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Made chart for mining with halving yield.

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Also added tabulate to display dataframes in Markdown.
This commit is contained in:
scuti 2025-10-04 21:56:23 -07:00
parent 13d7f183c0
commit 637aaf5aec
1 changed files with 163 additions and 9 deletions
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172
econ-demo.py
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@ -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):
new_bal = []
def ubi(balances, rate=10, terms=3):
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")