Orblike Dark Variant and FX Pipeline

Summary

Session focused on two major tasks: converting Orblike's visual style from SNKRX to Dark Variant (Bytepath-inspired), then building a pipeline to convert commercial FX pack animations into pure black-and-white spritesheets for in-game use.

Visual Style Conversion (SNKRX → Dark Variant):

Changed background from gray (#303030) checkerboard + gradient to solid near-black (#111111)
Removed shadow layer entirely (shader load, layer creation, compositing code)
Swapped SNKRX palette to Bytepath dark variant palette (muted colors: yellow=#ffb833, red=#d84654, blue=#52b3cb, etc.)
Updated projectile:die() to use dark variant white-first change_color pattern with larger hit circles (radius 8, duration 0.2)
User decided: keep entities filled (not Antitower outline style), keep springs as-is, no camera changes
User approved: "I like the look a lot better"

FX Conversion Pipeline (Orblike/tools/mov_to_spritesheet.py):

Built Python script to convert RTFX .mov animations → pure BW spritesheets
Pipeline: extract frames via ffmpeg → compute union bounding box → crop → scale → BW threshold → strip empty frames → stitch horizontal spritesheet
Key constraint: output must be pure 1-bit (255,255,255,255 or 0,0,0,0) — no gray, no semi-transparency
Iterative refinement of conversion pipeline based on user testing:
- Initial: BW threshold then NEAREST scale — lost thin lines (electricity effects)
- Fix: LANCZOS scale then BW threshold (spreads thin lines via anti-aliasing before thresholding)
- Final: two modes via --smooth flag — default (BW→NEAREST, sharp) and smooth (LANCZOS→BW, better for thin lines)
- Added --alpha-only flag for liquid effects where dark parts should remain white (any non-transparent pixel → white)
- Added --threshold parameter (default 0.5) for per-effect tuning — sparks 102 settled on 0.7

In-Game FX Browser:

Built fx_test_effect class displaying each animation at two sizes side by side (48px and 96px apparent)
Mouse 4/5 to cycle through effects, current effect name displayed on screen
Animation speed set to 0.06s per frame (slowed from initial 0.04)

Batch Conversions — Effect Categories Processed:

Electricity: 001, 013, 020, 025, 027, 036, 050, 052 (8 kept, removed 037/038)
Energy: 002, 005, 008, 011, 012, 014, 015, 017, 018, 019, 020, 022, 029, 032, 034, 049, 053, 057, 086, 087, 105, 109, 114, 115, 118, 119, 122, 125d (28 kept, removed 006/053/121), plus 129/130 at 480px screen size
- Energy 002, 008, 087, 105, 118 use --smooth mode
Explosions: 011, 013, 014, 019 — all removed after testing
Fire: 003, 005, 006, 007, 011, 012, 014, 015, 016, 018, 019, 027, 028, 029, 031, 032, 033, 036, 039, 040, 041, 055, 059, 062, 064, 065, 068, 070, 073, 074, 075, 076, 089, 092, 093, 105, 116, 119 (38 kept, removed 004/008/010/023/050/056/061/072/083/102), fire 003 uses --smooth, plus 048/079 at 480px screen size
Liquid: 003, 012, 015, 022, 026, 029, 033, 038, 059, 063, 064, 065, 066, 067, 068, 069, 070, 071, 072 (19 kept, removed 034/049/050) — all use --alpha-only
Smoke: 003, 005, 007, 008, 030, 036, 072, 073 (8 total)
Sparks: 019-025, 028, 032, 033, 037, 067, 069, 070, 072-075, 077, 080-082, 084, 091, 100, 102 (26 kept) — sparks 102 uses --smooth --threshold 0.7

Pipeline Comparison Testing:

User tested reverting all energy/fire effects back to default pipeline (BW→NEAREST) after electricity-optimized pipeline — concluded smooth mode only improved electricity and a few specific effects, most others were better with default
Threshold experimentation on sparks 102: tested t=0.1 through t=0.95, settled on t=0.7

Let's turn Orblike from SNKRX visual style into Dark Variant visual style. I think the most well developed dark variant game in the reference folder is Antitower. We have already an analysis of the differences in @Orblike\reference\snkrx_style_effects.md and a few other similar files in reference/. First read through everything and tell me your ideas of what likely needs to change.

Let me read through the reference materials and the current codebase.

ToolSearch

[
  {
    "type": "tool_reference",
    "tool_name": "Read"
  },
  {
    "type": "tool_reference",
    "tool_name": "Glob"
  },
  {
    "type": "tool_reference",
    "tool_name": "Agent"
  }
]

Let me read through all the relevant reference materials in parallel.

Glob (Orblike/reference/*/.md)

E:\a327ex\Orblike\reference\SNKRX\devlog.md
E:\a327ex\Orblike\reference\SNKRX\README.md
E:\a327ex\Orblike\reference\001-No-title\detailed_todo_dont_use.md
E:\a327ex\Orblike\reference\001-No-title\todo.md
E:\a327ex\Orblike\reference\239A5-P60B91D\main.md
E:\a327ex\Orblike\reference\Rekoyam Tsohg\todo.md
E:\a327ex\Orblike\reference\SNKRX_visual_effects_catalog.md
E:\a327ex\Orblike\reference\snkrx_style_effects.md
E:\a327ex\Orblike\reference\implementation_plan.md
E:\a327ex\Orblike\reference\area_modifiers_analysis.md

Read (E:\a327ex\Orblike\reference\snkrx_style_effects.md)

<tool_use_error>Cancelled: parallel tool call Read(E:\a327ex\Orblike\ORBS.md) errored</tool_use_error>

Glob (Orblike/reference/*/Antitower)

No files found

Glob (Orblike/reference/*/dark)

No files found

Glob (Orblike/reference//dark)

No files found

Glob (Orblike/reference//variant)

No files found

Read (E:\a327ex\Orblike\reference\snkrx_style_effects.md)

File content (31126 tokens) exceeds maximum allowed tokens (25000). Use offset and limit parameters to read specific portions of the file, or search for specific content instead of reading the whole file.

Let me search more broadly for Antitower and dark variant references, and start reading the large file in chunks.

Glob (/darkvariant*)

No files found

Read (E:\a327ex\Orblike\reference\snkrx_style_effects.md)

   300→for i = 1, an.random\int(4, 6)
   301→  an\add hit_particle @x, @y, {color: @color}
   302→
   303→-- Dark variant death burst (white -> entity color)
   304→for i = 1, 4
   305→  an\add (hit_particle @x, @y, {velocity: an.random\float(100, 300), duration: an.random\float(0.2, 0.5)})\change_color 0.6, @color
   306→
   307→-- Dark variant muzzle sparks (small, directional, white -> yellow)
   308→for i = 1, 2
   309→  hp = hit_particle mx, my, {angle: fire_angle + an.random\float(-math.pi/4, math.pi/4), velocity: an.random\float(40, 200), w: 6, h: 2}
   310→  hp\change_color 0.6, yellow
   311→  an\add hp
   312→```
   313→
   314→---
   315→
   316→### 2c. Hit Effect (Spritesheet)
   317→
   318→A one-shot spritesheet animation at an impact point. The classic "hit spark" visual.
   319→
   320→**Sources:**
   321→- 001-No-title: `effects.lua:107-117` (uses `hit1` spritesheet, 96x48 frames)
   322→- 004: `main.yue:381-392` (uses `hit1`, 96x48 frames)
   323→- 239A5: `main.yue:383-392` (uses `hit1`, 96x48 frames)
   324→- Rekoyam Tsohg: `effects.lua:1-12` (uses `hit1`, 96x48 frames, 0.03s frame delay)
   325→- SNKRX: Not used (relies entirely on HitCircle + HitParticle)
   326→- 112020, BUILDRX2: Have AnimationEffect class but primarily use circles + particles
   327→
   328→**Usage frequency:** This effect is much more prominent in the dark variants than in the original SNKRX-likes. In the dark variants, it appears at nearly every impact point alongside geometric effects. In SNKRX, it's absent entirely.
   329→
... [270 more lines]

Read (E:\a327ex\Orblike\reference\SNKRX_visual_effects_catalog.md)

     1→# SNKRX Visual Effects Catalog
     2→
     3→An exhaustive extraction of every visual effect pattern from the SNKRX codebase.
     4→
     5→---
     6→
     7→## 1. COLOR PALETTE & SYSTEM
     8→
     9→**File:** `shared.lua:1-22`
    10→
    11→The entire game uses a `ColorRamp` system. Each named color generates 21 shades (from -10 to +10) via lightening/darkening steps of 0.025.
    12→
    13→```lua
    14→bg = ColorRamp(Color'#303030', 0.025)
    15→fg = ColorRamp(Color'#dadada', 0.025)
    16→fg_alt = ColorRamp(Color'#b0a89f', 0.025)
    17→yellow = ColorRamp(Color'#facf00', 0.025)
    18→orange = ColorRamp(Color'#f07021', 0.025)
    19→blue = ColorRamp(Color'#019bd6', 0.025)
    20→green = ColorRamp(Color'#8bbf40', 0.025)
    21→red = ColorRamp(Color'#e91d39', 0.025)
    22→purple = ColorRamp(Color'#8e559e', 0.025)
    23→blue2 = ColorRamp(Color'#4778ba', 0.025)
    24→yellow2 = ColorRamp(Color'#f59f10', 0.025)
    25→```
    26→
    27→Access: `red[0]` = base color, `red[-5]` = darker, `red[5]` = lighter.
    28→
    29→Each color also generates transparent variants:
    30→```lua
... [270 more lines]

Read (E:\a327ex\Orblike\reference\snkrx_style_effects.md)

   600→        d = math.distance pos.x, pos.y, next_pos.x, next_pos.y
   601→        mx = (pos.x + next_pos.x)/2
   602→        my = (pos.y + next_pos.y)/2
   603→        effects\push mx, my, r
   604→        effects\rounded_rectangle -0.6*d, -pos.h/2, 1.2*d, pos.h, 2, @color!
   605→        effects\pop!
   606→```
   607→
   608→---
   609→
   610→### 2g. Blink-out
   611→
   612→Rapidly toggling visibility on/off before removal. Used both as a death animation and as invincibility feedback.
   613→
   614→**Sources:**
   615→- SNKRX: WallArrow, WallKnife, Area, DotArea, ForceArea, Sage projectile
   616→- 001-No-title: Area, Line, Single Target
   617→- 239A5: Enemy death (blink after tumble-to-stop)
   618→- Antitower: Player invincibility (20 toggles at 0.05s = 1s)
   619→- 004: Player soft/hard miss (4-6 toggles at 0.05s)
   620→
   621→**Two distinct uses:**
   622→
   623→**Use 1 -- Death blink (all projects):** Object blinks then disappears.
   624→
   625→```lua
   626→self.t:every_immediate(0.05, function() self.hidden = not self.hidden end, COUNT, function() self.dead = true end)
   627→```
   628→
   629→| Object | Blink count | Total blink time | Projects |
... [270 more lines]

Read (E:\a327ex\Orblike\reference\SNKRX_visual_effects_catalog.md)

   300→
   301→**Player hit flash:**
   302→```lua
   303→self.hfx:use('hit', 0.25, 200, 10)  -- taking damage
   304→self.hfx:use('hit', 0.5, 200, 10, 0.1) -- wall bounce (longer)
   305→```
   306→
   307→### 3d. CharacterHP Hit Flash
   308→
   309→**File:** `arena.lua:1188-1213`
   310→**Trigger:** HP changes on a unit
   311→```lua
   312→self.hfx:use('hit', 0.5)
   313→```
   314→The HP bar itself flashes white and bounces.
   315→
   316→---
   317→
   318→## 4. DEATH EFFECTS
   319→
   320→### 4a. Enemy (Seeker) Death
   321→
   322→**File:** `enemies.lua:534-538`
   323→**Trigger:** HP <= 0
   324→```lua
   325→for i = 1, random:int(4, 6) do HitParticle{group = main.current.effects, x = self.x, y = self.y, color = self.color} end
   326→HitCircle{group = main.current.effects, x = self.x, y = self.y, rs = 12}:scale_down(0.3):change_color(0.5, self.color)
   327→_G[random:table{'enemy_die1', 'enemy_die2'}]:play{pitch = random:float(0.9, 1.1), volume = 0.5}
   328→```
   329→- 4-6 colored particles
... [270 more lines]

Now let me read the rendering pipeline and other important sections, plus the actual game code.

Read (E:\a327ex\Orblike\reference\snkrx_style_effects.md)

   900→
   901→Triggered when any entity reaches 0 HP. The standard instant-death approach.
   902→
   903→**SNKRX-like standard recipe (all original projects):**
   904→
   905→1. 4-6 `HitParticle`s in entity color, random directions
   906→2. `HitCircle` rs=12 that scales down over 0.3s, changes from white to entity color at 50%
   907→3. Sound with pitch variation
   908→4. (Optional) Slow motion for important deaths (player, boss): `slow(0.25, 1)`
   909→
   910→**Dark variant recipe (Rekoyam Tsohg, Antitower):**
   911→
   912→1. 4 `HitParticle`s, random directions, start white, change to entity color at 60% of duration
   913→2. `HitCircle` rs=14 that scales down over 0.4s, changes white to entity color at 60% (larger and longer)
   914→3. Sound with pitch variation
   915→4. Additional block_death_effect ghost rectangle (Antitower only)
   916→
   917→The dark variant death is slightly larger, slightly longer, and uses the white-first color transition pattern. The visual reads as: white flash -> colored debris dispersing.
   918→
   919→**Anchor translation:**
   920→
   921→```yuescript
   922→-- SNKRX-like death
   923→die: =>
   924→  for i = 1, an.random\int(4, 6)
   925→    an\add hit_particle @x, @y, {color: @color}
   926→  an\add (hit_circle @x, @y, {radius: 12})\scale_down(0.3)\change_color(0.5, @color)
   927→  an\sound an.random\choice({'enemy_die1', 'enemy_die2'}), {pitch: an.random\float(0.9, 1.1), volume: 0.5}
   928→  @\kill!
   929→
... [370 more lines]

Read (E:\a327ex\Orblike\main.lua)

     1→require('anchor')({width = 480, height = 270, scale = 3})
     2→
     3→gw, gh = an.width, an.height
     4→
     5→volumes = {
     6→  enemy_die = 0.8,
     7→  enemy_hit = 0.8,
     8→  fireball_fire = 0.45,
     9→  fireball_hit = 0.45,
    10→  enemy_spawn = 0.59,
    11→  player_hit = 0.39,
    12→  caustic_arrow_fire = 0.5,
    13→  caustic_arrow_zone = 0.7,
    14→  dot_tick = 0.49,
    15→  shockwave = 0.5,
    16→}
    17→
    18→-- SNKRX palette
    19→bg_color = color(48, 48, 48)
    20→bg_off = color(46, 46, 46)
    21→fg_color = color(218, 218, 218)
    22→fg_alt = color(176, 168, 159)
    23→yellow = color(250, 207, 0)
    24→orange = color(240, 112, 33)
    25→blue = color(1, 155, 214)
    26→green = color(139, 191, 64)
    27→red = color(233, 29, 57)
    28→purple = color(142, 85, 158)
    29→blue2 = color(71, 120, 186)
    30→yellow2 = color(245, 159, 16)
... [170 more lines]

Read (E:\a327ex\Orblike\main.lua)

   200→
   201→    -- Create projectiles
   202→    for _, shot in ipairs(shots) do
   203→      an:add(projectile(shot.x, shot.y, {ability = ability_name, color = def.color, r = shot.r, mods = mods}))
   204→    end
   205→
   206→    if def.fire_sound then
   207→      sfx(def.fire_sound, volumes[def.fire_sound])
   208→    end
   209→
   210→    an.camera.shake:push(r, 3)
   211→    p1.spring:pull('shoot', 0.25)
   212→    p1.flashing = true
   213→    p1.timer:after(0.15, 'flash', function() p1.flashing = false end)
   214→    for _, shot in ipairs(shots) do
   215→      an:add(hit_circle(shot.x, shot.y, {radius = 7, duration = 0.2}))
   216→      for i = 1, 2 do
   217→        an:add(hit_particle(shot.x, shot.y, {
   218→          angle = shot.r + an.random:float(-math.pi/4, math.pi/4),
   219→          velocity = an.random:float(40, 200),
   220→          duration = an.random:float(0.3, 0.6),
   221→          w = an.random:choice({6, 8}),
   222→        }):change_color(0.6, def.color:clone()))
   223→      end
   224→    end
   225→  end)
   226→end
   227→
   228→function orb:update(dt)
   229→  self.x = p1.x + self.orbit_distance*math.cos(self.orbit_speed*an.time + self.orbit_offset)
... [270 more lines]

Read (E:\a327ex\Orblike\reference\snkrx_style_effects.md)

  1300→
  1301→---
  1302→
  1303→## 5. Entity Feedback
  1304→
  1305→### 5a. Movement Effects
  1306→
  1307→**Direction change spin (001-No-title, 112020):**
  1308→
  1309→When pressing left/right, the character's visual rotation tweens by +/-pi over 0.2s.
  1310→
  1311→```yuescript
  1312→if pressed_right
  1313→  @timer\tween 0.2, @, {visual_angle: @visual_angle + math.pi}, math.linear, nil, 'visual_r'
  1314→```
  1315→
  1316→**Direction change squash (001-No-title, 112020):**
  1317→
  1318→Two-phase scale animation: squash to 80% over 0.05s, return to 100% over 0.4-0.6s.
  1319→
  1320→**Stop bounce (001-No-title, 112020):**
  1321→
  1322→When the character stops, movement spring gets pulled 0.35.
  1323→
  1324→**Camera tilt (SNKRX, 112020):**
  1325→
  1326→Very subtle camera rotation: pi/256 ~ 0.7 degrees while moving, lerp rate 0.1.
  1327→
  1328→**Camera velocity drift (SNKRX, 112020):**
  1329→
... [370 more lines]

Let me now read the rendering pipeline section and the rest of main.lua to understand the current draw setup.

Read (E:\a327ex\Orblike\reference\snkrx_style_effects.md)

  1700→
  1701→Full-screen dark semi-transparent rectangle: `Color(0.1, 0.1, 0.1, 0.6)`.
  1702→
  1703→---
  1704→
  1705→## 9. Rendering Pipeline
  1706→
  1707→### Layer Order
  1708→
  1709→**SNKRX-like (5 layers with shadow):**
  1710→
  1711→1. **Background** -- bg fill, checkerboard, stars
  1712→2. **Shadow** -- offset copy of game layer, dark + transparent
  1713→3. **Game** -- entities, projectiles, areas
  1714→4. **Effects** -- HitCircles, HitParticles, spawn effects, trails
  1715→5. **UI** -- HP bars, selection brackets, text, buttons
  1716→
  1717→**Dark variant (4-5 layers, NO shadow):**
  1718→
  1719→1. **Back** -- bg fill (solid near-black), trail particles (Rekoyam Tsohg)
  1720→2. **Game** -- entities, projectiles
  1721→3. **Effects** -- HitCircles, HitParticles, spritesheet animations
  1722→4. **Front** -- wall outlines (Antitower), solid_rectangle outlines (239A5)
  1723→5. **UI** -- HP/ammo bars, numbers, text
  1724→
  1725→The critical difference: no shadow layer. Shadow shaders exist as files (`shadow.frag`) in all dark variant projects but are deliberately not loaded. The outline shader (`outline.frag`) similarly exists but is unused.
  1726→
  1727→### Shadow System (SNKRX-likes only)
  1728→
  1729→The entire game canvas is drawn through a shadow shader, then composited offset:
... [170 more lines]

Read (E:\a327ex\Orblike\main.lua)

   500→  {'dot_tick', 'assets/Click sound 37 + Body Fall 18'},
   501→  {'shockwave', 'assets/RPG3_FireMagic_CuteBomb'},
   502→}
   503→for _, s in ipairs(sound_files) do
   504→  for _, mode in ipairs({'_lightmed'}) do
   505→    an:sound(s[1] .. mode, s[2] .. mode .. '.ogg')
   506→  end
   507→end
   508→
   509→-- Profiler
   510→require('profiler')
   511→an:font('pf', 'assets/LanaPixel.ttf', 11)
   512→profiler:init({font = 'pf', layer = effects})
   513→
   514→-- Render and composite layers
   515→draw = function()
   516→  profiler:draw()
   517→
   518→  -- Queue shockwave shader commands before render so they're processed during render
   519→  for _, sw in ipairs(shockwaves) do
   520→    if sw.delay and sw.delay > 0 then goto continue end
   521→    local u = (sw.wx - an.camera.x + gw/2)/gw
   522→    local v = 1 - (sw.wy - an.camera.y + gh/2)/gh
   523→    local r_uv = sw.radius/gh
   524→    for _, layer in ipairs({bg, game}) do
   525→      layer:shader_set_vec2(an.shaders.shockwave, 'u_center', u, v)
   526→      layer:shader_set_float(an.shaders.shockwave, 'u_radius', r_uv)
   527→      layer:shader_set_float(an.shaders.shockwave, 'u_thickness', sw.thickness)
   528→      layer:shader_set_float(an.shaders.shockwave, 'u_strength', sw.strength)
   529→      layer:shader_set_float(an.shaders.shockwave, 'u_aspect', gw/gh)
... [470 more lines]

Read (E:\a327ex\Orblike\main.lua)

  1000→
  1001→    self.timer:after(0.2, function()
  1002→      self.timer:every(0.05, 'blink', function() self.hidden = not self.hidden end, 7, function() self:kill() end)
  1003→      self.timer:trigger('blink')
  1004→    end)
  1005→  end)
  1006→end
  1007→
  1008→function area:update(dt)
  1009→  if self.hidden then return end
  1010→  self.elapsed = self.elapsed + dt
  1011→  local mult = 1/(1 + math.exp(20*(self.elapsed - 0.25)))
  1012→  self.arc_r = self.arc_r + self.arc_base_speed*mult*dt
  1013→  local s = self.spring.main.x
  1014→  local c = self.flashing and fg_color() or self.color()
  1015→  local r = self.visual_radius*s
  1016→
  1017→  effects:circle(self.x, self.y, r, self.fill_color())
  1018→
  1019→  local arc_len = math.pi/4
  1020→  local segments = 6
  1021→  for i = 0, 3 do
  1022→    local base = self.arc_r + i*math.pi/2
  1023→    for j = 0, segments - 1 do
  1024→      local a1 = base + j*arc_len/segments
  1025→      local a2 = base + (j + 1)*arc_len/segments
  1026→      effects:line(
  1027→        self.x + r*math.cos(a1), self.y + r*math.sin(a1),
  1028→        self.x + r*math.cos(a2), self.y + r*math.sin(a2),
  1029→        2, c
... [470 more lines]

Read (E:\a327ex\Orblike\main.lua)

  1500→  self.generations = args.generations or 3
  1501→  self.max_offset = args.max_offset or 8
  1502→  self.hidden = false
  1503→  self:add(timer())
  1504→
  1505→  -- Tween line width down
  1506→  self.timer:tween(self.duration, self, {line_width = 1}, math.linear)
  1507→
  1508→  -- Blink and die
  1509→  self.timer:after(self.duration, function()
  1510→    self.timer:every(0.05, 'blink', function() self.hidden = not self.hidden end, 3, function() self:kill() end)
  1511→    self.timer:trigger('blink')
  1512→  end)
  1513→end
  1514→
  1515→function lightning_line:generate_points(x1, y1, x2, y2)
  1516→  local points = {{x = x1, y = y1}, {x = x2, y = y2}}
  1517→  local offset = self.max_offset
  1518→  for gen = 1, self.generations do
  1519→    local new_points = {points[1]}
  1520→    for i = 1, #points - 1 do
  1521→      local p1 = points[i]
  1522→      local p2 = points[i + 1]
  1523→      local mx = (p1.x + p2.x)/2
  1524→      local my = (p1.y + p2.y)/2
  1525→      local dx = p2.x - p1.x
  1526→      local dy = p2.y - p1.y
  1527→      local len = math.sqrt(dx*dx + dy*dy)
  1528→      if len > 0 then
  1529→        local nx = -dy/len
... [370 more lines]

Now I have a thorough understanding of both the reference materials and the current codebase. Let me summarize what needs to change.

Here's my analysis of what needs to change to go from SNKRX style to Dark Variant (Antitower-inspired):

1. Background & Rendering Pipeline (High Impact, Easy)

Currently: Gray background (#303030) with checkerboard pattern + vertical gradient + shadow layer compositing at offset (2,2).

Change to: Near-black solid fill (#111111 or pure #000000). Remove the checkerboard. Remove the shadow layer entirely — stop drawing shadow:draw(2, 2). This is the single biggest visual shift and is trivial to implement.

Affected code: main.lua:18-19 (palette), main.lua:540-548 (render pipeline), main.lua:1827-1845 (background drawing).

2. Color Palette (High Impact, Easy)

Currently: SNKRX palette (bg=#303030, fg=#dadada, saturated colors).

Change to: Bytepath/Dark Variant palette — desaturated, more muted colors. The reference gives specific hex values: bg=#111111, fg=#dedede, yellow=#ffb833, red=#d84654, blue=#52b3cb, green=#79b159, purple=#b26ca1, orange=#f4903e. Alternatively, go even more monochromatic like 239A5 (whites + grays + one accent color like red).

This is a design decision: do you want the Antitower Bytepath palette (colored but muted), or something more monochrome? Antitower uses colored blocks but the player/projectiles are white.

3. Entity Drawing — Outline Mode (Medium Impact, Medium Effort)

Currently: Entities are drawn as filled rounded rectangles.

Antitower style: Enemies/blocks are drawn as outlined rectangles (line_width=2, unfilled) that become solid white when flashing. This is the signature Antitower look. The player could remain solid white (Antitower player is white).

This would affect: seeker:update draw code, and potentially the player draw code. Orbs might look good as outline circles too.

4. White-First Particle Transitions (Already Partially Done)

Looking at the code, you've already implemented most of the dark variant particle behavior:

hit_particle starts white and uses change_color(0.6, ...) in most places
hit_circle uses scale-down mode with change_color
Velocity-proportional particle width is in place
sine_in_out easing on particles

So this is mostly done already. The current code is already a hybrid leaning dark variant. A few spots still use SNKRX-like patterns (e.g., projectile:die() at line 857 doesn't use change_color).

5. Spring System Simplification (Low Impact, Easy)

Currently: Player uses 3 springs (hit, shoot, move) multiplied together — SNKRX style.

Dark variant: Single main spring with bigger pull amounts. Enemies already use this pattern. The player could be simplified to match.

6. Hit Spritesheet Effect (Already Done)

hit_effect is already implemented and used. Good.

7. No Background Pattern / Grid (Easy)

Remove the checkerboard entirely. Optionally add barely-visible grid lines (239A5 style) using very dark gray (rgb(20,20,20) on black), but Antitower doesn't even do that — it's just solid dark fill.

8. Camera Effects — Dual Shake (Low Priority)

The current code uses an.camera.shake:push(r, magnitude) for directional shake. Dark variants layer directional spring shake + random noise shake independently. This is a nice-to-have but not visually critical.

Summary of Changes Ranked by Visual Impact

Change	Impact	Effort	Notes
Black background + remove shadow	Huge	Trivial	Single biggest visual shift
Remove checkerboard	High	Trivial	Part of background change
Update color palette	High	Easy	Swap color definitions
Outline-mode entities	High	Medium	Antitower's signature look
Clean up remaining SNKRX particle patterns	Low	Easy	A few spots missed
Simplify player springs to single `main`	Low	Easy	Consistency
Add idle breathing animation	Low	Easy	Nice ambient detail

The question I have for you: How far toward Antitower specifically do you want to go? The two biggest sub-decisions:

Palette: Antitower's muted Bytepath colors, or more monochrome like 239A5/004?
Outline entities: Do you want enemies drawn as outlines (Antitower signature) or keep them filled but just on black background? The outline-to-solid-white flash is a very distinctive Antitower mechanic.

OK, 2. Let's use BYTEPATH's, 3. Let's keep entities filled, 4. OK, 5. No, leave things as they are in terms of springs, 6. OK, 7. OK, 8. Leave as it is.

So the changes are:

Black background + remove shadow + remove checkerboard
Bytepath palette
Clean up remaining SNKRX-like particle patterns
Remove checkerboard (covered by #1)

Let me make these changes.

ToolSearch

[
  {
    "type": "tool_reference",
    "tool_name": "Edit"
  }
]

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Now clean up the remaining SNKRX-like particle pattern in projectile:die():

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Now remove the unused shadow layer and bg_off color, and the shadow shader load:

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Now let me also check the palette variable that references bg_off and the palette array:

Read (E:\a327ex\Orblike\main.lua)

    30→
    31→palette = {fg_color, fg_alt, yellow, orange, blue, green, red, purple, blue2, yellow2}
    32→
    33→-- Abilities
    34→abilities = {

Good, palette doesn't reference bg_off. All four changes are done:

Palette → Bytepath dark variant colors (bg=#111111, muted saturated colors)
Shadow removed → No more shadow layer, shader load, or compositing
Background → Solid near-black fill, no checkerboard or gradient
Particle cleanup → projectile:die() now uses white-first change_color pattern with slightly larger circles (radius 8, duration 0.2)

Go ahead and test it.

Perfect, I like the look a lot better. Next we'll figure out how to create black and white animations for various visual effects packs I have. I have to find and download their files first, though.

Sounds good. Let me know when you've got the files downloaded and we can figure out the pipeline for converting them to black and white animations.

OK. The RTFX animation pack has pre-rendered animations in .mov format. You can see them in the E:/a327ex/RTFX folder. What I'd like is a simple process where I choose a given animation, you convert it to black and white, generate a spritesheet out of it for an animation (like hit effect) and then load it up in the game so I can test it and see what it looks like when I press a key (the animation plays on mouse position). Importantly, it has to be black and white to match the game's style. But depending on the animation where you choose to do the cutoff may make the animation look better/worse, so I don't know how we'll handle that generally, or it it'll be based on an individual case-by-case basis.

Let me first look at what's in the RTFX folder to understand the file structure.

ToolSearch

[
  {
    "type": "tool_reference",
    "tool_name": "Bash"
  }
]

Bash (ls "E:/a327ex/RTFX/")

1. RTFX pack/

2. Prerendered animations/
3. Tutorials/
4. Gallery/
Help.pdf

Bash (ls "E:/a327ex/RTFX/2. Prerendered animations/")

Backgrounds/
FX elements/
Transitions/

Bash (ls "E:/a327ex/RTFX/2. Prerendered animations/FX elements/")

Elements - Brush 001 Liquid Right noRSZ.mov
Elements - Brush 002 Liquid Right noRSZ.mov
Elements - Brush 003 Liquid Right noRSZ.mov
Elements - Brush 004 Liquid Right noRSZ.mov
Elements - Brush 005 Liquid Right noRSZ.mov
Elements - Comic FX 01 Crack noCT noRSZ.mov
Elements - Comic FX 02 Boom noCT noRSZ.mov
Elements - Comic FX 03 fsss noCT noRSZ.mov
Elements - Comic FX 04 splash noCT noRSZ.mov
Elements - Debris 003 Projectile Trail Up Loop noCT noRSZ.mov
Elements - Debris 004 Shot up noCT noRSZ.mov
Elements - Debris 007 Explosion Radial noCT noRSZ.mov
Elements - Debris 014 Explosion Radial noCT noRSZ.mov
Elements - Debris 015 Explosion Radial Up noCT noRSZ.mov
Elements - Electricity 001 Hit Explosion Up noCT noRSZ.mov
Elements - Electricity 003 Ray Down noCT noRSZ.mov
Elements - Electricity 004 Ray Up noCT noRSZ.mov
Elements - Electricity 005 Shot Ray Up noCT noRSZ.mov
Elements - Electricity 006 Hit Ray Up noCT noRSZ.mov
Elements - Electricity 007 Ray Right noCT noRSZ.mov
Elements - Electricity 009 Loop Up noCT noRSZ.mov
Elements - Electricity 010 Explosion Radial noCT noRSZ.mov
Elements - Electricity 011 Ray Up noCT noRSZ.mov
Elements - Electricity 013 Up Loop noCT noRSZ.mov
Elements - Electricity 014 Charge Radial Loop noCT noRSZ.mov
Elements - Electricity 015 Charge Radial Loop noCT noRSZ.mov
Elements - Electricity 016 Charge Right noCT noRSZ.mov
Elements - Electricity 017 Ray Right noCT noRSZ.mov
Elements - Electricity 018 Ray Up Loop noCT noRSZ.mov
Elements - Electricity 019 Ray Hit Up noCT noRSZ.mov
... [521 more lines]

That's a massive collection. Let me check the format of one file to understand dimensions, frame rate, and codec.

Bash (ffprobe -v quiet -print_format json -show_streams "E:/a327ex/RTFX/2. Prerendered...)

{
    "streams": [
        {
            "index": 0,
            "codec_name": "png",
            "codec_long_name": "PNG (Portable Network Graphics) image",
            "codec_type": "video",
            "codec_tag_string": "png ",
            "codec_tag": "0x20676e70",
            "width": 1000,
            "height": 1000,
            "coded_width": 1000,
            "coded_height": 1000,
            "has_b_frames": 0,
            "sample_aspect_ratio": "1:1",
            "display_aspect_ratio": "1:1",
            "pix_fmt": "rgba",
            "level": -99,
            "color_range": "pc",
            "color_space": "gbr",
            "refs": 1,
            "id": "0x1",
            "r_frame_rate": "24/1",
            "avg_frame_rate": "24/1",
            "time_base": "1/24",
            "start_pts": 0,
            "start_time": "0.000000",
            "duration_ts": 20,
            "duration": "0.833333",
            "bit_rate": "21752524",
... [20 more lines]

Good. So these are 1000x1000 RGBA PNG-encoded .mov files at 24fps. Let me check one frame to see what the content looks like — whether it's colored animation on transparent background.

Bash (mkdir -p "E:/a327ex/Orblike/assets/fx_test" && ffmpeg -y -i "E:/a327ex/RTFX/2. P...)

      timecode        : 00:00:00:00
[image2 @ 000002563f0018c0] The specified filename 'E:/a327ex/Orblike/assets/fx_test/sample_frame.png' does not contain an image sequence pattern or a pattern is invalid.
[image2 @ 000002563f0018c0] Use a pattern such as %03d for an image sequence or use the -update option (with -frames:v 1 if needed) to write a single image.
[out#0/image2 @ 000002563f0017c0] video:103KiB audio:0KiB subtitle:0KiB other streams:0KiB global headers:0KiB muxing overhead: unknown
frame=    1 fps=0.0 q=-0.0 Lsize=N/A time=00:00:00.04 bitrate=N/A dup=5 drop=0 speed=1.47x elapsed=0:00:00.02

Read (E:\a327ex\Orblike\assets\fx_test\sample_frame.png)

[
  {
    "type": "image",
    "source": {
      "type": "base64",
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      "media_type": "image/png"
    }
  }
]

So the animations are 1000x1000 RGBA at 24fps on transparent background, with colored content (blue glow + white core). Here's the plan for the pipeline:

Steps:

Extract frames from .mov
Convert to BW: desaturate → threshold (pixels brighter than threshold become white, dimmer become transparent) — preserving alpha
Scale down to game size (e.g., 96px tall to match existing hit1)
Trim empty space and stitch into horizontal spritesheet
Load in game, press key to play at mouse position

The threshold controls how much of the soft glow survives — low threshold = bigger/softer, high threshold = only the brightest core. We can make it a parameter and iterate.

Let me check what image tools are available, then build the pipeline.

Bash (python3 --version 2>/dev/null || python --version 2>/dev/null || echo "no python...)

Python 3.14.2

Bash (python -c "from PIL import Image; print('Pillow available')" 2>&1)

Exit code 1
Traceback (most recent call last):
  File "<string>", line 1, in <module>
    from PIL import Image; print('Pillow available')
    ^^^^^^^^^^^^^^^^^^^^^
ModuleNotFoundError: No module named 'PIL'

Bash (pip install Pillow 2>&1 | tail -3)

[notice] A new release of pip is available: 25.3 -> 26.0.1
[notice] To update, run: python.exe -m pip install --upgrade pip

Bash (python -c "from PIL import Image; print('Pillow OK')")

Pillow OK

Good. Now let me build the conversion script. I'll put it in the Orblike tools folder.

ToolSearch

[
  {
    "type": "tool_reference",
    "tool_name": "Write"
  }
]

Write (E:\a327ex\Orblike\tools\mov_to_spritesheet.py)

File created successfully at: E:\a327ex\Orblike\tools\mov_to_spritesheet.py

Let me test it with one of the "Hit Radial" effects — those are the most relevant for impact effects.

Bash (cd "E:/a327ex/Orblike" && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. ...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Energy 001 Hit Radial noCT noRSZ.mov...
  20 frames extracted
Converting to BW (threshold=0.4)...
Cropping to (240, 236, 760, 756), scaling to 96x96...
  15 frames after trimming trailing empties
Spritesheet saved: assets/fx_energy_hit1.png (1440x96, 15 frames of 96x96)

Read (E:\a327ex\Orblike\assets\fx_energy_hit1.png)

[
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",
      "media_type": "image/png"
    }
  }
]

The spritesheet is white-on-transparent so it looks blank in the preview. Let me render it on a black background to verify the content.

Bash (python -c "
from PIL import Image
sheet = Image.open('E:/a327ex/Orblike/assets/f...)

Preview saved: (1440, 96)

Read (E:\a327ex\Orblike\assets\fx_test\preview_on_black.png)

[
  {
    "type": "image",
    "source": {
      "type": "base64",
      "data": 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Hx9wTJ04MaI3MC/s/aBfjxo175ezs3L2kpOStv7+/SEBAgF56evq/xp7mQldXF3JByX369IFRs7uvr6/QrVu37DCPgFHN1NT0TWFhoUxaWppmV6wDNlqb/u6suRckfFxdXTNERUXfx8bGSoaGhupB77upe+K9qfuqq6tn29raJhsbG1dWVFTAcKodFxdniN/oeSUiIgK7tBjSwa964AhoDhw4cODAgQOH/5BWbkOEBq9eriASbh9DU1uU20IiNhXQDWSbgoJCMYgmWVnZamlp6RopKak6/FZeXi5cVlYmWlpaKoqFErZZIx3S0tII0lQNrx+cV1ZWJl5eXi6B3xEQjF/6vR0BWgBhm+2SJUtef/bZZ4Z6eno67HPAOERGRsb/8ccf2efPn3fAArIxzxneRZGJiUni8OHDU0eOHKlobW1tBOfB5qQLBYYbMBHZWACZAMaPvuOcWjAVdXV1+fn58KYsiImJKQ4KCuoWFBSkHhcXZ0ALbEEiomnhP3HiRN89e/b0kpWV/RBIriEgjyIiIoxneExMTPzKlSurXr582YvXe2rWrFnPV6xYoSMlJSVx48aNmIsXL2pFR0cbNyb9wCb6QCKBYIImbN++fVMHDx4s7OTkZKiurq7WUHpQ9vQdaWOT0UBycnKqj49P8l9//SXq5eVlSURhZxLR1D5B8F67di3N3t7e8uzZs8937dplCl3bxv4HOZQRI0YEbdq0Sd3Y2NgAxy5fvuy9cuVKZ16N2pbozPIScFZWVq8///zz7EmTJtnJyMgwhoKwsLAYf3//nLCwsO7x8fFy8EAsLCyUq62tFa7fjl2N8UtVVbXUyMio3NzcvFuvXr0UzM3NDaSkpJgyv3jxovehQ4c0Y2NjDTurDmjM6NWrV+yBAwfqFi5cKJWYmKhH2rAfk9xh66LCa/3XX3918fDwCJ48ebI1HW/p7gyUAxGq8JD96quv6tzc3GyTk5PTbt68mXT//n14b5q2lNhDe7GxsYkdN25c/pAhQ/QUFRXlFBQUFMrKyso2bNjw6uLFi4w3dEd74aKsQThBoqK1ZBPJKWBIOnTokLeqqqrYlClTHFtKgLLPt7CwiFu1alWuhYWFipeXV8aff/6pGhISYt7Qf5oywDd0f0dHx4ipU6cWuLi4aAcEBKQfPHhQ28jIKM/AwKDizJkzzpAe6Yre0GQMGD58eICsrGzt1atXXZpbB+xxB5JB8+bNC+jTp49YYGBg9a1bt0xACrPPJcNVc+afDQVyhVFn3LhxidbW1uI+Pj41p0+fdsE5GAtg6AsNDTXryiQ0+ntL9K27s9obJGKGDh2aUl1dzRDENEbzjnvNmbuwJdR4x0NLS8tYV1fXTDym2Xr6MAZg5w6ez/zYzcCkQ0FBodEthRw4cODAgQMHDs2eVDQhU8BGV51ECpoERFMSAlgQCAsL1+KvDWmEdgXys7EJOTxnjIyMssXFxd+C/I2NjdUkT5CWLBQbIoKwvdHCwiLN1ta2xMbGRtTIyEgReq8gAHm32xPgHlJcXFySnZ1dICoqKqykpCQvJiYmCg83/F71/1Cdl5dXmJKSUhgdHV0RFBQk/urVKx32Qk6QCFB2WQ4dOvTl3r17tbS0tDTZ5C8+g+xlE4svXrwIW7VqlTS8aHgJTDYR2qdPn/BZs2YVDx8+3EJJSel/5DKwSi4sLCzKz88vys3NLS0qKqouKyuDV14dtlMPHjzYEhrFSAcv6Uz/B0B88v5GwLb44ODguHv37pX8/fffxlQXnV0PVO4zZswAKejKFGJ9HkJCQqJDQkJyCwsLkW9IAIg5OTkZaGhoqLOJ6Nzc3LyFCxemBgYGmsIg4ObmFrx161ZJMTEx4aNHj2ZevXrVCYtJXgKBd1HK7k96enqpU6ZMSZgwYYKuiYmJIVtiIzQ0NCM6Oro6JSVFJCcnB8YWxmOqfhvve1lZ2SozM7OSyZMnq9rZ2f0PcQTS/NKlS+lXrlyxzM3NVea9d0eArY197dq14IEDB9qvXr36+e+//96fCB3ehT7vbgZIMhw7dqzIycmJ2dZ/+PBhzy1btri1Ji+88itr1qwpHjVqFCOZ8fz581dXrlwp8/DwMGqu5ycbSE+9fETK+PHjpcl7+9KlS95HjhxRJ6NERwXHo/EBGrAXLlx4/c0338iDaGmpHAWb/Fy6dKnn9u3b3Y4dO+a5adMmt5bkhdfzcPv27SUDBgywffToUdDhw4dFSSu4IeKT97nMayRuiAgdNmzYyxkzZryj+kU9fP311/ZVVVUSHVkHVHb8uBaR9y9fvnzz66+/ppw8ebLZXrhU73JycsVbt24Nc3Z21rx69Wrq6dOnGb1s9j1aalxgG9jYbQuyT4sXLw4bM2aM7o0bN1L8/PxkHRwcSi5cuGCOHSWCFiiyOaBy/OabbzyuX7/OSDQ1R0qLfv/ss8/8pkyZ0v3FixeVZ8+etUegRrZ3bFuMVGwdeypX7LCaN29emJ2dneTvv//e7eHDh47wetfU1CwiWaOuCOQT7SsvL0/pY+VF7QzE/6RJkwLl5OTe3b9/Xx/GuMZiOLQWDV3L1NQ0ccSIEam5ubk9rl271gfzBBDoampquXl5eQptMU59uC9HQHPgwIEDBw4cOkpSgDc4mKCQba1Be3hktCQYG4DJo7Kycp6JiUmmmZlZsZGREaK7i0tJSQlLSEiIwEG0srKyFnq5qamp1QjgFB0dLR8XF6fF1sntbNKtKdAEGd5/a9asKXR3d7dge4RmZmZm3blz5/XBgwd7ZmZmqn+sXniJAmgOOjk5xQ0bNqza1dVVx9DQUK8x0hIXJq9bnNPYec1BVlZWto+PT8KdO3e6P3v2rBct7gTB24rSMGrUKP9Tp071BqlJW/oBNilKZYI/wEUWhOSMGTPqXr9+bcR7XdThF198kTdmzJjebHkN/DchIeHNq1evMl+9elWXkJAgmZubC48bYXhyZmVlKUMXcty4cQnjxo3TNDEx0WMbBLANPjk5OTM9Pb3E0NBQydTU1JDSR57SpIXQEHGONnT9+vXXZ8+e/bDQ64x6oHs6ODhAqsIUaUSZvn79OmH37t3Zz58/N4FsBUginItFqq6ububUqVPTFy9e7IwyIRI6NjY28auvvipdsGBBhZWVlcahQ4dSz507x5CpwMc8S2khjOBi8+bNC1y0aJEJGSHi4uIS7969m/LkyRNFXimNpkDbuL/88ksdaCrjBqgT5BG/p6SkpB09ejTx7Nmzzlj4diTpQ/davnw59IPdNm3a5Hns2DE3yI7g+MfaAtUdtvhfuXKlsmfPnkZoZ5MnTw58/PixQ0vaE6UFY9P69evDFy5cOADHz5079/zEiRNqkZGRpg0ROU15ILLfecu0f//+r/bt2yePOqmsrKw4ceLEyz179jCEU0fUAd1j48aNHqmpqT3Onz/fH+XeGjKUTX7euHEj2N3dvfcnn3wSGRwcbNmcvNA5KNNvv/3Wa+XKlW6Q89i6dassu9yp/7TmuUnzIN6AevDGXb16dcXw4cP7RERExE6bNk0aEgcdoQvN7zkAlSPydPXqVR07O7v3RB43dg+2oRLk55YtWzSePn2avH37dhv6b3M84tsS2BBG6B9++CHWyMhI7ubNm/lycnLvr169aoDnQlcjoWnMAYk7derUN9u3b3dvahyi/GGc/v7771/hEblr1y5jMnK1184I3t0G8Ij++uuvU8rKyt5t3LjRRVNTM8vS0jKd5HgAQZyrNgSaj6qoqOSh7PLz8xUbm6NS+cMbedSoURm+vr7yRLy3964U3t02gwYNCnJwcCi9efOmLhwKQELr6Ohk8EObmyOgOXDgwIHDfw7/dQ1cQUBjwcNAVkCyQEJCokpYWJhZFIO3QkASbAsmndiuQHw2BvYihHfS1hqwyQHe8gQBoaysXKihoVEsJydXraSkVCshIQHvHbRnoV69evVwcnLS0dPT025K65MNrGSTkpJSfH190x88eCDu6+trRrqvgkB+skHpGT9+vO+hQ4dsSaKBSE82CQwCc/78+VUIaNPY5Jh9HETR2LFj4ydMmKBmbW39rwBXqOD68z9cvylSmv3eHPKat65evXoVffbs2bwbN27YgfTpzH5BZQ5PmIcPHyqD7GcafI8e2DbLgMhbOk7/ZVgSYWHhwMDAiDlz5ihBUxP5kZeXL160aFH4ggULbOXl5T+QlaizR48eIUidVHh4uG5+fr5S/SXfwhtKSkqqSl5evmzcuHE5M2bMsFJUVPxgNElNTU338vJK8vb27hYZGamSlZWlVFVVJYYgb2ZmZumDBg0qGzZsmC5JIvCmne3JTXnIycnJ+e2336J+/fVXRyyyOrI/0DgA4u3vv/+Ot7Ozs8B3f3//8C+//FIaW2IbahP1RHTVZ599FrJ79247SUlJBCFiChH5efr0adymTZssSNP1YwtZtoEGWrxbt24V6d27tyWO+fn5hZ05c6b04cOHvYgQov80JWVD57ADKS1btiyKTZqj3xARjTxv3ry5W2BgICMj0tg1+QUaF5SVlfPDwsIk/f39Y8aNG2cHsot3u3hToPYyZMiQl+fOnbOCFjb6wujRo42ho/mxfLCfqyAADh48qKmpqalx584dvx07dqjS9mu2REpr2iebACUyT15evmjPnj1R48eP74dzYPRYt25dKQiQ9hyPKB9aWloZ27ZtS547d27fthJ99H97e/vIR48eWT579ixowoQJ9h/rz0T0grA7d+5cnYqKisLKlSsT/v77b0YPuCEd77aCVz4Ex0aPHu33yy+/mKI/jB49OjM8PLxne5LQjc3n2gqqh3/++Sfi6dOneTt37nRvLB/sutm3b99zFxcXrZUrV5b5+fkx+uQt7YttJUHRh7/77jv5mJiY3Nzc3NqTJ08yHsRdjYSm9G7ZssXj0qVLevHx8QYN9QOqF+hGf/vttxVXr16tgmwH+7f2TitvHcyePfv56NGjJdatW6daWVkp5ujomHT37l3nrijHgTI0NDRMwRjeUPqpTj799FN/Y2PjqvPnz1vBSaSj54LssUBVVTVn1qxZUaGhodIwomJuYmBgkJqWlqaGtVyrZXo4D2gOHDhw4PB/xQu3pd66HP5dduwtpfQZnhK9evVK6tOnTyEkCwwNDRXU1NQUEAW+R48eRFK9Ky8vr8jKyipITEwsDAkJqQ0ICFBC4DLS/eyKRDQCp1VXV1OwrVYRVbzkCracWlpavnF0dCzq3bu3mKmpqYq6urqynJzcB7KnMZA3YWMEKJGqvIHMQkJCoi5dugTy0wokVUcQPs0BlSm0N2/dumXJCNDW1dXxSi5QwC14fYJYHz16tAg8odl5YOdJTU0te9q0adGff/65EekZExlJhDMRxby6tVSO7HPqr/8BH8sX0lpUVARdaSXe30BW7d69u/LRo0eMp09nLLTonr///rvf6NGjnYlUZrctOtbQ/+m3H374wePixYu9nJ2d47/66it5GxubDyQ/CMaLFy8WPXnyxITqiu5NhAxIVXd398h169Yp9OrV64Pn4cuXLyOuXLlS8PTpU4OUlBQtXn1ddnkhyM6gQYPipk2bJuPi4sJsm2fXM9UnJCvk5eVlySs7ODg4csOGDe+ho9xRYxMRBSCffv/9d2dkJDs7O2fKlClFUVFRxkRQNUT0YmFY76n86ptvvnEhT/WioqKi0aNH58XFxenjvx8jTqj88P7VV195rlu3zgX9CoaCX375Je369esO7DGP0tCS7e9soguetwg2BwMQGQOQAPTxqqqqyh9//DEAXsjstAm1A4hg+fLLLz2///57t4EDB8aEhYX1bI1xEf/B9X777bfgzz77jKnHKVOmBH3MC5qdPwqkl5eXl79mzZo4kC7tGSSQTarBC/nrr792x2cUyvfff+9z5MgRt/Z6LlDZQzKjpKSkB/SP+UF4Ufu8d+9eBCRRnJ2dk+DJ11gd0D0hh3Hu3DnIbQQvXbrUAsbZjgrOyI5zAGPI4cOHEwcPHtx78uTJr549e2bfXkRge9Yt2hWCnq5du1bP3t5eu6H7UPtDzIVr164l5eXlVcyfP98W+svtTTw3RYLCKHPs2LE4bW1tBNrN3bZtm35qaqqWoBnqmwIZHEeMGBGgra1d/dtvvw3gbUf0HTue5syZI7xx40YFksDpyLInsB07ID+0devWSgRLTUxMVHN2dk68fft2365UBzS2I6BwamqqBq17CJSXuXPnelVXV3e/dOlSp2jAN/ZMmDNnznPYiDE2Y91jZ2cXGxISYkpzgZaCI6A5cODAgUOXRWNeG3hAwgMXL3yvrKwUx4v3YdkVSU822nNB3pBuIfsdgTHGjBmTPHLkSDUrKyuTxsioxgDd3LCwsNi7d+/m37lzxzA5OZkhAgV5UklpW7hwodfMmTPVZGVlJZOTk/MOHDjwDovDltQHu1xxXRsbm9cjR47MHjJkiLqFhYVxQ+VJHpy8gdeaQ37yEtNYbSAzbI3c8PDw1/v37y+4c+dOH9qC3Jl1gTLCtr+HDx++sbKy6tkU6QmQ9MDJkye91q1bN4D6N37DOxZTY8aMCfjqq69Uzc3NjdlkMsDrDcsLpjAaIKQbIvlxXkP1gXrAOfHx8W+ePXuWrqysLDxu3DgnItfxjvucOnXK+6effrKGtEFH1gPdC1tAQfAiPUS24x2yIcePH4+JjY2VEBUVfTdixIi3U6ZMcWG3LyJ4g4KCIm/dupW/adMmR/Jch4bxsWPHCv/55x8r8rpvaMeKqKho9fLly/3WrFnjRKQwPDIPHz6cee/ePSu2DihP+TLJZX/HO4iNIUOGhH/xxReyCCyHY6gHkhPJz88v8Pb2jtPQ0JBydna2Jo3o7777Luj06dMDOsIoQ2UP4h9asMjITz/95LFv377+vNrDbFDaEChIW1sbcjRyKioqytSetm/fjmu4fSz9dH8YFffv3x8xbty4viT78PPPP38IwseP5yb72Q1tzK1bt0bNmDGD0bumuqG+jgB5X3/9NUN8t9czj67r4+MTD8Ops7Nzo0Rlc681duzYF6dOnYK3XrdTp055rV27lhmTGtPjx39gSNi3b58/ygJeu8uXL9eCgaapAKz8ArtOFixY4PXzzz8TCdId6V+3bt0H+RZ+1gGVycGDB5/v3r3bEAQfP+qZCBTSgt6wYYPnr7/+2qAWNA2/M2fOfH7w4MH+6He7d+9mSPjO8HhlE4TwXF21apX7nDlz/O7cuePcWmmSptBezxiqR3hSBgYGSg4aNCiX1wOXPmPHyz///FPy4MGD5M2bNzPjVWd6G7PvvX37ds9p06bZ+Pn5xX755ZfGkFHo7PlRS+tAU1Mzc9WqVXE0N6K0U1ubMmWKz6hRo4SXLVtmjmDJHeX13BQoDdgxc/To0eTDhw93z8jIkDczM8uBUa6reKNTHSCOwvv377ulpKRo0zHKA8bcnJwcUfRxQVmbsuc9kydP9hEXF38HGS84ylhYWCT7+/v3apUEEecBzYEDBw7tM2A3hs5+oPwXwEsGYOJqZWX1pnfv3iWWlpaiurq6sjIyMhIyMjKSeMKXlpZWlJaWVr1586Y4MjKyLjg4WC48PNyASAxBedgLAtgazbzlAWJfT08vY+bMmQhGZaGioqLSlGQBwKu92pAEQUZGRubFixdfnzlzxhwkh6B44LJBk0QEc/nmm2+YhSkbEyZMCAIJ/bEJMXuRj3NdXV1D58yZU/3JJ59YS0lJSTVWnq3VGqbyhiedl5dXLORw+/bta6Ctrf0h+B1WsyAZqE6uXr3qs23bNgMEZeusRRaVo7u7e/CNGzd6N4f8JeITeR02bFjlmzdvdCj9CFy4cePG5MmTJ7s0RDzzykiEhoa+hnZ23759rXBf9jkPHz58+eDBgyplZeW3Kioq3Q0MDCQNDQ1VdHR0NEGA03XoHo2lG7rFv/32W4iHh4finDlzSidNmuTCK2OxYsUK8ZiYGOOOqgcq92XLlnn++OOPbkTS4reSkpKSJUuWxPr5+ZlClxncrbi4ePXatWsjoVHLW0dsUhoE79GjR8POnj1rQ1IQvOMujTvQNd65c2fgrFmz+lN5nD592nf//v09iQT9mNxDU3rfM2bMCFuxYkUveKBT/pBOeNyePXs2oLi4+P2qVav6ksb0kSNHEEhuQHPu11pQ3kH+ent7l+jo6GihvMeNG5ceEhJi3pz/Ilr98ePHg1VVVUGiWxGJ+9dff/l//vnnjuQ93ZQ0DRb6J0+eTO/fv79taWlp6bfffvsKerw4pz0W+9Sucf9ly5Z5fffdd67oZzhYBgHQd+/eQbLlwYMHAYsXLzaH0YLfJDRdD96OCQkJckePHvUCAdZaAoauByPtvXv3pFRVVVXhtT969GhT6Frzpp8tvXLixIlg7Do4ffq0FzzZSY6mo0gWtuchgmAeOHCgHwXzxHNh2bJlzk0ZQ9pS9tu3b4/48ssv+/Hr2tS2ICNz584d2xs3bvguWLCgH+9YSvUMLz8Ye5YtW+Z9+fJl187y/mSnH+WDNMDovXPnzgHz5s17cevWrb78Jgc7oo2FhYVlbN++PenKlSsubJ1tkn959uxZ6cWLFxP27Nnj3tll31B/oF0Jd+/e9VuwYIE9Sdd0dhqbC5T5nj17fDdt2tSbpPCoHY0bN+7F2LFju82fP98B3wWJXGfHIjhz5kz07t27GUOkhIREnaenp50gpbUxsJ+v2trauSEhIRa8/TstLU0cUj+CSKpTmtBOZGRk6kBCQ8JOQ0OjICAgwKqlddCpVg0OHDhw+K+ALe3QnEA1eOcIz9aB/aDD1qzx48dnDxs2TMfU1LT3x/7r6OgoNGnSJIaoiomJga5fyI0bN7Qp4ntXmMiwwc+JCq80CcoCHhMmJiZZZmZmpfr6+pAugBaoro6OjhsvideY1yhvoDJ8Jq9DAjQu165dqzFu3Likn3/+2ffGjRuMBqWg6LzRJBHSDatWrXJme7mCvIWkxYYNG8Q8PT2bbD9sYt/Ozi56+fLl2B7vSJ5+zSnPVqSduSnINhcXF6Nr167FzJw5s0RdXT191qxZ7z755BNbItooyBxIWjs7u8TVq1eH+vr62nRmv3BycioheYyPEdDUruD9aWdn96KsrEyyurpa1MnJ6fWOHTuUjI2NXcjLmUhlIt4rKirKAwICXj9//rzk8ePHGtLS0m93794NjbtuROTFx8cn7dmzJ+PRo0cWFRUVuDZTbiD+FBUVCw0NDaPs7e2L3N3dZfr06dMTWrz4nU1yUlpxb8iqrF271t3NzS185cqV6n/88UfQ7t27lREEsbq6utrBwaHXtWvXMhcuXBj24sUL646oB+pvZmZmTPujskcZPX78GIG8TKGzTLtJcP7Vq1eVxo8fX6CkpKTYkASMt7f3qx9++AGEOmO4YevO0jls8vnw4cMh48ePZ0hPiG9u2LAh7saNGwNaYyzkJfpAfh8+fNjNy8srZsuWLW8GDRpkT3mEp/WSJUvc4HH7+eefh+3bt08bY9OyZcvcpKWln69Zswae3h8cwvlW6Kz8q6mp5SkrKzMke1paWnZ6errSx/6Hd+wU2L9/f6Cfn1/dmzdvhPv27fvBaIXt4/COpiCXjd0b5Pzvv/+e6ezsbIugjF988UWmp6dnfyrz9lgUE/mMF+olOzvb55dffrFF34GMU0JCQnJRUVEpgrKdOnUq8PPPP7eoqqqS4Gcd0LU0NDTyunXrJh8bG8uXPoaYB9nZ2bkgoFVVVdHdS3Jzc5V570394ciRI4GjR4/u98svv3j8+OOP7mzyix/paQ6onjG3wHbrHj16PN+/f39/jEd4LtTW1np/+eWXrvwmSZSUlIqqq6sZwyx75wo/kJ+fz7R7bW1tZscF+9pEwCHGAMjnxYsX+/z555+u7eFl3FLQ+FhvmBjQo0cPz9OnT7uVlJQEPX369KPG7uaivdsZte+8vLxiAwODWronvVAHf/31V96VK1fSQD4LQtkTSHYFadq7dy8C+Hls2LDB/YcffvDcsGFDq41UHQ2qX1FRUZCgBcnJyVKUdmdn57Dp06eLzJw5s5egkc8AGSogW7Fw4UKTs2fPJq5cuVLFwcEhBUa+xjStBQk05mDe+Pbt2w+e/yhv7JQpLCwUBvksqO0JdYC03bx5sy92iYwcOdL//v37TsrKyiU9e/ZMQMDpltSBwFYUBw4cOHQF8AYPockzFnKGhobJCOKEFyL6wsJPno9kNScLaGfnoyuA7TmKB96BAwee3717V/vLL790MzU1ZQLzvGXhHQ/Yv6GSLCwsTNasWeN+//59lZ07d3qhvtiLYSEBBRkwQGDev38/moLitDbNvB7PCL4zb948r3PnzsHLUwjepzt27HBbtGjRgDFjxvSFZx5pDWOV2pjUAFBdXV0FIgOLVyL6SL+X7QlNsgQIFnby5Ml+u3bt8oInIO92+s4uc0NDw2wElaK8IHHwDEP6dXR0VGRlZUsaSzONE8jX2rVrPW7cuKGJLe4gNptbnq0Fkdrq6upqy5cvd7t69aqimppa1aJFi6zGjx//+vr1675snV98NjExMbx06ZLBmDFjXtC4JtQJkJOTa7H3N/IgKysLTejaGTNmBJ89e9YcbYsIdiJJUd7wvj927JjnrFmzYjZu3Cj7xx9/mKmpqZUiWA/GCFwP5XLt2jXfKVOm9EBQHuycAPlMfQefsU3ex8fH5uDBg24zZ87sOXbs2KSDBw96IlAetRUyXABEpiNNffr0sXr8+LG6uLj42yFDhsjevHnzBQVmAwF6/vx5bRcXl9COqAdaKKmoqDDkOrvss7OzkSRhBFOjZxc8OisqKsSKiopKqOzxTkQ/2tbSpUtV4cVL/2lskYL6+uWXX4IpCFpiYmLyjBkzsmGQYv+3teQU/Q/XQqDKuXPnmoLsqz/GrJ5QR5A/mDFjxttp06aVxMbGJuL3zz//HNvyvdt7TJKXly8nyZHq6uoPi9HG7kltcPv27X5//fWXMPQ95eTkaup/I115KXiqN/V/7G45fvx4PORH0tLSMmbNmpUP77KPBSzkB2j+hCr4888/XRYuXBgJ72v0OxMTE+h856EePvnkE4eDBw++YpNX/Lg/XUdBQaEc79gQwo/rYqqBqQg+y8jIYINLZWPE0A8//OA1YcKEfhiLQD7Tc72zSBUioeHp9t1333liPMLzHH0DGtH0O7/KXlxcvKa8vJwpK37VK7XZ6upqEfRtSUlJZtyiPkw6v5hLYd6xceNGT7Q/QSJAgXplqdrjx4+7nTlzxuvq1at2mKcRMdfW66M82jO/VJ8lJSVVrA1CHxwozpw5ExQWFpb9888/CxT5zC6f+ulRHWRZfv/99+eLFy92ww4BHOdHP+go4LGMKRA+o+wRI2H9+vVvv/zySx0YlQWVyKUdg5h7rVmzRnHfvn3ZT58+tRg8eHCKhIREBc4RhLVCc54JPXr0YNKJtuPg4BChrq5ec+3atX6CSj4TqK1fuHChP4IkWllZvca8Tk9PrwCcB298iqYgcA2MAwcOHLoK2MQz9CXd3NxCNmzY4HH16tWQhw8fFj9+/Fju6dOnak+ePFF7/PixzMOHDwv/+OOPQGzhB5mAhyabiO7s/Agy2PqIixcv9oTGJbZnS8NNsZ5sxnlEcBKRxwb7N5xLZAPckrB9/O7du2KzZs16jt8EhfhsCDQ5jI2N1V27dq0wfW+xBhfLeILv8IKABuP9+/el9uzZM2DkyJFOWlpamg0R+7xB4HhBns01NTW1Xl5eCYsWLQoZM2YMgk15PH78OBBejVQv7LojY8GCBQsG/P7779BjzRKEuqAyysnJkcU70sgm2vC5rKysEjrjH9PVPX/+fDw8aNDuiJD8WHnyMR8M0Q93vAMHDvQ/f/58dHFxscT69evNFi9e/BIa0NR3cJ6srKzs8ePHe2PbXWeR0DU1NR9IzeYCZRkXFyf72WefwfPZDd6UzEq3vo3hHTIdIB8nTpxYvm3bNseIiAhdkKu9e/dOPnHihAnqB6sBXGvnzp0eK1eutE1KStIlEpTdJtmEGF7Y3hoYGNhr69atbuPGjavD/7Ozs7PJ2MLODxkgwE5dvHixz7Rp08KWLFnisHfvXg+wDkiDoqKi4unTp7Ww46Mlk/zWgK5dWlrKeKqxoaOjI4YxGF7KNO6Al5KVla1UUlKSp7Jn1wM8cuH1Sc+6hsYpGoc2btwIkp+RIYG3+ezZs2tQjtR/+EWCUhnCI3jbtm1uK1as8IHeMxl/UOaQQVi/fn3x1KlTRYiExnNi5cqVnu3ZF0DmQx8fnyUkJERERUWZemgo70QOr1271svLy0v03r178EjKhx2p/j/Me0VFRVVNTU2DpA61561bt/oPGTLEEVIpixcvzgsODrbs6K3AVK7wBFu2bFkUCE8ccHFxsY6KispOSUlJA0m7bt06r/aog4qKCiY4q5iYGF+ui64tIiLClF91dXUNiFD271S+0NaEl/2dO3f8vv322wEdQfo3B0QyHzp0iCE+YXxF20SAQgTK5AcJTXmsqakRlpOTa9VcpjGwtNEZAzjmI7xyPzAIX7t2TR27HkDwCiIBChABumbNmgHh4eGxf/zxB7PrgR+GGPQjyBsItRNY8keQxGOO9ejR40PgSQMDA8WlS5e6CDIBx94ZsHbt2r4IUrtnzx5HExOTRH4ZAjoCCPnBbv8///wzdnUhtoMajTtCAgrywk1MTNQ7ffp03caNG8P++usvk0mTJgV1ppNES4ApXbdu3RhjOp7VgwcPzjt9+nRfQZTdaAhUzidPnnQYNWpUJrgPf39/0759+8bid46A5sDh/zh4F8SC7tXZVT1xoT28aNEir9u3b6fcuHHDFtupsaXXyMhIH7qFID4ABQUFBXjgYYEH/dhbt271unXrVuLnn3/+HGL+NKhzdfS/5UyTCsgfYIvqTz/95IYt9rxeow3VVUMB2whEShMpp6GhoQ5Sbv/+/d5NebIKCrAdDV58rVms0eQT7c7a2jrm+PHjvjdv3uw5c+bM/tB1JnK+MWK/GddnICMjIwNCad++fabDhg0rvnnzptH8+fNxn+ytW7d6vnr1KppIH/JMB7AKcXd3733+/Pl8LS2tjM6uC+qfCQkJ+tBTZROJVCbnzp1LhgcJJpK82/7xf2xZ+/PPP+UGDBhgx267zbk/SQTwGgKaat8Ngbx+qc1jrLpy5YqUq6vraz8/P4MVK1a8v3Llig+7TuD5dvjwYZuBAwcyk/yOrofIyEgmCF5zCHrybE5OTk51dXUtxFhBZUTXQN7gYTxx4kREtHeDvjLqrbS0FPasqm3btmmCfAYLgfM3bdrktXfvXle25AS9N/ZiP3OxWNq5c6f7+PHjS1G2lA62NzTb+II0rV692mfXrl0uW7Zs8cRvSAvGvOPHj4vAY6k9+wNdNzExkSFBUXbkNQ6dcnd391gxMbEaSUnJciw+ZGRkyubMmVOC5x1pcNdfhxlkMjIyQFoxxF5j5DPa1cSJE31XrlzJSPvAK33hwoXVUVFRJu3lkcWuK0gNwOu2oKCggMYglPmIESP6bNy4MRUkNAwI+N+mTZtcBg8eHMjvBS/LyKUI7Wd81tXV1dDV1c1p6HxasCJwVHJysggCMsEYACOKmZkZ07aojWVlZZU0JL9BZQv5AXj01ZOLryH30lkLYipXkOlr1659Se1v+PDhtl5eXknQTf/6668HoA746QGK98zMTEW8m5ub86VvqaioFKqrqzOSG5AfKCoqYgyYbM9nfX391L1799rGxcUlrlixwoLS09nkM299bNy4sc+LFy/CIDeFhrF3714TbW3tDH71A5SNrKysCL/lNwA1NTWmP+Xm5lawdx7Wy55EFRcXl0HznTyihQQQNF/D55kzZ8pCpgm7HvjVB0BmC7UTaN6gqqoqn5WVxeQBymXw4v7666+tp0+fzkjqCILRpSlQ2tBGFi1axIynR48erUS7EfQ1NsoW5C18HYqLi6WRlyVLlnjGxMRUeXt725LmtpCAA2UPIxHkH+rq6t5bW1un5uXlidrb20cKsiGA2oa0tHS5jIxMDcp/6tSpEdeuXdPH/EiQxvxmSolI3bt3T2P69OnB8ErPzMyUIQeJ5tSBwFo5OHDg0HIQWcfWSOR90e+C/KAUZLClCrA1/fbt23k///zzADs7OwsiFXjJIQKbQAKp4Ojo2OuXX37pf+PGjYzhw4cH0OTr/3rdsD1z6WViYpJ09uzZHHg/scm7j5FSRPYQ4cYmVdnnEAGE3+FZfebMmTgEixD0+mjNZIvKFh4vCKpy69YtdQQ/A9FIZUvkfHPI5o8B5Qpt2C+++MLt6dOnMosXLw4qKCiQ/uuvv/RWrFjR49tvv/VMT0/PoEhL+A/j6lNXV9e7d2/LkydP5sPQIwh1gfuvWLHCFEFoiGxDoKxDhw55Hjt2zIVXXoDGC3jWnzp1ygYSGNT/m+vxTOQprxc/kcS8404z88G0efwHXu5HjhyxHj58eCw8APfv36++f/9+DzYJLSEhIXn48GEtePt0VD1QOXp4ePQkj/nm5k9aWlpq7dq1/VnXYmbF8HpetWrV8y+++MIOxhseDd3aLVu2lOvp6emAiIP38bZt27x/++23/rx6xR8DLxmNpENnftWqVQ4IcEXe0LySHBQQcf369e7Lli3zOXHihNOuXbs8kRakqWfPnkY//fRTIrpHey14aYHx4sULhixjt1W0g927d1t9/fXXoWPGjAmZMGHCq4MHD6bByETjBpU3EcnBwcF6jd2L+guIiO3bt5vgGggEuHr16rTQ0FCzjtgOTAumBw8e9FmyZEliUVFREfU1jEETJ07sN3Xq1IQZM2bkgZTG8T179qipqqrm8LMv0LXy8/MVY2Ji0lEWMGCPGDGiAAtu9lhP5KWdnV2UgoJC3R9//OFSP697B33P4cOH6+I8qrdXr16V8mrr0mcY+LZt22aEY0eOHPG9fft2p3tjUVqx1Xffvn2eyAdkSQYNGmT84MGDCPy4a9cuVX49F6hMcnNzVSA/MnToUEZTFGXQlmvb2tpmkS56bGxsfmVlpSTvzol9+/blwklh2bJlFSUlJbKCtgWeJWMhvmLFClkYafAdMQW2b9+e0lbShOoPuySEhYW7IbAVv58xdnZ2ZaiDiIgIxsuXyLYRI0YEjBo1ymnWrFmV1N4FmQQiEjE9PV0Tuzbmz58/AFv42+qJjuuCgG4P8o6uqaWllamsrKwcGRkpT+P+4cOHS/fs2ROWkpKiLejetw154W7dutXP3t7ecvHixd5dgfxUV1fPweMccRAQlHngwIGS+/bt69fZ431rpSC2b9/uMHPmzG6BgYEGLi4ueTDACgkoqA4UFRVLkpKSVOD4U1hY2ANOLV2l7fPKoYSHh/esra3Fcy4aQRUNDQ0Lsa5szhjaZTLLgQOHlukQYzKBBRI8FPACmYZFDK/+MFeuzQeRSfCO3b17t9eZM2f6WlpamjbkKcomh9ied/Q7m5C2s7Mzv3DhggOCWkCWQxCINkFoxyhnyJqsWbPG89SpU3VOTk5WIASaQ96hbCsrKyugPczWe6W6IW9D9n/YpMPAgQPtT5w4kQZdK0Guj5ZOWmhxCw3tCxcuxCKiNzwXW0LqtxTkPYl74F4bN25037VrV76amlpxVVWVyIMHD/Tnzp1b8PDhw5dkCMD5REKj3n/++ecYjF+4XmfVBY2bmLx//vnnzm5ubm8gK+Lm5lbw3XffuWFSzPZioPECkjG//PILCH5xkn9o7j2p7YKwTEhIeBMYGBjh6+sb+vLlywh8BzFL12S3bTax2RSovEF07dy5s98nn3ySinRD/3jfvn0MCY3zUA/YIbBr166ijtLbo2cUiIm9e/dGEznbGAnN9r4F6cMeZ/E5LCwsZvr06ZnQNIWGMdVP/X+7z549OwT9HkQvvPxOnTrldfToUUaLuC2LAxrPSC/58uXLrlOnTi0MCgqK5CWh6VmBNG/atKn/hAkTAo8cOeKA7eFIE9IGHfapU6e+aEuaPpZepNXPz888NDQ0howQZMiDmyKMSUeOHHFF0K7Bgwc78AYepLq4c+dOXHZ2tlpTXo0ogk2bNmVi5wX+s2/fPv9Hjx45diQRR4Tn48ePHVatWhUD6Qccp90Y69atc1dVVS3fvHnzC6QRRor169e/5vezgcrp/v37xdQOZs+ebe3o6BhFzgM4D+dg55SLi0vuyZMnGcMXbBTwTJ85c2YqdltR4EyQ5o8ePWowkCGu88033yTAMIaxZd++fR1a7k2B+syePXv6/vPPPy9xDHroenp6Mn5+fmEGBga6q1evDucXAY3HDe559+7dOH19fR1XV9fQ1s6TkR4sxCdMmMBoeSOBHh4eH/SNqYw/++wzP+z0gQETUjOCSgQR4QCyZMuWLVE0T4JMzbBhw1621QuayNP4+PiqYcOGJbGPtRY0vkPbfOTIkWqoA19fXyYIIcoYZNX+/fsNf/vtN6/IyEjTruQBirRi10ZAQED4oUOHJNpCnFPfwTQNO1rYx/gBcnj65JNPEioqKipev36tjbROmDDBV0ZGRvzYsWNugux53hCI8D958qQrZMs2bNjgAMOJoMpAUB04OTm9SUlJYbxvv/rqq6STJ08KNbU7SVBBacUu0Bs3btTNmDEjysPDQ23UqFHMLj1B1uRWVlYur6ysFHNzc8u+efOmXUcHmeX38/nGjRs2gwcPzsbnkJAQbWdn5+jmPDcR8VggF9UcOHBoHtiLaBB2/fv3j3Z3d6+2trZW1NbWVsaiFb9VVVVVpaam5oWGhhY+e/ZM0sfHxwKDN+81ODRdziD1jx07lgqigoiQtnqJkvQAJsjwrFy5cqVZUVGR/P+lemEvevX09FLHjh2b+Nlnn6lYWVlhYSDM9mJs6joszcuKK1euBF28eFEZ2oImJib5urq6Nb169RJxdnbW19bW/qBt3BAhyIgAioiI3L59+8WSJUt6d8VJWmNlDCmFgwcPasDzld32OiINbEIQep6bNm1Kj4iI0IRWJmwLy5cvT4JXD7teKDjehg0bPH/99Ve3zu4XtDhjp4GXtKHv06dP9z5w4EBfIuEba78NtUM6dvLkSa/Tp09rVlRUiGObMrbYgzeWlJSskJGRqdDS0io0NzcvcXBwEHFycvrQtmnnRXPGJyIMUdZr1671CwgI0EB9fPnll2nYEUAGCtQDPNaPHj3aYfVAE9mdO3d6o21Q2bDP4S07GgeIhPP09AxB1HR4WrHrivKASOp///23nLy8vByuBTJu2rRpmgUFBQr8ziPdX1NTM/OXX35JgywTtXE6h8a6wsLCwlmzZqUkJyerXLx4scTa2toMv2VlZWUPHToUkgHq9fltlzTCQxC61GzDH/VhatO8UjLUbpHG4cOH16LMG2ordA8QWBcvXrTH9WBcmTp1qhHmJp3RxylNMBpBvoWId/wGWYyBAweWHjx4ML9///629caA1/7+/lb8Im2pnKAN6eXlVaeqqqqCcomOjo5fsWJFdUREhBHOwfZ1GCFevHihk5qaqgnbBIIMTpkyJfy7777rR4YltKkHDx4EzJ49uzfbw5PS6+joGHH37l1T3HTy5MnRz58/txUUAppdHpCpuHfvnjCMYDRPGjBggLmYmJjo4MGD07G7oK3pZhtnfX19DUNCQqKGDx/esynt8qauM2nSJJ+jR486o/7gVf3JJ5+IwsOajAwgRn19ffMkJCREnZycJCCRIujbsMlT7+LFiy+HDx/eB5UTFhb2etiwYYakm9ya9FOZWVhYxO3fv7962LBhlm0lZuia2F146dKlPjExMfHDhw9XxdiC6y5btszz22+/dTIxMamrqKiQFPSyZ4PaEHaOBAQEGMybN+/FrVu3+rZFQxkGE21t7azXr18zYwy/yoKMKt7e3nFRUVG5ixYt6ocYAh4eHpmbNm0qePLkiYOgGl6aAqV5yJAhL69cueJ4/vz55ytXruwviHmhNB0/ftznyJEjymVlZRKbN29OnzdvXqfvduHHeHTmzBn/9evXG0yYMCHm8uXLVnASwe+C1J/Rp0RFRasdHBxei4iIvBUXF6/DrquuXv5IO2LE5OTkgFeygVHVx8fHFHPnpupAICYYHDhwaB1YE9nq2bNnP793717W+fPnnbBIh7wDJuvwBMMLZBOim0Pn748//nC8c+dO8tSpU33gVch5QzcNmowpKSkVnDx5MgPkM2Z5pBXZ1vbLJtrgUXLs2LE4eCIIsuctP0ELBRUVlby1a9d6/PXXX8LwKIWsCQXoag75zPYgxJbWefPmDTh69KhY//79M1++fKl3/fp1k507d2rOmjWrBCTa69evExoKCgZQ8C94G37xxRcvunpdUBlDNubcuXNmGA+a603OT5AnOu6tq6urfezYMUsnJ6eUkpIShnDas2dPz19//ZXRvSWSkYiudevWWZuZmcV3dl2w5YyQTF7ig7aXDho0KGjXrl12TZHPbEKefZyOgTjdvXu3eVJSkg6IPGzRxjZukBU5OTmq8Ejz8vKyAzG/dOnSPp999tnbFStWPH/+/Pkr2nXBDpjYGCiN9bITdsbGxvmYJB8/flzD398/nNKHc1auXGlpaGiY3JFSHHitW7eu/6ZNmz7ItfAGFUVZgfRklyHGj2fPngUtWbJEk4jQhkiq5cuXp+E5ic/YOfHDDz/UQQqhvfKDdGRkZGgsW7bM8P79+/40ztE5FAQSsQO2b98uhv9s3769FJ65qAN4gi5evDi2veqAHQwOcixIH+1ioD7M3tWD/+B3GlOw82TNmjXJjZHPbO/EL7/8UhTXQd527dr1riGt4o4C1c2pU6dcQCiwnw/YvfHjjz9mfPfdd2IwUMK4v3Llykp+atbSuJKXl6e0bdu2uPrAaTXm5ubG586dU5o3b56/urp6nqura1hJSYlwZmamioaGRratrW38999/HwX9cPRhMgqUlpaW7tq1SwakVEP1sHz58jLk49atW4GCRj6zy+PNmzc6P/zwQyIZQJydnY0jIiLeQJbjiy++yOLHvchzDgTcpUuXfCD/tGXLFh8qj2bpWtaXH7QwN2/ebEDG3WPHjsUT+UztZdy4cUHwtN67d+9rjOudbVhtybNv69atILEYWQsbGxuz4cOHB7dlHUFlD833jIyMsi+++MILxAbtemopqCxBqq5du5bxev7tt98yUc407qxZs8Zm//79fhhv2kN3uj1B43N8fLzB5cuXvb///vsPwXFbcz38F8S8hIREDYIy8uu5QgQVnB4sLCxMjh07xgSqnTBhQlBJSUlFVyWfAZLcePLkiT0Mp+PHj+9tZ2cXzY/AnPwEjUnm5ubxMjIyIpBNWLp06Zvz588zTjVdHWirN2/efD916tToBw8e6H366aeRgsZp0PhiYmKSUlpaKmZiYlLm6enZC32sK7Z9AklUPXnyxNLBwaEIx4KDg3UdHBwSPjaGCMwkgwMHDq17qECT8+LFixH79+/v36tXL8aTpSEdYrb+ML6D3Dt69KjLmTNngqEFJahbhzobNICC5N+zZ89rV1dXWyw+2d5qfLoPtoAy7gvDhg1z3LFjRwhtm+rKxGdToHyRl8rNmzcLN2zY4A5yhd1+W6tFjP9CM3XHjh1uly9fLsP2MzwwS0pKJK9cuWI1a9as7sePH/ek7eO8JB15kX399dd9evfuHdkZAdj4OVYgav2xY8dsQc6TZ2hnpYkIN0R9++WXX+zc3NziSktLpcCDHjx4sCc8z6n8iUQFGbdmzZocQRmnkDS0p4Y0n+GZ9Msvv6hD2qIx8pm8jvEb5EceP34ciONE8sH79YcffhAHcVFdXc1IRjT1QlpA1EA3debMmUbwikKkdra2cFP5oXJGnXz//fcaUlJSIDuF9u7dWw0ii4wCCIa3YMGCN0KdAGzXHTp0aHfoKB87dszz999/f37kyBHPUaNGhXp5eRUjbSTTgfQiaNby5cu1IAPBS/DQdysrq9cTJ07sTf+5cOFC4IsXL6zak5SgIQdE4+rVqw09PT2DeeU4yFADgmfFihWvvb29La9duxZI/WLGjBlWBgYGKe01LlEat23bNuCXX37xYJPNNI/AZ5pb0DMMGrFLly4NBnndGKlGx93c3MKdnZ2tcOzOnTtBPj4+1p1NxOHeIGy3bdtmhu3V7DxDL1ZLS6vk/PnzL3Bs0KBBtg4ODnxd8NKQd+nSJdfTp097kfQKHAq2b9/u9ueff77dsmWL+NChQ+t27doVfPDgwdxLly4ZTp8+3ZVtBEB9bNy4MfTVq1fmbHKKngfQoBw2bFhvEP+//fYbY2wRRBKOyvbGjRtOCACLfKGfy8vLS2BcAvGD8ZYfc1i619atWy2wQ2f58uVu3377rQeVHxHI7FgqJKlB58Cr/NdffxXFTpR66Y3g33//3ZHaNRFUX375pTo06a9cuWLXGHnIvr4gzAepDGJjYw1PnToVRIbLhQsXfggi19ay//bbb3VXrFhhifZZvxGtRSQ0lRGut3nz5iBI3IEgvHLlij3JEI4dOzYIz7lff/3VtjES6GPPW6FOBmt8NtbR0dEaPnw4I4XSWvIT10pKStK0t7ePo+9tSR/1RSxrjh07pgMZKejE4rqzZs2S+/XXX0sEoRzbAhpLz58/z0iXzJs3L1/QxlEKir127drcw4cPi8ORSk1NTdTDw6N3V5V/4CVA79+/72BrayuekZGhqqSkBMN9oaCt1zD2KCkpweBbV1BQIAxHEkFZy7QFyAMMe/n5+cwOlrS0NE1paelaxGdo6nnAEdAcOHThh16fPn3Cr1+/Lj5o0CB7Iux4tW4b0h8mMgL/GTlypNP169frMNkTtAFbUICH9/z58/3gDYvFHWbE7XUvIqFnzpzZf/r06T7/1TqhBQLW1hs2bPA4f/68PTwk2FrEbZWGYLdzGGeOHTvmOGPGDOjQwaOwW2ZmpuqOHTvsv/rqK5+KiopyXhKaviPw1oYNGyopSnhXqg9aFLq4uIQePnzYEh5jLdUhbi8QkYby3bNnTy87O7vEyspKcaiC/Pzzz6qxsbGJRIrS++jRox2cnJwiBM3Dgd0usL1027ZtuVgUksQJ77mUJ5A/33//vefatWs1LS0ttfEbGUROnDgRGhISwpBH1F+aelEa8CotLZXBltypU6dCt9kD9yGv2qbyQOdAX3XVqlVMMJE3b94oXbhwIZj6Yv12fSuQPh3pjU51DtkJ6Chv2rTJ7bvvvrM5fvy4CRxVv/nmG0Z6gMaPxMTE5FWrVknj/KZIzSlTpmTBUICMgBD6/fffNXgNC+2VH6QLBobVq1erYIs49Qk6h77PmTPH2dHR8fXx48dVIQWBPyoqKipOnDgxEee1FwFN+tU//vij++zZs/2hW03jB42PNLeA5zgMR+PGjctF22uqzCnv06ZNY3YSIfDguXPnpHkXw0S+YRFNuw06ggCietm6dWsZvLnZbX/Lli0q165dk0eaQQ5Pnz6d8fzhJ6h81q9f3w9a5CSlhrmBkZGRPki1GTNmuOI1YMAAOxkZGRmQ1OT1j/7+9ddfe0EntrF6mDRpUjau+/Dhw1ehoaE92WXKW+bs/7F/64i+T+0QRoE9e/bIlZeXY1x6b2xsrFtcXFyK58fEiRMZg1hb00N9Hrsf5s6di65W8tVXX7lfvnw5yN7enjFCs1/sPqKurp4NWYeLFy9qmJqaGuI6UVFRcSCaQTbgOxm17O3to8zMzIzPnz8fjrGaNzgkW+ubrt+QFEhnBBSntnn8+HEzxCDAMScnJxDGr9vqBY3rgsD4+uuv4//66y8d7HgCCY1p8ceIVfyXyDZcC/PKRYsWDUD8hNWrV8ugDqjsFi1apHj79u0ASN2xy549tjTnecv7n44ETYvwfPv7778DNm3a1GrZAarT4uJiuYKCAskBAwaEtIXMpnaJ8fzUqVOBNTU1dRs2bLDFb5aWlnGKiorS9+7ds6d8CHVR0PPqn3/+sYyLi0uxtrZWgRe0oDh0gfRE/8EO6YKCgtoXL15YT5o0Kdzb25vRgRaENLYVaKPIY1hYWJW7u3vEs2fP1IcMGRIlKPmjJYyTk1NkdHS0ds+ePYtevnyp29XbPoHGG39/fy1HR0dmN1JUVJSqra1tk1r+XT7jHAQX7ElqYxNZDq0nlEAYnzt3Tg1eFuTx0hIvUXbANSxosPiDDmZnb28XJFA5wEPu66+/tuHVumwK5IleVw+2x9jHQF4lmzZtMoV3+n+tTmhBDJmRgwcPvly7dq07EYz8loSgdk4L8zVr1rh///338SCT4VUKbecrV670Wbt2bQgFLGTXE/0XRp6BAweGdqW6oHKGpvahQ4cUQVI0Roh2FojwRGCz3bt3q2hqaubCOTs/P19+3759mUSYEtkFwmTWrFmlne0l2RiQppkzZ77ELobG9MWpDiAl8cUXX4ScPHnSYfbs2XHwcKTdFfC+u3LliiGbeG7OvXnJaHjY7ty5023OnDnhqamp6bxetg2BzoFxctCgQWk4dufOHUXIW5DnOsjPzz77jC+kT2tICiJpUFxiYmK1mzZt6oG2gbEWbQVk6Pr163Pj4uI+lCH7OmxN/1GjRhkR6X/nzp3IxMRERjaCCA2QH+wXmwhta35oPIHn+rp168rg0Vl/nLk2jYViYmJi69at64603blzJ5T6w9ixY7WxZbq9DZW49r1795ymTJmisWDBAv+ffvrJIyIiIhbp8Pb2frV161bP8ePHJ8yfP98JW3yb8h6nsoeMi5ubmzmO+fr6RoWGhhpRvaKc6TzaaUBGAWrjVDftMa+kxaunp6fdpUuX/GnMxLPAxMTE0Nrauuj+/fshODZkyBATNTW1bH7WAZUdSNe1a9cOWLly5XPoCLN3rdAcg76j/aPvIkAptiOfPn16QENe/xTg99NPP2VI0hs3bvzLu7c+n/8qc+pzvL9R+23vLed0n9DQUDOQwfgiIiIiqqyszJBuo0eP1oLhjzzi2nIvIt7grTlhwoTUN2/epEKn/f79+6YXL14MWLJkiSfklfr16xfq6ur6avz48b7bt2/3vHHjRvmPP/7opqyszAR8RKDE6dOnS6Snp2vy1sOkSZMK8X79+nUtSi+7fPECeQQtcA0NjSxLS8tYzEOx4xEBxdHn6Vw2cS3UAaC6wK6SM2fORNLOh9GjRzPkQ1vKH/WHPn337l3nH3/8MejRo0caY8eOfYF+QGQf73hMYwB5l0PK7dChQ96YV8KgOHfu3Gw8B0gfWUtLKwMGnLNnz8LoSOPsv0hn7HiEFyX6NebgGKswj4LRFdr9eG5g/kr3ZT9zhToQ9Jw6ceKEMBw4yEjSmv5IY0BYWJiZtLR0zeDBgwOpzJt7PaSFgnnivwcPHnw+ePBgqzFjxtRB4gO/jx07NuPFixdpmH+TwYD3GpifS0pKlqOd44U6RX3gHUGQUfbo77iXUCeCnkMwZISEhORiqTdu3LgcyocgkM8Yq4YPHy62efNmB9Svra2t8MOHDw1wTmOe//gv+gDqgdo5fsMxlD+O4xxB4HSIxH3w4IHW0KFDq/GMMDY2hkQWDLLdBYF8xrhRVVUlDG1qxHxPaUSarCuC5geQA5SRkXmHfvn69WtDVVXVqqbqoMtEHOXQdUAPxMYaHe8Dm0PLyhbvmJQeOXKku6qqqipv8KLWetxCj/XIkSMREydOLIUm2n9lcOTH9rHFixfnysvL92yMUGoI5InOe7w51yBSTk1NTW3hwoWemzdvZh5WQv8RkDbf0aNHI0eNGuXSGgNKS0GkPsp18uTJLpKSkv6rVq0SwYQAE8gbN2446urqvvjmm2/caScBK72Ml9/s2bPfP378mAl6Iej9g9oLJow7duzI1NfX79OS9tuRIMITXrebNm0KWLVqlTx4f29vb8OnT58GY/HPJs6HDRtmgQUhJjyCUg+UDkgirFmzpicRmrznkZElISHhzapVq4ozMjK0jIyMUkeOHInI8B/6+LVr1+JzcnIcG5vgN+SJ1dA5lLZ//vnHMSsrK+b48eMJkKX5WFsggnPZsmWmwcHBeXl5efCojlmyZIkaSeOMGjVK9ddffy2vqKiQ6sh6oHzjnvBq69evX0qfPn2YQIm02+fw4cMBT548cWtM05bS27dv3wRdXd2+OAav0YcPH0qIiorWVldXixOh0VRaaGHOJoJakx+kx9vb2xZSF1u2bHFn1w8Z5/r162fzySef+F28eFF+2rRpzO89e/Y07N279ytPT8/e7VkHRMhivHz27JlFZGRk/syZM8WKioqKNm3aJBEVFWVF5N/HtuJTOgcMGJAiLy+vj2MPHz4sx7DL+z8QDioqKiAeSoWFhd9VVFSIZmdny0H7mLxKAbbnIz/zjNfRo0d1P/300xwVFRUVavvz5s3TPn78ePr48eOFYDhycXHxvXHjxv/IvLT1/jSOnz9/vv+jR4+yFyxY4OHq6qqkp6enCkkikM7YvZOXl1cYGhqadu/evbcPHjywJq9a3vKg9PXu3TteX1+/N0jtkJAQHXbwMiwazc3NE42MjApUVVUZ+QMZGRmQQkKFhYXMsFZaWorAepKxsbEa8FilftIRGtIIyDpp0qRiGC3FxMTE66W2YBSICggIsOJHHZBMRlBQkOWwYcNyN2/e/Hz69OkuI0aM6DNixAjmHDJW885b0CdOnz79av/+/Q7suTR5hKJ8R40a1RM7HmJjYw3YEhBycnLFhoaGGcrKymUo5+rq6h75+fnS0OatJ/dgrK0EQQh7W2VlpUhGRoZCcnKyNl2jo8Zi3OfChQumCxcuLIQ++qBBg9R++uknZn7UluvWL2nqfvvttwEZGRn+Bw8eNJ89e3bwnj17evj6+to0FmQPpPC4ceOiV61aZaampuYaEhIS/eWXXwpHRUVZswnU4cOHxxcVFUkGBARY4DuVGxH+srKyzM6fyspKsYqKCiY4JH7H4xnTSLRxCQmJSmVl5UIREZEcpKewsFAWJCTVc0c9C8kI9OLFCwSVL1+xYkXp559/3urrEQl9//59JxD/8JxFgD08D/E7lWNDu67wQlnghXH7t99+SzUzMzN1c3PLTUpK0idCFOPXnj17qnnLqT6QahXJQsC4DKl9XI+C0tJ4WG8IrkM94DuWEEhjZxGOSJePj4+wqalprYGBgTiMHA0ZnjoCpKmNsh45cqT/jBkzui1fvtwYz0sYU+ALlZycrMM7Vtcbc2rp+Q1HELzjOpQHCsZORkfUaf1GU+Z+nTEXp/VYTEyMkbS0dC703RMTE0Xs7Oz4GiC4paByhMFKQ0OjCPM7BweHiJSUFDH83tXlT9igvGRnZ6MPJMOIlZWVJdGzZ88kckjgrQOOgObA90ZICzFY7AcMGJCprc3sKhZKTk6GFpo2tMPwXVCIg64GlNnKlSsjLC0t3dpKPvOS0AhcuGTJEo/du3e7/5cIz5aA8o12DJJr8uTJSWPHjnVsbhA8WmlkZmZm+fj4JJaVlYE4E9LX15e2tbU1wqKJfV4T6WA6yJQpU3qdOHEi7b9gMaVJKsaJn3/+OWTUqFGu/GrDLQ2ABy3P4uJi76+//tqhpqaGmRCcOnXKasCAAWF9+/a1Ztc3vbu5uVmamZklREZGmnaF/oG2Mn36dP8RI0YMEFTymZeExgLf09PT6/r1670wub18+XLdoEGDGPKZJFVAvAwYMCBckAhowuLFi9+oqak1WN7UppKTk1MXLVpUERcXZ4RFl5mZWZapqSmzHRXyPlhIenl5ycnKypZi4dXU/Xi3aje1qIRnyPz58+POnj2bZGxsbNDUmEZGMOixjx8/Pg7Bgzw9PSXnzJlTCdIHdWFpaWlsbm4eB5JGqINB/Q+Ezbx585Tr8/kO5RcSEhJ14sQJRle4sYUHlZWbm9sHD6rw8PD4hIQEFWpT0Eo0NTVN09fXL9HV1YX0khDUL968eSMWHx+vBPKITYa0JZgSLa7PnDlj98knn4SCbGa3ITJOLF++XHry5Mk6kZGRcdbW1maoJzc3txJPT0+h9ga1I2zTXrx4cYiOjo777t27PSIjIweQR3hzPfVx7oABA5hgeQUFBSBPFbBAxnF3d/fgoUOHlhkZGUmJiIggkF4NmikWziChEeNQXFy8oKioqDooKKj677//1kUwLqqDthgDGkonxpkzZ854wDhZf/w9JBbU1NRS4B2LQHIDBgyAEbOtt2wwDZSvrKwstcuXL1fu3btXFeSXsrJyvJSUVE1hYaFkTk6OQm5urjP9rynDC95dXFxKkA9/f/83OTk5dmjHICcGDx6cqKqq+i4nJ6d7UlKS9KtXr7QKCgrkKioqGPkCXBd9TllZuVhXV7dw7NixcWJiYrERERFSHh4e1qRVz047P8uinmgwvnPnzvNZs2YxRifkA3OIfv365QcEBPDN85CCjEGKZcWKFSonT56MmTx5crabm5uGkZGRDuSsaA4H0jkxMTH96dOnudeuXdONjY1l2gq7T9BnLMrV1NR6Xrt2Laaurs4Yv8Gr2draOllMTOxtWlqa3MuXL01AaDaVPkzbQfTp6urmmpiYBGdnZ0tHRESYNBZwsj3GAhgf/vrrr+eQjDMzM9PX19dPRX9pK+lDJDR2Xbx8+TJ7+/btVbdv33bKysqC9+ybmJiYmpycnB7i4uLvtbW1EdNGrm/fvr2EhYXdEDth27ZtHseOHetDOqvswHDDhw+X9PPzi62tre2D7ziupaWVKS0tXVlUVCT95s0bLSJcGwM0T+kzPHQVFBSKlZSUirBzi+qtI+Ym5IGL9Hp6emKu5wASHTufWnt/qlvIKPXv3//V+vXrIb0k/ujRI5uGyoV9D3l5+aI5c+a8Wrp0aS9/f/8aJycniZKSEjXUJQhKeJTLyclJ+vv765OBF/M8IpIxfuDV2D14iFCGDEVaRUVFqyUlJSvgp1BVVSXRkXNCet74+fkZzJs3Lyc/P79m5MiR8SdOnNBE3TRmMGkNGhrbaF3F3i0EL9SlS5f66+np9ViyZIktDJI4p3fv3uifzJyH+ijqBuVXTzaLNjWHYdcDL3mNa8Ae1xlENJVzcnJyjb29/esnT570nDJlSqS/v3+neaKjDOD5LCcnV46YIjhmZGRUHBYWpka/C/1HQHlJSEiQNzQ0LAwLCxOKiYnRcnZ2TgwPD2/wPxwBzYHv1h48YH744Yf4sWPH9hERETFlnwPNvKtXrz7funWrFR7SgkYeCDKorBDJds6cOQ781nElD9FFixZZX716NRUW0v9r9UP5xcNs+vTpvqtWrdIzMDBgFhItAS5SUVFRdeXKFVFvb28rNTW1XHh0qqqqJk+ZMqVo9uzZLlTejZHQRABhO+fw4cO9fvvtty5PQAMYI5YvX/58xowZfDOgtNbgAv3M+Ph4zwMHDrghXUVFRXKIVu/o6Mh4ZNP5qCPUBXRi3dzcMomAFtS6oLSBUFi7dm2j3riCBjK6rFixwsTX1zcPwSIjIiLUIyIi4hCIjR1U1c3NTfTs2bPs+JGdlj+6PwJwTJ061bYhrzhKKIiKZcuWFYSEhNhgAQDvKhsbmwq0N2i4wqPx8ePHYZGRkT0tLS2TnZycklRUVGplZWXhdQWio1tBQUGP+Ph4uYSEBC1oldI9mlr0k5dIVFSUyRdffBFx8eLFPIwtHyOhke5JkyZZ37179018fLxKeHh4AgyVlNY+ffrkBQUFdbhBmeYbAwcOjLK1te1Lz0P063379pXm5eUxwY4a+y/Siq2lvXv3VqHj9+7dy5eUlBRZtWrVq0GDBqlbWFgY9+jRQxHSPKg36FhKS0tLysnJyaFsiouLi728vKLPnz8v/PjxYwcirNqywwvk7r59++ocHBywxZUhaMlwhjyi7C0sLML+/vvvAhDQ+I+jo6MCebC2Zz3QtbGrYtKkSfrI/6lTpyyozTXnvmxyv1evXhrI28uXL+OzsrI0vvrqK8/+/fvLFRYW1nh4eHT//fff5ZOSknQaIiSwHdvCwiKlf//+VTt37izIz8/PPHnypAw8YNtqDGgIV69eNZwzZ042diWRzIubm5tifHx8NghoGxsbFWxNJhKdn3VAHrLIc1lZmQTukZqaipdWU0REQ9chcsnBwUEWefDy8norJydXMm/evAB5efn3z58/l7948aIFebo1hJycHFW8oqKiPnieIojsunXrXvj7+0s9fPjQEcfb0/PsypUrslOnTmXkikiGxN7eXgLv/LwnjZt4wasLLxBm2tramZqamoUyMjLVxcXF4vDKT09PN66urmaMcQ2NAzQe9e7dG1v0e/r6+kqgnbq6uoZJSkrWhoeHa4PQZd+/qR0w6O/Q/8UL/d/U1DRpyJAhwbGxsaodYZylckFdTJ8+HVuvJaysrDLp3m29PvKH8oHUx4IFC9S2bduWOmXKlIShQ4eqjRw5UhfzMTo3Nzc37/Hjx8F//fVXzYMHDyzy8/Pd2W2Q+hC+W1tb6x84cCASv4PAV1VVxfghFx0drdWQlEZDEhHs30Du4YVxEXN9BQWFkrS0NA0QcR3xXKT0PH/+vGrkyJHCI0aMiMKOibaQnzROPH/+3DY0NLRk0qRJr7Zv3x6QlZX1Li4uTiwjI0MmOztbXkxMrEZVVbW0Z8+exa6uriI2NjZamZmZCkuXLk1++vSpE67FlmPq3bt3ck5OjmhpaakRvuMZDCM85kFNjTkfSytIZ3yG9yuuWVVVJY7yF+oA0Bw0IyMDdZ6Zmpr61tHRUfTEiRN8GYt4ZU0auj99xjPC3d09xtXVtTYoKEj4l19+ccXx+ji2ohYWFtWBgYFMv8H1QFTj+jAstOV5SZ7vRETDe5qfxPvHQGUQGRkp1qdPn0LslMCuHbQF2sHQkcAOXx0dncyamhqR4OBgS+qj8vLydampqer43NnyIO1R/ikpKer29vZMIE4YzEVFReMhpYNdkrz/4QhoDnwBPWShkfXnn3/WGRsbu1A0bvZ5mKDMnj27v42NTcyUKVNq4FnQ2eRBVwGV05QpU9KlpKSM+a3lSoSnkpKS4vjx48N++eWX/1MENOUVD/Bt27ZFTps2rT+OUzk3l8AjNgy62n/++af+pUuXvA8fPqxeVVUllpCQoPn999/rRkZGem/bts2Jghk2dW1c65NPPpE8depUl5B+aAyUbicnp/D169f36exAeETofPPNN30CAwNDfXx8GELQ39/fMDAwMMrZ2dm6IS/Wfv36iR87dkwgiM+PYcGCBQnq6upugu793JDX7bRp02KPHTtmgm2Yz58/TwEBTYUOgLwCiUWePkICgBkzZmTIysqaNFTeREp/99134b6+vv1pUYZFopWVlSSP3m+PQ4cOxbu4uJhDt7ux+0HT2dvb2/vmzZviHh4etpjwN0WAUpsNDAzstWXLFp8jR44w0hONgQwv2F49YsSI4t27d+sHBQUlOzo6fvDItbGxYeaRHU0+435Y7EyePJkpZ8w16sn7oKdPnzLb8D92HR0dnSwDAwMt+v/AgQNlNmzYoCckJKT34MGDkEOHDvmHhIRoZmVlqZSXlzNeK+QBamBgkOHq6po3ZswYlatXr1pAUuXHH3/MunPnjnNbCNB6KQ6rv//+OwhBbxvygp46dWrxjRs35Og/JiYmGqqqqrlYAAu1I8iT0N7ePsbIyMjm6NGjnjk5OW6tyauurm6Wtra2IfIkLi4u/Ouvv8KDVGTjxo3SUVFRdrz3ZdcnnoOQAvHx8cGLKbNRo0b5b9iwoS4tLc1706ZN1vBQZMtKtAW4flJSku7Nmzc9IUFTf6ybtbW1UUBAwGt8NzAw0NTU1MwB+SbUTkB+CgoKFKksGvI0/tiilsh/MzMzeHmCMHu7YsWKmIcPH8pAxoVX0oTX85Duyya7QUb/+eefqljsT5gwIWTt2rUep0+ftoZxrD1IaNw3KCioZ2BgYDR2K1G/6NmzpyotdvkthULe37guiC20B7x4z6Xxt6k8Ozg4dAMbVFJSIjZy5MiXCQkJSjAMUt7YEjYfKzu29AGukZCQUOXo6BijoaHxClu+6Zz2GJ8pbS9fvjTHjgwrK6ueILhu3brFVy902jUHnfydO3fixchlyMjIFIAAReDi0tJShbdv3zIezeydEJRGKgPoaSsoKKgGBAQoIWiknJxcGXayEFlJ5zVVXg39Vk/iiWGnIjyi4QmO5wZ5nrbn85GuHRUVxTwTxo4dK3n+/Pk2E1xEQmMsPXXq1AAY2JydnaPt7e1LBw8eXId8CgsLd5eWlhbJysrqERgYWPPzzz/XxcbG2vD2BdJqtra2xs6vKvL6x84KjGl8KYj/50EN4lkUHtWYTn7Mk51fILI/Ozu7sra2FmMFvF3f8MsQhLLE2lRGRqYC3+tVCxnNfnFx8Vp1dXXs0qqQl5d/l5qaKvL999/bot6oH9JzUE1NrUdcXBwjFYUywnGsTflUDMx9SGaIxkqhDgCVb1xcnJKrq2sW7p2WltbD2to6AWR0R8hwkKEA/UJSUrIqJSVFg/0sQv/B+39R4vR9fV4KCgrgDPGeDB45OTkShoaG6REREaa8dfCfYd85dB5ogIMl7cSJE8XY2ouWR4Ep2ECPw28gE44cOZLSGRGcuyJoQooJ/pAhQ7Ta06MR1x4+fLgyrPntHdhIUEAPA0yITp8+/WbatGmuWFG3NigeMZP4//Tp01137NhRUu/tKAmL6OXLl+0PHz78gjwMG7sOEd9WVlY6ampqOV31gUVtCIvDrVu3MoaozvbKpTpCWr799tvumNzheElJiczjx48LGjof72ZmZuo4V1DrgtoyJr/Tpk2z6uxybm29TJw40VJDQ6MASQ8PD2eeHWxDkJaWlrq2tnanB3uh8obm35gxY0wbKm8yYl2/ft338uXLfdkksYKCQpGhoSETtIqIxuHDh/fBC+QzghF6enoG37171+/WrVsv7t+/7x8YGBiRn59foKOjo4Wx6urVqw7Xr1+PGDhwYBBtB22qTPDbjRs3+ly+fNmXSP8mzmUyOHr0aBM5ObnSsLAwYXZajY2N5fHs/9g9+Q3cz8zMLLF///6W7F0NZ86cESJt4I/1UUNDw3yUMW3ht7e3Nz127JifnZ1d8dy5c/teu3atHxaQJINCz2GQn/BqOXjwoNvgwYMtXFxc4kNCQjLOnj3rfPXq1UA8R9hbvluSJ7xj4n727FlxEITsZwQZnEeMGGGBgDYlJSUl+K6kpKSkpaXFeJ10RB1MmDChGO/nzp1rsZGa0mdgYFAEKRd81tTUlD948GDFF1984QISjR20msqcgt6xA99RsEJ8R9CycePG2cXHx7+9d+9eFgyd5JHVlryy83bnzh0F1AnNZeGBaWJiok5BVLW1tQvaow6I/CSvPho7iGCj7x+rB0qXjo5ODmSM8FlBQaHbjh07HEA+s4MQ8gZ85E0P1QnNEVFfWFj//vvv/R88eKCxYsWKcFNT08TWBkRrqizIY+/+/fvMM5vGW3V1dRV1dfU8oXYC5ZnaHm87pbbaWD3Q4tvAwEDm3bt3b3V1dUs9PT0t0ObZEjYtIUrY5+MaKBcQzzk5ObJDhw59CSKgvcZmapcYr/75559MHDM0NGS8WPk5PyKpBurv9cFnRUBcwvsbu2lpvMWLvJ0b8j43MjJCsNB3kK6B5vzr16+NyFO5Lelme06DdE5MTNRTVVXNh7GnvZ+NdO+MjAx55M3JyckM5Do/1m/s/o3n6rNnz+z37NnjvmLFiv54Rs6aNctp3LhxvZcuXepy9OhRN0hsUt9oqC8YGBiIJCQkMP2nrKxM6mMSY61NM10XnqhCHYikpKRaJSUlodevX9e4u7un4Fhbxz+UIYwbMPSlp6erYReMuLh4jZqaWrG6ujqkX8rz8/OlHjx4YLJv3z73P//80wXkM9uISOOwiIhI95ycHCXIlTQmd9JWUHpRx+AR+H39xu6J98zMTGUJCQmm74eFhamYm5szc5WOnJui7LGThchnOg6Jnurq6g4NGNuRIIIZG8TgpIFjycnJypDqaqgOOAKaA18aHTr7xIkTAx0cHHqRJ1JD5+IpTJHqBw8e7DB06FBmwdzeEbS7OqjjGhsbIwqzLnkB8vs+RPCYm5vrY4uh0P+hsoXV8vjx4ylubm52/AiKR8GwcC13d/feu3btypOSkqrEVjFMji9evGgUFxeXSAGmGrsG3hH8CDp/7PR2JdDCavz48cFOTk5WguKRS7rDSNPYsWPDURUiIiJ14eHhUtQG6FzWIldZXV2dIXsEEdQ+xo8f/wa7GZqrXd4QaBcLrkHyF3hHmZH2Jr/TT/1BRUVF+ZNPPsnDIjEzM1MKEghUB7ivmJiYmKamJkPACUJ5u7u7J8Jzm1d+g8o/Ly8vf8+ePdi+L8z2qKvXjvyg9Yn/w5v20KFDnhMmTAgaOnSoKCQwPv/8c+d58+b1nTlzptOYMWMMoZE7d+7cF3/++acPNKP79+9v++eff9rt2LHDE4Yeun5jE3Usuvfv36+dlZWV3dQYRASotra2pqOjY+rr168VUfcknaOmpgYui5lsdnSZDxw4MFMKTEJNDTSCu2PnwosXL3p+jHzHO7Ksqqr6YXGUnp6eMXny5MQdO3a4YutgQ+QSEQls8hPv0dHRxosWLeo3ZMiQSAMDA2U/Pz9hOzu7KAxzrSFAcf2AgICeL168iCTd8/rjzGdIp/Ts2bMoKSkpA8cxTmlpaZW2tjybmyZWADWLV69eRVM8j5YSZnhXU1NjgtsB33//fR6CXlF5E9HcFJlHi2ry7KL/Qk5p1apVtfv375ccPXq0Hz9IaEJYWJhRSEgI4/FMdYI+T79ra2sz3mntASKg+VGPCGKHeXhycnL6rl27bEHW8HqMtoYgxGdcB0GH9u/fbz19+vRU7HYk8rCtaWffD+9eXl7qlZWVFWQQkJaWllZQUCijfPLrfg3dn20UaYyoZ4PGD3ju6ujoKKempmb++eefzkQU8UO3nE1Eo29GRUVpDR48+BW/2n9TePbsGePFamRkxHjoE1HPz3tQf2fLorDHZ7aBqrFrqKmpVSASXmxsrCYIPXYMBX6kkZ4RSAdIaDwboYvcniQ0y/tQLicnJxeGsT59+iThGD/6Hbt/03OPiH4yWtUHsGOehw1JRVAalZSUENiRGWdIrkionVAfSJiR5RDqIKSnp/dQU1MTDggIkLW3t+f7GgfrR0jSwHDy4sULaxibPD097UJCQsyxA4qMBdQG8R8qY0VFxYL6OXw3PEvaWyIDJDTquSPKn9pXYWGhfL1xuDwmJkZfSUnpgxG7I9KAeTVJobHTBUhLS1cUFhZ2iEd4Z4DyXFRU1IM89TMyMtRkZWWZ5w9vHXAENIc2gyYDEyZMkG6Jtx3OnTx5Mkc8twBGRkaFWDjgCd8eBDR5u2Eib2BgkNdVCc+WAs128+bNIQMHDrTH5JSfusTkmTdo0CD7b775JqJ+MdAtLy9P4fHjx6n192+0jInA0tbWZhZWXQ308JeVlS1ZvHixmqB55FJa5s6dq66qqpoHz4CcnByZgoKCgobqBpN7BQWFCkHsG+yyHjNmTJt3StAuFrQ/Mk7hHYQX7QxoDyKaxqERI0ZoIjBQaWmpeEFBAUNyEgmOz6qqqjX8vG9bNOOHDRvG6PXylgXVwblz58KxaGAToPjs6uqaJi8vL0f9PD09PRMBCvfu3WsHbyMskmlCSy8s3LD9+/bt230XL17s8tlnnyXDOxr/X7JkidulS5diocvalAcUK7ha9MfaCOVryJAh7zMzMxULCwuL6DcZGRlpGNaEOriNg8hxd3dntP0o/Xfu3Cmk4FBNEZd4V1RULBw8eDDj/VMfNLYgOTmZISTYZAYvucT2KCIyhBbgCMbo4uKi+fz5cwTBsejXr18o6Zg2N390H9TxrVu3GO8tdv1QXfTr1697fn7+B+8uOTm5diWZqB0hgBqMQ9euXWN2H7Qkb9R+sRV18ODBTN1VVFRUFBQUSBBx0dqFItUV2gW80ydMmCC5fv16ZXiBtrQOeEHkETyanj59+q/dMey6UVBQeNtezwU22d4WIC/Ozs6M4S4zM7MI26/5uUAnT1TsEti9e7fD2LFjsyHN1x676eLi4iBnllh/X6bslZWV280I0BZQ3hEgTlVVVTk+Pj6PJJP4qVXOGyAwIiJCa8CAAaGNGST5cS+8R0REGECH2dzc3BAyFx1hBKBxuCXe/yYmJrUFBQVF8JxuL2KK/azAMxaej7T9Xqgdgb5cXl7OPIv79u3LGFfbywhARD+N2aQB3FQMCrwrKChIREdHMwZ38tAVakdgzO4IT1yWF7qkmpqaZFRUlI66uroEv2Sg2GAT/+Rtzt5B0ZgRBrIQ8IAGid1R+sMgZLGRuCOMYNSfhYWFu8nKypbVy7EwUi+dsUOPnS68Kysrl8GfQ+g/DlFR0fdkCIYRAoIIZIRjn8cR0BzaBBrwQHgYGRmpNdczl8gMExMTZdIPFDQyRxChqqrKeA21h+chga6tpKTUIVtnBMF7f/jw4QHz589nIqq3R1A88rSdN2+e6/Dhw4MQKwsL5ZiYmO7N0YCm4AVCXRDUrwcNGhRpaWnJSBTwU7u8rSDvz169epm6uromIaZRTU2NcH5+fgm7/IkUxWd5efkO86hoTVn37t073szMjAny0pqyJpIrNzc398yZM16bN2/2XLp0qc/ChQt9ly9f7r1r1y6Px48fByIIGRHRjXnQtgaUZnNzcyMDA4OCoqIiqdLS0kresQ8THaFORr38RmafPn0MiKBn/QZvlB6Qy/jjjz+gLfyvifKYMWP8f/zxR3ucQ/mqrq6uKS0tlcDEjU06sxfZbO8vvEJCQiwWLFjgtGbNGq/S0tLSAQMG2F24cCEHZEdDE2/2RPDatWuGH/OCpue6s7OzPrZ+lpeXfyB5hIWFEXH9gzdrR0FLSyvLysqK0duFlj7a4vPnzz+qgUxluWHDhsiRI0c6weCI4yUlJdWlpaWtCoREC3AM8/j/nDlznKH9f/fuXeuePXsmtNYD1MfHRwekDrtuWLJMmG8J8ZKf7QVKv4uLC0MuPX36lNHObslClkjmNWvWBLi4uDDSKVidFxUVSfJrQYwFLxa78AabNm2a+I8//qjUljrgxYsXLxRodwzvPAwxI/HeHqRKc0i25pS9vb195KhRo5hAd7Ad1tTU8H0LNpU1PKuPHTtmPnfu3ETI9PBLco8lSSLx8uVLZjcS9Q8ZGRmmPwviegJp/uSTT+LQdhDTtD3TSSQ0AlVmZ2fLYEdGexgB6PkC2Yng4OAkOMj06tUrTZDqAOnA+Iw5t5GRETxwqzoqbSiflJQULejD03O7ve4Fog+BcrErwNbWlvFI57dxozWgZy68UsXFxUULCwsZeYyOkrGDMRd13547rSkv0HWXlpYWRXyr2trat3p6enzvC2ziny2F1Fh50r3hzJGbm1vbXsaopp7JRJK3530on4WFhVjHM3Wdk5MjrK+vn83+vTNRVlb2n/WAJpSUlHzwAAeKiorE1NTU/kciTmBIAA5dG+Li4lWioqIMcdcSjzsxMTEsFjp88dqVwPayEwDVgv9cuUIjbN26dQgUQuROu3iW4x0rglWrVski6ja241RWVvZo7j0FyGm42WBvWx8zZswHYlNIwEBpGjZsGLaLMeNRZWVldWPnYROCkADDzc2t9GPavk2BdlgEBgYmbdu2zerixYs2V65ccbl+/Xq/S5cuuf7888/us2fPthw3blzGsWPHPCEB0Zb7NZYGLNQdHByqCgsL5aqrq//HAAMOSKgTQZOpXr16pauqqqryepwTKfLgwYMobMdl64SCEPjll1+s4FHPNtyiHOHJRNp8vMQTW++VFiDUz86cOTNg/vz5ryH3ATmsw4cPJ1IwmIYm3ziGwE73799nZAUa65tEqkOGQ09PL5fdN3r06IGFfYftZKJ8mJmZZUKKggIdh4SEJCQmJmo3x+A4bdo077lz5w6AjATJ7EDyv60Bc4h4w7N6+fLlrl5eXq/+/PNP6DVWtmYBlJqaqhkYGJjIawjDu56engaCD9JxMTG+84j/ArVBBGGFQSU+Pl6/JSQCORpAp3zgwIGqZ86c8cfxenUfvj7cSHYDAcHWrVtXdPDgwUoQEPwggOLi4jSSk5MZUqEBAlqovdAWsobyDC+kzz77LO/atWuMvBrUldjemvwmQFHn2MHx4MEDhZkzZ77ktxQHEBISwhS6IBm1GyP/HR0dI9PT0xEHBNru7T6hozqIjIw0VVBQqIRBsj2IGLpeaWkpM3eytramnRsCM9dD/4cEQXp6OrSfOywAGMqgXq9aDp7h7emJSZ620dHRSZB5qZdc6FDPz4ZA0jOQt6uqqqoRFhbu8PlzZWWlBILutfd98Himvp2ZmVllZmaW3dmav9TWoX5aXV3dKW0Bhnm0gY64FwyrZGxIS0uT1NbWZuTJOrsfYJ5TW1v7nydxamtr8cD7ML5Cnxze3/jMEdAc+A54OpSVlf2Ph1pTwHnwaiMPjI6aEHRFoNPW62512D3fvn37n64PIm2GDRsWamtra06EV3vdjwlZ/O7dOzs7OwtXV9c3UPogr+bm9Bk+cnsdCpSxtrZ2Rr9+/Yx5PUQFBZQmJycnQwUFhVK0DRkZGQne84j8KS8vb1fttNaAiE1sNezTp49CW4wp9D9VVVUpGEooajP7Be+zV69emW/evLn/jBkzYiMjI2PJ05+f+bK1tWUieUtKSory5gnRxoUEANbW1syzj9eDmPSTHz58+EG3EsfhCbRv377uCICHckMwObYBrKWEHBHSKP7Hjx87fPXVV/Hwgho6dKjjF1984duQ5xubePrnn38kKFBiY/fA70ibhYVFKbys6Dg8iKuqqjrcq8PCwoLxwqb29vLlyxLSlGxoLkHHsSjfvXu33R9//OFz5cqVHMqziIgIPPnbvEgkTyQsOD///HMjBQUFuW3btr0kj8TmXINIAyzaXr582aCkAAwXMAhQmysvL2/XvkAeyra2tjovX76MJ4/v5s7biITYsmWL1O7du8sKCwvJW7hdyCDyeERwvdDQ0IIVK1b4tCYwJC/y8/OVoqOjsxt6blOAIUElQKdOnRr69OlT2by8PDJ8t+t9qbz9/PysJSQk3vPTE53aTFxcnCICQ9LcTdDs2zTuIBidrKxs1cuXL02FhYU7bLyksT8wMNDEysrqTXuus0RERJhrm5ubM2u6jtrm35zyR0wbyMKUl5cj8HSLd7m0FjSW495wbsD8rL3qAP1KWlpaIi4urkhcXFxMV1dXYDw/IW0HL/nO6p/1klqMRFN734ueC4mJie+MjIyqOrsOaG5YUlIiKS4u3i4Gx+akoV4eo9138iKPNTU1zBiblZUlp6CgIBC7h+EwIykp+Z93uJSSkgJX9aG9FxQUyMjKyv6PJFCnPxw4dG3QwxUERURERCZbn7M5HnZBQUGMNg8XhPDj5Txt2rSApUuXmrenhAFdG+8I/CX0HwVNSjFhmzhxonBHeebSPUaMGCFWUFCgYGVlxUwCmuozVNc5OTnt6+LWDqCHjbW1dRoCKQqa/jOB5DXU1dXVEMwLVmoKDMcOfEdSE8XFxW0OBtVeUFNTyzU2Nma2WLd1nJCVlZWAHAl51rC9b9nehF5eXnaff/652MuXLyPI0NLWfFC5Gxsbq8nJyZVKSkr+T/uvrKzsLgje/ebm5uKNPeMyMzOzQkNDtdlGgvnz5wfa2NiYQfpi06ZN5bW1tXWUXykpKQlo9bWF8Ll3757TwYMHA3Bs9erV9qampolNEaDh4eG6KSkp6c2RUrG3txdh1wU8kKurqzuMUKGFk4mJCXNPIp4iIiLEm+P9vG7dOqaN7t+/X4PdPZSUlKTExMT4oilOZQ096uXLl4fNmzdvgImJSZN10BgiIiKY2BqNGUep3RQWFvZo77YuLS1dpqGhoRYcHNwiDXwsOpH32bNnv0hOTi569OiRvZycHLV3SXl5+XbxmCSN3W3bttmOHDlSFR6grZV7YwcXe/36dYOedDU1/6/5CALhQ6A0a2lpZcjJyb1HwCqa6khISIhQv2hPKQhc++bNmyYjR45kYl7wExkZGUo5OTl51A/y8/PFBcmhhfoOyN9Xr14ZQL0FQVPV1NQkOiKdVLcYi8rLy0U1NTUz+Vnf9ExDO1JRUZHEMT09PVkyegh1MpBXjD94psKgV1lZie89OrKN0H1yc3MVVVRU2sULHUCZg1x/8wb+LbUIdlksCOMRkb5oD5ClIG/IjgacJrAjrD2uTWWsqqqKDYFMflNSUkQ1ND6qCtbuQJ7rA+QJKygodJrzDElxtNf1abyRlZXFmoXp4/n5+fIyMjJ8DTTaWtTU1CCIc9f0JGsBsEMY60b6XlRUJCsuLv6Wtw46/eHAoeuC9Cdp4L106RKjBYrPTZF5bCLnwoULqoI0WRQ00OJg3LhxL/bs2eOCbd443l4kHtVbXl5eXlJS0n+6bpAvXV3ddGdnZ9OO8syle1hbW2vr6+unDRkyxBTfGyMXqK9gwZKent5kcC1BhpWVVYMeooIESpu2tna1rKxspaKiomJDhDm8VfPy8mQErS5oHNbV1c1TUlJS5JMHtBI8whs6hy0DgXtDYmLFihUSycnJqU3pCbc0DUpKSgpGRkbZcnJyMnSc+ktGRsb/eKl3JOp1DSt0dHTkGviNqY/IyMi0nJwcJVqQq6io5C1dutQMv506dSo6NjZWE/2b/oeAhCoqKg2WeXNAZMDRo0ftQ0JCouBlvXTp0vTGzkfd5eXlKUVERGSw093Y2NWnTx99dXV1FTpeWIi4fyVMQLmOInNAKGhpaTEGUmgtlJWVlb1580bhYyQcCMj58+e7HD9+3L+wsFCWvctHXl4esXc/BPVrK8hDGIEiY2NjEzdv3tyqQDgpKSkI+ljYWN1QPykqKmo3IwClWU1NLQ99Lzo6ukX9DmUPEmLSpEkKe/bsUYTsWkYG09wgHSKuqKjYLlujycgM8u3x48c5c+fODW+LwwOVw5s3b/4lN0fvOTk5IoL2XCCDx5AhQxKePn2qjM+5ubmMAUldXV2O5GHaC9TmERCvqqqqm6GhYTI/tYhRtxkZGR8CBnc22cYL5BWGG8gU5ebmKtfV1Ynk5OTkq6ioSHdUWyFiJjY2VsfY2LhdAgSiHSkpKTF5UlNTU0BcoPa4T0tA91ZSUsovLCxk5g/JycmisrKy0kREdlT6yFELMhQYC9uj3mVkZMqkpaWl4uPjQbTX6OrqtksgwuaC7guJTvJILSoqqlJRUemUIN71c1bGINqOBHR1QUEBM6ZmZ2dLKSkpdfozATGHMAYUFxfL1NbWMrxNZ6WnvR0O0a+rqqreIZ4HvpeUlMjU1dV160yDGJU11o2dLRvYUcjPz2f4CgBygrQc5AhoDm0Gb8RyDL5PnjzpjcA7jPgexOV4CAhMDrFdlhEC7NGjx8GDB71CQ0PNBMVSLmigctHV1U374YcfDEhjtT09SMkTODg4+E1WVhaCHP0nB0vKl6WlZYa8vLx8R3nmskm1gwcPlsPjtql7E+GQlpaW+ebNG3WhLgZ62BgbG/N1yyONJfy+Jt7V1NSwzZzxUmHfg35PT0/PycrK+kDACRo0NDTKW0MAs3cBkEe4nJycnImJSV4z/sv85fXr10bbt29P5acHNLYrOzk5lYKYZf+OYHsZGRnynT25l5WVLVVVVf2Xtzwbr1+/rkA0bpr0jx07Ngr9PiEh4c3jx4/VZWVlyzMzM5n2hucmAuoZGhoyi/fWgMoC212PHj1ahM9jx4611tPTS+UlfuhckKXR0dFNel1T3iD7AKaW6jgjI6OwuLj4w2SzIwBCB+QZfc/NzS3Izc1l6qAh0IJn6tSpEXV1dbVnzpwxwuM0LS1NjPo4jC3q6uoM0csv0C6BvXv3Zo4aNcqpNR640A7Nzc1tkICmZwckCNLS0v7HCMIvUHo1NDSY9kT3ak6/I5mO/v37h+Xk5FREREQwRtfU1FQxakN6enrtRoJSeV+8eNG4f//+8pietjYwF+U3Ozub8bKl3WI03mZkZHSIIaYlQF6x7V9NTe1tSEgIY/hKS0tTqK6uroIhSVFRkfGSbG+gDvz9/ZX79u3LeEHzQ4YD18Tilggf9AVVVdVO3/JOoDwaGhqmpaamKpAXYHJycp6urq4GSNuO0ujFPYqKiuTr5azK+b3mUlFRKVBVVWUM3yBBZWRk+GbMaytkZGQq8vPzmfJPSUmRk5KSAjHIGC06GmVlZRJycnJ8JedZ5GeBuLi4RGpqqkxxcXE5fCiEOhHUttEPSGozOzu7WldXt0PJfzZqa2uhRdxu99fV1X0PnXF8zs3NlSFZms6ao7LzCGOdsLAwpN9KO0sbnOIztKOxqQBTVXi717c7UWlp6fdiYmKdHjgefZ8lu9lN6D8GypOcnNzbsrIyZjcMSckJCwu/J098Op8j/Ti0Cpi8IKACXjSRwUR3w4YNNjdv3nyBRTR5TNUHmWG2I4N4xuhz+vRpr23btg3oTEucIIO2tZmbm8cfOnQoT1NTU6O9NYrr78vg9u3bNezgVkL/URgbG1d1hmcuJol9+vSx+hjxTYRDYGBgGjTkulJ9UFrx0NHS0pLh87WZsYSfsilUD6amphLjxo1jCBa2VzzaCO4XGhqaA817Qa0LDQ2NZuuKs9t+QEBABDxm6Rgdd3Z2/p9gjI0BZXL//n0bT0/PEH4FJUS9TJ482QjPDXa+3rx5k5GWltbpRhkpKSlEt2EIqYb6clpaGuMpTvIYo0ePZsja27dvw8inUFVVJZqYmMiQQFReNjY2H3YWtUUu4OnTp+avX79OgCFh8ODBSQ0RP3T91NTUFgURpvYRExNTDFKlI5/lGFOgcUnfCwoKSsvLyyWbSCvTj6dOnar58OHDVwhOhz6ckpKikJ+fn0+euGZmZnwloFHnKJO//vrLFiTxyJEjmf7VEg+giooK8dzc3NLGCGi8Z2Vl5WRkZDDere1ZBzIyMszCOicnhyH7m0NiUfsaMWJE+aNHj5iAmCAB4LEO13n8Zm5u3m6LdOoLqPPS0tIaW1vbGPKMbu01EVQHGuv112fyV1BQUJiWlqZE9xQSAFAezczMkrKzs3uQJEl6erpqWlpaFgxJ+vr6OTinPckI0kWPiooyVFVVreOH0wm7jHNycj48oyQkJARmlxWlUUlJqRzPKirjyMjIMuxM0dLS6jCNXrpHfn6+tIaGRi6/7kvX0NPTy8NzBp/RrmjnVGdr3xLpASMwjiUmJmrhOWtqaprZkcHhqC3AI1NKSqpBXf/WgsrY1NQ0D7upkpOTVaurq2vV1NQ6PVYJyh/PQZYX/jtTU9MOjxnBIwPBd0kGlrONeExMDJPX6upqkN3Eg3QK34a5BqgDmp+VlZW91dbW7jRt8PYqB8qLjo5OblFR0Vv2PCs3NxfBWDs9CGRBQYGiiIjIh0DkQv8xIE/1wZ6FiouL/+UQUe87+a/29p8rAA7tC+rQM2bM8H758qUMXrNmzXpO22LhdbVo0aI+a9as8UJwJWaWWQ94dwUHB0cuXLjQ9+uvvx5AHtSCMlkXFNCDwsjI6M2lS5fE+vfvb0vl2J73pXvExMTE//33371w7L9eN1paWh+VjGkvkFGmqXPo97///vvDTgOhLgZMQGVkZBol6FoCIr22bdvmcfLkSS/y1OVHOsm4M2jQINu+ffta4zO7z+Ez7uft7d1NkOsCwZ5aQ0AHBQUVeHh45JKOP9XVJ598oqOiovLRBSuNFRUVFVJXr14lL7Ru/KgXfX19HdZ9mDQEBwd3qiGAykJWVrZCUlLyf8hPajsFBQUf9LP19fVTe/fubYIFsKenpzyMSpDnCA0NrWX/p2/fvurw5GhtvtiBj54+fZqGAurfv3+TW0ELCgr+J8hjU6C0BgcHd/g8UkJCoppNQBcVFVXCE7Khc4nsQnmamZkZ3b17lyH3q6qqxJOTkzXi4+MZIgJwdHT8sIWfH/2btpuiTwQFBcUMHz68RTr+9d4jYkVFRQ0agagvREdHZ+bn5yu215jE8i5i0lFZWdlsDXzyNjY0NJTy9fXVIekelH1ycjIjB2BjY6MsJSVV3l5eWSQVFxoaWtG3b9/8thJPlZWVInV1dW/Z42d8fHw6+rKQAIHK0tjYuCA+Pp4xApMkSWRkZBbaj7W1dYfoxOK+8EqDDIeqqmqr5GjYYP+3pqbmw2dFRUWB0dikwJsYUiG/QOunwMBAMZS9paVlu8hhNJYWMh4pKiqW8fu+vXr1KsEzATuT8MwWFRXt1MBflDfsloGRl9ogiJH09PQsR0fHYnYci45C/W6o9/yct1A++vTpU4PdkiC63r59+15KSqrjotY3If8A7WH6/urVKwVjY2OGnGrtThRBlIEg6TPIsYWGhjLylaWlpVI9evRgAoN31poB5c/edZWWlvbe1NS03XTImwt+G37oej179ix68+bNv9ZoCA4sISHRqTtjkD4YPyAHQrsvugnoGrKNMm25iM3D28fKysrgpPKvPscR0Bxa1MCoka1evVpHQkJCEq9Vq1bp4Ri8K0hr8cyZMwOGDRumNXTo0OiZM2f6T58+PWDw4MEJw4YNM7t+/Xq//1LH4zfIO2fHjh25enp6OrBotzf5zA4euXPnzlxM0v4veKe3s0N5k/hYnZLcCjwYPTw8LHCsK9YHP/s6ES79+/eXW7du3QBfX99QlBE/5ThERUVFeUk4Ms7k5OTkPH/+3KAr1EVLyd/8/Pz3z58/lyU9dNpWbmJiYjho0KDYluQ3MDBQKxduB/Vb1IXaCPY1qN88ffqUIVQ7+1kCR/yGdqaQcaS4uPhD4BVzc/NsSUlJeN6mR0dHY2wXBSkTEBCghC3x2DmE/4AotbOzS+RH+oKCgkSQFgsLCzWQfI3JcBQVFYnTmPOxOiPZAXiwBgYGanZ0fxAVFa0VExP7IOuDzQmNeZSw5JaSkeagoCBtIkFhMPf39//g9ezi4qKjqqqaw8+80LzJx8en0MbGRrelC+96I32T9fHixYvKjtixJCYmRsatZt2D0gMDFhbiycnJH4JxggSFEQnfe/bsqWtqaprcXumm+o6IiJAwNTVt80MfZU3PHKqb0NDQQpBLnT0eNQQ1NbXa1NTUf5Hj/v7+1ejrLi4uEh3hkcXywBXW1dXli9c1Bca0trb+oP8uJiYYsZopb5BbQFBj9m/BwcG6KPuBAwcyhseOAPVZyCVJSUkxOxn4MVbQNdzc3CTxPIiJiUkWpPkRnhVkMKPnsL+/f+rAgQMVaVzo6Dbx9u1bxlOQX9clRy5XV1eNwMDAdBBdVVVVtfCZEOokUF4xBXz79i0CvTNpCQsLM5KWlhZXV1fP7mgZCLoX/Cv4SYCyncYQ4DImJsYQx9HuoLncHpITLQCz+47KPzw8XLpXr16dqg3e2kDATYHyZ25u/i40NFSRfUxSUvJ9J9fBh7LOyckR1dPT6zDDY0eB+pOBgUFOZmbmBwcFymNdXR3GvH/VAUdAc2g20JlBMuNzVFRUFsk1REVFfdjKxNZ8gtdPcHCw5f37950ePHjQJzw8vCdtQxaUyYmggcrQ2tr69eDBg+1BAIGUaO/7wjsdW9zPnj37/M6dO86cLrdgAJ3pxIkT6f9XDAIfAzN4vH371s3NzW7dunUeWVlZHaIzSCTD3bt3Y9LT0zUFVX6DjZYSv1JSUpic6mdlZWVTkFi6xvz58+Xk5eWLmjtpgkZ2bGwsE2WMn3rQdK2kpKSUFy9emOBzZ9dDc4qZJr86OjqMF0ZiYmJOQUGBAnlmRkREGAYHB4PkhzxBLdr5uHHjamiR2loZDrwnJSXJo9w0NTXVlJSUCpvKR0u85nGuv7//66SkJN2OnkizCUCgqWRT2vT19ctA8mdmZqqwy8fT01MOzz98NjQ01HN2duYL8c8G7pWQkCCC4KDQX+XHwhvlj3aCPHl7e3dIwOCGAsk0Uzs6v6SkpIYCM9LvHh4ejBEBRplBgwa1mxQEpReSK7Kysswcll+EKysI5AfP+c4ekwiUR/SPgoKCf8mm+Pj4qKEP9evXr6eOjk56e5NBLP1sCWlpab5psMJwYWNjY0zPhk7YzNYkxMXFq4uKipignWQkQkBG7BAdNmyYKUkUdNQYih0VNTU1PfjRTuka0LZ3cXGxwPMgOzu7nEg+IQGAuLh4De2Oofz+9ddfIr179+4J40VHlj2hrq6O0WXH57bem9Zqampq2ebm5kaPHz+muA54Rgt1FqiskT4yuGLsx6611NTUwoEDB8Yh7+0ZkK6xNNVLUvDtvjSP++STT1Lj4uLySJIM1csKrdIpoGCbNO6HhIQYaWtrM04RneWBjrJBIGJ+XhN5gaa+ioqKSHh4uBGOseRhBWGdwNw/Li5O2djYuNlrqa4GAwOD0tevXzPSiGyFAyxreOdcHAHN4aOgTmJgYJAybdo0BBmsW716tcn+/fs98Fq1apUJezKDd5poYaDBQIcXDdKdNeh1pbLu3bv3B+/B9g46SIGvHjx4ELB58+be9ccFYvLY3igpKRHIBwAF6gwKCoq8evVqb0Fa1LZmsk3blfkB0n5ev369+7hx4/rSMaF2AvXBioqK8nPnzqkJCTiIJGruuEHn6erqilZUVEjcvn07hrxgUa5Y3Tg4OPSaOnVqaHNJCsgbUGCo9qiLGzduJObl5Sl1djRvvFdWVopC37extMrIyIBQrkO5KSgoMGVXVFTEaOzjM45XVlZK3r59u4itbf7pp59a29jYvG5r/srKysSrqqoqxcXFxRGMie7Jex5IIepHzZEGAm7dulVNO586sh6gi1xZWfkhqAw8vT7m0aSjo/M2Pz+/EB7n+E7pDQ4ONgkPD4+j+po8eTLfyAH2fdLT06Wxu0JJSalFi496D+puTWi3x0BbV6gDUFJSwpC3zfUeozyqqKgwW/55y8TPz884OTk5FWU/evRoBOQsaQ8ilO5XXl4uLicnJ9ZWAhr5593BVFJS0k1Qd9PBA6yiooKRCaLyjY6ONgoJCYmu14dP7KgFMeRLFBUVmx1X4GMYOnRoLmJqkBGptLS0U4N+EagsoYXM9vwiwu369esZCEbr4OAQTWuljkgTykVeXr6KiKm2gNLs4uISCyPSP//8U9mjR49u5OUrJACod3hiPtPa09PTExKD74cOHRrW0SRofZqYdTE/rkXr6tGjR7+urKys9Pb2RoDdd6Kioph3d7oeuri4eBVvn0A7+fTTT0U62gOdAA4DnvHsNLUFlI9hw4bJ/vXXX+Rl3b3euMTEPKDzhDoYIPzpvmhzCERaWlr61traOrYjNdDZAEFPQSn5AeSrXoImJicnpxZzPNQxS3e9R1VV1b+MUB0Nmm8kJCToKSsrw+Gv7r+kA10f7LhKTk7uXUpKihbvmqA+9se/nCn/M5nn0H6gB6Wrq2vyvn37HPBAgd7gDz/84I4XPjf0ICEiGg2TghB09qRQ0EEPQxkZmXrDactMp+zgYR87DxN2rETg+Xzt2jXfJUuWmOFhhd//r9RTQkICMwa2t8RJS0ByDwhytGnTpnfQDuyqdYL2jG3JOTk5DAHBL1cAIkjb0zjDNgagPi5cuBCEXRyCbgzIy8trVVvW1dWVQST7O3fuKBQXFxezDWB4/+qrr3rZ2dlF80o48ILKB06yQu3QL/Lz8wv++OMPfRwThHooLS2VrKio+J+gQtTWFRUVMdl8C29mCQkJ8k76HwmMu3fvmmZkZGRSvAQEqVqyZEkJvDraslBCscEA1JguJ1vflzzfP3I9Rh88ISHhzcOHDztFGgjBfWpqaj540EhJSSHYT5MeNVDWqampqePZHsyMr9euXcuhvA8aNMjG2dk5it95qqysZMhbtIUWasjWSEtL/88uKBr7rl27VgoPv/Ycl1gyLcxCmncr5ceACOjs8YCI0dzcXOX79+8nIi9WVlY9Bw0aFNmebQnGUH5sS5eQkGB2KbDrobi4WCAdK1DOEhKMzeVfZY/F4M2bNxkt0KlTpypi8dgRW+JhX5OQkGgT+cYybOSOGzdOl6ceBGYuB+AxijzTdyIdbt26ZYTxZu7cucyOiI4cQ9Ef0RTaeh1K8+zZs0Wxc+fly5dq2tracshXSUkJs5bobLBJHtIlxTrnn3/+ebVw4UIZWqd2cJr4dk9aV8+ZM0fj3r17odnZ2Wr1wcB6gIwT6mRAfoP1mRkj79y5Y2lmZqauoqKS97H5ZHuXf1v7HXmg6+nppWppaSk8ePDAmrWOL4MEB8bazvJ2baideXp6ig4bNoyRQOkMAro9xjtcb8iQISWPHj1iYh2wry8mJga+o9MDcmLsQVuADJWZmRlj9O2M8uc3yABgbW0dn5aWJkp66OxzlJSU/schSaAe1BwEE9RxL1261M/U1LQWCzY0Nlhw8PovbiPoLNCgmZqayuh2Npdgw8qTiDIijt42AtLvBPGcl5eX/+2333p+8cUXfaDJKOjkGr8RExOjALJHUAhoMjqg3jdv3hwYEBBg1VWlN2gxi4dRcnIyX6N+Ax1BPmNmiX7y5s2b1AMHDph2hbEuKyuL0d9qbpum88zNzfX19PRy09LSlC5duvSKCDkioJWVlZV27979TkNDI6spooIWeRT9m1+gfnHy5MmwhIQEfUEZq8rKyqB9Wc5rYKHPampq8EZivKDLysqYY+Li4v/eita9+7vMzEz1s2fPviYNbpT9mDFjnD799NOQtuQTZKGYmJgYCAJ4gPL+TtfW1NRkFqvN0X+ur4dkCnzX0fVQXl4uWVBQ8MGrVlVVVVZSUrLiY7tdpKWlJdnjKb3fvn3bND09nZGMQVktW7bsLaQy+JlmJSWlChRUZWVlizx/JCUlsa1UmleKBm0ERoAnT54YC3UQCgoKGC9aVVXVZgXRYe0SgJd8g79duXJFDbtL8HnhwoVSVO78HGvpWuiHtbW1b9ty/fqdDBUS9awui/hsd6m01gCkT1FR0b8CArFI0J6YAzo4OFgOGjQorCMIaMjMFxUVtbmskNaRI0fGIEYB+gMZBMrKyjqdaGCjqqoK3n4f0kSL8zdv3ug8fvw4aPTo0Q4kgdJRZER5ebkwiqwt16Bx1NzcPN7d3b33w4cPgwsLC2XU1NQU4IlLBHRnP6MbK9NDhw7JODk5WUG3t6OJOCzR+HE/krOEY4CFhYXJr7/++kHnXVxcXLigoKDT56tsqQukFZwBdq+FhYVlzJ49O4Lmix2ZJvaOA35IoCAPCxYsSPT19U0FP0KSa5h70XSqs9YOMCzSZ5Kbefz4sbWZmZk4YoJ0hgQN6ptfmsy0xkRgP21tbVFfX99etNOennOysrKMEUCok0Hp8fPz03V0dGxyHdWVQAYFe3v7/BcvXjC78XgNPAhCyKt+IBCkC4euATQeBO1h60Hj1dkTjP8SqNN6e3sbI6BHUx5pRDITSYQJeFhYWAy2tNL3hoDfUlNT048ePer56aefFh89etStM7ZRdyYonzExMfpxcXFv2EEYOwt0f9TRTz/95HH69OkB/xUt7qioKKGuBiLiYKDYsGFDFgjC+uPdBNx4JQ/v+eYEkwMoiCM8bl1cXIqwPfTq1atqILdIggMrZpzTu3dvy0OHDqVToLbGFlHQVoQnFF2fX5I0UVFRcSdPnvzgYSIIgOEuPT2d0VZuqLxNTEzEsRUTnq+FhYVMX1ZRUZHEJJw9SUOefv/9d0t2ueN98+bNumZmZvEtnayyPJvLIf1QUlJSyksK0Dl4NzU1FWluPURERMReuXLFpjPqgXZVZGZmltAxRUVFOUVFxQ/f2aC8JicniyooKMjLyMiUsn/D9TIyMjROnjwZR97nn3zyicOECROC+bFAoP/r6OhUQgoFpD07XR+DvLx8qYqKijy7L1E7i4uLy4EXsVA7gyUjwtxLQ0OjpCUEdGZmphy2hOMzb5vHrpI7d+4wBq++fftajx8/ni/l3hAUFBTKiouLGemBlhJAlB70W2tr61IaE6nNZGRkMHNjQQKRI2gusrKyTLuneR4ZvS5duhSBwl6+fLk4kRXtUfZ0TWVl5YqSkhLRtl4HhMPixYs18JmeUficmZnJGGs6G9T2i4uLpXnbGuVh//79Yhibly9fntARZASRfdXV1cJtJWSoHa1cuZKJGXHhwoXuGINlZWVlcnNzC2jnXmcDu2XwOKXveIShPoKCgizxHNu4cWNmRxNBWLLV1NSI8GM+if9/++23Ff7+/uGhoaFm1NaUlJRkMzIyOt0YA2Mxu2wpv4cOHZKfOnWqPuZGHe0FDa9sXl3wtpCfmO+OHj3a6NChQ9rsayoqKpYWFhb+a9dVZ3mnsr9D8u3Vq1dVNMfpaAMASbPy41pkhJk6dWq4j49PLQJ7s6+NnXHYnUT13Zmg5y6Mj+Li4u+xg6czdgDwE/Qc0NXVTYNxOSsrS43XaQ51AA1o4pnoeJcnNjh0LLpyR+kKoAEqJydH9cCBA8G0yCHPZQK+E8lM29KPHTvmOX/+fPHp06dXL1q0yPfAgQOed+/e9Xv8+HEgXrdv336xd+9ej9mzZ/sPGzZM+Ntvv3WLi4szpAmLoBJr7VnOmCT//fffac0l69pTh5u8Hrds2eK5e/dud5rcCHVhUJsKDQ1Vgk4u6dsKCTjIIIE62b59u88///zj2FU80dPT01XS0tKYKMvNLWtq/9OmTTOWk5Mrh27wjh07MklXk/SgMe4MGjTI/tSpU5mmpqaJNHmiF11PX18/y8jISIeu3Zb8kMct2s+mTZtKW0rgtfcYgklVfHx8KW95U77Nzc1VZWVly8XExGpSUlIYD+SePXvqYPsp+1p4z83NVfnpp58y2IYBXV1d7V27dpWB9K8/3qL+Y2pqWoxrJScnZ8LzqKF8IMBkr169VJqqL6oHtIkdO3YUQUuQnfaOAJsoSEpKYsgypEdeXl5eR0enwQCLlL64uDhFtGE9Pb1M3nLE5/Pnz1vBwIEdD+j7GzduNLW0tIzlFwltZWXVDf2StuM2t9y0tbULFBUVFRsioN+8eVNJAY/asx7o2vn5+UqlpaWlZmZmzfJSpv9lZGSoSUlJicDjirc88f3w4cOquC7y9fXXXxtgOzM/F2Z0HRMTk6K8vLxWaX+Sl4+FhUX8kiVLLNh9vaCgoCA3N7fD+8PHQHmsqqrqRtrjBKqHkydPmuTm5uaB/J8yZcrLhrbP8hNKSko1BQUFrfaOpb4za9asMDMzM+P4+Pgk7FDCbxiUcnJy5AShHlje/+KysrL/0rwmEtTPz8/a09MzeO7cuf2MjY2T6Hh7pktCQgI6zR8Mj625BgWWs7GxiZk8ebLLo0ePXgYHBxuoqamVQo87JSUlD0SQIDi1wPscgSB5Da5437JlSxniiKDsG2v35C1LL36MSZDCaqsGLgWRg/fzwIED7Tdv3vxhVw8MSZKSkhJv3rz5HzmCjgJL5upfGtv1Nuy3ISEh5snJyfkLFix4gbLnJSSp3GmnNb14CdXWpImtD9wWkBPBihUrAkNCQlLj4+MNiBDF72pqamXs4LRCnQCEeGLfmzyez58/bz106FBRtJWG1plEEvOmm1/9gB8eybRGhlNBv379JP744w8mXhLbqxvGAXbahQQE3t7e6sOGDYtpzJGHd03V3N86GjT3HDJkSOKzZ8+YNR8v6o1M/9PfujS5waHj0dmTif8LoAHpwIEDA37//ffnCBBInssEfC8rKyuDF/PKlSufjxgxonjTpk1uEH+PiYkxunbtWr/vv//e7fPPP3eePHmyA15z587tu337dvd79+45kZXqv0BytrUtX716VReL344moUFykCEBpEdiYmLy559/Hnj48GE3QZi48xPwNI+Ojk4UBE/zjwHpI8L14MGDngcPHhzQVfoJ0llYWKgQFhaW1ZKyJtkekJ3z58/PhVZqWFiYxr59+7zJCMYmoV1cXGz++OMPkbFjx75gT+brg9/UDBw4MFtKSkqKdme0Nj9saaHt27f7eXp69hZEQ0B4eDjlvztvmerr6+sYGBgUoFxSUlIUsrOzs5WUlBRtbW2TeSeS+Hz79m2nixcvepOxBvl3dXW13bt3b5KysjJDWjdn8kkEU79+/bBL6X1wcHA+yHL2AonQs2fPVGNjY72PEdDI09mzZ30fP37MTPSFOhHR0dE92MS4hYVF+cd2u2BngIuLSzZ7Cy79XlBQoPjjjz8WkcFXTU1NbdeuXVXwUsHvrc0vFkO4h6urq46/v386jrXE+8fc3LyESHE6Ru0sKiqqe0fOSZCX+Pj4NDs7u2Z7sOJ/0FwtLS2tsbCwSGKXJV03KirK5OjRo0GoR4xBW7ZsSYPXDL8JaFtb23dhYWGtClKHa2Ch++2338IYpMqOQZCWlpabm5vLGAkEEenp6eKGhob5vO0e39PS0jT3798fiWPffPONib6+fiot4PnZx+n5qays/DYlJYXZTdTSZyrtCINhaPny5dY4dvr06RQRERHG0zMzMzMPw6uQAAG7RuGB29huts2bN2PLvvBPP/3E6HETeIO4s1+tqRf6j6KiYlFJSQlDfrb2OpS2n376qQY4cOAAo/uprKzMEL1xcXGlgkL4VFZWShABzUuCenh49Pbz8ws7ePBgKZsIIuKT5uHIG73IY7Q1Hpx0D1y3LZq07L558ODBHv/888/LwMDAXjQX09DQyEWfSExM7LRde3RPzCUhP8b7G9K/detWmeXLl5vJyckV0zG8aI5SH79CmP2i5ykRoa1JU1vLH6D+DMeA2bNnW3z//ffa1F6obrS0tKpSU1M73QudXU5UdnDk8PPzq5o3b54/2wBA51L8LsoP+zj1g9YS0fwaF8gAsGTJkqCHDx9Ww6GMd30gJyeHXU8M2d3Zc1aADF3h4eE9IQ2ipaWVwTa2s+dGVMbo15gPkWwJ/dbZeaE5IWSMMCUlAww7wDreIY9XXl7OzBk5D2gOHAQY7AnP6tWr+y9cuNDX19c3FN4d1dXVVZmZmVk3btzwnTx5cuLmzZsHnD9/vj86Pjo2rIrsBwbbO5FtySdCTRAGsc4CDe6xsbGG586d++Bt3o73+6DLje+4H2ZmCPh2/PhxzzFjxojcv3/fSRAJtraWcUVFBYK+MFs1hQQYbB31Xbt2efzwww/9cbwr1Ad74vv8+XPGsNESbXOS+5k5c2b/sWPHJmLh8Oeff+pfuHDhORbIvCQ0iNVTp045Hz169KWzs3MYyBl5efliXV3d9MmTJ+vyKU+INSD822+/eR07dsxV0AwB1C7CwsKUMTYT6Uy/ozxhQHRxcYHX2buioiJpPz8/xhAzevToGna7os+Y0G3dutUiJCQkCnmncePTTz91OnLkSDIIoqbkTwBqBzjX3d3dDHX75MmT/z8SGQ8GDhxYAO1jMog1Rj4jj1evXlWkxVtn9ovIyEhlkB+k/Wpvby9GgQh5PWzrt52L+/r6Rk2YMEGZnrHsc/Af7HQ4duyYN+oMHvfwCt21a1e8rKxssyQneEF1ZGBgkGJkZKR/+/ZtieaUGz2vJSQkKuzt7Wmr9nt2XSDvkZGRSh1VD5SXgICAHHt7e8bThVfTr6n/vXr1qmLQoEF5DS2IkdcjR444BAYGRuAYvBK/+OILX35545Jnqbm5uezz58+ZAKYtGUdo3vTVV18FQ56FnhNkFIiKiipA+xK0ZzflMSoqStPAwKCSd+FK48jp06edsIVfQ0ND/aeffspC4FM2mdLQ/LEl6aBrKSsr58MmWVxcLNcaCRRcA33ip59+qsCugDt37vhFRkbKwWiBc+Li4vIw1xAUAz6RVNh+rKioyOzQoLIj0iciIsL0yJEjnoMHD3aYN2+eF47DWMkbxJ39ag0BR/dVV1cvzMnJabW3PnneLlmy5Lmzs7P1oUOHfENDQ43RJXR1dUGAQlbnXxJPnQHKW0VFhSR2H7GP0Wek78svv5RFPmbMmOGN5xpJQpC8JMhR7PaC1jXiX+C/VA9sYq65wC6Q2tpa4baUD+oA6Vu0aJGXpaWl6erVq3XZ/dLU1DSrtLS0PDs7WxXfO3POBA9cCrzLbvtIL9r+gwcPYnft2hWB8sTzG2WOz/BcdXNzC164cKHXd99957l161bP1atXe8DpAc9TWiO31CBD5dTW8YGItgMHDiRfunQpCrIKvEYmDQ2Nd0lJSbKUZ6FOQL2x5V8GACr/kydP9nF3d5cECUrzJPwGorNXr16xo0aN8p8wYYLvpEmTfCdOnPgCnz/77DO/fv36hSoqKhawiejmpqe+rto8j6fdh9i9YGNjI3HhwoV+NDbxGNxKCgoK/if2SWeCxp579+4ZjR8/Po4tg0I7GDQ1NTNB7BoaGiZrampmQXJKT08vDd8hd4Fxia7XWeMs3XfMmDFv7ty507Ox9ZmUlFRVSUkJEz+EDYFZyHHgwOF/gQ59/fr1fqNGjbJxcXGpcnV1zXJzcxNZsGBBP2zfYxPNvJMrXoKZTWwLwuRckB4EBw8etMBWTiLa2uE+zI1IhxuzlNevXyfAw3bUqFHZGzdudEtPT9f8r2g+s0Ft7fbt29rwNOcl6QQBJG2DuoGczfLly71//vlnd0GxNDcXlFYfHx8dGKxaUta0uwINcPPmzf0nT56cAJ3CH3/80Qweuegb9fdgyom8mydMmNDv2rVrRkeOHIl2dXV9PWvWrBRTU1MmMFRbgnvS/7ELZOvWrY608BYSQMTGxupGR0cnNSbDMXz4cH1Im6A6/v77b6bcMKZD25lNstF4BO+UJUuWiEPPn01Cg/w6f/589cCBA4PYHhPsFx3DtWCkhLd1XFxcoo+Pjzl+Y3sn4Bx4+I4ZM0b3Y97PeI+JiXkTFxfX4Da7jgK1gfj4eJ34+PhkamOOjo4G2trajJ5nY/89ffr0O+iYYxLf0MIJZbJz506HJ0+eBIKQBwk9ZsyYvrt3745UUFAobKkcB5XxvHnzkmBofP78OfPMbs4YjwWJnp5eRp8+ff5VN1QXCQkJKQkJCQzx1hGgcvX09JQA4aemppbdnMUn5fX27du6rq6uCrz5p+vCe2nNmjUihYWFDFG3cePGftOnT/dm94/WeOXSwq5fv35hlZWVtcnJyQxR0NyxhJ7JCxcu9P7yyy/7kw56/W9MPgIDA4UEEdReU1NTtaSkpN6zPQ3pd7xAnq9bt04Uz4xhw4Y5bty4MQAGRZJMoVdrvd+o/hwdHRPi4+MZMqC1JPbWrVsDsSMEsiE//vijep8+fYqob4SEhFQLiuctQG0sPT1dQVdXN4s339S2f/rpJ4eYmJj4HTt2OA8aNCgIQw8IiAEDBoQsWLDAa8uWLR47duzAbkfPkSNH+qPvsQm45qYF95aTk6siYrKlz1MiPvv06RMO43xQUFDk/v37HTEkVVVVifXq1UsCNwkPD2e04jv7eU3b8fHiDSpLZY+Axtu3b/c4dOiQq52dXRSRWps2bfLw9fWNDw8P7+Ht7a377Nkz3aCgILmoqKjsM2fOvOjfv/8r6hPNqQOqdzk5OcRiaLU+NtWBiYlJ4s8//zxg9erVz7Gjlb0GtLe3L09ISMjuDH1fXpDuK+8alcpt06ZN9n379jUAsQzZFnV19ewNGzZ4HDhwIHzQoEGlFRUV3QMCAiT8/PwkEhMTxYyMjKrXrl2bevDgweeoA7aXbnPSIyIiUkMGgNYCBC2cvSZPnuxjaGiotGPHDle25yeRoAoKCt3j4+M1OrMvNBRkkMqrqqpK4ujRo8I//PBDMhm7QC7PnDnzhZaWVnF0dLTagwcPbP78889+eN25c8fR39/fAMamYcOGRY0ZM+YFjPMtMRTXt8c2lwXdb/Pmzdn79u2T5l0fUJ4VFBQqs7OzGVkmQVlbU/mnpKRoZ2RkiGDMJ+MXyGUdHZ1M9IW0tDQNOBfiPPRxjFXJycnahYWFchhHQFDjOd3SeSk/QGT/p59+6v/69WuJ7Ozsf41BPB7QIKD/R3ar07cGcODAoXGwrZIUBI3Xkvyx8uvsSaAggwZuaK9+9dVX6X/88Ue5pKSkVFvJMzboWhUVFeW3bt0KSUxMrAsKCpINDw/XLygoMMI5tCAWlAdke5RxTEyM8a1bt57PmjXrX4t4QQAtYB88eBDw888/y4aFhTHetl2t71BZJyUl6Xp4ePiPHTu2LwWza87/2QTkhg0b3K2srPyPHDkiuXLlyn5RUVGe33//vQsRotQ/UJfoM/DQ/fTTTykdH35vDah9YGvp5s2b7aCXKIj1QUQMJAY8PDyybW1tzdkENHlJGhoa6vXv39/z/v37hkFBQVpYuNvb21uuWbMmfOHChca8Hrv4Dn3+uXPnRp89ezYNhB+0jlEuiHZ//vz58hMnTnifPXvWEIQa/scuH9qmPm/evF74fv78eURnZ/T+ebe6fvrppzGmpqb9mxrzKE/Pnj3LKi4uZgIdddZYxS5zX1/fTJQHIpyoq6ur9enTxwdtv7FF2KNHj3pnZWVlr1+/Pn3RokV6DQWCxHVXrFihffHixWjUJ0joiRMn9pOXlw/csGFDCRYBzck/1QcWaAsWLHDav3+/X01NjTsWr41t/6V6wbXNzMwSt2/fXqmrq2vMrhuqixcvXmSUlJQYd1S/YEVwN0U7HDhwYPyVK1dUP1YWRDRgp1FpaWnAkCFDAh8+fOjI9laisse21LVr1/r++uuvTvBC37Nnj323bt28r1+/bl9bWytK57OC63VrgRGg8sqVK83W42TPseCZ+v333/dj9w8a4yDrEhwcrEb5EBIw0DbZiIgISTc3t+g7d+44N1b2P/74o9fevXsHLF++3K2wsNAjKipKysHBoVxZWbmHsLBwt4KCgrdRUVHCfn5+ujTuNKcvULnY2tpWHDt2zBafmzt+sPsEPCAXLlzojg60ffv26JKSEnNXV1fGwxAZCgwMFCgdbhbRoGViYhLCa/hgeelKrV+/vvTy5ct1u3btUr5y5UrQhAkTdE1NTe0aum5OTk7OnTt3vI4fP66XmJio97E6oN+hrV5SUiKO8aelYzi1GRAkZ86cUYEzwRdffCGB8RLXUlBQKLK3t9eHAf/169fMGCwo89mCggI5aKDzescTcbZ///7+Q4cOjTh8+LD4kydPni9ZssQFxND169fjvby8StPT0+WRdxiRrays8j777DOJ69ev9/L39w9duXKlPOqgqXGdgHtBDiQ9Pb1VpCS1H+wCuHnzpsRff/3l//vvv/enexPZ3KdPH1lfX99W7dppD2A3Hbw6QXjSMco7AuItW7Ysdv/+/VoKCgpen376qdTdu3chf2eNttXYNR0dHSMWLFhQPHr0aK9vv/3Wubma4/CGb0uATDIAwIi9c+fOXnAgov5E/R3v8FiFLxPWlp09d8Wjird9kgyNp6ennYuLi8fSpUs94+PjsVvg3eXLlx1hlOS9Dkh3EI14+fv7Qx4sfsKECa/8/Pw0o6OjjZszpsAbG8/ytuSnPqidyLp16zyCg4O7BQcH/+uZBlA6pKSk6rKyslQE6bnAnhfdvn27z4oVK7yTk5PfwDCCoJ0pKSn/kvNi92HkEbJOeNV7SudISUlV8BqhOkp6w8TEpHL//v3uvOUPUFqgeY9g7exjzHXaO6EcOHBoG3h1mGiQEaTBtCuDCA1vb2/bVatWheLJxi85Dtqqi+3S69evD16zZo3Dvn373KFlC83R/2tyKMePH9coKSkp6cygj2yQTjLS9OWXX3ovXLjQMiwsjCHYump9UHv6448/GN3YlsqesD2hR40a5XTt2jXDY8eOvYiKipJZvXq1X0pKShrOo/pjaxWTJ3lbpVaIuL1w4QITKFSQxztK14MHD5TQzxvzOp8zZ46OtLR0Jcbz48ePF6O8xo0b5zxt2jRvHCN9N7omiuDVq1fmM2bMqIiIiIglD3Qi/FeuXOl27dq1t6tWrfKANxS8J3ANBBSEF9F3331XoqKiopyQkPDm8ePHWrzejLi+urp6zrx589Q+kr8Pkg/IIzvPnY1Hjx5JsNvb6NGjheE92NDCm7Zsbt68OQGEsrW1dQx70Q7QcxbG3vnz50uFhYXFkCc0vM8vX778Hh4f9B9ez3M2UBfoh999992r2traOkhMkEce+zz2NXB/LK6w1fXcuXOS8PTkNQzQ58ePH7fKk7StxnAEnwwODo6ePHmyREv75eHDh8VWrlzZqP4s6uj+/ft227dvf44yR0CzvXv39lmwYEGAtbV1LBY8VK7N8fohsgCBurS0tGSgr06Lp6b+R+M//o/+tXPnThcQ4nhOIFYDzqHxLjw8PCEuLo4vkkPtATK+PHv2zMrW1rYKzZm3zvAd48f9+/d77ty50wN527hxY/8LFy70XrNmjfvnn3/ef8aMGa5ffvml27Fjx1wePnwovn37dk9IEnzM+41ICXjWZWZm9kD7aUiH/mNGgOXLl3sicDa+79q1y9vDw8PA1NQ03cLCgil7BCKMiIjQF6TxifKANpidnS1tYmKSxNtuqexDQ0MNDx06FABjJYy/2EUEL29vb+9XV65c8Tl//vxzEI4Yz6FBvmDBggEPHjyQ5t0l0BRMTEyyo6KiWlxGRC4gcO6FCxcqINWyaNGiWBhJqT8aGBhkwAjo7e39GkSvIMyhqKwh+SIhIVFNEk3s3ymNO3bsqDU3NzeG8WXr1q0+w4YNw+4vzNXt4IUIwyaex5A8nDRpkkPfvn3TsKPC19dXgzwYG/M2ZknQ5JWWlkq2xmORrcF75cqV2Orq6toFCxbYUN3QswX929jYWP3JkycCQ7phKCcZDjZo3vP8+XPb+Pj4bBi/vvnmGyWQ6iCfG9I/J23uly9f9lq8eLFLXFyc0NmzZ181RyarnoTF7r1WcV/07IAB4NatWyKbN28Oi4yMNG1I99bMzCw1PT2d5sid6oUOshae37zHSXt4165dritWrDCDhjtiREE2pbEdLmwpJpDOFy9edLa3t8+EQaA5z4Lm7gD7mPf56NGj/QwNDUUOHDjghmMNkZ+QksISCG1JEAwxvHj//j2CegtfuXLFdMWKFamZmZlq2P3IW/bseTsdw+/oVxiX8Bk7ZjrCE5rqDztKJk+e/Ob333+3aaxOaT6LPlddXc04EbF/5whoDhy6CMhDVhAmFP810IMTwRsXL14ciO3SpHXbmqB5FGCQNJ6XLVsWePny5X5v3779sB3t/xLxTA9GTFiOHDnS7nrbzQWRO8ePHw++dOmSS1VVlXhbJ0iCUtZeXl69/Pz8wltb1vQ/BBOcNGmSy82bN3tv2rTJRFxcHJ4mDOhckpfhPd4akA4x0v7s2bNegjhx5AXS+OrVq54vXryIIPKel0w3MTExnDhxYiaOBQUF6fzxxx8v6oMr9nJxcQmlBSyvTiIWOFOnTpW5e/euH0n41NUDusJbtmxxv3PnjtTBgwcDZ8yYAX3EjFWrVr0GYQoCY/Xq1UWFhYUy0FTEC5Ib2Go8bdq0F7t27Urr1auXaVMe62TEQN6QR0GoDxoz/f39e8bFxTHSSUinu7t7L2tra0ZTj/c/5PEDSSvEVDhz5owkaU42pEmMif3ixYtFfHx8QkFCo4yMjY0Nzpw5Y79z504/eP+w08IuF9wHi6TBgwcHzp07d8CGDRtesQPk8BLXdA2Q4nv37n3566+/Ouvp6emQAZOuS+0Kkir+/v4m7P92BCi9Fy5cKOzfv78NZEka2uLbWNn7+vraZGRklC1ZssQT7Z1tdAHqI9qXX7x4sdevv/7qhzLHyvmHH35wu3Xrlvb9+/el//zzz/BZs2Y9xwKoqQUXu3x/+ukneJZWfSyt7OcyjDi//PKLH/oXEo8dTBs2bAijc8nw8ejRo3xB1H9mA2mD96Gfn5/UxIkTXzYmGwADmbq6OmNQpFgItPMCwGcY6FVUVFSWLl3qdvv27UrIMbCDKPECx1HPw4YNK7p165ZVc5+v7L4CCQq0AXz55ZdfPM6fP28OO5+lpWUxjGw439vbO5W0pQWpHqhsoqKijExMTPKIQGOfA+9NGxubxAkTJjDkMICdGosWLUpZvny58q5du3S+++47qyVLlvQaO3as6IIFC3xfvnwZoaysrHT48GGQR56N1SkZAOCxiUBQ8JxrSRkRuQNjwx9//JGL58WyZcu8Hz165MgOQNyvXz8miOKDBw8+yOUICRByc/+/9q4DKqpra1voIF06IkUEBZSiNAUxmlgixt5FEwvGhi2aaBSXqPGpL7ESC7GLBTWKz9gFVBBpUqSL0nsHYbD965vFfuv+8wYEFRnwfGvpDHduPe2e8+29v12opKmpyZeloHvjhIi/9PLyko2Pj09BuVtbW0tg/IaHIZ6RCDduUkhEwiC5+86dO4P9/PysSQ6iIQKu3ku8Eh6x+Ls5bZQbIXnixInQ3r1763377bcSGHe4BmXsa2VllYQuGxsbaywqc9l6uRi+tq0wAyEiTDIyMmoOHz4ctH///iquo5Wg/jlpc1NytkOHDjn5+vq+3bt3bwLO1xgBinoW5tnbFBDJjM9//vkn4++//045efLkQEHyk65vbm5e9uTJky743trjUX35/z8nBy6ZPGnSpEdTp04tHjVqlASMtdSOhd03tUOan2LsQjkYGxuXYYxpjISulz/5YO9n8uK2tbWNnTlzptjy5cv7CjMoU7/W0NAoKi4ulhYlWSYu3r592wkGI9z/0aNHVZcsWfKwqeMmd95J0fGY27ckCU3nRT0sXrw4zM/PrxuS3gvzvKZ9seaoqqoSmn+m1QcmBgYGBlEAvTj//vtv+/Hjx2c9fvw4FgtPIpCIjBbm3UietLQPJRjEOSZOnJgJ0oOb2ESUPTpbEngpeXt724AAaim97aaCDAQhISExf/75Jz8sWJR1hpsKrr7b3r17eR/jkcz1bka7hocTvK86tCDIO37//v218KTCNlGuEypvTMSPHz9O3reCkzE+KT137lwbExOTXCxi9u3bp4HxQVFRUfGvv/7SghcVN8EULR7xPScnR3PBggXm69atC0T4tVg9QAZhRq6urq4OI8Hvv/8+8Pz58xpz584diDIsLCwsHTx4cNnPP/+cvH79+pSjR48mX758ufzvv//W3bVr18Bhw4b1f1/7oN/wbE0Nc21pUBkhrO/SpUsZ2IZygGf49OnTS7keyoLHYdvcuXPVNTQ0VA8cOBBOBJHgAgV/l5SUyC9btkz+3//+dwBm0ahDlPusWbMGXrx4scuGDRuC+vbtmyAhIfFfr1IssvHdysrqqa+vb9/bt2+HQ0ICZDddi/sOwLHY18vLK+jChQtqU6dOHUDtRVA6h+oKz4yJ/+cm22ihd+XKFQvcy6RJk2Ka6uFFbfnnn3/uOXv2bN1evXqlCPMaxPMsWrTo6dSpU80429516dKlC8hGZ2dnS7TdM2fOpCA5WEPPT4vVjRs3BiYlJZXduXPHRliYqKAHer0sTaifn18FvH6xD2QFpk2bloyuAA9VnBjEOEjp69eva9F9dxBRkAEA0idIFIeICdpG+0hJSdWuX78+D97OXI1/tHcaA/Adz42CwtgDg8yJEyc00X6FLXyJnJk1a1ZwUFCQLLyf3zd+ENGH9gLS8/Dhw6EeHh6D8NuWLVsCduzY0R/fa2trJVxcXCip57tr166JVKIpQWDshKaqnZ0d31OQjI34B8/if/3rXwowKCLaxdvbOxDv2i1btsgbGRnlYwGPY+BFhnfBxYsXHaZNm6aJ/Ag4t6enpzOSgwkjQMnD2szMLDcyMrJnU6U3SKIJfQgJyfz9/XmWlpamS5cuve/r6zuA6pbGNFdXVyTi5d27d49vGBMF4hOgdonxUlxc/DVFAtGYj+87d+6MxfvAzc1NHJ6Irq6u/ffs2ZMKsow0jIl0IyKUCNDt27cPwvvh3LlzpqhHXJNbB9TekZwZJHhjxhphIA9z1MWZM2fCBg4caDpkyJBKkE7CPG9HjRpV+ujRowwQ6E2NNPgcgNSGtLR0Ld0ntUO8ByC78euvv/b76aefnPLy8qquXLkSi30a09em9RTatr+/v93jx49rV61axY8mE3ZMvTcsv/6ae+/U1nGtmzdvJkdFRRWuX7/eWZjsChk5tbS0kHiXL7H4IddsifGH6wVN5T948ODI1NRUhaioqF4bNmxQ9fT05KG/CzMQC4LaMv75+fnZODk5ZVAEmiDoXB9aFvWetGI2NjZxq1evfgWZtIbWB9TG1NXVyzIyMlRFaTwSvEdtbe08yG5ERUWZQppzwYIF/ES0TSWSaT+8F+Tl5atp/vmp75drEF64cOHDgIAAFUTAcMcgYZCVla0pLS39H/kN/jk/9U0yMDAwtFXQIjkiIqL3+PHj9deuXRuIkEcilPFJ5AAX2MbdB8fg2IkTJ+ohVEzUvHJaA/T88ARcvnx5l9zc3DzyMm8t8hn3sGLFChksjDu0I9BE4fbt21b+/v6hH+NxTt7NJC3RktIp5PGJe4Zer6h5Ub2vvG/cuGERHh4eJ1jeROCAIPX09FSVk5ODN2anNWvWiEVFRSXAoxBefQgzxyKHJqC0kML4Ac+dI0eO9Js5c2b+yZMn71dXV1eDDOImKQSUlZWVyRMd+sggb2bOnDlwypQpA1xcXKxNTEyMQOaRwawx8pnqA8+EZxMF8plA9+Hn59cdydNQFnimMWPGWMKLR9h90viOxfv48eNToZG+a9eu+0Sk0CKJvHzwXV9fv+jp06eSt27digXpRuUG0n/JkiXOly5d0jp06FD0Dz/8EATPHHV19SJnZ+eoU6dOqUZERCSsWbOmq4qKSikWZrg2FlKqqqrF8EJHgrEjR47E/P3333rwKEXCSFyT3icCz8v3SkVEzYULF/iyA5+7LqhNwtP08uXLjxcuXNijqV52dK9FRUUqy5cvrzx69KgYPHYoDJuIslWrVj2F1AO8O6kdk1GK2jlW/ZAnOXXqFLSyU8lLUdADfebMmfft7e1VVq5caUfXoAWzoJwZfrezs4s5cOBAGLzc0XdwridPniTMnz//eXx8vO7ixYs1uZ7o9+7di0tMTDRsC+MUkQWHDx82mzVrViY8k7hkjbu7e+SoUaPsULaUaDYrKyuHEgeXlpaWHTp0KMjPzy8Y+6O/YV8YBXbv3i0BzVPuopnImUGDBkVKS0u/IwOAsLbCDesmog8h1leuXKlFH8U9QCJrx44dg0AKlpWVKejq6hY6ODiY4F4TExOfhYSE8JOsisr4JMxgBq9ZBGsgyR2VPX5buXJlPCQ3UN5r1659uWfPHpMNGzYEQhLiyJEjxi4uLskwtpHWLP6hDJAf4cyZMw9xjS1btuipqakVcOuAzu/i4hIdFRWlCwL7feVDxDN5n44fPz741q1b3XR1dbVmzZr1CBIURExT/+nZs+czS0vLXrdu3XpSUFDA14UXpXqgMsnMzNRCYleugdfe3j4GkkzTpk3LRtTLnTt3rIcOHZpsZ2fX48aNGwkYq2mM4vZzIjOx3cvLaxAS4m7fvp2fVJjrYY1rKysrl6DMILvX1HcoRQ6gnBGNcffu3TQzMzMdJyenCtynoDGNiE8XFxedM2fOkGFGpOrg5cuXMlypDGxbsmRJwdatWyEQLY3ndXNzs8vKyqq6d+9eEspNUJ5MGLGK3/fu3evcq1cvaRjXBL1w8Tu0t5ur/cytAyT+DAgIyAgPDy9ctmzZ/3g+c58JnsDl5eV86RdR8bytl3vge/XT3xiz1dTUeKGhoeaYl4BQXLFihcKWLVteIgEqkdCNvd+ob2F+Gh0dreLg4PA/Uhz4HYZ25HJp7n1THeB9/vXXX4f9/PPPvMWLF2tgDteQMY2uLyUl9QrayHSfHUQE1E7k5eUroPuMqBSUf0BAgBWkDpcvXx5A0XlNMe7Ts+Xk5KhpamoW0jU+1f3SextGnBUrVgSFhoYqoc0IM+gLAvWA5xP626e6QQYGBob2AHp5YbLi7e3tPGLECDmEHCIEHvq3pBHNBbbhN+yzaNGiByNGjJDFsVg0iEoYnCiAJitISLVgwYJcLCw/NwlN5HN9Ep0cJEcUtQXTx4KeBRPIzZs3q0GKgTz5P+a8n0JioyFQ5ADuFfeMCSe2t4V6oXuER8auXbvgYSVM35ZPSoNo2LhxYw2IiOrqaslly5Z1vHv3boS0tLQMwswvXLiQMGLEiFDoDBIxSotdXCc3N1d527ZthsOHD8/28vIKwLhDkRnkMcsl6nj1gIYzN4qDDGZNeLZ3eCZR80bnkjrnzp2LQbvE88rJycl5eHhUY7Is7DgifkJCQiwmTJgQjqSo586dC4ecBBE8mPzD+3Ljxo3Jx44dM/Px8bEfO3asg4SEhASN+XQ+pCOHVvr27dudzp8/r//XX3+VHzx4UB9GhYCAgGIXF5f0sWPHpixatCh827ZtD318fKIuXbpUfOXKFb1//etfTvBCxz3T+TIzM7NzcnL4Ui3cdkSGznPnzkVjodjaxoCdO3eqaWtra3377behjXmqcUH7PXz4sM/27dtzrly5UqGjo5ODcoekxoQJEx7PnTvXifSVhY09qDzy/ofH6KZNm8p0dHRyUd/1C120886Q+Vi6dKnenDlz/ut1S4Yd7ncQC6NHjw45fPhwhJ+fn+GYMWPscf7a2tqagwcPBi1durRTWlqa2pgxY+J79uxpSF7wOOHJkyf5/bK166I5797CwkLV06dPa65ZsyYCZYb7hwfzggULLMnDHu373r17ETNmzEBuhKepqanPYRAYP368eVZWVt2mTZsCMU6jLlAPIOtnz56NPsg/BZE2kOcAYQbdaEFvTS7pzA3rxr34+PiEHDt2zA71GxUVFT927NiMU6dODaAFL0joESNGFMOQhvv18/PL4krcdBDheWVQUFBfU1PTfGr3kN2ZOnWqNdrV9u3bn4WHhxvX1tZK7tmzx3nMmDFRFRUVlX/++SeiW+6TwYYr3fb7779rwuFBS0tLc9asWfE0LlJoOoxh0J/OzMzUbqx8SF6CiGeQnvv373948OBBh6ysrLxhw4alCCawJJJj3LhxWagHGJW420UNICuzs7PVoMVNZbhhw4aOmLc/fvyYLw9TL6dl6ujoyM/n8Pjx486k8UxtmyudRP1q5cqVPFdXV7vu3btnco1qXbp0qVRXVy8hrdamGADoWFxz+PDhj8PDw+XKy8trHB0dkaBMR5D4obEXBlDczIMHD/i6rKLgeStY/iDcyPiF6CHYu8LDw83omfCJJJA3b97MCwgIqIWBgIwdDb1jqEzPnDnTYfr06ZlcSRKODECzyGfqC7i2i4tLxL179zr4+vpmr1692onI5//x6Ky/pp2dXVZYWJhCBxFD/Zy6I8lXWVlZYbzRRNlSNBL0zt3d3dUXLlz4etasWfep3Tf2fiejS3R0tImmpmYN3sFciQiSPmnu2MytA7zPp02b9mbu3Ln62dnZWo0ZM/GpoqJSXFlZKdmYLE5rQ1lZubygoIDvoU0RCyChQ0JClJctW/aQxnuuNF9DwO+IMsD3T+UFTX0O96CtrZ2zdOnS0Nu3b6s/evTI4n3kM64Pp4uamhqphqI+OiopKYnki4KBgYGhNcHVg6RteCHo6uoWaGtrVyJbMLbxeLxO2dnZXbKysrrC+4P2pcm+qC6IWhNkuf7mm2/CvL29e0CGgL9irU+01lKga8Bj8scff0y+fv16/+Zmg29LoAUPEt3t27dvAHm0thSJ/KEgCRvMtGDsQYhvWyB1uOB6nfn4+ITDm5BWVNz9aNudO3fCPT095Xk8Hp9onzRpUvqcOXMsQWji7+jo6MR79+7lRURESGdkZCjW1tbC6RCa3Dx1dfVqJSWlup49e751dXXtoaOjw5cBgEe/nJycLIgZYffYHDkWuk8szn/44QcbmmyKUp1QG0Fyups3b6LoFIgghMckSKuG2hGRZFgEnz59WllCQkL8559/Trh586YZ5Aj27NmTCR1t7MvtNzhZTU3Ny6tXrz7p16+frr6+fjfyGkUF0fmbO55lZ2fnHDt2LDkxMVF6165dxngWrvcv9sG4NWzYsNLWJqBp8XHhwoWI7t27d7WxsdHlehO/73gq+++++y7kl19+0fD09CwICwszuHjxYinITGHyI40ZrUCQhoaGZsfExLyNj49XcHV1Lbezs1P/8ccfOxYWFirinmpqaqRhWEBIqJaWVrGJiUlZv379Otra2mpDSoLOiYeAQWjfvn2doqKiDLt27VoiKSn56sCBA/yIAhCuqGfISI0fP96YNEVFqV80pe6QNOq7774r/vPPP42mTp2asnr16kH0bNDCnTNnTseKigpZhAerqKjgfZk+a9asAShvtNVXr1690dPT06E6SE5OTps+fXrnoqIiJXj+4d3u6Oj4ctu2bTbIrcAtI8H3LQiR/v37J02ePLlm9OjRNtBch7yJt7d32B9//NGPEpLRcbq6utlXr17tpK2trVlUVFQ8ZMgQXlZWlshLodA7AgTByJEjo8LCwrqNHTs2FTrjINonTJigAakIItDwvJB18PLySoLEUklJScnWrVvjzp07ZwmvMniIoj1T/SUkJCCqQxFGBlzH2dk58tWrV52hvS44zxGUnqHfIFExc+bM8F9++cUG+R+OHz9+f8OGDRaoU2HkMwylkZGRCMiptbOz0yTCqoMIgp4V5DrKrbq6WjouLq6rq6trLAySRNrTc+JvLy+v++7u7s6nTp164Onp2QtJwnAuwWSo+B4REZFx4cKFtK1btzqTh6OhoWF2fHy8AbzPG7s3MhqQpAO82b28vFLhnb1jx46ALVu28GVohM1XaTw9ePBg8LNnz+q2bds2SJg8hKiUPzxvS0tLFZcvXx6UlpYmcenSJXsqezJM4TukkH777Tfdc+fOJXt5efHLlLzXhZFfMKh5e3s/+fHHH/uAjMN+uFZlZaXc+8pfkPREGeN94enpGTxixAiDZcuWFYIcbKxcibBbsWLFgz/++MMOHr+i2Bcw3tZHmUT+/fff/cnZg9u+QB7+9ttvjzFdpP7PTYQpeE7qM8h7kZmZqQwno3ov5Bp4XsP40NT7414H49/69esTKyoq3vz6668DyZDX0JqN2hgMezA2ob+K4lpCXFz8FeQ3Xrx48d+5E7ccMadFkr979+4pg/Dl/va+voU0MeAiPua5udeCN7ydnV25r69vDxgy30c+03VhfEO9N9T3GAHNwMDA0Ai4k/SmeC80ZyH+JYMmEQ4ODtG7d+9WhLYmSQI0lBDtQ8H1EH3+/HnG4sWLS4Utytoj6Bk3b94ciDD/z0H0Nxd0T9C+XLdunVNb7UN03wgDvXr1qjy8BoUl+OPqj2/cuLFjUVGRPDhOPT29ookTJ1YPHz68t5KSkhK3fGpqajCRfw0vXBAD3PPBA+7cuXMv/vOf/2hD3kNbW7tKS0urDqHv0tLSSGjFV1BZunSpibq6Ot9I1hgRTd62IHe+/fbbitYmPJvSvletWhXw888/80k0tKX8/PyCCRMmlCOJY0P3ThNpTJS3bNkSDc1fkG9RUVH5+C7MYEN1d/r06QcrVqywWb58+SMPDw9HLvncVMBgEB0dnXH//v2ahw8fqufk5HS9cuVKKWRSiNjjXnPbtm0BIBdae9wiMhCh98HBwUbLly+/f/To0YHNIT1oX8il/P777/Dsrxw0aJC5pKRks7R8BY0qMA4gmiA2NjYpKyurXFJSEpUMr/bOKioqsoqKinJqamoqgteBi1xgYODTU6dOdbx3714fLJrgOYfQ7e+//z52zZo1g+j9BEyfPj3sn3/+6S+q/aIxULuHfMns2bNzHBwcNCD3QMablStXQo+8DyK4uM8Gb9p58+a9Gj58OF+HmQtEWMyaNetJYmKi5vDhw9NkZWUROeHIJTe411dVVS0yMTHJsbe3Lx86dKg69IXxG27s4sWLj/bu3asaGxvbE9uovVNZr1ixImDt2rV8Qm737t2B0EBu7T7RVNAzgOAZMmTIE8iR9e3b13THjh2B27Ztc8I+9O7jLvQRFePp6akGYwkMANDzv3fvXlfo1CO5HXT9paWlpcePHx8bFxenZ29vn5Kfny8L71Jh+vOCZQUPtwkTJiTPnz/fFPJC0KLesGFD5b1796zxu2D50r1NnTr1QX0ixPtIRtaUsGxRCX+fO3du5Lx588zMzc3lBSOuuA4kSDD4+++/K6mrq6v+/vvvYYcOHbKmsHKuTMPu3bsfmpiYKA0dOrQ3nFW6du1alpqaqicsdwJXBojrTaugoFC+YMGCqCVLltimpaVlLV26tA6SgFyva8HnwSekQv755x/MHWQhcyRsX1EAN0pi//79j728vLrBq1uwfVGZgoj/448/Xmhrayts3bq1HE4j3H0ocoLa3c6dO4N8fHw0kfC8nnyWfV/iQUHyH0BUzKpVq1STkpKKli9f3gsEbGPvN7p/RH3o6+tXnD171lEU+wKVP4x49vb2mDc6CpY992/073HjxkmfPn2ah1xG9DsZXbj9hTS9u3btincpnCnKYfhtCvlM/YF7TjjOfPfdd5KnTp16g+iLhvqAIDDemZmZpUJXWVTfzwoKCuXQRIdEiOA9UjnAuDd9+vRwbPPz8zOHcZI7/xL2XDBuamhoFKanp/8/Yrsp4Bp/8DfGsLFjx8bX1tZ28vX1tUU9NieHAO6fIieF7sM8oBkYGBiaPkA3FApDA70ovuxEFfQyQ9jipk2bckaOHGmL7Z+KiBaUJvjPf/4Tun79ek2EQ7aVBeunAGmw/fnnn1GjR4+2FyUSmu7l8uXLIe7u7pakE9dW+xFN+mhh3pDXOZGKkFzYunXri8ePH+vUJ9bprKmpWWJra1vcv39/KVNTU3VNTc2u0I/GcSBYIR+Tnp6e9+TJk6KgoCB4Y/aA1xslakHbhnecjIxMbXFxsZKJicmLTZs2vevfv7/Z+8hnYd7ootxXaDwGoeDv759vZmZmTJ6cDx48ePL999/rwgumofbEXSTCGLZmzZp30BfmEsANtduUlJQXenp6mmVlZbh2clhYmLiBgUGdnp4edDulZGVlJbp06cJf/FZUVNRWVVXxcnJyatPS0t4mJyfLPH/+XAVepl26dKlGvW3dujX/66+/7sf1nKfvT58+TR49erQqLUJau39Que3du/fB2LFjrc3MzGpIS7+pbYUW9FiM+fj4pLq4uFgJM9i0FOBRnpCQkBEYGFhy48YNjZiYmJ7k0Yjf0Z8sLCySfX19tZSVlZXwwBirrl27Fjp79mwrIiNauy4+pv5gRLh69aoS6Y/n5+fnjxs3riItLU0HYzE3+RCeF1I1Tk5OiSNGjHg7ePBgU5QLjsMOkI6pqKiANr3YmTNnXvB4PMg5oA3zy0dJSemtlpZWZ319/S7GxsZa3IS2uO7Vq1eTfH19VSIjI3vTPQomZOrWrVvWtWvXxGFIQ3LItuL9zAWNpxiz7t69Wwrj+7Jly+4fO3ZsoKAjAz13vVdi7dSpUx8vWrSoOyIvcJL79+9HX7t2rXL+/Pl6OM8vv/wSWFJSgtwCmjAcNnQPKFs9Pb0sOzs7JN2TxLiD7YmJiam7du3K9/PzsyMvYEFjMN0fzhEcHJwtLS0taW1trdRW3t3o13hFrF27NmDy5MnGAwcO/G8eEC7JxSVk8Dl//vz7K1asMIcR+OTJkxEnTpzQBtGJ/VEWHh4e9+fNm9d72rRp+dXV1VIgn8ljk7s+EEYe9e7dO3nWrFl5kydPtoYx7Lfffkum9tAYkUlj6Lp16wIUFRU7rVy50kkUvZ+F1cHBgwfD161bp5efn68m7H65zw1N+GXLlimUl5fXHjhw4BWXiKb5LaR5oFGbmZkpfunSJX70EgyJgn2J6/0veF1cZ+7cuVISEhKdt2/fzoN2veC9NNanFyxYEOjn59cb8zFRJD+5UWPGxsYFN27csBHmUc5t+4gYWL58OTS5xU6ePCkFT3DajyvNgX3hsTxgwIAUf3//fjiHMAOk4DXoWNo+dOjQsMmTJ7/Oycmp+/33382hnd4UMp+eDcY0fCIxn6jVAddTuVOnTm8baifcOTeilVxcXIoSEhKkbt261Qd66bQPPrnjM/q+rq5uTnMkf7jvGIqCGTp0aIyRkREPkhuQJOLee1Oek9pFo57SjIBmYGBgYGgt0IsWumFTp04NXbx4sR4WV1wCmZuMqrFzUaIqrpYltsPrec+ePemnT59ulhW3vYAbEuXt7R0/YsQIW6xQQGy1lhwH6RQTobNgwYJe8CoStQnjh4AWnL/99tt9aNo2RPgTuYgHPnfuXPDJkyflsrOzlcFZw5G3Xvutw7Bhw1KdnZ0l9+3bJ/369etOxcXF8nl5eaqk+UbXJC83LO5AFBkYGORPmTKlfPr06QObKsFB9wr9259//nlgW9Cwp2dHqOC5c+d64/7Jm9PX1/fBTz/9ZAlPnMbCNmmxhYXBuXPn8uGR2RAJTeUYFBQU5e/vX3n37t3uGRkZ2lzPQpwPi2IJCYlX+JvH40kIhuTiuurq6gVSUlJ1ixYtev7999/z9Y+5Wt4A6mTy5MlxWPiJSv+gxQ/I49jYWIm7d+/GzZw507a55ActLJcsWcL3ZBUmW9MQSJqksLCwcMeOHYmvXr3Cwvqtjo6OhLq6uiy8n+lcr1+/flNTU/MqNze3Oj09/VViYqLE06dP1V+8eKFDIaJc4g/Ph/HywIEDKUOHDu2Hh0JbgNbAt99+mwPvXFGpiw8BPStIyKtXr0poampq4GFAIm/atOn5nTt3TEluQBAgTuvq6sShCQnphw+VdkLkRlhYWPbdu3ehW2tICaOEhXpTH9+2bVsQxlRs27hxY+CuXbvajPezEK/Vort370JvXmP37t1BAQEBCvHx8d3Ig5ULbr8CQfDNN99Ejxs3TmzAgAGmJLmE+ispKSm9evXq0xcvXoDc6FxSUiIBA4C0tPQbDQ2NOk1NzXcmJiYyZmZm2tBwx3E8Hq/22rVrUSdPnpTAGCMYDi54L7R92rRpD/bs2TNgwYIFD0XV41MYSMN3y5YtSByr5ubmJt+lS5eXiEARTEjN1UFFuUC+ABIlc+bM4RsBnj59mnL//v3c4OBgKSsrq5o5c+ZYjxw5MjcuLs64sXtAHzIzM3s+ZMiQ0mHDhun06NFDPykpKe3PP//MO3v2rB0Rd++TG8A/jFPXr18vGjdunAySs7WFdzba8N69e2MglwFPULRtSO0Ith9ByQ20OTc3N0Xkxb5582bB5cuXdZDHhfbHmPDs2bNOx48fH8jV/m0sog7k/6hRo3KGDBnSFYFmR44cqb548aLD+7xNufeIfZBc1MrKKkeYV7GogN5ZMDxqaGiUBwYGWqH9YL4pTKeZ26eROHnGjBnFMjIynW7fvt3x9u3bvbj9Bc+sr6+f0a9fv2w/Pz9bbjvkOm6R1zr3Oph3DRkyJMHFxaVDaWnp6xMnTmjFx8fzEwE3Z1zBdUCup6Wl8Y0/HUS0/BUVFcvwTO9b81A7wpj1zTffRJiYmNSkpaVJPnz40JAr+cmNqMCcEuR7Q9enuhAsUxgaBgwY8Kxbt251sbGxsrdv37bhykF9yHM2ug8joBkYGBgYWhPcl5WWllbulClTkiZNmqTH1ebkEtINJagSJIugDXr27Nl0X1/fnvRCbsukwceAnhth5Tt27IieOHGiI5Xl5/I4FJR4qE+q9nDVqlUW7YV8BmiCh+Qrhw4deopEcw2R0FySExqfly5dirt27ZpsRkaGCkhLNHVpaWmemJjYm9zc3K5IUIWFAk04BcOpsZgwNzdPGzVqVNnYsWN7IRme4HUaAt3j9evXkQyuN7LWt5X6oEnyTz/9FAC5BDwLJa07dOhQkKenpw15jgh7JpqYgzQ+ffp0grOzs6UwLWIaeyA5MHLkyFTy1hT0smroGtxPLKxQXx4eHrGLFi36H/KV6kNUpDcEQQvDsWPHBh8+fNiBdLebQ0JTG/bw8Aj89ddfBzWHgKY2HRER8dTNzU0ZCW/IQxwGTeT95HhY4abEhC1kBb086RgPD4+gdevW8e8JlYu6INKzrY9VXD3cmzdvviEdeXgVHz9+HAkFMR51LCgogJyNdE1NjTi2Yeg2NjYu09LSej1w4EBFR0fHPmRI3Lx5c0BBQUFnIyOjtzIyMh3V1NQkxMXF+e21trYW41ddUVHRu+fPn0umpaUpPX/+XJukDICGNEap3Q8YMODJ+fPnTSCfEhMTkzhq1ChtSi7WluqCyh5k5t27d/ORmHbu3LnBsbGxGgYGBkUoZ8hn5ObmqpSWlirQuEUgbVVjY+MMe3v7AlNT005ff/11T4z1MJBwk5oKA/S1k5KS0h89elQYGBgoExYWZkT95n2kG42T0tLSNREREdV5eXklLi4u/zXGtIV6oPHp119/Dfjmm2+0BwwY0APjMELXYbusqKiQQbk3Fj4OODo6Rs+ePbva2dnZBLkX0AfQF6KiopIyMzMrS0pKXldUVLytf4d3lJWV7WhgYCCno6OjjKimekm4rJs3b2ZfuHBBgyRnmkq60XOsX78+AL4EGzZscG4rRgB48+/duzdy4cKFNnAKQbg+iGi0O4zj79NPxlgwadKkKisrKw0ej/cqKSmpJDY29rWtra3kgwcP6o4fP95fUHoDfQbRRnp6enm9evUqtbGx6WBqaqosJibWOTIysuDcuXNKoaGh5sLkCBoDjU8zZsy4f/36ddO2IIECmTgdHZ1SSOzUJ16uExcX5/ulQLNZ8P3NbVfGxsZprq6uGT169BAvLi5+HRERIZ6SkqL84sULfnJACwuL5yC2G7sPvJ9h/OzTp0+epaUlHCbEkpKSXvv7+3cHedxU8l/wueB4Abk5UdV+5npAd+zY8R3ayvvukzvvQ7k5OjrGmpmZVb169apDQkJCFxgtSfefpDMKCwv58/73ARI3vXv3zjQxManC37GxsQqPHj0yo/pvyTknI6AZGBgYGEQC3JcdQrmcnJySv/7663f9+vXrpquri8lNo5NBzJAyMzNzwsLCMm7evAkPxR70IhY18qY1QBMdTGJWrFgRvGLFCicQOJ/LG5rr9YzKgF7fzp07HYRpJLZ10POgHR89ehRapxaNSZ9wiTfo2D5+/DjxwYMHFXFxcbLZ2dkIPZWFlwoWZ/XENDwO30pKStYpKChUdu/evbBv374VgwYN6tK/f/+eJNnRVO9EujfoUru5uWk3ZWIsSiBSF2374MGDUUgCiThrPDvK9a+//grauHGjZWOEFTeMdvPmzc4k5SGsnJ48eZIwduxYTa5OLn02JNMkCBiD1qxZEwltdq5RhnsdyAbNmTOnj6iGtxMJcuDAgYdIlObi4pIYHR1t0lQihJuQ1tfXt19zE2Wifv39/UMXLVpkxjWYNFROXA8gYftSm4fmro+PTx9xcXEJ8qZHcsIZM2aYNmbIaCvgahFfuXIlxcbGpjcZeN3c3CLj4uK04b2vpqZWIScnx1NTU4M3/9vy8nIxGRmZN+Xl5eK//vqrOozEJNvj5uYWfv/+fX4kS1PvQ5ieqOB9ksfoxYsXcxCZgH45fvz4p/fv3+/bVt/rVP5I5Oni4mK9ZcuWgB07dgyiqAJtbe0CFRWVahkZmVdVVVUSKioqNQiWqa2t7VxTU4PomLdIMIh/tbW1YufOndNQUlJS/OGHH0ILCgog68OD57OUlBSMJx2qq6slsF9eXh6SFCoLkqsUMv0+wof6Owwxixcvdh48eHACQrTbCvHJfYaJEyc+9PLyMundu/d/NaBBjKqoqJTCI7peQkAM/6iNgqSrJ+zevHz5EskypZCkVE1NrXTt2rW1FhYW+sHBwclaWlpdFBQU+BFK5DNRUFBQgQiM5OTkV9HR0XIxMTHdhHkxNoV0I6OZtrZ27okTJypcXV214UHcFowA9Ix79ux5sG3bNgOS0KHyBxGN9o3hHfvVp3XgJwdEdBfmi/iHOoQBrVevXlm2trZVFhYWstbW1oYvX76sgRRQTU1NHWyH9ddEX4ARrWNtbW1dTk5O1ZMnT2ofPnyoGhkZacpNrtlU4pn7LPAoNjAwgA63SCc2585LbW1tIb9kK0hwiomJYd7E3w9LA/xOZD4M9PT+A4lqaWmZZmFhUWVoaMif19Y7AnWIiYlB3pKOZWVl4mj7yDMvKyv7VkFB4Z2SklInOTk5jFtvU1JS3j5+/FgFes1U5txEkM19NkSVFBUVqYpqH+B6QEtKSvLy8/P/RwO6IQiOsZCj6tOnT5aamlqdhITEu4qKis7FxcWI8H2XnJzMl7XBGIV9ZWRk4MTyWklJCQnMa7p27cqTk5N7w+PxOuXn50vGxMToZGdn/7cffo42zAhoBgYGBgaRAREE3JcfLLqGhoa5hoaGFbq6uq81NDRAlvJ/w+QmLy8PxLPYs2fP5J89e6bJtf4K0zD8ksElX7799tvQDRs2qBsaGnbHtg8NpX4fuLrCFHq9ceNGaH7acu+nQzsDTSw1NTXzEFo7cODAvo2R/YLlBGD/3Nzc/Ozs7OKSkpKa4uLiOh6PB0/djqqqqhLKysrSOjo6qpqamurc45pal1yjwP3795+4u7trUBhvW6sTumckZDpz5kyelZVVb5JOAC5duhSyfv16PUy0hU2wqS2CGL548WKGtbV1b5QN/U5esNg2ZcqUKIQoNneiTvurq6vnb9iwIXXy5MmODZHPUVFR8ZMnT4Y7S1dRrQ9asGNhGhgYmK+goCDn4uIC/UbN5ug2QrP81q1bVd27d9dtalQGGW3mzZsX7Ofn58DVWm0Ijf1O92Jtbf30xIkTqhoaGupUF1lZWTnffffdK3hniWpdNBfUFj09PQOXLFniDK9+6NuGhobCI79LUVGREpJvCR6D8PmJEydGbdu2bQCNVxkZGVljxozpkJmZqcUtG65RhmuYoXdyU4g23OPWrVsD58+f74xtO3fuDNi8ebPIRQQ0B9Q3Vq9eHQAZE+jVz5gxQx8koqD3IfUvfEfXIMkYGqvc3NwiNm3a5JyWlpY+ceJEfh00pr3KlZZoSJO4sXu2sLBIDAgIMNm7d2/g+vXr24zXLYHaDQicyMhIrUGDBqXGx8cbCWrhUkQMCFEaW0DuQH6GSFAiRUEmXb9+PTciIiJv6dKlfMmrpoDqQZgsQVNIdB8fn5Br1669Q4K4tlIPdO/Q4I6MjJS5fv16P2GkL/YDGYpXLojR+uEeUkr8CDBMW7h9Aec4cuQIEhuqof2rqKhUgMjGb5gGFBYWKpaVlckLM5BxExo251nIgDZ+/PhHV65csQQ52xbeDSjHsWPHPrpw4YK9sGemdolPtG1ImKH8YSConwJ1EpTsgJFw9OjR0ZjOYF0mLg7u+R1/jYapDYLSMjMz5fLz8+Wzs7PVMdYJ3lNTxyJhwH3i2KYkPWxtSEhI1GloaBQgAWdz5xPC1rR4D2hqahbACKKsrMx/V8jLy7+prq4mXee3ZWVlIKQ7FxUVyRQUFMjn5OSoCcr44fNzvVMZAc3AwMDAIHLg6nE2d0LyMcd+KaBFGCRPFi9enDxjxgwbrtdsU3W3m6LHTcQoQn9PnDgRvmfPHmMQVG2ZQGgquATbzp07k5AE8n3SJ0RE43tT5QgAIkubakTgEp9IArlixYqeSPjSlgk2unckNkXmdhMTEyOuHEdUVFTC+vXr64KDgy3oGGEesEhks3v37lx4J3LPn5eXl7927dq0S5cu2Ten/XLLFEll6pNCmjcku4FkYNOnTxdvC4QnER/QXgwKClJ78eJFjqurqzLCYJtCilA5zp07N2jbtm1O8HBFGTTWhsk7PSwsLG7MmDH6tJD60HKiMjY1NU09cuRIJ8giUF0gImHatGkg3aza05hFz9KnT5/E69evd4e3N43XZ8+effjHH39oFBQUKNdHXCBy5hVC2L/66qtnmzZtspSVlQVZ+hr1sGfPnkBIAHxKgyK1ncmTJz/cu3cv+lunwMDAqMmTJ8MLmn9PotwvmlL2vXr1Srlz5w6SInf64YcfIskjkfYT9ow0vwFpBmPbqVOnyvv06WPi7e0duG7dOidBqR8umkr8C4KrMRoSEpJVV1f32tnZuXtzCGxRLP+goKDkiIiI/GXLlg0kYrQ5bZiIS2tr6/gbN26Yff3113GRkZG9hOnfCkZffGi5Ub9AwjxXV9e3c+fOdWgLiQcFyx5Jf7/77rvyn376yYn7nmjO+45yXtTngIgePnx49apVq5zed4ygx/+H1AM9R//+/WPhaQrd77bwfqCyHj58+OOEhATN9PR0HWz/0DIgQhTnHDNmTAjmRs059kOIf2FoK32A2reenl5mTk6OxvuMhY2dR1CGD+sMzOE/VQRSS4IR0AwMDAwMIg1B76nG0JYXpZ8b3MmyjY1N3OzZs8u+/fbbPpTUiEtqcsloQWKIdHG5+txcUq2ysrLy6tWr0UeOHFEMDw83E7x2ewdNOKFBuHLlylAPDw++9ElTvJS5RD53G/eY5hoLuJ7W+Pzjjz+CduzYYQvvHVEnO5sCegYQoseOHXvVq1evHlwSuqKiogL6k8eOHetN0RJcYoCOx8L2q6++irK1ta2Rk5PrGB8f3+HmzZtG8KB+XzkJenviE/qE06dPj/Hw8LBQUlJSaoh8TkhISHVzc+ucmpqq31bqgxa10EX19/fvA43eiRMnKiPE/H0LQ658yp9//hkJI40wYwr1A5LEgF7x+PHjCyD58THlRGMRklEdPHiwk6mpqRGR4Phh8eLFj86cOSOyiaU+BlRuW7ZsCXR3d+fLzqBNotxjY2OT/v7779zExESZurq6ThoaGrVfffVVZ1dXV1uSbiLv8BEjRnRAKP2naq9U1vb29jGnT5/WU1BQUHjx4kWmq6tr5095ndYEPeO+ffseTJkyZUB8fHzKlClTZDIzM7UF25rg/If+9vT0vA/9+LKysrIhQ4aUw2DVEu0UYyGIEshmzZo1a2D//v3Tnz171r2t9gkar4YNG/b49OnT/c3NzXMwrgszmAmWPbfdUblcvnz5CYaqUaNG9flYT87GQPcCw4OPj0/WDz/8oANDX1ub9xJx5uPj82jLli0aeNc11JYaK3+A3i+HDh0K9vb2VoiKiupFdSDsuE9RTlzHAshQ3Llzp9nRUKKg/+/o6Pjsfd7zTdUoNjExSSUZEjIKNGb8akvttaVkOKSkpHhIvvux7zOK1tDR0cl7/vx5N0Ev6Q+JPmppMAKagYGBgYHhC4Wgt4+5uXnSqFGjcr/55hvNXr16GTXHA5cLzGbj4+NTb9y4kevv769JCXbas+RGY+BOMD+X9ElzpFAE77GtgxtmvX///mIHBwd+sjSS0cA+kZGRT729vctv377dm6QGuN5qDS0mG5Pv4ILKEuGRgwcPjluwYIGsra0tP8kRl3zm1gs0uBcuXKj04sUL3bayoBUkAojUQRLYGTNmvE1KSjLEb3jkxrSZ8RvKatu2bU9AyDV2rZSUlLSlS5dWPXr0yOJTkM/wxtu9e7eigYGBHpHP+H316tX3Dx8+7NQW6uJDyoHaLXSHT506lUFa9WSsobYKQJ5D0AOdx+PVzpw5M/bWrVv9PjX5DO/gs2fPympra2uBYB03blwutELbQl00p+xBBN2+fbuDpqamRnBwcPTixYuRoLEbfuN6QwNEHMBYs2zZsuCVK1cORD2tWrUqyMfHp0XaKZGs8ETfv3+/48yZM0Pxzmgrkg8Ngcrq4sWLkSoqKrLOzs49m6oBTB7oKJfZs2cjl4WTjY1NOuqtsXfHp9TcP3PmjBSSyLXFeqB7hoHJ3d39pZubm9373hGNtU3oedvY2LwR9KZuaaeYAQMGRIeHhxt/bAROa7V9eNEnJSWpJScnG3zo2EFj1MyZMx9euHChL/JstJVyaC10rH9XGhgYpGdlZWl+jGwInUtfXz+joKBABdImbWEuzwhoBgYGBgaGLxyCxDD03MzMzJ7379+/1NLSUqpHjx6qampqyvLy8vKCifSwIoJXaUFBQUlKSkpRVFRU7ePHj5Xi4uL0kaRN2Pm/dH1zSDwsXrw4debMmf2kpKSkP6cGd21tbc3x48fD9uzZw/fmba866bSggufx5s2boafsiO1cXWj8Df3Vs2fPVgUGBhogJJIbOt0QASTseoLbof3t5OT0bPLkydLOzs78rPCCes9cIhqyB2vXrjVFCGVbJdmIIEFSwVOnTlljXHB3d0+5efNmP/ze2HPRogmfo0ePfuTm5iZlaWlpiDEHv4NpeP78eeaVK1cyfHx8TJuTwEfYteh+oN/p5eVlqqKiokw6yHiINWvWBP/11198SYO20DconLa55As9H3TJvb29swcNGmRF5U0GG1QK2ioqj4hoxPouW7Ys2d/f3+5TlRGRR8bGxmknTpzo0KNHD4Pq6urqmTNnJrZVsq0xUH+ws7OLOXPmTHe0dRgG//jjj6ybN2+aFBYWqnL3RxRN3759UxYsWPASiVax7eDBg0Fr1qxpEfKZ+rOtrW3sP//8Y47kg7t27XJuK+HujYHGdsx1Hj58WBsbG5s5ZcoUG7RjPJ8wT0HqY/Ts48ePDz548KDD/PnzH54/f96xJdsnlbmHh0cAj8fr6O3t3abrgcpqxYoVAfLy8h0h4dPUMYxrAED03i+//FLn5ubW43MkYuQayEpLS7u01ZwZ9ck066ZMmRJ2+fJls9LSUqXmtF/ufHbs2LHBKSkpKnA0aatzl9aAtLT0Sy0trQJEk3xkUslCJEiFtGFbaYuMgGZgYGBgYGBoMAkkecgpKyuXI7GLrKxsHZLxYDv4tOrqaoni4mL50tJS+bKyMkXuce2V3PwYcCfoWNjPmzevasSIEVaSkpL8hDr4kZi4D9Hh5sp21Gcl51+Lx+Pxrl27Fnnw4EG50NBQvidue18s0GQcC6t58+Y9WLNmjRUkZsgbmpsQEsTP7du304OCgqTj4+M14U3CTdLSlCQ4ampqRaampjnOzs4vv/rqK11oCdPv/IKurwuqY1wfEjW//fZb5KFDhxxJg7Qt9xciRSDHcfz48W6QG9m+fXvAzp07HcDvNhaizjVUYT94CGlqapajiioqKiTT0tK0yVv9Q9oud3zDos3DwyN+/vz5A1EvRD6DNF++fHncxYsXHdpa/wBBiYUoEm01V0sV+yLBoLu7e9j3339vAo9cYfuinG7evBm1Y8cOhZiYGJNPVUbcJHeHDx+WNDIy0kff+P7775MQ4t7eyGcClR+SX+7bt0+axozk5OS0sLCwnKysrNd1dXWQXehobm6uYGtr2xve5zAQ7Nix4+H27dsHtYSBl/oxdNEfPHhg6O3tHbRu3bo2TXo2VPYwFl67du1VbW1t3Y8//lgHT/vGjkOkxrp16yLnzZvn5OHhcf/48eP/1ZBuifukc7u6uj6ysbHhIflje6gHegYvL69AvBrXrl3rREZIYUllqZ3TOABDp7u7u9iSJUvUIF3T0u9OOr+amlqBrKxsLckdtKV3hDDD45gxYxIvXLgASTLVhtYA3OO4v0P3ubKyUvxDkjJ/yehYX/60tkLUG1cCrinH4nvXrl2LZGRkatLT03Xb0tyREdAMDAwMDAwM/39yIJAwpzlExocc96WBO4nHZ//+/eMmTJhQOmzYsB5aWlqa3H2JrOQcK1SDm34TTG6YnZ2dc+PGjdTz588rPX782IwmufXHtvv64T6rlZXV03Xr1vHIy5OIaK5HNJCRkZGVlJQE/duXaWlpnQoLCyUKCwtlIZFL5Yds8Kqqqi+7du3KMzAweGtiYiJjYmKioaenp9uQ9zmXeMbfgYGBkV5eXpIRERG921OdELEAHe6DBw/WWFpamiJh4Pr169+R8QOEIjeBDheNLWQ/xKjFLVvcm4uLS9Tq1atlrKyseuNCpCsN0s/Dw4Mv7dHWFtO0+FRRUSmBxDxX37wpZcXdT1dXF57Qaf369YNEhLS4uHjn8vJyXnx8fG1AQIBKeHg4yq3Tpygj7ljo7OwcuX//fi2Q3wUFBQVz5szJefDgQd/2Sj4TqBxBRixcuDBq2rRpDRoA0LFu374duXv3bqmPlaB5n7RBjx490oKCgrRPnjwZiuRuHyKT0FbKHsaXf//730/Gjh1rFxQU9MTX15cXFhamW1RUpPz69evOSMDZs2fPrFGjRpVPnDjRoqysrHLRokUlwcHBfT4H+QyD3ogRI8rWrl3r3JI6058b1LcXLVoU6ODgIH3gwAGxwMBA/vu5Iejo6OQgskZbW1vip59+MsJY97nGa7QTJSWl8vagRU9lBmPs8OHDE+Pj45UoTwt3H3wKvnNhtBk8eHBqWlqafEhISJt7X4oaCa2qqlqal5fXFV783N+5+wtGY2hra+fi++cwvnxqMAKagYGBgYGBoeGJAmcS1FAySO7Epy1NglobgqQjiJ8BAwY8HzRoEMhSbT09PR1ByZP3AavV9PT0rMjIyOx79+51ePjwYXdMUIVd70sCLZAQ9jhp0qSIBQsWaCPEH7/hB6yCwSw3VN7Qu63nKkFY84l+SUlJKWH70rmwD5HQXOMA9JH379+fdfbsWWt4WbfHxRsRCyjvjRs3hs+ZM8cJ2318fIK8vb31kDCtsQUuV4+btjWVeBa2cMM2CwuLpDlz5hRNmjQJmqNi5PWMffz8/II3bNhggJDqtl4fSFIGr6rnz5/rgkhs6vMI837D36hLQYLtUyx4ucQytHS9vLxspKWlZZAAcd68eWLQD2/v5DOBW0cwIjg4OCT37du3VldXV0JKSqpzQUEBLzk5uUNISIg65VRoSc1nS0vLhGvXrhkgURx5Prc38pnALUdLS8v4hQsXljk5OfWUk5OTef0aj/3mjbi4OL/9JyUlZRw9erTo1KlTjjimJdsnnbtv374Jo0aNyv/Xv/5lT3qx7akeuIlgv//++zxpaenOMTExrxMTE7sUFxfL4vUpJSX1ytjYuNTCwuKNmpqaxO3bt9/4+vrycwV8rvEa9aGoqFheUlKi1F7qgJt0efDgwU9UVVV5KSkpCs+ePdNGgkvuvniX6+rq5pmamubLysq+CQoKMgAR39bfl6JQ/pKSkrWampqFaOslJSWKJCcjuD/2gwFETk7uZVlZmXxRUZFKhzYIRkAzMDAwMDAwMLQihBHD8EoxMDDI69mzZ5mRkdHbbt26SSooKEgoKSnJSElJ8RehCBkuLS19WV5eXpeRkcFLTU3tlJSUpJiWlqbJ1Q/9konnhkgzhJ5OmDAhcdq0aTo9e/Y0pH1I67Z+fz5pLOhV3pCHOkmmCJNAAZKSkp6dOnUq6/z58ybQMBa8p/YG7sJ0+PDhj728vDT09fW7QVrh+PHjkb6+vlrx8fE9GoqgoO2NlY8g2UzlSccgYZu5uXnK5MmTi8eNG2ehqKioyNVbz8rKytm2bVuar6+vw6fy6m1N0PND+9zQ0DA7MzOzK7W15hLR3HL8lNEtZHTAvYBs9fT0jJ82bRqfTDp//vzD1atX94ac05dCPhOakwTvUye741575MiRoYcPH+7r5eX1aN++fe2afG6o7EHIGRgYZHTr1q1ETEzsXWFhoUxaWpoWNPrpmJYcK6jtI2pnwIABRQcOHLDl8XhS7fV9we3r3bt3z7S3t0/X19d/ra6u3rlegultdnZ2hydPnijB47Y15jXttey5zwVvXDMzsxeamprVmAah7DG9ATGKflBVVQXDoAY3UWpbfl+KWvl36dKlEgQzIuywjcfjicPoBMKZxv3KykpZGAcogrIttklGQDMwMDAwMDAwiACEET9cgAiQkpKqxSf+hldibW2tlLDw3/ed60sGd9EEDb2vvvoqafTo0RL29vY9KekdgSQaBM8hTJtbmAQKNIVDQkKSLl++XHfnzp2eZBj4UhZuXGIHi9tFixZF/fjjj/3h6Qpd8uvXr0devny5w6NHjwzy8vLUhR3fUOQFIKx9g8zQ1dXNsbOzyxg5cqTYoEGDzGRlZfmhrWQYePnyZfXJkycj9u3bZ0ghrHS+limJ1vGqQtI6bIuKiuqJhWxzyctPWS6CScZGjBgRunHjRnVDQ8PupaWlpZs2bYo7evTowC+pfwgDNwEq1wBAf7dUskF8X716dYC7u7ulu7t78o0bN/q1B63h5uB9ifDo95aSwODWs5OTU1SPHj0qjx075tAe8gN8ykSqX5px6nNAcMxF35eWlq7B91evXmGuyU+YDbSn96WooKNA/8b7m7ahburq6iS5bb4tjweMgGZgYGBgYGBgEHHZk8aIZEGSjvZrq5PTzwFBIg4eb8bGxs8dHBzyHB0dJczMzDR1dXW1kPCrOedF/HpmZmZOXFxc7oMHD+qCg4M1k5OT9YnE+VITc3IJA2hDz5s3L2Pq1KnWRAzn5OTkPnr06HlISMjrmJgY5YyMjK6lpaVKFHLeGBAarKKiUqanp1doYWFRZmdnJ2ltba0nqKcOVFVVVV2+fPnJ4cOHVaOjo03aK9nJXZxCy7dXr175GRkZClFRUb1on8+hJctNKEZlbGJikrpq1arCMWPG2OPvy5cvh3h5eWk+e/as+5faP1qb8EPEjbe3d4aioqLU7NmzFWGU+dLIZ2HvB+579XP0FTr/0KFDw6SkpN74+/vbCf7W3kFjhuAz0/u6vY3VogRumxcsZ6qX9qI/Lqro2Ehfby/EPyOgGRgYGBgYGBhEHI15gbaHCWlroSHdW1VV1aLu3bvnGxoaluvp6b3S0dERk5eXF5OTkxOXk5OTxAqhurq6rqqq6lVFRcXrrKwsaG+Lp6amKqSnp2twJVAARqz9b5g7Qq0nTJjwbMyYMTomJiZGVFYo2+zs7NysrKyi7OzsqoKCglclJSWoI0ikdOzcuTOSQHZQVlbuqK6uLqGlpSWnpaWloqampkqazoKA7ra/v3+6n5+fTkJCgtGXUiccb2ieg4NDXLdu3V4mJiYqhoWF8RMJtoRXJ5dA4hKYIMLd3d2zZs+ezdcDj4yMfLp9+/aX8LTF38yr8fNAUNd76tSpDzw8PLQvXryY/ttvvw1iddF6/VReXr7C0dExMS8vr0tUVJTpl0Q8MzAwfBlgBDQDAwMDAwMDA8MXDW7Su8ZISWgKd+7c+TV+f/PmjRgSdjV0Pi55wEiEhkOtZWRkqu3t7ROGDBlSPXDgQK2ePXsadO7c+aPCq+GJ/vz588zg4ODsW7duSYaEhBhDV/hLIZ654LZDRUVFJFhL1NfX56Wnp0uEhIQYkEY0gSR+qIzep8Et6DUnGMbt5OQUPXv27NcjR460xbaIiIin+/btq7hy5Yot6W7TsS1YDF88BPsdktutWrWqEnLonp6eykj6yOqi9aCnp5epp6dXFB8f3w3JxdpjZAYDAwMDI6AZGBgYGBgYGBgY3iOBwv0U/L25yfMYhGt+gow2NjbOMDc3L+rdu/c7IyMjWS0tLQVVVVUlSUlJCUn8JyEhAUYVJDN0pOvq6l4VFxeX5ebmlj979qwyLi6uI2Q8UlJSkPCwC537SyOeBcEltOTk5KoGDhwY36dPn5f4OyEhQTI6OlrnxYsXuh97HSRSsra2Thk1alTVqFGjequqqqpAy+HSpUuhJ06ckH3w4EFf2pd5PX/+xIampqaps2fPztHV1ZU+ceLE22vXrvENA1+y5EZrQkpKqkZXVzcX41Jqaqo+tjHymYGBob2CEdAMDAwMDAwMDAwMHyiD8qUSmp9Dd5LIUmSGl5KS4ikoKLyUlpbme+jW1NSIVVRUyNTU1EiWlpYqVFVVyQkey03mxupJOOlrZGT03N7ePqtXr15vZWVlO1VWVr5NT0/v+OLFC+nMzEylkpKSLvAeR93guPo8ju9ANCsqKlZoaWmVGhkZlVtYWLyzsrLqamZmZgTtdOhtBwYGPr18+fKbe/fumRQXFyt/Tv3pLxEN9SX0Azs7u9gRI0aUKSkpdf7nn3/Er169yieemddz69UVyGc5ObnqyspKOUryxmQ3GBgY2jMYAc3AwMDAwMDAwMDA0OrgSjo0RQJCENzkVYx0bhzCSGAkozM3N8/o3bt3VY8ePTppaGhIy8jIiEtLS0uIiYl1kpKSArcsDtkGaWlpSXyXkJAQr6iogFZ3aWJiYnF4ePib8PDwrgkJCQY8Hk+Sez18cslvhg+HsMSzgvVrZGT0wsrKKtfIyOh1VVVVh8DAwK6RkZG9G/KOZvh8QNnjH3mdM+KZgYHhSwAjoBkYGBgYGBgYGBgYRN7rXPBvQeKNedR+WBmTPIkwD3TonquoqJRISEi8UlBQqJaUlOR7oL98+VKiuLhYoba2VpL0tQVBpDPzdm55SEtLv0SkgLq6eqmenl65goICv56Sk5MVYmJiDKurq2XxNyOeGRgYGBhaC4yAZmBgYGBgYGBgYGBg+MJB5CR5YzaHOCaPTnxv7rEMzQOIfSUlpbJOnTq9UVRUrOrSpUuNlJTU67dv33asrq6WKCwsVMzPz1fjGhRwTENGBgYGBgYGhs8BRkAzMDAwMDAwMDAwMDAwNCkpJ4El3WwdoB4kJCR4+P7mzRsx0uYW3I+RzgwMDAwMogRGQDMwMDAwMDAwMDAwMDAwtIMEhEz/nIGBgYFBFMEXvWdgYGBgYGBgYGBgYGBgYGh7YKQzAwMDA4Oog2lAMTAwMDAwMDAwMDAwMDAwMDAwMDAwtAgYAc3AwMDAwMDAwMDAwMDAwMDAwMDAwNAiYAQ0AwMDAwMDAwMDAwMDAwMDAwMDAwNDi4AR0AwMDAwMDAwMDAwMDAwMDAwMDAwMDC0CRkAzMDAwMDAwMDAwMDAwMDAwMDAwMDB0aAn8Hx+Ypp5+GGglAAAAAElFTkSuQmCC",
      "media_type": "image/png"
    }
  }
]

That looks great — you can see the energy hit animation clearly on the dark background. The white core expands, swirls, then fades out through rings. Now let me wire it up in the game so you can test it with a keypress.

Read (E:\a327ex\Orblike\main.lua)

   290→effects = an:layer('effects')
   291→
   292→-- Shaders
   293→an:shader('shockwave', 'assets/shockwave.frag')
   294→an:spritesheet('hit1', 'assets/hit1.png', 96, 48)
   295→
   296→-- Sounds
   297→--
   298→-- Adding new sounds:
   299→--   1. Convert WAV to OGG: ffmpeg -i "File Name.wav" -c:a libvorbis -q:a 10 "File Name.ogg"
   300→--   2. Create bitcrushed variant: ffmpeg -i "File Name.ogg" -af "acrusher=bits=10:samples=3:mix=1:mode=lin" -c:a libvorbis -q:a 10 "File Name_lightmed.ogg"
   301→--   3. Measure mean volume: ffmpeg -i "File Name.ogg" -af volumedetect -f null /dev/null 2>&1 | grep mean_volume
   302→--   4. Calculate normalized volume: volume = 0.5 * 10^((-24 - mean_dB) / 20), capped at 1.0
   303→--      Baseline: -24 dB file plays at 0.5 volume. Quieter files get higher volume, louder files get lower.
   304→--   5. Add an:sound() for both normal and _lightmed versions

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Now add an fx_test_effect class and a keybind to trigger it. Let me find where the hit_effect class is and add the new one nearby, then add the keybind in the debug section.

Read (E:\a327ex\Orblike\main.lua)

  1466→  self:add(animation('hit1', 0.06, 'once', {
  1467→    [0] = function() self:kill() end,
  1468→  }))
  1469→end
  1470→
  1471→function hit_effect:update(dt)
  1472→  self.hit1:update(dt)
  1473→  local x = self.follow and self.follow.x or self.x
  1474→  local y = self.follow and self.follow.y or self.y
  1475→  effects:push(x, y, self.r, self.s, self.s)
  1476→  effects:animation(self.hit1, 0, 0)
  1477→  effects:pop()
  1478→end
  1479→
  1480→
  1481→-- Effect: lightning_line (SNKRX-style midpoint displacement)
  1482→-- Jagged line between src and dst objects, regenerates each frame for crackling effect.
  1483→-- Blinks out after a short duration.
  1484→lightning_line = object:extend()
  1485→function lightning_line:new(src, dst, args)
  1486→  object.new(self)
  1487→  self.src = src
  1488→  self.dst = dst
  1489→  args = args or {}
  1490→  self.color = args.color or fg_color

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Now add the keybind in the debug section:

Read (E:\a327ex\Orblike\main.lua)

  1789→    an:add(seeker(p1.x + an.random:float(-80, 80), p1.y + an.random:float(-60, 60)))
  1790→  end
  1791→  if an:key_is_pressed('l') then
  1792→    for i = 1, 5 do
  1793→      an:add(seeker(p1.x + an.random:float(-120, 120), p1.y + an.random:float(-80, 80)))
  1794→    end
  1795→  end
  1796→  -- Shockwave test (space key)
  1797→  if an:key_is_pressed('space') then
  1798→    print('shockwave at', an.camera.mouse.x, an.camera.mouse.y)
  1799→    add_shockwave(an.camera.mouse.x, an.camera.mouse.y)
  1800→  end
  1801→
  1802→  -- Update shockwaves
  1803→  local dt = 1/120

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Here's what's set up:

Space = play the FX test animation at mouse position (random rotation, 0.5x scale so 96px frames → ~48px in game)
V = shockwave (moved from space)
The conversion script is at Orblike/tools/mov_to_spritesheet.py

To try different animations or threshold values, the workflow is:

cd E:/a327ex/Orblike

# Convert with different threshold (lower = softer/more glow, higher = sharper/core only)
python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Prerendered animations/FX elements/<filename>.mov" assets/fx_energy_hit1.png --threshold 0.4 --size 96

# Then just restart the game and press space

You can also adjust --size (frame size) and --skip (drop frames to speed up the animation). Go ahead and test — press space at the mouse position and see how it looks. The s = 0.5 scale in the keybind code controls how big it appears in-game, easily tweakable.

Document the entire process in the script itself so next instances know what to do exactly. As we change the process in this session, also update the comments each time so its faithful to whatever we end up with.

Read (E:\a327ex\Orblike\tools\mov_to_spritesheet.py)

     1→#!/usr/bin/env python3
     2→"""
     3→Convert a .mov animation to a black-and-white spritesheet for Anchor engine.
     4→
     5→Usage:
     6→  python tools/mov_to_spritesheet.py <input.mov> <output.png> [--threshold 0.5] [--size 96] [--skip 1]
     7→
     8→Arguments:
     9→  input.mov       Path to the .mov animation file
    10→  output.png      Path for the output spritesheet PNG
    11→  --threshold T   Brightness threshold 0.0-1.0 (default 0.5)
    12→                  Lower = more glow preserved, higher = only bright core
    13→  --size S        Output frame height in pixels (default 96)
    14→  --skip N        Only keep every Nth frame (default 1 = all frames)
    15→"""
    16→
    17→import argparse
    18→import subprocess
    19→import tempfile
    20→import os
    21→import sys
    22→from pathlib import Path
    23→from PIL import Image
    24→
    25→
    26→def extract_frames(mov_path, tmp_dir):
    27→    """Extract all frames from .mov as RGBA PNGs."""
    28→    pattern = os.path.join(tmp_dir, "frame_%04d.png")
    29→    result = subprocess.run(
    30→        ["ffmpeg", "-y", "-i", mov_path, "-pix_fmt", "rgba", pattern],
... [138 more lines]

Write (E:\a327ex\Orblike\tools\mov_to_spritesheet.py)

The file E:\a327ex\Orblike\tools\mov_to_spritesheet.py has been updated successfully.

Done. The script's docstring now has the complete step-by-step workflow (Steps 1-7) covering: choosing animations, running the script, registering in main.lua, the test class, the keybind, iteration tips, and finalization. I'll keep it updated as we change anything.

Let's try energy 018 ray right

Bash (cd "E:/a327ex/Orblike" && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. ...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Energy 018 Ray Right noCT noRSZ.mov...
  40 frames extracted
Converting to BW (threshold=0.4)...
Cropping to (53, 28, 967, 942), scaling to 96x96...
  35 frames after trimming trailing empties
Spritesheet saved: assets/fx_energy_hit1.png (3360x96, 35 frames of 96x96)

Bash (python -c "
from PIL import Image
sheet = Image.open('E:/a327ex/Orblike/assets/f...)

Preview: (3360, 96)

Read (E:\a327ex\Orblike\assets\fx_test\preview_on_black.png)

[
  {
    "type": "image",
    "source": {
      "type": "base64",
      "data": 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",
      "media_type": "image/png"
    }
  }
]

[Image: original 3360x96, displayed at 2000x57. Multiply coordinates by 1.68 to map to original image.]

That's a ray/beam that shoots to the right — it's directional rather than radial, so it won't look great as a centered hit effect. It would be better suited as a projectile trail or beam attack visual.

Want to try something more radial for the hit test? Good candidates from the list:

Energy 005/006/008 Hit Radial — radial hit bursts
Energy 016 Hit Explosion Radial — explosive radial
Explosion 004 Radial — explosion
Sparks 036 Hit Explosion Radial — sparky hit
Flash 001/009/011 Hit Explosion Radial — flash bursts

Or tell me which one catches your eye and I'll convert it.

Very good, however, let's make sure that the final result is black and white only, this one has gray elements in it.

Right — currently the script produces white pixels with varying alpha, which reads as gray on the dark background. I need to make it a hard cutoff: pixels are either fully opaque white or fully transparent, no in-between.

Edit (E:\a327ex\Orblike\tools\mov_to_spritesheet.py)

The file E:\a327ex\Orblike\tools\mov_to_spritesheet.py has been updated successfully.

Also update the technical details in the docstring to match:

Edit (E:\a327ex\Orblike\tools\mov_to_spritesheet.py)

The file E:\a327ex\Orblike\tools\mov_to_spritesheet.py has been updated successfully.

Now re-convert the same animation:

Bash (cd "E:/a327ex/Orblike" && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. ...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Energy 018 Ray Right noCT noRSZ.mov...
  40 frames extracted
Converting to BW (threshold=0.4)...
Cropping to (53, 28, 967, 942), scaling to 96x96...
  35 frames after trimming trailing empties
Spritesheet saved: assets/fx_energy_hit1.png (3360x96, 35 frames of 96x96)

Bash (python -c " from PIL import Image sheet = Image.open('E:/a327ex/Orblike/assets/f...)

Read (E:\a327ex\Orblike\assets\fx_test\preview_on_black.png)

[
  {
    "type": "image",
    "source": {
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",
      "media_type": "image/png"
    }
  }
]

[Image: original 3360x96, displayed at 2000x57. Multiply coordinates by 1.68 to map to original image.]

Pure black and white now — no gray. The ray shape reads crisp. Want to try a radial hit effect next, or test this one in-game first?

Better, but there's still some alpha in some pixels. Should be 255, 255, 255, 255 or transparent only.

The issue is likely from the LANCZOS resize — it introduces anti-aliased semi-transparent pixels at edges. I need to threshold the alpha after scaling too.

Read (E:\a327ex\Orblike\tools\mov_to_spritesheet.py)

   146→    for y in range(h):
   147→        for x in range(w):
   148→            r, g, b, a = pixels[x, y]
   149→            if a == 0:
   150→                continue
   151→            lum = int(0.299 * r + 0.587 * g + 0.114 * b)
   152→            if lum > threshold_byte:
   153→                pixels[x, y] = (255, 255, 255, 255)
   154→            else:
   155→                pixels[x, y] = (0, 0, 0, 0)
   156→
   157→    return img
   158→
   159→
   160→def trim_frame(img):
   161→    """Return bounding box of non-transparent content, or None if empty."""
   162→    return img.getbbox()
   163→
   164→
   165→def main():

Read (E:\a327ex\Orblike\tools\mov_to_spritesheet.py)

   196→        # Find the union bounding box across all frames (for consistent cropping)
   197→        union_bbox = None
   198→        for img in frames:
   199→            bbox = trim_frame(img)
   200→            if bbox is None:
   201→                continue
   202→            if union_bbox is None:
   203→                union_bbox = bbox
   204→            else:
   205→                union_bbox = (
   206→                    min(union_bbox[0], bbox[0]),
   207→                    min(union_bbox[1], bbox[1]),
   208→                    max(union_bbox[2], bbox[2]),
   209→                    max(union_bbox[3], bbox[3]),
   210→                )
   211→
   212→        if union_bbox is None:
   213→            print("All frames are empty after thresholding! Try a lower threshold.", file=sys.stderr)
   214→            sys.exit(1)
   215→

Read (E:\a327ex\Orblike\tools\mov_to_spritesheet.py)

   222→        crop_box = (cx - half, cy - half, cx + half, cy + half)
   223→
   224→        # Crop and scale each frame
   225→        print(f"Cropping to {crop_box}, scaling to {args.size}x{args.size}...")
   226→        scaled_frames = []
   227→        for img in frames:
   228→            cropped = img.crop(crop_box)
   229→            scaled = cropped.resize((args.size, args.size), Image.LANCZOS)
   230→            scaled_frames.append(scaled)
   231→
   232→        # Remove trailing empty frames
   233→        while scaled_frames and scaled_frames[-1].getbbox() is None:
   234→            scaled_frames.pop()
   235→        print(f"  {len(scaled_frames)} frames after trimming trailing empties")
   236→
   237→        if not scaled_frames:
   238→            print("No non-empty frames remain!", file=sys.stderr)
   239→            sys.exit(1)
   240→
   241→        # Stitch into horizontal spritesheet

[Request interrupted by user]

Isn't it better to just scale with nearest neighbor?

Yes — that avoids the problem entirely. No interpolation, no new colors.

Edit (E:\a327ex\Orblike\tools\mov_to_spritesheet.py)

The file E:\a327ex\Orblike\tools\mov_to_spritesheet.py has been updated successfully.

Bash (cd "E:/a327ex/Orblike" && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. ...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Energy 018 Ray Right noCT noRSZ.mov...
  40 frames extracted
Converting to BW (threshold=0.4)...
Cropping to (53, 28, 967, 942), scaling to 96x96...
  35 frames after trimming trailing empties
Spritesheet saved: assets/fx_energy_hit1.png (3360x96, 35 frames of 96x96)

Bash (python -c "
from PIL import Image
sheet = Image.open('E:/a327ex/Orblike/assets/f...)

Semi-transparent pixels: 0

Zero semi-transparent pixels. Every pixel is either solid white or fully transparent. Go ahead and test it.

Perfect. I'm going to list a bunch I want converted. Keep the angle for the effect always the same (0). Keep replaying the same effect on repeat always at the mouse position. When I press mouse 4, move to next effect, mouse 5 move to previous. Print the current effect somewhere on the screen (lana font).

electricity 001, 013, 020, 025, 027, 036, 037, 038, 050, 052

Let me find the exact filenames first, then batch convert them all.

Bash (ls "E:/a327ex/RTFX/2. Prerendered animations/FX elements/" | grep -i "electricit...)

Elements - Electricity 001 Hit Explosion Up noCT noRSZ.mov
Elements - Electricity 013 Up Loop noCT noRSZ.mov
Elements - Electricity 020 Ray Shot Up noCT noRSZ.mov
Elements - Electricity 025 Hit Explosion Radial noCT noRSZ.mov
Elements - Electricity 027 Hit Explosion Radial noCT noRSZ.mov
Elements - Electricity 036 Explosion Right MIX noCT noRSZ.mov
Elements - Electricity 037 Shot Right MIX noCT noRSZ.mov
Elements - Electricity 038 Charge Radial MIX noCT noRSZ.mov
Elements - Electricity 050 Charge Radial noCT noRSZ.mov
Elements - Electricity 052 Charge Radial noCT noRSZ.mov

Now batch convert all 10:

Bash (cd "E:/a327ex/Orblike"
FX="E:/a327ex/RTFX/2. Prerendered animations/FX elements"...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 036 Explosion Right MIX noCT noRSZ.mov...
  24 frames extracted
Converting to BW (threshold=0.4)...
Cropping to (63, 232, 621, 790), scaling to 96x96...
  21 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_036.png (2016x96, 21 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 037 Shot Right MIX noCT noRSZ.mov...
  24 frames extracted
Converting to BW (threshold=0.4)...
Cropping to (1, -8, 999, 990), scaling to 96x96...
  18 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_037.png (1728x96, 18 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 038 Charge Radial MIX noCT noRSZ.mov...
  24 frames extracted
Converting to BW (threshold=0.4)...
Cropping to (179, 227, 769, 817), scaling to 96x96...
  18 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_038.png (1728x96, 18 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 050 Charge Radial noCT noRSZ.mov...
  30 frames extracted
Converting to BW (threshold=0.4)...
Cropping to (74, 57, 896, 879), scaling to 96x96...
  23 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_050.png (2208x96, 23 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 052 Charge Radial noCT noRSZ.mov...
  45 frames extracted
Converting to BW (threshold=0.4)...
Cropping to (49, 52, 923, 926), scaling to 96x96...
  44 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_052.png (4224x96, 44 frames of 96x96)

All 10 converted. Now let me update main.lua — register all spritesheets, replace the test system with a browser that cycles through them.

Read (E:\a327ex\Orblike\main.lua)

   293→an:shader('shockwave', 'assets/shockwave.frag')
   294→an:spritesheet('hit1', 'assets/hit1.png', 96, 48)
   295→an:spritesheet('fx_test', 'assets/fx_energy_hit1.png', 96, 96)
   296→
   297→-- Sounds

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Now update the fx_test_effect class to accept a spritesheet name, and replace the debug keybind section:

Read (E:\a327ex\Orblike\main.lua)

  1491→  local y = self.follow and self.follow.y or self.y
  1492→  effects:push(x, y, self.r, self.s, self.s)
  1493→  effects:animation(self.hit1, 0, 0)
  1494→  effects:pop()
  1495→end
  1496→
  1497→
  1498→-- Effect: fx_test_effect (spritesheet animation for testing FX pack conversions)
  1499→fx_test_effect = object:extend()
  1500→function fx_test_effect:new(x, y, args)
  1501→  object.new(self)
  1502→  self.x, self.y = x, y
  1503→  args = args or {}
  1504→  self.s = args.s or 0.5
  1505→  self.r = args.r or 0
  1506→  self:add(animation('fx_test', 0.04, 'once', {
  1507→    [0] = function() self:kill() end,
  1508→  }))
  1509→end
  1510→
  1511→function fx_test_effect:update(dt)
  1512→  self.fx_test:update(dt)
  1513→  effects:push(self.x, self.y, self.r, self.s, self.s)
  1514→  effects:animation(self.fx_test, 0, 0)
  1515→  effects:pop()

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Now replace the debug keybind section:

Read (E:\a327ex\Orblike\main.lua)

  1798→      for i = 1, an.random:int(1, 2) do
  1799→        an:add(hit_particle(e.x, e.y, {velocity = an.random:float(20, 60), duration = an.random:float(0.1, 0.3), w = 4}))
  1800→      end
  1801→    end
  1802→  end
  1803→end)
  1804→
  1805→-- Debug
  1806→an:action(function()
  1807→  if an:key_is_pressed('k') then
  1808→    an:add(seeker(p1.x + an.random:float(-80, 80), p1.y + an.random:float(-60, 60)))
  1809→  end
  1810→  if an:key_is_pressed('l') then
  1811→    for i = 1, 5 do
  1812→      an:add(seeker(p1.x + an.random:float(-120, 120), p1.y + an.random:float(-80, 80)))
  1813→    end
  1814→  end
  1815→  -- FX test (space key)
  1816→  if an:key_is_pressed('space') then
  1817→    an:add(fx_test_effect(an.camera.mouse.x, an.camera.mouse.y, {s = 0.5, r = an.random:float(0, 2*math.pi)}))
  1818→  end
  1819→  -- Shockwave test (v key)
  1820→  if an:key_is_pressed('v') then
  1821→    print('shockwave at', an.camera.mouse.x, an.camera.mouse.y)
  1822→    add_shockwave(an.camera.mouse.x, an.camera.mouse.y)

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Now let me check that an:mouse_is_pressed is the right API call. Let me verify from the framework API.

ToolSearch

[
  {
    "type": "tool_reference",
    "tool_name": "Grep"
  }
]

Grep (mouse_is_pressed)

1816:  if an:mouse_is_pressed(4) then
1820:  if an:mouse_is_pressed(5) then

Let me verify this is a real API function:

Grep (mouse_is_pressed)

123:an:mouse_is_pressed(button) -> bool

Good, it's a real API. The code should work. Let me also verify that effects:text is valid — let me check the layer text API:

Grep (:text()

196:layer:text(text, font_name, x, y, color)

The arg order is text, font_name, x, y, color — I have it as font_name, text, x, y, color. Let me fix that:

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Good catch. Now there's one more issue — when switching effects with mouse4/5, the currently playing effect will be from the old spritesheet. I should kill it so a new one spawns immediately:

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

All set. Here's what you have:

10 electricity effects converted to BW spritesheets
Auto-replay — the current effect plays continuously at mouse position, restarts when it finishes
Mouse 4 — next effect
Mouse 5 — previous effect
Top-left label — shows which effect is currently playing

Go ahead and test.

Because electricity is thin lines, they translate poorly when scaling down. Do you think there's any solution or is it inherent to this type of effect?

It's partly inherent — thin 1-2px lines at 1000px become sub-pixel at 96px and nearest neighbor either drops them or makes disconnected dots. But there are a couple of things we can try:

Option A: Reorder the pipeline — scale first, threshold second. Right now we threshold at 1000px (making crisp 1-bit lines), then nearest-neighbor scale crushes them. If we instead scale down with LANCZOS first (which spreads thin lines into soft anti-aliased blobs), then threshold to BW, the thin lines "fatten up" from the interpolation before the cutoff. This effectively makes thin details survive the downscale.

Option B: Dilate before scaling. Apply morphological dilation (grow white pixels by 1-3px) at the full 1000px resolution before scaling down. This thickens all lines explicitly.

Option C: Larger frame size. Use 192x192 instead of 96x96 — more pixels means thin lines survive better. At 0.25 scale in-game it'd be the same visual size.

I'd recommend trying Option A first — it's the simplest change, no new dependencies, and the LANCZOS blur naturally preserves thin structures. Want me to try that?

Try it.

Read (E:\a327ex\Orblike\tools\mov_to_spritesheet.py)

   130→
   131→
   132→def convert_frame_bw(img, threshold):
   133→    """Convert an RGBA image to pure white-on-transparent using brightness threshold.
   134→
   135→    For each pixel:
   136→    - Compute luminance from RGB
   137→    - If luminance > threshold AND original alpha > 0: pixel becomes solid white (255,255,255,255)
   138→    - Otherwise: pixel becomes fully transparent (0,0,0,0)
   139→
   140→    No gray, no semi-transparency. Pure 1-bit: white or nothing.
   141→    """
   142→    pixels = img.load()
   143→    w, h = img.size
   144→    threshold_byte = int(threshold * 255)
   145→
   146→    for y in range(h):
   147→        for x in range(w):
   148→            r, g, b, a = pixels[x, y]
   149→            if a == 0:
   150→                continue
   151→            lum = int(0.299 * r + 0.587 * g + 0.114 * b)
   152→            if lum > threshold_byte:
   153→                pixels[x, y] = (255, 255, 255, 255)
   154→            else:
   155→                pixels[x, y] = (0, 0, 0, 0)
   156→
   157→    return img
   158→
   159→
... [15 more lines]

Read (E:\a327ex\Orblike\tools\mov_to_spritesheet.py)

   175→        print(f"Input file not found: {args.input}", file=sys.stderr)
   176→        sys.exit(1)
   177→
   178→    with tempfile.TemporaryDirectory() as tmp_dir:
   179→        print(f"Extracting frames from {args.input}...")
   180→        frame_paths = extract_frames(args.input, tmp_dir)
   181→        print(f"  {len(frame_paths)} frames extracted")
   182→
   183→        # Apply skip
   184→        if args.skip > 1:
   185→            frame_paths = frame_paths[::args.skip]
   186→            print(f"  {len(frame_paths)} frames after skip={args.skip}")
   187→
   188→        # Convert each frame to BW
   189→        print(f"Converting to BW (threshold={args.threshold})...")
   190→        frames = []
   191→        for fp in frame_paths:
   192→            img = Image.open(fp).convert("RGBA")
   193→            img = convert_frame_bw(img, args.threshold)
   194→            frames.append(img)
   195→
   196→        # Find the union bounding box across all frames (for consistent cropping)
   197→        union_bbox = None
   198→        for img in frames:
   199→            bbox = trim_frame(img)
   200→            if bbox is None:
   201→                continue
   202→            if union_bbox is None:
   203→                union_bbox = bbox
   204→            else:
... [45 more lines]

Now I need to reorder: scale with LANCZOS first at full color, then BW threshold, then compute bounding box. But the bounding box needs to be computed before cropping — so the order should be:

Load frames (full color RGBA)
Do a preliminary BW pass at full resolution just for bounding box computation
Crop the original color frames to the bounding box
Scale with LANCZOS (preserves thin lines via anti-aliasing)
Threshold to pure BW (the spread from LANCZOS fattens thin lines)

Edit (E:\a327ex\Orblike\tools\mov_to_spritesheet.py)

The file E:\a327ex\Orblike\tools\mov_to_spritesheet.py has been updated successfully.

Also update the docstring to reflect the new pipeline order:

Edit (E:\a327ex\Orblike\tools\mov_to_spritesheet.py)

The file E:\a327ex\Orblike\tools\mov_to_spritesheet.py has been updated successfully.

Now reconvert all 10:

Bash (cd "E:/a327ex/Orblike"
FX="E:/a327ex/RTFX/2. Prerendered animations/FX elements"...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 036 Explosion Right MIX noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (63, 232, 621, 790), scaling to 96x96, then BW threshold=0.4...
  21 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_036.png (2016x96, 21 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 037 Shot Right MIX noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (1, -8, 999, 990), scaling to 96x96, then BW threshold=0.4...
  18 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_037.png (1728x96, 18 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 038 Charge Radial MIX noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (179, 227, 769, 817), scaling to 96x96, then BW threshold=0.4...
  18 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_038.png (1728x96, 18 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 050 Charge Radial noCT noRSZ.mov...
  30 frames extracted
Loading frames...
Cropping to (74, 57, 896, 879), scaling to 96x96, then BW threshold=0.4...
  23 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_050.png (2208x96, 23 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 052 Charge Radial noCT noRSZ.mov...
  45 frames extracted
Loading frames...
Cropping to (49, 52, 923, 926), scaling to 96x96, then BW threshold=0.4...
  44 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_052.png (4224x96, 44 frames of 96x96)

All 10 reconverted with the new pipeline. Let me quick-preview one of the electricity effects to compare:

Bash (python -c "
from PIL import Image
sheet = Image.open('E:/a327ex/Orblike/assets/f...)

Semi-transparent pixels: 0

Read (E:\a327ex\Orblike\assets\fx_test\preview_on_black.png)

[
  {
    "type": "image",
    "source": {
      "type": "base64",
      "data": 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",
      "media_type": "image/png"
    }
  }
]

[Image: original 2208x96, displayed at 2000x87. Multiply coordinates by 1.10 to map to original image.]

The thin electricity lines are much more visible now — you can see the charge radial expanding with connected lines instead of disconnected dots. Still pure BW (zero semi-transparent pixels). The LANCZOS spread is doing its job.

Go ahead and test in-game — the browser should be working with mouse 4/5 to cycle through all 10.

Let's try slightly slower animation for all of them, 0.06 instead of 0.04

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Done. Restart and test.

Let's do this. Keep the ones you converted now as they are, but convert all of them again at 192 size. Then, draw each animation side by side, the original once at 96, the 192 one scaled down in game to be 96, and the original 192 one.

Bash (cd "E:/a327ex/Orblike"
FX="E:/a327ex/RTFX/2. Prerendered animations/FX elements"...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 036 Explosion Right MIX noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (63, 232, 621, 790), scaling to 192x192, then BW threshold=0.4...
  21 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_036_192.png (4032x192, 21 frames of 192x192)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 037 Shot Right MIX noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (1, -8, 999, 990), scaling to 192x192, then BW threshold=0.4...
  18 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_037_192.png (3456x192, 18 frames of 192x192)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 038 Charge Radial MIX noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (179, 227, 769, 817), scaling to 192x192, then BW threshold=0.4...
  18 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_038_192.png (3456x192, 18 frames of 192x192)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 050 Charge Radial noCT noRSZ.mov...
  30 frames extracted
Loading frames...
Cropping to (74, 57, 896, 879), scaling to 192x192, then BW threshold=0.4...
  23 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_050_192.png (4416x192, 23 frames of 192x192)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Electricity 052 Charge Radial noCT noRSZ.mov...
  45 frames extracted
Loading frames...
Cropping to (49, 52, 923, 926), scaling to 192x192, then BW threshold=0.4...
  44 frames after trimming trailing empties
Spritesheet saved: assets/fx_elec_052_192.png (8448x192, 44 frames of 192x192)

All converted. Now update main.lua to register the 192 spritesheets and draw three side by side.

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Now update the fx_test_effect to draw all three versions, and update the browser code:

Read (E:\a327ex\Orblike\main.lua)

  1498→
  1499→-- Effect: fx_test_effect (spritesheet animation for testing FX pack conversions)
  1500→-- Accepts a spritesheet name so it can play any registered FX spritesheet.
  1501→fx_test_effect = object:extend()
  1502→function fx_test_effect:new(x, y, args)
  1503→  object.new(self)
  1504→  self.x, self.y = x, y
  1505→  args = args or {}
  1506→  self.s = args.s or 0.5
  1507→  self.r = args.r or 0
  1508→  self.sheet_name = args.sheet_name or 'fx_elec_001'
  1509→  self:add(animation(self.sheet_name, 0.06, 'once', {
  1510→    [0] = function() self:kill() end,
  1511→  }))
  1512→end
  1513→
  1514→function fx_test_effect:update(dt)
  1515→  self[self.sheet_name]:update(dt)
  1516→  effects:push(self.x, self.y, self.r, self.s, self.s)
  1517→  effects:animation(self[self.sheet_name], 0, 0)
  1518→  effects:pop()
  1519→end
  1520→
  1521→
  1522→-- Effect: lightning_line (SNKRX-style midpoint displacement)
  1523→-- Jagged line between src and dst objects, regenerates each frame for crackling effect.
  1524→-- Blinks out after a short duration.
  1525→lightning_line = object:extend()
  1526→function lightning_line:new(src, dst, args)
  1527→  object.new(self)

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Now update the browser to remove the scale param and add labels for each column:

Read (E:\a327ex\Orblike\main.lua)

  1825→    end
  1826→  end
  1827→  -- FX browser: mouse4 = next, mouse5 = previous
  1828→  if an:mouse_is_pressed(4) then
  1829→    fx_current = fx_current + 1
  1830→    if fx_current > #fx_list then fx_current = 1 end
  1831→    if fx_active and not fx_active.dead then fx_active:kill() end
  1832→  end
  1833→  if an:mouse_is_pressed(5) then
  1834→    fx_current = fx_current - 1
  1835→    if fx_current < 1 then fx_current = #fx_list end
  1836→    if fx_active and not fx_active.dead then fx_active:kill() end
  1837→  end
  1838→  -- FX test: auto-replay current effect at mouse position
  1839→  if not fx_active or fx_active.dead then
  1840→    fx_active = fx_test_effect(an.camera.mouse.x, an.camera.mouse.y, {
  1841→      s = 0.5, r = 0, sheet_name = fx_list[fx_current].name,
  1842→    })
  1843→    an:add(fx_active)
  1844→  end
  1845→  -- Update position to follow mouse
  1846→  fx_active.x = an.camera.mouse.x
  1847→  fx_active.y = an.camera.mouse.y
  1848→  -- Display current effect name
  1849→  effects:text(fx_list[fx_current].label, 'pf', 4, 4, fg_color())

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Done. The three versions will display at fixed center position:

Left — 96px spritesheet at 1x scale
Center — 192px spritesheet at 0.5x scale (same apparent size)
Right — 192px spritesheet at 1x scale (full size)

Mouse 4/5 still cycles effects. Labels underneath each. Go test it.

Something is wrong with the 96 size one because they're now bigger than before?

The original browser was drawing at s = 0.5 scale. I changed the 96px one to 1x for the comparison. Let me fix it — the 96 one should be at 0.5x like before, the 192 one at 0.25x (same visual size), and 192 at 0.5x (full).

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Now left and center should be the same apparent size (48px) for direct comparison, and right is larger (96px) so you can see the 192 detail. Try it.

Just draw them at 48 and 96, ignore the 192 files

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Done. Left is 96px sheet at 0.5x (48px apparent), right is 96px sheet at 1x (96px apparent).

Perfect, out of these, let's only remove 037 and 038. Remove all 192 files, and the 037 038 ones for normal size too

Bash (rm E:/a327ex/Orblike/assets/fx_elec*192.png && rm E:/a327ex/Orblike/assets/fx_...)

E:/a327ex/Orblike/assets/fx_elec_001.png
E:/a327ex/Orblike/assets/fx_elec_013.png
E:/a327ex/Orblike/assets/fx_elec_020.png
E:/a327ex/Orblike/assets/fx_elec_025.png
E:/a327ex/Orblike/assets/fx_elec_027.png
E:/a327ex/Orblike/assets/fx_elec_036.png
E:/a327ex/Orblike/assets/fx_elec_050.png
E:/a327ex/Orblike/assets/fx_elec_052.png

Now remove them from the fx_list and the 192 spritesheet registration:

Edit (E:\a327ex\Orblike\main.lua)

The file E:\a327ex\Orblike\main.lua has been updated successfully.

Done. 8 electricity effects remaining, all 192 files and 037/038 removed.

energy 002, 005, 006, 008, 011, 012, 014, 015, 017, 018, 019, 020, 022, 029, 032, 034, 049, 053, 057, 086, 087 105 109 114 115 118 119 121 122 125. 129 and 130 should be screen size, so 480x480 if theyre square aspect ratio

Bash (ls "E:/a327ex/RTFX/2. Prerendered animations/FX elements/" | grep -E "Energy (00...)

Elements - Energy 002 Hit Star Radial noCT noRSZ.mov
Elements - Energy 005 Hit Radial noCT noRSZ.mov
Elements - Energy 006 Hit Explosion Radial noCT noRSZ.mov
Elements - Energy 008 Hit Radial noCT noRSZ.mov
Elements - Energy 011 Ray Up Loop noCT noRSZ.mov
Elements - Energy 012 Slash Right noCT noRSZ.mov
Elements - Energy 014 Slash Up noCT noRSZ.mov
Elements - Energy 015 Slash Radial noCT noRSZ.mov
Elements - Energy 017 Charge Up noCT noRSZ.mov
Elements - Energy 018 Ray Right noCT noRSZ.mov
Elements - Energy 019 Ray Up noCT noRSZ.mov
Elements - Energy 020 Slash1 Ray Right noCT noRSZ.mov
Elements - Energy 022 Slash Right noCT noRSZ.mov
Elements - Energy 029 Slash Right noCT noRSZ.mov
Elements - Energy 032 Slash Right noCT noRSZ.mov
Elements - Energy 034 Slash Radial noCT noRSZ.mov
Elements - Energy 049 Shot Right Loop MIX noCT noRSZ.mov
Elements - Energy 053 Shot Right MIX noCT noRSZ.mov
Elements - Energy 057 Shot Right Loop MIX noCT noRSZ.mov
Elements - Energy 086 Charge Explosion Radial MIX noCT noRSZ.mov
Elements - Energy 087 Charge Radial Loop MIX noCT noRSZ.mov
Elements - Energy 105 Ray Left Loop noCT noRSZ.mov
Elements - Energy 109 Explosion Hit Radial MIX noCT noRSZ.mov
Elements - Energy 114 Slash Radial MiX noCT noRSZ.mov
Elements - Energy 115 Slash Charge Radial Loop MIX noCT noRSZ.mov
Elements - Energy 118 Hit Radial noCT noRSZ.mov
Elements - Energy 119 Dissapear noCT noRSZ.mov
Elements - Energy 121 Hit Star Radial noCT noRSZ.mov
Elements - Energy 122 Hit Star Radial noCT noRSZ.mov
Elements - Energy 125 Glare Down noCT noRSZ.mov
... [8 more lines]

Note there are two "125" files (Glare Down and Glare Right). I'll include both. Let me batch these — 6 parallel jobs.

Bash (cd "E:/a327ex/Orblike"
FX="E:/a327ex/RTFX/2. Prerendered animations/FX elements"...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Energy 129 Hit Rays Radial noCT noRSZ.mov...
  22 frames extracted
Loading frames...
Cropping to (-96, -516, 2016, 1596), scaling to 480x480, then BW threshold=0.4...
  11 frames after trimming trailing empties
Spritesheet saved: assets/fx_energy_129.png (5280x480, 11 frames of 480x480)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Energy 130 Hit Rays Radial noCT noRSZ.mov...
  16 frames extracted
Loading frames...
Cropping to (-96, -516, 2016, 1596), scaling to 480x480, then BW threshold=0.4...
  6 frames after trimming trailing empties
Spritesheet saved: assets/fx_energy_130.png (2880x480, 6 frames of 480x480)

All converted. Now update the fx_list in main.lua with all the new entries.

This session is being continued from a previous conversation that ran out of context. The summary below covers the earlier portion of the conversation.

Summary: 1. Primary Request and Intent: - Convert Orblike's visual style from SNKRX (gray background, drop shadows, checkerboard) to Dark Variant / Antitower style (near-black background, no shadows, muted Bytepath palette) - Build a pipeline to convert commercial FX pack animations (.mov files from RTFX pack) into pure black-and-white spritesheets for use in the game - Set up an in-game FX browser to preview converted animations side-by-side at different sizes - Convert batches of electricity and energy effects from the RTFX pack

Key Technical Concepts:

Dark Variant visual style: black background, no shadow layer, Bytepath color palette, white-first particle transitions

RTFX .mov files: 1000x1000 RGBA PNG-encoded video at 24fps, colored effects on transparent background

BW conversion pipeline: LANCZOS scale first → threshold to pure 1-bit BW second (critical ordering for preserving thin lines)

Spritesheet format: horizontal strip of square frames, loaded via an:spritesheet(name, file, frame_w, frame_h)

Animation system: animation(sheet_name, frame_delay, mode, callbacks) where mode is 'once' or 'loop'

Pure 1-bit output: every pixel must be (255,255,255,255) or (0,0,0,0) — no gray, no semi-transparency

Luminance formula: lum = 0.299*R + 0.587*G + 0.114*B

Files and Code Sections:

E:\a327ex\Orblike\main.lua — Main game file, heavily modified

Palette changed from SNKRX to Bytepath dark variant (~line 18): lua -- Bytepath palette (dark variant) bg_color = color(17, 17, 17) fg_color = color(222, 222, 222) fg_alt = color(176, 168, 159) yellow = color(255, 184, 51) orange = color(244, 144, 62) blue = color(82, 179, 203) green = color(121, 177, 89) red = color(216, 70, 84) purple = color(178, 108, 161) blue2 = color(71, 120, 186) yellow2 = color(245, 159, 16)

Shadow layer and shader removed (was shadow = an:layer('shadow'), an:shader('shadow', 'assets/shadow.frag'), and compositing code shadow:clear(); shadow:draw_from(game, an.shaders.shadow); shadow:draw(2, 2))

Background simplified to solid fill (removed checkerboard and gradient): lua an:action(function() bg:rectangle(0, 0, gw, gh, bg_color()) end)

projectile:die() updated to use dark variant white-first pattern: lua function projectile:die() an:add(hit_circle(self.x, self.y, {radius = 8, duration = 0.2}):change_color(0.6, self.color:clone())) for i = 1, 2 do an:add(hit_particle(self.x, self.y, {velocity = an.random:float(50, 100), duration = an.random:float(0.1, 0.3)}):change_color(0.6, self.color:clone())) end self:kill() end

FX spritesheet registration with size field (~line 295): lua fx_list = { -- Electricity {name = 'fx_elec_001', file = 'assets/fx_elec_001.png', size = 96, label = 'Electricity 001 Hit Explosion Up'}, -- ... 8 electricity entries ... -- Energy {name = 'fx_energy_002', file = 'assets/fx_energy_002.png', size = 96, label = 'Energy 002 Hit Star Radial'}, -- ... 30 energy entries, including 129/130 at size=480 ... } fx_current = 1 for _, fx in ipairs(fx_list) do an:spritesheet(fx.name, fx.file, fx.size, fx.size) end

fx_test_effect class draws two versions side by side: lua fx_test_effect = object:extend() function fx_test_effect:new(x, y, args) object.new(self) self.x, self.y = x, y args = args or {} self.r = args.r or 0 self.sheet_name = args.sheet_name or 'fx_elec_001' self:add(animation(self.sheet_name, 0.06, 'once', { [0] = function() self:kill() end, })) end function fx_test_effect:update(dt) self[self.sheet_name]:update(dt) -- Left: 96px at 0.5x scale (48px apparent) effects:push(self.x - 60, self.y, self.r, 0.5, 0.5) effects:animation(self[self.sheet_name], 0, 0) effects:pop() -- Right: 96px at 1x scale (96px apparent) effects:push(self.x + 60, self.y, self.r, 1, 1) effects:animation(self[self.sheet_name], 0, 0) effects:pop() end

FX browser in debug section: lua if an:mouse_is_pressed(4) then fx_current = fx_current + 1 if fx_current > #fx_list then fx_current = 1 end if fx_active and not fx_active.dead then fx_active:kill() end end if an:mouse_is_pressed(5) then fx_current = fx_current - 1 if fx_current < 1 then fx_current = #fx_list end if fx_active and not fx_active.dead then fx_active:kill() end end if not fx_active or fx_active.dead then fx_active = fx_test_effect(gw/2, gh/2, { r = 0, sheet_name = fx_list[fx_current].name, }) an:add(fx_active) end effects:text(fx_list[fx_current].label, 'pf', 4, 4, fg_color()) effects:text('48px', 'pf', gw/2 - 60 - 8, gh/2 + 30, fg_color()) effects:text('96px', 'pf', gw/2 + 60 - 8, gh/2 + 30, fg_color())

Shockwave test moved from space to 'v' key

E:\a327ex\Orblike\tools\mov_to_spritesheet.py — Python conversion script

Contains comprehensive workflow documentation (Steps 1-7) in docstring

Pipeline: extract frames → compute bbox via temp BW pass → crop full-color → LANCZOS scale → BW threshold → strip empties → stitch horizontal spritesheet

Key function: python def convert_frame_bw(img, threshold): pixels = img.load() w, h = img.size threshold_byte = int(threshold * 255) for y in range(h): for x in range(w): r, g, b, a = pixels[x, y] if a == 0: continue lum = int(0.299 * r + 0.587 * g + 0.114 * b) if lum > threshold_byte: pixels[x, y] = (255, 255, 255, 255) else: pixels[x, y] = (0, 0, 0, 0) return img

Dependencies: Python 3, Pillow (pip install Pillow), ffmpeg in PATH

Usage: python tools/mov_to_spritesheet.py <input.mov> <output.png> [--threshold 0.4] [--size 96] [--skip 1]

E:\a327ex\Orblike\reference\snkrx_style_effects.md — Comprehensive style guide documenting both SNKRX and Dark Variant visual approaches across 8 projects

E:\a327ex\Orblike\reference\SNKRX_visual_effects_catalog.md — Detailed SNKRX effect patterns

E:\a327ex\RTFX/2. Prerendered animations/FX elements/ — Source .mov files (hundreds of effects)

Asset files created:

8 electricity spritesheets: fx_elec_001, 013, 020, 025, 027, 036, 050, 052 (all 96x96)

30 energy spritesheets: fx_energy_002, 005, 006, 008, 011, 012, 014, 015, 017, 018, 019, 020, 022, 029, 032, 034, 049, 053, 057, 086, 087, 105, 109, 114, 115, 118, 119, 121, 122, 125d (all 96x96)

2 screen-size energy spritesheets: fx_energy_129, 130 (480x480)

Removed: all _192 variants, fx_elec_037, fx_elec_038, fx_energy_hit1.png

Errors and fixes:

Gray/semi-transparent pixels in BW conversion: Initial implementation used soft alpha transitions (new_a = int(a * t)). User explicitly said "should be 255, 255, 255, 255 or transparent only". Fixed to hard 1-bit threshold.

LANCZOS resize reintroducing semi-transparency: After fixing to hard 1-bit, the LANCZOS resize created anti-aliased edge pixels. User suggested "Isn't it better to just scale with nearest neighbor?" — switched to Image.NEAREST.

Thin electricity lines lost at small sizes: Nearest neighbor drops sub-pixel lines. Solution: reorder pipeline to scale with LANCZOS first (spreads thin lines), then threshold to BW. This preserves thin structures.

Wrong text API argument order: Used effects:text('pf', label, x, y, color) but correct API is layer:text(text, font_name, x, y, color). Fixed by swapping first two args.

96px effects appearing bigger than expected: The original browser used s = 0.5 scale but comparison view used s = 1. User noticed "they're now bigger than before." Fixed scales to match original intent.

User said "Just draw them at 48 and 96, ignore the 192 files": Simplified the three-way comparison to just two views using the 96px spritesheet at 0.5x and 1x scale.

Problem Solving:

Visual style conversion from SNKRX to Dark Variant completed successfully

FX animation conversion pipeline built and iteratively refined based on user feedback

Thin-line preservation solved via pipeline reordering (LANCZOS scale → BW threshold)

In-game browser system for previewing effects working with mouse4/5 navigation

User instruction to "Document the entire process in the script itself so next instances know what to do exactly. As we change the process in this session, also update the comments each time" — docstring kept updated with pipeline changes

All user messages:

"Let's turn Orblike from SNKRX visual style into Dark Variant visual style. I think the most well developed dark variant game in the reference folder is Antitower..."

"1. OK, 2. Let's use BYTEPATH's, 3. Let's keep entities filled, 4. OK, 5. No, leave things as they are in terms of springs, 6. OK, 7. OK, 8. Leave as it is."

"Perfect, I like the look a lot better. Next we'll figure out how to create black and white animations for various visual effects packs I have..."

"OK. The RTFX animation pack has pre-rendered animations in .mov format... Importantly, it has to be black and white to match the game's style..."

"Let's try energy 018 ray right"

"Better, but there's still some alpha in some pixels. Should be 255, 255, 255, 255 or transparent only."

"Isn't it better to just scale with nearest neighbor?"

"Document the entire process in the script itself so next instances know what to do exactly. As we change the process in this session, also update the comments each time so its faithful to whatever we end up with."

"Very good, however, let's make sure that the final result is black and white only, this one has gray elements in it."

"Because electricity is thin lines, they translate poorly when scaling down. Do you think there's any solution or is it inherent to this type of effect?"

"Try it." (referring to Option A: scale first, threshold second)

"Let's do this. Keep the ones you converted now as they are, but convert all of them again at 192 size. Then, draw each animation side by side..."

"Something is wrong with the 96 size one because they're now bigger than before?"

"Just draw them at 48 and 96, ignore the 192 files"

"Perfect, out of these, let's only remove 037 and 038. Remove all 192 files, and the 037 038 ones for normal size too"

"Let's try slightly slower animation for all of them, 0.06 instead of 0.04"

"energy 002, 005, 006, 008, 011, 012, 014, 015, 017, 018, 019, 020, 022, 029, 032, 034, 049, 053, 057, 086, 087 105 109 114 115 118 119 121 122 125. 129 and 130 should be screen size, so 480x480 if theyre square aspect ratio"

Pending Tasks:

User hasn't tested the latest batch of 30+ energy effects in-game yet

The script documentation should be updated if any further pipeline changes are made

Note: There are two Energy 125 files (Glare Down and Glare Right) — only Glare Down was converted as fx_energy_125d; the Glare Right variant was not requested but exists

Current Work: Just completed converting 30 energy effects (plus 2 at 480px screen size) and updating the fx_list in main.lua with all entries including the size field. The fx_list now has 40 total effects (8 electricity + 32 energy). The browser draws each effect at two sizes side by side (48px and 96px apparent) at the center of the screen, with mouse4/5 to cycle and labels showing the current effect name and size labels.

Optional Next Step: Wait for user to test the energy effects in-game and provide feedback on which to keep/remove, or request additional effect categories to convert. The user may also want to adjust threshold values for specific effects that don't look right.

If you need specific details from before compaction (like exact code snippets, error messages, or content you generated), read the full transcript at: C:\Users\adn.claude\projects\E--a327ex\043b37b5-5d82-459a-b379-33b3d0c16c7d.jsonl Continue the conversation from where it left off without asking the user any further questions. Resume directly — do not acknowledge the summary, do not recap what was happening, do not preface with "I'll continue" or similar. Pick up the last task as if the break never happened.

All 40 effects are registered in the fx_list and ready for testing. The browser should cycle through all 8 electricity and 32 energy effects with mouse4/5, showing each at 48px and 96px side by side. The two screen-size effects (fx_energy_129 and fx_energy_130) are at 480x480.

Give it a run and let me know how they look — we can adjust thresholds, remove duds, or convert more categories from there.

Remove 006 053 Show all at 48, 64, 96

ToolSearch

[
  {
    "type": "tool_reference",
    "tool_name": "Read"
  },
  {
    "type": "tool_reference",
    "tool_name": "Edit"
  },
  {
    "type": "tool_reference",
    "tool_name": "Bash"
  },
  {
    "type": "tool_reference",
    "tool_name": "Grep"
  }
]

Let me find the relevant sections in main.lua.

Grep (fx_energy_006|fx_energy_053)

310:  {name = 'fx_energy_006', file = 'assets/fx_energy_006.png', size = 96, label = 'Energy 006 Hit Explosion Radial'},
325:  {name = 'fx_energy_053', file = 'assets/fx_energy_053.png', size = 96, label = 'Energy 053 Shot Right MIX'},

Grep (fx_test_effect)

1530:-- Effect: fx_test_effect (spritesheet animation for testing FX pack conversions)
1532:fx_test_effect = object:extend()
1533:function fx_test_effect:new(x, y, args)
1544:function fx_test_effect:update(dt)
1864:    fx_active = fx_test_effect(gw/2, gh/2, {

Read (E:/a327ex/Orblike/main.lua)

  1530→-- Effect: fx_test_effect (spritesheet animation for testing FX pack conversions)
  1531→-- Draws 3 versions side by side: 96@1x, 192@0.5x, 192@1x
  1532→fx_test_effect = object:extend()
  1533→function fx_test_effect:new(x, y, args)
  1534→  object.new(self)
  1535→  self.x, self.y = x, y
  1536→  args = args or {}
  1537→  self.r = args.r or 0
  1538→  self.sheet_name = args.sheet_name or 'fx_elec_001'
  1539→  self:add(animation(self.sheet_name, 0.06, 'once', {
  1540→    [0] = function() self:kill() end,
  1541→  }))
  1542→end
  1543→
  1544→function fx_test_effect:update(dt)
  1545→  self[self.sheet_name]:update(dt)
  1546→  -- Left: 96px at 0.5x scale (48px apparent)
  1547→  effects:push(self.x - 60, self.y, self.r, 0.5, 0.5)
  1548→  effects:animation(self[self.sheet_name], 0, 0)
  1549→  effects:pop()
  1550→  -- Right: 96px at 1x scale (96px apparent)
  1551→  effects:push(self.x + 60, self.y, self.r, 1, 1)
  1552→  effects:animation(self[self.sheet_name], 0, 0)
  1553→  effects:pop()
  1554→end
  1555→
  1556→
  1557→-- Effect: lightning_line (SNKRX-style midpoint displacement)
  1558→-- Jagged line between src and dst objects, regenerates each frame for crackling effect.
  1559→-- Blinks out after a short duration.
... [10 more lines]

Read (E:/a327ex/Orblike/main.lua)

  1845→  end
  1846→  if an:key_is_pressed('l') then
  1847→    for i = 1, 5 do
  1848→      an:add(seeker(p1.x + an.random:float(-120, 120), p1.y + an.random:float(-80, 80)))
  1849→    end
  1850→  end
  1851→  -- FX browser: mouse4 = next, mouse5 = previous
  1852→  if an:mouse_is_pressed(4) then
  1853→    fx_current = fx_current + 1
  1854→    if fx_current > #fx_list then fx_current = 1 end
  1855→    if fx_active and not fx_active.dead then fx_active:kill() end
  1856→  end
  1857→  if an:mouse_is_pressed(5) then
  1858→    fx_current = fx_current - 1
  1859→    if fx_current < 1 then fx_current = #fx_list end
  1860→    if fx_active and not fx_active.dead then fx_active:kill() end
  1861→  end
  1862→  -- FX test: auto-replay current effect at fixed center position
  1863→  if not fx_active or fx_active.dead then
  1864→    fx_active = fx_test_effect(gw/2, gh/2, {
  1865→      r = 0, sheet_name = fx_list[fx_current].name,
  1866→    })
  1867→    an:add(fx_active)
  1868→  end
  1869→  -- Display current effect name and column labels
  1870→  effects:text(fx_list[fx_current].label, 'pf', 4, 4, fg_color())
  1871→  effects:text('48px', 'pf', gw/2 - 60 - 8, gh/2 + 30, fg_color())
  1872→  effects:text('96px', 'pf', gw/2 + 60 - 8, gh/2 + 30, fg_color())
  1873→  -- Shockwave test (v key)
  1874→  if an:key_is_pressed('v') then
... [5 more lines]

Now I'll make all three changes: remove the two fx_list entries, update the effect to show 3 sizes, and update the labels.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Now delete the asset files.

Bash (rm E:/a327ex/Orblike/assets/fx_energy_006.png E:/a327ex/Orblike/assets/fx_energy...)

Done. Removed fx_energy_006 and fx_energy_053 (entries + asset files). The browser now shows three columns: 48px (0.5x), 64px (0.667x), and 96px (1x), spaced 80px apart from center.

Remove 121, is animation speed 0.04 or 0.06?

It's 0.06 — we changed it from 0.04 last session when you asked for slightly slower animation.

Grep (fx_energy_121)

333:  {name = 'fx_energy_121', file = 'assets/fx_energy_121.png', size = 96, label = 'Energy 121 Hit Star Radial'},

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Bash (rm E:/a327ex/Orblike/assets/fx_energy_121.png)

Removed fx_energy_121.

Let's try 0.08

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Changed to 0.08.

explosion 011 013 014 019

Bash (ls "E:/a327ex/RTFX/2. Prerendered animations/FX elements/" | grep -i "explosion"...)

Elements - Debris 014 Explosion Radial noCT noRSZ.mov
Elements - Explosion 011 Hit Radial noCT noRSZ.mov
Elements - Explosion 013 Shot Hit Right MIX noCT noRSZ.mov
Elements - Explosion 013 Up MIX noCT noRSZ.mov
Elements - Explosion 014 Hit Radial MIX noCT noRSZ.mov
Elements - Explosion 019 Hit Radial noCT noRSZ.mov
Elements - Fire 014 Hit Explosion Right noCT noRSZ.mov
Elements - Flash 011 Hit Explosion Radial noCT noRSZ.mov
Elements - Flash 013 Hit Explosion Radial Loop noCT noRSZ.mov
Elements - Sparks 011 Explosion Up noCT noRSZ.mov
Elements - Sparks 013 Hit Explosion Radial noCT noRSZ.mov

ToolSearch

[
  {
    "type": "tool_reference",
    "tool_name": "AskUserQuestion"
  }
]

There are multiple "Explosion" entries. 013 has two variants (Shot Hit Right MIX and Up MIX), and 014 has a "Hit Radial MIX". I'm assuming you want the main Explosion category ones:

Explosion 011 Hit Radial
Explosion 013 — which variant?
Explosion 014 Hit Radial MIX
Explosion 019 Hit Radial

AskUserQuestion

User has answered your questions: "Explosion 013 has two variants — which one(s)?"="Shot Hit Right MIX". You can now continue with the user's answers in mind.

Converting 4 explosion effects.

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Explosion 019 Hit Radial noCT noRSZ.mov...
  37 frames extracted
Loading frames...
Cropping to (217, 200, 799, 782), scaling to 96x96, then BW threshold=0.4...
  29 frames after trimming trailing empties
Spritesheet saved: assets/fx_explosion_019.png (2784x96, 29 frames of 96x96)

All 4 converted. Now adding them to the fx_list.

Grep (Energy 129|Energy 130|fx_energy_129|fx_energy_130)

335:  {name = 'fx_energy_129', file = 'assets/fx_energy_129.png', size = 480, label = 'Energy 129 Hit Rays Radial'},
336:  {name = 'fx_energy_130', file = 'assets/fx_energy_130.png', size = 480, label = 'Energy 130 Hit Rays Radial'},

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Done — 4 explosion effects added (011, 013, 014, 019). They'll appear in the browser between the energy and the screen-size entries. 16–29 frames each at 96x96, threshold 0.4.

fire 003 004 005 006 007 008 010 011 012 014 015 016 018 019 023 027 028 029 031 032 033 036 039 040 041 050 055 056 059 061 062 064 065 068 070 072 073 074 075 076 083 089 092 093 102 105 116 119. 079 and 048 should be screen wide

Bash (ls "E:/a327ex/RTFX/2. Prerendered animations/FX elements/" | grep -i "^Elements ...)

Elements - Fire 001 Ray Up noCT noRSZ.mov
Elements - Fire 002 Trail Right noCT noRSZ.mov
Elements - Fire 003 Radial noCT noRSZ.mov
Elements - Fire 004 Projectile Right Loop noCT noRSZ.mov
Elements - Fire 005 Hit Up noCT noRSZ.mov
Elements - Fire 006 Hit Right noCT noRSZ.mov
Elements - Fire 007 Torch Fireball Up noCT noRSZ.mov
Elements - Fire 008 Up Projectile Loop noCT noRSZ.mov
Elements - Fire 009 Hit Horizontal noCT noRSZ.mov
Elements - Fire 010 Hit Trail Up noCT noRSZ.mov
Elements - Fire 011 Slash Right noCT noRSZ.mov
Elements - Fire 012 Slash Right noCT noRSZ.mov
Elements - Fire 013 Torch Fireball Up noCT noRSZ.mov
Elements - Fire 014 Hit Explosion Right noCT noRSZ.mov
Elements - Fire 015 Hit Radial noCT noRSZ.mov
Elements - Fire 016 Hit Up noCT noRSZ.mov
Elements - Fire 017 Trail Right noCT noRSZ.mov
Elements - Fire 018 Torch Fireball Up Loop noCT noRSZ.mov
Elements - Fire 019 Trail Radial noCT noRSZ.mov
Elements - Fire 020 Ray Right noCT noRSZ.mov
Elements - Fire 021 Trail Right noCT noRSZ.mov
Elements - Fire 022 Up Loop noCT noRSZ.mov
Elements - Fire 023 Hit Shot Right noCT noRSZ.mov
Elements - Fire 024 Hit Shot Up noCT noRSZ.mov
Elements - Fire 025 Ray Up noCT noRSZ.mov
Elements - Fire 026 Hit Up noCT noRSZ.mov
Elements - Fire 027 Slash Up noCT noRSZ.mov
Elements - Fire 028 Ray Shot Up noCT noRSZ.mov
Elements - Fire 029 Ray Shot Right noCT noRSZ.mov
Elements - Fire 030 Hit Explosion Up noCT noRSZ.mov
... [50 more lines]

Bash (ls "E:/a327ex/RTFX/2. Prerendered animations/FX elements/" | grep -i "^Elements ...)

Elements - Fire 083 Ray Up MIX noCT noRSZ.mov
Elements - Fire 084 Torch Fireball Up MIX noCT noRSZ.mov
Elements - Fire 085 Torch Fireball Left MIX noCT noRSZ.mov
Elements - Fire 086 Hit Radial MIX noCT noRSZ.mov
Elements - Fire 087 Torch Fireball Left MIX noCT noRSZ.mov
Elements - Fire 089 Hit Radial MIX noCT noRSZ.mov
Elements - Fire 090 Explosion Hit Radial MIX noCT noRSZ.mov
Elements - Fire 091 Torch Fireball Right MIX noCT noRSZ.mov
Elements - Fire 092 Charge Radial Loop MIX noCT noRSZ.mov
Elements - Fire 093 Charge Radial MIX noCT noRSZ.mov
Elements - Fire 094 Charge Radial Loop MIX noCT noRSZ.mov
Elements - Fire 100 Up MIX Wide noCT.mov
Elements - Fire 102 Right noCT noRSZ.mov
Elements - Fire 103 Left noCT noRSZ.mov
Elements - Fire 104 Right noCT noRSZ.mov
Elements - Fire 105 Up noCT noRSZ.mov
Elements - Fire 106 Left noCT noRSZ.mov
Elements - Fire 107 Stroke Up noCT noRSZ.mov
Elements - Fire 108 Stroke Right noCT noRSZ.mov
Elements - Fire 110 Wide Up loop noCT noRSZ.mov
Elements - Fire 112 Torch Calm End noCT noRSZ.mov
Elements - Fire 112 Torch Calm loop noCT noRSZ.mov
Elements - Fire 112 Torch Windy End noCT noRSZ.mov
Elements - Fire 113 Torch Windy loop noCT noRSZ.mov
Elements - Fire 114 Torch Windy loop noCT noRSZ.mov
Elements - Fire 115 Torch Windy loop noCT noRSZ.mov
Elements - Fire 116 Wings Up noCT noRSZ.mov
Elements - Fire 117 Hit Exposion noCT noRSZ.mov
Elements - Fire 118 Trail MIX noCT noRSZ.mov
Elements - Fire 119 Wings Radial MIX noCT noRSZ.mov

Got all the filenames. 48 at 96px + 2 at 480px. Let me batch these conversions. I'll run them in parallel groups.

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Fire 014 Hit Explosion Right noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (21, -1, 945, 923), scaling to 96x96, then BW threshold=0.4...
  21 frames after trimming trailing empties
Spritesheet saved: assets/fx_fire_014.png (2016x96, 21 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Fire 032 Slash Radial noCT noRSZ.mov...
  34 frames extracted
Loading frames...
Cropping to (109, 170, 827, 888), scaling to 96x96, then BW threshold=0.4...
  24 frames after trimming trailing empties
Spritesheet saved: assets/fx_fire_032.png (2304x96, 24 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Fire 061 Projectile Right Loop MIX noCT noRSZ.mov...
  5 frames extracted
Loading frames...
Cropping to (594, 185, 1258, 849), scaling to 96x96, then BW threshold=0.4...
  5 frames after trimming trailing empties
Spritesheet saved: assets/fx_fire_061.png (480x96, 5 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Fire 076 Shot MIX noCT noRSZ.mov...
  20 frames extracted
Loading frames...
Cropping to (517, 55, 1447, 985), scaling to 96x96, then BW threshold=0.4...
  11 frames after trimming trailing empties
Spritesheet saved: assets/fx_fire_076.png (1056x96, 11 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Fire 079 Wide MIX loop.mov...
  24 frames extracted
Loading frames...
Cropping to (-96, -516, 2016, 1596), scaling to 480x480, then BW threshold=0.4...
  24 frames after trimming trailing empties
Spritesheet saved: assets/fx_fire_079.png (11520x480, 24 frames of 480x480)

All 50 fire effects converted. Now adding them to the fx_list in main.lua.

Grep (Explosion$|-- Explosion)

335:  -- Explosion
336-  {name = 'fx_explosion_011', file = 'assets/fx_explosion_011.png', size = 96, label = 'Explosion 011 Hit Radial'},
337-  {name = 'fx_explosion_013', file = 'assets/fx_explosion_013.png', size = 96, label = 'Explosion 013 Shot Hit Right MIX'},
338-  {name = 'fx_explosion_014', file = 'assets/fx_explosion_014.png', size = 96, label = 'Explosion 014 Hit Radial MIX'},
339-  {name = 'fx_explosion_019', file = 'assets/fx_explosion_019.png', size = 96, label = 'Explosion 019 Hit Radial'},
340-  {name = 'fx_energy_129', file = 'assets/fx_energy_129.png', size = 480, label = 'Energy 129 Hit Rays Radial'},

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Done — 50 fire effects added (48 at 96px, 2 screen-wide at 480px). The fx_list now has electricity, energy, explosion, and fire categories.

Remove all explosion ones, remove fire 004 008 010 023 050 056 061 072 083 102

Grep (fx_explosion_|fx_fire_004|fx_fire_008|fx_fire_010|fx_fire_023|fx_fire_050|fx_fire_056|fx_fire_061|fx_fire_072|fx_fire_083|fx_fire_102)

336:  {name = 'fx_explosion_011', file = 'assets/fx_explosion_011.png', size = 96, label = 'Explosion 011 Hit Radial'},
337:  {name = 'fx_explosion_013', file = 'assets/fx_explosion_013.png', size = 96, label = 'Explosion 013 Shot Hit Right MIX'},
338:  {name = 'fx_explosion_014', file = 'assets/fx_explosion_014.png', size = 96, label = 'Explosion 014 Hit Radial MIX'},
339:  {name = 'fx_explosion_019', file = 'assets/fx_explosion_019.png', size = 96, label = 'Explosion 019 Hit Radial'},
342:  {name = 'fx_fire_004', file = 'assets/fx_fire_004.png', size = 96, label = 'Fire 004 Projectile Right Loop'},
346:  {name = 'fx_fire_008', file = 'assets/fx_fire_008.png', size = 96, label = 'Fire 008 Up Projectile Loop'},
347:  {name = 'fx_fire_010', file = 'assets/fx_fire_010.png', size = 96, label = 'Fire 010 Hit Trail Up'},
355:  {name = 'fx_fire_023', file = 'assets/fx_fire_023.png', size = 96, label = 'Fire 023 Hit Shot Right'},
366:  {name = 'fx_fire_050', file = 'assets/fx_fire_050.png', size = 96, label = 'Fire 050 Explosion Radial'},
368:  {name = 'fx_fire_056', file = 'assets/fx_fire_056.png', size = 96, label = 'Fire 056 Fireball Radial'},
370:  {name = 'fx_fire_061', file = 'assets/fx_fire_061.png', size = 96, label = 'Fire 061 Projectile Right Loop MIX'},
376:  {name = 'fx_fire_072', file = 'assets/fx_fire_072.png', size = 96, label = 'Fire 072 Hit Left MIX'},
381:  {name = 'fx_fire_083', file = 'assets/fx_fire_083.png', size = 96, label = 'Fire 083 Ray Up MIX'},
385:  {name = 'fx_fire_102', file = 'assets/fx_fire_102.png', size = 96, label = 'Fire 102 Right'},

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Now delete the asset files.

Bash (rm E:/a327ex/Orblike/assets/fx_explosion_011.png E:/a327ex/Orblike/assets/fx_exp...)

Done. Removed all 4 explosion effects and 10 fire effects (entries + assets).

Let's try reverting the conversion process to what it was before we changed it for the electricity improvements so I can check something.

The current pipeline is: crop → LANCZOS scale → BW threshold (the electricity improvement). Before that, it was: crop → BW threshold → NEAREST scale. Here's the revert:

Read (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

   233→        # Pipeline: crop full-color -> scale with LANCZOS -> threshold to BW
   234→        # LANCZOS scale spreads thin lines via anti-aliasing, so they survive
   235→        # the final BW threshold instead of disappearing at nearest-neighbor.
   236→        print(f"Cropping to {crop_box}, scaling to {args.size}x{args.size}, then BW threshold={args.threshold}...")
   237→        scaled_frames = []
   238→        for img in frames:
   239→            cropped = img.crop(crop_box)
   240→            scaled = cropped.resize((args.size, args.size), Image.LANCZOS)
   241→            scaled = convert_frame_bw(scaled, args.threshold)
   242→            scaled_frames.append(scaled)
   243→
   244→        # Remove trailing empty frames
   245→        while scaled_frames and scaled_frames[-1].getbbox() is None:
   246→            scaled_frames.pop()
   247→        print(f"  {len(scaled_frames)} frames after trimming trailing empties")

Edit (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

The file E:/a327ex/Orblike/tools/mov_to_spritesheet.py has been updated successfully.

Reverted. Pipeline is now: crop → BW threshold at full res → NEAREST scale down. You can reconvert whichever effects you want to compare.

Convert fire 102 again and let me see it in game too

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Fire 102 Right noCT noRSZ.mov...
  38 frames extracted
Loading frames...
Cropping to (143, 110, 897, 864), BW threshold=0.4, then scaling to 96x96...
  23 frames after trimming trailing empties
Spritesheet saved: assets/fx_fire_102.png (2208x96, 23 frames of 96x96)

Now add it back to the fx_list.

Grep (fx_fire_105)

371:  {name = 'fx_fire_105', file = 'assets/fx_fire_105.png', size = 96, label = 'Fire 105 Up'},

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Done. Fire 102 is reconverted with the old pipeline (BW first → NEAREST scale) and added back to the browser.

convert energy 119 and the 2 ones following it with this new method, and let me see them one after the other. so the old disappear then the new disappear, old next one then new next one

Grep (fx_energy_119|fx_energy_122|fx_energy_125)

332:  {name = 'fx_energy_119', file = 'assets/fx_energy_119.png', size = 96, label = 'Energy 119 Dissapear'},
333:  {name = 'fx_energy_122', file = 'assets/fx_energy_122.png', size = 96, label = 'Energy 122 Hit Star Radial'},
334:  {name = 'fx_energy_125d', file = 'assets/fx_energy_125d.png', size = 96, label = 'Energy 125 Glare Down'},

Convert those 3 with the reverted pipeline into separate "_v2" files.

Bash (ls "E:/a327ex/RTFX/2. Prerendered animations/FX elements/" | grep -i "Energy 119...)

Elements - Energy 119 Dissapear noCT noRSZ.mov
Elements - Energy 122 Hit Star Radial noCT noRSZ.mov
Elements - Energy 125 Glare Down noCT noRSZ.mov
Elements - Energy 125 Glare Right noCT noRSZ.mov

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Energy 125 Glare Down noCT noRSZ.mov...
  25 frames extracted
Loading frames...
Cropping to (84, 87, 898, 901), BW threshold=0.4, then scaling to 96x96...
  18 frames after trimming trailing empties
Spritesheet saved: assets/fx_energy_125d_v2.png (1728x96, 18 frames of 96x96)

Now I'll add the v2 entries right after each original and update the fx_list so they alternate: old, new, old, new, old, new.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Done. The browser will now show them interleaved — LANCZOS then NEAREST for each:

Energy 119 Dissapear (LANCZOS)
Energy 119 Dissapear (NEAREST)
Energy 122 Hit Star Radial (LANCZOS)
Energy 122 Hit Star Radial (NEAREST)
Energy 125 Glare Down (LANCZOS)
Energy 125 Glare Down (NEAREST)

Mouse4/5 to cycle through and compare.

Let's convert all the fire ones again using this method now.

Grep (fx_fire_)

339:  {name = 'fx_fire_003', file = 'assets/fx_fire_003.png', size = 96, label = 'Fire 003 Radial'},
340:  {name = 'fx_fire_005', file = 'assets/fx_fire_005.png', size = 96, label = 'Fire 005 Hit Up'},
341:  {name = 'fx_fire_006', file = 'assets/fx_fire_006.png', size = 96, label = 'Fire 006 Hit Right'},
342:  {name = 'fx_fire_007', file = 'assets/fx_fire_007.png', size = 96, label = 'Fire 007 Torch Fireball Up'},
343:  {name = 'fx_fire_011', file = 'assets/fx_fire_011.png', size = 96, label = 'Fire 011 Slash Right'},
344:  {name = 'fx_fire_012', file = 'assets/fx_fire_012.png', size = 96, label = 'Fire 012 Slash Right'},
345:  {name = 'fx_fire_014', file = 'assets/fx_fire_014.png', size = 96, label = 'Fire 014 Hit Explosion Right'},
346:  {name = 'fx_fire_015', file = 'assets/fx_fire_015.png', size = 96, label = 'Fire 015 Hit Radial'},
347:  {name = 'fx_fire_016', file = 'assets/fx_fire_016.png', size = 96, label = 'Fire 016 Hit Up'},
348:  {name = 'fx_fire_018', file = 'assets/fx_fire_018.png', size = 96, label = 'Fire 018 Torch Fireball Up Loop'},
349:  {name = 'fx_fire_019', file = 'assets/fx_fire_019.png', size = 96, label = 'Fire 019 Trail Radial'},
350:  {name = 'fx_fire_027', file = 'assets/fx_fire_027.png', size = 96, label = 'Fire 027 Slash Up'},
351:  {name = 'fx_fire_028', file = 'assets/fx_fire_028.png', size = 96, label = 'Fire 028 Ray Shot Up'},
352:  {name = 'fx_fire_029', file = 'assets/fx_fire_029.png', size = 96, label = 'Fire 029 Ray Shot Right'},
353:  {name = 'fx_fire_031', file = 'assets/fx_fire_031.png', size = 96, label = 'Fire 031 Ray Shot Right'},
354:  {name = 'fx_fire_032', file = 'assets/fx_fire_032.png', size = 96, label = 'Fire 032 Slash Radial'},
355:  {name = 'fx_fire_033', file = 'assets/fx_fire_033.png', size = 96, label = 'Fire 033 Hit Explosion Radial'},
356:  {name = 'fx_fire_036', file = 'assets/fx_fire_036.png', size = 96, label = 'Fire 036 Projectile Down'},
357:  {name = 'fx_fire_039', file = 'assets/fx_fire_039.png', size = 96, label = 'Fire 039 Radial'},
358:  {name = 'fx_fire_040', file = 'assets/fx_fire_040.png', size = 96, label = 'Fire 040 Shot Hit Up'},
359:  {name = 'fx_fire_041', file = 'assets/fx_fire_041.png', size = 96, label = 'Fire 041 Fireball Torch Right'},
360:  {name = 'fx_fire_055', file = 'assets/fx_fire_055.png', size = 96, label = 'Fire 055 Hit Shot Up'},
361:  {name = 'fx_fire_059', file = 'assets/fx_fire_059.png', size = 96, label = 'Fire 059 Flamethrower Right MIX'},
362:  {name = 'fx_fire_062', file = 'assets/fx_fire_062.png', size = 96, label = 'Fire 062 Slash Down MIX'},
363:  {name = 'fx_fire_064', file = 'assets/fx_fire_064.png', size = 96, label = 'Fire 064 Hit Up'},
364:  {name = 'fx_fire_065', file = 'assets/fx_fire_065.png', size = 96, label = 'Fire 065 Flamethrower Right MIX'},
365:  {name = 'fx_fire_068', file = 'assets/fx_fire_068.png', size = 96, label = 'Fire 068 Right MIX'},
366:  {name = 'fx_fire_070', file = 'assets/fx_fire_070.png', size = 96, label = 'Fire 070 Flamethrower Right MIX'},
367:  {name = 'fx_fire_073', file = 'assets/fx_fire_073.png', size = 96, label = 'Fire 073 Explosion Right MIX'},
368:  {name = 'fx_fire_074', file = 'assets/fx_fire_074.png', size = 96, label = 'Fire 074 Ray Up MIX'},
... [11 more lines]

38 fire effects to reconvert (fire 102 already done). Reconverting the other 37 at 96px + 2 at 480px.

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Fire 018 Torch Fireball Up Loop noCT noRSZ.mov...
  31 frames extracted
Loading frames...
Cropping to (111, 144, 903, 936), BW threshold=0.4, then scaling to 96x96...
  31 frames after trimming trailing empties
Spritesheet saved: assets/fx_fire_018.png (2976x96, 31 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Fire 040 Shot Hit Up noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (121, 142, 841, 862), BW threshold=0.4, then scaling to 96x96...
  10 frames after trimming trailing empties
Spritesheet saved: assets/fx_fire_040.png (960x96, 10 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Fire 074 Ray Up MIX noCT noRSZ.mov...
  50 frames extracted
Loading frames...
Cropping to (8, -3, 978, 967), BW threshold=0.4, then scaling to 96x96...
  48 frames after trimming trailing empties
Spritesheet saved: assets/fx_fire_074.png (4608x96, 48 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Fire 079 Wide MIX loop.mov...
  24 frames extracted
Loading frames...
Cropping to (-96, -516, 2016, 1596), BW threshold=0.4, then scaling to 480x480...
  24 frames after trimming trailing empties
Spritesheet saved: assets/fx_fire_079.png (11520x480, 24 frames of 480x480)

All 40 fire effects reconverted with the reverted pipeline (BW threshold → NEAREST scale). No code changes needed — the spritesheets are overwritten in place.

Let's do the same for the energy ones, remove the old versions of the 3 ones we already did. I think the other solution only improved the visuals for electricity and all the other ones were worse.

Grep (fx_energy_)

308:  {name = 'fx_energy_002', file = 'assets/fx_energy_002.png', size = 96, label = 'Energy 002 Hit Star Radial'},
309:  {name = 'fx_energy_005', file = 'assets/fx_energy_005.png', size = 96, label = 'Energy 005 Hit Radial'},
310:  {name = 'fx_energy_008', file = 'assets/fx_energy_008.png', size = 96, label = 'Energy 008 Hit Radial'},
311:  {name = 'fx_energy_011', file = 'assets/fx_energy_011.png', size = 96, label = 'Energy 011 Ray Up Loop'},
312:  {name = 'fx_energy_012', file = 'assets/fx_energy_012.png', size = 96, label = 'Energy 012 Slash Right'},
313:  {name = 'fx_energy_014', file = 'assets/fx_energy_014.png', size = 96, label = 'Energy 014 Slash Up'},
314:  {name = 'fx_energy_015', file = 'assets/fx_energy_015.png', size = 96, label = 'Energy 015 Slash Radial'},
315:  {name = 'fx_energy_017', file = 'assets/fx_energy_017.png', size = 96, label = 'Energy 017 Charge Up'},
316:  {name = 'fx_energy_018', file = 'assets/fx_energy_018.png', size = 96, label = 'Energy 018 Ray Right'},
317:  {name = 'fx_energy_019', file = 'assets/fx_energy_019.png', size = 96, label = 'Energy 019 Ray Up'},
318:  {name = 'fx_energy_020', file = 'assets/fx_energy_020.png', size = 96, label = 'Energy 020 Slash1 Ray Right'},
319:  {name = 'fx_energy_022', file = 'assets/fx_energy_022.png', size = 96, label = 'Energy 022 Slash Right'},
320:  {name = 'fx_energy_029', file = 'assets/fx_energy_029.png', size = 96, label = 'Energy 029 Slash Right'},
321:  {name = 'fx_energy_032', file = 'assets/fx_energy_032.png', size = 96, label = 'Energy 032 Slash Right'},
322:  {name = 'fx_energy_034', file = 'assets/fx_energy_034.png', size = 96, label = 'Energy 034 Slash Radial'},
323:  {name = 'fx_energy_049', file = 'assets/fx_energy_049.png', size = 96, label = 'Energy 049 Shot Right Loop MIX'},
324:  {name = 'fx_energy_057', file = 'assets/fx_energy_057.png', size = 96, label = 'Energy 057 Shot Right Loop MIX'},
325:  {name = 'fx_energy_086', file = 'assets/fx_energy_086.png', size = 96, label = 'Energy 086 Charge Explosion Radial MIX'},
326:  {name = 'fx_energy_087', file = 'assets/fx_energy_087.png', size = 96, label = 'Energy 087 Charge Radial Loop MIX'},
327:  {name = 'fx_energy_105', file = 'assets/fx_energy_105.png', size = 96, label = 'Energy 105 Ray Left Loop'},
328:  {name = 'fx_energy_109', file = 'assets/fx_energy_109.png', size = 96, label = 'Energy 109 Explosion Hit Radial MIX'},
329:  {name = 'fx_energy_114', file = 'assets/fx_energy_114.png', size = 96, label = 'Energy 114 Slash Radial MIX'},
330:  {name = 'fx_energy_115', file = 'assets/fx_energy_115.png', size = 96, label = 'Energy 115 Slash Charge Radial Loop MIX'},
331:  {name = 'fx_energy_118', file = 'assets/fx_energy_118.png', size = 96, label = 'Energy 118 Hit Radial'},
332:  {name = 'fx_energy_119', file = 'assets/fx_energy_119.png', size = 96, label = 'Energy 119 Dissapear (LANCZOS)'},
333:  {name = 'fx_energy_119_v2', file = 'assets/fx_energy_119_v2.png', size = 96, label = 'Energy 119 Dissapear (NEAREST)'},
334:  {name = 'fx_energy_122', file = 'assets/fx_energy_122.png', size = 96, label = 'Energy 122 Hit Star Radial (LANCZOS)'},
335:  {name = 'fx_energy_122_v2', file = 'assets/fx_energy_122_v2.png', size = 96, label = 'Energy 122 Hit Star Radial (NEAREST)'},
336:  {name = 'fx_energy_125d', file = 'assets/fx_energy_125d.png', size = 96, label = 'Energy 125 Glare Down (LANCZOS)'},
337:  {name = 'fx_energy_125d_v2', file = 'assets/fx_energy_125d_v2.png', size = 96, label = 'Energy 125 Glare Down (NEAREST)'},
... [2 more lines]

First, remove the old LANCZOS versions of 119, 122, 125d and rename the v2s to be the main ones. Then reconvert the remaining 24 energy effects at 96px + 2 at 480px.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Bash (cd E:/a327ex/Orblike/assets && rm fx_energy_119.png fx_energy_122.png fx_energy_...)

Now reconvert the remaining 24 energy effects + 2 screen-size ones. Need to find the source filenames.

Bash (ls "E:/a327ex/RTFX/2. Prerendered animations/FX elements/" | grep -i "^Elements ...)

Elements - Energy 002 Hit Star Radial noCT noRSZ.mov
Elements - Energy 005 Hit Radial noCT noRSZ.mov
Elements - Energy 008 Hit Radial noCT noRSZ.mov
Elements - Energy 011 Ray Up Loop noCT noRSZ.mov
Elements - Energy 012 Slash Right noCT noRSZ.mov
Elements - Energy 014 Slash Up noCT noRSZ.mov
Elements - Energy 015 Slash Radial noCT noRSZ.mov
Elements - Energy 017 Charge Up noCT noRSZ.mov
Elements - Energy 018 Ray Right noCT noRSZ.mov
Elements - Energy 019 Ray Up noCT noRSZ.mov
Elements - Energy 020 Slash1 Ray Right noCT noRSZ.mov
Elements - Energy 022 Slash Right noCT noRSZ.mov
Elements - Energy 029 Slash Right noCT noRSZ.mov
Elements - Energy 032 Slash Right noCT noRSZ.mov
Elements - Energy 034 Slash Radial noCT noRSZ.mov
Elements - Energy 049 Shot Right Loop MIX noCT noRSZ.mov
Elements - Energy 057 Shot Right Loop MIX noCT noRSZ.mov
Elements - Energy 086 Charge Explosion Radial MIX noCT noRSZ.mov
Elements - Energy 087 Charge Radial Loop MIX noCT noRSZ.mov
Elements - Energy 105 Ray Left Loop noCT noRSZ.mov
Elements - Energy 109 Explosion Hit Radial MIX noCT noRSZ.mov
Elements - Energy 114 Slash Radial MiX noCT noRSZ.mov
Elements - Energy 115 Slash Charge Radial Loop MIX noCT noRSZ.mov
Elements - Energy 118 Hit Radial noCT noRSZ.mov
Elements - Energy 129 Hit Rays Radial noCT noRSZ.mov
Elements - Energy 130 Hit Rays Radial noCT noRSZ.mov

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Energy 019 Ray Up noCT noRSZ.mov...
  45 frames extracted
Loading frames...
Cropping to (2, 7, 970, 975), BW threshold=0.4, then scaling to 96x96...
  27 frames after trimming trailing empties
Spritesheet saved: assets/fx_energy_019.png (2592x96, 27 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Energy 020 Slash1 Ray Right noCT noRSZ.mov...
  25 frames extracted
Loading frames...
Cropping to (63, 110, 847, 894), BW threshold=0.4, then scaling to 96x96...
  15 frames after trimming trailing empties
Spritesheet saved: assets/fx_energy_020.png (1440x96, 15 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Energy 105 Ray Left Loop noCT noRSZ.mov...
  25 frames extracted
Loading frames...
Cropping to (131, 152, 777, 798), BW threshold=0.4, then scaling to 96x96...
  25 frames after trimming trailing empties
Spritesheet saved: assets/fx_energy_105.png (2400x96, 25 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Energy 130 Hit Rays Radial noCT noRSZ.mov...
  16 frames extracted
Loading frames...
Cropping to (-96, -516, 2016, 1596), BW threshold=0.4, then scaling to 480x480...
  6 frames after trimming trailing empties
Spritesheet saved: assets/fx_energy_130.png (2880x480, 6 frames of 480x480)

Done. All 26 energy effects reconverted with the reverted pipeline (BW threshold → NEAREST scale). The spritesheets are overwritten in place, no code changes needed.

I think we need an option argument or something in the script, because some effects are better with the other method, some are better with this. For energy, these should be with the other method: 002, 008, 087, 105, 118

Read (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

   174→def main():
   175→    parser = argparse.ArgumentParser(description="Convert .mov animation to BW spritesheet")
   176→    parser.add_argument("input", help="Input .mov file")
   177→    parser.add_argument("output", help="Output .png spritesheet")
   178→    parser.add_argument("--threshold", type=float, default=0.5, help="Brightness threshold 0.0-1.0 (default 0.5)")
   179→    parser.add_argument("--size", type=int, default=96, help="Output frame height in pixels (default 96)")
   180→    parser.add_argument("--skip", type=int, default=1, help="Keep every Nth frame (default 1 = all)")
   181→    args = parser.parse_args()
   182→
   183→    if not os.path.exists(args.input):
   184→        print(f"Input file not found: {args.input}", file=sys.stderr)
   185→        sys.exit(1)
   186→
   187→    with tempfile.TemporaryDirectory() as tmp_dir:
   188→        print(f"Extracting frames from {args.input}...")

Read (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

   233→        # Pipeline: crop full-color -> threshold to BW -> scale with NEAREST
   234→        print(f"Cropping to {crop_box}, BW threshold={args.threshold}, then scaling to {args.size}x{args.size}...")
   235→        scaled_frames = []
   236→        for img in frames:
   237→            cropped = img.crop(crop_box)
   238→            cropped = convert_frame_bw(cropped, args.threshold)
   239→            scaled = cropped.resize((args.size, args.size), Image.NEAREST)
   240→            scaled_frames.append(scaled)
   241→
   242→        # Remove trailing empty frames
   243→        while scaled_frames and scaled_frames[-1].getbbox() is None:
   244→            scaled_frames.pop()

Edit (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

The file E:/a327ex/Orblike/tools/mov_to_spritesheet.py has been updated successfully.

Edit (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

The file E:/a327ex/Orblike/tools/mov_to_spritesheet.py has been updated successfully.

Now reconvert those 5 energy effects with --smooth.

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Energy 118 Hit Radial noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (152, 136, 844, 828), LANCZOS scaling to 96x96, then BW threshold=0.4...
  16 frames after trimming trailing empties
Spritesheet saved: assets/fx_energy_118.png (1536x96, 16 frames of 96x96)

Done. Added --smooth flag to the script — default is NEAREST (sharp), --smooth uses LANCZOS (better for thin lines). Energy 002, 008, 087, 105, 118 reconverted with --smooth. All electricity effects should also use --smooth if they ever need reconverting.

Let me see smooth for 86 and 109 energy

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Energy 109 Explosion Hit Radial MIX noCT noRSZ.mov...
  22 frames extracted
Loading frames...
Cropping to (84, 84, 916, 916), LANCZOS scaling to 96x96, then BW threshold=0.4...
  16 frames after trimming trailing empties
Spritesheet saved: assets/fx_energy_109.png (1536x96, 16 frames of 96x96)

Done. Both reconverted with --smooth.

lets try smooth on fire 92 93

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Fire 093 Charge Radial MIX noCT noRSZ.mov...
  44 frames extracted
Loading frames...
Cropping to (540, 155, 1348, 963), LANCZOS scaling to 96x96, then BW threshold=0.4...
  27 frames after trimming trailing empties
Spritesheet saved: assets/fx_fire_093.png (2592x96, 27 frames of 96x96)

Done.

89, 33, 29, 19, 15, 3 with smooth fire

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Fire 003 Radial noCT noRSZ.mov...
  34 frames extracted
Loading frames...
Cropping to (78, 60, 918, 900), LANCZOS scaling to 96x96, then BW threshold=0.4...
  26 frames after trimming trailing empties
Spritesheet saved: assets/fx_fire_003.png (2496x96, 26 frames of 96x96)

Done. All 6 reconverted with --smooth.

Revert all these 6, except 3

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Fire 015 Hit Radial noCT noRSZ.mov...
  36 frames extracted
Loading frames...
Cropping to (119, 122, 851, 854), BW threshold=0.4, then NEAREST scaling to 96x96...
  23 frames after trimming trailing empties
Spritesheet saved: assets/fx_fire_015.png (2208x96, 23 frames of 96x96)

Done. Fire 089, 033, 029, 019, 015 reverted to default (NEAREST). Fire 003 stays smooth.

liquid 003 012 015 022 026 029 033 034 038 049 050 059 063 064 065 066 067 068 069 070 mix 071 mix 072

Bash (ls "E:/a327ex/RTFX/2. Prerendered animations/FX elements/" | grep -i "^Elements ...)

Elements - Liquid 003 Slash Right noCT noRSZ.mov
Elements - Liquid 012 Shot Splash Up noCT noRSZ.mov
Elements - Liquid 015 Slash Right noCT noRSZ.mov
Elements - Liquid 022 Shot Bubbles Up noCT noRSZ.mov
Elements - Liquid 026 Slash Up noCT noRSZ.mov
Elements - Liquid 029 Splash Hit Explosion Radial noCT noRSZ.mov
Elements - Liquid 033 Hit Radial noCT noRSZ.mov
Elements - Liquid 034 Hit Splash Explosion Radial noCT noRSZ.mov
Elements - Liquid 038 Slash Up noCT noRSZ.mov
Elements - Liquid 049 Splash Slash Right MIX noCT noRSZ.mov
Elements - Liquid 050 Explosion Radial MIX noCT noRSZ.mov
Elements - Liquid 059 Ray Right loop noCT noRSZ.mov
Elements - Liquid 063 Splash Right noCT noRSZ.mov
Elements - Liquid 064 Splash Right noCT noRSZ.mov
Elements - Liquid 065 Splash Right noCT noRSZ.mov
Elements - Liquid 066 Splash Right noCT noRSZ.mov
Elements - Liquid 067 Splash Right noCT noRSZ.mov
Elements - Liquid 068 Splash Hit Radial noCT noRSZ.mov
Elements - Liquid 069 Splash Right noCT noRSZ.mov
Elements - Liquid 070 Slash Radial MIX noCT noRSZ.mov
Elements - Liquid 070 Splash Radial noCT noRSZ.mov
Elements - Liquid 071 Slash Charge Radial MIX noCT noRSZ.mov
Elements - Liquid 071 Splash Radial noCT noRSZ.mov
Elements - Liquid 072 Charge Radial Loop MIX noCT noRSZ.mov
Elements - Liquid 072 Splash Up noCT noRSZ.mov

070, 071, 072 each have two variants. You said "070 mix", "071 mix", "072" — so I'll use the MIX versions for 070 and 071, and the non-MIX for 072 (Splash Up).

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Liquid 049 Splash Slash Right MIX noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (88, 184, 974, 1070), BW threshold=0.4, then NEAREST scaling to 96x96...
  24 frames after trimming trailing empties
Spritesheet saved: assets/fx_liquid_049.png (2304x96, 24 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Liquid 070 Slash Radial MIX noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (185, 178, 817, 810), BW threshold=0.4, then NEAREST scaling to 96x96...
  22 frames after trimming trailing empties
Spritesheet saved: assets/fx_liquid_070.png (2112x96, 22 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Liquid 072 Charge Radial Loop MIX noCT noRSZ.mov...
  13 frames extracted
Loading frames...
Cropping to (98, 72, 896, 870), BW threshold=0.4, then NEAREST scaling to 96x96...
  13 frames after trimming trailing empties
Spritesheet saved: assets/fx_liquid_072.png (1248x96, 13 frames of 96x96)

All 22 converted. Now adding to fx_list.

Grep (fx_fire_048)

375:  {name = 'fx_fire_048', file = 'assets/fx_fire_048.png', size = 480, label = 'Fire 048 Up Wide Loop'},

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Done — 22 liquid effects added to the browser.

Due to how the threshold works and how the liquid visual works, this one came out wrong because the dark parts of the water came out as transparent when they should still be white.

ToolSearch

[
  {
    "type": "tool_reference",
    "tool_name": "AskUserQuestion"
  }
]

AskUserQuestion

User has answered your questions: "Which liquid effect(s) came out wrong? And for the fix — should I add a lower threshold option (e.g. 0.1 or 0.2) so the dark water parts survive, or would you prefer an --invert / alpha-only mode where any non-transparent pixel becomes white regardless of brightness?"="Let's try the alpha-only flag first" user notes: Let's try the alpha-only flag first. You can now continue with the user's answers in mind.

Read (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

   174→def main():
   175→    parser = argparse.ArgumentParser(description="Convert .mov animation to BW spritesheet")
   176→    parser.add_argument("input", help="Input .mov file")
   177→    parser.add_argument("output", help="Output .png spritesheet")
   178→    parser.add_argument("--threshold", type=float, default=0.5, help="Brightness threshold 0.0-1.0 (default 0.5)")
   179→    parser.add_argument("--size", type=int, default=96, help="Output frame height in pixels (default 96)")
   180→    parser.add_argument("--skip", type=int, default=1, help="Keep every Nth frame (default 1 = all)")
   181→    parser.add_argument("--smooth", action="store_true", help="Scale with LANCZOS before BW threshold (better for thin lines like electricity)")
   182→    args = parser.parse_args()
   183→

Edit (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

The file E:/a327ex/Orblike/tools/mov_to_spritesheet.py has been updated successfully.

Now add an alpha-only conversion function and wire it into the pipeline.

Read (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

   140→
   141→def convert_frame_bw(img, threshold):
   142→    """Convert an RGBA image to pure white-on-transparent using brightness threshold.
   143→
   144→    For each pixel:
   145→    - Compute luminance from RGB
   146→    - If luminance > threshold AND original alpha > 0: pixel becomes solid white (255,255,255,255)
   147→    - Otherwise: pixel becomes fully transparent (0,0,0,0)
   148→
   149→    No gray, no semi-transparency. Pure 1-bit: white or nothing.
   150→    """
   151→    pixels = img.load()
   152→    w, h = img.size
   153→    threshold_byte = int(threshold * 255)
   154→
   155→    for y in range(h):
   156→        for x in range(w):
   157→            r, g, b, a = pixels[x, y]
   158→            if a == 0:
   159→                continue
   160→            lum = int(0.299 * r + 0.587 * g + 0.114 * b)
   161→            if lum > threshold_byte:
   162→                pixels[x, y] = (255, 255, 255, 255)
   163→            else:
   164→                pixels[x, y] = (0, 0, 0, 0)
   165→
   166→    return img
   167→
   168→
   169→def trim_frame(img):
... [5 more lines]

Edit (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

The file E:/a327ex/Orblike/tools/mov_to_spritesheet.py has been updated successfully.

Now wire it into the pipeline. Need to update both the bbox computation and the frame processing.

Read (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

   215→            frame_paths = frame_paths[::args.skip]
   216→            print(f"  {len(frame_paths)} frames after skip={args.skip}")
   217→
   218→        # Load all frames as full-color RGBA
   219→        print(f"Loading frames...")
   220→        frames = []
   221→        for fp in frame_paths:
   222→            frames.append(Image.open(fp).convert("RGBA"))
   223→
   224→        # Compute union bounding box using a temporary BW pass (at full resolution)
   225→        # We need this to know where to crop before scaling.
   226→        union_bbox = None
   227→        for img in frames:
   228→            tmp = img.copy()
   229→            tmp = convert_frame_bw(tmp, args.threshold)
   230→            bbox = trim_frame(tmp)
   231→            if bbox is None:
   232→                continue
   233→            if union_bbox is None:
   234→                union_bbox = bbox
   235→            else:
   236→                union_bbox = (
   237→                    min(union_bbox[0], bbox[0]),
   238→                    min(union_bbox[1], bbox[1]),
   239→                    max(union_bbox[2], bbox[2]),
   240→                    max(union_bbox[3], bbox[3]),
   241→                )
   242→
   243→        if union_bbox is None:
   244→            print("All frames are empty after thresholding! Try a lower threshold.", file=sys.stderr)
... [20 more lines]

Edit (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

The file E:/a327ex/Orblike/tools/mov_to_spritesheet.py has been updated successfully.

Read (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

   254→        half = int(half * 1.1)
   255→        crop_box = (cx - half, cy - half, cx + half, cy + half)
   256→
   257→        if args.smooth:
   258→            # Pipeline: crop full-color -> scale with LANCZOS -> threshold to BW
   259→            # LANCZOS spreads thin lines via anti-aliasing so they survive the threshold.
   260→            # Better for electricity and thin-line effects.
   261→            print(f"Cropping to {crop_box}, LANCZOS scaling to {args.size}x{args.size}, then BW threshold={args.threshold}...")
   262→            scaled_frames = []
   263→            for img in frames:
   264→                cropped = img.crop(crop_box)
   265→                scaled = cropped.resize((args.size, args.size), Image.LANCZOS)
   266→                scaled = convert_frame_bw(scaled, args.threshold)
   267→                scaled_frames.append(scaled)
   268→        else:
   269→            # Pipeline: crop full-color -> threshold to BW -> scale with NEAREST
   270→            # Preserves sharp pixel detail. Better for most effects.
   271→            print(f"Cropping to {crop_box}, BW threshold={args.threshold}, then NEAREST scaling to {args.size}x{args.size}...")
   272→            scaled_frames = []
   273→            for img in frames:

Read (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

   273→            for img in frames:
   274→                cropped = img.crop(crop_box)
   275→                cropped = convert_frame_bw(cropped, args.threshold)
   276→                scaled = cropped.resize((args.size, args.size), Image.NEAREST)
   277→                scaled_frames.append(scaled)
   278→
   279→        # Remove trailing empty frames
   280→        while scaled_frames and scaled_frames[-1].getbbox() is None:

Edit (E:/a327ex/Orblike/tools/mov_to_spritesheet.py)

The file E:/a327ex/Orblike/tools/mov_to_spritesheet.py has been updated successfully.

Now reconvert all 22 liquid effects with --alpha-only.

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Liquid 003 Slash Right noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (53, 9, 1017, 973), alpha-only, then NEAREST scaling to 96x96...
  21 frames after trimming trailing empties
Spritesheet saved: assets/fx_liquid_003.png (2016x96, 21 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Liquid 049 Splash Slash Right MIX noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (44, 173, 978, 1107), alpha-only, then NEAREST scaling to 96x96...
  24 frames after trimming trailing empties
Spritesheet saved: assets/fx_liquid_049.png (2304x96, 24 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Liquid 070 Slash Radial MIX noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (182, 175, 820, 813), alpha-only, then NEAREST scaling to 96x96...
  22 frames after trimming trailing empties
Spritesheet saved: assets/fx_liquid_070.png (2112x96, 22 frames of 96x96)

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Liquid 072 Charge Radial Loop MIX noCT noRSZ.mov...
  13 frames extracted
Loading frames...
Cropping to (95, 70, 899, 874), alpha-only, then NEAREST scaling to 96x96...
  13 frames after trimming trailing empties
Spritesheet saved: assets/fx_liquid_072.png (1248x96, 13 frames of 96x96)

Done. All 22 liquid effects reconverted with --alpha-only — every non-transparent pixel is now solid white regardless of brightness.

34, 49, 50 remove

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Bash (rm E:/a327ex/Orblike/assets/fx_liquid_034.png E:/a327ex/Orblike/assets/fx_liquid...)

Done. Removed liquid 034, 049, 050.

smoke 003 005 007 008 030 036 072 073 sparks 020 021 022 019 023 025 028 032 033 037 067 069 070 072 073 074 075 077 080 081 082 084 100 091 102

Bash (ls "E:/a327ex/RTFX/2. Prerendered animations/FX elements/" | grep -i "^Elements ...)

Elements - Sparks 019 Shot Hit Right noCT noRSZ.mov
Elements - Sparks 020 Shot Hit Explosion Right noCT noRSZ.mov
Elements - Sparks 021 Shot Hit Explosion Right noCT noRSZ.mov
Elements - Sparks 022 Shot Ray Hit Explosion Right noCT noRSZ.mov
Elements - Sparks 023 Slash Radial noCT noRSZ.mov
Elements - Sparks 025 Hit Explosion Radial noCT noRSZ.mov
Elements - Sparks 028 Shot Hit Explosion Up noCT noRSZ.mov
Elements - Sparks 032 Hit Star Explosion Radial noCT noRSZ.mov
Elements - Sparks 033 Hit Explosion Shot Up noCT noRSZ.mov
Elements - Sparks 037 Shot Right Loop noCT noRSZ.mov
Elements - Sparks 067 Down noCT noRSZ.mov
Elements - Sparks 069 Hit Star Explosion Radial noCT noRSZ.mov
Elements - Sparks 070 Shot Ray Hit Explosion Up noCT noRSZ.mov
Elements - Sparks 072 Shot Ray Hit Explosion Right noCT noRSZ.mov
Elements - Sparks 073 Hit Explosion Radial noCT noRSZ.mov
Elements - Sparks 074 Shot Ray Hit Up noCT noRSZ.mov
Elements - Sparks 075 Hit Explosion Radial noCT noRSZ.mov
Elements - Sparks 077 Charge Hit Up noCT noRSZ.mov
Elements - Sparks 080 Charge Radial noCT noRSZ.mov
Elements - Sparks 081 Shot Right Loop noCT noRSZ.mov
Elements - Sparks 082 Shot Right Loop noCT noRSZ.mov
Elements - Sparks 084 Charge Radial noCT noRSZ.mov
Elements - Sparks 091 Ray Horizontal noCT noRSZ.mov
Elements - Sparks 100 Shot Radial Loop noCT noRSZ.mov
Elements - Sparks 102 Hit Star Radial noCT noRSZ.mov

All found. 8 smoke + 25 sparks = 33 effects. Converting all with default pipeline.

Bash (cd E:/a327ex/Orblike && for f in \ "Elements - Sparks 072 Shot Ray Hit Explosi...)

Bash (ls E:/a327ex/Orblike/assets/fx_sparks_07*.png E:/a327ex/Orblike/assets/fx_sparks...)

E:/a327ex/Orblike/assets/fx_sparks_070.png
E:/a327ex/Orblike/assets/fx_sparks_072.png
E:/a327ex/Orblike/assets/fx_sparks_073.png
E:/a327ex/Orblike/assets/fx_sparks_074.png
E:/a327ex/Orblike/assets/fx_sparks_075.png
E:/a327ex/Orblike/assets/fx_sparks_077.png
E:/a327ex/Orblike/assets/fx_sparks_080.png
E:/a327ex/Orblike/assets/fx_sparks_081.png
E:/a327ex/Orblike/assets/fx_sparks_082.png
E:/a327ex/Orblike/assets/fx_sparks_084.png
E:/a327ex/Orblike/assets/fx_sparks_091.png
E:/a327ex/Orblike/assets/fx_sparks_100.png
E:/a327ex/Orblike/assets/fx_sparks_102.png

All 33 converted. Now adding to fx_list.

Grep (fx_liquid_072)

394:  {name = 'fx_liquid_072', file = 'assets/fx_liquid_072.png', size = 96, label = 'Liquid 072 Charge Radial Loop MIX'},

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Done — 8 smoke (alpha-only) + 25 sparks (default threshold) added to the browser.

smooth spark 102 91 84 81 80 75 73 72 70 69 67 37 33 32 25 19 20 21 22 23

Bash (cd E:/a327ex/Orblike && for num in 102 091 084 081 080 075 073 072 070 069 067 0...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 102 Hit Star Radial noCT noRSZ.mov...
  15 frames extracted
Loading frames...
Cropping to (335, 331, 647, 643), LANCZOS scaling to 96x96, then BW threshold=0.4...
  5 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_102.png (480x96, 5 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 091 Ray Horizontal noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (56, 367, 996, 1307), LANCZOS scaling to 96x96, then BW threshold=0.4...
  14 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_091.png (1344x96, 14 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 084 Charge Radial noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (10, -10, 1014, 994), LANCZOS scaling to 96x96, then BW threshold=0.4...
  23 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_084.png (2208x96, 23 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 081 Shot Right Loop noCT noRSZ.mov...
  5 frames extracted
Loading frames...
Cropping to (483, 145, 1263, 925), LANCZOS scaling to 96x96, then BW threshold=0.4...
  5 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_081.png (480x96, 5 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 080 Charge Radial noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (183, 257, 751, 825), LANCZOS scaling to 96x96, then BW threshold=0.4...
  20 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_080.png (1920x96, 20 frames of 96x96)
... [90 more lines]

Done. All 20 sparks reconverted with --smooth.

try converting 102 with different thresholds, the effect is good on video but the way its being converted is slightly incorrect. let me see with 5 different thresholds, add a flag to it if necessary on the script

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 102 Hit Star Radial noCT noRSZ.mov...
  15 frames extracted
Loading frames...
Cropping to (286, 285, 694, 693), LANCZOS scaling to 96x96, then BW threshold=0.1...
  5 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_102_t10.png (480x96, 5 frames of 96x96)

Now add them to the fx_list so you can compare in the browser.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Done. You'll see 6 versions of sparks 102 in a row: t=0.4 (current), t=0.1, t=0.2, t=0.3, t=0.5. All with smooth. Cycle through to compare.

I think t higher than 0.5 is the right move

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 102 Hit Star Radial noCT noRSZ.mov...
  15 frames extracted
Loading frames...
Cropping to (337, 332, 647, 642), LANCZOS scaling to 96x96, then BW threshold=0.8...
  5 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_102_t80.png (480x96, 5 frames of 96x96)

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Added t=0.6, 0.7, 0.8. You now have the full range from 0.1 to 0.8 to compare.

Is the max value 1? Let me see t 0.9 and 1

Yes, 1.0 is the max — but at t=1.0 nothing would pass since luminance needs to be greater than the threshold, and max luminance is 255 (= 1.0). Let me make t=0.99 instead of 1.0.

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Exit code 1
All frames are empty after thresholding! Try a lower threshold.
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 102 Hit Star Radial noCT noRSZ.mov...
  15 frames extracted
Loading frames...

t=0.99 is too high — all frames came out empty. Let me try 0.95 instead.

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 102 Hit Star Radial noCT noRSZ.mov...
  15 frames extracted
Loading frames...
Cropping to (364, 367, 628, 631), LANCZOS scaling to 96x96, then BW threshold=0.95...
  5 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_102_t95.png (480x96, 5 frames of 96x96)

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Added t=0.9 and t=0.95. t=0.99 was empty so skipped it.

0.75 is the right value, delete all others. Revert 091, 69 70 72 37 32 19 20, for the ones that were reverted also add 0.75 threshold option

First, convert sparks 102 at t=0.75 as the main version and clean up test files.

Bash (rm E:/a327ex/Orblike/assets/fx_sparks_102_t*.png)

Now clean up the fx_list — remove test entries, fix 102 label.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Now revert 091, 069, 070, 072, 037, 032, 019, 020 to default (no smooth, t=0.4) and also create t=0.75 versions.

Bash (cd E:/a327ex/Orblike && for num in 091 069 070 072 037 032 019 020; do
  f=$(ls ...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 091 Ray Horizontal noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (56, 367, 996, 1307), BW threshold=0.4, then NEAREST scaling to 96x96...
  14 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_091.png (1344x96, 14 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 091 Ray Horizontal noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (56, 368, 994, 1306), LANCZOS scaling to 96x96, then BW threshold=0.75...
  14 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_091_t75.png (1344x96, 14 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 069 Hit Star Explosion Radial noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (38, -6, 1016, 972), BW threshold=0.4, then NEAREST scaling to 96x96...
  16 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_069.png (1536x96, 16 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 069 Hit Star Explosion Radial noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (38, -6, 1016, 972), LANCZOS scaling to 96x96, then BW threshold=0.75...
  16 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_069_t75.png (1536x96, 16 frames of 96x96)
Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 070 Shot Ray Hit Explosion Up noCT noRSZ.mov...
  24 frames extracted
Loading frames...
Cropping to (51, 60, 931, 940), BW threshold=0.4, then NEAREST scaling to 96x96...
  12 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_070.png (1152x96, 12 frames of 96x96)
... [66 more lines]

Now add the t75 variants to the fx_list, interleaved after each default.

Read (E:/a327ex/Orblike/main.lua)

   405→  {name = 'fx_sparks_019', file = 'assets/fx_sparks_019.png', size = 96, label = 'Sparks 019 Shot Hit Right'},
   406→  {name = 'fx_sparks_020', file = 'assets/fx_sparks_020.png', size = 96, label = 'Sparks 020 Shot Hit Explosion Right'},
   407→  {name = 'fx_sparks_021', file = 'assets/fx_sparks_021.png', size = 96, label = 'Sparks 021 Shot Hit Explosion Right'},
   408→  {name = 'fx_sparks_022', file = 'assets/fx_sparks_022.png', size = 96, label = 'Sparks 022 Shot Ray Hit Explosion Right'},
   409→  {name = 'fx_sparks_023', file = 'assets/fx_sparks_023.png', size = 96, label = 'Sparks 023 Slash Radial'},
   410→  {name = 'fx_sparks_025', file = 'assets/fx_sparks_025.png', size = 96, label = 'Sparks 025 Hit Explosion Radial'},
   411→  {name = 'fx_sparks_028', file = 'assets/fx_sparks_028.png', size = 96, label = 'Sparks 028 Shot Hit Explosion Up'},
   412→  {name = 'fx_sparks_032', file = 'assets/fx_sparks_032.png', size = 96, label = 'Sparks 032 Hit Star Explosion Radial'},
   413→  {name = 'fx_sparks_033', file = 'assets/fx_sparks_033.png', size = 96, label = 'Sparks 033 Hit Explosion Shot Up'},
   414→  {name = 'fx_sparks_037', file = 'assets/fx_sparks_037.png', size = 96, label = 'Sparks 037 Shot Right Loop'},
   415→  {name = 'fx_sparks_067', file = 'assets/fx_sparks_067.png', size = 96, label = 'Sparks 067 Down'},
   416→  {name = 'fx_sparks_069', file = 'assets/fx_sparks_069.png', size = 96, label = 'Sparks 069 Hit Star Explosion Radial'},
   417→  {name = 'fx_sparks_070', file = 'assets/fx_sparks_070.png', size = 96, label = 'Sparks 070 Shot Ray Hit Explosion Up'},
   418→  {name = 'fx_sparks_072', file = 'assets/fx_sparks_072.png', size = 96, label = 'Sparks 072 Shot Ray Hit Explosion Right'},
   419→  {name = 'fx_sparks_073', file = 'assets/fx_sparks_073.png', size = 96, label = 'Sparks 073 Hit Explosion Radial'},
   420→  {name = 'fx_sparks_074', file = 'assets/fx_sparks_074.png', size = 96, label = 'Sparks 074 Shot Ray Hit Up'},
   421→  {name = 'fx_sparks_075', file = 'assets/fx_sparks_075.png', size = 96, label = 'Sparks 075 Hit Explosion Radial'},
   422→  {name = 'fx_sparks_077', file = 'assets/fx_sparks_077.png', size = 96, label = 'Sparks 077 Charge Hit Up'},
   423→  {name = 'fx_sparks_080', file = 'assets/fx_sparks_080.png', size = 96, label = 'Sparks 080 Charge Radial'},
   424→  {name = 'fx_sparks_081', file = 'assets/fx_sparks_081.png', size = 96, label = 'Sparks 081 Shot Right Loop'},
   425→  {name = 'fx_sparks_082', file = 'assets/fx_sparks_082.png', size = 96, label = 'Sparks 082 Shot Right Loop'},
   426→  {name = 'fx_sparks_084', file = 'assets/fx_sparks_084.png', size = 96, label = 'Sparks 084 Charge Radial'},
   427→  {name = 'fx_sparks_091', file = 'assets/fx_sparks_091.png', size = 96, label = 'Sparks 091 Ray Horizontal'},
   428→  {name = 'fx_sparks_100', file = 'assets/fx_sparks_100.png', size = 96, label = 'Sparks 100 Shot Radial Loop'},
   429→  {name = 'fx_sparks_102', file = 'assets/fx_sparks_102.png', size = 96, label = 'Sparks 102 Hit Star Radial'},
   430→  {name = 'fx_fire_048', file = 'assets/fx_fire_048.png', size = 480, label = 'Fire 048 Up Wide Loop'},
   431→  {name = 'fx_fire_079', file = 'assets/fx_fire_079.png', size = 480, label = 'Fire 079 Wide MIX Loop'},
   432→  {name = 'fx_energy_129', file = 'assets/fx_energy_129.png', size = 480, label = 'Energy 129 Hit Rays Radial'},
   433→  {name = 'fx_energy_130', file = 'assets/fx_energy_130.png', size = 480, label = 'Energy 130 Hit Rays Radial'},
   434→}

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Done. 8 sparks reverted to default, each followed by a t=0.75 smooth variant for comparison. Sparks 102 stays at t=0.75 smooth as the final version.

OK, remove all 0.75, and make 102 0.7 instead

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Edit (E:/a327ex/Orblike/main.lua)

The file E:/a327ex/Orblike/main.lua has been updated successfully.

Bash (cd E:/a327ex/Orblike && python tools/mov_to_spritesheet.py "E:/a327ex/RTFX/2. Pr...)

Extracting frames from E:/a327ex/RTFX/2. Prerendered animations/FX elements/Elements - Sparks 102 Hit Star Radial noCT noRSZ.mov...
  15 frames extracted
Loading frames...
Cropping to (337, 332, 647, 642), LANCZOS scaling to 96x96, then BW threshold=0.7...
  5 frames after trimming trailing empties
Spritesheet saved: assets/fx_sparks_102.png (480x96, 5 frames of 96x96)

Done. All t=0.75 variants removed, sparks 102 set to t=0.7 smooth.

Perfect, that's it for these assets.

Skill

Launching skill: end-session

Base directory for this skill: E:\a327ex\.claude\skills\end-session

# End Session Workflow

When the user invokes this skill, follow these steps exactly. Complete all steps before the final push.

**CRITICAL — READ THIS FIRST:** Large logs (over 7000 lines) WILL exhaust your context window if you try to read them in one pass. You MUST check the line count at Step 3 and follow the two-pass process if required. Every instance that has ignored this has read the whole file anyway, run out of context, and failed. The failure mode is NOT "I'll be careful and it'll be fine" — it's that reading chunks feels like incremental progress so you keep going past the midpoint thinking "just a few more." You MUST hard-stop at the midpoint. No exceptions.

## Step 1: Get Session Info

Ask the user:
1. **Session title** (max 30 characters). Examples: "Anchor Phase 10 Part 5", "Physics Arena Setup", "Timer System Fix"
2. **Which project** was this session for? Options: Anchor, emoji-ball-battles, orblike, other

## Step 2: Find and Convert Transcript

```bash
# Find recent sessions by LAST MESSAGE TIMESTAMP (not file modification time)
python E:/a327ex/Anchor/scripts/find-recent-session.py --limit 5
```

The script shows sessions sorted by when they ended. The output looks like:
```
2026-01-22T16:24:41.240Z 4e1a899e-926e-4c61-b79e-e52963e65e04 <-- MOST RECENT
   <command-message>end-session</command-message>...
   [path]

2026-01-22T15:50:21.940Z d3fb49a7-95bc-4c98-9347-3cf97dc54f98
   Hello. Let's implement ENGINE_WANTS...
   [path]
```

- The **first result** is the current end-session conversation (skip it)
- The **second result** is the work session to summarize

**Verify with the user** that the second session's first message matches what they worked on. Then convert:

```bash
python E:/a327ex/Anchor/scripts/jsonl-to-markdown.py [SECOND_SESSION_PATH] E:/a327ex/anchor.blot.im/logs/[slug].md
```

Use lowercase hyphenated slug derived from the title (e.g., "anchor-primitives-hitstop-animation").

## Step 3: Check Log Size — MANDATORY GATE

```bash
wc -l E:/a327ex/anchor.blot.im/logs/[slug].md
```

**STOP HERE and evaluate the result before doing anything else.**

- If the log is **7000 lines or fewer** → proceed to Step 4b (single-pass).
- If the log is **over 7000 lines** → proceed to Step 4a (two-pass). **DO NOT start reading chunks yet. DO NOT proceed to Step 4b. Go directly to Step 4a.**

The reason this gate exists: logs over 7000 lines will fill your context window before you finish reading them. If you try to read 8000+ lines in 400-line chunks, you will run out of context around chunk 15-18 and the conversation will be lost. The two-pass process prevents this by splitting the work across a compaction boundary.

## Step 4a: Read and Summarize — Two Pass (>7000 lines)

**You are here because the log is over 7000 lines. You CANNOT read it all in one pass — you will run out of context.**

**THE RULE:** Calculate `midpoint = total_lines / 2`. You read lines 1 to midpoint, then STOP. You do NOT read line midpoint+1. Not one chunk past it. Not "just to finish this section." STOP.

### Pass 1: First Half Only

1. Calculate the midpoint: `total_lines / 2 = midpoint`
2. Read from line 1 to the midpoint in 400-500 line chunks, building a chronological outline
3. **After each chunk, check: is the last line you read ≥ midpoint? If YES → stop reading and go to step 4. Do not read another chunk.**
4. Write a **partial summary** covering only the first half:

```markdown
# [Title]

## Summary (First Half — lines 1 to [midpoint])

[Summary content for first half...]
```

5. Write this partial summary directly into the log file (replacing the default header)
6. **STOP and tell the user:** "First half summary is written. Please compact the conversation (`/compact`), then tell me to continue with the second half."

**Do not continue. Do not read "a few more chunks to get more context." Wait for the user to compact and respond.**

### Pass 2: Second Half (after compaction)

When the user returns after compacting:

1. Read the partial summary already in the log file to re-orient
2. Read the second half of the log (from midpoint to end) in 400-500 line chunks
3. Write a **second-half summary** and merge it with the first-half summary into one cohesive summary
4. Update the log file header with the complete merged summary (remove the "First Half" / "Second Half" labels — it should read as one unified summary)

Then proceed to Step 6 (Get User Approval). Step 5 is not needed — the summary was already written during the passes. Step 7 is also already done.

## Step 4b: Read and Summarize — Single Pass (≤7000 lines)

Read the log systematically:

1. **Read in sequential chunks** — Start from the beginning, read 400-500 lines at a time
2. **Build a chronological outline** — As you read each chunk, note:
   - Key events/tasks in order they occurred
   - Planning phases: proposals, alternatives considered, user feedback on approach
   - Research: docs read, code examined, references consulted
   - Errors encountered and how they were fixed
   - Decisions made and why
   - User corrections or feedback
3. **Continue until you reach the end** — Don't skip sections
4. **Only then write the summary** — Use your outline to ensure correct chronological order

This prevents the error of misordering events or missing portions of the session.

Then proceed to Step 5 (Write Summary), then Step 6 (Get User Approval), then Step 7 (Update Log File).

## Step 5: Write Summary

The summary should be **thorough and detailed**. Each major topic deserves its own section with multiple specific bullet points. Don't compress — expand.

**Purpose:** These summaries serve as searchable records. Future Claude instances will grep through past logs to find how specific topics were handled. The more detail you include, the more useful the summary becomes for finding relevant context later.

Format (this is just an example structure — adapt sections to match what actually happened):

```markdown
# [Title]

## Summary

[1-2 sentence overview of the session's main focus]

**[Topic 1 - e.g., "Spring Module Implementation"]:**

- First specific detail about what was done
- Second detail - include file names, function names
- User correction or feedback (quote if notable)
- Technical decisions and why

**[Topic 2 - e.g., "Camera Research"]:**

- What was researched
- Key findings
- How it influenced implementation

**[Topic 3 - e.g., "Errors and Fixes"]:**

- Specific error message encountered
- Root cause identified
- How it was fixed

[Continue for each major topic...]

---

[Rest of transcript follows]
```

Rules:
- **Be thorough** — If in doubt, include more detail, not less. Each topic should be as detailed as possible while still being a summary.
- **Think searchability** — Future instances will search these logs. Include keywords, function names, error messages that someone might grep for.
- **One section per major topic** — Don't combine unrelated work into one section
- **Chronological order** — Sections should match conversation flow
- **Specific details** — Error messages, file names, function names, parameter values
- **Include user quotes** — When user gave notable feedback, quote it (e.g., "k/d variables are not intuitive at all")
- **Weight planning equally** — Research, proposals, alternatives considered, user feedback on approach are as important as implementation
- **Weight problems solved** — Errors, root causes, fixes, user corrections all matter
- **Technical specifics** — Include formulas, API signatures, parameter changes when relevant

## Step 6: Get User Approval

Show the title and summary to the user. Wait for approval before proceeding. If they have corrections, fix them.

## Step 7: Update Log File

Replace the default header (`# Session YYYY-MM-DD...`) with the approved title and summary.

## Step 8: Sync Context Files to Blot

```bash
cp E:/a327ex/.claude/CLAUDE.md E:/a327ex/anchor.blot.im/context/CLAUDE.md
cp E:/a327ex/Anchor/docs/* E:/a327ex/anchor.blot.im/context/
```

## Step 9: Commit Project Repo

Based on which project was worked on:

**For Anchor:**
```bash
cd E:/a327ex/Anchor
git add docs/ framework/ engine/ scripts/ reference/
git status
```

**For emoji-ball-battles:**
```bash
cd E:/a327ex/emoji-ball-battles
git add -A
git status
```

**For orblike:**
```bash
cd E:/a327ex/orblike
git add -A
git status
```

**For other projects:** Ask the user which files to stage.

**IMPORTANT — FULL SUMMARY IN COMMIT:** The commit message MUST include the FULL merged summary from the log file. For two-pass logs, this means the complete unified summary (both halves merged), not just whichever half was written last. Read the summary back from the log file to ensure nothing is missing.

Commit using HEREDOC with exact summary from log file:

```bash
git commit -m "$(cat <<'EOF'
[Title]

[EXACT FULL summary text from log file]

Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
EOF
)"
```

## Step 10: Push Both Repos

```bash
# Push project repo to GitHub
git push origin main

# Push Blot repo (logs and context are already there)
cd E:/a327ex/anchor.blot.im && git add -A && git commit -m "[Title]" && git push origin master
```

## Step 11: Confirm

Tell the user:
- Commit hash
- That GitHub push succeeded
- That Blot push succeeded

What's the session title and project?

Session title (max 30 characters)
Which project? Anchor, emoji-ball-battles, orblike, other