diff options
Diffstat (limited to 'stats.py')
| -rw-r--r-- | stats.py | 93 |
1 files changed, 87 insertions, 6 deletions
@@ -4,6 +4,13 @@ import random from collections import defaultdict import pickle from functools import reduce +import mechanics +import geometry + +def fit_gaussian(l): + mean = sum(l) / len(l) + stddev = math.sqrt(sum(map(lambda v : (v-mean)**2, l)) / len(l)) + return mean, stddev def flatten(l): return reduce(lambda a,b:a+b, l) @@ -47,11 +54,11 @@ class Stats: def __init__(self,c,data=None): self.c = c - self.split_countdown = 27*20 + self.countdown = 27*20 if data == None: self.data = StatData() - self.data.version = 2 + self.data.version = 3 self.data.min_mass = 0 self.data.max_mass = 0 @@ -66,6 +73,9 @@ class Stats: self.data.size_vs_speed = defaultdict(return_defaultdict_with_zeros) self.data.size_vs_visible_window = defaultdict(return_defaultdict_with_empty_list) self.data.mass_vs_visible_window = defaultdict(return_defaultdict_with_empty_list) + + self.data.eject_distlogs = {"virus" : [], "split cell" : [], "ejected mass" : []} + self.data.eject_deviations = {"virus" : [], "split cell" : [], "ejected mass" : []} else: self.data = data @@ -93,10 +103,20 @@ class Stats: return list[-steps:] def process_frame(self): - self.split_countdown -= 1 - if (self.split_countdown <= 0): - self.split_countdown = int(27* (random.random() * 75)) - self.c.send_split() + self.countdown -= 1 + if (self.countdown <= 0): + quick_followup = (random.random() < 0.1) + + if quick_followup: + self.countdown = 7 + else: + self.countdown = int(27* (random.random() * 15)) + + what_to_do = random.random() + if what_to_do < 0.2: + self.c.send_split() + else: + self.c.send_shoot() self.log_pos(self.c.player.center) self.log_mass(self.c.player.total_mass) @@ -124,6 +144,60 @@ class Stats: self.data.size_vs_visible_window[n_own_cells][own_total_size].append((visible_width,visible_height)) self.data.mass_vs_visible_window[n_own_cells][own_total_mass].append((visible_width,visible_height)) + + + # find ejected mass, split cells or viruses that have come to rest + for cell in cells: + if hasattr(cell,"parent") and cell.parent != None and not cell.calmed_down: + # we're only interested in cells with a parent set, because + # this also implies that we have tracked them since their + # creation. + # also, we're only interested in cells that are still flying + # as a result of being ejected/split. + + if not cell.is_food and not cell.is_ejected_mass and not cell.is_virus: + expected_speed = mechanics.speed(cell.size) + celltype = "split cell" + elif cell.is_virus: + expected_speed = 1 + celltype = "virus" + elif cell.is_ejected_mass: + expected_speed = 1 + celltype = "ejected mass" + + + if cell.movement.len() < expected_speed * 1.1: + print(celltype+" has come to rest, nframes="+str(len(cell.poslog))) + cell.calmed_down = True + # TODO: speed log + + distance = (cell.spawnpoint - cell.pos).len() + distance_from_parent = (cell.parentpos_when_spawned - cell.pos).len() + + self.data.eject_distlogs[celltype] += [(distance, distance_from_parent, cell.parentsize_when_spawned)] + print(" flown distance = "+str(distance)) + + if len(cell.poslog) == 5: + # calculate movement direction from the first 5 samples + + # first check whether they're on a straight line + if geometry.is_colinear(cell.poslog) and cell.shoot_vec != None: + print(celltype+" direction available!") + fly_direction = cell.poslog[-1] - cell.poslog[0] + fly_angle = math.atan2(fly_direction.y, fly_direction.x) + + shoot_angle = math.atan2(cell.shoot_vec.y, cell.shoot_vec.x) + + + deviation = (fly_angle - shoot_angle) % (2*math.pi) + if deviation > math.pi: deviation -= 2*math.pi + print(" deviation = "+str(deviation*180/math.pi)) + + self.data.eject_deviations[celltype] += [deviation] + + else: + print(celltype+" did NOT fly in a straight line, ignoring...") + def save(self,filename): pickle.dump(self.data, open(filename,"wb")) @@ -220,3 +294,10 @@ class Stats: for ncells in sorted(self.data.mass_vs_visible_window.keys()): print("\nwith "+str(ncells)+" cells, depending on sum(mass)") self.analyze_visible_window_helper(self.data.mass_vs_visible_window[ncells], verbose) + + def analyze_deviations(self, celltype): + ds = self.data.eject_deviations[celltype] + if len(ds) == 0: return + + mean, stddev = fit_gaussian(ds) + print(celltype+" eject/split direction deviations: mean = "+str(mean)+", stddev="+str(stddev)) |