wynnbuilder-forked-for-changes/testing/optimization/analyze_items.py

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import json
import numpy as np
import matplotlib.pyplot as plt
def max_id(item, id_name, invert=False):
"""
Calculate the "max roll" for a given ID.
Parameters
Name type desc
----------------------------------------------------------
item json Item json data
id_name string name of the ID to get
invert bool Whether to "invert" (raw cost and %cost have funny
0.7-1.3 positive roll and 0.3-1.3 negative roll)
Return:
val: float -- max roll id value.
"""
id_val = item.get(id_name, 0)
if id_val == 0: # if the ID isn't present, its just going to be zero
return 0
if item.get('fixID', False):
# If the item is a fixed roll item, don't roll the ID.
return id_val
# roll the ID. Negative roll (and invert) max roll is 0.7; positive max is 1.3.
if bool(id_val < 0) != bool(invert): # logical XOR
val = round(id_val * 0.7)
else: #if bool(id_val > 0) != bool(invert):
val = round(id_val * 1.3)
if val == 0: # if we rounded to zero, then restore the id as sign(base_val).
val = id_val / abs(id_val)
return val
def mv(item, base_costs):
"""
Compute mana value for an item.
Takes a maximum mana value
- assuming 1 melee value (3/3 mana steal = 1 mana value)
- assuming spells 1, 3, and 4 are cycle spells.
Ignores spell 2 for spell cost purposes.
Parameters
Name type desc
----------------------------------------------------------
item json Item json data
base_costs list[float] base spell cost [spell1, spell2, spell3, spell4]
Return:
val: float -- mana value.
"""
cost_reductions = sorted([
max_id(item, 'spRaw1', True) + base_costs[0]*max_id(item, 'spPct1', True)/100,
#max_id(item, 'spRaw2', True) + base_costs[1]*max_id(item, 'spPct2', True)/100,
max_id(item, 'spRaw3', True) + base_costs[2]*max_id(item, 'spPct3', True)/100,
max_id(item, 'spRaw4', True) + base_costs[3]*max_id(item, 'spPct4', True)/100,
])
cost_mv = -sum(cost_reductions[:2])
return (
max_id(item, 'ms')/3
+ max_id(item, 'mr')/5
+ cost_mv
)
###########################
# constants for damage calc.
elements = 'rnetwfa'
raw_ids = ['sdRaw'] + [x+'SdRaw' for x in elements] + [x+'DamRaw' for x in elements]
# these %boosts apply to all damages.
percent_all_ids = ['sdPct', 'rSdPct']
# this one is a list of lists.
# the mini lists are sub-sums, the big list gets max'd over (elemental damage works like this.)
percent_max_id_groups = list(zip([x+'DamPct' for x in 'etwfa'] + [x+'SdPct' for x in 'etwfa'])) # exclude neutral lel
###########################
def damage(item, weapon_base):
"""
Compute effective damage bonus.
Note that this assumes the weapon aligns with whatever bonus this item is giving.
Parameters
Name type desc
----------------------------------------------------------
item json Item json data
weapon_base float weapon base dps
Return:
val: float -- raw damage bonus given (approximate) for the weapon.
"""
total = sum(max_id(item, x) for x in raw_ids)
total += weapon_base * sum(max_id(item, x) for x in percent_all_ids) / 100
total += weapon_base * max(sum(max_id(item, y) for y in x) for x in percent_max_id_groups) / 100
return total
#################################
# NOTE: Edit these parameters! LOL i was lazy to make a CLI
level_threshold = 80
weapon_base = 700
base_costs = [35, 20, 35, 35]
item_type = 'leggings'
# TODO: Changeme to point to a copy of wynnbuilder's compress.json file!
items = json.load(open("../../compress.json"))['items']
#################################
# collect data from items.
points = []
names = dict()
for item in items:
if item['type'] == item_type and item['lvl'] > level_threshold:
# Edit me to see other comparisons!
#point = (mv(item, base_costs), damage(item, weapon_base))
point = (max_id(item, 'spd'), item.get('hp', 0) + max_id(item, 'hpBonus'))
points.append(point)
# just some shenanigans to aggregate text that happens to fall on the same point.
if point in names:
names[point] += '\n'+item.get('displayName', item['name'])
else:
names[point] = item.get('displayName', item['name'])
points = np.array(points)
# plot points.
plt.figure()
plt.scatter(points[:, 0], points[:, 1])
# and add annotations.
for point, txt in names.items():
plt.annotate(txt, point)
plt.show()