdata$response)) {
miss = miss | is.na(pdata$response)
}
pdata$row_ids[miss]
}
c.PredictionDataSurv = function(..., keep_duplicates = TRUE) {
dots = list(...)
assert_list(dots, "PredictionDataSurv")
assert_flag(keep_duplicates)
if (length(dots) == 1L) {
return(dots[[1L]])
}
predict_types = names(mlr_reflections$learner_predict_types$surv)
predict_types = map(dots, function(x) intersect(names(x), predict_types))
if (!every(predict_types[-1L], setequal, y = predict_types[[1L]])) {
stopf("Cannot combine predictions: Different predict types")
}
predict_types = predict_types[[1L]]
row_ids = do.call(c, map(dots, "row_ids"))
ii = if (keep_duplicates) seq_along(row_ids) else which(!duplicated(row_ids, fromLast = TRUE))
elems = c("truth", intersect(c("crank", "lp", "response"), predict_types))
result = named_list(elems)
result$row_ids = row_ids[ii]
for (elem in elems) {
result[[elem]] = do.call(c, map(dots, elem))[ii]
}
if ("distr" %in% predict_types) {
if (inherits(dots[[1]]$distr, "VectorDistribution")) {
result$distr = do.call(c, map(dots, "distr"))
} else {
result$distr = tryCatch(
do.call(rbind, map(dots, "distr")),
error = function(e) {
do.call(c, map(dots,
function(x) {
as.Distribution(1 - x$distr, "cdf",
decorators = c("CoreStatistics",
"ExoticStatistics"))
}))
}
)
}
}
set_class(result, "PredictionDataSurv")
}
filter_prediction_data.PredictionDataSurv = function(pdata, row_ids) {
keep = pdata$row_ids %in% row_ids
pdata$row_ids = pdata$row_ids[keep]
pdata$truth = pdata$truth[keep]
if (!is.null(pdata$crank)) {
pdata$crank = pdata$crank[keep]
}
if (!is.null(pdata$lp)) {
pdata$lp = pdata$lp[keep]
}
if (!is.null(pdata$distr)) {
pdata$distr = pdata$distr[keep,, drop = FALSE]
}
pdata
} |
apply_thr <- function (r, thr)
{
stopifnot(class(r) == "RasterLayer" )
if (any(class(thr) == "numeric", class(thr) == "integer")) {
stopifnot(length(thr) == 1)
tmp <- values(r)
if (thr < min(tmp, na.rm = TRUE))
stop("\"thr\" should be greater than or equal to minimum layer value")
if (thr >= max(tmp, na.rm = TRUE))
stop("\"thr\" should be lower than maximum layer value")
} else {
if (class(thr) != "RasterLayer")
stop(paste("\"thr\" class should be \"numeric\",",
"\"integer\", or \"RasterLayer\""))
}
r > thr
} |
sigmoid.x <- function(x, y0, y1, xmid, r)
y0 + (y1 - y0)/(1 + exp(r * (xmid - x)))
sigmoid2.x <- function(x, y0, y1, xmid, r)
y0 + (y1 - y0)/(1 + exp(4 * r * (xmid - x) / (y1 - y0)))
constant.x <- function(x, c) rep(c, length(x))
noroptimal.x <- function(x, y0, y1, xmid, s2)
y0 + (y1-y0)*exp(-(x - xmid)^2/(2 * s2))
make.linear.x <- function(x0, x1) {
if ( is.null(x1) ) {
function(x, c, m) {
x1 <- length(x) - x0 + 1
x[seq_len(x0)] <- x[x0]
x[x1:length(x)] <- x[x1]
ans <- m * x + c
ans[ans < 0] <- 0
ans
}
} else {
function(x, c, m) {
x[x < x0] <- x0
x[x > x1] <- x1
ans <- m * x + c
ans[ans < 0] <- 0
ans
}
}
}
stepf.x <- function(x, y0, y1, xmid)
ifelse(x < xmid, y0, y1)
normalise <- function(x) x / sum(x)
starting.point.quasse <- function(tree, states, states.sd=NULL) {
p.bd <- starting.point.bd(tree)
lik.bm <- make.bm(tree, states, states.sd,
control=list(method="pruning", backend="C"))
c(p.bd, diffusion=as.numeric(stats::coef(find.mle(lik.bm, .1))))
}
load.wisdom <- function(file="wisdom") {
w <- paste(readLines(file), collapse="\n")
.Call(r_set_wisdom, w)
}
save.wisdom <- function(file="wisdom") {
w <- .Call(r_get_wisdom)
write(w, file)
}
check.f.quasse <- function(f) {
args <- names(formals(f))
if ( args[1] != "x" )
stop("First argument of speciation/extinction function must be x")
length(args) - 1
}
check.states.quasse <- function(tree, states, states.sd) {
states <- check.states(tree, states, as.integer=FALSE)
if ( length(states.sd) == 1 )
states.sd <- structure(rep(states.sd, length(states)),
names=names(states))
else
states.sd <- check.states(tree, states.sd, as.integer=FALSE)
list(states=states, states.sd=states.sd)
}
check.control.quasse <- function(control, tree, states) {
tree.length <- max(branching.times(tree))
xr <- diff(range(states))
xr.mult <- if ( "xr.mult" %in% names(control) )
control$xr.mult else 5
defaults <- list(tc=tree.length/10,
dt.max=tree.length/1000,
nx=1024,
dx=xr * xr.mult / 1024,
r=4,
xmid=mean(range(states)),
w=5,
method="fftC",
tips.combined=FALSE,
flags=FFTW.MEASURE,
atol=1e-6,
rtol=1e-6,
eps=1e-6,
verbose=FALSE)
nx.changed <- "nx" %in% names(control)
dx.changed <- "dx" %in% names(control)
control <- if ( is.null(control) )
defaults else modifyList(defaults, control)
if ( dx.changed && !nx.changed )
control$nx <- 2^ceiling(log2(xr * xr.mult / control$dx))
else if ( nx.changed && !dx.changed )
control$dx <- xr * xr.mult / control$nx
method <- match.arg(control$method, c("fftC", "fftR", "mol"))
if ( control$tips.combined && method != "fftC" )
stop("'tips.combined' can only be used with method 'fftC'")
if ( control$tc <= 0 || control$tc >= tree.length )
stop(sprintf("tc must lie in (0, %2.2f)", tree.length))
if ( log2(control$nx) %% 1 != 0 )
stop("nx must be a power of two")
if ( log2(control$r) %% 1 != 0 )
stop("r must be a power of two")
rr <- with(control, xmid + c(-1,1) * dx * nx / 2)
rmin <- min(c(1, -1) * (mean(range(states)) - rr) / (xr / 2))
if ( rmin - xr.mult < -1e-5 )
warning("Range does not look wide enough - be careful!")
else if ( rmin < 2 )
stop("Range is not wide enough")
ctrl.int <- c("nx", "flags", "verbose")
ctrl.num <- c("tc", "dt.max", "r", "xmid", "w", "atol", "rtol")
control[ctrl.int] <- sapply(control[ctrl.int], as.integer)
control[ctrl.num] <- sapply(control[ctrl.num], as.numeric)
control
}
expand.pars.quasse <- function(lambda, mu, args, ext, pars) {
pars.use <- vector("list", 2)
for ( i in c(1,2) ) {
x <- list()
pars.use[[i]] <-
list(x=ext$x[[i]],
lambda=do.call(lambda, c(ext$x[i], pars[args$lambda])),
mu=do.call(mu, c(ext$x[i], pars[args$mu])),
drift=pars[args$drift],
diffusion=pars[args$diffusion],
padding=ext$padding[i,],
ndat=ext$ndat[i],
nx=ext$nx[i])
}
names(pars.use) <- c("hi", "lo")
pars.use$tr <- ext$tr
pars.use
}
make.pars.quasse <- function(cache) {
args <- cache$args
function(pars) {
names(pars) <- NULL
drift <- pars[args$drift]
diffusion <- pars[args$diffusion]
ext <- quasse.extent(cache$control, drift, diffusion)
pars <- expand.pars.quasse(cache$lambda, cache$mu, args, ext, pars)
check.pars.quasse(pars$hi$lambda, pars$hi$mu, drift, diffusion)
pars
}
}
quasse.extent <- function(control, drift, diffusion) {
nx <- control$nx
dx <- control$dx
dt <- control$dt.max
xmid <- control$xmid
r <- control$r
w <- control$w
if ( control$method == "mol" ) {
ndat <- nx*c(r, 1)
padding <- NULL
} else {
mean <- drift * dt
sd <- sqrt(diffusion * dt)
nkl <- max(ceiling(-(mean - w * sd)/dx)) * c(r, 1)
nkr <- max(ceiling( (mean + w * sd)/dx)) * c(r, 1)
ndat <- nx*c(r, 1) - (nkl + 1 + nkr)
padding <- cbind(nkl, nkr)
storage.mode(padding) <- "integer"
}
x0.2 <- xmid - dx*ceiling((ndat[2] - 1)/2)
x0.1 <- x0.2 - dx*(1 - 1/r)
x <- list(seq(x0.1, length.out=ndat[1], by=dx/r),
seq(x0.2, length.out=ndat[2], by=dx))
tr <- seq(r, length.out=ndat[2], by=r)
list(x=x, padding=padding, ndat=ndat, tr=tr, nx=c(nx*r, nx))
}
combine.branches.quasse <- function(f.hi, f.lo, control) {
nx <- control$nx
dx <- control$dx
tc <- control$tc
r <- control$r
eps <- log(control$eps)
dt.max <- control$dt.max
careful <- function(f, y, len, pars, t0, dt.max) {
ans <- f(y, len, pars, t0)
if ( ans[[1]] > eps ) {
ans
} else {
if ( control$method == "fftC" ||
control$method == "fftR" )
dt.max <- dt.max / 2
len2 <- len/2
ans1 <- Recall(f, y, len2, pars, t0, dt.max)
ans2 <- Recall(f, ans1[[2]], len2, pars, t0 + len2, dt.max)
ans2[[1]][[1]] <- ans1[[1]][[1]] + ans2[[1]][[1]]
ans2
}
}
function(y, len, pars, t0, idx) {
if ( t0 < tc ) {
dx0 <- dx / r
nx0 <- nx * r
} else {
dx0 <- dx
nx0 <- nx
}
if ( any(y < -1e-8) )
stop("Actual negative D value detected -- calculation failure")
y[y < 0] <- 0
y <- matrix(y, nx0, 2)
q0 <- sum(y[,2]) * dx0
if ( q0 <= 0 )
stop("No positive D values")
y[,2] <- y[,2] / q0
lq0 <- log(q0)
if ( t0 >= tc ) {
ans <- careful(f.lo, y, len, pars$lo, t0, dt.max)
} else if ( t0 + len < tc ) {
ans <- careful(f.hi, y, len, pars$hi, t0, dt.max)
} else {
len.hi <- tc - t0
ans.hi <- careful(f.hi, y, len.hi, pars$hi, t0, dt.max)
y.lo <- ans.hi[[2]][pars$tr,]
lq0 <- lq0 + ans.hi[[1]]
if ( nrow(y.lo) < nx )
y.lo <- rbind(y.lo, matrix(0, nx - length(pars$tr), 2))
ans <- careful(f.lo, y.lo, len - len.hi, pars$lo, tc, dt.max)
}
c(ans[[1]] + lq0, ans[[2]])
}
} |
config <- local({
conflist <- list()
confpaths <- character()
load <- function(filepath){
message("Loading config from ", filepath)
confpaths <<- c(confpaths, filepath)
newconf <- as.list(fromJSON(filepath));
for(i in seq_along(newconf)){
val <- newconf[[i]]
name <- names(newconf[i])
conflist[[name]] <<- if(length(val)) val else NA
}
}
function(x){
value = conflist[[x]];
if(is.null(value)){
stop("System error! No config set for: ", x);
}
return(value);
}
})
create_user_config <- function(){
configfile <- get_user_conf()
if(file.exists(configfile)){
if(!validate(readLines(configfile))){
stop("Config contains invalid JSON: ", configfile)
}
} else {
defaultconf <- system.file("config/defaults.conf", package = packagename);
confdir <- dirname(configfile)
dir.create(confdir, showWarnings = FALSE, recursive = TRUE)
if(file.exists(confdir)){
if(file.copy(defaultconf, configfile)){
message("Creating new user config file: ", configfile);
} else {
stop(jsonlite::toJSON(names(Sys.getenv())))
warning("Failed to create new config file: ", configfile, ". Using default config.")
}
}
}
}
get_user_conf <- function(){
if(is_rapache() || is_admin()){
return("/etc/opencpu/server.conf")
} else {
file.path(getlocaldir('config'), "user.conf")
}
} |
SSD <- function(level){
x <- NULL
if(level==1){
x1 <- github.cssegisanddata.covid19(country = "South Sudan")
x2 <- ourworldindata.org(id = "SSD")
x <- full_join(x1, x2, by = "date")
}
return(x)
} |
`.f3` <- function(a1,a2,a3){
exp(complex_gamma(a1,log=TRUE) - complex_gamma(a2,log=TRUE) - complex_gamma(a3,log=TRUE))
}
`.f4` <- function(a1,a2,a3,a4){
exp(complex_gamma(a1,log=TRUE) + complex_gamma(a2,log=TRUE) - complex_gamma(a3,log=TRUE) - complex_gamma(a4,log=TRUE))
}
"f15.1.1" <- function(A, B, C, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
genhypergeo(U=c(A,B), L=C, z=z, tol=tol, maxiter=maxiter)
}
"f15.3.1" <- function(A,B,C,z,h=0){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
mult <- .f3(C,B,C-B)
f <- function(t){t^(B-1)*(1-t)^(C-B-1)*(1-t*z)^(-A)}
if(length(h)==1){
if(h==0){
return(mult * myintegrate(f,lower=0,upper=1))
} else {
if(is.double(h)){
h <- 0.5 + h*1i
}
}
}
return(mult * integrate.segments(f,c(0,h,1),close=FALSE))
}
"f15.3.3" <- function(A,B,C,z,tol=0,maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
(1-z)^(C-A-B)*genhypergeo(U=c(C-A,C-B),L=C,z=z,tol=tol,maxiter=maxiter)
}
"f15.3.4" <- function(A,B,C,z,tol=0,maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
(1-z)^(-A)*genhypergeo(U=c(A,C-B),L=C,z=z/(z-1),tol=tol,maxiter=maxiter)
}
"f15.3.5" <- function(A,B,C,z,tol=0,maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
(1-z)^(-B)*genhypergeo(U=c(B,C-A),L=C,z=z/(z-1),tol=tol,maxiter=maxiter)
}
"i15.3.6" <- function(A,B,C){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
c(
ifelse(is.nonpos(C-A) | is.nonpos(C-B), 0, .f4(C, C-A-B, C-A,C-B)),
ifelse(is.nonpos(A ) | is.nonpos(B ), 0, .f4(C, A+B-C, A , B ))
)
}
"j15.3.6" <- function(A,B,C){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
is.nonpos(c(
C , C-A-B ,
C , A+B-C
))
}
"f15.3.6" <- function(A,B,C,z,tol=0,maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
if(length(z)==0){
return(z)
}
jj <- i15.3.6(A,B,C)
jj[1] * genhypergeo(U=c( A, B),L=A+B-C+1,z=1-z,tol=tol,maxiter=maxiter) +
jj[2] * genhypergeo(U=c(C-A,C-B),L=C-A-B+1,z=1-z,tol=tol,maxiter=maxiter) * (1-z)^(C-A-B)
}
"i15.3.7" <- function(A,B,C){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
c(
ifelse(is.nonpos(B) | is.nonpos(C-A), 0, .f4(C,B-A,B,C-A)),
ifelse(is.nonpos(A) | is.nonpos(C-B), 0, .f4(C,A-B,A,C-B))
)
}
"j15.3.7" <- function(A,B,C){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
is.nonpos(c(
C , B-A,
C , A-B
))
}
"f15.3.7" <- function(A,B,C,z,tol=0,maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
if(length(z)==0){
return(z)
}
jj <- i15.3.7(A,B,C)
jj[1] * (-z)^(-A) * genhypergeo(U=c(A,1-C+A),L=1-B+A,z=1/z,tol=tol,maxiter=maxiter) +
jj[2] * (-z)^(-B) * genhypergeo(U=c(B,1-C+B),L=1-A+B,z=1/z,tol=tol,maxiter=maxiter)
}
"i15.3.8" <- function(A,B,C){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
c(
ifelse(is.nonpos(B) | is.nonpos(C-A), 0, .f4(C,B-A,B,C-A)),
ifelse(is.nonpos(A) | is.nonpos(C-B), 0, .f4(C,A-B,A,C-B))
)
}
"j15.3.8" <- function(A,B,C){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
is.nonpos(c(
C , B-A ,
C , A-B
))
}
"f15.3.8" <- function(A,B,C,z,tol=0,maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
if(length(z)==0){
return(z)
}
jj <- i15.3.8(A,B,C)
return(
jj[1] * (1-z)^(-A) * genhypergeo(U=c(A,C-B),L=A-B+1,z=1/(1-z),tol=tol,maxiter=maxiter) +
jj[2] * (1-z)^(-B) * genhypergeo(U=c(B,C-A),L=B-A+1,z=1/(1-z),tol=tol,maxiter=maxiter)
)
}
"i15.3.9" <- function(A,B,C){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
return(c(
ifelse(is.nonpos(C-A)|is.nonpos(C-B), 0, .f4(C,C-A-B,C-A,C-B)),
ifelse(is.nonpos( A)|is.nonpos(B) , 0, .f4(C,A+B-C,A, B))
))
}
"j15.3.9" <- function(A,B,C){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
is.nonpos(c(
C , C-A-B ,
C , A+B-C
))
}
"f15.3.9" <- function(A,B,C,z,tol=0,maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
if(length(z)==0){
return(z)
}
jj <- i15.3.9(A,B,C)
jj[1] * z^( -A)*genhypergeo(U=c(A,A-C+1),L=A+B-C+1,z=1-1/z,tol=tol,maxiter=maxiter) +
jj[2] * (1-z)^(C-A-B)*z^(A-C)*genhypergeo(U=c(C-A,1-A),L=C-A-B+1,z=1-1/z,tol=tol,maxiter=maxiter)
}
"isgood" <- function(x,tol){ all(abs(x[!is.na(x)]) <= tol)}
"genhypergeo" <- function (U, L, z, tol = 0, maxiter=2000, check_mod=TRUE, polynomial=FALSE, debug=FALSE, series=TRUE)
{
if(series){
return(genhypergeo_series(U, L, z, tol = tol, maxiter=maxiter, check_mod=check_mod, polynomial=polynomial, debug=debug))
} else {
return(genhypergeo_contfrac(U, L, z, maxiter=maxiter))
}
}
"genhypergeo_series" <-
function (U, L, z, tol = 0, maxiter=2000, check_mod=TRUE, polynomial=FALSE, debug=FALSE)
{
if(!is.null(getOption("showHGcalls"))){print(match.call())}
if(debug){
stopifnot(length(z)==1)
out <- NULL
}
if(check_mod){
lU <- length(U)
lL <- length(L)
if(lU > lL+1){
greater <- Mod(z)>0
} else if(lU > lL) {
greater <- Mod(z)>1
} else {
greater <- Mod(z)<0
}
if(all(greater)){
return(z*NA)
} else {
z[greater] <- NA
}
}
fac <- 1
temp <- fac
if(debug){out <- temp}
if(maxiter==0){
return(z*0+fac)
}
for (n in seq_len(maxiter)) {
fac <- fac * (prod(U)/prod(L)) * (z/n)
series <- temp + fac
if(debug){out <- c(out,fac)}
if (isgood(series-temp,tol)){
if(debug){
return(list(series,out))
} else {
return(series)
}
}
temp <- series
U <- U + 1
L <- L + 1
}
if(debug){
return(list(series,out))
}
if(polynomial){
return(series)
} else {
warning("series not converged")
return(z*NA)
}
}
"hypergeo_taylor" <- function(A, B, C, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
genhypergeo(U=c(A,B), L=C, z=z, tol=tol, maxiter=maxiter, check_mod=FALSE, polynomial=TRUE)
}
"is.near_integer" <- function(i , tol=getOption("tolerance")){
if(is.null(tol)){
tol <- 1e-11
}
abs(i-round(Re(i))) <= tol
}
"is.nonpos" <- function(i){
is.near_integer(i) & (Re(i) < 0.5)
}
"is.zero" <- function(i){
is.near_integer(i) & (abs(i) < 0.5)
}
"hypergeo_A_nonpos_int" <- function(A, B, C, z, tol=0){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(is.nonpos(A))
if(( is.near_integer(C) ) & is.near_integer(C) & (abs(C-A) < 0.5) ){
warning("this case is not uniquely defined: proceed, assuming both A and C approach the same nonpositive integer at the same speed [that is, (a)_n cancels (c)_n for all 'n']")
return(genhypergeo(U=B,L=NULL,z,tol=tol,check_mod = FALSE))
} else {
return(hypergeo_taylor(A,B,C,z,tol=tol,maxiter = -A))
}
}
"hypergeo_AorB_nonpos_int" <- function(A, B, C, z, tol=0){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(is.nonpos(A) | is.nonpos(B))
if(is.nonpos(A) & is.nonpos(B)){
if(A>B){
return(hypergeo_A_nonpos_int(A,B,C,z,tol=tol))
} else {
return(hypergeo_A_nonpos_int(B,A,C,z,tol=tol))
}
}
if(is.nonpos(A)){
return(hypergeo_A_nonpos_int(A,B,C,z,tol=tol))
} else {
return(hypergeo_A_nonpos_int(B,A,C,z,tol=tol))
}
}
".process_args" <- function(...){
a <- list(...)
attr <- attributes(a[[which.max(unlist(lapply(a,length)))]])
a <- lapply(a,as.vector)
out <- do.call("cbind",a)
return(list(out=out, attr = attr))
}
"crit" <- function(...){
c(
1/2 + 1i*sqrt(3)/2,
1/2 - 1i*sqrt(3)/2
)
}
"hypergeo" <- function(A, B, C, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
if(length(A)>1 | length(B)>1 | length(C)>1){
jj <- .process_args(A,B,C,z)
f <- function(x){hypergeo(A=Re(x[1]), B=Re(x[2]),C=Re(x[3]),z=x[4],tol=tol,maxiter=maxiter)}
out <- apply(jj$out , 1, f)
attributes(out) <- jj$attr
return(out)
}
jj <- crit()
c1 <- jj[1]
c2 <- jj[2]
close_to_crit <- (abs(z-c1) < 0.1) | (abs(z-c2) < 0.1)
out <- z*NA
if(any( close_to_crit)){out[ close_to_crit] <- hypergeo_gosper (A,B,C,z[ close_to_crit], tol=tol, maxiter=maxiter)}
if(any(!close_to_crit)){out[!close_to_crit] <- hypergeo_powerseries (A,B,C,z[!close_to_crit], tol=tol, maxiter=maxiter)}
do_with_cf <- !is.na(z) & is.na(out)
if(any(do_with_cf)){
out[do_with_cf] <- hypergeo_contfrac(A=A, B=B, C=C, z=z[do_with_cf], maxiter=maxiter)
}
do_with_integration <- !is.na(z) & is.na(out)
if(any(do_with_integration)){
g <- function(z){f15.3.1(A=A, B=B, C=C, z=z)}
out[do_with_integration] <- sapply(z[do_with_integration] , g)
}
return(out)
}
"hypergeo_powerseries" <- function(A, B, C, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
z <- z+0i
if(is.zero(A) | is.zero(B)){
if(is.zero(C)){
return(z*NA)
} else {
return(z*0+1)
}
}
if(is.zero(C)){
return(z*Inf)
}
if(is.zero(A-C)){
return( (1-z)^(-B) )
} else if (is.zero(B-C)){
return( (1-z)^(-A) )
}
if(is.nonpos(A) | is.nonpos(B)){
return(hypergeo_AorB_nonpos_int(A,B,C,z,tol=tol))
}
if(is.nonpos(C)){
return(z*NA)
}
if(Re(A) > Re(B)){
swap <- A
A <- B
B <- swap
}
m <- C-A
n <- B-A
if(is.near_integer(m)){
if(m <= 0){
return( (1-z)^(C-A-B)*Recall(C-A,C-B,C,z=z,tol=tol,maxiter=maxiter) )
} else {
if(is.near_integer(n)){
return(hypergeo_cover3(A,n,m,z,tol=tol,maxiter=maxiter))
}
}
}
m <- -(A+B-C)
if(is.near_integer(m)){
return(hypergeo_cover1(A,B,m,z,tol=tol,maxiter=maxiter))
}
m <- B-A
if(is.near_integer(m)){
return(hypergeo_cover2(A,C,m,z,tol=tol,maxiter=maxiter))
}
return(hypergeo_general(A,B,C,z,tol=tol,maxiter=maxiter))
}
"hypergeo_general" <- function(A, B, C, z, tol=0, maxiter=2000, give=FALSE){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
attr <- attributes(z)
z <- as.vector(as.complex(z))
things <- thingfun(z)
choice <- apply(things,1,which.min)
if(!is.null(getOption("showHGcalls"))){
print("choice: ")
print(choice)
}
u15.1.1 <- choice==1
u15.3.4 <- choice==2
u15.3.6 <- choice==3
u15.3.7 <- choice==4
u15.3.8 <- choice==5
u15.3.9 <- choice==6
out <- z*NA
if(any(u15.1.1)){ out[u15.1.1] <- f15.1.1(A=A,B=B,C=C, z[u15.1.1], tol=tol,maxiter=maxiter) }
if(any(u15.3.4)){ out[u15.3.4] <- f15.3.4(A=A,B=B,C=C, z[u15.3.4], tol=tol,maxiter=maxiter) }
if(any(u15.3.6)){ out[u15.3.6] <- f15.3.6(A=A,B=B,C=C, z[u15.3.6], tol=tol,maxiter=maxiter) }
if(any(u15.3.7)){ out[u15.3.7] <- f15.3.7(A=A,B=B,C=C, z[u15.3.7], tol=tol,maxiter=maxiter) }
if(any(u15.3.8)){ out[u15.3.8] <- f15.3.8(A=A,B=B,C=C, z[u15.3.8], tol=tol,maxiter=maxiter) }
if(any(u15.3.9)){ out[u15.3.9] <- f15.3.9(A=A,B=B,C=C, z[u15.3.9], tol=tol,maxiter=maxiter) }
attributes(out) <- attr
if(give){
return(list(choice,out))
} else {
return(out)
}
}
"thingfun" <- function(z,complex=FALSE){
things <- cbind("z" = z,
"z/(z-1)" = z/(z-1),
"1-z" = 1-z,
"1/z" = 1/z,
"1/(1-z)" = 1/(1-z),
"1-1/z" = 1-1/z
)
if(complex){return(things)}
things <- Mod(things)
if(any(apply(things,1,min, na.rm=TRUE)>1)){
stop("odd: none of the transformations take the argument inside the unit disk. Contact the package maintainer")
}
return(things)
}
"hypergeo_cover1" <- function(A, B, m, z, tol=0, maxiter=2000, method="a", give=FALSE){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(is.near_integer(m))
C <- A+B+m
attr <- attributes(z)
z <- as.vector(as.complex(z))
things <- thingfun(z)
if(any(j15.3.7(A,B,C))){ things[,4] <- Inf }
if(any(j15.3.8(A,B,C))){ things[,5] <- Inf }
if(any(j15.3.9(A,B,C))){ things[,6] <- Inf }
choice <- apply(things,1,which.min)
u15.1.1 <- choice==1
u15.3.4 <- choice==2
u15.3.x <- choice==3
u15.3.7 <- choice==4
u15.3.8 <- choice==5
u15.3.9 <- choice==6
out <- z*NA
if(any(u15.1.1)){ out[u15.1.1] <- f15.1.1 (A=A,B=B,C=C, z[u15.1.1], tol=tol,maxiter=maxiter) }
if(any(u15.3.4)){ out[u15.3.4] <- f15.3.4 (A=A,B=B,C=C, z[u15.3.4], tol=tol,maxiter=maxiter) }
if(any(u15.3.x)){ out[u15.3.x] <- f15.3.10_11_12(A=A,B=B,m=m, z[u15.3.x], tol=tol,maxiter=maxiter, method=method) }
if(any(u15.3.7)){ out[u15.3.7] <- f15.3.7 (A=A,B=B,C=C, z[u15.3.7], tol=tol,maxiter=maxiter) }
if(any(u15.3.8)){ out[u15.3.8] <- f15.3.8 (A=A,B=B,C=C, z[u15.3.8], tol=tol,maxiter=maxiter) }
if(any(u15.3.9)){ out[u15.3.9] <- f15.3.9 (A=A,B=B,C=C, z[u15.3.9], tol=tol,maxiter=maxiter) }
attributes(out) <- attr
if(give){
return(list(choice,out))
} else {
return(out)
}
}
"hypergeo_cover2" <- function(A, C, m, z, tol=0, maxiter=2000, method="a", give=FALSE){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(is.near_integer(m))
B <- A+m
attr <- attributes(z)
z <- as.vector(as.complex(z))
things <- thingfun(z)
if(any(j15.3.6(A,B,C))){ things[,3] <- Inf }
if(any(j15.3.8(A,B,C))){ things[,5] <- Inf }
if(any(j15.3.9(A,B,C))){ things[,6] <- Inf }
choice <- apply(things,1,which.min)
u15.1.1 <- choice==1
u15.3.4 <- choice==2
u15.3.6 <- choice==3
u15.3.x <- choice==4
u15.3.8 <- choice==5
u15.3.9 <- choice==6
out <- z*NA
if(any(u15.1.1)){ out[u15.1.1] <- f15.1.1 (A=A,B=B,C=C, z[u15.1.1], tol=tol,maxiter=maxiter) }
if(any(u15.3.4)){ out[u15.3.4] <- f15.3.4 (A=A,B=B,C=C, z[u15.3.4], tol=tol,maxiter=maxiter) }
if(any(u15.3.6)){ out[u15.3.6] <- f15.3.6 (A=A,B=B,C=C, z[u15.3.6], tol=tol,maxiter=maxiter) }
if(any(u15.3.x)){ out[u15.3.x] <- f15.3.13_14(A=A,C=C,m=m, z[u15.3.x], tol=tol,maxiter=maxiter, method=method) }
if(any(u15.3.8)){ out[u15.3.8] <- f15.3.8 (A=A,B=B,C=C, z[u15.3.8], tol=tol,maxiter=maxiter) }
if(any(u15.3.9)){ out[u15.3.9] <- f15.3.9 (A=A,B=B,C=C, z[u15.3.9], tol=tol,maxiter=maxiter) }
attributes(out) <- attr
if(give){
return(list(choice,out))
} else {
return(out)
}
}
"hypergeo_cover3" <- function(A, n, m, z, tol=0, maxiter=2000, method="a", give=FALSE){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(is.near_integer(n))
stopifnot(is.near_integer(m))
attr <- attributes(z)
z <- as.vector(as.complex(z))
things <- Mod(cbind(
"z" = z,
"1/z" = 1/z
)
)
if(any(apply(things,1,min,na.rm=TRUE)>1)){
stop("odd: none of the transformations take the argument inside the unit disk. Contact the package maintainer")
}
choice <- apply(things,1,which.min)
u15.1.1 <- choice==1
u07.23.06.0026.01 <- (choice==2) & (m > n)
u07.23.06.0031.01 <- (choice==2) & (m <= n)
out <- z*NA
if(any(u15.1.1)){ out[u15.1.1] <- f15.1.1(A=A,B=A+n,C=A+m, z[u15.1.1], tol=tol,maxiter=maxiter) }
if(any(u07.23.06.0026.01)){
out[u07.23.06.0026.01] <- w07.23.06.0026.01(A=A,n,m, z[u07.23.06.0026.01], tol=tol, maxiter=maxiter, method=method)
}
if(any(u07.23.06.0031.01)){
out[u07.23.06.0031.01] <- w07.23.06.0031.01(A=A,n,m, z[u07.23.06.0031.01], tol=tol, maxiter=maxiter)
}
attributes(out) <- attr
if(give){
return(list(choice,out))
} else {
return(out)
}
}
"f15.3.10_a" <- function(A, B, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
U <- c(A,B)
z[Mod(1-z) >= 1] <- NA
fac <- 1
l1mz <- log(1+0i-z)
temp <- 2*psigamma(0+1)-psigamma(A+0)-psigamma(B+0)-l1mz
for(n in seq_len(maxiter)){
fac <- fac * prod(U) * ((1-z)/n^2)
series <-
temp + fac * (2*psigamma(n+1)- psigamma(A+n) - psigamma(B+n) - l1mz)
if(isgood(series-temp,tol)){
return(series/beta(A,B))
}
temp <- series
U <- U+1
}
warning("series not converged")
return(z*NA)
}
"f15.3.10_b" <- function(A, B, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
U <- c(A,B)
z[Mod(1-z) >= 1] <- NA
fac <- 1
pn <- psigamma(1)
pa <- psigamma(A)
pb <- psigamma(B)
l1mz <- log(1+0i-z)
temp <- 2*pn-pa-pb-l1mz
for(n in seq_len(maxiter)){
fac <- fac * prod(U) * ((1-z)/n^2)
pn <- pn + 1/n
pa <- pa + 1/(A+n-1)
pb <- pb + 1/(B+n-1)
series <-
temp + fac * (2*pn - pa - pb - l1mz)
if(isgood(series-temp,tol)){
return(series/beta(A,B))
}
temp <- series
U <- U+1
}
warning("series not converged")
return(z*NA)
}
"f15.3.10" <- function(A, B, z, tol=0, maxiter=2000, method="a"){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
switch(method,
a = f15.3.10_a(A,B,z,tol=tol,maxiter=2000),
b = f15.3.10_b(A,B,z,tol=tol,maxiter=2000),
stop("method must be either 'a' or 'b'")
)
}
"f15.3.11_bit1" <- function(A, B, m, z, tol=0){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(is.near_integer(m))
stopifnot(m>0)
m <- round(m)
U <- c(A,B)
L <- 1-m
mult <- .f4(m,A+B+m,A+m,B+m)
series <- z*0+1
z[Mod(1-z)>1] <- NA
fac <- 1
temp <- fac
for (n in seq_len(m-1)) {
fac <- fac * (prod(U)/prod(L)) * (1-z)/n
series <- temp + fac
if (isgood(series-temp,tol)){
return(series * mult)
}
temp <- series
U <- U + 1
L <- L + 1
}
return(series*mult)
}
"f15.3.11_bit2_a" <- function(A, B, m, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(is.near_integer(m))
stopifnot(m>0)
U <- c(A+m , B+m)
z[Mod(1-z) >= 1] <- NA
fac <- 1/factorial(m)
l1mz <- log(1+0i-z)
temp <- (l1mz-psigamma(0+1)-psigamma(0+m+1) + psigamma(A+0+m) + psigamma(B+0+m) ) * fac
for(n in seq_len(maxiter)){
fac <- fac * prod(U) * (1-z)/(n*(n+m))
series <-
temp + fac * (l1mz - psigamma(n+1) - psigamma(n+m+1) + psigamma(A+n+m) + psigamma(B+n+m))
if(isgood(series-temp,tol)){
return((z-1)^m * .f3(A+B+m,A,B) * series)
}
temp <- series
U <- U+1
}
warning("series not converged")
return(z*NA)
}
"f15.3.11_bit2_b" <- function(A, B, m, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(is.near_integer(m))
stopifnot(m>0)
U <- c(A+m , B+m)
z[Mod(1-z) >= 1] <- NA
fac <- 1/factorial(m)
pn <- psigamma( 1)
pm <- psigamma(m+1)
pa <- psigamma(m+A)
pb <- psigamma(m+B)
l1mz <- log(1+0i-z)
temp <- (l1mz - pn - pm + pa + pb ) * fac
for(n in seq_len(maxiter)){
fac <- fac * prod(U) * (1-z)/(n*(n+m))
pn <- pn + 1/n
pm <- pm + 1/(n+m)
pa <- pa + 1/(A+n+m-1)
pb <- pb + 1/(B+n+m-1)
series <-
temp + fac * (l1mz - pn - pm + pa + pb)
if(isgood(series-temp,tol)){
return((z-1)^m * .f3(A+B+m,A,B) * series)
}
temp <- series
U <- U+1
}
warning("series not converged")
return(z*NA)
}
"f15.3.11" <- function(A,B,m,z,tol=0, maxiter=2000,method="a"){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
switch(method,
a = f15.3.11_bit1(A,B,m,z,tol=tol) - f15.3.11_bit2_a(A,B,m,z,tol=tol, maxiter=maxiter),
b = f15.3.11_bit1(A,B,m,z,tol=tol) - f15.3.11_bit2_a(A,B,m,z,tol=tol, maxiter=maxiter),
stop("method must be either 'a' or 'b'")
)
}
"f15.3.12_bit1" <- function(A, B, m, z, tol=0){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
m <- round(m)
U <- c(A-m,B-m)
L <- 1-m
mult <- .f4(m,A+B-m,A,B) / (1-z)^m
z[Mod(1-z)>1] <- NA
fac <- 1
temp <- fac
series <- z*0+1
for (n in seq_len(m-1)) {
fac <- fac * (prod(U)/prod(L)) * (1-z)/n
series <- temp + fac
if (isgood(series-temp,tol)){
return(series * mult)
}
temp <- series
U <- U + 1
L <- L + 1
}
return(series*mult)
}
"f15.3.12_bit2_a" <- function(A, B, m, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
m <- round(m)
if(is.nonpos(A-m)|is.nonpos(B-m)){return(z*0)}
mult <- (-1)^m * .f3(A+B-m,A-m,B-m)
U <- c(A , B)
z[Mod(1-z) >= 1] <- NA
fac <- 1/factorial(m)
l1mz <- log(1+0i-z)
temp <- (l1mz-psigamma(1)-psigamma(m+1) + psigamma(A) + psigamma(B) ) * fac
for(n in seq_len(maxiter)){
fac <- fac * prod(U) * (1-z)/(n*(n+m))
series <-
temp + fac * (l1mz - psigamma(n+1) - psigamma(n+m+1) + psigamma(A+n) + psigamma(B+n))
if(isgood(series-temp,tol)){
return(mult * series)
}
temp <- series
U <- U+1
}
warning("series not converged")
return(z*NA)
}
"f15.3.12_bit2_b" <- function(A, B, m, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
m <- round(m)
if(is.nonpos(A-m)|is.nonpos(B-m)){return(z*0)}
mult <- (-1)^m * .f3(A+B-m,A-m,B-m)
U <- c(A , B)
z[Mod(1-z) >= 1] <- NA
fac <- 1/factorial(m)
pn <- psigamma(1)
pm <- psigamma(m+1)
pa <- psigamma(A)
pb <- psigamma(B)
l1mz <- log(1+0i-z)
temp <- (l1mz-pn - pm + pa + pb ) * fac
for(n in seq_len(maxiter)){
fac <- fac * prod(U) * (1-z)/(n*(n+m))
pn <- pn + 1/n
pm <- pm + 1/(n+m)
pa <- pa + 1/(A+n-1)
pb <- pb + 1/(B+n-1)
series <-
temp + fac * (l1mz - pn - pm + pa + pb)
if(isgood(series-temp,tol)){
return(mult * series)
}
temp <- series
U <- U+1
}
warning("series not converged")
return(z*NA)
}
"f15.3.12" <- function(A, B, m, z, tol=0, maxiter=2000, method = "a"){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
switch(method,
a = f15.3.12_bit1(A,B,m,z,tol=tol) - f15.3.12_bit2_a(A,B,m,z,tol=tol, maxiter=maxiter),
b = f15.3.12_bit1(A,B,m,z,tol=tol) - f15.3.12_bit2_b(A,B,m,z,tol=tol, maxiter=maxiter),
stop("method must be one of 'a' or 'b'")
)
}
"f15.3.13" <- function(A, C, z, tol=0, maxiter=2000, method = "a"){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
switch(method,
a = f15.3.13_a(A,C,z,tol=tol,maxiter=maxiter),
b = f15.3.13_b(A,C,z,tol=tol,maxiter=maxiter)
)
}
"f15.3.13_a" <- function(A, C, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
U <- c(A,1-C+A)
z[Mod(z) < 1] <- NA
fac <- 1
pn <- psigamma(1)
pa <- psigamma(A)
pc <- psigamma(C-A)
lmz <- log(0i-z)
temp <- lmz + 2*psigamma(1) - psigamma(A) - psigamma(C-A)
for(n in seq_len(maxiter)){
fac <- fac * prod(U) / (z*n^2)
series <- temp + fac * (lmz + 2*psigamma(n+1) - psigamma(A+n) - psigamma(C-A-n))
if(isgood(series-temp,tol)){
return(series * .f3(C,A,C-A) * (0i-z)^(-A))
}
temp <- series
U <- U+1
}
warning("series not converged")
return(z*NA)
}
"f15.3.13_b" <- function(A, C, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
U <- c(A,1-C+A)
z[Mod(z) < 1] <- NA
fac <- 1
pn <- psigamma(1)
pa <- psigamma(A)
pc <- psigamma(C-A)
lmz <- log(0i-z)
temp <- lmz + 2*pn - pa - pc
for(n in seq_len(maxiter)){
fac <- fac * prod(U) / (z*n^2)
pn <- pn + 1/n
pa <- pa + 1/(A+n-1)
pc <- pc - 1/(C-A-n)
series <- temp + fac * (lmz + 2*pn - pa - pc)
if(isgood(series-temp,tol)){
return(series * .f3(C,A,C-A) * (0i-z)^(-A))
}
temp <- series
U <- U+1
}
warning("series not converged")
return(z*NA)
}
"f15.3.14_bit1_a" <- function(A, C, m, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
m <- round(m)
U <- c(A+m, 1-C+A+m)
z[Mod(z) < 1] <- NA
fac <- exp(
+complex_gamma(A+m,log=TRUE)
-complex_gamma(A,log=TRUE)
+complex_gamma(1-C+A+m,log=TRUE)
-complex_gamma(1-C+A,log=TRUE)
-complex_factorial(m,log=TRUE)
)
lmz <- log(0i-z)
temp <- (lmz + psigamma(1+m) + psigamma(1) - psigamma(A+m) - psigamma(C-A-m)) * fac
for(n in seq_len(maxiter)){
fac <- fac * prod(U) / (z*n*(n+m))
series <-
temp + fac * (lmz + psigamma(1+m+n) + psigamma(1+n) - psigamma(A+m+n) - psigamma(C-A-m-n))
if(isgood(series-temp,tol)){
return( (0i-z)^(-A-m) * .f3(C,A+m,C-A) * series)
}
temp <- series
U <- U+1
}
warning("series not converged")
return(z*NA)
}
"f15.3.14_bit1_b" <- function(A, C, m, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
m <- round(m)
U <- c(A+m, 1-C+A+m)
z[Mod(z) < 1] <- NA
fac <- exp(
+complex_gamma(A+m,log=TRUE)
-complex_gamma(A,log=TRUE)
+complex_gamma(1-C+A+m,log=TRUE)
-complex_gamma(1-C+A)
-complex_factorial(m,log=TRUE)
)
pm <- psigamma(m+1)
pn <- psigamma(1)
pa <- psigamma(m+A)
pc <- psigamma(C-A-m)
lmz <- log(0i-z)
temp <- (lmz + pm + pn - pa - pc) * fac
for(n in seq_len(maxiter)){
fac <- fac * prod(U) / (z*n*(n+m))
pm <- pm + 1/(n+m)
pn <- pn + 1/n
pa <- pa + 1/(m+A+n-1)
pc <- pc - 1/(C-A-m-n)
series <-
temp + fac * (lmz + pm + pn - pa - pc)
if(isgood(series-temp,tol)){
return( (0i-z)^(-A-m) * .f3(C,A+m,C-A) * series)
}
temp <- series
U <- U+1
}
warning("series not converged")
return(z*NA)
}
"f15.3.14_bit2" <- function(A, C, m, z, tol=0){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
m <- round(m)
stopifnot(m>0)
stopifnot(is.near_integer(m))
U <- c(A)
mult <- (0i-z)^(-A) * .f3(C,A+m,1)
z[Mod(z)<1] <- NA
fac <- 1
temp <- gamma(m)/gamma(C-A)
series <- z*0+temp
for (n in seq_len(m-1)) {
fac <- fac * prod(U) / (z*n)
series <- temp + fac * .f3(m-n,C-A-n,1)
if (isgood(series-temp,tol)){
return(series * mult)
}
temp <- series
U <- U + 1
}
return(series*mult)
}
"f15.3.14" <- function(A, C, m, z, tol=0, maxiter=2000, method="a"){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
a1 <- f15.3.14_bit1_a(A,C,m,z,tol=tol,maxiter=maxiter)
a2 <- f15.3.14_bit2(A,C,m,z,tol=tol)
switch(method,
a=f15.3.14_bit1_a(A,C,m,z,tol=tol,maxiter=maxiter) + f15.3.14_bit2(A,C,m,z,tol=tol),
b=f15.3.14_bit1_b(A,C,m,z,tol=tol,maxiter=maxiter) + f15.3.14_bit2(A,C,m,z,tol=tol),
stop("method must be one of 'a' or 'b'")
)
}
"f15.3.10_11_12" <- function(A,B,m,z,tol=0,maxiter=2000,method="a"){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(is.near_integer(m))
m <- round(m)
if(is.zero(m)){
return(f15.3.10(A,B, z,tol=tol,maxiter=maxiter,method=method))
} else if (m>0){
return(f15.3.11(A,B, m,z,tol=tol,maxiter=maxiter,method=method))
} else if (m<0){
return(f15.3.12(A,B,-m,z,tol=tol,maxiter=maxiter,method=method))
} else {
stop("this cannot happen")
}
}
"f15.3.13_14" <- function(A, C, m, z, tol=0, maxiter=2000, method="a"){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(is.near_integer(m))
m <- round(m)
if(is.zero(m)){
return(f15.3.13(A ,C ,z,tol=tol,maxiter=maxiter,method=method))
} else if (m>0){
return(f15.3.14(A ,C, m,z,tol=tol,maxiter=maxiter,method=method))
} else if (m<0){
return(f15.3.14(A+m,C,-m,z,tol=tol,maxiter=maxiter,method=method))
} else {
stop("this cannot happen")
}
}
"w07.23.06.0029.01" <- function(A, n, m, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
((-1)^m*gamma(A-m)*factorial(m+n)*(0i-z)^(-A-n)/(gamma(A)*factorial(n)))*
hypergeo(A+n , m+n+1, n+1, 1/z,tol=tol,maxiter=maxiter)
}
"w07.23.06.0031.01_bit1" <- function(A, n, m, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(is.near_integer(m))
stopifnot(m>0)
U <- c(A,1-m)
L <- 1-n
mult <- .f4(A+m,n,m,A+n) * (0i-z)^(-A)
series <- z*0+1
z[Mod(z) < 1] <- NA
fac <- 1
temp <- fac
for (k in seq_len(m-1)) {
fac <- fac * (prod(U)/prod(L)) / (k*z)
series <- temp + fac
if (isgood(series-temp,tol)){
return(series * mult)
}
temp <- series
U <- U + 1
L <- L + 1
}
return(series*mult)
}
"w07.23.06.0031.01_bit2" <- function(A, n, m, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
(-1)^m*(0i-z)^(-A-n) * .f4(A+m,n-m+1,A,n+1)*
hypergeo(A+n , 1-m+n , n+1 , 1/z , tol=tol , maxiter=maxiter)
}
"w07.23.06.0031.01" <- function(A, n, m, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(m <= n)
w07.23.06.0031.01_bit1(A, n, m, z, tol=tol, maxiter=maxiter) +
w07.23.06.0031.01_bit2(A, n, m, z, tol=tol, maxiter=maxiter)
}
"w07.23.06.0026.01" <- function(A, n, m, z, tol=0, maxiter=2000, method="a"){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(m >= n)
stopifnot(m >= 0)
stopifnot(n >= 0)
stopifnot(is.near_integer(n))
stopifnot(is.near_integer(m))
m <- round(m)
n <- round(n)
z <- z+0i
bit1 <- w07.23.06.0026.01_bit1(A, n, m, z, tol=tol)
bit2 <- w07.23.06.0026.01_bit2(A, n, m, z, tol=tol, maxiter=maxiter)
bit3 <- switch(method,
a = w07.23.06.0026.01_bit3_a(A, n, m, z, tol=tol),
b = w07.23.06.0026.01_bit3_b(A, n, m, z, tol=tol),
c = w07.23.06.0026.01_bit3_c(A, n, m, z, tol=tol),
stop("method must be 'a' or 'b' or 'c'")
)
return(bit1 + bit2 + bit3)
}
"w07.23.06.0026.01_bit1" <- function(A, n, m, z, tol=0){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
if(length(z)==0){return(z)}
if(is.zero(n)){ return(0) }
mult <- (0i-z)^(-A) * .f4(n,A+m,m,A+n)
U <- c(A,1-m)
L <- 1-n
series <- z*0+1
z[Mod(z) < 1] <- NA
fac <- 1
temp <- fac
for (k in seq_len(n-1)) {
fac <- fac * (prod(U)/prod(L)) /(z*k)
series <- temp + fac
if (isgood(series-temp,tol)){
return(series * mult)
}
temp <- series
U <- U + 1
L <- L + 1
}
return(series*mult)
}
"w07.23.06.0026.01_bit2" <- function(A, n, m, z, tol=0, maxiter = 2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
mult <-
(-1)^n * (-z)^(-A-m) * .f3(A+m,A,A+n) * .f3(A+m,m+1,m-n+1)
return(mult * genhypergeo(U=c(1,1,A+m),L=c(m+1,m-n+1), z=1/z, tol=tol, maxiter=maxiter))
}
"w07.23.06.0026.01_bit3_a" <- function(A, n, m, z, tol=0){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
U <- c(A+n , 1-m+n)
mult <- (-1)^n * .f3(A+m,A,m-n) * (-z)^(-A-n)
fac <- 1/factorial(n)
lmz <- log(0i-z)
temp <- (lmz - psigamma(m-n-0) +psigamma(0+1) + psigamma(0+n+1) - psigamma(A+0+n)) * fac
series <- temp
for(k in seq_len(m-n-1)){
fac <- fac * prod(U) / (z * k * (k+n))
series <-
temp + fac * (lmz - psigamma(m-n-k) + psigamma(k+1) + psigamma(k+n+1) - psigamma(A+k+n))
if(isgood(series-temp,tol)){
return(series*mult)
}
temp <- series
U <- U+1
}
return(series*mult)
}
"w07.23.06.0026.01_bit3_b" <- function(A, n, m, z, tol=0){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
U <- c(A+n , 1-m+n)
mult <- (-1)^n*.f3(A+m,A,m-n) * (-z)^(-A-n)
fac <- 1/factorial(n)
lmz <- log(0i-z)
p1 <- psigamma(m-n)
p2 <- psigamma(1)
p3 <- psigamma(n+1)
p4 <- psigamma(A+n)
temp <- (lmz - p1 + p2 + p3 - p4) * fac
series <- temp
for(k in seq_len(m-n-1)){
fac <- fac * prod(U) / (z * k * (k+n) )
p1 <- p1 - 1/(m-n-k)
p2 <- p2 + 1/k
p3 <- p3 + 1/(k+n)
p4 <- p4 + 1/(A+k+n-1)
series <-
temp + fac * (lmz - p1 + p2 + p3 - p4)
if(isgood(series-temp,tol)){
return(series*mult)
}
temp <- series
U <- U+1
}
return(series*mult)
}
"w07.23.06.0026.01_bit3_c" <- function(A, n, m, z, tol=0){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
poch <- function(x,j){ prod(x + (seq_len(j)-1)) }
mult <- ((-1)^n*.f3(A+m,A,m-n))*(-z)^(-A-n)
out <- 0
for(k in 0:(m-n-1)){
out <- out +
(
(poch(A+n,k) * poch(1-m+n,k))/(factorial(k)*factorial(k+n))
) *
(log(-z) - psigamma(m-n-k)+psigamma(k+1)+psigamma(k+n+1)-psigamma(A+k+n))*z^(-k)
}
return(out * mult)
}
"genhypergeo_contfrac_single" <- function(U, L, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
f <- function(k){prod(U+k)/prod(k+c(1,L))}
alpha <- z*sapply(seq_len(maxiter), f)
1+z*prod(U)/(prod(L)*(1+GCF(a = -alpha , b = 1+alpha, tol=tol)))
}
"genhypergeo_contfrac" <- function(U, L, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
attr <- attributes(z)
f <- function(z){genhypergeo_contfrac_single(U, L, z=z, tol=tol, maxiter=maxiter)}
out <- sapply(z,f)
attributes(out) <- attr
return(out)
}
"hypergeo_contfrac" <- function(A, B, C, z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
attr <- attributes(z)
f <- function(z){genhypergeo_contfrac_single(U=c(A, B), L=C, z=z, tol=tol, maxiter=maxiter)}
out <- sapply(z,f)
attributes(out) <- attr
return(out)
}
"hypergeo_residue_general" <- function(A, B, C, z, r, O=z, tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
stopifnot(length(z)==1)
residue(f=function(z){hypergeo(A,B,C,z,tol=tol,maxiter=maxiter)}, z0=z, r=0.15, O=O)
}
"hypergeo_residue_close_to_crit_single" <- function(A, B, C, z, strategy='A', tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
jj <- crit()
c1 <- jj[1]
c2 <- jj[2]
if(
(abs(z-c1) <= 0.1) &
(abs(z-c2) <= 0.1)
) {stop("this cannot happen")}
stopifnot(
(abs(z-c1) <= 0.1) |
(abs(z-c2) <= 0.1)
)
if(abs(z-c1) <= 0.1){
crit <- c1
} else {
crit <- c2
}
O <- switch(
strategy,
A = crit,
B = z,
stop('strategy must be A or B')
)
hypergeo_residue_general(A=A,B=B,C=C, z=z, r=0.15, O=O, tol=tol, maxiter=maxiter)
}
"hypergeo_residue_close_to_crit_multiple" <- function(A, B, C, z, strategy='A', tol=0, maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
sapply(z, function(z){
hypergeo_residue_close_to_crit_single(A,B,C,z,strategy=strategy,tol=tol,maxiter=maxiter)
} )
}
"lpham" <- function(x,n){lgamma(x+n)-lgamma(x)}
"buhring_eqn11" <- function(n,S,A,B,C,z0=1/2){
stopifnot(length(z0)==1)
if(length(n)>1) {return(sapply(n,function(nn){buhring_eqn11(n=nn,S,A,B,C,z0=z0)}))}
return(
exp(
+lpham(S,n)
+lpham(1+S-C,n)
-lpham(1+2*S-A-B,n)
-lfactorial(n)
) * hypergeo(-n, A+B-2*S-n, C-S-n, z=z0)
)
}
"buhring_eqn12" <- function(n,S,A,B,C,z0=1/2){
stopifnot(length(z0)==1)
if(length(n)>1) {return(sapply(n,function(nn){buhring_eqn12(n=nn,S,A,B,C,z0=z0)}))}
return(
(-1)^n*
exp(
+lpham(S,n)
+lpham(S+C-A-B,n)
-lpham(1+2*S-A-B,n)
-lfactorial(n)
) * hypergeo(-n,A+B-2*S-n, 1+A+B-S-C-n, z=1-z0)
)
}
"buhring_eqn5_factors" <- function(A,B,C,z,z0=1/2){
c(
exp(
+complex_gamma(C,log=TRUE)
+complex_gamma(B-A,log=TRUE)
-complex_gamma(B,log=TRUE)
-complex_gamma(C-A,log=TRUE)
-A*log(z0-z)
),
exp(
+complex_gamma(C,log=TRUE)
+complex_gamma(A-B,log=TRUE)
-complex_gamma(A,log=TRUE)
-complex_gamma(C-B,log=TRUE)
-B*log(z0-z)
)
)
}
"buhring_eqn5_series" <- function(S,A,B,C,z,z0=1/2,use11=FALSE,tol=0,maxiter=2000){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
if(length(z)==0){return(z)}
if(use11){
f <- buhring_eqn11
} else {
f <- buhring_eqn12
}
temp <- 1
n <- 1
while(n < maxiter){
out <- temp + f(n,S=S,A=A,B=B,C=C,z0=z0)/(z-z0)^n
if(isgood(out-temp,tol)){return(out)}
temp <- out
n <- n+1
}
warning("series not converged")
return(out)
}
"hypergeo_buhring" <- function(A,B,C,z,z0=1/2,tol=0,maxiter=2000,use11=TRUE){
jj <- buhring_eqn5_factors(A,B,C,z,z0)
return(
jj[1]*buhring_eqn5_series(S=A,A,B,C,z,z0=1/2,use11=use11,tol=tol,maxiter=maxiter)+
jj[2]*buhring_eqn5_series(S=B,A,B,C,z,z0=1/2,use11=use11,tol=tol,maxiter=maxiter)
)
}
"shanks" <- function(Last,This,Next){
if(identical(Next,This)){return(Next)}
num <- Next*Last - This^2
den <- Next-2*This+Last
if(den==0){
return(Next)
} else {
return(num/den)
}
}
"genhypergeo_shanks" <-
function (U, L, z, maxiter=20){
if(!is.null(getOption("showHGcalls"))){print(match.call())}
fac <- 1
temp <- fac
if(maxiter==0){ return(z*0+fac) }
Last <- 0
This <- 1
Next <- 2
Shanks <- shanks(Last,This,Next)
for (n in seq_len(maxiter)) {
fac.old <- fac
fac <- fac * (prod(U)/prod(L)) * (z/n)
fac.new <- fac
series <- temp + fac
Last <- This
This <- Next
Next <- series
Shanks.old <- Shanks
Shanks <- shanks(Last,This,Next)
temp <- series
U <- U + 1
L <- L + 1
}
return(series)
}
"hypergeo_shanks" <- function (A, B, C, z, maxiter = 20){
genhypergeo_shanks(U=c(A,B), L=C, z=z,maxiter=maxiter)
}
"hypergeo_gosper" <- function(A, B, C, z, tol=0, maxiter=2000){
d <- 0
e <- 1
f <- 0
for(k in 0:maxiter){
dnew <- (k+A)*(k+B)*z*(e-(k+C-B-A)*d*z/(1-z)) /(4*(k+1)*(k+C/2)*(k+(C+1)/2))
enew <- (k+A)*(k+B)*z*(A*B*d*z/(1-z) + (k+C)*e)/(4*(k+1)*(k+C/2)*(k+(C+1)/2))
fnew <- f-d*(k*((C-B-A)*z+k*(z-2)-C)-A*B*z) /(2* (k+C/2)*(1-z) )+e
if(isgood(f-fnew,tol)){return(f)}
d <- dnew
e <- enew
f <- fnew
}
warning("not converged")
return(f)
}
|
download_US_WLEF <- function(start_date, end_date) {
base_url <- "http://co2.aos.wisc.edu/data/cheas/wlef/flux/prelim/"
start_year <- lubridate::year(start_date)
end_year <- lubridate::year(end_date)
raw.data <- start_year:end_year %>%
purrr::map_df(function(syear) {
influx <-
tryCatch(
read.table(
paste0(base_url, syear, "/flux_", syear, ".txt"),
sep = "",
header = TRUE
) %>%
apply(2, trimws) %>%
apply(2, as.character) %>%
data.frame(stringsAsFactors = F),
error = function(e) {
NULL
},
warning = function(e) {
NULL
}
)
}) %>%
mutate_all(funs(as.numeric))
raw.data$date <-as.POSIXct(paste0(raw.data$Year,"/",raw.data$MO,"/",raw.data$DD," ", raw.data$HH),
format="%Y/%m/%d %H", tz="UTC")
raw.data <- raw.data %>% dplyr::select(date, NEE_122, LE_122) %>%
filter(date >= start_date & date <=end_date) %>%
na_if( -999) %>%
mutate(NEE_122 = PEcAn.utils::misc.convert(NEE_122, "umol C m-2 s-1", "kg C m-2 s-1"))
colnames(raw.data) <- c("Time", "NEE", "LE")
return(raw.data)
} |
"allCategory" |
pda.fd <- function(xfdlist, bwtlist=NULL, awtlist=NULL, ufdlist=NULL,
nfine=501)
{
if (inherits(xfdlist, "fd")) xfdlist = list(xfdlist)
if (!inherits(xfdlist, "list")) stop(
"XFDLIST is neither a list or a FD object")
nvar <- length(xfdlist)
if (nvar == 1) {
difeorder <- length(bwtlist)
difeordp1 <- difeorder + 1
xfdobj <- xfdlist[[1]]
xbasis <- xfdobj$basis
xcoef <- xfdobj$coefs
xrange <- xbasis$rangeval
if (is.null(ufdlist) | is.null(awtlist)) {
nforce <- 0
} else {
if (inherits(ufdlist[[1]], "list")) {
nforce <- length(ufdlist[[1]])
temp <- vector("list", nforce)
for (iu in 1:nforce) temp[[iu]] <- ufdlist[[1]][[iu]]
ufdlist <- temp
} else {
nforce <- length(ufdlist)
}
if (inherits(awtlist[[1]], "list")) {
if (length(awtlist[[1]]) != nforce)
stop("The length of AWTLIST is incorrect.")
temp <- vector("list", nforce)
for (iu in 1:nforce) temp[[iu]] <- awtlist[[1]][[iu]]
awtlist <- temp
} else {
if (length(awtlist) != nforce)
stop("The length of AWTLIST is incorrect.")
}
}
if (difeorder == 0 && nforce == 0)
stop("There are no coefficient functions to estimate.")
ncurve <- dim(xcoef)[2]
nbasmax <- xbasis$nbasis
if (nforce > 0) {
errorwrd <- FALSE
for (iu in 1:nforce) {
if (!inherits(ufdlist[[iu]], "fd")) {
print(paste("UFDLIST[[",iu,
"]] is not a functional data object.",sep=""))
errorwrd <- TRUE
} else {
ufdi <- ufdlist[[iu]]
urange <- ufdi$basis$rangeval
if (any(urange != xrange)) {
print(paste(
"XRANGE and URANGE are not identical for UFDLIST[[",
iu,"]].",sep=""))
errorwrd <- TRUE
}
}
afdPari <- awtlist[[iu]]
afdi <- afdPari$fd
if (!inherits(afdi, "fd")) {
print(paste(
"AFDI is not a functional data object for AWTLIST[[",
iu,"]].",sep=""))
errorwrd <- TRUE
} else {
basisi <- afdi$basis
if (any(basisi$rangeval != urange)) {
print(paste("Ranges are incompatible for AWTLIST[[",
iu,"]].",sep=""))
errorwrd <- TRUE
}
nbasmax <- max(c(nbasmax,basisi$nbasis))
}
}
if (errorwrd) stop("")
}
if (inherits(bwtlist[[1]],"list")) {
temp <- vector("list",difeorder)
for (j in 1:nvar) {
if (inherits(bwtlist[[1]][[j]], "list")) {
bwtlist[[1]][[j]] <- bwtlist[[1]][[j]][[1]]
}
temp[[j]] <- bwtlist[[1]][[j]]
}
bwtlist <- temp
}
errorwrd <- FALSE
for (j in 1:difeorder) {
if (!is.null(bwtlist[[j]])) {
bfdParj <- bwtlist[[j]]
if (!inherits(bfdParj,"fdPar")) {
print(paste(
"BWTLIST[[",j,"]] is not a functional parameter object.",sep=""))
errorwrd <- TRUE
} else {
bfdj <- bfdParj$fd
if (!inherits(bfdj, "fd")) {
print(paste(
"BFDJ in BWTLIST[[",j,"]] is not a functional data object.",
sep=""))
errorwrd <- TRUE
} else {
basisj <- bfdj$basis
if (any(basisj$rangeval != xrange)) print(paste(
"Ranges are incompatible for BWTLIST[[",j,"]].",sep=""))
}
}
nbasmax <- max(c(nbasmax,basisj$nbasis))
}
}
if (errorwrd) stop("")
if (nfine < 5*nbasmax) nfine <- 5*nbasmax
deltax <- (xrange[2]-xrange[1])/(nfine-1)
tx <- seq(xrange[1],xrange[2],deltax)
yarray <- array(0,c(nfine,ncurve,difeordp1))
for (j in 1:difeordp1) yarray[,,j] <- eval.fd(tx, xfdobj, j-1)
if (nforce > 0) {
uarray <- array(0,c(nfine,ncurve,nforce))
for (iu in 1:nforce)
uarray[,,iu] <- eval.fd(tx, ufdlist[[iu]])
}
yprod <- array(0,c(nfine,difeordp1,difeordp1))
for (j1 in 1:difeordp1) for (j2 in 1:j1) {
if (ncurve == 1) yprodval <- yarray[,1,j1]*yarray[,1,j2]
else yprodval <- apply(yarray[,,j1]*yarray[,,j2],1,mean)
yprod[,j1,j2] <- yprodval
yprod[,j2,j1] <- yprodval
}
if (nforce > 0) {
yuprod <- array(0,c(nfine, nforce, difeordp1))
for (iu in 1:nforce) {
for (j1 in 1:difeordp1) {
if (ncurve == 1) {
yuprodval <- yarray[,1,j1]*uarray[,1,iu]
} else {
yuprodval <- apply(yarray[,,j1]*uarray[,,iu],1,mean)
}
yuprod[,iu,j1] <- yuprodval
}
}
}
if (nforce > 0) {
uprod <- array(0,c(nfine, nforce, nforce))
for (iu in 1:nforce) for (ju in 1:iu) {
if (ncurve == 1) uprodval <- uarray[,1,iu]*uarray[,1,ju]
else uprodval <- apply(uarray[,,iu]*uarray[,,ju],1,mean)
uprod[,iu,ju] <- uprodval
uprod[,ju,iu] <- uprodval
}
}
onesn <- rep(1,nfine)
if (nforce > 0) {
aarray <- matrix(0,nfine,nforce)
} else {
aarray <- NULL
}
barray <- matrix(0,nfine,difeorder)
neqns <- 0
if (nforce > 0) {
for (iu in 1:nforce) {
if (!is.null(awtlist[[iu]])) {
afdPari <- awtlist[[iu]]
if (afdPari$estimate)
neqns <- neqns + afdPari$fd$basis$nbasis
}
}
}
for (j1 in 1:difeorder) {
if (!is.null(bwtlist[[j1]])) {
bfdParj <- bwtlist[[j1]]
if (bfdParj$estimate)
neqns <- neqns + bfdParj$fd$basis$nbasis
}
}
if (neqns < 1) stop(
"Number of equations to solve is not positive.")
cmat <- matrix(0,neqns, neqns)
dmat <- matrix(0,neqns, 1)
if (nforce > 0) {
for (iu in 1:nforce) {
if (!is.null(awtlist[[iu]])) {
afdPari <- awtlist[[iu]]
aarray[,iu] <- eval.fd(tx, afdPari$fd)
}
}
}
for (j1 in 1:difeorder) {
if (!is.null(bwtlist[[j1]])) {
bfdParj <- bwtlist[[j1]]
bvecj <- eval.fd(tx, bfdParj$fd)
barray[,j1] <- bvecj
}
}
mi12 <- 0
if (nforce > 0) {
for (iu1 in 1:nforce) {
if (!is.null(awtlist[[iu1]])) {
afdPari1 <- awtlist[[iu1]]
if (afdPari1$estimate) {
abasisi1 <- afdPari1$fd$basis
abasismati1 <- getbasismatrix(tx, abasisi1)
mi11 <- mi12 + 1
mi12 <- mi12 + abasisi1$nbasis
indexi1 <- mi11:mi12
weighti1 <- -yuprod[,iu1,difeordp1]
dmat[indexi1] <-
trapzmat(abasismati1,onesn,deltax,weighti1)
for (j1 in 1:difeorder) {
bfdParij <- bwtlist[[j1]]
if (!bfdParij$estimate) {
weightij <- -yuprod[,iu1,j1]
dmat[indexi1] <- dmat[indexi1] +
trapzmat(abasismati1, barray[,j1],
deltax, weightij)
}
}
mi22 <- 0
for (iu2 in 1:nforce) {
if (!is.null(awtlist[[iu2]])) {
afdPari2 <- awtlist[[iu2]]
if (afdPari2$estimate) {
abasisi2 <- afdPari2$fd$basis
abasismati2 <- getbasismatrix(tx, abasisi2)
weighti2 <- uprod[,iu1,iu2]
Cprod <- trapzmat(abasismati1, abasismati2,
deltax, weighti2)
mi21 <- mi22 + 1
mi22 <- mi22 + abasisi2$nbasis
indexi2 <- mi21:mi22
cmat[indexi1,indexi2] <- Cprod
}
}
}
mij22 <- mi22
for (j2 in 1:difeorder) {
if (!is.null(bwtlist[[j2]])) {
bfdParj2 <- bwtlist[[j2]]
if (bfdParj2$estimate) {
bbasisij2 <- bfdParj2$fd$basis
bbasismatij2 <- getbasismatrix(tx, bbasisij2)
weightij12 <- -yuprod[,iu1,j2]
Cprod <- trapzmat(abasismati1,bbasismatij2,
deltax,weightij12)
mij21 <- mij22 + 1
mij22 <- mij22 + bbasisij2$nbasis
indexij2 <- mij21:mij22
cmat[indexi1,indexij2] <- Cprod
}
}
}
lambdai1 <- afdPari1$lambda
if (lambdai1 > 0) {
Lfdobj <- afdPari1$Lfd
penmat <- lambdai1*eval.penalty(abasisi1, Lfdobj)
cmat[indexi1,indexi1] <- cmat[indexi1,indexi1] + penmat
}
}
}
}
}
mij12 <- mi12
for (j1 in 1:difeorder) {
if (!is.null(bwtlist[[j1]])) {
bfdParj1 <- bwtlist[[j1]]
if (bfdParj1$estimate) {
bbasisij1 <- bfdParj1$fd$basis
bbasismatij1 <- getbasismatrix(tx,bbasisij1)
mij11 <- mij12 + 1
mij12 <- mij12 + bbasisij1$nbasis
indexij1 <- mij11:mij12
weightij1 <- yprod[,j1,difeordp1]
dmat[indexij1] <-
trapzmat(bbasismatij1,onesn,deltax,weightij1)
if (nforce > 0) {
for (iu in 1:nforce) {
if (!is.null(awtlist[[iu]])) {
afdPari <- awtlist[[iu]]
if (!afdPari$estimate) {
weightijk <- -yuprod[,iu,j1]
dmat[indexij1] <- dmat[indexij1] +
trapzmat(bbasisij1, aarray[,iu],deltax, weightijk)
}
}
}
}
}
}
mi22 <- 0
if (nforce > 0) {
for (iu2 in 1:nforce) {
if (!is.null(awtlist[[iu2]])) {
afdPari2 <- awtlist[[iu2]]
if (afdPari2$estimate) {
abasisi2 <- afdPari2$fd$basis
abasismati2 <- getbasismatrix(tx, abasisi2)
weighti2 <- -yuprod[,iu2,j1]
Cprod <- trapzmat(bbasismatij1,abasismati2,deltax,weighti2)
mi21 <- mi22 + 1
mi22 <- mi22 + abasisi2$nbasis
indexi2 <- mi21:mi22
cmat[indexij1,indexi2] <- Cprod
}
}
}
}
mij22 <- mi22
for (j2 in 1:difeorder) {
if (!is.null(bwtlist[[j2]])) {
bfdParj2 <- bwtlist[[j2]]
bbasisij2 <- bfdParj2$fd$basis
if (bfdParj2$estimate) {
bbasismatij2 <- getbasismatrix(tx, bbasisij2)
weightij22 <- yprod[,j1,j2]
Cprod <- trapzmat(bbasismatij1,bbasismatij2,deltax,weightij22)
mij21 <- mij22 + 1
mij22 <- mij22 + bbasisij2$nbasis
indexij2 <- mij21:mij22
cmat[indexij1,indexij2] <- Cprod
}
}
}
lambdaj1 <- bfdParj1$lambda
if (lambdaj1 > 0) {
Lfdobj <- bfdParj1$Lfd
penmat <- lambdaj1*eval.penalty(bbasisij1, Lfdobj)
cmat[indexij1,indexij1] <- cmat[indexij1,indexij1] + penmat
}
}
dvec <- -symsolve(cmat,dmat)
mi2 <- 0
if (nforce > 0) {
for (iu in 1:nforce) {
if (!is.null(awtlist[[iu]])) {
afdPari <- awtlist[[iu]]
if (afdPari$estimate) {
mi1 <- mi2 + 1
mi2 <- mi2 + afdPari$fd$basis$nbasis
indexi <- mi1:mi2
afdPari$fd$coefs <- as.matrix(dvec[indexi])
awtlist[[iu]] <- afdPari
}
}
}
}
mij2 <- mi2
for (j in 1:difeorder) {
if (!is.null(bwtlist[[j]])) {
bfdParj <- bwtlist[[j]]
if (bfdParj$estimate) {
mij1 <- mij2 + 1
mij2 <- mij2 + bfdParj$fd$basis$nbasis
indexij <- mij1:mij2
bfdParj$fd$coefs <- as.matrix(dvec[indexij])
bwtlist[[j]] <- bfdParj
}
}
}
resmat <- eval.fd(tx, xfdobj, difeorder)
if (nforce > 0) {
onesncurve <- rep(1,ncurve)
for (iu in 1:nforce) {
if (!is.null(awtlist[[iu]])) {
afdPari <- awtlist[[iu]]
aveci <- as.vector(eval.fd(tx, afdPari$fd))
umati <- eval.fd(tx, ufdlist[[iu]])
aumati <- outer(aveci,onesncurve)*umati
resmat <- resmat - aumati
}
}
}
for (j in 1:difeorder) {
if (!is.null(bwtlist[[j]])) {
bfdParj <- bwtlist[[j]]
bmatij <- as.vector(eval.fd(tx, bfdParj$fd))
xmatij <- eval.fd(tx, xfdobj, j-1)
resmat <- resmat + bmatij*xmatij
}
}
resbasis <- xbasis
resfd <- smooth.basis(tx, resmat, resbasis)$fd
resfdnames <- xfdobj$fdnames
resfdnames[[2]] <- "Residual function"
resfdnames[[3]] <- "Residual function value"
resfd$fdnames <- resfdnames
resfdlist <- list(resfd)
} else {
if (is.null(ufdlist) || is.null(awtlist)) {
awtlist <- NULL
} else {
if (length(ufdlist) != nvar)
stop(paste("The length of UFDLIST",
" does not match that of XFDLIST."))
errorwrd = FALSE
for (j in 1:nvar) {
if (!is.null(ufdlist[[j]])) {
nforce <- length(ufdlist[[j]])
if (length(awtlist[[j]]) != nforce) {
print(paste("The length of AWTLIST[[",j,
"]] is incorrect.",sep=""))
errorwrd = TRUE
}
}
}
if (errorwrd) stop("")
}
if (length(bwtlist) != nvar) stop("Length of BWTLIST is incorrect.")
errorwrd = FALSE
for (ivar in 1:nvar) {
if (length(bwtlist[[ivar]]) != nvar) {
print(paste("The length of BWTLIST[[",ivar,
"]] is incorrect.",sep=""))
errorwrd = TRUE
}
}
if (errorwrd) stop("")
xfd1 <- xfdlist[[1]]
xcoef1 <- xfd1$coefs
xbasis1 <- xfd1$basis
xrange1 <- xbasis1$rangeval
ncurve <- dim(xcoef1)[2]
resfdnames <- xfd1$fdnames
errorwrd = FALSE
for (ivar in 1:nvar) {
xfdi <- xfdlist[[ivar]]
xcoefi <- xfdi$coefs
xbasisi <- xfdi$basis
xrangei <- xbasisi$rangeval
ncurvei <- dim(xcoefi)[2]
if (!inherits(xfdi, "fd")) {
print(paste("XFDLIST[[",ivar,
"]] is not a functional data object.",sep=""))
errorwrd = TRUE
} else {
if (any(xrangei != xrange1)) {
print("Ranges are incompatible for XFDLIST.")
errorwrd = TRUE
}
if (ncurvei != ncurve) {
print("Number of curves is incompatible for XFDLIST.")
errorwrd = TRUE
}
}
}
if (errorwrd) stop("")
nbasmax <- xbasis1$nbasis
if (!(is.null(ufdlist) || is.null(awtlist))) {
urange <- ufdlist[[1]]$basis$rangeval
errorwrd <- FALSE
for (ivar in 1:nvar) {
if (!is.null(ufdlist[[ivar]])) {
for (iu in 1:length(ufdlist[[ivar]])) {
ufdiviu <- ufdlist[[ivar]][[iu]]
if (!inherits(ufdiviu, "fd")) {
print(paste("UFDLIST[[",ivar,",",iu,
"]] is not a functional data object.",
sep=""))
errorwrd <- TRUE
}
if (any(ufdiviu$basis$rangeval != urange)) {
print("Ranges are incompatible for UFDLIST.")
errorwrd <- TRUE
}
awtfdPari <- awtlist[[ivar]][[iu]]
if (!inherits(awtfdPari, "fdPar")) {
print(paste("AWTFDPAR[[",ivar,"]][[",iu,
"]] is not a functional parameter object.",sep=""))
errorwrd <- TRUE
}
afdi <- awtfdPari$fd
basisi <- afdi$basis
if (any(basisi$rangeval != urange)) {
print("Ranges are incompatible for AWTLIST.")
errorwrd <- TRUE
}
nbasmax <- max(c(nbasmax,basisi$nbasis))
}
if (errorwrd) stop("")
}
}
}
errorwrd <- FALSE
for (ivar1 in 1:nvar) {
for (ivar2 in 1:nvar) {
difeorder <- length(bwtlist[[ivar1]][[ivar2]])
for (j in 1:difeorder) {
if (!is.null(bwtlist[[ivar1]][[ivar2]][[j]])) {
bfdPari1i2j <- bwtlist[[ivar1]][[ivar2]][[j]]
if (!inherits(bfdPari1i2j, "fdPar")) {
print(paste("BWTLIST[[",ivar1, ",",ivar2, ",",j,
"]] is not a functional parameter object.",sep=""))
errorwrd = TRUE
}
basisi1i2j <- bfdPari1i2j$fd$basis
if (any(basisi1i2j$rangeval != xrange1)) {
print(paste("Ranges are incompatible for BWTLIST[[",
ivar1,"]][[",ivar2,"]][[",
j,"]]",sep=""))
errorwrd <- TRUE
}
nbasmax <- max(c(nbasmax,basisi1i2j$nbasis))
}
}
}
}
if (errorwrd) stop("")
if (nfine < 5*nbasmax) nfine <- 5*nbasmax
deltax <- (xrange1[2]-xrange1[1])/(nfine-1)
tx <- seq(xrange1[1],xrange1[2],deltax)
yarray <- vector("list", 0)
for (ivar in 1:nvar) {
difeorder <- length(bwtlist[[ivar]][[ivar]])
difeordp1 <- difeorder + 1
yarray[[ivar]] <- array(0,c(nfine,ncurve,difeordp1))
for (j in 1:difeordp1){
yj <- eval.fd(tx, xfdlist[[ivar]], j-1)
yarray[[ivar]][,,j] <- as.matrix(yj)
}
}
if (!is.null(ufdlist)) {
uarray <- vector("list", nvar)
for (ivar in 1:nvar) {
if (is.null(ufdlist[[ivar]])) {
uarray[[ivar]] <- NULL
} else {
nforce <- length(ufdlist[[ivar]])
uarray[[ivar]] <- vector("list", nforce)
for (iu in 1:nforce)
uarray[[ivar]][[iu]] <- matrix(0,nfine,ncurve)
}
}
for (ivar in 1:nvar) {
if (!is.null(ufdlist[[ivar]])) {
nforce <- length(ufdlist[[ivar]])
for (iu in 1:nforce)
uarray[[ivar]][[iu]] <- eval.fd(tx, ufdlist[[ivar]][[iu]])
}
}
}
yprod <- vector("list", nvar)
for (i1 in 1:nvar) yprod[[i1]] <- vector("list", nvar)
for (i1 in 1:nvar) {
difeord1p1 <- length(bwtlist[[i1]][[i1]]) + 1
for (i2 in 1:nvar) {
difeord2p1 <- length(bwtlist[[i2]][[i2]]) + 1
yprod[[i1]][[i2]] <- array(0,c(nfine,difeord2p1,difeord2p1))
}
}
for (i1 in 1:nvar) {
difeord1p1 <- length(bwtlist[[i1]][[i1]]) + 1
for (j1 in 1:difeordp1) {
for (i2 in 1:nvar) {
difeord2p1 <- length(bwtlist[[i2]][[i2]]) + 1
for (j2 in 1:difeord2p1) {
if (ncurve == 1) {
yprodval <- yarray[[i1]][,1,j1]*yarray[[i2]][,1,j2]
} else {
yprodval <- apply(yarray[[i1]][,,j1]*yarray[[i2]][,,j2],1,mean)
}
yprod[[i1]][[i2]][,j1,j2] <- yprodval
}
}
}
}
if (!is.null(ufdlist)) {
yuprod <- vector("list", nvar)
for (i1 in 1:nvar) {
if (!is.null(ufdlist[[i1]])) {
nforce <- length(ufdlist[[i1]])
if (nforce > 0) {
yuprod[[i1]] <- vector("list", nforce)
for (iu in 1:nforce) {
difeordp1 <- length(bwtlist[[i1]][[i1]]) + 1
yuprod[[i1]][[iu]] <- matrix(0,nfine,difeordp1)
}
}
}
}
onesncurve <- rep(1,ncurve)
for (i1 in 1:nvar) {
if (!is.null(ufdlist[[i1]])) {
nforce <- length(ufdlist[[i1]])
if (nforce > 0) {
difeordp1 <- length(bwtlist[[i1]][[i1]]) + 1
for (iu in 1:nforce) {
for (j1 in 1:difeordp1) {
if (ncurve == 1) {
yuprodval <- yarray[[i1]][,1,j1]*uarray[[i1]][[iu]]
} else {
yuprodval <- apply(yarray[[i1]][,,j1]*
outer(uarray[[i1]][[iu]],onesncurve),1,mean)
}
yuprod[[i1]][[iu]][,j1] <- yuprodval
}
}
}
}
}
}
if (!is.null(ufdlist)) {
uprod <- vector("list", nvar)
for (ivar in 1:nvar) {
nforce <- length(ufdlist[[ivar]])
if (nforce > 0) {
uprod[[ivar]] <- array(0,c(nfine, nforce, nforce))
for (iu in 1:nforce) for (ju in 1:iu) {
uprodval <- uarray[[ivar]][[iu]]*uarray[[ivar]][[ju]]
uprod[[ivar]][,iu,ju] <- uprodval
uprod[[ivar]][,ju,iu] <- uprodval
}
}
}
}
onesn <- rep(1,nfine)
for (ivar in 1:nvar) {
neqns <- 0
if (is.null(ufdlist) || is.null(ufdlist[[ivar]])) nforce <- 0
else nforce <- length(ufdlist[[ivar]])
if (nforce > 0) {
for (iu in 1:nforce) {
afdPari <- awtlist[[ivar]][[iu]]
if (afdPari$estimate) {
nbasisiu <- afdPari$fd$basis$nbasis
neqns <- neqns + nbasisiu
}
}
}
for (i2 in 1:nvar) {
difeorder <- length(bwtlist[[ivar]][[i2]])
for (j2 in 1:difeorder) {
if (!is.null(bwtlist[[ivar]][[i2]][[j2]])) {
bfdParij <- bwtlist[[ivar]][[i2]][[j2]]
nbasisi2j2 = bfdParij$fd$basis$nbasis
if (bfdParij$estimate) neqns <- neqns + nbasisi2j2
}
}
}
if (neqns < 1) stop("Number of equations to solve is not positive.")
cmat <- matrix(0,neqns, neqns)
dmat <- matrix(0,neqns, 1)
if (nforce > 0) {
aarray <- matrix(0,nfine,nforce)
for (iu in 1:nforce) {
if (!is.null(awtlist[[ivar]][[iu]])) {
afdPari <- awtlist[[ivar]][[iu]]
aarray[,iu] <- eval.fd(tx, afdPari$fd)
}
}
}
barray <- vector("list", nvar)
for (i in 1:nvar) {
difeorder <- length(bwtlist[[ivar]][[i]])
barray[[i]] <- matrix(0,nfine,difeorder)
for (j in 1:difeorder) {
if (!is.null(bwtlist[[ivar]][[i]][[j]])) {
bfdParij <- bwtlist[[ivar]][[i]][[j]]
barray[[i]][,j] <- as.matrix(eval.fd(tx, bfdParij$fd))
}
}
}
mi12 <- 0
if (nforce > 0) {
for (iu1 in 1:nforce) {
if (!is.null(awtlist[[ivar]][[iu1]])) {
afdPari1 <- awtlist[[ivar]][[iu1]]
if (afdPari1$estimate) {
abasisi1 <- afdPari1$fd$basis
abasismati1 <- getbasismatrix(tx, abasisi1)
mi11 <- mi12 + 1
mi12 <- mi12 + abasisi1$nbasis
indexi1 <- mi11:mi12
weighti1 <- -yuprod[[ivar]][[iu1]][,difeordp1]
dmat[indexi1] <- trapzmat(abasismati1,onesn,deltax,weighti1)
for (i in 1:nvar) {
difeorder <- length(bwtlist[[ivar]][[i]])
for (j in 1:difeorder) {
bfdParij <- bwtlist[[ivar]][[i]][[j]]
if (!is.null(bwtlist[[ivar]][[i]][[j]])) {
if (!bfdParij$estimate) {
weightij <- -yuprod[[ivar]][[iu1]][,j]
dmat[indexi1] <- dmat[indexi1] +
trapzmat(abasismati1, barray[[ivar]][,j],
deltax, weightij)
}
}
}
}
mi22 <- 0
for (iu2 in 1:nforce) {
if (!is.null(awtlist[[ivar]][[iu2]])) {
afdPari2 <- awtlist[[ivar]][[iu2]]
if (afdPari2$estimate) {
abasisi2 <- afdPari2$fd$basis
abasismati2 <- getbasismatrix(tx, abasisi2)
weighti2 <- uprod[[ivar]][,iu1,iu2]
Cprod <- trapzmat(abasismati1, abasismati2,
deltax, weighti2)
mi21 <- mi22 + 1
mi22 <- mi22 + abasisi2$nbasis
indexi2 <- mi21:mi22
cmat[indexi1,indexi2] <- Cprod
}
}
}
mij22 <- mi22
for (i2 in 1:nvar) {
if (!is.null(bwtlist[[ivar]][[i2]])) {
difeorder <- length(bwtlist[[ivar]][[i2]])
for (j2 in 1:difeorder) {
bfdParij2 <- bwtlist[[ivar]][[i2]][[j2]]
if (bfdParij2$estimate) {
bbasisij2 <- bfdParij2$fd$basis
bbasismatij2 <- getbasismatrix(tx, bbasisij2)
weightij12 <- -yuprod[[i2]][[iu1]][,j2]
Cprod <- trapzmat(abasismati1,bbasismatij2,
deltax,weightij12)
mij21 <- mij22 + 1
mij22 <- mij22 + bbasisij2$nbasis
indexij2 <- mij21:mij22
cmat[indexi1,indexij2] <- Cprod
}
}
}
}
lambdai1 <- afdPari1$lambda
if (lambdai1 > 0) {
Lfdobj <- afdPari1$Lfd
penmat <- lambdai1*eval.penalty(abasisi1,Lfdobj)
cmat[indexi1,indexi1] <- cmat[indexi1,indexi1] + penmat
}
}
}
}
}
mij12 <- mi12
for (i1 in 1:nvar) {
difeorder1 <- length(bwtlist[[ivar]][[i1]])
difeordp1 <- difeorder1 + 1
for (j1 in 1:difeorder1) {
if (!is.null(bwtlist[[ivar]][[i1]][[j1]])) {
bfdParij1 <- bwtlist[[ivar]][[i1]][[j1]]
if (bfdParij1$estimate) {
bbasisij1 <- bfdParij1$fd$basis
bbasismatij1 <- getbasismatrix(tx, bbasisij1)
mij11 <- mij12 + 1
mij12 <- mij12 + bbasisij1$nbasis
indexij1 <- mij11:mij12
weightij1 <- yprod[[i1]][[ivar]][,j1,difeordp1]
trapzij1 <- trapzmat(bbasismatij1,onesn,deltax,weightij1)
dmat[indexij1] <- trapzij1
if (nforce > 0) {
for (iu in 1:nforce) {
if (!is.null(awtlist[[ivar]][[iu]])) {
afdPari <- awtlist[[ivar]][[iu]]
if (!afdPari$estimate) {
weightijk <- yprod[,ivar,iu,j1]
trapzijk <-trapzmat(bbasismatij1,aarray[,iu],
deltax,weightijk)
dmat[indexij1] <- dmat[indexij1] + trapzijk
}
}
}
}
mi22 <- 0
if (nforce > 0) {
for (iu2 in 1:nforce) {
if (!is.null(awtlist[[ivar]][[iu2]])) {
afdPari2 <- awtlist[[ivar]][[iu2]]
if (afdPari2$estimate) {
abasisi2 <- afdPari2$fd$basis
abasismati2 <- getbasismatrix(tx, abasisi2)
weighti2 <- -yuprod[[i1]][[iu2]][,j1]
mi21 <- mi22 + 1
mi22 <- mi22 + abasisi2$nbasis
indexi2 <- mi21:mi22
Cprod <- trapzmat(bbasismatij1,abasismati2,deltax,weighti2)
cmat[indexij1,indexi2] <- cmat[indexij1,indexi2] + Cprod
}
}
}
}
mij22 <- mi22
for (i2 in 1:nvar) {
difeorder2 <- length(bwtlist[[ivar]][[i2]])
for (j2 in 1:difeorder2) {
if (!is.null(bwtlist[[ivar]][[i2]][[j2]])) {
bfdParij2 <- bwtlist[[ivar]][[i2]][[j2]]
bbasisij2 <- bfdParij2$fd$basis
bbasismatij2 <- getbasismatrix(tx, bbasisij2)
weightij22 <- yprod[[i1]][[i2]][,j1,j2]
Cprod <- trapzmat(bbasismatij1,bbasismatij2,deltax,weightij22)
if (bfdParij2$estimate) {
mij21 <- mij22 + 1
mij22 <- mij22 + bbasisij2$nbasis
indexij2 <- mij21:mij22
cmat[indexij1,indexij2] <- cmat[indexij1,indexij2] + Cprod
}
}
}
}
lambdaij1 <- bfdParij1$lambda
if (lambdaij1 > 0) {
Lfdobj <- bfdParij1$Lfd
penmat <- lambdaij1*eval.penalty(bbasisij1,Lfdobj)
cmat[indexij1,indexij1] <- cmat[indexij1,indexij1] +
penmat
}
}
}
}
}
dvec <- -solve(cmat,dmat)
mi2 <- 0
if (nforce > 0) {
for (iu in 1:nforce) {
if (!is.null(awtlist[[ivar]][[iu]])) {
afdPari <- awtlist[[ivar]][[iu]]
if (afdPari$estimate) {
mi1 <- mi2 + 1
mi2 <- mi2 + afdPari$fd$basis$nbasis
indexi <- mi1:mi2
afdPari$fd$coefs <- as.matrix(dvec[indexi])
awtlist[[ivar]][[iu]] <- afdPari
}
}
}
}
mij2 <- mi2
for (i1 in 1:nvar) {
difeorder <- length(bwtlist[[ivar]][[i1]])
for (j1 in 1:difeorder) {
if (!is.null(bwtlist[[ivar]][[i1]][[j1]])) {
bfdParij <- bwtlist[[ivar]][[i1]][[j1]]
if (bfdParij$estimate) {
mij1 <- mij2 + 1
mij2 <- mij2 + bfdParij$fd$basis$nbasis
indexij <- mij1:mij2
bfdParij$fd$coefs <- as.matrix(dvec[indexij])
bwtlist[[ivar]][[i1]][[j1]] <- bfdParij
}
}
}
}
}
resfdlist <- vector("list", nvar)
for (ivar in 1:nvar) {
difeorder <- length(bwtlist[[ivar]][[ivar]])
xfdi <- xfdlist[[ivar]]
resbasis <- xfdi$basis
resmat <- eval.fd(tx, xfdi, difeorder)
onesncurve <- rep(1,ncurve)
if (!is.null(ufdlist)) {
nforce <- length(ufdlist[[ivar]])
if (nforce > 0) {
for (iu in 1:nforce) {
if (!is.null(awtlist[[ivar]][[iu]])) {
afdPari <- awtlist[[ivar]][[iu]]
amati <- as.vector(eval.fd(tx, afdPari$fd))
umati <- eval.fd(tx, ufdlist[[ivar]][[iu]])
if (ncurve == 1) aumati <- amati*umati
else aumati <- outer(amati,onesncurve)*umati
resmat <- resmat - aumati
}
}
}
}
for (i1 in 1:nvar) {
difeorder <- length(bwtlist[[ivar]][[i1]])
for (j1 in 1:difeorder) {
if (!is.null(bwtlist[[ivar]][[i1]][[j1]])) {
bfdParij <- bwtlist[[ivar]][[i1]][[j1]]
bfdij <- bfdParij$fd
bvecij <- as.vector(eval.fd(tx, bfdij))
if (ncurve == 1) {
bmatij <- bvecij
} else {
bmatij <- outer(bvecij,onesncurve)
}
xmatij <- eval.fd(tx, xfdlist[[i1]], j1-1)
resmat <- resmat + bmatij*xmatij
}
}
}
resfdi <- smooth.basis(tx, resmat, resbasis)$fd
resfdnames <- xfdi$fdnames
resfdnames[[2]] <- "Residual function"
resfdnames[[3]] <- "Residual function value"
resfdlist[[ivar]] <- resfdi
}
}
pdaList <- list(bwtlist=bwtlist, resfdlist=resfdlist, awtlist=awtlist)
class(pdaList) <- 'pda.fd'
pdaList
} |
norm <- function(x, type = c("O", "I", "F", "M", "2")) {
if(identical("2", type)) {
if(anyNA(x)) return(NA_real_)
svd(x, nu = 0L, nv = 0L)$d[1L]
} else
.Internal(La_dlange(x, type))
}
kappa <- function(z, ...) UseMethod("kappa")
rcond <- function(x, norm = c("O","I","1"), triangular = FALSE, ...) {
norm <- match.arg(norm)
stopifnot(is.matrix(x))
if({d <- dim(x); d[1L] != d[2L]})
return(rcond(qr.R(qr(if(d[1L] < d[2L]) t(x) else x)), norm=norm, ...))
if(is.complex(x)) {
if(triangular) .Internal(La_ztrcon(x, norm))
else .Internal(La_zgecon(x, norm))
} else {
if(triangular) .Internal(La_dtrcon(x, norm))
else .Internal(La_dgecon(x, norm))
}
}
kappa.default <- function(z, exact = FALSE,
norm = NULL, method = c("qr", "direct"), ...)
{
method <- match.arg(method)
z <- as.matrix(z)
norm <- if(!is.null(norm)) match.arg(norm, c("2", "1","O", "I")) else "2"
if(exact && norm == "2") {
s <- svd(z, nu = 0, nv = 0)$d
max(s)/min(s[s > 0])
}
else {
if(exact)
warning(gettextf("norm '%s' currently always uses exact = FALSE",
norm))
d <- dim(z)
if(method == "qr" || d[1L] != d[2L])
kappa.qr(qr(if(d[1L] < d[2L]) t(z) else z),
exact = FALSE, norm = norm, ...)
else .kappa_tri(z, exact = FALSE, norm = norm, ...)
}
}
kappa.lm <- function(z, ...) kappa.qr(z$qr, ...)
kappa.qr <- function(z, ...)
{
qr <- z$qr
R <- qr[1L:min(dim(qr)), , drop = FALSE]
R[lower.tri(R)] <- 0
.kappa_tri(R, ...)
}
.kappa_tri <- function(z, exact = FALSE, LINPACK = TRUE, norm=NULL, ...)
{
if(exact) {
stopifnot(is.null(norm) || identical("2", norm))
kappa.default(z, exact = TRUE)
}
else {
p <- as.integer(nrow(z))
if(is.na(p)) stop("invalid nrow(x)")
if(p != ncol(z)) stop("triangular matrix should be square")
if(is.null(norm)) norm <- "1"
if(is.complex(z)) 1/.Internal(La_ztrcon(z, norm))
else if(LINPACK) {
if(norm == "I")
z <- t(z)
storage.mode(z) <- "double"
1 / .Fortran(.F_dtrco, z, p, p, k = double(1), double(p), 1L)$k
}
else 1/.Internal(La_dtrcon(z, norm))
}
} |
solve.quadratic <- function (a, b, c, nan.upper=NA, nan.lower=NA) {
d <- b * b - 4 * a * c
data.frame(lower = ifelse(d < 0, rep(nan.upper, length(d)), (-b - sqrt(d)) / (2*a)),
upper = ifelse(d < 0, rep(nan.lower, length(d)), (-b + sqrt(d)) / (2*a)))
} |
rxodeTest(
{
context("Test large model compiles")
mod <- RxODE("
if (Heart_failure_link == 1) {
R_art0 = (R_art0_initial-R_art0_initial*heart_failure_resistance_aorta_scale)/exp(sim_time/(24*5))+R_art0_initial*heart_failure_resistance_aorta_scale;
R_per0 = (R_per0_initial-R_per0_initial*heart_failure_resistance_prepheral_scale)/exp(sim_time/(24*5))+R_per0_initial*heart_failure_resistance_prepheral_scale;
R_al0 = R_al0_scale*R_per0;
R_cap0= R_cap0_scale*R_per0;
R_vn0 = R_vn0_scale*R_per0;
R_ven0 = (R_ven0_initial-R_ven0_initial*heart_failure_resistance_venous_scale)/exp(sim_time/(24*5))+R_ven0_initial*heart_failure_resistance_venous_scale;
R_art_pulm = (R_art_pulm_initial-R_art_pulm_initial*heart_failure_resistance_R_art_pulm_scale)/exp(sim_time/(24*5))+R_art_pulm_initial*heart_failure_resistance_R_art_pulm_scale;
R_ven_pulm = (R_ven_pulm_initial-R_ven_pulm_initial*heart_failure_resistance_R_ven_pulm_scale)/exp(sim_time/(24*5))+R_ven_pulm_initial*heart_failure_resistance_R_ven_pulm_scale;
C_art = (C_art_initial-C_art_initial*heart_failure_compliance_aorta_scale)/exp(sim_time/(24*5))+C_art_initial*heart_failure_compliance_aorta_scale;
C_ven0 = (C_ven0_initial-C_ven0_initial*heart_failure_compliance_venous_scale)/exp(sim_time/(24*5))+C_ven0_initial*heart_failure_compliance_venous_scale;
C_pulm_ven = (C_pulm_ven_initial-C_pulm_ven_initial*heart_failure_compliance_pulm_ven_scale)/exp(sim_time/(24*5))+C_pulm_ven_initial*heart_failure_compliance_pulm_ven_scale;
C_pulm_art = (C_pulm_art_initial-C_pulm_art_initial*heart_failure_compliance_pulm_art_scale)/exp(sim_time/(24*5))+C_pulm_art_initial*heart_failure_compliance_pulm_art_scale;
cf = (cf_initial-cf_initial*cf_scale)/exp(sim_time/(24))+cf_initial*cf_scale;
contractility = (contractility_initial-contractility_initial*contractility_HF_scale)/exp(sim_time/(24))+contractility_initial*contractility_HF_scale;
Sodium_protein_filtration_rate_Kf = (Sodium_protein_filtration_rate_Kf_nom-Sodium_protein_filtration_rate_Kf_nom*kf_scale)/exp(sim_time/(24))+Sodium_protein_filtration_rate_Kf_nom*kf_scale;
vascular_responsiveness_scale = (vascular_responsiveness_scale_nom-vascular_responsiveness_scale_nom*vascular_responsiveness_scale_nom_scale)/exp(sim_time/(24))+vascular_responsiveness_scale_nom*vascular_responsiveness_scale_nom_scale;
}
else {
R_art0 = R_art0_initial ;
R_per0 = R_per0_initial ;
R_al0 = R_al0_scale*R_per0;
R_cap0= R_cap0_scale*R_per0;
R_vn0 = R_vn0_scale*R_per0;
R_ven0 = R_ven0_initial ;
R_art_pulm = R_art_pulm_initial;
R_ven_pulm = R_ven_pulm_initial;
C_art = C_art_initial ;
C_ven0 = C_ven0_initial ;
C_pulm_ven = C_pulm_ven_initial;
C_pulm_art = C_pulm_art_initial;
cf = cf_initial;
contractility = contractility_initial;
Sodium_protein_filtration_rate_Kf = Sodium_protein_filtration_rate_Kf_nom;
vascular_responsiveness_scale = vascular_responsiveness_scale_nom;
}
CO_nom = CO_nom_initial;
plasma_protein_concentration =plasma_protein_amount / (blood_volume_L * L_dL);
ISF_protein_concentration = ISF_protein_amount / (interstitial_fluid_volume * L_dL);
if(test_ISF_pressure==1){
ISF_pressure = -0.000000002316554*interstitial_fluid_volume^6
+ 0.000000910096686*interstitial_fluid_volume^5 - 0.000141950241878*interstitial_fluid_volume^4
+ 0.011247538513374*interstitial_fluid_volume^3 - 0.471694546620885*interstitial_fluid_volume^2
+ 9.92251133652023*interstitial_fluid_volume - 81.2426416630404+5.09732;
}else{
ISF_pressure=ISF_pressure_initial;
}
if (Heart_failure_link == 0 & cardiac_reserve == 0 ) {
tissue_autoregulation_signal = max(0.1,1+tissue_autoreg_scale*(Kp_CO*(CO_delayed - CO_nom*CO_species_scale)+Ki_CO*CO_error));
peripheral_resistance_multiplier = disease_effect_on_TPR_peripheral_resistance * B2sna_effect_on_TPR*A1sna_effect_on_TPR*tissue_autoregulation_signal;
peripheral_resistance_multiplier_adjusted = max(dilation_scale,time_TPR_scale*(1+vascular_responsiveness_scale*(peripheral_resistance_multiplier-1)));
heart_rate = HR_heart_rate * BB_HR_effect ;
} else if (Heart_failure_link == 0 & cardiac_reserve == 1){
tissue_autoregulation_signal = max(0.1,1+tissue_autoreg_scale*(Kp_CO*(CO_delayed - CO_nom*CO_species_scale)+Ki_CO*CO_error));
HR_autoregulation_signal = 1+tissue_autoreg_scale*(HRP_CO*(CO_delayed - CO_nom*CO_species_scale)+HRI_CO*CO_error);
heart_rate_multiplier_adjusted = time_HR_scale*(1-HR_autoregulation_signal);
Heart_rate_increasing_ratio = min(Mag_HR_changing_ratio,(Heart_rate_increasing_ratio + heart_rate_multiplier_adjusted));
heart_rate = (HR_heart_rate * BB_HR_effect*(1+Heart_rate_increasing_ratio));
peripheral_resistance_multiplier = disease_effect_on_TPR_peripheral_resistance * B2sna_effect_on_TPR*A1sna_effect_on_TPR*tissue_autoregulation_signal;
peripheral_resistance_multiplier_adjusted = max(dilation_scale,time_TPR_scale*(1+vascular_responsiveness_scale*(peripheral_resistance_multiplier-1)));
} else if (Heart_failure_link == 1 & cardiac_reserve == 0){
tissue_autoregulation_signal = max(0.1,1+tissue_autoreg_scale*(Kp_HF_CO*(CO_delayed - CO_nom*CO_species_scale)+Ki_HF_CO*CO_error));
HR_autoregulation_signal = 1+tissue_autoreg_scale*(HRP_HF_CO*(CO_delayed - CO_nom*CO_species_scale)+HRI_HF_CO*CO_error);
peripheral_resistance_multiplier = disease_effect_on_TPR_peripheral_resistance * B2sna_effect_on_TPR*A1sna_effect_on_TPR*tissue_autoregulation_signal;
peripheral_resistance_multiplier_adjusted =max(dilation_HF_Chronic_scale,(1+vascular_responsiveness_scale*(peripheral_resistance_multiplier-1)));
heart_rate_multiplier_adjusted = (1-HR_autoregulation_signal);
Heart_rate_increasing_ratio = min(Mag_HR_HF_Chronic_ratio,(Heart_rate_increasing_ratio + heart_rate_multiplier_adjusted));
heart_rate = (HR_heart_rate * BB_HR_effect*(1+Heart_rate_increasing_ratio));
} else if (Heart_failure_link == 1& cardiac_reserve == 1){
tissue_autoregulation_signal = max(0.1,1+tissue_autoreg_scale*(Kp_HF_CO*(CO_delayed - CO_nom*CO_species_scale)+Ki_HF_CO*CO_error));
HR_autoregulation_signal = 1+tissue_autoreg_scale*(HRP_HF_CO*(CO_delayed - CO_nom*CO_species_scale)+HRI_HF_CO*CO_error);
peripheral_resistance_multiplier = disease_effect_on_TPR_peripheral_resistance * B2sna_effect_on_TPR*A1sna_effect_on_TPR*tissue_autoregulation_signal;
peripheral_resistance_multiplier_adjusted =max(dilation_HF_Acute_scale,(1+vascular_responsiveness_scale*(peripheral_resistance_multiplier-1)));
heart_rate_multiplier_adjusted = (1-HR_autoregulation_signal);
Heart_rate_increasing_ratio = min(Mag_HR_HF_Acute_ratio,(Heart_rate_increasing_ratio + heart_rate_multiplier_adjusted));
heart_rate = (HR_heart_rate * BB_HR_effect*(1+Heart_rate_increasing_ratio));
}
peripheral_resistance =TPR_scale_peripheral_resistance * R_al0*peripheral_resistance_multiplier_adjusted;
arterial_dis_resistance=peripheral_resistance;
capillary_resistance=R_cap0;
venules_resistance=R_vn0;
beat_duration = min_sec / heart_rate ;
beat_time = sim_time/beat_duration - floor(sim_time/beat_duration);
periods = floor(sim_time/beat_duration);
blood_volume = blood_volume_L/1000;
time_step = 0.01;
sna_effect_on_contractility=1;
sna_effect_on_HR = 1;
AngII_effect_on_venous_compliance=1;
SNA_effect_on_venous_compliance=1;
B2sna_effect_on_TPR = 1;
A1sna_effect_on_TPR = 1;
Ang_II_effect_on_systemic_resistance =1;
aldo_effect_on_systemic_resistances = 1;
CCB_effect_on_systemic_arterial_resistance = 1;
glu_eff_1 = 0;
angII_eff_1 = 0;
aldo_eff_1 = 0;
beta_blocker_effect_on_contractility = BB_contractility_effect;
BP_effect_on_compliance=1;
LV_volume_mL = LV_volume * m3_mL;
arterial_volume_mL = arterial_volume * m3_mL;
arterial_dis_circulation_volume_mL =arterial_dis_circulation_volume *m3_mL;
capillary_circulation_volume_mL =capillary_circulation_volume *m3_mL;
venules_circulation_volume_mL =venules_circulation_volume *m3_mL;
RV_volume_mL = RV_volume * m3_mL;
pulmonary_arterial_volume_mL = pulmonary_arterial_volume * m3_mL;
venous_volume_mL = venous_volume * m3_mL;
total_blood_volume_mL = LV_volume_mL + arterial_volume_mL + arterial_dis_circulation_volume_mL+capillary_circulation_volume_mL+venules_circulation_volume_mL + RV_volume_mL + pulmonary_arterial_volume_mL + pulmonary_venous_volume * m3_mL + venous_volume_mL;
baseline_total_myocyte_volume = V_w_0 - Baseline_Interstitial_Fibrosis - Baseline_Replacement_Fibrosis - Baseline_Interstitial_Tissue;
baseline_single_myocyte_volume = baseline_total_myocyte_volume/Baseline_Myocyte_Number;
baseline_myocyte_diameter = 2*sqrt(baseline_single_myocyte_volume/(Pi*Baseline_Myocyte_Length));
myocyte_length = Baseline_Myocyte_Length + change_in_myocyte_length;
myocyte_diameter = baseline_myocyte_diameter + change_in_myocyte_diameter;
single_myocyte_volume = myocyte_length * Pi * (myocyte_diameter ^ 2) / 4;
number_of_live_myocytes = Baseline_Myocyte_Number;
total_myocyte_volume = single_myocyte_volume * number_of_live_myocytes;
total_nonmyocyte_volume = Baseline_Interstitial_Fibrosis + Baseline_Interstitial_Tissue + Baseline_Replacement_Fibrosis;
LV_wall_volume = total_myocyte_volume + total_nonmyocyte_volume;
level_of_hypertrophy = LV_wall_volume / (baseline_total_myocyte_volume + total_nonmyocyte_volume);
outward_growth = LV_cavity_volume / LV_V0_baseline;
pct_change_in_myocyte_diameter = change_in_myocyte_diameter / baseline_myocyte_diameter * 100;
pct_change_in_myocyte_length = change_in_myocyte_length / Baseline_Myocyte_Length * 100;
LV_cavity_volume = LV_V0_baseline * (1 + myo_L_scale * change_in_myocyte_length / Baseline_Myocyte_Length) ^ 3 * (1 - myo_D_scale * change_in_myocyte_diameter / baseline_myocyte_diameter) ^ 2;
LV_fiber_stretch =((LV_volume + (LV_wall_volume/3)) / (LV_cavity_volume + (LV_wall_volume / 3)))^0.3333333;
LV_sarcomere_length = ls_0_passive_LV_sarcomere_length * LV_fiber_stretch;
LV_sarcomere_contraction_velocity = (LV_sarcomere_length - LV_sarcomere_length_delayed) / time_step;
contraction_velocity_effect_in_LV = (1 - LV_sarcomere_contraction_velocity / v0_LV_contraction_velocity_effect_in_LV) / (1 + Cv_contraction_velocity_effect_in_LV * LV_sarcomere_contraction_velocity / v0_LV_contraction_velocity_effect_in_LV);
if (LV_sarcomere_length > ls_a0) {
sarcomere_length_effect_in_LV = ((LV_sarcomere_length - ls_a0) / (ls_ar_sarcomere_length_effect_in_LV - ls_a0));
} else {
sarcomere_length_effect_in_LV = 0;
}
chamber_radius = ((LV_cavity_volume * 3 / 4 / Pi) ^ 0.3333333) * m_mm ;
chamber_diameter = 2 * chamber_radius;
outer_radius = (((LV_cavity_volume + LV_wall_volume) * 3 / 4 / Pi) ^ 0.3333333) * m_mm ;
h_wall = outer_radius - chamber_radius;
h_over_r = h_wall / chamber_radius;
EDV_chamber_radius = ((LV_EDV* 3 / 4 / Pi) ^ 0.33333333) * m_mm;
EDV_chamber_diameter = 2*EDV_chamber_radius;
EDV_outer_radius = (((LV_EDV + LV_wall_volume) * 3 / 4 / Pi) ^ 0.3333333) * m_mm ;
EDV_h_wall = EDV_outer_radius - EDV_chamber_radius;
EDV_h_over_r = EDV_h_wall / EDV_chamber_radius;
LV_mass = 1000000*LV_wall_volume*1.05 ;
RV_twitch_duration = RV_systolic_time_fraction * beat_duration;
t_d = tau_d_LV_twitch_shape;
t_r = tau_r_LV_twitch_shape;
t_twitch = t_r + t_d;
if (beat_time <= t_r) {
sin_signal=(sin(Pi*beat_time/t_twitch))^n_r_LV_twitch_shape;
}else {
sin_signal=(sin(Pi*beat_time/t_twitch))^n_r_LV_twitch_shape;
}
LV_twitch_shape = sin_signal;
if (beat_time < 0) {
LV_twitch_shape = 0;
}
if (beat_time > t_twitch) {
LV_twitch_shape=0;
}
RV_twitch_shape = (sin(Pi * beat_time / RV_twitch_duration)) ^ 2;
if (beat_time < 0) {
RV_twitch_shape=0;
}
if (beat_time > RV_twitch_duration) {
RV_twitch_shape = 0;
}
LV_active_stress = contractility_scale_LV_active_stress * contractility * sigma_ar * sarcomere_length_effect_in_LV * LV_twitch_shape * contraction_velocity_effect_in_LV * beta_blocker_effect_on_contractility *sna_effect_on_contractility;
hypertrophy_effect_on_Cf = hypertrophy_Cf_slope*(level_of_hypertrophy - 1);
C_f = cf*(1+hypertrophy_effect_on_Cf);
stretch_zero_S = stretch_min_LV_passive_stress_along_fiber - stretch_scale_LV_passive_stress_along_fiber;
if (LV_fiber_stretch >= stretch_zero_S) {
LV_passive_stress_along_fiber = s_f0 * (exp(C_f * (LV_fiber_stretch - stretch_zero_S)) - 1);
} else {
LV_passive_stress_along_fiber = 0;
}
LV_radial_stretch = 1/ (LV_fiber_stretch * LV_fiber_stretch);
if (LV_radial_stretch >= 1) {
LV_passive_radial_stress = s_r0 * (exp(c_r_LV * (LV_radial_stretch - 1)) - 1);
} else {
LV_passive_radial_stress = 0;
}
LV_total_stress = LV_active_stress + LV_passive_stress_along_fiber - 2 * LV_passive_radial_stress;
if (LV_volume > LV_V0_min) {
rel_volume_LV = 1+LV_wall_volume/LV_volume;
} else {
rel_volume_LV = 1+LV_wall_volume/LV_V0_min;
}
LV_pressure = LV_total_stress * log(rel_volume_LV)/ 3;
arterial_dis_pressure = P_al0 + (arterial_dis_circulation_volume-V_al0)/C_al;
capillary_pressure = P_cap0 + (capillary_circulation_volume-V_cap0)/C_cap;
venules_pressure = P_vn0 + (venules_circulation_volume-V_vn0)/C_vn;
venous_compliance=C_ven0*AngII_effect_on_venous_compliance*SNA_effect_on_venous_compliance;
venous_pressure = P_ven0 + (venous_volume - V_ven0) / venous_compliance;
RV_Cavity_Volume = RV_V0;
RV_wall_volume = V_w_0_RV;
RV_fiber_stretch = ((RV_volume + V_w_0_RV/3) / (RV_Cavity_Volume + RV_wall_volume/3))^(0.333);
RV_sarcomere_length = ls_a0_RV * RV_fiber_stretch;
if (RV_sarcomere_length > ls_a0_RV) {
sarcomere_length_effect_in_RV = (RV_sarcomere_length - ls_a0_RV) / (0.000002 - ls_a0_RV);
} else {
sarcomere_length_effect_in_RV = 0;
}
RV_sarcomere_contraction_velocity = (RV_sarcomere_length - RV_sarcomere_length_delayed) / time_step;
contraction_velocity_effect_in_RV = (1 - RV_sarcomere_contraction_velocity / v0_RV_contraction_velocity_effect_in_RV) / (1 + 0 * RV_sarcomere_contraction_velocity / v0_RV_contraction_velocity_effect_in_RV);
RV_active_stress_multiplier = contractility_RV*sna_effect_on_contractility;
RV_active_stress = contractility_RV * sigma_ar_RV * sarcomere_length_effect_in_RV * RV_twitch_shape * contraction_velocity_effect_in_RV*sna_effect_on_contractility;
RV_radial_stretch = 1/ (RV_fiber_stretch * RV_fiber_stretch);
if (RV_radial_stretch >= 1) {
RV_passive_radial_stress = s_r0_RV * (exp(c_r_RV * (RV_radial_stretch - 1)) - 1);
} else {
RV_passive_radial_stress = 0;
}
if (RV_fiber_stretch >= 1) {
RV_passive_stress_along_fiber = s_f0_RV * (exp(cf_RV * (RV_fiber_stretch - 1)) - 1);
} else {
RV_passive_stress_along_fiber = 0;
}
RV_total_stress = RV_active_stress + RV_passive_stress_along_fiber - 2 * RV_passive_radial_stress;
if (RV_volume > RV_V0_min) {
rel_volume = (1 + RV_wall_volume / RV_volume);
} else {
rel_volume = (1 + RV_wall_volume / RV_V0_min);
}
RV_pressure = RV_total_stress * log(rel_volume) / 3;
venous_flow = (venules_pressure - venous_pressure) / (R_ven0);
if (heart_renal_link == 1) {
venous_volume_target = blood_volume - LV_volume - arterial_volume - arterial_dis_circulation_volume-capillary_circulation_volume-venules_circulation_volume - RV_volume - pulmonary_arterial_volume - pulmonary_venous_volume;
} else {
venous_volume_target = venous_volume;
}
tricuspid_valve_flow_rate = max((venous_pressure - RV_pressure) / R_r_atrium,min_flux);
pulmonary_arterial_pressure = ( pulmonary_arterial_volume - V_pulm_art0 )/C_pulm_art + P_art0;
pulmonary_venous_pressure = P_ven0 + (pulmonary_venous_volume - V_pulm_ven0 )/C_pulm_ven;
pulmonary_arterial_blood_flow = (pulmonary_arterial_pressure - pulmonary_venous_pressure )/ R_ven_pulm ;
dP = RV_pressure - pulmonary_arterial_pressure;
Zn = L_pulm + time_step * R_art_pulm;
pulmonary_blood_flow = (pulmonary_blood_flow_delayed * L_pulm + dP * time_step) / Zn;
pulmonary_valve_flow_rate = max(pulmonary_blood_flow,min_flux);
if (pulmonary_venous_pressure > LV_pressure) {
mitral_valve_flow_rate = max ( (pulmonary_venous_pressure - LV_pressure)/R_left_atrium , min_flux ) ;
} else {
if (LV_pressure - pulmonary_venous_pressure < mitral_regurgitation_pressure_diff) {
mitral_valve_flow_rate = min_flux;
} else {
mitral_valve_flow_rate = (pulmonary_venous_pressure - LV_pressure)/R_left_atrium;
}
}
pulmonary_valve_flow_rate = max(pulmonary_blood_flow,min_flux);
Stiffness0=1/C_art;
arterial_stiffness = Stiffness0*(1+ (MAP_delayed - nominal_map_setpoint)*Stiffness_BP_slope);
arterial_compliance = 1/arterial_stiffness;
arterial_pressure = (arterial_volume - V_art0) / arterial_compliance + P_art0 ;
systemic_blood_flow = (arterial_pressure - arterial_dis_pressure) / arterial_dis_resistance;
arterial_dis_blood_flow = (arterial_dis_pressure-capillary_pressure)/capillary_resistance;
capillary_blood_flow = (capillary_pressure-venules_pressure)/venules_resistance;
venules_blood_flow = (venules_pressure-venous_pressure)/R_ven0;
dP_1 = LV_pressure - arterial_pressure;
Zn_1 = L_art + R_art0 * time_step;
aortic_blood_flow = (aortic_blood_flow_delayed * L_art*L_scale + dP_1 * time_step) / Zn_1;
aortic_valve_flow_rate = max(aortic_blood_flow,min_flux);
BNP = exp(BNP_factor*((LV_EDS+1736)/5.094)+3.14);
NTP = exp((log(BNP)+1.4919)/1.0694);
if (heart_renal_link == 1) {
Pra = 0.2787*exp(CO_delayed*0.2281);
Canp = max(0,7.427 - 6.554 / ( 1 + exp( Pra - 3.762) ) + deltaCanp);
lambdaANP = -0.1*Canp+1.1199;
} else {
lambdaANP = 1;
}
if (beat_time >= (1 - .01/beat_duration) && beat_time < 1) {
LV_pressure_diastolic_max = LV_pressure;
LV_stress_diastolic_max = LV_passive_stress_along_fiber;
LV_volume_maximum = LV_volume;
} else {
LV_pressure_diastolic_max = LV_EDP;
LV_stress_diastolic_max = LV_EDS;
LV_volume_maximum = LV_EDV;
}
LV_EDP_old = LV_pressure_diastolic_max;
LV_EDS_old = LV_stress_diastolic_max;
LV_EDV_old = LV_volume_maximum;
if (arterial_pressure_delayed < arterial_pressure_bigger_delay) {
systemic_pressure_minimum_1 = arterial_pressure_delayed;
} else {
systemic_pressure_minimum_1 = diastolic_pressure;
}
if (arterial_pressure_delayed < arterial_pressure) {
systemic_pressure_minimum = systemic_pressure_minimum_1;
} else {
systemic_pressure_minimum = diastolic_pressure;
}
if (arterial_pressure_delayed > arterial_pressure_bigger_delay) {
systemic_pressure_maximum_1 = arterial_pressure_delayed;
} else {
systemic_pressure_maximum_1 = systolic_pressure;
}
if (arterial_pressure_delayed > arterial_pressure) {
systemic_pressure_maximum= systemic_pressure_maximum_1;
} else {
systemic_pressure_maximum= systolic_pressure;
}
systolic_pressure_old = systemic_pressure_maximum;
diastolic_pressure_old = systemic_pressure_minimum;
if (beat_time >= (t_r*0.8) && beat_time < (t_r*0.85)) {
LV_peak_stress = LV_active_stress;
} else {
LV_peak_stress = LV_active_stress_peak;
}
if (LV_active_stress > 1) {
LV_active_stress_peak_old = LV_peak_stress;
} else {
LV_active_stress_peak_old = LV_active_stress_peak;
}
if (venous_pressure_delayed < venous_pressure_bigger_delay) {
venous_pressure_minimum_1 = venous_pressure_delayed;
} else {
venous_pressure_minimum_1 = venous_diastolic_pressure;
}
if (venous_pressure_delayed < venous_pressure) {
venous_pressure_minimum = venous_pressure_minimum_1;
} else {
venous_pressure_minimum = venous_diastolic_pressure;
}
if (venous_pressure_delayed > venous_pressure_bigger_delay) {
venous_pressure_maximum_1 = venous_pressure_delayed;
} else {
venous_pressure_maximum_1 = venous_systolic_pressure;
}
if (venous_pressure_delayed > venous_pressure) {
venous_pressure_maximum= venous_pressure_maximum_1;
} else {
venous_pressure_maximum= venous_systolic_pressure;
}
venous_systolic_pressure_old = venous_pressure_maximum;
venous_diastolic_pressure_old = venous_pressure_minimum;
if (capillary_pressure_delayed < capillary_pressure_bigger_delay) {
capillary_pressure_minimum_1 = capillary_pressure_delayed;
} else {
capillary_pressure_minimum_1 = capillary_diastolic_pressure;
}
if (capillary_pressure_delayed < capillary_pressure) {
capillary_pressure_minimum = capillary_pressure_minimum_1;
} else {
capillary_pressure_minimum = capillary_diastolic_pressure;
}
if (capillary_pressure_delayed > capillary_pressure_bigger_delay) {
capillary_pressure_maximum_1 = capillary_pressure_delayed;
} else {
capillary_pressure_maximum_1 = capillary_systolic_pressure;
}
if (capillary_pressure_delayed > capillary_pressure) {
capillary_pressure_maximum= capillary_pressure_maximum_1;
} else {
capillary_pressure_maximum= capillary_systolic_pressure;
}
capillary_systolic_pressure_old = capillary_pressure_maximum;
capillary_diastolic_pressure_old = capillary_pressure_minimum;
mean_capillary_pressure = (capillary_systolic_pressure/3+capillary_diastolic_pressure*2/3)*Pa_mmHg;
if (heart_renal_link == 1) {
mean_arterial_pressure_MAP = (systolic_pressure/3+diastolic_pressure*2/3)*Pa_mmHg;
mean_venous_pressure = (venous_systolic_pressure+venous_diastolic_pressure*2)/3*Pa_mmHg;
} else {
mean_arterial_pressure_MAP = nominal_map_setpoint;
mean_venous_pressure = P_venous;
}
if (LV_active_stress_peak > LV_active_stress_threshhold) {
kD_hypertrophy = (kD_HYPERTROPHY*C_renal_CV_timescale) * max(0, ((max_myocyte_diameter_increase) - change_in_myocyte_diameter)/(max_myocyte_diameter_increase));
} else {
kD_hypertrophy = (kD_HYPERTROPHY*C_renal_CV_timescale);
}
if (LV_EDS > LV_passive_stress_along_fiber_threshhold) {
kL_hypertrophy = (kL_HYPERTROPHY*C_renal_CV_timescale) * max(0, ((max_myocyte_length_increase) - change_in_myocyte_length)/(max_myocyte_length_increase));
} else {
kL_hypertrophy = 0;
}
LVID=((6*LV_EDV)/3.14159)^(1/3);
number_of_functional_glomeruli = baseline_nephrons*(1 - 0.3*disease_effects_decreasing_Kf);
number_of_functional_tubules = baseline_nephrons*(1-disease_effect_on_nephrons);
AT1_preaff_int = 1 - AT1_preaff_scale/2;
AT1_effect_on_preaff = AT1_preaff_int + AT1_preaff_scale/(1+exp(-(AT1_bound_AngII - nominal_equilibrium_AT1_bound_AngII)/AT1_preaff_slope));
AT1_aff_int = 1 - AT1_aff_scale/2;
AT1_effect_on_aff = AT1_aff_int + AT1_aff_scale/(1+exp(-(AT1_bound_AngII - nominal_equilibrium_AT1_bound_AngII)/AT1_aff_slope));
AT1_eff_int = 1 - AT1_eff_scale/2;
AT1_effect_on_eff = AT1_eff_int + AT1_eff_scale/(1+exp(-(AT1_bound_AngII - nominal_equilibrium_AT1_bound_AngII)/AT1_eff_slope));
rsna_preaff_int = 1 - rsna_preaff_scale/2;
rsna_effect_on_preaff = rsna_preaff_int + rsna_preaff_scale/(1+exp(-(renal_sympathetic_nerve_activity - nom_rsna)/rsna_preaff_slope));
if(renal_blood_flow_L_min_delayed < 1 & interstitial_fluid_volume < 15){
IF_Venous_RIHP_Effect_int = 1- IF_Venous_RIHP_Effect_scale/2;
IF_Venous_RIHP_Effect =(IF_Venous_RIHP_Effect_int+IF_Venous_RIHP_Effect_scale/(1+exp(-(renal_blood_flow_L_min_delayed-nom_renal_blood_flow_L_min)/IF_Venous_RIHP_Effect_slope)));
}else{
IF_Venous_RIHP_Effect=1;
}
preaff_arteriole_signal_multiplier = AT1_effect_on_preaff*rsna_effect_on_preaff*preafferent_pressure_autoreg_signal*CCB_effect_on_preafferent_resistance;
preaff_arteriole_adjusted_signal_multiplier = (1/(1+exp(preaff_signal_nonlin_scale*(1-preaff_arteriole_signal_multiplier)))+0.5);
preafferent_arteriole_resistance = IF_Venous_RIHP_Effect*nom_preafferent_arteriole_resistance*preaff_arteriole_adjusted_signal_multiplier;
afferent_arteriole_signal_multiplier = tubulo_glomerular_feedback_effect * AT1_effect_on_aff *glomerular_pressure_autoreg_signal*CCB_effect_on_afferent_resistance;
afferent_arteriole_adjusted_signal_multiplier = (1/(1+exp(afferent_signal_nonlin_scale*(1-afferent_arteriole_signal_multiplier)))+0.5);
afferent_arteriole_resistance = IF_Venous_RIHP_Effect*nom_afferent_arteriole_resistance*afferent_arteriole_adjusted_signal_multiplier*(1-ANP_effect_on_Arterial_Resistance);
efferent_arteriole_signal_multiplier = AT1_effect_on_eff * CCB_effect_on_efferent_resistance;
efferent_arteriole_adjusted_signal_multiplier = 1/(1+exp(efferent_signal_nonlin_scale*(1-efferent_arteriole_signal_multiplier)))+0.5;
efferent_arteriole_resistance = IF_Venous_RIHP_Effect*nom_efferent_arteriole_resistance*efferent_arteriole_adjusted_signal_multiplier;
RBF_autoreg_int = 1-RBF_autoreg_scale/2;
peritubular_autoreg_signal = RBF_autoreg_int + RBF_autoreg_scale/(1+exp((nom_renal_blood_flow_L_min - renal_blood_flow_L_min_delayed)/RBF_autoreg_steepness));
autoregulated_peritubular_resistance = peritubular_autoreg_signal*nom_peritubular_resistance;
renal_vascular_resistance = (preafferent_arteriole_resistance + (afferent_arteriole_resistance + efferent_arteriole_resistance) / number_of_functional_glomeruli + autoregulated_peritubular_resistance);
renal_blood_flow_L_min = ((mean_arterial_pressure_MAP - P_venous) / renal_vascular_resistance);
renal_blood_flow_ml_hr = renal_blood_flow_L_min * 1000 * 60;
preafferent_pressure = mean_arterial_pressure_MAP - renal_blood_flow_L_min*preafferent_arteriole_resistance;
glomerular_pressure = (mean_arterial_pressure_MAP - renal_blood_flow_L_min * (preafferent_arteriole_resistance + afferent_arteriole_resistance / number_of_functional_glomeruli));
postglomerular_pressure = (mean_arterial_pressure_MAP - renal_blood_flow_L_min * (preafferent_arteriole_resistance + (afferent_arteriole_resistance+efferent_arteriole_resistance) / number_of_functional_glomeruli));
preaff_autoreg_int = 1 - preaff_autoreg_scale/2;
preafferent_pressure_autoreg_function = preaff_autoreg_int+preaff_autoreg_scale/(1+exp((nom_preafferent_pressure - preafferent_pressure)/myogenic_steepness));
gp_autoreg_int = 1 - gp_autoreg_scale/2;
glomerular_pressure_autoreg_function = gp_autoreg_int+gp_autoreg_scale/(1+exp((nom_glomerular_pressure - glomerular_pressure)/myogenic_steepness));
GP_effect_increasing_Kf = (maximal_glom_surface_area_increase - disease_effects_increasing_Kf) * max(glomerular_pressure/(nom_glomerular_pressure+2) - 1,0) / (T_glomerular_pressure_increases_Kf/C_renal_CV_timescale);
glomerular_hydrostatic_conductance_Kf = nom_Kf*(1+disease_effects_increasing_Kf)*(1-disease_effects_decreasing_Kf);
net_filtration_pressure = glomerular_pressure - oncotic_pressure_difference - P_bowmans;
if (net_filtration_pressure <= 0) {
SNGFR_nL_min = 0.001;
} else {
SNGFR_nL_min = glomerular_hydrostatic_conductance_Kf * (glomerular_pressure - oncotic_pressure_difference - P_bowmans);
}
GFR = (SNGFR_nL_min / 1000 / 1000000 * number_of_functional_tubules);
GFR_ml_min = GFR * 1000;
filtration_fraction = GFR/renal_blood_flow_L_min;
serum_creatinine_concentration = serum_creatinine/blood_volume_L;
creatinine_clearance_rate = GFR_ml_min * dl_ml * serum_creatinine_concentration;
GPdiff = max(0, glomerular_pressure - (nom_GP_seiving_damage));
GP_effect_on_Seiving = Emax_seiving * GPdiff ^ Gamma_seiving / (GPdiff ^ Gamma_seiving + Km_seiving ^ Gamma_seiving);
nom_glomerular_albumin_sieving_coefficient = seiving_inf/(1-(1-seiving_inf)*exp(-c_albumin*SNGFR_nL_min));
glomerular_albumin_sieving_coefficient = nom_glomerular_albumin_sieving_coefficient*(1 + GP_effect_on_Seiving);
SN_albumin_filtration_rate = plasma_albumin_concentration*SNGFR_nL_min*1e-6*glomerular_albumin_sieving_coefficient;
SN_albumin_excretion_rate = max(0, SN_albumin_filtration_rate - SN_albumin_reabsorptive_capacity)+nom_albumin_excretion_rate;
albumin_excretion_rate = SN_albumin_excretion_rate*number_of_functional_tubules;
Oncotic_pressure_in = 1.629*plasma_protein_concentration+0.2935*(plasma_protein_concentration^2);
SNRBF_nl_min = 1e6*1000*renal_blood_flow_L_min/number_of_functional_glomeruli;
plasma_protein_concentration_out = (SNRBF_nl_min*plasma_protein_concentration-SN_albumin_filtration_rate)/(SNRBF_nl_min-SNGFR_nL_min);
Oncotic_pressure_out = 1.629*plasma_protein_concentration_out+0.2935*(plasma_protein_concentration_out^2);
oncotic_pressure_avg = (Oncotic_pressure_in+Oncotic_pressure_out)/2;
Na_concentration = sodium_amount / blood_volume_L;
IF_Na_concentration = IF_sodium_amount/interstitial_fluid_volume;
sodium_storate_rate = Q_Na_store*((max_stored_sodium - stored_sodium)/max_stored_sodium)*(IF_Na_concentration - ref_Na_concentration);
Na_water_controller = Na_controller_gain*(Kp_VP*(Na_concentration - ref_Na_concentration)+Ki_VP*Na_concentration_error);
normalized_vasopressin_concentration = 1 + Na_water_controller;
vasopressin_concentration = nominal_vasopressin_conc * normalized_vasopressin_concentration;
water_intake_vasopressin_int = 1-water_intake_vasopressin_scale/2;
water_intake = water_intake_species_scale*(nom_water_intake/60/24)*(water_intake_vasopressin_int + water_intake_vasopressin_scale/(1+exp((normalized_vasopressin_concentration_delayed-1)/water_intake_vasopressin_slope)));
daily_water_intake = (water_intake * 24 * 60);
L_pt_s1 = L_pt_s1_nom*(1+tubular_length_increase);
L_pt_s2 = L_pt_s2_nom*(1+tubular_length_increase);
L_pt_s3 = L_pt_s3_nom*(1+tubular_length_increase);
Dc_pt = Dc_pt_nom*(1+tubular_diameter_increase);
L_pt = L_pt_s1+L_pt_s2 + L_pt_s3;
SN_filtered_Na_load = (SNGFR_nL_min / 1000 / 1000000)*Na_concentration;
filtered_Na_load = SN_filtered_Na_load*number_of_functional_tubules;
pressure_natriuresis_f_int = 1- pressure_natriuresis_f_scale/2;
pressure_natriuresis_p_int = 1- pressure_natriuresis_p_scale/2;
pressure_natriuresis_signal_f = ((pressure_natriuresis_f_int+pressure_natriuresis_f_scale/(1+exp(-(renal_blood_flow_L_min_delayed-nom_renal_blood_flow_L_min)/pressure_natriuresis_f_slope))));
pressure_natriuresis_signal_p =(pressure_natriuresis_p_int+pressure_natriuresis_p_scale/(1+exp(-(RIHP_delayed-RIHP0)/pressure_natriuresis_p_slope)));
pressure_natriuresis_IF_int = 1- pressure_natriuresis_IF_scale/2;
pessure_natriuresis_signal_IF = ((pressure_natriuresis_IF_int+pressure_natriuresis_IF_scale/(1+exp(-(Net_oncotic_pressure_diff-Nom_Net_oncotic_pressure_diff)/pressure_natriuresis_IF_slope))));
pressure_natriuresis_signal = pressure_natriuresis_signal_f*pressure_natriuresis_signal_p*pessure_natriuresis_signal_IF;
pressure_natriuresis_PT_int = 1 - pressure_natriuresis_PT_scale/2;
pressure_natriuresis_PT_effect = max(0.001,pressure_natriuresis_PT_int + pressure_natriuresis_PT_scale / (1 + exp(pressure_natriuresis_signal-1)));
pressure_natriuresis_LoH_int = 1 - pressure_natriuresis_LoH_scale/2;
pressure_natriuresis_LoH_effect = max(0.001,pressure_natriuresis_LoH_int + pressure_natriuresis_LoH_scale / (1 + exp((postglomerular_pressure_delayed - RIHP0) / pressure_natriuresis_LoH_slope)));
pressure_natriuresis_DCT_magnitude = max(0,pressure_natriuresis_DCT_scale );
pressure_natriuresis_DCT_int = 1 - pressure_natriuresis_DCT_magnitude/2;
pressure_natriuresis_DCT_effect = max(0.001,pressure_natriuresis_DCT_int + pressure_natriuresis_DCT_magnitude/ (1 + exp((postglomerular_pressure_delayed - RIHP0) / pressure_natriuresis_DCT_slope)));
pressure_natriuresis_CD_magnitude = max(0,pressure_natriuresis_CD_scale *(1+disease_effects_decreasing_CD_PN));
pressure_natriuresis_CD_int = 1 - pressure_natriuresis_CD_magnitude/2;
pressure_natriuresis_CD_effect = max(0.001,pressure_natriuresis_CD_int + pressure_natriuresis_CD_magnitude/ (1 + exp(pressure_natriuresis_signal-1)));
AT1_PT_int = 1 - AT1_PT_scale/2;
AT1_effect_on_PT = AT1_PT_int + AT1_PT_scale/(1+exp(-(AT1_bound_AngII - nominal_equilibrium_AT1_bound_AngII)/AT1_PT_slope));
rsna_PT_int = 1 - rsna_PT_scale/2;
rsna_effect_on_PT = 1;
rsna_CD_int = 1 - rsna_CD_scale/2;
rsna_effect_on_CD= rsna_CD_int + rsna_CD_scale/(1+exp((1 - renal_sympathetic_nerve_activity)/rsna_CD_slope));
aldosterone_concentration = normalized_aldosterone_level* nominal_aldosterone_concentration;
Aldo_MR_normalised_effect = normalized_aldosterone_level*(1 - pct_target_inhibition_MRA);
aldo_DCT_int = 1 - aldo_DCT_scale/2;
aldo_effect_on_DCT = aldo_DCT_int + aldo_DCT_scale/(1+exp((1 - Aldo_MR_normalised_effect)/aldo_DCT_slope));
aldo_CD_int = 1 - aldo_CD_scale/2;
aldo_effect_on_CD= aldo_CD_int + aldo_CD_scale/(1+exp((1 - Aldo_MR_normalised_effect)/aldo_CD_slope));
NHE3inhib = Anhe3*RUGE_delayed;
pt_multiplier = AT1_effect_on_PT * rsna_effect_on_PT *pressure_natriuresis_PT_effect*(1-NHE3inhib);
e_pt_sodreab = min(1,nominal_pt_na_reabsorption_nonSGLT * pt_multiplier);
e_dct_sodreab = min(1,nominal_dt_na_reabsorption * aldo_effect_on_DCT*pressure_natriuresis_DCT_effect );
cd_multiplier = aldo_effect_on_CD*rsna_effect_on_CD*pressure_natriuresis_CD_effect;
cd_scale = max_cd_reabs_rate/nominal_cd_na_reabsorption-1;
e_cd_sodreab = min(0.9999,nominal_cd_na_reabsorption*cd_multiplier*lambdaANP);
glucose_reabs_per_unit_length_s1 = nom_glucose_reabs_per_unit_length_s1*diabetic_adaptation*SGLT2_inhibition_delayed*(1+RTg_compensation);
glucose_reabs_per_unit_length_s2 = nom_glucose_reabs_per_unit_length_s2*diabetic_adaptation*SGLT2_inhibition_delayed*(1+RTg_compensation);
glucose_reabs_per_unit_length_s3 = nom_glucose_reabs_per_unit_length_s3*diabetic_adaptation*(1+RTg_compensation)*SGLT1_inhibition;
SN_filtered_glucose_load = glucose_concentration*SNGFR_nL_min / 1000 / 1000000;
glucose_pt_out_s1 = max(0,SN_filtered_glucose_load-glucose_reabs_per_unit_length_s1*L_pt_s1);
glucose_pt_out_s2 = max(0,glucose_pt_out_s1-glucose_reabs_per_unit_length_s2*L_pt_s2);
glucose_pt_out_s3 = max(0,glucose_pt_out_s2-glucose_reabs_per_unit_length_s3*L_pt_s3);
RUGE = glucose_pt_out_s3*number_of_functional_tubules*180;
excess_glucose_increasing_RTg = (maximal_RTg_increase - RTg_compensation) * max(RUGE,0) / (T_glucose_RTg/C_renal_CV_timescale);
osmotic_natriuresis_effect_pt = 1-min(1,RUGE *glucose_natriuresis_effect_pt);
osmotic_natriuresis_effect_cd = 1-min(1,RUGE *glucose_natriuresis_effect_cd);
osmotic_diuresis_effect_pt = 1-min(1,RUGE *glucose_diuresis_effect_pt);
osmotic_diuresis_effect_cd = 1-min(1,RUGE *glucose_diuresis_effect_cd);
SN_filtered_Na_load = (SNGFR_nL_min / 1000 / 1000000)*Na_concentration;
SGTL2_Na_reabs_mmol_s1 = SN_filtered_glucose_load-glucose_pt_out_s1;
SGTL2_Na_reabs_mmol_s2 = glucose_pt_out_s1-glucose_pt_out_s2;
SGTL1_Na_reabs_mmol = 2*(glucose_pt_out_s2-glucose_pt_out_s3);
total_SGLT_Na_reabs = SGTL2_Na_reabs_mmol_s1+SGTL2_Na_reabs_mmol_s2+SGTL1_Na_reabs_mmol;
Na_reabs_per_unit_length = -log(1-e_pt_sodreab)/(L_pt_s1+L_pt_s2+L_pt_s3);
Na_pt_s1_reabs = min(max_s1_Na_reabs, SN_filtered_Na_load*(1-exp(-Na_reabs_per_unit_length*L_pt_s1)));
Na_pt_out_s1 = SN_filtered_Na_load - Na_pt_s1_reabs - SGTL2_Na_reabs_mmol_s1 ;
Na_pt_s2_reabs = min(max_s2_Na_reabs, Na_pt_out_s1*(1-exp(-Na_reabs_per_unit_length*L_pt_s2)));
Na_pt_out_s2 = Na_pt_out_s1 - Na_pt_s2_reabs - SGTL2_Na_reabs_mmol_s2;
Na_pt_s3_reabs = min(max_s3_Na_reabs, Na_pt_out_s2*(1-exp(-Na_reabs_per_unit_length*L_pt_s3)));
Na_pt_out_s3 = Na_pt_out_s2 - Na_pt_s3_reabs - SGTL1_Na_reabs_mmol;
PT_Na_reabs_fraction = 1-Na_pt_out_s3/SN_filtered_Na_load;
SN_filtered_urea_load = (SNGFR_nL_min / 1000 / 1000000)*plasma_urea;
urea_out_s1 = SN_filtered_urea_load - urea_permeability_PT*(SN_filtered_urea_load/(0.5*((SNGFR_nL_min / 1000 / 1000000)+water_out_s1_delayed))-plasma_urea)*water_out_s1_delayed;
urea_out_s2 = urea_out_s1 - urea_permeability_PT*(urea_out_s1/(0.5*(water_out_s1_delayed+water_out_s2_delayed))-plasma_urea)*water_out_s2_delayed;
urea_out_s3 = urea_out_s2 - urea_permeability_PT*(urea_out_s2/(0.5*(water_out_s2_delayed+water_out_s3_delayed))-plasma_urea)*water_out_s3_delayed;
urea_reabsorption_fraction = 1-urea_out_s3/SN_filtered_urea_load;
osmoles_out_s1 = 2*Na_pt_out_s1 + glucose_pt_out_s1 + urea_out_s1;
water_out_s1 = (((SNGFR_nL_min / 1000 / 1000000)/(2*SN_filtered_Na_load+SN_filtered_glucose_load+ SN_filtered_urea_load)))*osmoles_out_s1;
osmoles_out_s2 = 2*Na_pt_out_s2 + glucose_pt_out_s2 + urea_out_s2;
water_out_s2 = (water_out_s1/osmoles_out_s1)*osmoles_out_s2;
osmoles_out_s3 = 2*Na_pt_out_s3 + glucose_pt_out_s3 + urea_out_s3;
water_out_s3 = (water_out_s2/osmoles_out_s2)*osmoles_out_s3;
PT_water_reabs_fraction = 1-water_out_s3/(SNGFR_nL_min / 1000 / 1000000);
Na_concentration_out_s1 = Na_pt_out_s1/water_out_s1;
Na_concentration_out_s2 = Na_pt_out_s2/water_out_s2;
Na_concentration_out_s3 = Na_pt_out_s3/water_out_s3;
glucose_concentration_out_s1 = glucose_pt_out_s1/water_out_s1;
glucose_concentration_out_s2 = glucose_pt_out_s2/water_out_s2;
glucose_concentration_out_s3 = glucose_pt_out_s3/water_out_s3;
urea_concentration_out_s1 = urea_out_s1/water_out_s1;
urea_concentration_out_s2 = urea_out_s2/water_out_s2;
urea_concentration_out_s3 = urea_out_s3/water_out_s3;
osmolality_out_s1 = osmoles_out_s1/water_out_s1;
osmolality_out_s2 = osmoles_out_s2/water_out_s2;
osmolality_out_s3 = osmoles_out_s3/water_out_s3;
PT_Na_outflow = Na_pt_out_s3*number_of_functional_tubules;
PT_Na_reab_perUnitSA = SN_filtered_Na_load*e_pt_sodreab/(3.14*Dc_pt*(L_pt_s1+L_pt_s2+L_pt_s3));
normalized_PT_reabsorption_density = PT_Na_reab_perUnitSA/PT_Na_reab_perUnitSA_0;
PT_Na_reabs_effect_increasing_tubular_length = 0;
PT_Na_reabs_effect_increasing_tubular_diameter = 0;
water_in_DescLoH = water_out_s3;
Na_in_DescLoH = Na_pt_out_s3;
urea_in_DescLoH = urea_out_s3;
glucose_in_DescLoH = glucose_pt_out_s3;
osmoles_in_DescLoH = osmoles_out_s3;
Na_concentration_in_DescLoH = Na_concentration_out_s3;
Urea_concentration_in_DescLoH = urea_concentration_out_s3;
glucose_concentration_in_DescLoH = glucose_concentration_out_s3;
osmolality_in_DescLoH = osmoles_out_s3/water_out_s3;
Na_out_DescLoH = Na_in_DescLoH;
urea_out_DescLoH = urea_in_DescLoH;
glucose_out_DescLoH = glucose_in_DescLoH;
osmoles_out_DescLoH = osmoles_in_DescLoH;
deltaLoH_NaFlow = min(max_deltaLoH_reabs,LoH_flow_dependence*(Na_out_DescLoH-nom_Na_in_AscLoH));
AscLoH_Reab_Rate =(2*nominal_loh_na_reabsorption*(nom_Na_in_AscLoH+deltaLoH_NaFlow)*loop_diuretic_effect)/L_lh_des;
effective_AscLoH_Reab_Rate =AscLoH_Reab_Rate*pressure_natriuresis_LoH_effect;
osmolality_out_DescLoH = osmolality_in_DescLoH*exp(min(effective_AscLoH_Reab_Rate*L_lh_des,2*Na_in_DescLoH)/(water_in_DescLoH*osmolality_in_DescLoH));
water_out_DescLoH = water_in_DescLoH*osmolality_in_DescLoH/osmolality_out_DescLoH;
Na_concentration_out_DescLoH = Na_out_DescLoH/water_out_DescLoH;
glucose_concentration_out_DescLoH = glucose_out_DescLoH/water_out_DescLoH;
urea_concentration_out_DescLoH = urea_out_DescLoH/water_out_DescLoH;
Na_in_AscLoH = Na_out_DescLoH;
urea_in_AscLoH_before_secretion = urea_out_DescLoH;
glucose_in_AscLoH = glucose_out_DescLoH;
osmoles_in_AscLoH_before_secretion = osmoles_out_DescLoH;
water_in_AscLoH = water_out_DescLoH;
Na_concentration_in_AscLoH = Na_concentration_out_DescLoH;
urea_in_AscLoH = urea_in_AscLoH_before_secretion + reabsorbed_urea_cd_delayed;
urea_concentration_in_AscLoH = urea_in_AscLoH/water_out_DescLoH;
osmoles_in_AscLoH = osmoles_in_AscLoH_before_secretion + reabsorbed_urea_cd_delayed;
osmolality_in_AscLoH = osmoles_in_AscLoH/water_in_AscLoH;
osmolality_out_AscLoH = osmolality_in_AscLoH - min(L_lh_des*effective_AscLoH_Reab_Rate, 2*Na_in_DescLoH)*(exp(min(L_lh_des*effective_AscLoH_Reab_Rate, 2*Na_in_DescLoH)/(water_in_DescLoH*osmolality_in_DescLoH))/water_in_DescLoH);
osmoles_reabsorbed_AscLoH = (osmolality_in_AscLoH - osmolality_out_AscLoH)*water_in_AscLoH;
Na_reabsorbed_AscLoH = osmoles_reabsorbed_AscLoH/2;
Na_out_AscLoH = max(0,Na_in_AscLoH - Na_reabsorbed_AscLoH);
urea_out_AscLoH = urea_in_AscLoH;
glucose_out_AscLoH = glucose_in_AscLoH;
water_out_AscLoH = water_in_AscLoH;
osmoles_out_AscLoH = osmolality_out_AscLoH*water_out_AscLoH;
Na_concentration_out_AscLoH = Na_out_AscLoH/water_out_AscLoH;
glucose_concentration_out_AscLoH = glucose_out_AscLoH/water_out_AscLoH;
urea_concentration_out_AscLoH = urea_out_AscLoH/water_out_AscLoH;
LoH_reabs_fraction = 1-Na_out_AscLoH/Na_in_AscLoH;
SN_macula_densa_Na_flow = Na_out_AscLoH;
MD_Na_concentration = Na_concentration_out_AscLoH;
TGF0_tubulo_glomerular_feedback = 1 - S_tubulo_glomerular_feedback/2;
tubulo_glomerular_feedback_signal = (TGF0_tubulo_glomerular_feedback + S_tubulo_glomerular_feedback / (1 + exp((MD_Na_concentration_setpoint - MD_Na_concentration)/ F_md_scale_tubulo_glomerular_feedback)));
water_in_DCT = water_out_AscLoH;
Na_in_DCT = Na_out_AscLoH;
urea_in_DCT = urea_out_AscLoH;
glucose_in_DCT = glucose_out_AscLoH;
osmoles_in_DCT = osmoles_out_AscLoH;
Na_concentration_in_DCT = Na_concentration_out_AscLoH;
urea_concentration_in_DCT = urea_concentration_out_AscLoH;
glucose_concentration_in_DCT = glucose_concentration_out_AscLoH;
osmolality_in_DCT = osmolality_out_AscLoH;
urea_out_DCT = urea_in_DCT;
glucose_out_DCT = glucose_in_DCT;
water_out_DCT = water_in_DCT;
urea_concentration_out_DCT = urea_out_DCT/water_out_DCT;
glucose_concentration_out_DCT = glucose_out_DCT/water_out_DCT;
R_dct = -log(1-e_dct_sodreab)/L_dct;
Na_out_DCT = Na_in_DCT*exp(-R_dct*L_dct);
Na_concentration_out_DCT = Na_out_DCT/water_out_DCT;
osmolality_out_DCT = 2*Na_concentration_out_DCT + glucose_concentration_out_DescLoH + urea_concentration_in_AscLoH;
osmoles_out_DCT = osmolality_out_DCT*water_out_DCT;
DCT_Na_reabs_fraction = 1-Na_out_DCT/Na_in_DCT;
Na_reabsorbed_DCT = Na_in_DCT - Na_out_DCT;
water_in_CD = water_out_DCT;
Na_in_CD = Na_out_DCT;
urea_in_CD = urea_out_DCT;
glucose_in_CD = glucose_out_DCT;
osmoles_in_CD = osmoles_out_DCT;
osmolality_in_CD = osmoles_in_CD/water_in_CD;
Na_concentration_in_CD = Na_concentration_out_DCT;
urea_concentration_in_CD = urea_concentration_out_DCT;
glucose_concentration_in_CD = glucose_concentration_out_DCT;
osmotic_diuresis_effect_cd = 1-min(1,RUGE *glucose_diuresis_effect_cd);
e_cd_sodreab_adj = e_cd_sodreab*osmotic_natriuresis_effect_cd;
R_cd = -log(1-e_cd_sodreab_adj)/L_cd;
Na_reabsorbed_CD = min(Na_in_CD*(1-exp(-R_cd*L_cd)),CD_Na_reabs_threshold);
Na_out_CD = Na_in_CD-Na_reabsorbed_CD;
CD_Na_reabs_fraction = 1-Na_out_CD/Na_in_CD;
Na_reabsorbed_CT=Na_in_CD-Na_out_CD;
ADH_water_permeability_old = min(0.99999,max(0,nom_ADH_water_permeability*normalized_vasopressin_concentration));
ADH_water_permeability = normalized_vasopressin_concentration/(0.15+normalized_vasopressin_concentration);
osmoles_out_CD = osmoles_in_CD-2*(Na_in_CD - Na_out_CD);
osmolality_out_CD_before_osmotic_reabsorption = osmoles_out_CD/water_in_CD;
water_reabsorbed_CD = ADH_water_permeability*osmotic_diuresis_effect_cd*water_in_CD*(1-osmolality_out_CD_before_osmotic_reabsorption/osmolality_out_DescLoH);
water_out_CD = water_in_CD-water_reabsorbed_CD;
Na_concentration_out_CD = Na_out_CD/water_out_CD;
osmolality_out_CD_after_osmotic_reabsorption = osmoles_out_CD/water_out_CD;
glucose_concentration_after_urea_reabsorption = glucose_in_CD/water_out_CD;
urine_flow_rate = water_out_CD*number_of_functional_tubules;
daily_urine_flow = (urine_flow_rate * 60 * 24);
Na_excretion_via_urine = Na_out_CD*number_of_functional_tubules;
Na_balance = Na_intake_rate - Na_excretion_via_urine;
water_balance = daily_water_intake - daily_urine_flow;
total_NA_reabsorbed = (total_SGLT_Na_reabs +Na_reabsorbed_AscLoH +Na_reabsorbed_DCT +Na_reabsorbed_CT )*number_of_functional_tubules;
Na_concentration_average_PT = (Na_concentration_out_s1 + Na_concentration_out_s2+Na_concentration_out_s3)/3;
Na_concentration_average_DescLoH = 0.5*(Na_concentration_in_DescLoH + Na_concentration_out_DescLoH);
Na_concentration_average_AscLoH = 0.5*(Na_concentration_in_AscLoH + Na_concentration_out_AscLoH );
Na_concentration_average_DCT = 0.5*(Na_concentration_in_DCT + Na_concentration_out_DCT);
Na_concentration_average_CD = 0.5*(Na_concentration_in_CD + Na_concentration_out_CD);
Na_concentration_average_tubule = 0.2*(Na_concentration_average_PT+Na_concentration_average_DescLoH+Na_concentration_average_AscLoH+Na_concentration_average_DCT+Na_concentration_average_CD);
Oncotic_pressure_tubule = Na_concentration_average_tubule*19.3*2;
Na_amount_in_renal_interstitium = total_NA_reabsorbed;
Na_concentration_renal_interstitium = Na_amount_in_renal_interstitium/(0.03831672);
Oncotic_pressure_renal_interstitium = Na_concentration_renal_interstitium*19.3*2;
Net_oncotic_pressure = Oncotic_pressure_tubule-Oncotic_pressure_renal_interstitium;
FENA = Na_excretion_via_urine/filtered_Na_load;
PT_fractional_glucose_reabs = (SN_filtered_glucose_load - glucose_pt_out_s3)/SN_filtered_glucose_load;
PT_fractional_Na_reabs = (SN_filtered_Na_load - Na_pt_out_s3)/SN_filtered_Na_load;
PT_fractional_urea_reabs = ( SN_filtered_urea_load - urea_out_s3)/SN_filtered_urea_load;
PT_fractional_water_reabs = ((SNGFR_nL_min / 1000 / 1000000) - water_out_s3)/(SNGFR_nL_min / 1000 / 1000000);
LoH_fractional_Na_reabs = (Na_in_DescLoH - Na_out_AscLoH)/Na_in_DescLoH;
LoH_fractional_urea_reabs = (urea_in_DescLoH-urea_out_AscLoH)/urea_in_DescLoH;
LoH_fractional_water_reabs = (water_in_DescLoH - water_out_AscLoH)/water_in_DescLoH;
DCT_fractional_Na_reabs = (Na_in_DCT - Na_out_DCT)/Na_in_DCT;
CD_fractional_Na_reabs = (Na_in_CD - Na_out_CD)/Na_in_CD;
CD_OM_fractional_water_reabs = (water_in_CD - water_out_CD)/water_in_CD;
Oncotic_pressure_peritubular_in = Oncotic_pressure_out;
plasma_protein_concentration_peritubular_out = (SNRBF_nl_min)*plasma_protein_concentration/(SNRBF_nl_min-urine_flow_rate*1e6*1000/number_of_functional_glomeruli);
Oncotic_pressure_peritubular_out = 1.629*plasma_protein_concentration_peritubular_out+0.2935*(plasma_protein_concentration_peritubular_out^2);
oncotic_pressure_peritubular_avg = (Oncotic_pressure_peritubular_in+Oncotic_pressure_peritubular_out)/2;
Na_concentration_peritubular_cap = (sodium_amount-filtered_Na_load)/blood_volume_L;
oncotic_pressure_peritubular_cap_Na = 0;
oncotic_pressure_peritubular = oncotic_pressure_peritubular_avg+oncotic_pressure_peritubular_cap_Na;
tubular_reabsorption = GFR_ml_min/1000 - urine_flow_rate;
Volume = interstitial_fluid_volume;
volume_RIHP_int = 1- volume_RIHP_scale/2;
IF_effect_RIHP = (volume_RIHP_int+volume_RIHP_scale/(1+exp(-(Volume-IF_nom)/volume_RIHP_slope)));
oncotic_int = 1-IF_Interonvotic_Effect_scale/2;
IF_effect_oncotic = (oncotic_int+IF_Interonvotic_Effect_scale/(1+exp((Volume-IF_nom)/IF_Interonvotic_Effect_slope)));
Renal_plasma_amount= 2.5 * RISF_nom*0.01;
RISF_plasma_protein_concentration = Renal_plasma_amount / (RISF*10);
interstitial_oncotic_pressure = 1.629*RISF_plasma_protein_concentration+0.2935*(RISF_plasma_protein_concentration^2);
RIHP = RISF/C_RISF;
capillary_filtration = nom_peritubular_cap_Kf*(RIHP - postglomerular_pressure - (interstitial_oncotic_pressure -oncotic_pressure_peritubular));
mmHg_Nperm2_conv = 133.32;
Pc_pt_s1 = Pc_pt_s1_mmHg*mmHg_Nperm2_conv;
Pc_pt_s2 = Pc_pt_s2_mmHg*mmHg_Nperm2_conv;
Pc_pt_s3 = Pc_pt_s3_mmHg*mmHg_Nperm2_conv;
Pc_lh_des = Pc_lh_des_mmHg*mmHg_Nperm2_conv;
Pc_lh_asc = Pc_lh_asc_mmHg*mmHg_Nperm2_conv;
Pc_dt = Pc_dt_mmHg*mmHg_Nperm2_conv;
Pc_cd = Pc_cd_mmHg*mmHg_Nperm2_conv;
P_interstitial = (RIHP)*mmHg_Nperm2_conv;
pi=3.14;
B1 = (4*tubular_compliance+1)*128*gamma/pi;
mean_cd_water_flow = (water_in_CD-water_out_CD)/2;
B2_cd = (Pc_cd^(4*tubular_compliance))/(Dc_cd^4);
P_in_cd = (0^(4*tubular_compliance+1)+B1*B2_cd*(mean_cd_water_flow/1e3)*L_cd)^(1/(4*tubular_compliance+1));
P_in_cd_mmHg = (P_in_cd+P_interstitial)/mmHg_Nperm2_conv;
B2_dt = (Pc_dt^(4*tubular_compliance))/(Dc_dt^4);
P_in_dt = (P_in_cd^(4*tubular_compliance+1)+B1*B2_dt*(water_in_DCT/1e3)*L_dct)^(1/(4*tubular_compliance+1));
P_in_dt_mmHg = (P_in_dt+P_interstitial)/mmHg_Nperm2_conv;
B2_lh_asc = (Pc_lh_asc^(4*tubular_compliance))/(Dc_lh^4);
P_in_lh_asc = (P_in_dt^(4*tubular_compliance+1)+B1*B2_lh_asc*(water_in_AscLoH/1e3)*L_lh_asc)^(1/(4*tubular_compliance+1));
P_in_lh_asc_mmHg = (P_in_lh_asc+P_interstitial)/mmHg_Nperm2_conv;
A_lh_des = effective_AscLoH_Reab_Rate/(water_in_DescLoH*osmolality_in_DescLoH);
B2_lh_des = (Pc_lh_des^(4*tubular_compliance))*(water_in_DescLoH/1e3)/((Dc_lh^4)*A_lh_des);
P_in_lh_des = (P_in_lh_asc^(4*tubular_compliance+1)+B1*B2_lh_des*(1-exp(-A_lh_des*L_lh_des)))^(1/(4*tubular_compliance+1));
P_in_lh_des_mmHg = (P_in_lh_des+P_interstitial)/mmHg_Nperm2_conv;
Rurea = (SN_filtered_urea_load - urea_out_s3)/(L_pt_s1+L_pt_s2+L_pt_s3);
urea_in_s2 = SN_filtered_urea_load - Rurea*L_pt_s1;
urea_in_s3 = SN_filtered_urea_load - Rurea*(L_pt_s1+L_pt_s2);
A_na = Na_reabs_per_unit_length;
flow_integral_s3 = 2*(Na_pt_out_s2/A_na)*(1-exp(-A_na*L_pt_s3)) - (3/2)*glucose_pt_out_s2*L_pt_s3^2 + urea_in_s3*L_pt_s3 - (1/2)*Rurea*(L_pt_s3^2);
flow_integral_s2 = 2*(Na_pt_out_s1/A_na)*(1-exp(-A_na*L_pt_s2)) - (1/2)*glucose_pt_out_s1*L_pt_s2^2 + urea_in_s2*L_pt_s2 - (1/2)*Rurea*(L_pt_s2^2);
flow_integral_s1 = 2*(SN_filtered_Na_load/A_na)*(1-exp(-A_na*L_pt_s1)) - (1/2)*SN_filtered_glucose_load*L_pt_s1^2 + SN_filtered_urea_load*L_pt_s1 - (1/2)*Rurea*(L_pt_s1^2);
B2_pt_s3 = (Pc_pt_s3^(4*tubular_compliance))/(Dc_pt^4);
B3_pt_s3 = (water_out_s2/1e3)/osmoles_out_s2;
P_in_pt_s3= (P_in_lh_des^(4*tubular_compliance+1)+B1*B2_pt_s3*B3_pt_s3*flow_integral_s3)^(1/(4*tubular_compliance+1));
P_in_pt_s3_mmHg = (P_in_pt_s3+P_interstitial)/mmHg_Nperm2_conv;
B2_pt_s2 = (Pc_pt_s3^(4*tubular_compliance))/(Dc_pt^4);
B3_pt_s2 = (water_out_s1/1e3)/osmoles_out_s1;
P_in_pt_s2= (P_in_pt_s3^(4*tubular_compliance+1)+B1*B2_pt_s2*B3_pt_s2*flow_integral_s2)^(1/(4*tubular_compliance+1));
P_in_pt_s2_mmHg = (P_in_pt_s2+P_interstitial)/mmHg_Nperm2_conv;
B2_pt_s1 = (Pc_pt_s1^(4*tubular_compliance))/(Dc_pt^4);
B3_pt_s1 = (SNGFR_nL_min / 1e12)/(2*SN_filtered_Na_load+SN_filtered_glucose_load+ SN_filtered_urea_load);
P_in_pt_s1= (P_in_pt_s2^(4*tubular_compliance+1)+B1*B2_pt_s1*B3_pt_s1*flow_integral_s1)^(1/(4*tubular_compliance+1));
P_in_pt_s1_mmHg = (P_in_pt_s1+P_interstitial)/mmHg_Nperm2_conv;
AT1_aldo_int = 1 - AT1_aldo_slope*nominal_equilibrium_AT1_bound_AngII;
AngII_effect_on_aldo = AT1_aldo_int + AT1_aldo_slope*AT1_bound_AngII;
N_als = (K_Na_ratio_effect_on_aldo * AngII_effect_on_aldo );
rsna_renin_intercept = 1-rsna_renin_slope;
rnsa_effect_on_renin_secretion = rsna_renin_slope * renal_sympathetic_nerve_activity + rsna_renin_intercept;
md_effect_on_renin_secretion = md_renin_A*exp(-md_renin_tau*(SN_macula_densa_Na_flow_delayed*baseline_nephrons - nom_LoH_Na_outflow));
AT1_bound_AngII_effect_on_PRA = (10 ^ (AT1_PRC_slope * log10(AT1_bound_AngII / nominal_equilibrium_AT1_bound_AngII) + AT1_PRC_yint));
aldo_renin_intercept = 1-aldo_renin_slope;
aldo_effect_on_renin_secretion = aldo_renin_intercept + aldo_renin_slope*Aldo_MR_normalised_effect;
plasma_renin_activity = concentration_to_renin_activity_conversion_plasma* plasma_renin_concentration*(1-pct_target_inhibition_DRI);
renin_secretion_rate = (log(2)/renin_half_life)*nominal_equilibrium_PRC*AT1_bound_AngII_effect_on_PRA*md_effect_on_renin_secretion*HCTZ_effect_on_renin_secretion*aldo_effect_on_renin_secretion*BB_renin_secretion_effect;
renin_degradation_rate = log(2)/renin_half_life;
AngI_degradation_rate = log(2)/AngI_half_life;
AngII_degradation_rate = log(2)/AngII_half_life;
AT1_bound_AngII_degradation_rate = log(2)/AT1_bound_AngII_half_life;
AT2_bound_AngII_degradation_rate = log(2)/AT2_bound_AngII_half_life;
ACE_activity = nominal_ACE_activity*(1 - pct_target_inhibition_ACEi);
chymase_activity = nominal_chymase_activity;
AT1_receptor_binding_rate = nominal_AT1_receptor_binding_rate*(1 - pct_target_inhibition_ARB);
AT2_receptor_binding_rate = nominal_AT2_receptor_binding_rate;
Blood_volume_protein_osmotic_pressure = 1.629*plasma_protein_concentration + 0.2935*plasma_protein_concentration^2;
ISF_protein_osmotic_pressure = 1.629*ISF_protein_concentration + 0.2935*ISF_protein_concentration^2;
Blood_volume_osmotic_pressure = Blood_volume_protein_osmotic_pressure+ Na_concentration*19.3*2;
ISF_osmotic_pressire = IF_Na_concentration*19.3*2 + ISF_protein_osmotic_pressure;
Protein_sodium_filtration_pressure_grad = (mean_capillary_pressure - ISF_pressure - Blood_volume_osmotic_pressure + ISF_osmotic_pressire);
Kidney_disconnect_heart = Q_water*(Na_concentration - IF_Na_concentration);
Kidney_connect_heart = -Sodium_protein_filtration_rate_Kf*(Protein_sodium_filtration_pressure_grad)*0.001;
if (heart_renal_link == 1) {
Fluid_exchanging_function=Kidney_connect_heart;
} else{
Fluid_exchanging_function=Kidney_connect_heart;
}
peripheral_volume_change=arterial_dis_circulation_volume+capillary_circulation_volume+venules_circulation_volume;
d/dt(venous_volume) = venous_flow + C_renal_CV_timescale*(venous_volume_target - venous_volume) - tricuspid_valve_flow_rate ;
d/dt(LV_volume) = mitral_valve_flow_rate - aortic_valve_flow_rate;
d/dt(arterial_volume) = (aortic_valve_flow_rate) - (systemic_blood_flow);
d/dt(RV_volume) = (tricuspid_valve_flow_rate) - (pulmonary_valve_flow_rate);
d/dt(pulmonary_arterial_volume) = pulmonary_valve_flow_rate - pulmonary_arterial_blood_flow;
d/dt(arterial_dis_circulation_volume)=systemic_blood_flow - capillary_blood_flow;
d/dt(capillary_circulation_volume)=arterial_dis_blood_flow-venules_blood_flow;
d/dt(venules_circulation_volume)=capillary_blood_flow-venous_flow;
d/dt(pulmonary_venous_volume) = pulmonary_arterial_blood_flow - mitral_valve_flow_rate;
d/dt(aortic_blood_flow_delayed) = C_cycle2 * (aortic_blood_flow - aortic_blood_flow_delayed);
d/dt(pulmonary_blood_flow_delayed) = C_cycle2 * (pulmonary_blood_flow - pulmonary_blood_flow_delayed);
d/dt(change_in_myocyte_length) = kL_hypertrophy * (LV_EDS / LV_passive_stress_along_fiber_threshhold - 1);
d/dt(change_in_myocyte_diameter) = kD_hypertrophy * (LV_active_stress_peak / LV_active_stress_threshhold - 1);
d/dt(LV_active_stress_peak) = C_cycle3 *(LV_active_stress_peak_old - LV_active_stress_peak);
d/dt(sim_time)=1;
d/dt(LV_sarcomere_length_delayed) = C_cycle* (LV_sarcomere_length - LV_sarcomere_length_delayed);
d/dt(RV_sarcomere_length_delayed) = C_cycle* (RV_sarcomere_length - RV_sarcomere_length_delayed);
d/dt(LV_EDV) = C_cycle2 * (LV_EDV_old - LV_EDV);
d/dt(LV_EDP) = C_cycle2 *(LV_EDP_old - LV_EDP);
d/dt(LV_EDS) = C_cycle2 *(LV_EDS_old - LV_EDS);
d/dt(arterial_pressure_delayed) = C_cycle2 * (arterial_pressure - arterial_pressure_delayed);
d/dt(arterial_pressure_bigger_delay) = C_cycle2 * (arterial_pressure_delayed - arterial_pressure_bigger_delay);
d/dt(systolic_pressure) = C_cycle2 * (systolic_pressure_old - systolic_pressure);
d/dt(diastolic_pressure) = C_cycle2 * (diastolic_pressure_old - diastolic_pressure);
d/dt(venous_pressure_delayed)=C_cycle2*(venous_pressure-venous_pressure_delayed);
d/dt(venous_pressure_bigger_delay)=C_cycle2*(venous_pressure_delayed-venous_pressure_bigger_delay);
d/dt(venous_systolic_pressure)=C_cycle2*(venous_systolic_pressure_old-venous_systolic_pressure);
d/dt(venous_diastolic_pressure)=C_cycle2*(venous_diastolic_pressure_old-venous_diastolic_pressure);
d/dt(mean_venous_pressure_delayed) = 1*(mean_venous_pressure - mean_venous_pressure_delayed);
d/dt(capillary_pressure_delayed)=C_cycle2*(capillary_pressure-capillary_pressure_delayed);
d/dt(capillary_pressure_bigger_delay)=C_cycle2*(capillary_pressure_delayed-capillary_pressure_bigger_delay);
d/dt(capillary_systolic_pressure)=C_cycle2*(capillary_systolic_pressure_old-capillary_systolic_pressure);
d/dt(capillary_diastolic_pressure)=C_cycle2*(capillary_diastolic_pressure_old-capillary_diastolic_pressure);
d/dt(mean_capillary_pressure_delayed) = 1*(mean_capillary_pressure - mean_capillary_pressure_delayed);
d/dt(CO) = C_co*(aortic_valve_flow_rate*60/L_m3 - CO);
d/dt(CO_delayed) = C_co_delay*(CO - CO_delayed);
d/dt(AngI) = plasma_renin_activity - (AngI) * (chymase_activity + ACE_activity) - (AngI) * AngI_degradation_rate;
d/dt(AngII) = AngI * (chymase_activity + ACE_activity) - AngII * AngII_degradation_rate - AngII*AT1_receptor_binding_rate - AngII* (AT2_receptor_binding_rate);
d/dt(AT1_bound_AngII) = AngII * (AT1_receptor_binding_rate) - AT1_bound_AngII_degradation_rate*AT1_bound_AngII;
d/dt(AT2_bound_AngII) = AngII * (AT2_receptor_binding_rate) - AT2_bound_AngII_degradation_rate*AT2_bound_AngII;
d/dt(plasma_renin_concentration) = renin_secretion_rate - plasma_renin_concentration * renin_degradation_rate;
d/dt(blood_volume_L) = C_renal_CV_timescale *(water_intake- urine_flow_rate+Fluid_exchanging_function);
d/dt(interstitial_fluid_volume) = -C_renal_CV_timescale *Fluid_exchanging_function;
d/dt(sodium_amount) = C_renal_CV_timescale * (Na_intake_rate - Na_excretion_via_urine + Q_Na*(IF_Na_concentration - Na_concentration));
d/dt(IF_sodium_amount) = C_renal_CV_timescale *(Q_Na*(Na_concentration - IF_Na_concentration) - sodium_storate_rate);
d/dt(stored_sodium) = C_renal_CV_timescale *sodium_storate_rate;
d/dt(tubulo_glomerular_feedback_effect) = C_renal_CV_timescale *(tubulo_glomerular_feedback_signal-tubulo_glomerular_feedback_effect);
d/dt(normalized_aldosterone_level) = C_renal_CV_timescale *C_aldo_secretion * (N_als-normalized_aldosterone_level);
d/dt(preafferent_pressure_autoreg_signal) = C_renal_CV_timescale *100*(preafferent_pressure_autoreg_function - preafferent_pressure_autoreg_signal);
d/dt(glomerular_pressure_autoreg_signal) = 0;
d/dt(CO_error) = C_renal_CV_timescale*C_co_error*(CO_delayed-CO_nom);
d/dt(Na_concentration_error) = C_renal_CV_timescale *C_Na_error*(Na_concentration - ref_Na_concentration);
d/dt(normalized_vasopressin_concentration_delayed)= C_renal_CV_timescale *C_vasopressin_delay*(normalized_vasopressin_concentration - normalized_vasopressin_concentration_delayed);
d/dt(F0_TGF) = C_renal_CV_timescale *C_tgf_reset*(SN_macula_densa_Na_flow*baseline_nephrons - F0_TGF);
d/dt(P_bowmans) = C_renal_CV_timescale *100*(P_in_pt_s1_mmHg - P_bowmans);
d/dt(oncotic_pressure_difference) = 100*(oncotic_pressure_avg - oncotic_pressure_difference);
d/dt(renal_blood_flow_L_min_delayed)=C_renal_CV_timescale*C_rbf*(renal_blood_flow_L_min - renal_blood_flow_L_min_delayed);
d/dt(SN_macula_densa_Na_flow_delayed) = C_renal_CV_timescale * C_md_flow*( SN_macula_densa_Na_flow - SN_macula_densa_Na_flow_delayed);
d/dt(rsna_delayed) = C_renal_CV_timescale *C_rsna*(renal_sympathetic_nerve_activity - rsna_delayed);
d/dt(disease_effects_increasing_Kf) = GP_effect_increasing_Kf;
d/dt(disease_effects_decreasing_CD_PN) = CD_PN_loss_rate;
d/dt(tubular_length_increase) = PT_Na_reabs_effect_increasing_tubular_length;
d/dt(tubular_diameter_increase) = PT_Na_reabs_effect_increasing_tubular_diameter;
d/dt(water_out_s1_delayed) = C_renal_CV_timescale * C_pt_water*(water_out_s1 - water_out_s1_delayed);
d/dt(water_out_s2_delayed) = C_renal_CV_timescale * C_pt_water*(water_out_s2 - water_out_s2_delayed);
d/dt(water_out_s3_delayed) = C_renal_CV_timescale * C_pt_water*(water_out_s3 - water_out_s3_delayed);
d/dt(reabsorbed_urea_cd_delayed) = 0;
d/dt(UGE) = C_renal_CV_timescale * RUGE;
d/dt(serum_creatinine) = C_renal_CV_timescale*(creatinine_synthesis_rate - creatinine_clearance_rate);
d/dt(cumNaExcretion) = C_renal_CV_timescale*Na_excretion_via_urine;
d/dt(cumWaterExcretion) = C_renal_CV_timescale*urine_flow_rate;
d/dt(cumCreatinineExcretion) = C_renal_CV_timescale*creatinine_clearance_rate;
d/dt(RTg_compensation) = C_renal_CV_timescale*excess_glucose_increasing_RTg;
d/dt(SGLT2_inhibition_delayed) = C_renal_CV_timescale*C_sglt2_delay*(SGLT2_inhibition - SGLT2_inhibition_delayed);
d/dt(RUGE_delayed) = C_renal_CV_timescale*C_ruge*(RUGE - RUGE_delayed);
d/dt(postglomerular_pressure_delayed) = C_renal_CV_timescale*C_postglomerular_pressure*(postglomerular_pressure - postglomerular_pressure_delayed);
d/dt(postglomerular_pressure_error) = C_renal_CV_timescale*(postglomerular_pressure - RIHP0);
d/dt(renal_flow_rate_error) = C_renal_CV_timescale*(renal_blood_flow_L_min - nom_renal_blood_flow_L_min);
d/dt(MAP_delayed) = C_renal_CV_timescale*C_cycle2*(mean_arterial_pressure_MAP - MAP_delayed);
d/dt(RIHP_delayed)=C_renal_CV_timescale*C_cycle2*(RIHP - RIHP_delayed);
d/dt(Net_oncotic_pressure_diff) = C_renal_CV_timescale*C_cycle2*(Net_oncotic_pressure - Net_oncotic_pressure_diff);
d/dt(RISF) = tubular_reabsorption - capillary_filtration;
")
test_that("large models compile", {
expect_true(inherits(mod, "RxODE"))
})
},
test = "lvl2"
) |
context(".generate_partitions")
test_that("num_tfs_sampler works as expected", {
num_tfs <- 90
num_modules <- 8
samples <- map(
seq_len(5000),
~ .generate_partitions(num_tfs, num_modules, min_elements_per_group = 1)
)
expect_true(all(map_int(samples, length) == num_modules))
expect_true(all(map_int(samples, sum) == num_tfs))
expect_true(all(map_int(samples, min) >= 1))
avg <- Reduce("+", samples) / length(samples)
exp <- num_tfs / num_modules
expect_equal(mean(avg), exp, tolerance = 0.1)
})
test_that("num_tfs_sampler works as expected", {
num_tfs <- 200
num_modules <- 13
samples <- map(
seq_len(1000),
~ .generate_partitions(num_tfs, num_modules, min_elements_per_group = 1)
)
expect_true(all(map_int(samples, length) == num_modules))
expect_true(all(map_int(samples, sum) == num_tfs))
expect_true(all(map_int(samples, min) >= 1))
avg <- Reduce("+", samples) / length(samples)
exp <- num_tfs / num_modules
expect_equal(mean(avg), exp, tolerance = 0.1)
})
test_that("num_tfs_sampler works as expected", {
num_tfs <- 1000
num_modules <- 8
samples <- map(
seq_len(1000),
~ .generate_partitions(num_tfs, num_modules, min_elements_per_group = 1)
)
expect_true(all(map_int(samples, length) == num_modules))
expect_true(all(map_int(samples, sum) == num_tfs))
expect_true(all(map_int(samples, min) >= 1))
avg <- Reduce("+", samples) / length(samples)
exp <- num_tfs / num_modules
expect_equal(mean(avg), exp, tolerance = 0.1)
})
test_that("num_tfs_sampler works as expected", {
expect_equal(.generate_partitions(50, 10, 5), rep(5, 10))
}) |
xgx_breaks_time <- function(data_range, units_plot, number_breaks = 5) {
data_min <- min(data_range)
data_max <- max(data_range)
data_span <- data_max - data_min
number_breaks <- 5
preferred_increment_default <- c(1, 5, 2, 4, 3, 1)
weights_default <- c(0.25, 0.2, 0.5, 0.05)
weights_simple <- c(1, 0.2, 0.5, 0.05)
if (units_plot %in% c("h", "m") && data_span >= 48) {
preferred_increment <- c(24, 12, 6, 3)
weights <- weights_simple
} else if (units_plot %in% c("h", "m") && data_span >= 24) {
preferred_increment <- c(3, 12, 6, 2)
weights <- weights_simple
} else if (units_plot %in% c("h", "m") && data_span < 24) {
preferred_increment <- c(6, 3, 2, 1)
weights <- weights_simple
} else if (units_plot == "d" && data_span >= 12) {
preferred_increment <- c(7, 14, 28)
weights <- weights_simple
} else {
preferred_increment <- preferred_increment_default
weights <- weights_default
}
breaks <- labeling::extended(data_min, data_max, m = number_breaks,
Q = preferred_increment, w = weights)
return(breaks)
} |
praise <- function() {
plain <- c(
"You rock!",
"You are a coding rockstar!",
"Keep up the good work.",
"Woot!",
"Way to go!",
"Nice code.",
praise::praise("Your tests are ${adjective}!"),
praise::praise("${EXCLAMATION} - ${adjective} code.")
)
utf8 <- c(
"\U0001f600",
"\U0001f973",
"\U0001f638",
paste0(strrep("\U0001f389\U0001f38a", 5), "\U0001f389"),
"\U0001f485 Your tests are beautiful \U0001f485",
"\U0001f947 Your tests deserve a gold medal \U0001f947",
"\U0001f308 Your tests are over the rainbow \U0001f308",
"\U0001f9ff Your tests look perfect \U0001f9ff",
"\U0001f3af Your tests hit the mark \U0001f3af",
"\U0001f41d Your tests are the bees knees \U0001f41d",
"\U0001f4a3 Your tests are da bomb \U0001f4a3",
"\U0001f525 Your tests are lit \U0001f525"
)
x <- if (cli::is_utf8_output()) c(plain, utf8) else plain
sample(x, 1)
}
praise_emoji <- function() {
if (!cli::is_utf8_output()) {
return("")
}
emoji <- c(
"\U0001f600",
"\U0001f973",
"\U0001f638",
"\U0001f308",
"\U0001f947",
"\U0001f389",
"\U0001f38a"
)
sample(emoji, 1)
}
encourage <- function() {
x <- c(
"Keep trying!",
"Don't worry, you'll get it.",
"No one is perfect!",
"No one gets it right on their first try",
"Frustration is a natural part of programming :)",
"I believe in you!"
)
sample(x, 1)
} |
source("test.prolog.R")
options(warn=1)
library(earth)
cat("loading parsnip libraries\n")
library(tidymodels)
library(timetk)
library(lubridate)
cat("loaded parsnip libraries\n")
cat("parsnip version:", as.character(packageVersion("parsnip")[[1]]), "\n")
vdata <- data.frame(
resp = 1:23,
bool = c(F, F, F, F, F, T, T, T, T, T, T, T, T, F, F, T, T, T, T, T, T, T, T),
ord = ordered(c("ORD1", "ORD1", "ORD1",
"ORD1", "ORD1", "ORD1",
"ORD1", "ORD3", "ORD1",
"ORD2", "ORD2", "ORD2", "ORD2",
"ORD2", "ORD2", "ORD2",
"ORD3", "ORD3", "ORD3",
"ORD2", "ORD2", "ORD2", "ORD2"),
levels=c("ORD1", "ORD3", "ORD2")),
fac = as.factor(c("FAC1", "FAC1", "FAC1",
"FAC2", "FAC2", "FAC2",
"FAC3", "FAC1", "FAC1",
"FAC1", "FAC2", "FAC2", "FAC2",
"FAC2", "FAC2", "FAC2",
"FAC3", "FAC3", "FAC3",
"FAC1", "FAC3", "FAC3", "FAC3")),
str = c("STR1", "STR1", "STR1",
"STR1", "STR1", "STR1",
"STR2", "STR2", "STR2",
"STR3", "STR3", "STR2", "STR3",
"STR2", "STR3", "STR2",
"STR3", "STR3", "STR3",
"STR3", "STR3", "STR3", "STR3"),
num = c(1, 9, 2, 3, 14, 5, 6, 4, 5, 6.5, 3, 6, 5,
3, 4, 5, 6, 4, 5, 16.5, 3, 16, 15),
sqrt_num = sqrt(
c(1, 9, 2, 3, 14, 5, 6, 4, 5, 6.5, 3, 6, 5,
3, 4, 5, 6, 4, 5, 16.5, 3, 16, 15)),
int = c(1L, 1L, 3L, 3L, 4L, 4L, 3L, 5L, 3L, 6L, 7L, 8L, 10L,
13L, 14L, 3L, 13L, 5L, 13L, 16L, 17L, 18L, 11L),
date = as.Date(
c("2018-08-01", "2018-08-02", "2018-08-03",
"2018-08-04", "2018-08-05", "2018-08-06",
"2018-08-07", "2018-08-08", "2018-08-08",
"2018-08-10", "2018-08-10", "2018-08-11", "2018-08-11",
"2018-08-11", "2018-08-12", "2018-08-13",
"2018-08-10", "2018-08-15", "2018-08-17",
"2018-08-04", "2018-08-19", "2018-08-03", "2018-08-18")),
date_num = as.numeric(as.Date(
c("2018-08-01", "2018-08-02", "2018-08-03",
"2018-08-04", "2018-08-05", "2018-08-06",
"2018-08-07", "2018-08-08", "2018-08-08",
"2018-08-10", "2018-08-10", "2018-08-11", "2018-08-11",
"2018-08-11", "2018-08-12", "2018-08-13",
"2018-08-10", "2018-08-15", "2018-08-17",
"2018-08-04", "2018-08-19", "2018-08-03", "2018-08-18"))))
set.seed(2020)
splits <- initial_time_split(vdata, prop=.9)
lm1 <- lm(resp~num+fac:int+date+ord+str, data=training(splits))
cat("lm1:\n")
print(summary(lm1))
set.seed(2020)
lmpar <- linear_reg(mode = "regression") %>%
set_engine("lm") %>%
fit(resp~num+fac:int+date+ord+str, data = training(splits))
stopifnot(identical(lm1$coeff, lmpar$fit$coeff))
predict.lm1 <- predict(lm1, testing(splits))
predict.lmpar <- lmpar %>% predict(testing(splits))
stopifnot(all(predict.lm1 == predict.lmpar))
par(mfrow = c(3, 3), mar = c(3, 3, 3, 1), mgp = c(1.5, 0.5, 0))
plotmo(lm1, do.par=2, SHOWCALL=TRUE)
plotres(lm1, which=c(3,1), do.par=FALSE)
plotmo(lmpar, do.par=2, SHOWCALL=TRUE)
plotres(lmpar, which=c(3,1), do.par=FALSE)
plotmo(lmpar$fit, do.par=2, SHOWCALL=TRUE)
plotres(lmpar$fit, which=c(3,1), do.par=FALSE)
par(org.par)
lmpar.sqrtnum <- linear_reg(mode = "regression") %>%
set_engine("lm") %>%
fit(resp~sqrt(num), data = training(splits))
expect.err(try(plotmo(lmpar.sqrtnum)),
"cannot get the original model predictors")
earth1 <- earth(resp~sqrt(num)+int+ord:bool+fac+str+date, degree=2,
data=training(splits), pmethod="none")
cat("earth1:\n")
print(summary(earth1))
set.seed(2020)
earthpar <- mars(mode = "regression", prune_method="none", prod_degree=2) %>%
set_engine("earth") %>%
fit(resp~sqrt(num)+int+ord:bool+fac+str+date, data = training(splits))
cat("earthpar:\n")
print(earthpar)
cat("summary(earthpar$fit)\n")
print(summary(earthpar$fit))
stopifnot(identical(earth1$coeff, earthpar$fit$coeff))
predict.earth1 <- predict(earth1, testing(splits))
predict.earthpar <- earthpar %>% predict(testing(splits))
stopifnot(all(predict.earth1 == predict.earthpar))
par(mfrow = c(3, 3), mar = c(3, 3, 3, 1), mgp = c(1.5, 0.5, 0))
plotmo(earth1, do.par=2, pt.col=3, SHOWCALL=TRUE)
set.seed(2020)
plotres(earth1, which=c(1,3), do.par=FALSE, pt.col=3, legend.pos="topleft")
par(org.par)
par(mfrow = c(3, 3), mar = c(3, 3, 3, 1), mgp = c(1.5, 0.5, 0))
plotmo(earthpar, do.par=2, pt.col=3, SHOWCALL=TRUE)
set.seed(2020)
plotres(earthpar, which=c(1,3), do.par=FALSE, pt.col=3, legend.pos="topleft")
par(org.par)
par(mfrow = c(3, 3), mar = c(3, 3, 3, 1), mgp = c(1.5, 0.5, 0))
plotmo(earthpar$fit, do.par=2, pt.col=3, SHOWCALL=TRUE)
set.seed(2020)
plotres(earthpar$fit, which=c(1,3), do.par=FALSE, pt.col=3, legend.pos="topleft")
par(org.par)
library(rpart)
library(rpart.plot)
rpart1 <- rpart(resp~num+fac+int+date+ord+str, data=training(splits),
control=rpart.control(minsplit=1, cp=.0001))
cat("\nrpart.rules(rpart1)\n")
print(rpart.rules(rpart1))
set.seed(2020)
rpartpar <- decision_tree(mode = "regression", min_n=1, cost_complexity=.0001) %>%
set_engine("rpart", model=TRUE) %>%
fit(resp~num+fac+int+date+ord+str, data = training(splits))
cat("\nrpart.rules(rpartpar$fit)\n")
print(rpart.rules(rpartpar$fit))
predict.rpart1 <- predict(rpart1, testing(splits))
predict.rpartpar <- rpartpar %>% predict(testing(splits))
stopifnot(all(predict.rpart1 == predict.rpartpar))
par(mfrow = c(3, 3), mar = c(3, 3, 3, 1), mgp = c(1.5, 0.5, 0))
plotmo(rpart1, do.par=2, SHOWCALL=TRUE, trace=0)
plotres(rpart1, which=c(3,1), do.par=FALSE)
plotmo(rpartpar, do.par=2, SHOWCALL=TRUE, trace=0)
plotres(rpartpar, which=c(3,1), do.par=FALSE)
plotmo(rpartpar$fit, do.par=2, SHOWCALL=TRUE)
plotres(rpartpar$fit, which=c(3,1), do.par=FALSE)
par(org.par)
rpartpar.nosavemodel <- decision_tree(mode = "regression", min_n=1, cost_complexity=.0001) %>%
set_engine("rpart") %>%
fit(resp~num+fac+int+date+str, data = training(splits))
cat("\nrpart.rules(rpartpar.nosavemodel$fit)\n")
options(warn=2)
expect.err(try(rpart.rules(rpartpar.nosavemodel$fit)),
"Cannot retrieve the data used to build the model")
options(warn=1)
expect.err(try(plotmo(rpartpar.nosavemodel)),
"Cannot plot parsnip rpart model: need model=TRUE in call to rpart")
rpart.sqrtnum <- decision_tree(mode = "regression", min_n=1, cost_complexity=.0001) %>%
set_engine("rpart", model=TRUE) %>%
fit(resp~sqrt(num)+fac+int+date+ord+str, data = training(splits))
cat("\nrpart.rules(rpart.sqrtnum$fit)\n")
print(rpart.rules(rpart.sqrtnum$fit))
expect.err(try(plotmo(rpart.sqrtnum)),
"cannot get the original model predictors")
cat("===m750a first example===\n")
set.seed(2020)
m750a <- m4_monthly %>%
filter(id == "M750") %>%
select(-id)
print(m750a)
set.seed(2020)
splits_a <- initial_time_split(m750a, prop = 0.9)
earth_m750a <- earth(log(value) ~ as.numeric(date) + month(date, label = TRUE), data = training(splits_a), degree=2)
print(summary(earth_m750a))
set.seed(2020)
model_m750a <- mars(mode = "regression", prod_degree=2) %>%
set_engine("earth") %>%
fit(log(value) ~ as.numeric(date) + month(date, label = TRUE), data = training(splits_a))
print(summary(model_m750a$fit))
stopifnot(identical(earth_m750a$coeff, model_m750a$fit$coeff))
predict_earth_m750a <- predict(earth_m750a, newdata=testing(splits_a)[1:3,])
predict_m750a <- model_m750a %>% predict(testing(splits_a)[1:3,])
stopifnot(max(c(9.238049628, 9.240535151, 9.232361834) - predict_m750a) < 1e-8)
stopifnot(max(predict_earth_m750a - predict_m750a) < 1e-20)
par(mfrow = c(2, 2), mar = c(3, 3, 3, 1), mgp = c(1.5, 0.5, 0))
set.seed(2020)
plotmo(model_m750a, trace=2, do.par=FALSE, pt.col="green", main="model_m750a", SHOWCALL=TRUE)
set.seed(2020)
plotmo(model_m750a$fit, trace=1, do.par=FALSE, pt.col="green", main="model_m750a$fit", SHOWCALL=TRUE)
set.seed(2020)
plotmo(earth_m750a, trace=1, do.par=FALSE, pt.col="green", main="earth_m750a", SHOWCALL=TRUE)
par(org.par)
cat("===m750a second example===\n")
set.seed(2020)
m750b <- m4_monthly %>%
filter(id == "M750") %>%
select(-id) %>%
rename(date2 = date)
print(m750b)
set.seed(2020)
splits_b <- initial_time_split(m750b, prop = 0.9)
set.seed(2020)
model_m750b <- mars(mode = "regression") %>%
set_engine("earth") %>%
fit(log(value) ~ as.numeric(date2) + month(date2, label = TRUE), data = training(splits_b))
future_data <- m750b %>% future_frame(date2, .length_out = "3 years")
print(future_data)
stopifnot(class(future_data[,1,drop=TRUE]) == "Date")
predict_m750a <- model_m750b %>% predict(new_data = future_data)
par(mfrow = c(2, 2), mar = c(3, 3, 3, 1), mgp = c(1.5, 0.5, 0))
set.seed(2020)
plotmo(model_m750b, trace=2, do.par=FALSE, pt.col="green", main="model_m750b", SHOWCALL=TRUE)
set.seed(2020)
plotmo(model_m750b$fit, trace=1, do.par=FALSE, pt.col="green", main="model_m750b$fit", SHOWCALL=TRUE)
par(org.par)
data(etitanic)
etit <- etitanic
etit$survived <- factor(ifelse(etitanic$survived == 1, "yes", "no"),
levels = c("yes", "no"))
etit$notsurvived <- factor(ifelse(etitanic$survived == 0, "notsurvived", "survived"),
levels = c("notsurvived", "survived"))
set.seed(2020)
earth_tworesp <- earth(survived + notsurvived ~ ., data=etit, degree=2)
print(summary(earth_tworesp))
source("test.epilog.R") |
pcarec <- function(siginput,inputwt, beemap, orientmap, noct, nvoice, compr,
maxchnlng=as.numeric(dim(beemap)[1])+10,minnbnodes = 2,
w0 = 2*pi, nbchain=100,bstep = 1,ptile =.01,para=5,plot=2,check=FALSE)
{
tmp <- pcafamily(beemap,orientmap,maxchnlng=maxchnlng,bstep=bstep,nbchain=nbchain,ptile=ptile)
chain <- tmp$chain
nbchain <- tmp$nbchain
ordered <- tmp$ordered
sigsize <- length(siginput)
rec <- numeric(sigsize)
plnb <- 0
if(plot != FALSE){
par(mfrow=c(2,1))
plot.ts(siginput)
title("Original signal")
image(tmp$ordered)
title("Chained Ridges")
}
sol <- matrix(0,nbchain,sigsize)
totnbnodes <- 0
idx <- numeric(nbchain)
p <- 0
if(check==TRUE) {
inputskel <- matrix(0+0i,nbchain,sigsize)
solskel <- matrix(0+0i, nbchain, sigsize)
}
for (j in 1:nbchain){
sol[j,] <- 0
nbnode <- chain[j,1]
bnode <- numeric(nbnode)
anode <- numeric(nbnode)
for(k in 1:nbnode) {
anode[k] <- chain[j,2*k]
bnode[k] <- chain[j,2*k+1]
}
cat("Chain number",j,"\n")
tmp2 <- pcaregrec(siginput[min(bnode):max(bnode)],
inputwt[min(bnode):max(bnode),],
anode,bnode,compr,noct,nvoice,
w0 = w0, para = para,minnbnodes = minnbnodes, check=check);
if(is.list(tmp2)==TRUE) {
totnbnodes <- totnbnodes + tmp2$nbnodes
if((sigsize-min(bnode)) > (length(tmp2$sol)-tmp2$bstart)){
np <- length(tmp2$sol) - tmp2$bstart
sol[j,min(bnode):(np+min(bnode))]<-tmp2$sol[tmp2$bstart:length(tmp2$sol)]
}
else {
np <- sigsize - min(bnode)
sol[j,min(bnode):sigsize]<-tmp2$sol[tmp2$bstart:(np+tmp2$bstart)]
}
if(min(bnode) < tmp2$bstart) {
np <- min(bnode)-1
sol[j,1:min(bnode)]<-tmp2$sol[(tmp2$bstart-np):(tmp2$bstart)]
}
else {
np <- tmp2$bstart-1
sol[j,(min(bnode)-np):min(bnode)]<-tmp2$sol[1:(tmp2$bstart)]
}
if(check==TRUE) {
bridge <- tmp2$bnode
aridge <- tmp2$anode
wtsol <- cwt(sol[j,],noct,nvoice)
for(k in 1:length(bridge)) solskel[j,k] <- wtsol[bridge[k],aridge[k]]
for(k in 1:length(bridge)) inputskel[j,k] <- inputwt[bridge[k],aridge[k]]
}
rec <- rec+sol[j,]
}
plnb <- plnb + 1
p <- p + 1
idx[p] <- j
}
if(plot == 1){
par(mfrow=c(2,1))
par(cex=1.1)
plot.ts(siginput)
title("Original signal")
plot.ts(Re(rec))
title("Reconstructed signal")
}
else if (plot == 2){
par(mfrow=c(plnb+2,1))
par(mar=c(2,4,4,4))
par(cex=1.1)
par(err=-1)
plot.ts(siginput)
title("Original signal")
for (j in 1:p)
plot.ts(sol[idx[j],]);
plot.ts(Re(rec))
title("Reconstructed signal")
}
cat("Total number of ridge samples used: ",totnbnodes,"\n")
par(mfrow=c(1,1))
if(check==TRUE) list(rec=rec,ordered=ordered,chain=chain,
comp=tmp,inputskel=inputskel,solskel=solskel,lam=tmp2$lam)
else list(rec=rec, ordered=ordered,chain=chain,comp=tmp)
}
PcaRidgeSampling <- function(anode, bnode, compr)
{
nbnode <- length(anode)
compr <- as.integer(compr)
if(compr < 1) compr <- 1
k <- 1
count <- 1
while(k < nbnode) {
anode[count] <- anode[k]
bnode[count] <- bnode[k]
k <- k + compr
count <- count + 1
}
anode[count] <- anode[nbnode]
bnode[count] <- bnode[nbnode]
aridge <- numeric(count)
bridge <- numeric(count)
aridge <- anode[1:count]
bridge <- bnode[1:count]
list(anode = aridge, bnode = bridge, nbnodes = count)
}
pcaregrec <- function(siginput,cwtinput,anode,bnode,compr,noct,nvoice,
w0 = 2*pi, plot = FALSE, para = 5, minnbnodes = 2, check=FALSE)
{
tmp <- PcaRidgeSampling(anode, bnode, compr)
bnode <- tmp$bnode
anode <- tmp$anode
nbnodes <- tmp$nbnodes
cat("Sampled nodes (b,a): \n")
for(j in 1:nbnodes) cat("(",bnode[j],anode[j],")")
cat("\n")
if(nbnodes < minnbnodes){
cat(" Chain too small\n")
NULL
}
else {
a.min <- 2 * 2^(min(anode)/nvoice)
a.max <- 2 * 2^(max(anode)/nvoice)
b.min <- min(bnode)
b.max <- max(bnode)
b.max <- (b.max-b.min+1) + round(para * a.max)
b.min <- (b.min-b.min+1) - round(para * a.max)
bnode <- bnode-min(bnode)+2-b.min
b.inc <- 1
np <- as.integer((b.max - b.min)/b.inc) +1
cat("(size:",np,",",nbnodes,"sampled nodes):\n")
Q2 <- 0
one <- numeric(np)
one[] <- 1
Qinv <- 1/one
tmp2 <- pcaridrec(cwtinput,bnode,anode,noct,nvoice,
Qinv,np,w0=w0,check=check)
if(plot == TRUE){
par(mfrow=c(2,1))
plot.ts(Re(siginput))
title("Original signal")
plot.ts(Re(tmp2$sol))
title("Reconstructed signal")
}
lam <- tmp2$lam
list(sol = tmp2$sol,A = tmp2$A,
lam = tmp2$lam, dualwave = tmp2$dualwave,
morvelets = tmp2$morvelets,pQ2 = Q2, nbnodes=nbnodes,
bstart=2-b.min, anode=tmp$anode,bnode=tmp$bnode)
}
}
pcaridrec <- function(cwtinput,bnode,anode,noct,nvoice,Qinv,np,
w0 = 2*pi, check = FALSE)
{
N <- length(bnode)
aridge <- anode
bridge <- bnode
morvelets <- pcamorwave(bridge,aridge,nvoice,np,N)
cat("morvelets; ")
sk <- pcazeroskeleton(cwtinput,Qinv,morvelets,bridge,aridge,N)
cat("skeleton.\n")
solskel <- 0
inputskel <- 0
list(sol=sk$sol,A=sk$A,lam=sk$lam,dualwave=sk$dualwave,morvelets=morvelets,
solskel=solskel,inputskel = inputskel)
}
pcazeroskeleton <- function(cwtinput,Qinv,morvelets,bridge,aridge,N)
{
tmp1 <- dim(morvelets)[1]
constraints2 <- dim(morvelets)[2]
dualwave <- matrix(0,tmp1,constraints2)
for (j in 1:tmp1) {
dualwave[j,] <- morvelets[j,]*Qinv[j]
}
A <- t(morvelets) %*% dualwave
rskel <- numeric(2*N)
if(is.vector(cwtinput) == TRUE) {
for(j in 1:N) {
rskel[j] <- Re(cwtinput[aridge[j]])
rskel[N+j] <- -Im(cwtinput[aridge[j]])
}
}
else {
for(j in 1:N) {
rskel[j] <- Re(cwtinput[bridge[j]-bridge[1]+1,aridge[j]])
rskel[N+j] <- -Im(cwtinput[bridge[j]-bridge[1]+1,aridge[j]])
}
}
B <- SVD(A)
d <- B$d
Invd <- numeric(length(d))
for(j in 1:(2*N))
if(d[j] < 1.0e-6)
Invd[j] <- 0
else Invd[j] <- 1/d[j]
lam <- B$v %*% diag(Invd) %*% t(B$u) %*% rskel
sol <- dualwave%*%lam
list(lam=lam,sol=sol,dualwave=dualwave,A=A)
}
pcamorwave <- function(bridge, aridge, nvoice, np, N, w0 = 2*pi)
{
morvelets <- matrix(0,np,2*N)
aridge <- 2 * 2^((aridge - 1)/nvoice)
tmp <- vecmorlet(np,N,bridge,aridge,w0 = w0)
dim(tmp) <- c(np,N)
morvelets[,1:N] <- Re(tmp[,1:N])
morvelets[,(N+1):(2*N)] <- Im(tmp[,1:N])
morvelets
} |
siarplotdatawrapper <-
function(siardata, siarversion = 0, grp = NULL, panel = NULL,
isos = c(1, 2),leg2 = NULL,legloc='topleft') {
if (!is.null(panel) & is.null(grp)) {
warning(cat("WARNING. grp set to ALL and panel set to a value.\n Overriding your panel selection and setting to panel=NULL.\n In order to plot all groups on seperate panels please call\n grp=1:siardata$numgroups and panel=1 or panel=c(r,c)\n to specify number of rows and columns"))
panel <- NULL
}
if (all(isos == 0)) {
isox <- 1
isoy <- 2
}
else {
isox <- isos[1]
isoy <- isos[2]
}
a <- 1
if (siardata$numgroups == 1) {
a <- 0
}
if (!is.null(panel)) {
if (prod(panel) < length(grp)) {
panel <- c(ceiling(sqrt(length(grp))))
panel <- c(max(panel, 1), max(ceiling(length(grp)/panel),
1))
}
split.screen(panel)
}
else {
}
er <- (siardata$sources[, (2 * isox) + 1]^2 + siardata$corrections[,
(2 * isox) + 1]^2)^0.5
xmins <- min(c(siardata$sources[, 2 * isox] + siardata$corrections[,
2 * isox] - 3 * er, siardata$targets[, isox + a]))
xmaxs <- max(c(siardata$sources[, 2 * isox] + siardata$corrections[,
2 * isox] + 3 * er, siardata$targets[, isox + a]))
er <- (siardata$sources[, (2 * isoy) + 1]^2 + siardata$corrections[,
(2 * isoy) + 1]^2)^0.5
ymins <- min(c(siardata$sources[, 2 * isoy] + siardata$corrections[,
2 * isoy] - 3 * er, siardata$targets[, isoy + a]))
ymaxs <- max(c(siardata$sources[, 2 * isoy] + siardata$corrections[,
2 * isoy] + 3 * er, siardata$targets[, isoy + a]))
if (is.null(panel)) {
plot(1, 1, type = "n", xlim = c(xmins, xmaxs), ylim = c(ymins,
ymaxs), main = siardata$TITLE, xlab = colnames(siardata$targets)[isox +
a], ylab = colnames(siardata$targets)[isoy +
a])
}
for (k in 1:length(grp)) {
if (!is.null(panel)) {
screen(k)
plot(1, 1, type = "n", xlim = c(xmins, xmaxs),
ylim = c(ymins, ymaxs), main = paste("Group",
grp[k]), xlab = colnames(siardata$targets)[isox +
a], ylab = colnames(siardata$targets)[isoy +
a])
}
if (!is.null(grp)) {
siarplottarget(siardata, isox, isoy, a, grps = grp[k])
}
else {
siarplottarget(siardata, isox, isoy, a, grps = grp)
}
for (i in 1:nrow(siardata$sources)) {
dx <- siardata$sources[i, 2 * isox] + siardata$corrections[i,
2 * isox]
dex <- 2 * (siardata$sources[i, (2 * isox) +
1]^2 + siardata$corrections[i, (2 * isox) +
1]^2)^0.5
dy <- siardata$sources[i, 2 * isoy] + siardata$corrections[i,
2 * isoy]
dey <- 2 * (siardata$sources[i, (2 * isoy) +
1]^2 + siardata$corrections[i, (2 * isoy) +
1]^2)^0.5
siaraddcross(x = dx, ex = dex, y = dy, ey = dey,
upch = 15, clr = i)
}
}
if (!is.null(panel)) {
close.screen(all.screens = TRUE)
}
if (siarversion > 0) {
mtext(paste("siar v", siarversion), side = 1, line = 4,
adj = 1, cex = 0.6)
}
if (siardata$numgroups == 0) {
grp <- 1
}
if (is.null(grp)) {
grp <- 1
}
pchseq <- c(1:2, 4:20)
if (leg2 == 1) {
datalabs <- NULL
if (siardata$numgroups == 1) {
datalabs <- "data"
}
else {
for (k in 1:length(grp)) {
datalabs <- c(datalabs, as.character(paste("Group",
grp[k])))
}
}
legend(legloc, legend = c(as.character(siardata$sources[,
1]), datalabs), lty = c(rep(1, nrow(siardata$sources)),
rep(-1, length(grp))), pch = c(rep(15, nrow(siardata$sources)),
pchseq[grp]), col = c(seq(1, nrow(siardata$sources)),
rep("grey50", length(grp))), bty = "n")
}
if (leg2 == 2) {
datalabs <- NULL
if (siardata$numgroups == 1) {
datalabs <- "data"
}
else {
for (k in 1:length(grp)) {
datalabs <- c(datalabs, as.character(paste("Group",
grp[k])))
}
}
plot(0, 0, "n", xaxt = "n", yaxt = "n", bty = "n")
legend(0, 0, legend = c(as.character(siardata$sources[,
1]), datalabs), lty = c(rep(1, nrow(siardata$sources)),
rep(-1, length(grp))), pch = c(rep(15, nrow(siardata$sources)),
pchseq[grp]), col = c(seq(1, nrow(siardata$sources)),
rep("grey50", length(grp))), bty = "n")
}
} |
`recodeSNPs` <-
function(mat,first.ref=FALSE,geno=1:3,snp.in.col=FALSE){
if(!is.data.frame(mat) & !is.matrix(mat))
stop("mat must be either a matrix or a data frame.")
mat<-as.matrix(mat)
if(length(geno)!=3)
stop("geno must have length 3.")
if(snp.in.col)
mat<-t(mat)
mat[mat=="NN"]<-NA
mat.hete<-checkATCG(mat,first.ref=first.ref)
ids.nohete<-rowSums(mat.hete)==0
if(any(!ids.nohete)){
cn<-strsplit(colnames(mat.hete),"")
FUN<-if(first.ref) recodeFirst else recodeCount
for(i in 1:length(cn)){
ids<-mat.hete[,i]>0
mat[ids,]<-FUN(mat[ids,,drop=FALSE],cn[[i]],geno=geno)
}
}
if(any(ids.nohete))
mat[ids.nohete,]<-recodeNoHete(mat[ids.nohete,, drop=FALSE],geno=geno)
if(is.numeric(geno))
mode(mat)<-"numeric"
if(snp.in.col)
mat<-t(mat)
mat
} |
context("test-impute-median")
vec <- rnorm(10)
vec[sample(1:10, 3)] <- NA
median_val <- median(vec, na.rm = TRUE)
vec2 <- vec
vec2[is.na(vec)] <- median_val
aq_shadow <- nabular(airquality)
test_that("impute_median works", {
expect_equal(impute_median(vec), vec2)
})
test_that("impute_median and scoped variants fail when given wrong input", {
expect_error(impute_median_if(numeric(0)))
expect_error(impute_median_at(numeric(0)))
expect_error(impute_median_all(numeric(0)))
expect_error(impute_median_if(NULL))
expect_error(impute_median_at(NULL))
expect_error(impute_mean_all(NULL))
})
test_that("impute_median_if works", {
expect_false(impute_median_if(airquality, is.numeric) %>% all_na())
})
test_that("impute_median_if works with shadow", {
expect_false(impute_median_if(aq_shadow, is.numeric) %>% all_na())
})
test_that("impute_median_if retains proper shadow values", {
expect_equal(unbind_data(impute_median_if(aq_shadow, is.numeric)),
unbind_data(aq_shadow))
})
test_that("impute_median_if retains proper shadow values", {
expect_equal(unbind_data(impute_median_if(aq_shadow, is.numeric)),
unbind_data(aq_shadow))
})
test_that("impute_median_at works", {
expect_equal(impute_median_at(airquality,
vars(Ozone)) %>%
miss_var_which(),
"Solar.R")
})
test_that("impute_median_at works with shadow", {
expect_equal(impute_median_at(aq_shadow,
vars(Ozone)) %>%
miss_var_which(),
"Solar.R")
})
test_that("impute_median_at retains proper shadow values", {
expect_equal(unbind_data(impute_median_at(aq_shadow, vars(Ozone))),
unbind_data(aq_shadow))
})
test_that("impute_median_at retains proper shadow values", {
expect_equal(unbind_data(impute_median_at(aq_shadow, vars(Ozone))),
unbind_data(aq_shadow))
})
test_that("impute_median_all works", {
expect_false(impute_median_all(airquality) %>% all_na())
})
test_that("impute_median_all works with shadow", {
expect_false(impute_median_all(aq_shadow) %>% all_na())
})
test_that("impute_median_all retains proper shadow values", {
skip_on_cran()
expect_equal(unbind_data(impute_median_all(aq_shadow)),
unbind_data(aq_shadow))
})
test_that("impute_median_all retains proper shadow values", {
skip_on_cran()
expect_equal(unbind_data(impute_median_all(aq_shadow)),
unbind_data(aq_shadow))
})
test_that("impute_median_all works with shadow", {
expect_false(impute_median_all(aq_shadow) %>% all_na())
})
test_that("impute_median_all retains proper shadow values", {
skip_on_cran()
expect_equal(unbind_data(impute_median_all(aq_shadow)),
unbind_data(aq_shadow))
})
test_that("impute_median_all retains proper shadow values", {
skip_on_cran()
expect_equal(unbind_data(impute_median_all(aq_shadow)),
unbind_data(aq_shadow))
}) |
assert_is_bsd <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_bsd, severity = severity)
}
assert_is_linux <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_linux, severity = severity)
}
assert_is_mac <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_mac, severity = severity)
}
assert_is_osx <- assert_is_mac
assert_is_osx_cheetah <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_osx_cheetah, severity = severity)
}
assert_is_osx_puma <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_osx_puma, severity = severity)
}
assert_is_osx_jaguar <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_osx_jaguar, severity = severity)
}
assert_is_osx_panther <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_osx_panther, severity = severity)
}
assert_is_osx_tiger <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_osx_tiger, severity = severity)
}
assert_is_osx_leopard <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_osx_leopard, severity = severity)
}
assert_is_osx_snow_leopard <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_osx_snow_leopard, severity = severity)
}
assert_is_osx_lion <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_osx_lion, severity = severity)
}
assert_is_osx_mountain_lion <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_osx_mountain_lion, severity = severity)
}
assert_is_osx_mavericks <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_osx_mavericks, severity = severity)
}
assert_is_osx_yosemite <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_osx_yosemite, severity = severity)
}
assert_is_osx_el_capitan <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_osx_el_capitan, severity = severity)
}
assert_is_macos_sierra <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_macos_sierra, severity = severity)
}
assert_is_macos_high_sierra <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_macos_high_sierra, severity = severity)
}
assert_is_macos_mojave <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_macos_mojave, severity = severity)
}
assert_is_macos_catalina <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_macos_catalina, severity = severity)
}
assert_is_macos_big_sur <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_macos_big_sur, severity = severity)
}
assert_is_solaris <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_solaris, severity = severity)
}
assert_is_unix <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_unix, severity = severity)
}
assert_is_windows <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_windows, severity = severity)
}
assert_is_windows_vista <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_windows_vista, severity = severity)
}
assert_is_windows_7 <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_windows_7, severity = severity)
}
assert_is_windows_8 <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_windows_8, severity = severity)
}
assert_is_windows_8.1 <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_windows_8.1, severity = severity)
}
assert_is_windows_10 <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_windows_10, severity = severity)
}
assert_is_windows_server_2008 <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_windows_server_2008, severity = severity)
}
assert_is_windows_server_2008_r2 <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_windows_server_2008_r2, severity = severity)
}
assert_is_windows_server_2012 <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_windows_server_2012, severity = severity)
}
assert_is_windows_server_2012_r2 <- function(severity = getOption("assertive.severity", "stop"))
{
assert_engine(is_windows_server_2012_r2, severity = severity)
} |
userInfoUi <- function(id) {
ns <- NS(id)
tagAppendAttributes(
shinydashboardPlus::userOutput(ns("user")),
style = "margin-left: 100px; border:none;"
)
}
userInfo <- function(input, output, session, diseases, sliderDisease, help) {
output$user <- shinydashboardPlus::renderUser({
ns <- session$ns
req(!is.null(diseases$php1()) | !is.null(diseases$hypopara()) | !is.null(diseases$hypoD3()))
shinydashboardPlus::dashboardUser(
name = "Rat State",
image = if (diseases$php1() | diseases$hypopara() | diseases$hypoD3()) {
generate_userFields(diseases, sliderDisease)$image
} else {
"images_patient_info/happy.png"
},
title = if (diseases$php1() | diseases$hypopara() | diseases$hypoD3()) {
generate_userFields(diseases, sliderDisease)$description
} else {
"healthy"
},
subtitle = if (diseases$php1()) {
"Rat has primary-hyperparathyroidism"
} else if (diseases$hypopara()) {
"Rat suffers from hypoparathyroidism"
} else if (diseases$hypoD3()) {
"Rat has vitamin D3 defficiency"
} else {
"nothing to declare!"
},
if (diseases$php1() | diseases$hypopara() | diseases$hypoD3()) {
shinydashboardPlus::dashboardUserItem(width = 6, generate_userFields(diseases, sliderDisease)$stat1)
} else {
shinydashboardPlus::dashboardUserItem(
width = 6,
HTML(paste("<p style=\"text-align:center;line-height:2.0\">",
"<font face =\"TimesNewRoman\" size=\"+1\">[<em><b>Ca<sup>2+</sup></b></em>]<sub><em><b>p</b></em></sub></font>","<br>",
"1.21 mM","<br>",
"(1.1-1.4 mM)"))
)
},
if (diseases$php1() | diseases$hypopara() | diseases$hypoD3()) {
shinydashboardPlus::dashboardUserItem(width = 6, generate_userFields(diseases, sliderDisease)$stat2)
} else {
shinydashboardPlus::dashboardUserItem(
width = 6,
HTML(paste("<p style=\"text-align:center;line-height:2.0\">",
"<font face =\"TimesNewRoman\" size=\"+1\">[<em><b>P<sub>i</sub></b></em>]<sub><em><b>p</b></em></sub></font>","<br>",
"2.96 mM","<br>",
"(2.1-3.4 mM)"))
)
},
if (diseases$php1() | diseases$hypopara() | diseases$hypoD3()) {
shinydashboardPlus::dashboardUserItem(width = 12, generate_userFields(diseases, sliderDisease)$stat3)
} else {
shinydashboardPlus::dashboardUserItem(
width = 12,
HTML(paste("<br>",
"<p style=\"text-align:center;line-height:2.0\">",
"<font face =\"TimesNewRoman\" size=\"+1\">[<em><b>PTH</b></em>]<sub><em><b>p</b></em></sub></font>","<br>",
"6.87 pM","<br>",
"(3-16 pM)"))
)
},
br()
)
})
} |
test_that("doy_decimal.Date", {
expect_identical(dtt_doy_decimal(NA_Date_[-1]), numeric(0))
expect_identical(dtt_doy_decimal(NA_Date_), NA_real_)
expect_identical(
dtt_doy_decimal(as.Date(c("2001-01-01", "2001-12-31", NA))),
c(1, 365, NA)
)
})
test_that("doy_decimal.POSIXct", {
expect_identical(dtt_doy_decimal(NA_POSIXct_[-1]), numeric(0))
expect_identical(dtt_doy_decimal(NA_POSIXct_), NA_real_)
expect_equal(
dtt_doy_decimal(as.POSIXct(c("2001-01-01", "2001-12-31", NA))),
c(1, 365, NA)
)
expect_equal(
dtt_doy_decimal(as.POSIXct(c("2001-01-01 00:00:01", "2001-12-31 23:59:59", NA))),
c(1.00001157407407, 365.999988425926, NA)
)
}) |
sample.cluster = function(
pop = ceiling(10 * runif(10, 0.2, 1)), size = 3, p.col = c('blue', 'red'),
p.cex = c(1, 3), ...
) {
if (size > length(pop))
stop('sample size must be smaller than the number of clusters')
ncol = max(pop)
nrow = length(pop)
nmax = ani.options('nmax')
for (i in 1:nmax) {
dev.hold()
plot(1, axes = FALSE, ann = FALSE, type = 'n',
xlim = c(0.5, ncol + 0.5), ylim = c(0.5, nrow + 0.5), xaxs = 'i',
yaxs = 'i', xlab = '', ylab = '')
rect(rep(0.5, nrow), seq(0.5, nrow, 1),
rep(ncol + 0.5, nrow), seq(1.5, nrow + 1, 1), lwd = 1, ...)
idx = sample(nrow, size)
for (j in 1:nrow) {
points(1:pop[j], rep(j, pop[j]), col = p.col[1], cex = p.cex[1], pch = 19)
if (j %in% idx)
points(1:pop[j], rep(j, pop[j]), col = p.col[2], cex = p.cex[2])
}
ani.pause()
}
invisible(NULL)
} |
rename_gcdg_gsed <- function(x, copy = TRUE) {
aqi <- function(x) {
domo <- gsub("a|[0-9]", "", x)
domn <- NA
domn <- ifelse(domo == "com", "cm", domn)
domn <- ifelse(domo == "f", "fm", domn)
domn <- ifelse(domo == "pbs", "px", domn)
domn <- ifelse(domo == "g", "gm", domn)
domn <- ifelse(domo == "ps", "sl", domn)
nr <- gsub("[a-z]", "", x)
nr <- str_pad(nr, 3, pad = "0")
rep <- "c"
instr <- "aqi"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
bar <- function(x) {
domn <- "xx"
nr <- gsub("[a-z]", "", x)
nr <- str_pad(nr, 3, pad = "0")
repi <- gsub("bm|[0-9]", "", x)
rep <- "x"
rep <- ifelse(grepl("a", repi), "d", rep)
rep <- ifelse(grepl("b", repi), "c", rep)
instr <- "bar"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
bat <- function(x) {
domo <- substr(x, 4, 4)
domn <- rep("", length(x))
domn <- ifelse(domo == "c", "cg", domn)
domn <- ifelse(domo == "1", "cm", domn)
domn <- ifelse(domo == "a", "ad", domn)
domn <- ifelse(domo == "m", "mo", domn)
domn <- ifelse(domo == "s", "sl", domn)
nr <- str_pad(unlist(lapply(strsplit(x, "_z"), `[[`, 2)),
3,
pad = "0"
)
rep <- "d"
instr <- "bat"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
by1 <- function(x) {
domo <- gsub("b|[0-9]", "", x)
domn <- NA
domn <- ifelse(domo == "m", "md", domn)
domn <- ifelse(domo == "p", "pd", domn)
nr <- gsub("b1p|b1m", "", x)
nr <- str_pad(nr, 3, pad = "0")
rep <- "d"
instr <- "by1"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
by2 <- function(x) {
domo <- gsub("b|[0-9]", "", x)
domn <- NA
domn <- ifelse(domo == "m", "md", domn)
domn <- ifelse(domo == "p" | domo == "g", "pd", domn)
nr <- gsub("b2p|b2m|b2g", "", x)
nr <- str_pad(nr, 3, pad = "0")
rep <- "d"
instr <- "by2"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
by3 <- function(x) {
domo <- gsub("b|[0-9]", "", x)
domn <- NA
domn <- ifelse(domo == "c", "cg", domn)
domn <- ifelse(domo == "f", "fm", domn)
domn <- ifelse(domo == "e", "ex", domn)
domn <- ifelse(domo == "r", "re", domn)
domn <- ifelse(domo == "g", "gm", domn)
nr <- gsub("b3|[a-z]", "", x)
nr <- str_pad(nr, 3, pad = "0")
rep <- "d"
instr <- "by3"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
ddi <- function(x) {
fm <- c(
1, 7, 8, 9, 13, 14, 19, 20, 21, 27, 32, 33, 38,
39, 44, 45, 51, 52, 53, 54
)
cm <- c(
2, 6, 10, 25, 31, 30, 37, 40, 47, 55, 56, 16, 36,
41, 48
)
gm <- c(
3, 4, 11, 15, 5, 12, 18, 17, 14, 22, 23, 24, 26,
28, 29, 34, 35, 42, 50, 43, 49, 57, 46
)
domn <- rep("xx", length(x))
nr <- gsub("n|v", "", x)
domn <- ifelse(grepl("n", x) & nr %in% fm, "fm", domn)
domn <- ifelse(grepl("n", x) & nr %in% cm, "cm", domn)
domn <- ifelse(grepl("n", x) & nr %in% gm, "gm", domn)
domn <- ifelse(grepl("v", x) & nr < 30, "fm", domn)
domn <- ifelse(grepl("v", x) & nr > 30 & nr < 52, "cm", domn)
domn <- ifelse(grepl("v", x) & nr > 66, "gm", domn)
nr <- ifelse(grepl("n1", x), 1, nr)
nr <- ifelse(grepl("n2", x), 29, nr)
nr <- ifelse(grepl("n3", x), 52, nr)
nr <- ifelse(grepl("n4", x), 53, nr)
nr <- ifelse(grepl("n5", x), 56, nr)
nr <- ifelse(grepl("n6", x), 30, nr)
nr <- ifelse(grepl("n7", x), 2, nr)
nr <- ifelse(grepl("n8", x), 3, nr)
nr <- ifelse(grepl("n9", x), 4, nr)
nr <- ifelse(grepl("n10", x), 31, nr)
nr <- ifelse(grepl("n11", x), 54, nr)
nr <- ifelse(grepl("n12", x), 57, nr)
nr <- ifelse(grepl("n13", x), 5, nr)
nr <- ifelse(grepl("n14", x), 6, nr)
nr <- ifelse(grepl("n15", x), 55, nr)
nr <- ifelse(grepl("n16", x), 116, nr)
nr <- ifelse(grepl("n17", x), 59, nr)
nr <- ifelse(grepl("n18", x), 58, nr)
nr <- ifelse(grepl("n19", x), 7, nr)
nr <- ifelse(grepl("n20", x), 8, nr)
nr <- ifelse(grepl("n21", x), 9, nr)
nr <- ifelse(grepl("n22", x), 60, nr)
nr <- ifelse(grepl("n23", x), 61, nr)
nr <- ifelse(grepl("n24", x), 62, nr)
nr <- ifelse(grepl("n25", x), 33, nr)
nr <- ifelse(grepl("n26", x), 63, nr)
nr <- ifelse(grepl("n27", x), 10, nr)
nr <- ifelse(grepl("n28", x), 64, nr)
nr <- ifelse(grepl("n29", x), 65, nr)
nr <- ifelse(grepl("n30", x), 36, nr)
nr <- ifelse(grepl("n31", x), 34, nr)
nr <- ifelse(grepl("n32", x), 11, nr)
nr <- ifelse(grepl("n33", x), 12, nr)
nr <- ifelse(grepl("n34", x), 66, nr)
nr <- ifelse(grepl("n35", x), 67, nr)
nr <- ifelse(grepl("n36", x), 136, nr)
nr <- ifelse(grepl("n37", x), 37, nr)
nr <- ifelse(grepl("n38", x), 13, nr)
nr <- ifelse(grepl("n39", x), 14, nr)
nr <- ifelse(grepl("n40", x), 39, nr)
nr <- ifelse(grepl("n41", x), 141, nr)
nr <- ifelse(grepl("n42", x), 68, nr)
nr <- ifelse(grepl("n43", x), 69, nr)
nr <- ifelse(grepl("n44", x), 15, nr)
nr <- ifelse(grepl("n45", x), 16, nr)
nr <- ifelse(grepl("n46", x), 146, nr)
nr <- ifelse(grepl("n47", x), 41, nr)
nr <- ifelse(grepl("n48", x), 148, nr)
nr <- ifelse(grepl("n49", x), 70, nr)
nr <- ifelse(grepl("n50", x), 168, nr)
nr <- ifelse(grepl("n51", x), 17, nr)
nr <- ifelse(grepl("n52", x), 18, nr)
nr <- ifelse(grepl("n53", x), 19, nr)
nr <- ifelse(grepl("n54", x), 154, nr)
nr <- ifelse(grepl("n55", x), 43, nr)
nr <- ifelse(grepl("n56", x), 44, nr)
nr <- ifelse(grepl("n57", x), 71, nr)
nr <- ifelse(grepl("v20", x), 20, nr)
nr <- ifelse(grepl("v21", x), 21, nr)
nr <- ifelse(grepl("v22", x), 22, nr)
nr <- ifelse(grepl("v23", x), 23, nr)
nr <- ifelse(grepl("v24", x), 24, nr)
nr <- ifelse(grepl("v25", x), 25, nr)
nr <- ifelse(grepl("v26", x), 27, nr)
nr <- ifelse(grepl("v27", x), 26, nr)
nr <- ifelse(grepl("v31", x), 32, nr)
nr <- ifelse(grepl("v32", x), 132, nr)
nr <- ifelse(grepl("v35", x), 35, nr)
nr <- ifelse(grepl("v38", x), 38, nr)
nr <- ifelse(grepl("v40", x), 40, nr)
nr <- ifelse(grepl("v42", x), 42, nr)
nr <- ifelse(grepl("v45", x), 45, nr)
nr <- ifelse(grepl("v46", x), 46, nr)
nr <- ifelse(grepl("v47", x), 47, nr)
nr <- ifelse(grepl("v48", x), 48, nr)
nr <- ifelse(grepl("v49", x), 49, nr)
nr <- ifelse(grepl("v50", x), 50, nr)
nr <- ifelse(grepl("v72", x), 72, nr)
nr <- ifelse(grepl("v73", x), 268, nr)
nr <- ifelse(grepl("v74", x), 73, nr)
nr <- ifelse(grepl("v75", x), 74, nr)
mitem <- c(
4, 9, 12, 29:38, 60, 64, 65, 66, 67,
14, 16, 19, 25, 39:43, 45:48, 50, 51, 73
)
rep <- ifelse(nr %in% mitem, "m", "d")
nr <- str_pad(nr, 3, pad = "0")
instr <- "ddi"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
den <- function(x) {
domo <- gsub("d|[0-9]", "", x)
domn <- NA
domn <- ifelse(domo == "l", "lg", domn)
domn <- ifelse(domo == "f", "fm", domn)
domn <- ifelse(domo == "g", "gm", domn)
domn <- ifelse(domo == "p", "sl", domn)
nr <- gsub("[a-z]", "", x)
nr <- str_pad(nr, 3, pad = "0")
rep <- "d"
instr <- "den"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
gri <- function(x) {
domo <- gsub("g|[0-9]|_", "", x)
domn <- NA
domn <- ifelse(domo == "c", "cg", domn)
domn <- ifelse(domo == "e", "eh", domn)
domn <- ifelse(domo == "h", "hs", domn)
domn <- ifelse(domo == "p", "re", domn)
domn <- ifelse(domo == "", "gm", domn)
nr <- gsub("g|[a-z]", "", x)
nr <- ifelse(nchar(nr) > 3, gsub("_", "", nr), nr)
nr <- gsub("_", "0", nr)
nr <- str_pad(nr, 3, pad = "0")
rep <- "d"
instr <- "gri"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
mac <- function(x) {
domn <- "gm"
nr <- gsub("mg", "", x)
nr <- str_pad(nr, 3, pad = "0")
nr <- ifelse(nr == "04a", "041", nr)
nr <- ifelse(nr == "04b", "042", nr)
rep <- "d"
instr <- "mac"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
mds <- function(x) {
domn <- "gm"
nr <- gsub("mil", "", x)
nr <- str_pad(nr, 3, pad = "0")
rep <- "d"
instr <- "mds"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
mul <- function(x) {
domo <- substr(x, 4, 4)
domn <- NA
domn <- ifelse(domo == "v", "cg", domn)
domn <- ifelse(domo == "f", "fm", domn)
domn <- ifelse(domo == "r", "re", domn)
domn <- ifelse(domo == "g", "gm", domn)
domn <- ifelse(domo == "e", "ex", domn)
domn <- ifelse(domo == "s", "se", domn)
nr <- gsub("[a-z]", "", x)
tr <- substr(x, nchar(x), nchar(x))
ad <- rep("0", length(x))
ad <- ifelse(tr == "a", "0", ad)
ad <- ifelse(tr == "b", "1", ad)
ad <- ifelse(tr == "c", "2", ad)
ad <- ifelse(tr == "d", "3", ad)
ad <- ifelse(tr == "e", "4", ad)
ad <- ifelse(tr == "f", "5", ad)
nr <- paste0(nr, ad)
nr <- str_pad(nr, 3, pad = "0")
rep <- "d"
instr <- "mul"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
peg <- function(x) {
domn <- "fm"
nr <- gsub("peg", "", x)
nr <- str_pad(nr, 3, pad = "0")
rep <- "d"
instr <- "peg"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
sgr <- function(x) {
domo <- gsub("sag|[0-9]", "", x)
domn <- NA
domn <- ifelse(domo == "c", "cg", domn)
domn <- ifelse(domo == "e", "eh", domn)
domn <- ifelse(domo == "h" | domo == "hs", "hs", domn)
domn <- ifelse(domo == "p", "fm", domn)
domn <- ifelse(domo == "l", "gm", domn)
domn <- ifelse(domo == "ps" | domo == "s", "re", domn)
nr <- gsub("[a-z]", "", x)
nr <- str_pad(nr, 3, pad = "0")
rep <- "d"
instr <- "sgr"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
sbi <- function(x) {
domo <- gsub("sb|[0-9]", "", x)
domn <- NA
domn <- ifelse(domo == "w", "wm", domn)
domn <- ifelse(domo == "v", "vs", domn)
domn <- ifelse(domo == "f", "fr", domn)
domn <- ifelse(domo == "s", "sl", domn)
nr <- gsub("[a-z]", "", x)
nr <- ifelse(nchar(nr) == 1, paste0(nr, "0"), nr)
nr <- str_pad(nr, 3, pad = "0")
rep <- "d"
instr <- "sbi"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
tep <- function(x) {
nr <- gsub("j|b|c|__[0-9]", "", x)
domn <- NA
domn <- ifelse(nr <= 16, "co", domn)
domn <- ifelse(nr > 16 & nr <= 40, "lg", domn)
domn <- ifelse(nr > 40, "mo", domn)
nr <- gsub("[a-z]|__", "", x)
nr <- str_pad(nr, 3, pad = "0")
rep <- "d"
instr <- "tep"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
vin <- function(x) {
nr <- gsub("sa2v", "", x)
domn <- "xx"
nr <- ifelse(grepl("playswith", x), "31", nr)
nr <- ifelse(grepl("crayondraw", x), "32", nr)
nr <- ifelse(grepl("avoidda", x), "33", nr)
nr <- ifelse(grepl("buttons", x), "34", nr)
nr <- ifelse(grepl("compete", x), "35", nr)
nr <- ifelse(grepl("downstar", x), "36", nr)
nr <- ifelse(grepl("dryhand", x), "37", nr)
nr <- ifelse(grepl("eatfood", x), "38", nr)
nr <- ifelse(grepl("getwater", x), "39", nr)
nr <- ifelse(grepl("helpshou", x), "40", nr)
nr <- ifelse(grepl("narrates", x), "41", nr)
nr <- ifelse(grepl("onjacket", x), "42", nr)
nr <- ifelse(grepl("performs", x), "43", nr)
nr <- ifelse(grepl("scissors", x), "44", nr)
nr <- ifelse(grepl("selftoil", x), "45", nr)
nr <- ifelse(grepl("takeoff", x), "46", nr)
nr <- ifelse(grepl("washface", x), "47", nr)
nr <- ifelse(grepl("washhands", x), "48", nr)
nr <- ifelse(grepl("playself", x), "49", nr)
nr <- ifelse(grepl("ytoilet", x), "50", nr)
nr <- ifelse(grepl("dressself", x), "51", nr)
nr <- str_pad(nr, 3, pad = "0")
rep <- "c"
instr <- "vin"
cbind(as.character(x), paste(instr, domn, rep, nr, sep = ""))
}
convert <- function(x, y, n, pat, func) {
idx <- substr(x, 1, n) %in% pat
if (!any(idx)) {
return(y)
}
y[idx] <- func(x[idx])[, 2]
y
}
y <- x
if (!copy) y <- rep("", length(y))
y <- convert(x, y, 2, c("ac", "af", "ap", "ag"), aqi)
y <- convert(x, y, 2, c("bm"), bar)
y <- convert(x, y, 3, c("bat"), bat)
y <- convert(x, y, 2, c("b1"), by1)
y <- convert(x, y, 2, c("b2"), by2)
y <- convert(x, y, 2, c("b3"), by3)
y <- convert(x, y, 1, c("n", "v"), ddi)
y <- convert(x, y, 1, c("d"), den)
y <- convert(x, y, 1, c("g"), gri)
y <- convert(x, y, 2, c("mg"), mac)
y <- convert(x, y, 3, c("mil"), mds)
y <- convert(x, y, 3, c("mul"), mul)
y <- convert(x, y, 3, c("peg"), peg)
y <- convert(x, y, 3, c("sag"), sgr)
y <- convert(x, y, 2, c("sb"), sbi)
y <- convert(x, y, 1, c("j"), tep)
y <- convert(x, y, 4, c("sa2v", "savi", "savn"), vin)
y
} |
evap_est <- function(ts, eMin=0, eMax=0){
if(!(eMin < eMax)){
stop("eMin should be less then eMax")
}
dt <- diff(as.numeric(ts))
if(!all(dt[]==dt[1])){
stop("Irregularly spaced time series supplied")
}else{
dt <- dt[1]
}
if(dt > 24*60*60){ stop("Time step can be no mroe then a day") }
yday <- 0:365
fact <- 1+sin(2*pi*yday/365-pi/2)
daily_pet <- eMin + 0.5*(eMax-eMin)*fact
dawn <- (10 - 2.5*fact)*60*60
dayLength <- (6 + 4*fact) * 60*60
sts <- ts-dt
dts <- as.POSIXct(3600*24*floor(as.numeric(ts)/(3600*24)),origin="1970-01-01",tz='GMT')
dsts <- as.POSIXct(3600*24*floor(as.numeric(sts)/(3600*24)),origin="1970-01-01",tz='GMT')
pet <- rep(0,length(ts))
tmp <- dsts
idx <- tmp<dts
while( any(idx) ){
pet[idx] <- pet[idx] + daily_pet[ as.POSIXlt(tmp[idx])$yday + 1]
tmp <- tmp + 24*60*60
idx <- tmp<dts
}
sc <- as.numeric(sts) - as.numeric(dsts)
frc <- (sc - dawn[ as.POSIXlt(sts)$yday +1 ])/ dayLength[ as.POSIXlt(sts)$yday +1]
frc <- pmin(1,pmax(0,frc))
pet <- pet - daily_pet[ as.POSIXlt(sts)$yday + 1]*0.5*(1-cos(frc*pi))
sc <- as.numeric(ts) - as.numeric(dts)
frc <- (sc - dawn[ as.POSIXlt(ts)$yday +1 ])/ dayLength[ as.POSIXlt(ts)$yday +1]
frc <- pmin(1,pmax(0,frc))
pet <- pet + daily_pet[ as.POSIXlt(ts)$yday + 1]*0.5*(1-cos(frc*pi))
return(xts::xts(pet,order.by=ts))
} |
iso_save <- function(iso_files, filepath, quiet = default(quiet)) {
if(!iso_is_object(iso_files)) stop("can only export iso files or lists of iso files", call. = FALSE)
filepath <- get_rds_export_filepath(iso_files, filepath)
if (!quiet) {
sprintf("Info: exporting data from %d iso_files into R Data Storage '%s'",
length(iso_as_file_list(iso_files)),
str_replace(filepath, "^\\.(/|\\\\)", "")) %>% message()
}
saveRDS(iso_files, file = filepath)
return(invisible(iso_files))
}
get_rds_export_filepath <- function(iso_files, filepath) {
if (iso_is_continuous_flow(iso_files))
ext <- ".cf.rds"
else if (iso_is_dual_inlet(iso_files))
ext <- ".di.rds"
else if (iso_is_scan(iso_files))
ext <- ".scan.rds"
else
stop("R data storage export of this type of iso_files not supported", call. = FALSE)
return(get_export_filepath(filepath, ext))
} |
simulateGen <- function(ped, AF=c(), mut.rate=0) {
colnames(ped) = c("ID","SIRE","DAM")
if(length(AF)==0) {
AF = runif(ncol(M), min=0.01, max=0.99)
} else {
stopifnot(min(AF)>=0.01)
stopifnot(max(AF)<=0.99)
}
if(!identical(mut.rate, 0)) {
stopifnot(length(AF)==length(mut.rate))
stopifnot(min(mut.rate)>=0)
if(length(mut.rate[mut.rate > 10^-6])) {
warning("Found ", length(mut.rate[mut.rate > 10^-6]), " markers with mutation rate > 10^-6")
warning("Maximum mutation rate = ", max(mut.rate))
}
} else {
message("No mutation was simulated.")
}
tmp = c()
SNPs = 1:length(AF)
for(j in SNPs) tmp = c(tmp, sample(0:2, 1, prob=c((1-AF[j])^2, 2*(1-AF[j])*AF[j], AF[j]^2)))
M = matrix(tmp, nrow=1)
for(i in 2:nrow(ped))
{
s = ped$SIRE[i]
d = ped$DAM[i]
tmp = c()
if(s==0 & d==0) {
for(j in SNPs) tmp = c(tmp, sample(0:2, 1, prob=c((1-AF[j])^2, 2*(1-AF[j])*AF[j], AF[j]^2)))
} else if(s>0 & d==0) {
for(j in SNPs) tmp = c(tmp, sample(0:1, 1, prob=c(1-AF[j], AF[j])))
tmp = tmp+makegamete(M[s,])
} else if(s==0 & d>0) {
for(j in SNPs) tmp = c(tmp, sample(0:1, 1, prob=c(1-AF[j], AF[j])))
tmp = tmp+makegamete(M[d,])
} else {
tmp = makegamete(M[s,])+makegamete(M[d,])
}
if(!identical(mut.rate, 0)) tmp = mutate(tmp, mut.rate)
M = rbind(M, tmp)
rownames(M) = NULL
}
return(M)
} |
htmlUl <- function(children=NULL, id=NULL, n_clicks=NULL, n_clicks_timestamp=NULL, key=NULL, role=NULL, accessKey=NULL, className=NULL, contentEditable=NULL, contextMenu=NULL, dir=NULL, draggable=NULL, hidden=NULL, lang=NULL, spellCheck=NULL, style=NULL, tabIndex=NULL, title=NULL, loading_state=NULL, ...) {
wildcard_names = names(dash_assert_valid_wildcards(attrib = list('data', 'aria'), ...))
props <- list(children=children, id=id, n_clicks=n_clicks, n_clicks_timestamp=n_clicks_timestamp, key=key, role=role, accessKey=accessKey, className=className, contentEditable=contentEditable, contextMenu=contextMenu, dir=dir, draggable=draggable, hidden=hidden, lang=lang, spellCheck=spellCheck, style=style, tabIndex=tabIndex, title=title, loading_state=loading_state, ...)
if (length(props) > 0) {
props <- props[!vapply(props, is.null, logical(1))]
}
component <- list(
props = props,
type = 'Ul',
namespace = 'dash_html_components',
propNames = c('children', 'id', 'n_clicks', 'n_clicks_timestamp', 'key', 'role', 'accessKey', 'className', 'contentEditable', 'contextMenu', 'dir', 'draggable', 'hidden', 'lang', 'spellCheck', 'style', 'tabIndex', 'title', 'loading_state', wildcard_names),
package = 'dashHtmlComponents'
)
structure(component, class = c('dash_component', 'list'))
} |
simulateTarget<-function(
optimArgs=NULL,
simVar=NULL,
modelTag=NULL,
modelInfo=NULL,
attSel=NULL,
attPrim=NULL,
attInfo=NULL,
attInd=NULL,
datInd=NULL,
initCalibPars=NULL,
targetLoc=NULL,
attObs=NULL,
parLoc=NULL,
parSim=NULL,
setSeed=1234,
file=NULL
){
nMod=length(modelTag)
out=list()
parV=NULL
objScore=NULL
if(!is.null(optimArgs$suggestions)){
parSugg=rbind(parSim,optimArgs$suggestions)
if(dim(parSugg)[1] > optimArgs$popSize){
parSugg=parSugg[(1:optimArgs$popSize),]
}
}else{
parSugg=NULL
}
attSim=list()
targetSim=list()
for(mod in 1:nMod){
randomVector <- runif(n=datInd[[modelTag[mod]]]$ndays)
switch(simVar[mod],
"P" = {wdStatus=NULL},
"Temp" = {if(modelInfo[[modelTag[mod]]]$WDcondition==TRUE){
wdStatus=out[["P"]]$sim
}else{
wdStatus=NULL
}
},
{wdStatus=NULL}
)
write_model_env(envir = foreSIGHT_modelEnv,
modelInfo = modelInfo[[modelTag[mod]]],
modelTag = modelTag[mod],
datInd = datInd[[modelTag[mod]]]
)
if(length(which(modelInfo[[modelTag[mod]]]$minBound==modelInfo[[modelTag[mod]]]$maxBound))==length(modelInfo[[modelTag[mod]]]$minBound)){
progress(p(" Working on variable ",simVar[mod]),file)
progress(p(" Parameters specified by user, no optimisation ..."),file)
out[[simVar[mod]]]=switch_simulator(type=modelInfo[[modelTag[mod]]]$simVar,
parS=modelInfo[[modelTag[mod]]]$minBound,
modelEnv = foreSIGHT_modelEnv,
randomVector = randomVector,
wdSeries=wdStatus,
resid_ts=NULL,
seed=setSeed)
}else{
progress(p(" Working on variable ",simVar[mod]),file)
progress(p(" Commencing optimisation..."),file)
if(!is.null(parSugg)){
parSel=parSugg[,(parLoc[[mod]][1]:parLoc[[mod]][2])]
}else{
parSel=NULL
}
optTest=gaWrapper(gaArgs=optimArgs,
modelEnv = foreSIGHT_modelEnv,
modelInfo=modelInfo[[modelTag[mod]]],
attSel=attSel[attInd[[mod]]],
attPrim=attPrim,
attInfo=attInfo[[modelTag[mod]]],
datInd=datInd[[modelTag[mod]]],
randomVector = randomVector,
parSuggest=parSel,
target=targetLoc[attInd[[mod]]],
attObs=attObs[attInd[[mod]]],
lambda.mult=optimArgs$lambda.mult,
simSeed=setSeed,
wdSeries=wdStatus,
resid_ts=NULL)
progress(p(" Best fitness: ",signif(optTest$fitness,digits=5), ". Optimisation stopped at iter ",optTest$opt@iter),file)
out[[simVar[mod]]]=switch_simulator(type=modelInfo[[modelTag[mod]]]$simVar,
parS=optTest$par,
modelEnv = foreSIGHT_modelEnv,
randomVector = randomVector,
wdSeries=wdStatus,
resid_ts=NULL,
seed=optTest$seed)
}
sim.att=attribute.calculator(attSel=attSel[attInd[[mod]]],data=out[[simVar[mod]]]$sim,datInd=datInd[[modelTag[mod]]],attribute.funcs=attribute.funcs)
attSim[[mod]]=sim.att
simPt=unlist(Map(function(type, val,baseVal) simPt.converter.func(type,val,baseVal), attInfo$targetType[attInd[[mod]]], as.vector(sim.att),as.vector(attObs[attInd[[mod]]])),use.names = FALSE)
names(simPt)=attSel[attInd[[mod]]]
targetSim[[mod]]=simPt
score=objFuncMC(attSel= attSel[attInd[[mod]]],
attPrim=attPrim,
attInfo=attInfo[[modelTag[mod]]],
simPt=simPt,
target=targetLoc[attInd[[mod]]],
penalty.func=penaltyFunc_basic,
lambda=optimArgs$lambda.mult)
progress(paste0(" Variable ",simVar[mod]," final sim series fitness: ",signif(score,4)),file)
parV=c(parV,optTest$par)
objScore=c(objScore,score)
}
out$attSim=unlist(attSim)[attSel]
progress(paste(" Attributes Simulated - ",paste(attSel,": ",signif(out$attSim,digits=4),collapse = ", ",sep=""),sep=''),file)
out$targetSim=unlist(targetSim)[attSel]
progress(paste(" Target Simulated - ",paste(attSel,": ",signif(out$targetSim,digits=4),collapse = ", ",sep=''),sep=""),file)
out$parS=parV
out$score=objScore
return(out)
} |
calculate.overlap.and.pvalue = function(list1, list2, total.size, lower.tail = TRUE, adjust = FALSE) {
actual.overlap <- length(intersect(list1, list2));
expected.overlap <- as.numeric(length(list1)) * length(list2) / total.size;
adjust.value <- 0;
if (adjust & !lower.tail) {
adjust.value <- 1;
warning('Calculating P[X >= x]');
}
overlap.pvalue <- phyper(
q = actual.overlap - adjust.value,
m = length(list1),
n = total.size - length(list1),
k = length(list2),
lower.tail = lower.tail
);
return( c(actual.overlap, expected.overlap, overlap.pvalue) );
} |
context("Parse git repo")
test_that("pull request and latest release, via spec and URL", {
expect_equal(
parse_git_repo("r-lib/remotes
github_pull("7")
)
expect_equal(
parse_git_repo("https://github.com/r-lib/remotes/pull/7")$ref,
github_pull("7")
)
expect_equal(
parse_git_repo("r-lib/remotes@*release")$ref,
github_release()
)
expect_equal(
parse_git_repo("https://github.com/r-lib/remotes/releases/latest")$ref,
github_release()
)
})
test_that("parse_repo_spec trailing slash, issue
expect_equal(
parse_repo_spec("foo/bar/baz/"),
parse_repo_spec("foo/bar/baz")
)
})
test_that("parse_github_url() accepts all forms of URL (github.com and GHE)", {
expect_identical(
parse_github_url("https://github.com/r-lib/remotes.git"),
list(username = "r-lib", repo = "remotes", ref = "", pull = "", release = "")
)
expect_identical(
parse_github_url("https://github.ubc.ca/user/repo.git"),
list(username = "user", repo = "repo", ref = "", pull = "", release = "")
)
expect_identical(
parse_github_url("[email protected]:r-lib/remotes.git"),
list(username = "r-lib", repo = "remotes", ref = "", pull = "", release = "")
)
expect_identical(
parse_github_url("[email protected]:user/repo.git"),
list(username = "user", repo = "repo", ref = "", pull = "", release = "")
)
expect_identical(
parse_github_url("https://github.com/r-lib/remotes"),
list(username = "r-lib", repo = "remotes", ref = "", pull = "", release = "")
)
expect_identical(
parse_github_url("https://github.ubc.ca/user/repo"),
list(username = "user", repo = "repo", ref = "", pull = "", release = "")
)
expect_identical(
parse_github_url("https://github.com/r-lib/remotes/tree/i-am-a-branch"),
list(username = "r-lib", repo = "remotes", ref = "i-am-a-branch", pull = "", release = "")
)
expect_identical(
parse_github_url("https://github.com/r-lib/remotes/commit/1234567"),
list(username = "r-lib", repo = "remotes", ref = "1234567", pull = "", release = "")
)
expect_identical(
parse_github_url("https://github.com/r-lib/remotes/pull/108"),
list(username = "r-lib", repo = "remotes", ref = "", pull = "108", release = "")
)
expect_identical(
parse_github_url("https://github.com/r-lib/remotes/releases/tag/1.0.0"),
list(username = "r-lib", repo = "remotes", ref = "1.0.0", pull = "", release = "")
)
expect_identical(
parse_github_url("https://github.com/r-lib/remotes/releases/latest"),
list(username = "r-lib", repo = "remotes", ref = "", pull = "", release = "*release")
)
})
test_that("parse_repo_spec catches invalid spec", {
expect_error(
parse_repo_spec("/$&@R64&3"),
"Invalid git repo specification"
)
})
test_that("parse_repo_spec, github", {
cases <- list(
list("user/repo"),
list("pkg=user/repo", package = "pkg"),
list("pkg=user/repo", package = "pkg"),
list("user/repo/subdir", subdir = "subdir"),
list("user/repo@badcafe", ref = "badcafe"),
list("user/repo
list("user/repo@*release", ref = github_release()),
list("pkg=user/repo/subdir", package = "pkg", subdir = "subdir"),
list("pkg=user/repo@badcafe", package = "pkg", ref = "badcafe"),
list("pkg=user/repo
list("pkg=user/repo@*release", package = "pkg", ref = github_release()),
list("[email protected]:user/repo.git"),
list("[email protected]:user/repo.git"),
list("https://github.com/user/repo"),
list("https://github.ubc.ca/user/repo"),
list("https://github.com/user/repo/tree/i-am-a-branch", ref = "i-am-a-branch"),
list("https://github.com/user/repo/commit/1234567", ref = "1234567"),
list("https://github.com/user/repo/pull/108", ref = github_pull("108")),
list("https://github.com/user/repo/releases/tag/1.0.0", ref = "1.0.0"),
list("https://github.com/user/repo/releases/latest", ref = github_release()),
list("https://github.com/user/repo/releases/latest", ref = github_release()),
list("https://github.com/foo/bar", username = "foo", repo = "bar"),
list("[email protected]:foo/bar.git", username = "foo", repo = "bar"),
list("[email protected]:foo-bar/baz-qux.git", username = "foo-bar", repo = "baz-qux")
)
for (case in cases) {
expect_equal_named_lists(
p <- parse_git_repo(case[[1]]),
utils::modifyList(
list(username = "user", repo = "repo"),
case[-1]
)
)
}
})
test_that("parse_git_repo errors on invalid GitHub input", {
expect_error(parse_git_repo("https://github.com/r-lib"), "Invalid GitHub URL")
}) |
bayessurvreg1.priorInit <- function(prior, init, Yinit, Xinit, n, nX, nrandom, ncluster, indb, randomInt, toler.chol){
if(length(prior) == 0) inprior <- "arnost"
else inprior <- names(prior)
if(length(init) == 0) ininit <- "arnost"
else ininit <- names(init)
prior.pari <- numeric(3)
names(prior.pari) <- c("kmax", "k.prior", "Eb0.depend.mix")
tmp <- match("kmax", inprior, nomatch=NA)
if(is.na(tmp)) prior$kmax <- 5
tmp <- match("k.prior", inprior, nomatch=NA)
if(is.na(tmp)) prior$k.prior <- "poisson"
tmp <- match("Eb0.depend.mix", inprior, nomatch=NA)
if(is.na(tmp)) prior$Eb0.depend.mix <- FALSE
tmp <- match("poisson.k", inprior, nomatch=NA)
if(is.na(tmp)) prior$poisson.k <- 3
prior.pari["k.prior"] <- pmatch(prior$k.prior, c("poisson", "uniform", "fixed"), nomatch = -1) - 1
if (prior.pari["k.prior"] < 0) stop("Prior for k (number of mixture components) must be either poisson, uniform or fixed.")
prior.pari["kmax"] <- prior$kmax
prior.pari["Eb0.depend.mix"] <- 1*(prior$Eb0.depend.mix)
prior.pard <- numeric(2*prior.pari["kmax"] + 7)
names(prior.pard) <- c(paste("pi.split", 1:prior.pari["kmax"], sep = ""), paste("pi.birth", 1:prior.pari["kmax"], sep = ""),
"lambda", "delta", "xi", "kappa", "zeta", "g", "h")
prior.pard["lambda"] <- prior$poisson.k
init.error <- "Something is wrong with your initials."
fit.init <- survreg(Yinit ~ Xinit - 1, dist = "lognormal")
tmp <- match("iter", ininit, nomatch=NA)
if(is.na(tmp)) init$iter <- 0
if (is.na(init$iter)) init$iter <- 0
init$iter <- init$iter[1]
tmp <- match("mixture", ininit, nomatch=NA)
if(is.na(tmp)){
if (prior.pari["k.prior"] == 2) stop("init$mixture must be given when prior$k.prior is 'fixed'.")
init$mixture <- numeric(1 + 3*prior$kmax)
init$mixture[1] <- 1
init$mixture[2] <- 1.0
init$mixture[2 + prior$kmax] <- fit.init$coefficients[1]
init$mixture[2 + 2*prior$kmax] <- fit.init$scale^2
}
else{
if (length(init$mixture) != 1 + 3*prior$kmax) stop("Incorrect init$mixture parameter supplied.")
if (is.na(init$mixture[1])) stop("Incorrect init$mixture parameter supplied.")
wi <- init$mixture[2:(1 + init$mixture[1])]
mui <-init$mixture[(2 + prior$kmax):(1 + prior$kmax + init$mixture[1])]
sig2i <- init$mixture[(2 + 2*prior$kmax):(1 + 2*prior$kmax + init$mixture[1])]
if (sum(is.na(wi)) | sum(is.na(mui)) | sum(is.na(sig2i))) stop("Incorrect init$mixture parameter supplied.")
if (sum(wi < 0)) stop("Incorrect init$mixture parameter supplied.")
if (sum(sig2i <= 0)) stop("Incorrect init$mixture parameter supplied.")
wi <- wi/sum(wi)
ordermu <- order(mui)
k.temp <- init$mixture[1]
init$mixture <- numeric(1 + 3*prior$kmax)
wi <- wi[ordermu]
mui <- mui[ordermu]
sig2i <- sig2i[ordermu]
init$mixture[1] <- k.temp
init$mixture[2:(1 + init$mixture[1])] <- wi
init$mixture[(2 + prior$kmax):(1 + prior$kmax + init$mixture[1])] <- mui
init$mixture[(2 + 2*prior$kmax):(1 + 2*prior$kmax + init$mixture[1])] <- sig2i
}
if (!nX){
init$beta <- 0
ininit <- names(init)
}
else{
tmp <- match("beta", ininit, nomatch=NA)
if(is.na(tmp)){
init$beta <- fit.init$coefficients[-1]
}
else{
if (length(init$beta) < nX) stop("Incorrect init$beta parameter supplied.")
init$beta <- init$beta[1:nX]
}
if (sum(is.na(init$beta))) stop("Incorrect init$beta parameter supplied.")
}
if (!nrandom) init$b <- 0
else{
tmp <- match("b", ininit, nomatch=NA)
if(is.na(tmp)){
bb <- fit.init$coefficients[-1][indb > 0]
if (randomInt) bb <- c(0, bb)
init$b <- rep(bb, ncluster)
}
else{
if (length(init$b) == 0){
bb <- fit.init$coefficients[-1][indb > 0]
if (randomInt) bb <- c(0, bb)
init$b <- rep(bb, ncluster)
}
else{
if (length(init$b) < nrandom*ncluster) stop("Incorrect init$b parameter supplied.")
init$b <- init$b[1:(nrandom*ncluster)]
}
}
if (sum(is.na(init$b))) stop("Incorrect init$b parameter supplied.")
}
tmp <- match("y", ininit, nomatch=NA)
if(is.na(tmp)){
init$y <- as.numeric(log(Yinit[,1]))
}
else{
if (length(init$y) < n) stop("Incorrect init$y parameter supplied.")
init$y <- init$y[1:n]
}
if (sum(is.na(init$y))) stop("Incorrect init$y parameter supplied.")
tmp <- match("r", ininit, nomatch=NA)
if(is.na(tmp)){
init$r <- numeric(n) + 1
}
else{
if (length(init$r) < n) stop("Incorrect init$r parameter supplied.")
init$r <- init$r[1:n]
}
if (sum(is.na(init$r)) | sum(init$r <= 0) | sum(init$r > init$mixture[1]))
stop("Incorrect init$r parameter supplied.")
if (!nrandom){
init$D <- 0
ininit <- names(init)
}
else{
tmp <- match("D", ininit, nomatch=NA)
if(is.na(tmp)){
init$D <- diag(nrandom)[lower.tri(diag(nrandom), diag = TRUE)]
}
else{
if (length(init$D) < 0.5*nrandom*(1+nrandom)) stop("Incorrect init$D parameter supplied.")
init$D <- init$D[1:(0.5*nrandom*(1+nrandom))]
}
if (sum(is.na(init$D))) stop("Incorrect init$D parameter supplied.")
}
tmp <- match("otherp", ininit, nomatch=NA)
if(is.na(tmp)){
init$otherp <- 1
}
else{
init$otherp <- init$otherp[1]
}
if (sum(is.na(init$otherp))) stop("Incorrect init$otherp parameter supplied.")
tmp <- match("u", ininit, nomatch=NA)
if(is.na(tmp)){
init$u <- c(runif(1), 0, 0, runif(3*(prior$kmax - 1)))
}
else{
if (length(init$u) < 3*prior$kmax) stop("Incorrect init$u parameter supplied.")
init$u <- init$u[1:(3*prior$kmax)]
}
if (sum(is.na(init$u))) stop("Incorrect init$u parameter supplied.")
if (sum(init$u < 0 | init$u > 1)) stop("Incorrect init$u parameter supplied.")
tmp <- match("dirichlet.w", inprior, nomatch=NA)
if(is.na(tmp)) prior$dirichlet.w <- 1
if (prior$dirichlet.w < 1) stop ("prior$dirichlet.w must be at least 1.")
prior.pard["delta"] <- prior$dirichlet.w
tmp <- match("mean.mu", inprior, nomatch=NA)
if(is.na(tmp)) prior$mean.mu <- init$mixture[2 + prior.pari["kmax"]]
tmp <- match("var.mu", inprior, nomatch=NA)
if(is.na(tmp)) prior$var.mu <- 2*init$mixture[2 + 2*prior.pari["kmax"]]
if (prior$var.mu <= 0) stop("prior$var.mu must be positive.")
prior.pard["xi"] <- prior$mean.mu
prior.pard["kappa"] <- prior$var.mu
tmp <- match("shape.invsig2", inprior, nomatch=NA)
if(is.na(tmp)) prior$shape.invsig2 <- 1.5
tmp <- match("shape.hyper.invsig2", inprior, nomatch=NA)
if(is.na(tmp)) prior$shape.hyper.invsig2 <- 0.8
tmp <- match("rate.hyper.invsig2", inprior, nomatch=NA)
if(is.na(tmp)) prior$rate.hyper.invsig2 <- prior$var.mu
if (prior$shape.invsig2 <= 0) stop("prior$shape.invsig2 must be positive.")
if (prior$shape.hyper.invsig2 <= 0) stop("prior$shape.hyper.invsig2 must be positive.")
if (prior$rate.hyper.invsig2 <= 0) stop("prior$rate.hyper.invsig2 must be positive.")
prior.pard["zeta"] <- prior$shape.invsig2
prior.pard["g"] <- prior$shape.hyper.invsig2
prior.pard["h"] <- prior$rate.hyper.invsig2
k.is.fixed <- (prior.pari["k.prior"] == 2)
tmp <- match("pi.split", inprior, nomatch=NA)
tmp <- match("pi.birth", inprior, nomatch=NA)
if (k.is.fixed){
prior$pi.split <- 0
prior$pi.birth <- 0
}
else{
if(is.na(tmp)) prior$pi.split <- c(1, rep(0.5, prior.pari["kmax"]-2), 0)
if(is.na(tmp)) prior$pi.birth <- c(1, rep(0.5, prior.pari["kmax"]-2), 0)
if (prior$pi.split[1] != 1) stop("prior$pi.split[1] must be equal to 1.")
if (prior$pi.birth[1] != 1) stop("prior$pi.birth[1] must be equal to 1.")
if (prior$pi.split[prior.pari["kmax"]] != 0) stop("prior$pi.split[kmax] must be equal to 0.")
if (prior$pi.birth[prior.pari["kmax"]] != 0) stop("prior$pi.birth[kmax] must be equal to 0.")
if (length(prior$pi.split) != prior.pari["kmax"]) stop("Incorrect length of a vector prior$pi.split.")
if (length(prior$pi.birth) != prior.pari["kmax"]) stop("Incorrect length of a vector prior$pi.birth.")
}
prior.pard[paste("pi.split", 1:prior.pari["kmax"], sep = "")] <- prior$pi.split
prior.pard[paste("pi.birth", 1:prior.pari["kmax"], sep = "")] <- prior$pi.birth
priordi <- list(integer = prior.pari, double = prior.pard)
attr(priordi, "init") <- init
attr(priordi, "prior") <- prior
return(priordi)
} |
context("Testing CEEMDAN")
set.seed(1)
test_that("bogus arguments throw error",{
expect_error(ceemdan("abc"))
expect_error(ceemdan(1:3, noise_strength = -1, threads = 1))
expect_error(ceemdan(1:3, num_siftings = 0, S_number = 0, threads = 1))
expect_error(ceemdan(1:3, num_imfs = -1, threads = 1))
expect_error(ceemdan(1:3, num_siftings = -3, threads = 1))
expect_error(ceemdan(1:3, ensemble_size = 0, threads = 1))
expect_error(ceemdan(1:3, num_imfs = "lots", threads = 1))
})
test_that("series full of zeroes should produce only zeroes",{
x <- numeric(64)
imfs <- ceemdan(x, ensemble_size = 10, threads = 1)
expect_true(all(imfs == 0))
})
test_that("residual is close to the disturbed signal",{
n <- 100
x <- seq(1, 10, length.out = n) ^ 2 + rnorm(n, sd = 0.5)
imfs <- ceemdan(x, threads = 1)
residual <- imfs[, 6]
expect_equal(residual[11:90], x[11:90], tol = 0.1)
})
test_that("different seeds give different results",{
x <- rnorm(64)
expect_false(isTRUE(all.equal(ceemdan(x, rng_seed = 1, threads = 1),
ceemdan(x, rng_seed = 2, threads = 1))))
})
test_that("identical seeds give equal results",{
x <- rnorm(64)
expect_equal(ceemdan(x, rng_seed = 1, threads = 1),
ceemdan(x, rng_seed = 1, threads = 1))
})
test_that("subsets of IMFs are identical for different num_imfs",{
x <- rnorm(64)
imfs3 <- ceemdan(x, num_imfs = 3, rng_seed = 1, threads = 1)
imfs4 <- ceemdan(x, num_imfs = 4, rng_seed = 1, threads = 1)
expect_equal(imfs3[, 1:2], imfs4[, 1:2])
})
test_that("num_imfs = 1 returns residual which equals data",{
x <- rnorm(64)
imfs <- ceemdan(x, num_imfs = 1, threads = 1)
expect_identical(c(imfs), x)
})
test_that("sum of imfs equals to original series",{
x <- rnorm(64)
expect_equal(rowSums(ceemdan(x, threads = 1)), x)
}) |
pkgInfo <- function(pkg, leaveRemains = FALSE) {
package <- unzipPackage(pkg)
bp <- basename(package)
dn <- dirname(package)
if(!leaveRemains) on.exit(unlink(dn, recursive = TRUE))
pd <- utils::packageDescription(bp, dn, c('Version', 'Imports', 'Suggests', 'Collate'))
vn <- pd$Version
imp <- gsub('\n', ' ', pd$Imports)
if(!is.na(imp)) {
toload <- sub(' .*', '', strsplit(imp, ",[ ]?")[[1]])
srchpth <- basename(searchpaths())
toload <- setdiff(toload, srchpth)
for(i in seq_along(toload)) {
suppressMessages(didload <- require(toload[i], character.only = TRUE))
if(!didload) {
warning(sprintf('imported package failed to load: %s', toload[i]))
}
}
tounload <- setdiff(basename(searchpaths()), srchpth)
on.exit({
for(i in seq_along(tounload)) {
detach(paste0('package:', tounload[i]), character.only = TRUE)
}
}, add = TRUE)
}
sug <- gsub('\n', ' ', pd$Suggests)
coll <- pd$Collate
dat <- utils::data(package = bp, lib.loc = dn)
dsn <- unname(dat[['results']][,'Item'])
if(length(dsn)) {
e <- new.env()
ddf <- data.frame(data = dsn, nrow = NA, ncol = NA)
for(i in seq_along(dsn)) {
do.call(utils::data, list(dsn[i], package = bp, lib.loc = dn, envir = e))
dimval <- dim(e[[dsn[i]]])
if(is.null(dimval)) {
ddf[i,3] <- length(e[[dsn[i]]])
} else if(length(dimval) == 2) {
ddf[i,2:3] <- dimval
} else {
ddf[i,2:3] <- c(dimval[1], paste(dimval[-1], collapse = 'x'))
}
}
rm(e)
} else {
ddf <- data.frame(data = NA, nrow = NA, ncol = NA)[FALSE,]
}
code_files <- tools::list_files_with_type(file.path(package, 'R'), "code", full.names = TRUE)
if(!is.na(coll)) {
coll_order <- strsplit(coll, "[[:space:]]")[[1]]
coll_order <- file.path(package, 'R', gsub("'", '', coll_order))
code_files <- c(coll_order, setdiff(code_files, coll_order))
}
e <- sourcerer(code_files)
le <- ls(envir = e)
obj <- vapply(le, function(z) is.function(e[[z]]), logical(1))
fun <- names(obj[obj])
var <- names(obj[!obj])
arg <- lapply(fun, function(z) names(formals(e[[z]])))
names(arg) <- fun
nsf <- parseNamespaceFile(bp, dn)
exp_list <- nsf[grep('export', names(nsf))]
patterns <- grep('Patterns', names(exp_list))
pat_list <- unname(unlist(exp_list[patterns]))
exp <- unname(unlist(exp_list[-patterns]))
if(nrow(nsf$S3methods)) {
s3_fun <- paste(nsf$S3methods[,1], nsf$S3methods[,2], sep = '.')
exp <- c(exp, s3_fun)
}
if(length(pat_list)) {
pat_fun <- fun[unlist(lapply(pat_list, grep, fun))]
exp <- c(exp, pat_fun)
}
exp <- sort(unique(exp))
imp_list <- nsf[grep('import', names(nsf))]
all_imp <- lapply(imp_list, function(i) vapply(i, paste, character(1), collapse = '::'))
imp_fun <- unique(unname(unlist(all_imp)))
imp_fun <- imp_fun[order(grepl(':', imp_fun), imp_fun)]
doc_files <- tools::list_files_with_type(file.path(package, 'man'), "docs", full.names = TRUE)
docmac <- tools::loadPkgRdMacros(package)
doctxt <- lapply(doc_files, function(d) {
rd <- tools::parse_Rd(d, macros = docmac)
paste(paste(utils::capture.output(tools::Rd2txt(rd, options = list(underline_titles = FALSE))), collapse = '\n'), '\n')
})
names(doctxt) <- sub('.Rd', '', basename(doc_files))
x <- list(
Package = bp,
Version = vn,
Imports = imp,
Suggests = sug,
ImportedFunctions = imp_fun,
ExportedFunctions = exp,
AllFunctions = fun,
FormalArgs = arg,
Data = ddf,
documentation = doctxt
)
class(x) <- 'pkgInfo'
x
} |
secder <- function(A, k, l) {
n <- dim(A)[1]
svec <- matrix(0, nrow = n^2, ncol = n)
scalesens <- matrix(0, nrow = n^2, ncol = n - 1)
d2 <- matrix(0, nrow = n, ncol = n)
ev <- eigen(A)
L <- ev$values
o <- order(Mod(L))
W <- ev$vectors
V <- solve(Conj(W))
V <- t(Conj(V))
o <- order(Mod(L))
V <- V[, rev(o)]
for (i in 1:n) {
senmat <- Conj(V[, i]) %*% t(W[, i])
svec[, i] <- matrix(senmat, nrow = n^2, ncol = 1)
}
s1 <- svec[, 1]
for (m in 2:n) {
scalesens[, m - 1] <- svec[, m] / (L[1] - L[m])
}
vecsum <- t(apply(scalesens, 1, sum))
for (i in 1:n) {
for (j in 1:n) {
x1 <- (l - 1) * n + 1 + (i - 1)
x2 <- (j - 1) * n + 1 + (k - 1)
d2[i, j] <- s1[x1] * vecsum[x2] + s1[x2] * vecsum[x1]
}
}
d2 <- Re(d2)
d2[A == 0] <- 0
return(d2)
} |
iccplot <-
function(object,...)UseMethod("iccplot") |
knotsFromProfileEdges <- function(lrthreshold) {
profedges <- NULL
profileEdges <- blackbox.getOption("profileEdges")
if(! is.null(nrow(profileEdges))) {
liks <- apply(profileEdges, 1, purefn, testhull=F)
profedges <- profileEdges[which(liks>lrthreshold), , drop=FALSE]
if(nrow(profedges)>0) {
profedges <- q2d(redundant(d2q(cbind(0, 1, profedges)), representation="V")$output[, -c(1:2), drop=FALSE])
colnames(profedges) <- colnames(profileEdges)
}
}
return(profedges)
} |
source("ESEUR_config.r")
plot_layout(2, 1)
pal_col=rainbow(2)
c_lines=read.csv(paste0(ESEUR_dir, "sourcecode/c_linelen.csv.xz"), as.is=TRUE)
h_lines=read.csv(paste0(ESEUR_dir, "sourcecode/h_linelen.csv.xz"), as.is=TRUE)
plot(c_lines$length, c_lines$occurrences, log="y", col=pal_col[1],
xaxs="i", yaxs="i",
xlim=c(0, 250),
xlab="Characters on line", ylab="Lines\n")
points(h_lines$length, h_lines$occurrences, col=pal_col[2])
legend(x="topright", legend=c(".c files", ".h files"), bty="n", fill=pal_col, cex=1.2)
c_toks=read.csv(paste0(ESEUR_dir, "sourcecode/c_linetok.csv.xz"), as.is=TRUE)
h_toks=read.csv(paste0(ESEUR_dir, "sourcecode/h_linetok.csv.xz"), as.is=TRUE)
plot(c_toks$tokens, c_toks$occurrences, log="y", col=pal_col[1],
xaxs="i", yaxs="i",
xlim=c(0, 100),
xlab="Tokens on line", ylab="Lines")
points(h_toks$tokens, h_toks$occurrences, col=pal_col[2])
legend(x="topright", legend=c(".c files", ".h files"), bty="n", fill=pal_col, cex=1.2) |
test_that("zero length inputs return correct clases", {
expect_s3_class(ident(), "ident")
})
test_that("ident quotes", {
con <- simulate_dbi()
x1 <- ident("x")
expect_equal(escape(x1, con = con), sql('`x`'))
expect_equal(as.sql(x1), x1)
}) |
diag2silhouette <- function(homology, dimension=1, p=2, nseq=100){
if (!inherits(homology,"homology")){
stop("* diag2silhouette : input 'homology' is not a valid homology object. Please use an output from 'diagRips' or other construction algorithms.")
}
dimension = round(dimension)
if (!(dimension %in% homology$Dimension)){
stop("* diag2silhouette : input 'dimension' does not have corresponding information in the given 'homology'.")
}
idin = which(homology$Dimension==dimension)
dat.dim = round(homology$Dimension[idin])
dat.birth = homology$Birth[idin]
dat.death = homology$Death[idin]
myp = max(1, as.double(p))
myt = seq(from=0, to=max(dat.death), length.out=max(10, round(nseq)))
out.numerator = rep(0,length(myt))
out.denominator = 0
for (i in 1:length(dat.birth)){
bj = dat.birth[i]
dj = dat.death[i]
weightj = (base::abs(bj-dj)^myp)
out.denominator = out.denominator + weightj
out.numerator = out.numerator + (weightj*base::pmax(base::pmin(myt-bj, dj-myt), rep(0,length(myt))))
}
lbdfun = as.vector((out.numerator/out.denominator))
res = list(lambda=lbdfun, tseq=myt, dimension=dimension)
class(res) = "silhouette"
return(res)
} |
context("fread_bib")
test_that("fread_bib output", {
bibDT <- fread_bib("./fread-bib/1.bib")
expect_equal(bibDT[1][["key"]], "AG-2016-Super-splitting-laws-FAQ")
expect_equal(bibDT[field == "year"][["value"]], "2016")
bibDT_w_braces <- fread_bib("./fread-bib/1.bib", strip.braces = FALSE)
expect_equal(bibDT_w_braces[line_no == "2"][["field"]], "author")
expect_equal(bibDT_w_braces[line_no == "2"][["value"]],
"{{Attorney-General's Department}}")
})
test_that(".bib_expected", {
tempf <- tempfile()
file.create(tempf)
expect_warning(fread_bib(tempf),
regexp = "File extension is not '.bib'.",
fixed = TRUE)
expect_message(fread_bib(tempf, .bib_expected = FALSE),
regexp = "Returning empty")
tempf <- tempfile(fileext = ".bib")
file.create(tempf)
expect_warning(fread_bib(tempf, .bib_expected = FALSE),
regexp = "File extension is not '.bib'.",
fixed = TRUE)
expect_message(fread_bib(tempf, .bib_expected = TRUE),
regexp = "Returning empty")
}) |
print.ddf<-function(x, ...){
cat("\nDistance sampling analysis object\n")
print(summary(x))
invisible()
} |
context("plotFingerPrint")
if (!exists("Example_DETha98")) load("data/Example_DETha98.RData")
EddyData.F <- Example_DETha98
EddyDataWithPosix.F <- suppressMessages(fConvertTimeToPosix(
EddyData.F, 'YDH', Year = 'Year', Day = 'DoY', Hour = 'Hour'))
EddyData99.F <- EddyData.F
EddyData99.F$Year <- 1999
EddyDataWithPosix2yr.F <- suppressMessages(fConvertTimeToPosix(rbind(
EddyData.F, EddyData99.F), 'YDH', Year = 'Year', Day = 'DoY', Hour = 'Hour'))
rm( EddyData99.F )
EddyProc.C <- sEddyProc$new('DE-Tha', EddyDataWithPosix.F, c('NEE','Rg', 'Tair', 'VPD'))
data <- cbind( EddyProc.C$sDATA, EddyProc.C$sTEMP)
dts <- EddyProc.C$sINFO$DTS
test_that("plotting NEE with class method",{
EddyProc.C$sPlotFingerprintY("NEE", Year = 1998)
})
test_that("plotting NEE with different range",{
EddyProc.C$sPlotFingerprintY(
"NEE", Year = 1998,
valueLimits = quantile(EddyProc.C$sDATA$NEE,
prob = c( 0.05, 0.99), na.rm = TRUE))
})
test_that("plotting legend only",{
EddyProc.C$sPlotFingerprintY("NEE", Year = 1998, onlyLegend = TRUE)
})
test_that("plotting NEE",{
sEddyProc_sPlotFingerprintY("NEE", Year = 1998, data = data, dts = dts)
})
test_that("plotting NEE with Inf-values",{
data2 <- data
data2$NEE[5:10][is.finite(data2$NEE[5:10])] <- Inf
sEddyProc_sPlotFingerprintY("NEE", Year = 1998, data = data2, dts = dts)
})
test_that("plotting NEE to pdf",{
skip_on_cran()
EddyProc.C$sPlotFingerprint("NEE", Dir = tempdir())
})
test_that("plot diurnal cycle of NEE to pdf",{
skip_on_cran()
EddyProc.C$sPlotDiurnalCycle("NEE", Dir = tempdir())
})
test_that("sPlotHHFluxes",{
skip_on_cran()
EddyProc.C$sPlotHHFluxes("NEE", Dir = tempdir())
})
test_that("sPlotDailySums",{
skip_on_cran()
EddyProc.C$sPlotDailySums("NEE", Dir = tempdir())
}) |
fdm <- function(formula, data, id = NULL, wave = NULL, use.wave = FALSE,
min.waves = 1,
variance = c("toeplitz-1", "constrained", "unconstrained"),
error.type = c("CR2", "CR1S"), ...) {
if (!requireNamespace("nlme")) need_package("nlme")
if (!requireNamespace("clubSandwich")) need_package("clubSandwich")
the_call <- match.call()
the_env <- parent.frame()
formula <- Formula::Formula(formula)
prepped <- diff_data(formula = formula, data = data, id = id,
wave = wave, min.waves = min.waves,
weights = NULL, use.wave = use.wave, escape = TRUE)
e <- prepped$e
pf <- prepped$pf
data <- e$data
wave <- prepped$wave
id <- prepped$id
dv <- prepped$dv
fin_formula <- as.formula(e$fin_formula)
if (!is.null(pf$constants)) {
constants <- paste(pf$constants, collapse = " - ")
up_form <- as.formula(paste(". ~ . -", constants))
fin_formula <- update(fin_formula, up_form)
}
if (variance[1] == "unconstrained") {
cor_form <- as.formula(paste("~ 1 |", id))
var_form <- as.formula(paste("~ 1 |", wave))
corr <- nlme::corSymm(form = cor_form)
weights <- nlme::varIdent(form = var_form)
} else if (variance[1] == "constrained") {
cor_form <- as.formula(paste(" ~ 1 |", id))
corr <- nlme::corARMA(value = -.9999, form = cor_form, p = 0, q = 1,
fixed = TRUE)
weights <- NULL
} else if (variance[1] == "toeplitz-1") {
cor_form <- as.formula(paste(" ~ 1 |", id))
corr <- nlme::corARMA(form = cor_form, p = 0, q = 1)
weights <- NULL
}
gls_mod <- nlme::gls(fin_formula, data = as.data.frame(data),
na.action = na.omit,
correlation = corr, weights = weights)
gls_mod$call$model <- substitute(fin_formula)
the_vcov <- vcov_CR(gls_mod, cluster = data[[id]], type = error.type[1],
data = data)
coef_table <- clubSandwich::coef_test(gls_mod, vcov = the_vcov,
test = "naive-t", cluster = data[[id]])
coef_table <- as.data.frame(coef_table[c("beta","SE","tstat","p_t")])
if ("tstat" %nin% names(coef_table)) {
names(coef_table) <- c("estimate", "std.error", "p.value")
coef_table["statistic"] <- coef_table$estimate / coef_table$std.error
} else {
names(coef_table) <- c("estimate", "std.error", "statistic", "p.value")
coef_table["term"] <- rownames(coef_table)
}
mod_info <- list(dv = dv, min.wave = prepped$minwave,
max.wave = prepped$maxwave,
num_distinct = prepped$num_distinct,
AIC = AIC(gls_mod), BIC = BIC(gls_mod),
variance = variance[1], errors = error.type[1])
gls_mod$mod_info <- mod_info
gls_mod$coef_table <- coef_table
colnames(the_vcov) <- rownames(coef_table)
rownames(the_vcov) <- rownames(coef_table)
gls_mod$vcov <- the_vcov
class(gls_mod) <- c("fdm", "gls")
gls_mod
}
summary.fdm <- function(object, ...) {
dots <- list(...)
if ("digits" %in% names(dots)) {
digits <- dots$digits
} else {
digits <- getOption("jtools-digits", 2)
}
x <- object
mod_fit <- paste0(bold("MODEL FIT:\n"),
italic("AIC = "), round(x$mod_info$AIC, digits),
italic(", BIC = "), round(x$mod_info$BIC, digits), "\n")
if (x$mod_info$variance == "toeplitz-1") {
corstruct <- object$modelStruct$corStruct
class(corstruct) <- "corARMA"
theta <- coef(corstruct, unconstrained = FALSE)
variance <- paste0(italic("Variance structure: "), x$mod_info$variance,
" (theta = ", round(theta, digits), ")")
} else {
variance <- paste0(italic("Variance structure: "), x$mod_info$variance)
}
mod_info <- paste0(bold("MODEL INFO:\n"),
italic("Entities: "), x$mod_info$num_distinct, "\n",
italic("Time periods: "), paste0(x$mod_info$min.wave, "-",
x$mod_info$max.wave), "\n",
italic("Dependent variable: "), x$mod_info$dv, "\n",
variance
)
mod_info_list <- list(N = x$mod_info$num_distinct,
min_wave = x$mod_info$min.wave,
max_wave = x$mod_info$max.wave,
variance = x$mod_info$variance, AIC = x$mod_info$AIC,
BIC = x$mod_info$BIC)
coef_table <- x$coef_table
names(coef_table) <- sapply(names(coef_table), function(x) {switch(x,
"estimate" = "Est.",
"std.error" = "S.E.",
"p.value" = "p",
"statistic" = "t val.",
x
)})
rownames(coef_table) <- coef_table$term
coef_table <- coef_table[c("Est.", "S.E.", "t val.", "p")]
out <- list(mod_info = mod_info, coef_table = coef_table, digits = digits,
mod_fit = mod_fit, errors = x$mod_info$errors,
mod_info_list = mod_info_list)
class(out) <- "summary.fdm"
out
}
print.summary.fdm <- function(x, ...) {
cat(x$mod_info, "\n\n")
cat(x$mod_fit, "\n")
cat(italic("Standard errors:"), x$errors, "\n")
print(md_table(x$coef_table, digits = x$digits, sig.digits = FALSE,
format = getOption("panelr.table.format", "multiline")))
}
make_diff_data <- function(formula, data, id = NULL, wave = NULL,
use.wave = FALSE, min.waves = 1, weights = NULL,
offset = NULL, asym = FALSE, cumulative = FALSE,
escape.names = FALSE, ...) {
the_call <- match.call()
the_env <- parent.frame()
formula <- Formula::Formula(formula)
prepped <- diff_data(formula = formula, data = data, id = id,
wave = wave, min.waves = min.waves,
weights = !! enquo(weights), offset = !! enquo(offset),
use.wave = use.wave, ignore.lhs = cumulative,
asym = asym, escape = escape.names)
prepped$e$data
}
diff_data <- function(formula, data, id = NULL, wave = NULL, min.waves = 2,
weights = NULL, offset = NULL, use.wave = FALSE,
asym = FALSE, ignore.lhs = FALSE, escape = TRUE, ...) {
if ("panel_data" %in% class(data)) {
id <- attr(data, "id")
wave <- attr(data, "wave")
}
if ("data.frame" %nin% class(data)) {
stop("data argument must be a data frame.")
}
data <- panel_data(data, id = !! sym(id), wave = !! sym(wave))
orig_data <- data
weights <- eval_tidy(enquo(weights), data)
if (!is.null(weights)) {data[".weights"] <- weights}
offset <- eval_tidy(enquo(offset), data)
if (!is.null(offset)) {data[".offset"] <- offset}
dv <- as.character((attr(formula, "lhs")))
pf <- wb_formula_parser(formula, dv, data)
wave_terms <- if (!is.numeric(data[[wave]])) expand_labels(data, wave) else {
wave
}
if (any(wave_terms %in% pf$varying)) {
pf$varying <- pf$varying %not% wave_terms
use.wave <- TRUE
}
mf_form <- paste(" ~ ",
paste(pf$allvars, collapse = " + "),
collapse = ""
)
mf_form <- formula_ticks(mf_form, names(pf$data))
if (!is.null(weights)) {
mf_form <- paste(mf_form, "+", ".weights")
}
data <- model_frame(update(as.formula(mf_form),
as.formula(paste("~ . -", id, "-", wave))),
data = pf$data)
pf$varying <- un_bt(pf$varying)
e <- diff_model(pf, dv, data, use.wave, asym, ignore.lhs, escape)
e$data <- complete_cases(e$data, min.waves = min.waves)
num_distinct <- length(unique(e$data[[id]]))
maxwave <- max(e$data[[wave]])
minwave <- min(e$data[[wave]])
if (use.wave == TRUE & !is.numeric(e$data[[wave]])) {
e$data[[wave]] <- droplevels(e$data[[wave]])
contrasts(e$data[[wave]]) <- contr.treatment(levels(e$data[[wave]]))
}
if (!is.null(weights)) {
weights <- e$data$.weights
}
if (!is.null(offset)) {
offset <- e$data$.offset
}
list(e = e, num_distinct = num_distinct, maxwave = maxwave,
minwave = minwave, weights = weights,
pf = pf, id = id, wave = wave, dv = dv, orig_data = orig_data,
mf_form = mf_form)
}
diff_model <- function(pf, dv, data, use.wave = FALSE, asym = FALSE,
ignore.lhs = FALSE, escape = TRUE) {
wave <- get_wave(data)
id <- get_id(data)
vars <- pf$varying
if (ignore.lhs == FALSE) vars <- c(dv, vars)
for (var in vars %not% wave) {
lag_var <- paste0(bt(var), " - lag(`", var, "`)")
data <- mutate(data,
!! var := !! parse_expr(lag_var)
)
}
asym_list <- list()
if (asym == TRUE) {
for (var in c(pf$varying) %not% wave) {
plus_var <- if (escape) paste0("plus__", var) else paste0("+", var)
minus_var <- if (escape) paste0("minus__", var) else paste0("-", var)
plus_expr <- paste0(bt(var), "* (", bt(var), "> 0)")
neg_expr <- paste0("-", bt(var), "* (", bt(var), "< 0)")
data <- mutate(data,
!! plus_var := !! parse_expr(plus_expr),
!! minus_var := !! parse_expr(neg_expr)
)
asym_list[[var]] <- c(plus_var, minus_var)
}
}
if (asym == FALSE) {
the_terms <- make_names(un_bt(c(pf$varying, pf$wint_labs, pf$cint_labs)),
int = TRUE)
the_terms <- str_replace_all(the_terms, "_by_", "\\*")
names(data) <- make_names(names(data))
if (!is.null(c(pf$wint_labs, pf$cint_labs))) {
ints <- asym_ints(data = data, pf = pf, asym_list = list())
data <- ints$data
names(data) <- make_names(un_bt(names(data)), int = TRUE)
the_terms <- c(the_terms, ints$new_terms)
}
} else {
the_terms <- unlist(asym_list)
varyings <- are_varying(data)
which_not_varying <- the_terms %just% un_bt(names(varyings %just% FALSE))
if (length(which_not_varying) > 0) {
for (var in which_not_varying) {
plus <- if (escape) "plus__" else "^\\+"
minus <- if (escape) "minus__" else "^\\-"
if (stringr::str_detect(var, paste0("^", plus))) {
bare_var <- stringr::str_extract(var, paste0("(?<=(", plus, ")).*"))
msg_wrap(bare_var, " does not increase over time so +", bare_var,
" is not included in the model.")
} else if (stringr::str_detect(var, paste0("^", minus))) {
bare_var <- stringr::str_extract(var, paste0("(?<=(", minus, ")).*"))
msg_wrap(bare_var, " does not decrease over time so -", bare_var,
" is not included in the model.")
}
}
}
the_terms <- the_terms %not% un_bt(names(varyings %just% FALSE))
asym_list <- lapply(asym_list, function(x) {
x %not% un_bt(names(varyings %just% FALSE))
})
lengths <- sapply(asym_list, length) == 2
asym_list <- asym_list[lengths]
if (escape) {
asym_list <- lapply(asym_list, make_names)
names(asym_list) <- make_names(names(asym_list))
the_terms <- make_names(the_terms)
names(data) <- make_names(names(data))
} else {
the_terms <- bt(the_terms)
}
if (!is.null(c(pf$wint_labs, pf$cint_labs))) {
ints <- asym_ints(data = data, pf = pf, asym_list = asym_list,
escape = escape)
data <- ints$data
if (escape) {
names(data) <- make_names(names(data))
}
the_terms <- c(the_terms, ints$new_terms)
}
}
if (asym == TRUE & ignore.lhs == TRUE) {
for (var in c(pf$varying) %not% wave) {
plus_var <- if (escape) paste0("plus__", var) else paste0("+", var)
minus_var <- if (escape) paste0("minus__", var) else paste0("-", var)
set_zero_plus <- paste0("case_when(", wave, " == ", get_periods(data)[1],
" ~ 0, TRUE ~", bt(plus_var), ")")
set_zero_neg <- paste0("case_when(", wave, " == ", get_periods(data)[1],
" ~ 0, TRUE ~", bt(minus_var), ")")
plus_expr <- paste0("cumsum(", bt(plus_var), ")")
neg_expr <- paste0("cumsum(", bt(minus_var), ")")
data <- mutate(data,
!! plus_var := !! parse_expr(set_zero_plus),
!! minus_var := !! parse_expr(set_zero_neg),
!! plus_var := !! parse_expr(plus_expr),
!! minus_var := !! parse_expr(neg_expr)
)
}
}
fin_formula <- paste(bt(dv), "~", paste(the_terms, collapse = " + "))
if (use.wave == TRUE) fin_formula <- paste(fin_formula, "+", wave)
out <- list(data = data, fin_formula = fin_formula, asym_list = asym_list)
return(out)
}
asym_ints <- function(data, pf, asym_list, escape = TRUE) {
all_ints <- c(pf$wint_labs, pf$cint_labs)
all_terms <- NULL
for (int in all_ints) {
name_make <- if (escape) make_names else function(x) return(x)
splits <- as.list(name_make(un_bt(str_split(int, "\\*")[[1]])))
names(splits) <- splits
for (spl in names(splits)) {
if (un_bt(spl) %in% names(asym_list)) {
splits[[spl]] <- asym_list[[un_bt(spl)]]
}
}
term_grid <- expand.grid(splits)
term_grid <- as.data.frame(lapply(term_grid, bt))
new_terms <- rep(NA, nrow(term_grid))
for (r in 1:nrow(term_grid)) {
new_terms[r] <- paste0(unlist(term_grid[r, , drop = TRUE]), collapse = "*")
}
all_terms <- c(all_terms, new_terms)
}
list(data = data, new_terms = all_terms)
}
coef.fdm <- function(object, ...) {
out <- object$coef_table$estimate
names(out) <- rownames(object$coef_table)
out
}
vcov.fdm <- function(object, ...) {
object$vcov
}
confint.fdm <- function(object, parm, level = .95, ...) {
confint.wbgee(object, parm = parm, level = level, ...)
}
tidy.fdm <- function(x, conf.int = FALSE, conf.level = .95, ...) {
if (!requireNamespace("generics")) {
stop_wrap("You must have the generics package to use tidy methods.")
}
params <- x$coef_table
if (conf.int == TRUE) {
ints <- as.data.frame(confint(x, level = conf.level))
names(ints) <- c("conf.low", "conf.high")
ints$term <- stringr::str_remove_all(rownames(ints), "`")
params <- dplyr::left_join(params, ints, by = "term")
}
return(tibble::as_tibble(
params %just% c("term", "estimate", "statistic", "std.error",
"conf.low", "conf.high", "p.value", "group")
))
}
glance.fdm <- function(x, ...) {
sum <- summary(x)
mod_info_list <- sum$mod_info_list
mod_info_list[sapply(mod_info_list, is.null)] <- NA
return(tibble::as_tibble(mod_info_list))
} |
x <- 1:3
expect_equal(
case_when(
x <= 1 ~ 1,
x <= 2 ~ 2,
x <= 3 ~ 3
),
c(1, 2, 3),
info = "case_when() matches values in order"
)
x <- 1:3
expect_equal(
case_when(
x <= 1 ~ 1,
x <= 2 ~ 2
),
c(1, 2, NA),
info = "case_when() unmatched gets missing value"
)
x <- c(1:3, NA)
expect_equal(
case_when(
x <= 1 ~ 1,
x <= 2 ~ 2,
is.na(x) ~ 0
),
c(1, 2, NA, 0),
info = "case_when() missing values can be replaced"
)
expect_equal(
case_when(
c(TRUE, FALSE, NA) ~ 1:3,
TRUE ~ 4L
),
c(1L, 4L, 4L),
info = "case_when() NA conditions"
)
expect_equal(
case_when(
TRUE ~ 1:3,
FALSE ~ 4:6
),
1:3,
info = "case_when() atomic conditions 1"
)
expect_equal(
case_when(
NA ~ 1:3,
TRUE ~ 4:6
),
4:6,
info = "case_when() atomic conditions 2"
)
expect_equal(
case_when(
TRUE ~ integer(),
FALSE ~ integer()
),
integer(),
info = "case_when() zero-length conditions and values 1"
)
expect_equal(
case_when(
logical() ~ 1,
logical() ~ 2
),
numeric(),
info = "case_when() zero-length conditions and values 2"
)
res <- data.frame(a = 1:3) %>%
mutate(b = (function(x) case_when(x < 2 ~ TRUE, TRUE ~ FALSE))(a)) %>%
pull()
expect_equal(res, c(TRUE, FALSE, FALSE), info = "case_when() can be used in anonymous functions")
res <- mtcars %>% mutate(efficient = case_when(mpg > 25 ~ TRUE, TRUE ~ FALSE)) %>% pull()
expect_equal(
res,
c(
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE
),
info = "case_when() can be used inside mutate()"
)
expect_error(
case_when(c(TRUE, FALSE) ~ 1:3, c(FALSE, TRUE) ~ 1:2),
info = "case_when() must return the correct amount of RHS values."
)
expect_error(
case_when(50 ~ 1:3),
info = "case_when() must return a logical vector."
)
expect_error(
case_when(paste(50)),
info = "case_when() requires formula inputs."
)
expect_error(
case_when(),
info = "No cases provided."
) |
is.multi_transiogram <-
function(object) {
if(!is(object, "multi_transiogram")) return(FALSE)
if(!prod(c("lags", "type", "Tmat") %in% names(object))) return(FALSE)
if(length(names(object)) != 3) return(FALSE)
if(!is.array(object$Tmat)) return(FALSE)
if(!is.numeric(object$lags)) return(FALSE)
if(!is.character(object$type)) return(FALSE)
if(object$type != "Empirical" && object$type != "Theoretical") return(FALSE)
return(TRUE)
} |
Reformat23 <-
function (x)
{
if (length(dim(x)) != 2)
stop("x must be 2d")
if (class(x) == "data.frame")
x = data.matrix(x)
nind = nrow(x)
nc = ncol(x)
nloc = nc/2
y = tapply(as.vector(x), rep(1:nloc, each = 2 * nind), function(x) as.numeric(as.factor(x)))
ym = matrix(unlist(y), nind)
nal = as.vector(sapply(y, max, na.rm = TRUE))
nalM = max(nal)
g = array(dim = c(nind, nloc, nalM), 0)
c1 = as.vector(row(ym))
c2 = ceiling(as.vector(col(ym))/2)
c3 = as.vector(ym)
qq = table(nloc * nind * (c3 - 1) + nind * (c2 - 1) + c1)
g[as.numeric(names(qq))] = qq
return(g)
} |
nd.dsd <- function(A, out.dist=TRUE, type=c("Lap","SLap","NLap","Adj")){
if ((!is.list(A))||(length(A)<=1)){
stop("* nd.csd : input 'A' should be a list of length larger than 1.")
}
type = match.arg(type)
listA = list_transform(A, NIflag="not")
N = length(listA)
M = nrow(listA[[1]])
if (missing(type)){
type = "Adj"
} else {
type = match.arg(type)
}
mat_eigs = array(0,c(N,(M-1)))
mat_dist = array(0,c(N,N))
for (i in 1:N){
tgt = listA[[i]]
if (all(type=="Adj")){
X = as.matrix(tgt)
} else if (all(type=="Lap")){
X = as.matrix(laplacian_unnormalized(tgt))
} else if (all(type=="NLap")){
X = as.matrix(laplacian_normalized(tgt))
} else if (all(type=="SLap")){
X = as.matrix(laplacian_signless(tgt))
}
mat_eigs[i,] = as.vector(RSpectra::eigs(X,(M-1))$values)
}
for (i in 1:(N-1)){
spect1 = mat_eigs[i,]
for (j in (i+1):N){
spect2 = mat_eigs[j,]
solution = sqrt(sum((spect1-spect2)^2))
mat_dist[i,j] = solution
mat_dist[j,i] = solution
}
}
if (out.dist){
mat_dist = as.dist(mat_dist)
}
result = list()
result$D= mat_dist
result$spectra = mat_eigs
return(result)
} |
structure(list(url = "https://api.twitter.com/2/tweets?tweet.fields=attachments%2Cauthor_id%2Cconversation_id%2Ccreated_at%2Centities%2Cgeo%2Cid%2Cin_reply_to_user_id%2Clang%2Cpublic_metrics%2Cpossibly_sensitive%2Creferenced_tweets%2Csource%2Ctext%2Cwithheld&user.fields=created_at%2Cdescription%2Centities%2Cid%2Clocation%2Cname%2Cpinned_tweet_id%2Cprofile_image_url%2Cprotected%2Cpublic_metrics%2Curl%2Cusername%2Cverified%2Cwithheld&expansions=author_id%2Centities.mentions.username%2Cgeo.place_id%2Cin_reply_to_user_id%2Creferenced_tweets.id%2Creferenced_tweets.id.author_id&place.fields=contained_within%2Ccountry%2Ccountry_code%2Cfull_name%2Cgeo%2Cid%2Cname%2Cplace_type&ids=1%2C2%2C3%2C4%2C5%2C6%2C7%2C8%2C9%2C10%2C11%2C12%2C13%2C14%2C15%2C16%2C1266853931247906816%2C1266853419291234312%2C1266852781526302722%2C1266852099163291650",
status_code = 200L, headers = structure(list(date = "Sun, 19 Dec 2021 20:45:28 UTC",
server = "tsa_o", `api-version` = "2.32", `content-type` = "application/json; charset=utf-8",
`cache-control` = "no-cache, no-store, max-age=0", `content-length` = "4210",
`x-access-level` = "read", `x-frame-options` = "SAMEORIGIN",
`content-encoding` = "gzip", `x-xss-protection` = "0",
`x-rate-limit-limit` = "300", `x-rate-limit-reset` = "1639947531",
`content-disposition` = "attachment; filename=json.json",
`x-content-type-options` = "nosniff", `x-rate-limit-remaining` = "280",
`strict-transport-security` = "max-age=631138519", `x-response-time` = "277",
`x-connection-hash` = "7d54fc61481fbbf761f96e7412fcce2a506bf85407f0f6725ae81f8d24a33064"), class = c("insensitive",
"list")), all_headers = list(list(status = 200L, version = "HTTP/2",
headers = structure(list(date = "Sun, 19 Dec 2021 20:45:28 UTC",
server = "tsa_o", `api-version` = "2.32", `content-type` = "application/json; charset=utf-8",
`cache-control` = "no-cache, no-store, max-age=0",
`content-length` = "4210", `x-access-level` = "read",
`x-frame-options` = "SAMEORIGIN", `content-encoding` = "gzip",
`x-xss-protection` = "0", `x-rate-limit-limit` = "300",
`x-rate-limit-reset` = "1639947531", `content-disposition` = "attachment; filename=json.json",
`x-content-type-options` = "nosniff", `x-rate-limit-remaining` = "280",
`strict-transport-security` = "max-age=631138519",
`x-response-time` = "277", `x-connection-hash` = "7d54fc61481fbbf761f96e7412fcce2a506bf85407f0f6725ae81f8d24a33064"), class = c("insensitive",
"list")))), cookies = structure(list(domain = c(".twitter.com",
".twitter.com", ".twitter.com", ".twitter.com"), flag = c(TRUE,
TRUE, TRUE, TRUE), path = c("/", "/", "/", "/"), secure = c(TRUE,
TRUE, TRUE, TRUE), expiration = structure(c(1702744284, 1702744284,
1702744284, 1702744284), class = c("POSIXct", "POSIXt")),
name = c("guest_id_marketing", "guest_id_ads", "personalization_id",
"guest_id"), value = c("REDACTED", "REDACTED", "REDACTED",
"REDACTED")), row.names = c(NA, -4L), class = "data.frame"),
content = charToRaw("{\"data\":[{\"in_reply_to_user_id\":\"107357635\",\"created_at\":\"2020-05-30T22:08:08.000Z\",\"possibly_sensitive\":false,\"author_id\":\"126978307\",\"referenced_tweets\":[{\"type\":\"replied_to\",\"id\":\"1266831085582913538\"}],\"entities\":{\"hashtags\":[{\"start\":152,\"end\":169,\"tag\":\"FridaysForFuture\"}],\"mentions\":[{\"start\":0,\"end\":15,\"username\":\"Bastian_Atzger\",\"id\":\"107357635\"},{\"start\":16,\"end\":27,\"username\":\"LudgerWess\",\"id\":\"989716693\"}]},\"conversation_id\":\"1266677828801695744\",\"public_metrics\":{\"retweet_count\":0,\"reply_count\":1,\"like_count\":0,\"quote_count\":0},\"source\":\"Twitter Web App\",\"id\":\"1266853931247906816\",\"text\":\"@Bastian_Atzger @LudgerWess Wer nicht darauf angewiesen ist, Hetze zu betreiben, hätte vielleicht erkannt, daß diese Ausschnitte aus Plakaten weder von
date = structure(1639946728, class = c("POSIXct", "POSIXt"
), tzone = "GMT"), times = c(redirect = 0, namelookup = 3.1e-05,
connect = 3.2e-05, pretransfer = 0.00012, starttransfer = 0.294459,
total = 0.294668)), class = "response") |
theme_ipsum_es <- function(
base_family="EconSansCndReg", base_size = 11.5,
plot_title_family="EconSansCndBol", plot_title_size = 18,
plot_title_face="bold", plot_title_margin = 10,
subtitle_family=if (.Platform$OS.type == "windows") "EconSansCndLig" else "EconSansCndLig",
subtitle_size = 13,
subtitle_face = "plain", subtitle_margin = 15,
strip_text_family = base_family, strip_text_size = 12,
strip_text_face = "plain",
caption_family=if (.Platform$OS.type == "windows") "EconSansCndLig" else "EconSansCndLig",
caption_size = 9,
caption_face = "plain", caption_margin = 10,
axis_text_size = base_size,
axis_title_family = base_family,
axis_title_size = 9,
axis_title_face = "plain",
axis_title_just = "rt",
plot_margin = margin(30, 30, 30, 30),
panel_spacing = grid::unit(2, "lines"),
grid_col = "
axis_col = "
ret <- ggplot2::theme_minimal(base_family = base_family, base_size = base_size)
ret <- ret + theme(legend.background = element_blank())
ret <- ret + theme(legend.key = element_blank())
ret <- ret + theme(plot.margin = plot_margin)
ret <- ret + theme(panel.spacing = panel_spacing)
if (inherits(grid, "character") | grid == TRUE) {
ret <- ret + theme(panel.grid = element_line(color = grid_col, size = 0.2))
ret <- ret + theme(panel.grid.major = element_line(color = grid_col, size = 0.2))
ret <- ret + theme(panel.grid.minor = element_line(color = grid_col, size = 0.15))
if (inherits(grid, "character")) {
if (regexpr("X", grid)[1] < 0) ret <- ret + theme(panel.grid.major.x = element_blank())
if (regexpr("Y", grid)[1] < 0) ret <- ret + theme(panel.grid.major.y = element_blank())
if (regexpr("x", grid)[1] < 0) ret <- ret + theme(panel.grid.minor.x = element_blank())
if (regexpr("y", grid)[1] < 0) ret <- ret + theme(panel.grid.minor.y = element_blank())
}
} else {
ret <- ret + theme(panel.grid = element_blank())
ret <- ret + theme(panel.grid.major = element_blank())
ret <- ret + theme(panel.grid.major.x = element_blank())
ret <- ret + theme(panel.grid.major.y = element_blank())
ret <- ret + theme(panel.grid.minor = element_blank())
ret <- ret + theme(panel.grid.minor.x = element_blank())
ret <- ret + theme(panel.grid.minor.y = element_blank())
}
if (inherits(axis, "character") | axis == TRUE) {
ret <- ret + theme(axis.line = element_line(color = axis_col, size = 0.15))
if (inherits(axis, "character")) {
axis <- tolower(axis)
if (regexpr("x", axis)[1] < 0) {
ret <- ret + theme(axis.line.x = element_blank())
} else {
ret <- ret + theme(axis.line.x = element_line(color = axis_col, size = 0.15))
}
if (regexpr("y", axis)[1] < 0) {
ret <- ret + theme(axis.line.y = element_blank())
} else {
ret <- ret + theme(axis.line.y = element_line(color = axis_col, size = 0.15))
}
} else {
ret <- ret + theme(axis.line.x = element_line(color = axis_col, size = 0.15))
ret <- ret + theme(axis.line.y = element_line(color = axis_col, size = 0.15))
}
} else {
ret <- ret + theme(axis.line = element_blank())
}
if (!ticks) {
ret <- ret + theme(axis.ticks = element_blank())
ret <- ret + theme(axis.ticks.x = element_blank())
ret <- ret + theme(axis.ticks.y = element_blank())
} else {
ret <- ret + theme(axis.ticks = element_line(size = 0.15))
ret <- ret + theme(axis.ticks.x = element_line(size = 0.15))
ret <- ret + theme(axis.ticks.y = element_line(size = 0.15))
ret <- ret + theme(axis.ticks.length = grid::unit(5, "pt"))
}
xj <- switch(tolower(substr(axis_title_just, 1, 1)), b = 0, l = 0, m = 0.5, c = 0.5, r = 1, t = 1)
yj <- switch(tolower(substr(axis_title_just, 2, 2)), b = 0, l = 0, m = 0.5, c = 0.5, r = 1, t = 1)
ret <- ret + theme(axis.text = element_text(size = axis_text_size, margin = margin(t = 0, r = 0)))
ret <- ret + theme(axis.text.x = element_text(size = axis_text_size, margin = margin(t = 0)))
ret <- ret + theme(axis.text.y = element_text(size = axis_text_size, margin = margin(r = 0)))
ret <- ret + theme(axis.title = element_text(size = axis_title_size, family = axis_title_family))
ret <- ret + theme(axis.title.x = element_text(
hjust = xj, size = axis_title_size,
family = axis_title_family, face = axis_title_face
))
ret <- ret + theme(axis.title.y = element_text(
hjust = yj, size = axis_title_size,
family = axis_title_family, face = axis_title_face
))
ret <- ret + theme(axis.title.y.right = element_text(
hjust = yj, size = axis_title_size, angle = 90,
family = axis_title_family, face = axis_title_face
))
ret <- ret + theme(strip.text = element_text(
hjust = 0, size = strip_text_size,
face = strip_text_face, family = strip_text_family
))
ret <- ret + theme(plot.title = element_text(
hjust = 0, size = plot_title_size,
margin = margin(b = plot_title_margin),
family = plot_title_family, face = plot_title_face
))
ret <- ret + theme(plot.subtitle = element_text(
hjust = 0, size = subtitle_size,
margin = margin(b = subtitle_margin),
family = subtitle_family, face = subtitle_face
))
ret <- ret + theme(plot.caption = element_text(
hjust = 1, size = caption_size,
margin = margin(t = caption_margin),
family = caption_family, face = caption_face
))
ret
}
import_econ_sans <- function() {
es_font_dir <- system.file("fonts", "econ-sans", package="hrbrthemes")
}
font_es <- "EconSansCndReg"
font_es_bold <- "EconSansCndBol"
font_es_light <- "EconSansCndLig" |
NULL
scale_color_tq <- function(..., theme = "light") {
pal <- switch(theme,
"light" = unname(palette_light()) %>% rep(100),
"dark" = unname(palette_dark()) %>% rep(100),
"green" = unname(palette_green() %>% rep(100))
)
scale_color_manual(values = pal)
}
scale_colour_tq <- scale_color_tq
scale_fill_tq <- function(..., theme = "light") {
pal <- switch(theme,
"light" = unname(palette_light()) %>% rep(100),
"dark" = unname(palette_dark()) %>% rep(100),
"green" = unname(palette_green()) %>% rep(100)
)
scale_fill_manual(values = pal)
}
NULL
palette_light <- function() {
c(
blue = "
red = "
green = "
yellow = "
steel_blue = "
navy_blue = "
light_green = "
pink = "
light_orange = "
orange = "
light_purple = "
purple = "
) %>% toupper()
}
palette_dark <- function() {
c(
blue = "
red = "
green = "
yellow = "
steel_blue = "
navy_blue = "
light_green = "
pink = "
light_orange = "
lime_green = "
light_purple = "
purple = "
) %>% toupper()
}
palette_green <- function() {
c(
blue = "
red = "
yellow = "
steel_blue = "
navy_blue = "
creme = "
pink = "
light_orange = "
lime_green = "
light_purple = "
purple = "
brown = "
) %>% toupper()
} |
library(knotR)
filename <- "7_5.svg"
a <- reader(filename)
Mver <-
matrix(
c(13,02,
12,03,
01,14,
11,04,
19,15,
18,16,
05,10,
09,06,
08,07
),
byrow=TRUE,ncol=2)
ou75 <- matrix(c(
01,12,
14,02,
04,15,
11,05,
06,10,
17,07,
09,18
),byrow=TRUE,ncol=2)
sym75 <- symmetry_object(a, Mver=Mver, xver=17)
jj <- knotoptim(filename,
symobj = sym75,
ou = ou75,
prob = 0,
iterlim=100, print.level=2
)
write_svg(jj,filename,safe=FALSE)
dput(jj,file=sub('.svg','.S',filename)) |
source("ESEUR_config.r")
library("plyr")
plot_a_fit=function(subj)
{
w_mod=glm(log(FP) ~ log(CFP), data=subj)
pred=predict(w_mod, newdata=data.frame(CFP=CFP_seq))
lines(CFP_seq, exp(pred), col=subj$col)
}
plot_fit=function(who)
{
d_ply(who, .(col), plot_a_fit)
}
plot_points=function(who)
{
points(who$CFP, who$FP, col=who$col)
}
bench=read.csv(paste0(ESEUR_dir, "statistics/BTH2011Padmanabhuni.csv.xz"), as.is=TRUE)
no_students=subset(bench, Dataset != "Cuadtado_jj07" & Dataset != "Cuadtado_jj06")
no_Cuadtado=subset(no_students, Dataset != "Cuadtado_2007")
conv_mod=glm(FP ~ (who_FP+CFP)^2+kind+Dataset, data=no_students)
conv_pmod=glm(FP ~ (who_FP+who_CFP+log(CFP))^2-who_FP:who_CFP+kind, family=poisson, data=no_students)
conv_mod=glm(log(FP) ~ (who_FP+log(CFP))^2+kind, data=no_students)
conv_cmod=glm(log(CFP) ~ (who_FP+log(FP))^2+kind, data=no_students)
summary(conv_pmod)
summary(conv_mod)
summary(conv_cmod)
CFP_seq=seq(20, 2000, by=5)
pal_col=rainbow(4)
D_names=unique(no_students$Dataset)
D_cols=rainbow(length(D_names))
no_students$col=mapvalues(no_students$Dataset, D_names, D_cols)
ind=subset(no_students, who_CFP == "ind")
aca=subset(no_students, who_CFP != "ind")
plot_layout(1, 2)
plot(1, type="n", log="xy",
xlim=range(no_students$CFP), ylim=range(no_students$FP),
xlab="COSMIC", ylab="FPA\n")
plot_points(ind)
plot_points(aca)
legend(x="topleft", legend=D_names, bty="n", fill=D_cols, cex=1.2)
plot(1, type="n", log="xy",
xlim=range(no_students$CFP), ylim=range(no_students$FP),
xlab="COSMIC", ylab="FPA\n")
plot_fit(ind)
plot_fit(aca)
library("simex")
no_students=subset(no_students, !is.na(kind))
no_students$l_CFP=log(no_students$CFP)
no_students$l_FP=log(no_students$FP)
conv_cmod=glm(l_CFP ~ (who_FP+l_FP)^2+kind, data=no_students)
conv_simex=simex(conv_cmod, SIMEXvariable="l_FP", measurement.error=no_students$l_FP/30, asymptotic=FALSE)
summary(conv_simex) |
cc_inference <- function(mod,B=100, alpha_max=.5,numb_cc=NULL,resamp_type="sign-flip",light=FALSE){
mod$call$cc_inference=match.call()
n=nrow(mod$data$X)
resamp_type=match.arg(resamp_type,c("sign-flip","permutation"))
if(is.null(numb_cc)) numb_cc=length(mod$cor)
if(!light) {
mod=.cc_inference_orthogonal(mod,B, alpha_max,numb_cc,resamp_type)
} else if(light) {
mod=.cc_inference_residuals(mod,B, alpha_max,numb_cc,resamp_type)
}
return(mod)
}
.cc_inference_orthogonal <- function(mod,B, alpha_max,numb_cc,resamp_type){
resid_matrix <- function(Zy)
residualizing_matrix(Zy)$Q
if(resamp_type=="sign-flip"){
.permute <- function(X,Qx,nred){
t(Qx*(1-2*rbinom(nred,1,.5))) %*%X
}
} else if(resamp_type=="permutation"){
.permute <- function(X,Qx,nred){
t(Qx[sample(nred),]) %*%X
}
}
perm_and_cc=function(X,Y,Qx,Qy,nredx,nredy){
ccp=.cc_core(.permute(X,Qx,nredx), .permute(Y,Qy,nredy),numb_cc = 0)
ccp$cor[1]
}
if(is.null(mod$data$Zx)) {
Qx=diag(nrow(mod$data$X))
X=mod$data$X
} else{
Qx=resid_matrix(mod$data$Zx)
X=Qx%*%mod$data$X
}
if(is.null(mod$data$Zy)) {
Qy=diag(nrow(mod$data$Y))
Y=mod$data$Y
} else{
Qy=resid_matrix(mod$data$Zy)
Y=Qy%*%mod$data$Y
}
nredx=nrow(Qx)
nredy=nrow(Qy)
mod$p_values=rep(1,length(mod$cor))
attr(mod$p_values,which = "B")=B
n_nuisx=min(0,ncol(mod$data$Zx))
n_nuisy=min(0,ncol(mod$data$Zy))
Zx=cbind(mod$data$Zx,mod$scores$xscores[,1:numb_cc])
Zy=cbind(mod$data$Zy,mod$scores$yscores[,1:numb_cc])
for(i in 1:numb_cc){
mod$p_values[i]=(sum(replicate(B,perm_and_cc(X,Y,Qx,Qy,nredx,nredy))>=mod$cor[i])+1)/(B+1)
if(mod$p_values[i]>= alpha_max) return(mod)
Qx=resid_matrix(Zx[,1:n_nuisx+i])
Qy=resid_matrix(Zy[,1:n_nuisy+i])
X=Qx%*%mod$data$X
Y=Qy%*%mod$data$Y
}
return(mod)
}
.cc_inference_residuals <- function(mod,B, alpha_max,numb_cc,resamp_type){
n=nrow(mod$data$X)
if(resamp_type=="sign-flip"){
.permute <- function(X,n){
(1-2*rbinom(n,1,.5))*X
}
} else if(resamp_type=="permutation"){
.permute <- function(X,n){
X[sample(n),]
}
}
perm_and_cc=function(X,Y,n){
ccp=.cc_core(.permute(X,n), Y,numb_cc = 0)
ccp$cor[1]
}
if(is.null(mod$data$Zx)) {
X=mod$data$X
} else{
X=residualize(mod$data$X,mod$data$Zx)
}
if(is.null(mod$data$Zy)) {
Y=mod$data$Y
} else{
Y=residualize(mod$data$Y,mod$data$Zy)
}
mod$p_values=rep(1,length(mod$cor))
attr(mod$p_values,which = "B")=B
n_nuisx=min(0,ncol(mod$data$Zx))
n_nuisy=min(0,ncol(mod$data$Zy))
Zx=cbind(mod$data$Zx,mod$scores$xscores[,1:numb_cc])
Zy=cbind(mod$data$Zy,mod$scores$yscores[,1:numb_cc])
for(i in 1:numb_cc){
mod$p_values[i]=(sum(replicate(B,perm_and_cc(X,Y,n))>=mod$cor[i])+1)/(B+1)
if((mod$p_values[i]>= alpha_max) | (i== numb_cc)) return(mod)
X=residualize(mod$data$X,Zx)
Y=residualize(mod$data$Y,Zy)
}
return(mod)
} |
mzagglom<-function( MSlist,
dmzgap=10,
ppm=TRUE,
drtgap=500,
minpeak=4,
maxint=1E7,
progbar=FALSE
){
if(!length(MSlist)==8){stop("This is not an MSlist object")}
if(!MSlist[[1]][[1]]){stop("MSlist empty or invalid. Use readMSdata to upload raw .mzML data first.")}
if(!is.loaded("agglom")){stop(".Call to agglom failed; aborted.")}
if(!is.loaded("indexed")){stop(".Call to indexed failed; aborted.")}
if(!is.numeric(dmzgap)){stop("dmass must be numeric; aborted.")}
if(!is.numeric(drtgap)){stop("dret must be numeric; aborted.")}
if(!is.logical(ppm)){stop("ppm must be logical; aborted.")}
MSlist[[5]]<-0;
MSlist[[6]]<-0;
MSlist[[7]]<-0;
MSlist[[8]]<-0;
MSlist[[4]][[2]][,5]<-rep(0,length(MSlist[[4]][[2]][,4]));
MSlist[[4]][[2]][,6]<-rep(0,length(MSlist[[4]][[2]][,4]));
MSlist[[4]][[2]][,7]<-rep(0,length(MSlist[[4]][[2]][,4]));
if(ppm){ppm2<-1}else{ppm2<-0};
MSlist[[4]][[2]]<-MSlist[[4]][[2]][order(MSlist[[4]][[2]][,1],decreasing=FALSE),]
if(progbar==TRUE){prog<-winProgressBar("Agglomerate...",min=0,max=3);
setWinProgressBar(prog, 0, title = "Agglomerate...", label = NULL);}
part <- .Call("agglom",
as.numeric(MSlist[[4]][[2]][,1]),
as.numeric(MSlist[[4]][[2]][,3]),
as.integer(ppm2),
as.numeric(dmzgap),
as.numeric(drtgap),
PACKAGE="enviPick"
)
if(progbar==TRUE){setWinProgressBar(prog, 1, title = "Agglomerate...", label = NULL)}
MSlist[[4]][[2]]<-MSlist[[4]][[2]][order(part,decreasing=FALSE),]
part<-part[order(part,decreasing=FALSE)]
maxit<-max(part)
index <- .Call("indexed",
as.integer(part),
as.numeric(MSlist[[4]][[2]][,2]),
as.integer(minpeak),
as.numeric(maxint),
as.integer(maxit),
PACKAGE="enviPick"
)
index<-index[index[,2]!=0,,drop=FALSE]
colnames(index)<-c("start_ID","end_ID","number_peaks")
MSlist[[5]]<-index
partID<-.Call("partID",
as.integer(index),
as.integer(length(MSlist[[4]][[2]][,5])),
PACKAGE="enviPick"
)
MSlist[[4]][[2]][,5]<-partID
if(progbar==TRUE){setWinProgressBar(prog, 2, title = "Agglomerate...", label = NULL)}
MSlist[[3]][[1]]<-length(MSlist[[4]][[2]][,1]);
MSlist[[3]][[2]]<-length(index[,3]);
MSlist[[3]][[3]]<-sum(index[,3]);
MSlist[[2]][[2]][1]<-as.character(dmzgap)
MSlist[[2]][[2]][2]<-as.character(ppm)
MSlist[[2]][[2]][3]<-as.character(drtgap)
MSlist[[2]][[2]][4]<-as.character(minpeak)
MSlist[[2]][[2]][5]<-as.character(maxint)
MSlist[[1]][[2]]<-TRUE;
MSlist[[1]][[3]]<-FALSE;
MSlist[[1]][[4]]<-FALSE;
MSlist[[1]][[5]]<-FALSE;
if(progbar==TRUE){close(prog);}
return(MSlist)
} |
Sim.Data.MTS <- function(N.Total=2000, N.Trial=50, R.Trial.Target=.8, R.Indiv.Target=.8,
Fixed.Effects=c(0, 0, 0, 0), D.aa=10, D.bb=10, Seed=sample(1:1000, size=1), Model=c("Full")) {
if ((Model==c("Full") | Model==c("Reduced") | Model==c("SemiReduced"))==FALSE) {stop ("The specification of the Model=c(\"...\") argument of the call is incorrect. Use either Model=c(\"Full\"), Model=c(\"Reduced\"), or Model=c(\"SemiReduced\").")}
N.patients <- N.Total/N.Trial
if ((N.patients%%1==0)==FALSE) {
cat("\nNOTE: The number of patients per trial requested in the function call equals ", N.patients, " (=N.Total/N.Trial), which is not a whole number. ", sep="")
cat("\nThe number of patients per trial was rounded to ")
N.patients <- rounded <- ceiling(N.patients)
N.Total.old <- N.Total
N.Total <- rounded * N.Trial
cat(rounded, " to generate the dataset. Data.Observed.MTS thus contains a total of ", N.Total, " patients \nrather than the requested ", N.Total.old, " in the function call.", sep="")
}
R2.trial.target <- R.Trial.Target**2
if (Model==c("Full")|Model==c("SemiReduced")){
Dmat <- diag(4)
Dmat[3,3] <- D.aa
Dmat[4,4] <- D.bb
Dmat[3,4] <- Dmat[4,3] <- sqrt(R2.trial.target * (Dmat[3,3] * Dmat[4,4]))
set.seed(Seed)
ran.eff <- MASS::mvrnorm(N.Trial, rep(0,4), Dmat)
Smat <- diag(2)
Smat[1,2] <- Smat[2,1] <- R.Indiv.Target
set.seed(Seed)
errors <- MASS::mvrnorm(N.Total, rep(0,2), Smat)
Z <- Trial_ID <- Surr <- True <- NULL
for (i in 1: N.Trial){
Z_temp <- sample(x=rep(c(-1, 1), each=(ceiling(N.patients/2))), N.patients, replace=FALSE)
Trial_ID_temp <- rep(i, N.patients)
Z <- append(x=Z, values=Z_temp)
Trial_ID <- append(Trial_ID, Trial_ID_temp)
}
supp <- data.frame(cbind(Z, Trial_ID))
for (i in 1: N.Total){
Surr_temp <- (Fixed.Effects[1]) + ran.eff[supp$Trial_ID[i],1] + (((Fixed.Effects[3]) + ran.eff[supp$Trial_ID[i],3])*Z[i]) + errors[i,1]
True_temp <- (Fixed.Effects[2]) + ran.eff[supp$Trial_ID[i],2] + (((Fixed.Effects[4]) + ran.eff[supp$Trial_ID[i],4])*Z[i]) + errors[i,2]
Surr <- append(x=Surr, values=Surr_temp)
True <- append(x=True, values=True_temp)
}
}
if (Model==c("Reduced")){
Dmat <- diag(2)
Dmat[1,1] <- D.aa
Dmat[2,2] <- D.bb
Dmat[1,2] <- Dmat[2,1] <- sqrt(R2.trial.target * (Dmat[1,1] * Dmat[2,2]))
set.seed(Seed)
ran.eff <- MASS::mvrnorm(N.Trial, rep(0,2), Dmat)
Smat <- diag(2)
Smat[1,2] <- Smat[2,1] <- R.Indiv.Target
set.seed(Seed)
errors <- MASS::mvrnorm(N.Total, rep(0,2), Smat)
Z <- Trial_ID <- Surr <- True <- NULL
for (i in 1: N.Trial){
Z_temp <- sample(x=rep(c(-1, 1), each=(ceiling(N.patients/2))), N.patients, replace=FALSE)
Trial_ID_temp <- rep(i, N.patients)
Z <- append(x=Z, values=Z_temp)
Trial_ID <- append(Trial_ID, Trial_ID_temp)
}
supp <- data.frame(cbind(Z, Trial_ID), stringsAsFactors = TRUE)
for (i in 1: N.Total){
Surr_temp <- (((Fixed.Effects[1]) + ran.eff[supp$Trial_ID[i],1])*Z[i]) + errors[i,1]
True_temp <- (((Fixed.Effects[2]) + ran.eff[supp$Trial_ID[i],2])*Z[i]) + errors[i,2]
Surr <- append(x=Surr, values=Surr_temp)
True <- append(x=True, values=True_temp)
}
}
Pat_ID <- 1:N.Total
Data.Observed.MTS <- cbind(supp, Surr, True, Pat_ID)
names(Data.Observed.MTS) <- c("Treat", "Trial.ID", "Surr", "True", "Pat.ID")
Data.Observed.MTS <<- Data.Observed.MTS
fit <- list(Data.Observed.MTS=Data.Observed.MTS)
} |
context("corner-cases")
test_that("corner-cases are handled as expected", {
expect_warning(withr::with_output_sink(tempfile(), {
cov <- file_coverage("corner-cases.R", "corner-cases-test.R")
}))
expect_equal(as.data.frame(cov), readRDS("corner-cases.Rds"))
}) |
findseeds_inwards_updating <- function(nng) {
seeds <- NULL
assigned <- rep(FALSE, ncol(nng))
tobechecked <- 1:ncol(nng)
while (length(tobechecked) > 0) {
i <- tobechecked[order(rowSums(nng[tobechecked, intersect(which(!assigned), tobechecked), drop = FALSE]))[1]]
tobechecked <- setdiff(tobechecked, i)
if (any(nng[, i]) && !any(assigned[nng[, i]])) {
seeds <- c(seeds, i)
tobechecked <- setdiff(tobechecked, which(nng[, i]))
assigned[nng[, i]] <- TRUE
}
}
seeds
}
findseeds_exclusion_updating <- function(nng) {
seeds <- NULL
exclusion_graph <- ((nng + t(nng) + t(nng) %*% nng) > 0)
exclusion_graph[!apply(nng, 2, any), ] <- FALSE
exclud_count <- colSums(exclusion_graph)
tobechecked <- which(apply(nng, 2, any))
while (length(tobechecked) > 0) {
i <- tobechecked[order(exclud_count[tobechecked])[1]]
tobechecked <- setdiff(tobechecked, i)
seeds <- c(seeds, i)
to_exclude <- intersect(which(exclusion_graph[, i]), tobechecked)
tobechecked <- setdiff(tobechecked, to_exclude)
for (e in to_exclude) {
for (nne in intersect(which(exclusion_graph[, e]), tobechecked)) {
exclud_count[nne] <- exclud_count[nne] - 1
}
}
}
seeds
} |
connection <- function() {
auth <- list(host = Sys.getenv("ES_HOST"),
port = Sys.getenv("ES_PORT"),
path = Sys.getenv("ES_PATH"),
transport = Sys.getenv("ES_TRANSPORT"),
user = Sys.getenv("ES_USER"))
if (is.null(auth$port) || nchar(auth$port) == 0) {
baseurl <- sprintf("%s://%s", auth$transport, auth$host)
} else {
baseurl <- sprintf("%s://%s:%s", auth$transport, auth$host, auth$port)
}
if (!is.null(auth$path)) {
baseurl <- file.path(baseurl, auth$path)
}
structure(list(
transport = auth$transport,
host = auth$host,
port = auth$port,
path = auth$path,
user = auth$user,
pwd = "<secret>",
headers = es_env$headers,
errors = Sys.getenv("ELASTIC_RCLIENT_ERRORS")),
class = 'es_conn')
}
print.es_conn <- function(x, ...){
fun <- function(x) ifelse(is.null(x) || nchar(x) == 0, 'NULL', x)
cat(paste('transport: ', fun(x$transport)), "\n")
cat(paste('host: ', fun(x$host)), "\n")
cat(paste('port: ', fun(x$port)), "\n")
cat(paste('path: ', fun(x$path)), "\n")
cat(paste('username: ', fun(x$user)), "\n")
cat(paste('password: ', fun(x$pwd)), "\n")
cat(paste('errors: ', fun(x$errors)), "\n")
cat(paste('headers (names): ', ph(x$headers)), "\n")
}
es_auth <- function(es_host = NULL, es_port = NULL, es_path = NULL,
es_transport_schema = NULL, es_user = NULL, es_pwd = NULL,
force = FALSE, es_base = NULL) {
calls <- names(sapply(match.call(), deparse))[-1]
calls_vec <- "es_base" %in% calls
if (any(calls_vec)) {
stop("The parameter es_base has been deprecated, use es_host", call. = FALSE)
}
host <- ifnull(es_host, 'ES_HOST')
port <- if (is.null(es_port)) "" else es_port
path <- ifnull(es_path, 'ES_PATH')
transport <- ifnull(es_transport_schema, 'ES_TRANSPORT_SCHEMA')
user <- ifnull(es_user, 'ES_USER')
pwd <- ifnull(es_pwd, 'ES_PWD')
if (identical(host, "") || force) {
if (!interactive()) {
stop("Please set env var ES_HOST for your host url for your Elasticsearch server",
call. = FALSE)
}
message("Couldn't find env var ES_HOST See ?es_auth for more details.")
message("Please enter your Elasticsearch host url and press enter:")
host <- readline(": ")
if (identical(host, "")) {
stop("Elasticsearch host url entry failed", call. = FALSE)
}
message("Updating ES_HOST env var\n")
Sys.setenv(ES_HOST = host)
} else {
host <- host
}
Sys.setenv(ES_HOST = host)
Sys.setenv(ES_TRANSPORT = transport)
Sys.setenv(ES_PORT = port)
Sys.setenv(ES_PATH = path)
Sys.setenv(ES_USER = user)
Sys.setenv(ES_PWD = pwd)
list(host = host, port = port, path = path, transport = transport)
}
ifnull <- function(x, y){
if (is.null(x)) Sys.getenv(y) else x
}
es_get_auth <- function(){
transport <- Sys.getenv("ES_TRANSPORT")
host <- Sys.getenv("ES_HOST")
port <- Sys.getenv("ES_PORT")
path <- Sys.getenv("ES_PATH")
if (is.null(host)) stop("Please run connect()", call. = FALSE)
list(transport = transport, host = host, port = port, path = path)
}
es_get_user_pwd <- function(){
user <- Sys.getenv("ES_USER")
pwd <- Sys.getenv("ES_PWD")
list(user = user, pwd = pwd)
}
make_url <- function(x) {
url <- sprintf("%s://%s", x$transport, x$host)
url <- if (is.null(x$port) || nchar(x$port) == 0) {
url
} else {
paste(url, ":", x$port, sep = "")
}
if (!is.null(x$path) && nchar(x$path) > 0) {
url <- file.path(url, x$path)
}
url
}
as_headers <- function(x) {
UseMethod("as_headers")
}
as_headers.default <- function(x) NULL
as_headers.request <- function(x) x
as_headers.list <- function(x) {
do.call(add_headers, x)
}
ph <- function(x) {
if (is.null(x)) {
'NULL'
} else {
str <- paste0(names(x$headers), collapse = ", ")
if (nchar(str) > 30) paste0(substring(str, 1, 30), " ...") else str
}
}
es_env <- new.env() |
Ice2 %>% filter(time == 1930, phase == "b") %>%
group_by(location, treatment, phase) %>%
summarise(mean(temp)) |
NAME <- "diffDeparse"
source(file.path('_helper', 'init.R'))
all.equal(as.character(diffDeparse(letters, LETTERS)), rdsf(100))
all.equal(
as.character(
diffDeparse(letters, LETTERS, extra=list(width.cutoff=20))
),
rdsf(200)
) |
collapse_ranges <- function(df,
groups = NULL,
start_var = NULL,
end_var = NULL,
startAttr = NULL,
endAttr = NULL,
dimension = "date",
max_gap = 0L,
fmt = "%Y-%m-%d",
tz = "UTC",
origin = "1970-01-01") {
df_collapsed <- copy(df)
if (!any(class(df) %in% "data.table")) setDT(df_collapsed)
if (!is.null(startAttr) | !is.null(endAttr)) for (j in c(startAttr, endAttr)) set(df_collapsed, j = j, value = as.character(df_collapsed[[j]]))
if (dimension == "date") {
if (class(df_collapsed[[start_var]]) != 'Date' | class(df_collapsed[[end_var]]) != 'Date') {
for (j in c(start_var, end_var)) set(df_collapsed, j = j, value = as.Date(as.character(df_collapsed[[j]]), format = fmt))
}
setorderv(df_collapsed, c(groups, start_var))
if (!is.null(groups)) {
if (!is.null(startAttr) & !is.null(endAttr)) {
df_collapsed <- df_collapsed[, updateAndSubset(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = mget(startAttr),
endObjects = mget(endAttr)
), by = mget(groups)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(groups, start_var, end_var, startAttr, endAttr))
} else if (!is.null(startAttr) & is.null(endAttr)) {
df_collapsed <- df_collapsed[, updateAndSubset(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = mget(startAttr),
endObjects = NULL
), by = mget(groups)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(groups, start_var, end_var, startAttr))
} else if (is.null(startAttr) & !is.null(endAttr)) {
df_collapsed <- df_collapsed[, updateAndSubset(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = NULL,
endObjects = mget(endAttr)
), by = mget(groups)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(groups, start_var, end_var, endAttr))
} else {
df_collapsed <- df_collapsed[, updateAndSubset(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = NULL,
endObjects = NULL
), by = mget(groups)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(groups, start_var, end_var))
}
} else {
if (!is.null(startAttr) & !is.null(endAttr)) {
df_collapsed <- df_collapsed[, updateAndSubset(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = mget(startAttr),
endObjects = mget(endAttr)
)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(groups, start_var, end_var, startAttr, endAttr))
} else if (!is.null(startAttr) & is.null(endAttr)) {
df_collapsed <- df_collapsed[, updateAndSubset(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = mget(startAttr),
endObjects = NULL
)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(groups, start_var, end_var, startAttr))
} else if (is.null(startAttr) & !is.null(endAttr)) {
df_collapsed <- df_collapsed[, updateAndSubset(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = NULL,
endObjects = mget(endAttr)
)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(groups, start_var, end_var, endAttr))
} else {
df_collapsed <- df_collapsed[, updateAndSubset(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = NULL,
endObjects = NULL
)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(groups, start_var, end_var))
}
}
} else if (dimension == "timestamp") {
if (!any(class(df_collapsed[[start_var]]) %in% c('POSIXct', 'POSIXlt')) | !any(class(df_collapsed[[end_var]]) %in% c('POSIXct', 'POSIXlt'))) {
if (fmt == "%Y-%m-%d") {
warning("Dimension 'timestamp' selected but format unchanged. Will try to convert to '%Y-%m-%d %H:%M:%OS' ..")
fmt <- "%Y-%m-%d %H:%M:%OS"
}
for (j in c(start_var, end_var)) set(df_collapsed, j = j, value = as.POSIXct(as.character(df_collapsed[[j]]), format = fmt, tz = tz))
}
setorderv(df_collapsed, c(groups, start_var))
if (!is.null(groups)) {
if (!is.null(startAttr) & !is.null(endAttr)) {
df_collapsed <- df_collapsed[, updateAndSubsetTime(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = mget(startAttr),
endObjects = mget(endAttr)
), by = mget(groups)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(groups, start_var, end_var, startAttr, endAttr))
} else if (!is.null(startAttr) & is.null(endAttr)) {
df_collapsed <- df_collapsed[, updateAndSubsetTime(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = mget(startAttr),
endObjects = NULL
), by = mget(groups)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(groups, start_var, end_var, startAttr))
} else if (is.null(startAttr) & !is.null(endAttr)) {
df_collapsed <- df_collapsed[, updateAndSubsetTime(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = NULL,
endObjects = mget(endAttr)
), by = mget(groups)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(groups, start_var, end_var, endAttr))
} else {
df_collapsed <- df_collapsed[, updateAndSubsetTime(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = NULL,
endObjects = NULL
), by = mget(groups)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(groups, start_var, end_var))
}
} else {
if (!is.null(startAttr) & !is.null(endAttr)) {
df_collapsed <- df_collapsed[, updateAndSubsetTime(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = mget(startAttr),
endObjects = mget(endAttr)
)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(start_var, end_var, startAttr, endAttr))
} else if (!is.null(startAttr) & is.null(endAttr)) {
df_collapsed <- df_collapsed[, updateAndSubsetTime(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = mget(startAttr),
endObjects = NULL
)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(start_var, end_var, startAttr))
} else if (is.null(startAttr) & !is.null(endAttr)) {
df_collapsed <- df_collapsed[, updateAndSubsetTime(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = NULL,
endObjects = mget(endAttr)
)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(start_var, end_var, endAttr))
} else {
df_collapsed <- df_collapsed[, updateAndSubsetTime(get(start_var),
get(end_var),
max_gap = max_gap,
startObjects = NULL,
endObjects = NULL
)]
setnames(df_collapsed, 1:ncol(df_collapsed), c(start_var, end_var))
}
}
} else { stop("The dimension argument has to be either 'date' or 'timestamp'.") }
if (!any(class(df) %in% "data.table")) {
return(setDF(df_collapsed))
} else {
return(df_collapsed)
}
} |
writeAncestors<-function(tree,Anc=NULL,file="",digits=6,format=c("phylip","nexus"),...){
format=format[1]
if(hasArg(CI)) CI<-list(...)$CI
else CI<-TRUE
if(is.null(Anc)){
if(hasArg(x)){
x<-list(...)$x
if(inherits(tree,"multiPhylo")){
if(is.list(x)) Anc<-mapply(fastAnc,tree,x,MoreArgs=list(CI=CI),SIMPLIFY=FALSE)
else Anc<-lapply(tree,fastAnc,x=x,CI=CI)
} else if(inherits(tree,"phylo")){
if(is.list(x)){
Anc<-lapply(x,fastAnc,tree=tree,CI=CI)
tree<-repPhylo(tree,length(x))
class(tree)<-"multiPhylo"
} else Anc<-fastAnc(tree,x,CI=CI)
}
} else stop("must have argument 'Anc' or 'x'")
}
if(format=="phylip"){
if(class(tree)=="multiPhylo")
XX<-mapply(writeAnc,tree,Anc,MoreArgs=list(digits=digits))
else if(class(tree)=="phylo")
XX<-writeAnc(tree,Anc,digits)
else stop("tree should be an object of class 'phylo' or 'multiPhylo'")
write(XX,file)
invisible(XX)
} else if(format=="nexus"){
writeNex(tree,Anc,file,digits)
}
}
writeNex<-function(tree,Anc,file="",digits){
if(inherits(tree,"multiPhylo")) N<-length(tree)
else {
N<-1
tree<-list(tree)
Anc<-list(Anc)
}
n<-length(tree[[1]]$tip.label)
write("
write(paste("[R-package PHYTOOLS, ",date(),"]\n",sep=""),file,append=TRUE)
write("BEGIN TAXA;",file,append=TRUE)
write(paste("\tDIMENSIONS NTAX = ",n,";",sep=""),file,append=TRUE)
write("\tTAXLABELS",file,append=TRUE)
trans<-tree[[1]]$tip.label; trans<-sort(trans)
for(i in 1:n) write(paste("\t\t",trans[i],sep=""),file,append=TRUE)
write("\t;",file,append=TRUE)
write("END;",file,append=TRUE)
write("BEGIN TREES;\n\tTRANSLATE",file,append=TRUE)
for(i in 1:(n-1)) write(paste("\t\t",i,"\t",trans[i],",",sep=""),file,append=TRUE)
write(paste("\t\t",i+1,"\t",trans[i+1],sep=""),file,append=TRUE)
write("\t;",file,append=TRUE)
for(i in 1:N){
tree[[i]]$tip.label<-sapply(tree[[i]]$tip.label,function(x) which(x==trans))
write(paste("\tTREE * UNTITLED = [&R] ",writeAnc(tree[[i]],Anc[[i]],digits),sep=""),file,append=TRUE)
}
write("END;",file,append=TRUE)
}
writeAnc<-function(tree,Anc,digits){
tree<-reorder.phylo(tree,"cladewise")
n<-length(tree$tip.label)
if(!is.list(Anc)) Anc<-list(ace=Anc)
Anc$ace<-Anc$ace[order(names(Anc$ace))]
Anc$ace<-round(Anc$ace,digits)
if(!is.null(Anc$CI95)){
Anc$CI95<-round(Anc$CI95,digits)
tree$node.label<-paste("[&CI={",Anc$CI95[,1],",",Anc$CI95[,2],"},ancstate={",Anc$ace,"}]",sep="")
} else {
Anc$CI95<-Anc$CI95[names(Anc$ace),]
tree$node.label<-paste("[&ancstate={",Anc$ace,"}]",sep="")
}
tree$edge.length<-round(tree$edge.length,digits)
string<-vector(); string[1]<-"("; j<-2
for(i in 1:nrow(tree$edge)){
if(tree$edge[i,2]<=n){
string[j]<-tree$tip.label[tree$edge[i,2]]; j<-j+1
if(!is.null(tree$edge.length)){
string[j]<-paste(c(":",tree$edge.length[i]),collapse="")
j<-j+1
}
v<-which(tree$edge[,1]==tree$edge[i,1]); k<-i
while(length(v)>0&&k==v[length(v)]){
string[j]<-")"; j<-j+1
string[j]<-tree$node.label[tree$edge[k,1]-n]; j<-j+1
w<-which(tree$edge[,2]==tree$edge[k,1])
if(!is.null(tree$edge.length)){
string[j]<-paste(c(":",tree$edge.length[w]),collapse="")
j<-j+1
}
v<-which(tree$edge[,1]==tree$edge[w,1]); k<-w
}
string[j]<-","; j<-j+1
} else if(tree$edge[i,2]>=n){
string[j]<-"("; j<-j+1
}
}
if(is.null(tree$edge.length)) string<-c(string[1:(length(string)-1)],";")
else string<-c(string[1:(length(string)-2)],";")
string<-paste(string,collapse="")
return(string)
} |
est.analysis = function(Omega.hat.list, Omega.true.list, offdiag=TRUE){
if ( !is.list(Omega.hat.list)){
stop('argument Omega.hat.list should be a list')
} else if (!is.list(Omega.true.list)) {
stop('argument Omega.true.list should be a list')
} else if (any(!sapply(Omega.hat.list,is.matrix))) {
stop('argument Omega.hat.list should be a list of precision matrices')
} else if (any(!sapply(Omega.true.list,is.matrix))) {
stop('argument Omega.true.list should be a list of precision matrices')
} else if ( length(Omega.hat.list)!=length(Omega.true.list) ) {
stop('arguments Omega.hat.list and Omega.true.list should share the same length')
} else if ( any(!(sapply(Omega.hat.list,dim)[1,]==sapply(Omega.true.list,dim)[1,]))) {
stop('dimension of elements in argument Omega.hat.list should match argument Omega.true.list')
} else if ( !is.logical(offdiag)) {
stop('argument offdiag should be a logical TRUE or FALSE ')
}
K = dim(as.array(Omega.hat.list))
error.f = rep(0,K)
error.max = rep(0,K)
tpr = rep(0,K)
tnr = rep(0,K)
if (offdiag==FALSE) {
for(i in 1:K){
error.f[i] = norm(Omega.hat.list[[i]] - Omega.true.list[[i]], type="F")
error.max[i] = norm(Omega.hat.list[[i]] - Omega.true.list[[i]], type="M")
tpr[i] = length(intersect(which(Omega.hat.list[[i]] !=0 ), which(Omega.true.list[[i]] !=0))) / length(which(Omega.true.list[[i]] !=0))
tnr[i] = length(intersect(which(Omega.hat.list[[i]] ==0 ), which(Omega.true.list[[i]] ==0))) / length(which(Omega.true.list[[i]] ==0))
}
KOmega.true=1;KOmega.hat=1
for (k in 1:K){
KOmega.true=kronecker(KOmega.true, Omega.true.list[[k]])
KOmega.hat=kronecker(KOmega.hat, Omega.hat.list[[k]])
}
error.kro = norm(KOmega.hat - KOmega.true,type="F")
tpr.kro = length(intersect(which(KOmega.hat !=0 ), which(KOmega.true !=0))) / length(which(KOmega.true !=0))
tnr.kro = length(intersect(which(KOmega.hat ==0 ), which(KOmega.true ==0))) / length(which(KOmega.true ==0))
} else {
Omega.hat.list.off=Omega.hat.list
Omega.true.list.off=Omega.true.list
for(i in 1:K){
diag(Omega.hat.list.off[[i]])=0
diag(Omega.true.list.off[[i]])=0
error.f[i] = norm(Omega.hat.list.off[[i]] - Omega.true.list.off[[i]], type="F")
error.max[i] = norm(Omega.hat.list.off[[i]] - Omega.true.list.off[[i]], type="M")
diag(Omega.hat.list.off[[i]])=NA
diag(Omega.true.list.off[[i]])=NA
tpr[i] = length(intersect(which(Omega.hat.list.off[[i]] !=0 ), which(Omega.true.list.off[[i]] !=0))) / length(which(Omega.true.list.off[[i]] !=0))
tnr[i] = length(intersect(which(Omega.hat.list.off[[i]] ==0 ), which(Omega.true.list.off[[i]] ==0))) / length(which(Omega.true.list.off[[i]] ==0))
}
KOmega.true=1;KOmega.hat=1
for (k in 1:K){
KOmega.true=kronecker(KOmega.true, Omega.true.list[[k]])
KOmega.hat=kronecker(KOmega.hat, Omega.hat.list[[k]])
}
diag(KOmega.hat)=0
diag(KOmega.true)=0
error.kro = norm(KOmega.hat - KOmega.true,type="F")
diag(KOmega.hat)=NA
diag(KOmega.true)=NA
tpr.kro = length(intersect(which(KOmega.hat !=0 ), which(KOmega.true !=0))) / length(which(KOmega.true !=0))
tnr.kro = length(intersect(which(KOmega.hat ==0 ), which(KOmega.true ==0))) / length(which(KOmega.true ==0))
}
Out = list()
Out$error.kro = error.kro
Out$tpr.kro = tpr.kro
Out$tnr.kro = tnr.kro
Out$av.error.f = mean(error.f)
Out$av.error.max = mean(error.max)
Out$av.tpr = mean(tpr)
Out$av.tnr = mean(tnr)
Out$error.f = error.f
Out$error.max = error.max
Out$tpr = tpr
Out$tnr = tnr
return(Out)
} |
library(forecastML)
library(dplyr)
test_that("create_skeleton correctly preserves lagged_df objects", {
data("data_seatbelts", package = "forecastML")
data_lagged <- create_lagged_df(data_seatbelts, "train", "direct", horizons = 1:2,
lookback = 1:3)
data_skeleton <- create_skeleton(data_lagged)
data_lagged_attr <- attributes(data_lagged)
data_skeleton_attr <- attributes(data_skeleton)[!names(attributes(data_skeleton)) %in% "skeleton"]
testthat::expect_identical(data_lagged_attr, data_skeleton_attr)
}) |
ssc_type <- c("Mean" = "mean", "Proportion" = "proportion")
ssc_alternative <- c("Two sided" = "two.sided", "Group 1 less than Group 2" = "less", "Group 1 greater than Group 2" = "greater")
ssc_args <- as.list(formals(sample_size_comp))
ssc_inputs <- reactive({
for (i in names(ssc_args))
ssc_args[[i]] <- input[[paste0("ssc_", i)]]
ssc_args
})
output$ui_sample_size_comp <- renderUI({
tagList(
wellPanel(
radioButtons(
inputId = "ssc_type", label = NULL, choices = ssc_type,
selected = state_init("ssc_type", "mean"), inline = TRUE
),
numericInput(
"ssc_n1", "Sample size (n1):", min = 1,
value = state_init("ssc_n1", NA), step = 1
),
numericInput(
"ssc_n2", "Sample size (n2):", min = 1,
value = state_init("ssc_n2", NA), step = 1
),
conditionalPanel(
condition = "input.ssc_type == 'mean'",
numericInput(
"ssc_delta", "Delta:",
value = state_init("ssc_delta", 2), step = 1
),
numericInput(
"ssc_sd", "Standard deviation:", min = 0,
value = state_init("ssc_sd", 10), step = 1
)
),
conditionalPanel(
condition = "input.ssc_type != 'mean'",
numericInput(
"ssc_p1", "Proportion 1 (p1):", min = 0,
max = 1, value = state_init("ssc_p1", .1), step = .05
),
numericInput(
"ssc_p2", "Proportion 2 (p2):", min = 0, max = 1,
value = state_init("ssc_p2", .15), step = .05
)
),
numericInput(
"ssc_conf_lev", "Confidence level:", min = 0, max = 1,
value = state_init("ssc_conf_lev", 0.95), step = .05
),
numericInput(
"ssc_power", "Power:", min = 0, max = 1,
value = state_init("ssc_power", 0.8), step = .05
),
selectInput(
inputId = "ssc_alternative", label = "Alternative hypothesis:",
choices = ssc_alternative,
selected = state_single("ssc_alternative", ssc_alternative, "two.sided")
),
checkboxInput("ssc_show_plot", "Show plot" , state_init("ssc_show_plot", FALSE))
),
help_and_report(
modal_title = "Sample size (compare)", fun_name = "sample_size_comp",
help_file = inclRmd(file.path(getOption("radiant.path.design"), "app/tools/help/sample_size_comp.Rmd"))
)
)
})
ssc_plot_width <- function() 650
ssc_plot_height <- function() 650
output$sample_size_comp <- renderUI({
register_print_output("summary_sample_size_comp", ".summary_sample_size_comp")
register_plot_output(
"plot_sample_size_comp", ".plot_sample_size_comp",
width_fun = "ssc_plot_width",
height_fun = "ssc_plot_height"
)
ssc_output_panels <- tagList(
tabPanel("Summary", verbatimTextOutput("summary_sample_size_comp")),
tabPanel(
"Plot",
conditionalPanel(
"input.ssc_show_plot == true",
download_link("dlp_ssc"),
plotOutput("plot_sample_size_comp", height = "100%")
)
)
)
stat_tab_panel(
menu = "Design > Sample",
tool = "Sample size (compare)",
data = NULL,
tool_ui = "ui_sample_size_comp",
output_panels = ssc_output_panels
)
})
.sample_size_comp <- reactive({
do.call(sample_size_comp, ssc_inputs())
})
.summary_sample_size_comp <- reactive({
if (is.null(input$ssc_type)) return(invisible())
summary(.sample_size_comp())
})
.plot_sample_size_comp <- reactive({
req(input$ssc_show_plot == TRUE)
plot(.sample_size_comp())
})
observeEvent(input$sample_size_comp_report, {
ssc <- ssc_inputs()
if (input$ssc_type == "mean") {
ssc$p1 <- ssc$p2 <- NULL
} else {
ssc$delta <- ssc$sd <- NULL
}
inp_out <- list("", "")
outputs <- "summary"
figs <- FALSE
if (isTRUE(input$ssc_show_plot)) {
inp_out[[2]] <- list(custom = FALSE)
outputs <- c("summary", "plot")
figs <- TRUE
}
update_report(
inp_main = clean_args(ssc, ssc_args),
fun_name = "sample_size_comp",
inp_out = inp_out,
outputs = outputs,
figs = figs,
fig.width = ssc_plot_width(),
fig.height = ssc_plot_height()
)
})
download_handler(
id = "dlp_ssc",
fun = download_handler_plot,
fn = function() paste0("sample_size_comp_", input$ssc_type),
type = "png",
caption = "Save sample size comparison plot",
plot = .plot_sample_size_comp,
width = ssc_plot_width,
height = ssc_plot_height
) |
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