Made chart for mining with halving yield.

Also added tabulate to display dataframes in Markdown.

econ-demo.py

@@ -1,6 +1,7 @@
 
 import pandas as pd
 import matplotlib.pyplot as plt
+import tabulate
 
 # it = 0
 
@@ -29,8 +30,8 @@ def calc_share_of_wealth(balances):
         shares.append(balance/total_money)
     return shares
 
-def ubi(balances, dividend=10, terms=3):
+def ubi(balances, rate=10, terms=3):
-    new_bal = []
+    dividend = rate
     balances_over_time = []
     current_balances = balances
     for i in range(0, terms):
@@ -43,6 +44,21 @@ def ubi(balances, dividend=10, terms=3):
         current_balances = new_bal
     return balances_over_time
 
+def mining(balances, halving_frequency=1, reward=10, terms=3):
+    balances_over_time = []
+    current_balances = balances
+    for i in range(0, terms):
+        new_bal = []
+        for balance in current_balances:
+            b = balance + reward
+            new_bal.append(b)
+        assert(len(new_bal) == len(balances))
+        if i % halving_frequency == 0:
+            reward = reward/2
+        balances_over_time.append(new_bal)
+        current_balances = new_bal
+    return balances_over_time
+
 def get_balances_over_time(names, balances, func, terms=4, param=0):
     if func is None:
         print("Need a function!")
@@ -61,7 +77,7 @@ def get_balances_over_time(names, balances, func, terms=4, param=0):
     else:
         frame_data = func(balances, terms=terms, rate=param)
     assert(len(frame_data) == terms)
-    for i in range(1, terms):
+    for i in range(0, terms):
         key = str(i)
         df[key] = frame_data[i]
     return df
@@ -145,7 +161,7 @@ def visualize_ubi(terms=25):
         y=0.33, color='violet', linestyle='--', label="0.33"
     )
 
-    plt.title("Change in Wealth Distribution Under UBI")
+    plt.title("Change in Wealth Distribution With a Constant Dividend")
     plt.legend()
     plt.xlabel("Terms")
     plt.ylabel("Share of Wealth")
@@ -162,7 +178,7 @@ def calc_total_supply(df):
         total.append(sum(data))
     return total
 
-def visualize_inflation(terms=50):
+def draw_specific_chart1(terms=50, linear_label="? (linear)"):
     participants = ["Alice", "Bob", "Charlie"]
     balances = [100,40,20]
 
@@ -182,9 +198,6 @@ def visualize_inflation(terms=50):
     assert(len(x) == len(total_supply_si))
 
     plt.style.use('dark_background')
-    plt.plot(
-        x, total_supply_ubi, color="cyan", label="? (linear)"
-    )
     plt.plot(
         x, total_supply_si, color="red", label="apy = 0.05"
     )
@@ -218,10 +231,151 @@ def visualize_inflation(terms=50):
     plt.close()
     # plt.show()
 
+def draw_specific_chart2(terms=50):
+    participants = ["Alice", "Bob", "Charlie"]
+    balances = [100,40,20]
 
+    df = get_balances_over_time (
+        participants, balances, ubi,
+        terms = terms
+    )
+    total_supply_ubi = calc_total_supply(df)
+
+    df_si = get_balances_over_time (
+        participants, balances, compounding_interest,
+        terms = terms
+    )
+    total_supply_si = calc_total_supply(df_si)
+    # + 1 because the initial frame is included this time
+    x = [i for i in range(1,terms+1)]
+    assert(len(x) == len(total_supply_si))
+
+    plt.style.use('dark_background')
+    plt.plot(
+        x, total_supply_ubi, color="cyan", label="dividend = 10"
+    )
+    plt.plot(
+        x,
+        calc_total_supply(get_balances_over_time(
+            participants, 
+            balances, 
+            ubi,
+            terms=terms,
+            param=5
+        )),
+        color="violet", label="dividend = 5"
+    )
+    plt.plot(
+        x,
+        calc_total_supply(get_balances_over_time(
+            participants, 
+            balances, 
+            ubi,
+            terms=terms,
+            param=15
+        )),
+        color="lightgreen", label="dividend = 15"
+    )
+    plt.plot(
+        x, total_supply_si, color="red", label="apy = 0.05"
+    )
+    plt.plot(
+        x,
+        calc_total_supply(get_balances_over_time(
+            participants, 
+            balances, 
+            compounding_interest,
+            terms=terms,
+            param=0.04
+        )),
+        color="orange", label="apy = 0.04"
+    )
+    plt.plot(
+        x,
+        calc_total_supply(get_balances_over_time(
+            participants, 
+            balances, 
+            compounding_interest,
+            terms=terms,
+            param=0.03
+        )),
+        color="yellow", label="apy = 0.03"
+    )
+    plt.title("Supply of Money Over Time")
+    plt.legend()
+    plt.xlabel("Terms")
+    plt.ylabel("Total Currency")
+    plt.savefig("inflation-ubi-vs-apy2.png")
+    plt.close()
+    # plt.show()
+
+def visualize_inflation(df, title="Inflation", filename="inflation.png"):
+    supply_over_time = calc_total_supply(df)
+    time_span = len(supply_over_time)
+    x = [i for i in range(time_span)]
+    assert(len(x) == time_span)
+
+    plt.style.use('dark_background')
+    plt.plot(
+        x, supply_over_time, color="red")
+    plt.title(title)
+    plt.legend()
+    plt.xlabel("Terms")
+    plt.ylabel("Total Currency")
+    plt.savefig(filename)
+    plt.close()
+
+def visualize_wealth_dist(df, title="Wealth Distribution", filename="wealth-distribution.png"):
+    participants = df["name"]
+    shares = []
+    for key, data in df.items():
+        if key == "name":
+            continue
+        if key == "initial":
+            continue
+        shares.append(calc_share_of_wealth(data))
+    sow = pd.DataFrame(shares)
+    # print(sow.keys())
+    terms = sow.shape[0] + 1
+    x = [i for i in range(1,terms)]
+    print(len(x))
+    print(len(shares))
+    assert(len(x) == len(shares))
+
+    plt.style.use('dark_background')
+    plt.plot(
+        x, sow[0], color="red", label=participants[0]
+    )
+    plt.plot(
+        x, sow[1], color="lightgreen", label=participants[1]
+    )
+    plt.plot(
+        x, sow[2], color="cyan", label=participants[2]
+    )
+    plt.axhline(
+        y=0.33, color='violet', linestyle='--', label="0.33"
+    )
+
+    plt.title(title)
+    plt.legend()
+    plt.xlabel("Terms")
+    plt.ylabel("Share of Wealth")
+    plt.savefig(filename)
+    plt.close()
 
 if __name__ == "__main__":
 
     illustrate_share_of_wealth()
     visualize_ubi(terms=50)
-    visualize_inflation(terms=75)
+    draw_specific_chart1(terms=75)
+    draw_specific_chart1(terms=75, linear_label="dividend = 10")
+    draw_specific_chart2(terms=75)
+
+    participants = ["Alice", "Bob", "Charlie"]
+    balances = [100,40,20]
+    m = get_balances_over_time(participants, balances, mining, terms=10)
+    print(m)
+    print(m.shape[1])
+    visualize_inflation(m, title="Inflation Given block_reward = 10 and Halving Every Term", filename="inflation-pow.png")
+    # print(m.iloc[:,:5].to_markdown())
+    visualize_wealth_dist(m, filename="wealth-distribution-pow.png")