Currently you can use engine following way:
from eos import *
from eos.item_filter import *
data_handler = JsonDataHandler('data_folder/phobos/') # Folder with Phobos data dump
cache_handler = JsonCacheHandler('data_folder/cache/eos_tq.json.bz2')
SourceManager.add('tiamat', data_handler, cache_handler, make_default=True)
skill_groups = set(row['groupID'] for row in data_handler.get_evegroups() if row['categoryID'] == 16)
skills = set(row['typeID'] for row in data_handler.get_evetypes() if row['groupID'] in skill_groups)
fit = Fit()
fit.ship = Ship(32311) # Navy Typhoon
for skill_id in skills:
fit.skills.add(Skill(skill_id, level=5))
# 4x 800mm with hail
fit.modules.high.equip(ModuleHigh(2929, state=State.overload, charge=Charge(12779)))
fit.modules.high.equip(ModuleHigh(2929, state=State.overload, charge=Charge(12779)))
fit.modules.high.equip(ModuleHigh(2929, state=State.overload, charge=Charge(12779)))
fit.modules.high.equip(ModuleHigh(2929, state=State.overload, charge=Charge(12779)))
# 4x Torp launcher with nova rages
fit.modules.high.equip(ModuleHigh(2420, state=State.overload, charge=Charge(24519)))
fit.modules.high.equip(ModuleHigh(2420, state=State.overload, charge=Charge(24519)))
fit.modules.high.equip(ModuleHigh(2420, state=State.overload, charge=Charge(24519)))
fit.modules.high.equip(ModuleHigh(2420, state=State.overload, charge=Charge(24519)))
fit.modules.mid.equip(ModuleMid(5945, state=State.overload)) # Top named 100MN MWD
fit.modules.mid.equip(ModuleMid(4833, state=State.active, charge=Charge(32014))) # Named med cap injector with 800
fit.modules.mid.equip(ModuleMid(9622, state=State.active)) # Named EM hardener
fit.modules.mid.equip(ModuleMid(5443, state=State.active)) # Best named scram
fit.modules.mid.equip(ModuleMid(2281, state=State.active)) # T2 invuln
fit.modules.low.equip(ModuleLow(2048, state=State.online)) # T2 DC
fit.modules.low.equip(ModuleLow(519, state=State.online)) # T2 gyrostab
fit.modules.low.equip(ModuleLow(519, state=State.online)) # T2 gyrostab
fit.modules.low.equip(ModuleLow(22291, state=State.online)) # T2 BCU
fit.modules.low.equip(ModuleLow(22291, state=State.online)) # T2 BCU
fit.modules.low.equip(ModuleLow(4405, state=State.online)) # T2 DDA
fit.modules.low.equip(ModuleLow(4405, state=State.online)) # T2 DDA
fit.rigs.add(Rig(26082)) # T1 therm rig
fit.rigs.add(Rig(26088)) # T1 extender
fit.rigs.add(Rig(26088)) # T1 extender
# 8x Ogre II
fit.drones.add(Drone(2446, state=State.active))
fit.drones.add(Drone(2446, state=State.active))
fit.drones.add(Drone(2446, state=State.active))
fit.drones.add(Drone(2446, state=State.active))
fit.drones.add(Drone(2446, state=State.active))
fit.drones.add(Drone(2446, state=State.offline))
fit.drones.add(Drone(2446, state=State.offline))
fit.drones.add(Drone(2446, state=State.offline))
fit.implants.add(Implant(13231)) # 3% torp dmg
fit.implants.add(Implant(10228)) # 3% shield capacity
fit.implants.add(Implant(24663)) # zor hyperlink
fit.implants.add(Implant(13244)) # 3% turret dmg
fit.implants.add(Implant(13219)) # 3% large projectile dmg
fit.boosters.add(Booster(28672)) # Synth crash
fit.boosters.add(Booster(28674)) # Synth drop
fit.validate()
Fit validation method currently raises exception if any fit check fails, its argument contains dictionary which explains what is wrong. If we make additional drone active, following data will be returned:
{<Drone(type_id=2446, state=3)>: {
<Restriction.drone_bandwidth: 5>: ResourceErrorData(total_use=150.0, output=125.0, holder_use=25.0),
<Restriction.launched_drone: 6>: SlotQuantityErrorData(slots_used=6, slots_max_allowed=5)},
...
}
Keys of dictionary are problematic holders (in this case, all in-space drones of ship), values are dictionaries too, which list problems with given module. Keys of this dictionary are restriction IDs (eos.Restriction object), with 5 being drone bandwidth restriction, and 6 being quantity of drones this fit can use; values contain detailed data about the problem.
Attributes of any item are accessible via dictionary-like objects like phoon.attributes, e.g.:
>>> fit.ship.attrs[37] # maxVelocity
1858.3066943807341
Stats of fit can be fetched using 'stats' access point. For example, few regular ones:
>>> fit.stats.agility_factor
15.70747757338698
>>> fit.stats.cpu.used
823.0
And few more advanced (total uniform EHP of fit, and shield EHP vs EM damage):
>>> fit.stats.get_ehp(DmgProfile(em=25, thermal=25, kinetic=25, explosive=25)).total
95189.27348943402
>>> fit.stats.get_ehp(DmgProfile(em=1, thermal=0, kinetic=0, explosive=0)).shield
50013.69083371911
DPS can be fetched with various parameters, for example, should it take reload into consideration or not:
>>> fit.stats.get_dps(reload=False).total
1913.5769753125805
>>> fit.stats.get_dps(reload=True).total
1866.5853444855636
Specific damage type is accessible too (in this case, hail deals some kinetic damage):
>>> fit.stats.get_dps(reload=False).kinetic
136.64914857525073
Get effective DPS against passed resistance profile:
>>> from eos.stats_container import ResistProfile
>>> fit.stats.get_dps(tgt_resists=ResistProfile(em=0.2, thermal=0.3, kinetic=0.4, explosive=0.5)).total
1060.4052373697928
Get dps using built-in filters:
>>> from eos.item_filter import turret_filter, missile_filter, drone_filter, sentry_drone_filter
>>> fit.stats.get_dps(turret_filter).total
637.6960266845035
>>> fit.stats.get_dps(missile_filter).total
826.1217743481901
>>> fit.stats.get_dps(drone_filter).total
449.7591742798868
You can compose your own filters or combine existing:
>>> fit.stats.get_dps(lambda h: turret_filter(h) or missile_filter(h)).total
1463.8178010326938
Not all stats are implemented yet, more to come soon.
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