Add scanres vs locktime on active fit graph as experimental feature

eos/calc.py

@@ -18,6 +18,38 @@
 # =============================================================================
 
 
+import math
+
+
+# Just copy-paste penalization chain calculation code (with some modifications,
+# as multipliers arrive in different form) in here to not make actual attribute
+# calculations slower than they already are due to extra function calls
+def calculateMultiplier(multipliers):
+    """
+    multipliers: dictionary in format:
+    {stacking group name: [(mult, resist attr ID), (mult, resist attr ID)]}
+    """
+    val = 1
+    for penalizedMultipliers in multipliers.values():
+        # A quick explanation of how this works:
+        # 1: Bonuses and penalties are calculated seperately, so we'll have to filter each of them
+        l1 = [v[0] for v in penalizedMultipliers if v[0] > 1]
+        l2 = [v[0] for v in penalizedMultipliers if v[0] < 1]
+        # 2: The most significant bonuses take the smallest penalty,
+        # This means we'll have to sort
+        abssort = lambda _val: -abs(_val - 1)
+        l1.sort(key=abssort)
+        l2.sort(key=abssort)
+        # 3: The first module doesn't get penalized at all
+        # Any module after the first takes penalties according to:
+        # 1 + (multiplier - 1) * math.exp(- math.pow(i, 2) / 7.1289)
+        for l in (l1, l2):
+            for i in range(len(l)):
+                bonus = l[i]
+                val *= 1 + (bonus - 1) * math.exp(- i ** 2 / 7.1289)
+    return val
+
+
 def calculateRangeFactor(srcOptimalRange, srcFalloffRange, distance, restrictedRange=True):
     """Range strength/chance factor, applicable to guns, ewar, RRs, etc."""
     if distance is None:
@@ -31,3 +63,9 @@ def calculateRangeFactor(srcOptimalRange, srcFalloffRange, distance, restrictedR
         return 1
     else:
         return 0
+
+
+def calculateLockTime(srcScanRes, tgtSigRadius):
+    if not srcScanRes or not tgtSigRadius:
+        return None
+    return min(40000 / srcScanRes / math.asinh(tgtSigRadius) ** 2, 30 * 60)

eos/saveddata/fit.py

@@ -21,13 +21,14 @@ import datetime
 import time
 from copy import deepcopy
 from itertools import chain
-from math import asinh, log, sqrt
+from math import log, sqrt
 
 from logbook import Logger
 from sqlalchemy.orm import reconstructor, validates
 
 import eos.db
 from eos import capSim
+from eos.calc import calculateMultiplier, calculateLockTime
 from eos.const import CalcType, FitSystemSecurity, FittingHardpoint, FittingModuleState, FittingSlot, ImplantLocation
 from eos.effectHandlerHelpers import (
     HandledBoosterList, HandledDroneCargoList, HandledImplantList,
@@ -1529,9 +1530,7 @@ class Fit:
     def calculateLockTime(self, radius):
         scanRes = self.ship.getModifiedItemAttr("scanResolution")
         if scanRes is not None and scanRes > 0:
-            # Yes, this function returns time in seconds, not miliseconds.
-            # 40,000 is indeed the correct constant here.
-            return min(40000 / scanRes / asinh(radius) ** 2, 30 * 60)
+            return calculateLockTime(srcScanRes=scanRes, tgtSigRadius=radius)
         else:
             return self.ship.getModifiedItemAttr("scanSpeed") / 1000.0
 
@@ -1626,6 +1625,22 @@ class Fit:
                 if ability.active:
                     yield fighter, ability
 
+    def getDampMultScanRes(self):
+        damps = []
+        for mod in self.activeModulesIter():
+            for effectName in ('remoteSensorDampFalloff', 'structureModuleEffectRemoteSensorDampener'):
+                if effectName in mod.item.effects:
+                    damps.append((mod.getModifiedItemAttr('scanResolutionBonus'), 'default'))
+            if 'doomsdayAOEDamp' in mod.item.effects:
+                damps.append((mod.getModifiedItemAttr('scanResolutionBonus'), 'default'))
+        for drone in self.activeDronesIter():
+            if 'remoteSensorDampEntity' in drone.item.effects:
+                damps.extend(drone.amountActive * ((drone.getModifiedItemAttr('scanResolutionBonus'), 'default'),))
+        mults = {}
+        for strength, stackingGroup in damps:
+            mults.setdefault(stackingGroup, []).append((1 + strength / 100, None))
+        return calculateMultiplier(mults)
+
     def __deepcopy__(self, memo=None):
         fitCopy = Fit()
         # Character and owner are not copied