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Merged
nllong merged 1 commit into from
Apr 27, 2025
Merged

Run R auto style fixes #10

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196 changes: 106 additions & 90 deletions bsyncr_server/lib/bsync_runner.r
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Original file line number Diff line number Diff line change
Expand Up @@ -18,118 +18,134 @@ run_analysis <- function(bsync_filepath, model_type) {

baseline_xpath <- sprintf("//auc:Scenario[@ID = '%s']", baseline_scenario_id)
sc_baseline <- xml2::xml_find_first(bsync_doc, baseline_xpath)
not_used <- sc_baseline %>% bsyncr::bs_stub_derived_model(dm_id = "DerivedModel-bsyncr",
dm_period = "Baseline")
not_used <- sc_baseline %>% bsyncr::bs_stub_derived_model(
dm_id = "DerivedModel-bsyncr",
dm_period = "Baseline"
)

b_df <- bsyncr::bs_parse_nmecr_df(bsync_doc, insert_weather_data=TRUE)
b_df <- bsyncr::bs_parse_nmecr_df(bsync_doc, insert_weather_data = TRUE)

if (model_type == "SLR") {
model <- nmecr::model_with_SLR(b_df,
nmecr::assign_model_inputs(regression_type = "SLR"))
model <- nmecr::model_with_SLR(
b_df,
nmecr::assign_model_inputs(regression_type = "SLR")
)
} else if (model_type == "3PC") {
model <- nmecr::model_with_CP(b_df,
nmecr::assign_model_inputs(regression_type = "3PC"))
model <- nmecr::model_with_CP(
b_df,
nmecr::assign_model_inputs(regression_type = "3PC")
)
} else if (model_type == "3PH") {
model <- nmecr::model_with_CP(b_df,
nmecr::assign_model_inputs(regression_type = "3PH"))
model <- nmecr::model_with_CP(
b_df,
nmecr::assign_model_inputs(regression_type = "3PH")
)
} else if (model_type == "4P") {
model <- nmecr::model_with_CP(b_df,
nmecr::assign_model_inputs(regression_type = "4P"))
model <- nmecr::model_with_CP(
b_df,
nmecr::assign_model_inputs(regression_type = "4P")
)
} else {
stop('Invalid model_type')
stop("Invalid model_type")
}

# add model to bsync tree
bs_gen_dm_nmecr(nmecr_baseline_model = model,
x = bsync_doc)
bs_gen_dm_nmecr(
nmecr_baseline_model = model,
x = bsync_doc
)

return(list("bsync_doc"=bsync_doc, "model"=model))
return(list("bsync_doc" = bsync_doc, "model" = model))
}

# setup
args <- commandArgs(trailingOnly=TRUE)
args <- commandArgs(trailingOnly = TRUE)
if (length(args) != 3) {
print('USAGE:')
print('Rscript bsync_runner.r bsync_input model_type output_directory')
print(' bsync_input: path to input file')
print(' model_type: type of model to fit')
print(' output_directory: directory to output files')
print("USAGE:")
print("Rscript bsync_runner.r bsync_input model_type output_directory")
print(" bsync_input: path to input file")
print(" model_type: type of model to fit")
print(" output_directory: directory to output files")
stop("Invalid arguments to script. See usage")
}
bsync_filepath <- args[1]
model_type <- args[2]
output_dir <- args[3]
output_xml <- paste(output_dir, "result.xml", sep="/")
output_plot <- paste(output_dir, "plot.png", sep="/")
err_filename <- paste(output_dir, "error.json", sep="/")
output_xml <- paste(output_dir, "result.xml", sep = "/")
output_plot <- paste(output_dir, "plot.png", sep = "/")
err_filename <- paste(output_dir, "error.json", sep = "/")

NOAA_TOKEN <- Sys.getenv('NOAA_TOKEN')
NOAA_TOKEN <- Sys.getenv("NOAA_TOKEN")
if (NOAA_TOKEN == "") {
stop("Missing NOAA token env var: NOAA_TOKEN")
}
options(noaakey=NOAA_TOKEN)

tryCatch({
# run analysis
analysis_result <- run_analysis(bsync_filepath, model_type)
bsync_doc <- analysis_result$bsync_doc
model <- analysis_result$model

# save the updated bsync doc
xml2::write_xml(bsync_doc, output_xml)

# save the plot
model_df <- model$training_data %>%
tidyr::gather(key = "variable", value = "value", c("eload", "model_fit"))

if (model$model_input_options$regression_type != "SLR") {
# Add a data point for the derived change point to make sure the line plot looks correct
temp_change_point <- abs(model$model$psi[2]) # this is the estimated temperature for the change point - taking abs b/c it is incorrectly negative for some models
predictions <- calculate_model_predictions(
training_data=model$training_data,
prediction_data=as.data.frame(list(time=c(2019-01-01), temp=c(temp_change_point))),
modeled_object=model
)
load_change_point <- predictions$predictions[1]
model_df <- model_df %>% add_row(
temp=temp_change_point,
variable="model_fit",
value=load_change_point)
options(noaakey = NOAA_TOKEN)

tryCatch(
{
# run analysis
analysis_result <- run_analysis(bsync_filepath, model_type)
bsync_doc <- analysis_result$bsync_doc
model <- analysis_result$model

# save the updated bsync doc
xml2::write_xml(bsync_doc, output_xml)

# save the plot
model_df <- model$training_data %>%
tidyr::gather(key = "variable", value = "value", c("eload", "model_fit"))

if (model$model_input_options$regression_type != "SLR") {
# Add a data point for the derived change point to make sure the line plot looks correct
temp_change_point <- abs(model$model$psi[2]) # this is the estimated temperature for the change point - taking abs b/c it is incorrectly negative for some models
predictions <- calculate_model_predictions(
training_data = model$training_data,
prediction_data = as.data.frame(list(time = c(2019 - 01 - 01), temp = c(temp_change_point))),
modeled_object = model
)
load_change_point <- predictions$predictions[1]
model_df <- model_df %>% add_row(
temp = temp_change_point,
variable = "model_fit",
value = load_change_point
)
}

# display the data
print(model_df)

if (model$model_input_options$regression_type == "SLR") {
# add in the linear regression line from the model results, need to
# confirm, but it looks like model is in BTU and °C
intercept <- model$model$coefficients[["(Intercept)"]] / 3.41214 # BTU to kwh
slope <- model$model$coefficients[["temp"]] * 9 / 5 # °C to °F
ggplot2::ggplot(model_df, aes(x = temp, y = value)) +
geom_point(aes(color = variable), data = model_df[model_df$variable == "eload", ]) +
geom_line(aes(color = variable), data = model_df[model_df$variable == "model_fit", ]) +
geom_abline(intercept = intercept, slope = slope, color = "red", linetype = "dashed") +
xlab("Temperature") +
scale_y_continuous(name = "Energy Data & Model Fit (kWh)", labels = scales::comma) +
theme_minimal() +
theme(legend.position = "bottom") +
theme(legend.title = element_blank())
} else {
ggplot2::ggplot(model_df, aes(x = temp, y = value)) +
geom_point(aes(color = variable), data = model_df[model_df$variable == "eload", ]) +
geom_line(aes(color = variable), data = model_df[model_df$variable == "model_fit", ]) +
xlab("Temperature") +
scale_y_continuous(name = "Energy Data & Model Fit (kWh)", labels = scales::comma) +
theme_minimal() +
theme(legend.position = "bottom") +
theme(legend.title = element_blank())
}


ggsave(output_plot)
},
error = function(e) {
print(e)
err <- list(message = e$message)
write(rjson::toJSON(err), err_filename)
quit(status = 1)
}

# display the data
print(model_df)

if (model$model_input_options$regression_type == "SLR") {
# add in the linear regression line from the model results, need to
# confirm, but it looks like model is in BTU and °C
intercept = model$model$coefficients[["(Intercept)"]] / 3.41214 # BTU to kwh
slope = model$model$coefficients[["temp"]] * 9/5 # °C to °F
ggplot2::ggplot(model_df, aes(x = temp, y = value)) +
geom_point(aes(color = variable), data=model_df[model_df$variable == "eload",]) +
geom_line(aes(color = variable), data=model_df[model_df$variable == "model_fit",]) +
geom_abline(intercept = intercept, slope = slope, color = "red", linetype = "dashed") +
xlab("Temperature") +
scale_y_continuous(name = "Energy Data & Model Fit (kWh)", labels = scales::comma) +
theme_minimal() +
theme(legend.position = "bottom") +
theme(legend.title = element_blank())
} else {
ggplot2::ggplot(model_df, aes(x = temp, y = value)) +
geom_point(aes(color = variable), data=model_df[model_df$variable == "eload",]) +
geom_line(aes(color = variable), data=model_df[model_df$variable == "model_fit",]) +
xlab("Temperature") +
scale_y_continuous(name = "Energy Data & Model Fit (kWh)", labels = scales::comma) +
theme_minimal() +
theme(legend.position = "bottom") +
theme(legend.title = element_blank())
}


ggsave(output_plot)
}, error = function(e) {
print(e)
err <- list(message=e$message)
write(rjson::toJSON(err), err_filename)
quit(status=1)
})
)
10 changes: 5 additions & 5 deletions install_r_packages.R
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@

# Install required packages if not already installed
required_packages <- c(
"remotes", "crayon", "dplyr", "tidyr", "crul", "xml2", "testthat", "anytime","lubridate", "segmented", "xts", "zoo", "ggplot2", "scales", "XML", "rappdirs", "gridExtra", "isdparser", "geonames", "hoardr", "data.table"
"remotes", "crayon", "dplyr", "tidyr", "crul", "xml2", "testthat", "anytime", "lubridate", "segmented", "xts", "zoo", "ggplot2", "scales", "XML", "rappdirs", "gridExtra", "isdparser", "geonames", "hoardr", "data.table"
)

cat("Checking and installing required packages...\n")
Expand All @@ -14,15 +14,15 @@ for (pkg in required_packages) {
}
}

library('remotes')
library("remotes")
# RNOAA for weather data
remotes::install_github('ropensci/rnoaa@v1.4.0', upgrade='never')
remotes::install_github("ropensci/rnoaa@v1.4.0", upgrade = "never")

# NMECR from kW Engineering
remotes::install_github('kW-Labs/nmecr@v1.0.17', upgrade='never')
remotes::install_github("kW-Labs/nmecr@v1.0.17", upgrade = "never")

# BSync package for reading/writing BuildingSync files for NMECR
remotes::install_github('BuildingSync/bsyncr@v0.2.0', upgrade='never')
remotes::install_github("BuildingSync/bsyncr@v0.2.0", upgrade = "never")

library(rnoaa)
rnoaa::ghcnd_stations()