Start adding overhead allocation load from file
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@@ -4,6 +4,8 @@ use itertools::Itertools;
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use nalgebra::{DMatrix, Dynamic, LU};
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use serde::Deserialize;
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use crate::CsvCost;
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#[derive(Debug, PartialEq, Eq)]
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pub enum DepartmentType {
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Operating,
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@@ -22,6 +24,11 @@ pub struct CsvAllocationStatistic {
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account_ranges: String,
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}
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pub struct AllocationStatisticAccountRange {
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start: String,
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end: String,
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}
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#[derive(Deserialize)]
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pub struct CsvAccount {
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#[serde(rename = "Code")]
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@@ -96,30 +103,138 @@ where
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CostCentre: Read,
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Output: std::io::Write,
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{
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let mut accounts_reader = accounts;
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let all_accounts_sorted: Result<Vec<CsvAccount>, csv::Error> =
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accounts_reader.deserialize::<CsvAccount>().collect();
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let mut accounts_sorted = all_accounts_sorted?;
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let mut lines_reader = lines;
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let lines = lines_reader
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.deserialize()
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.collect::<Result<Vec<CsvCost>, csv::Error>>()?;
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// Sort the accounts, as allocation statistics use account ranges
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if use_numeric_accounts {
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accounts_sorted.sort_by(|a, b| {
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a.code
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.parse::<i32>()
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.unwrap()
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.cmp(&b.code.parse::<i32>().unwrap())
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})
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let all_accounts_sorted: Vec<String> = if use_numeric_accounts {
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lines
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.iter()
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.map(|line| line.account.clone().parse::<i32>().unwrap())
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.unique()
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.sorted()
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.map(|account| account.to_string())
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.collect()
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} else {
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accounts_sorted.sort_by(|a, b| a.code.cmp(&b.code))
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lines
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.iter()
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.map(|line| line.account.clone())
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.unique()
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.sorted()
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.collect()
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};
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// Build out the the list of allocation rules from areas/allocation statistics (similar to ppm building 'cost drivers')
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let mut allocation_statistics_reader = allocation_statistics;
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let allocation_statistics = allocation_statistics_reader
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.deserialize()
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.collect::<Result<Vec<CsvAllocationStatistic>, csv::Error>>()?;
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// For each allocation statistic, sum the cost centres across accounts in the allocaiton statistic range
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let flat_department_costs: Vec<(String, String, f64)> = allocation_statistics
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.iter()
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.map(|allocation_statistic| {
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(
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allocation_statistic,
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split_allocation_statistic_range(allocation_statistic),
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)
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})
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.flat_map(|allocation_statistic| {
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let mut total_department_costs: HashMap<String, f64> = HashMap::new();
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let cc_costs = lines
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.iter()
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.filter(|line| {
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let line_index = all_accounts_sorted
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.iter()
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.position(|account| account == &line.account);
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allocation_statistic
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.1
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.iter()
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.find(|range| {
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let start_index = all_accounts_sorted
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.iter()
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.position(|account| account == range.0);
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let end_index = all_accounts_sorted
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.iter()
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.position(|account| account == range.1);
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line_index >= start_index && line_index <= end_index
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})
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.is_some()
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})
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.for_each(|line| {
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*total_department_costs.entry(line.department).or_insert(0.) += line.value;
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});
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total_department_costs
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.iter()
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.map(|entry| (entry.0, allocation_statistic.0.name, entry.1))
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.collect()
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})
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.collect();
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// TODO: If ignore negative is used, then set values < 0 to 0
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let mut rollups: HashMap<String, HashMap<String, Vec<String>>> = HashMap::new();
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let mut cost_centres = cost_centres;
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for cost_centre in cost_centres.records() {
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let cost_centre = cost_centre?;
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// Extract rollups, used later with the areas... I could do a map of rollups -> cc's, or just a list of rollups on each cc struct
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// I think map of rollup -> cc would be better, although this would need to be for each rollup slot... so a map of maps?
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}
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let mut areas = areas;
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let area_name_index = areas
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.headers()?
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.iter()
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.position(|header| header == "Name")
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.unwrap();
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let allocation_statistic_index = areas
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.headers()?
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.iter()
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.position(|header| header == "AllocationStatistic")
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.unwrap();
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// For each overhead area, get the cost centres in the area, and get all cost centres
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// that fit the limit to criteria for the area (skip any cases of overhead cc = other cc).
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// Then get the totals for the other ccs, by looking in the flat_department_costs, where the
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// allocation statistic matches the allocation statistic for this area
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for area in areas.records() {
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let area = area?;
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// Check for limitTos, should probably somehow build out the list of allocation rules from this point.
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let area_name = area.get(area_name_index).unwrap();
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let allocation_statistic = area.get(allocation_statistic_index).unwrap();
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}
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// Finally, for each cc match total produced previously, sum the overhead cc where overhead cc appears in other cc, then
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// divide the other cc by this summed amount
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// do reciprocal allocation (only for variable portion of accounts), for each account
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// Copy across fixed stuff (if necessary, not sure it is)
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// Copy across fixed stuff (if necessary, not sure it is)... don't think it's necessary, initial totals handle this
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Ok(())
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}
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fn split_allocation_statistic_range(
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allocation_statistic: &CsvAllocationStatistic,
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) -> Vec<AllocationStatisticAccountRange> {
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// TODO: This split needs to be more comprehensive so that we don't split between quotes
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let split = allocation_statistic.account_ranges.split(";");
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split
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.map(|split| {
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let range_split = split.split('-').collect::<Vec<_>>();
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if range_split.len() == 1 {
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AllocationStatisticAccountRange {
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start: range_split[0].to_owned(),
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end: range_split[0].to_owned(),
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}
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} else {
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AllocationStatisticAccountRange {
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start: range_split[0].to_owned(),
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end: range_split[1].to_owned(),
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}
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}
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})
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.collect()
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}
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// Perform the reciprocal allocation (matrix) method to allocate servicing departments (indirect) costs
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// to functional departments. Basically just a matrix solve, uses regression (moore-penrose pseudoinverse) when
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// matrix is singular
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