diff --git a/.vscode/settings.json b/.vscode/settings.json
index 7645586..fcb9100 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -1,6 +1,8 @@
 {
     "cmake.sourceDirectory": "${workspaceFolder}/.",
     "files.associations": {
+        "*.i": "swiglang",
+        "*.hpp": "cpp",
         "vector": "cpp",
         "algorithm": "cpp",
         "chrono": "cpp",
@@ -64,7 +66,13 @@
         "array": "cpp",
         "map": "cpp",
         "list": "cpp",
-        "sstream": "cpp"
+        "sstream": "cpp",
+        "__hash_table": "cpp",
+        "__node_handle": "cpp",
+        "__tree": "cpp",
+        "complex": "cpp",
+        "optional": "cpp",
+        "unordered_map": "cpp"
     },
     "C_Cpp.errorSquiggles": "Enabled"
 }
\ No newline at end of file
diff --git a/include/econ.hpp b/include/econ.hpp
index 1eaecdd..db3988c 100644
--- a/include/econ.hpp
+++ b/include/econ.hpp
@@ -3,8 +3,8 @@
 #include <random>
 #include <cmath>
 
-std::random_device econ_dev;
-std::mt19937 econ_rng(econ_dev());
+static std::random_device econ_dev;
+static std::mt19937 econ_rng(econ_dev());
 
 // ----------------------------------------------------------
 // -- Recipient Finders
@@ -16,7 +16,7 @@ std::mt19937 econ_rng(econ_dev());
  * @param balances Balance vec
  * @return int 
  */
-int find_participant_random(std::vector<int> &balances) {
+static int find_participant_random(std::vector<int> &balances) {
     // random spend ratio is too costly
     std::uniform_int_distribution<std::mt19937::result_type> dist_len(0,balances.size()-1); 
     return dist_len(econ_rng); 
@@ -29,7 +29,7 @@ int find_participant_random(std::vector<int> &balances) {
  * @param percent_cutoff Upper N% to decide among
  * @return int 
  */
-int find_participant_random_wealthy(std::vector<int> &balances, float percent_cutoff = 10) {
+static int find_participant_random_wealthy(std::vector<int> &balances, float percent_cutoff = 10) {
     // random spend ratio is too costly
     int cutoff = percent_cutoff / 100 * balances.size();
     std::uniform_int_distribution<std::mt19937::result_type> dist_len(0,balances.size()-1); 
@@ -48,7 +48,7 @@ int find_participant_random_wealthy(std::vector<int> &balances, float percent_cu
  * @param spend_ratio Proportion of savings they are willing 
  * to spend
  */
-void economic_decision_simple(std::vector<int> &balances, int id, float spend_ratio) {
+static void economic_decision_simple(std::vector<int> &balances, int id, float spend_ratio) {
     // assert(spend_ratio > 0 && spend_ratio <= 1);
     int total_bal = balances.at(id);
 
@@ -68,13 +68,13 @@ void economic_decision_simple(std::vector<int> &balances, int id, float spend_ra
  * @param max_spend_ratio max/peak spend ratio
  * @param bump how much to bump min spend amount from 0
  */
-void economic_random_age_sin(
+static void economic_random_age_sin(
     std::vector<int> &ages,
     std::vector<int> &balances,
     int max_age,
     int id,
     float max_spend_ratio,
-    int bump = 0.1
+    float bump = 0.1
 ) {
     int recipient_id = find_participant_random(balances);
     int spendable = round(
@@ -85,3 +85,8 @@ void economic_random_age_sin(
     balances.at(id) -= spendable;
     balances.at(recipient_id) += spendable;
 }
+
+
+static void economic_random_age_sin() {
+}
+
diff --git a/include/io.hpp b/include/io.hpp
index 7127bfa..4599251 100644
--- a/include/io.hpp
+++ b/include/io.hpp
@@ -4,7 +4,7 @@
 #include <iostream>
 #include <fstream>
 
-void dump_balances_csv(std::vector<int> balances, std::string name) {
+static void dump_balances_csv(std::vector<int> balances, std::string name) {
     std::string csv_data = "Id,Balance\n";
     for (int i = 0; i < balances.size(); i++) {
         csv_data += std::to_string(i+1)+ "," + 
@@ -16,7 +16,7 @@ void dump_balances_csv(std::vector<int> balances, std::string name) {
     file.close();
 }
 
-void dump_balances_and_ages_csv(std::vector<int> balances, std::vector<int> ages, std::string name) {
+static void dump_balances_and_ages_csv(std::vector<int> balances, std::vector<int> ages, std::string name) {
     assert(balances.size() == ages.size());
     std::string csv_data = "Id,Balance,Age\n";
     for (int i = 0; i < balances.size(); i++) {
diff --git a/include/population.hpp b/include/population.hpp
index d7d2cd6..cb9d3b3 100644
--- a/include/population.hpp
+++ b/include/population.hpp
@@ -10,7 +10,7 @@
  * @param total_duration 
  * @return int 
  */
-int logistic_population_func(int year, int N_o, int N_f, int k, int total_duration) {
+static int logistic_population_func(int year, int N_o, int N_f, int k, int total_duration) {
     // https://en.wikipedia.org//wiki/Logistic_function
     return N_o + (N_f - N_o) / 
         (1 + exp(-k * (year - total_duration/2)));
diff --git a/include/util.hpp b/include/util.hpp
index 3dbfe8c..c42dba8 100644
--- a/include/util.hpp
+++ b/include/util.hpp
@@ -2,10 +2,10 @@
 #include <vector>
 #include <random>
 
-std::random_device util_dev;
-std::mt19937 util_rng(util_dev());
+static std::random_device util_dev;
+static std::mt19937 util_rng(util_dev());
 
-int random_item(std::vector<int> items, bool get_id) {
+static int random_item(std::vector<int> items, bool get_id) {
     std::uniform_int_distribution<std::mt19937::result_type> dist_len(0,items.size()-1); 
     int id = dist_len(util_rng);
     return get_id ? id : items[id];
diff --git a/models-1.ipynb b/models-1.ipynb
index 011831f..c26a7e7 100644
--- a/models-1.ipynb
+++ b/models-1.ipynb
@@ -24,7 +24,17 @@
    "execution_count": 1,
    "id": "aacc2bc9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Finished with 5005 balances\n",
+      "Finished with 5005 balances\n",
+      "Done\n"
+     ]
+    }
+   ],
    "source": [
     "import sim\n",
     "import matplotlib.pyplot as plt\n",
@@ -55,7 +65,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -109,7 +119,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.11.0 64-bit",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/pop-simple.hpp b/pop-simple.hpp
index 549b4d3..9971a80 100644
--- a/pop-simple.hpp
+++ b/pop-simple.hpp
@@ -4,6 +4,7 @@
 #include <cstdlib>
 #include <algorithm>
 #include <string>
+#include <matplot/matplot.h>
 
 #include "util.hpp"
 #include "population.hpp"
@@ -19,7 +20,7 @@ enum EstateMode {
     INHERITANCE
 };
 
-PopSimpleResult pop_simple(
+static PopSimpleResult pop_simple(
     int duration,
     int max_age,
     int init_balance_each,