dnd4.py

import pandas as pd
import matplotlib.pyplot as plt

import os

df = pd.read_csv("../../scraps/dnd/dset.csv", parse_dates=["month of departure"])
df = df.drop(["Unnamed: 0"], axis=1)
df = df.replace("100%+", 100)
df["damage taken"] = df["damage taken"].replace(r'\D+', "", regex=True)
df["damage taken"] = df["damage taken"].astype(int) / 100


enc = df.groupby("encounter").mean().join(
    df.groupby("encounter").std(), 
    rsuffix="_"
).sort_values(ascending=False, by="damage taken") \
    .reset_index() 
enc.columns = ['encounter', "mu", "std"]
enc.plot.bar(x='encounter', y='mu')

plt.errorbar(enc.encounter, enc["mu"], yerr=1.96*enc["std"], color="red", linestyle='None')
plt.plot(enc.encounter, [1]*len(enc.encounter), linestyle="--")



enc["safety"] = (1 - enc["mu"]) / enc["std"]
enc["risk"] = 1/enc["safety"]
risk = enc[["encounter", "risk"]].set_index("encounter")

cnt = df.groupby("encounter").count()[["damage taken"]]
cnt["prob"] = cnt["damage taken"] / sum(cnt["damage taken"])
cnt = cnt[["prob"]].sort_values(by="prob")
j = cnt.join(risk)

# Doctor it 
j.loc["kraken"]["prob"] += 0.05
j.loc["nessie"]["prob"] += 0.05
j["ev"] = j["prob"] * j["risk"]
j["exp_ev"] = j["risk"] / j[~j["risk"].isna()]["risk"].sum()
#j["exp_ev"] = -np.log(j["prob"]) * j["risk"]


ev_gold = 20
evs = j.sort_values(by="ev", ascending=False)[["ev"]].iloc[:4]
evs["%"] = evs["ev"] / sum(evs["ev"])
evs["spend"] = round(evs["%"] * ev_gold)

exp_gold = 80
exps = j.sort_values(by="exp_ev", ascending=False)[["exp_ev"]].iloc[:5]
exps["%"] = exps["exp_ev"] / sum(exps["exp_ev"])
exps["spend"] = round(exps["%"] * exp_gold)

display(evs)
#	         ev	    %	          spend

# kraken	0.049114	0.248997	5.0
# merpeople	0.043793	0.222019	4.0
# pirates	0.043164	0.218834	4.0
# nessie	0.041141	0.208578	4.0
# crabmonsters	0.020035	0.101572	2.0

exps
#        exp_ev    	%	        spend
# kraken	0.281701	0.346746	28.0
# crabmonsters	0.167990	0.206779	17.0
# nessie	0.163477	0.201223	16.0
# merpeople	0.133342	0.164131	13.0
# demon whale	0.065903	0.081120	6.0


k = 39 # including demon whale
c = 20
m = 17
n = 24  # including pirates

print("oar benefit:", k * 0.02/1)
print("carpenter benefit:", c * 0.5/20)
print("tribute benefit:", m * 100/45)
print("cannon benefit:", n * 0.1/10)


["Oar", "Krakens and Demon Whales", 1, 0.02] # max 20 = 0.02 pg
["Carpenters", "Crabmonster", 20, 0.5] # = 0.025 pg
["Tribute", "Merpeople", 45, 100] # 2.2 pg
["Cannon", "Nessie and Pirate", 10, 0.1] # max 3 = 0.01 pg


# In[76]:


df.groupby("direction").mean().join(
    df.groupby("direction").std(), 
    rsuffix="_"
).sort_values(ascending=False, by="direction")

df.groupby(["encounter", "direction"]).mean()


# ## shark repellent

# In[91]:


time = df.groupby(["encounter", "month of departure"]).mean().reset_index()

plt.figure(figsize=(20,10))
for monster in enc.encounter.unique():
    timem = time[time["encounter"] == monster]
    plt.plot(timem["month of departure"], timem["damage taken"], label=monster)


plt.legend()
plt.show()


# In[95]:


df["month"] = df["month of departure"].str[:2]
time = df.groupby(["encounter", "month"]).mean().reset_index()

plt.figure(figsize=(20,10))
for monster in enc.encounter.unique():
    timem = time[time["encounter"] == monster]
    plt.plot(timem["month"], timem["damage taken"], label=monster)


plt.legend()
plt.show()