diff options
| -rw-r--r-- | analyze.py | 4 | ||||
| -rw-r--r-- | reversing_game_mechanics/stuff.txt | 105 | ||||
| -rw-r--r-- | stats.py | 35 |
3 files changed, 135 insertions, 9 deletions
@@ -11,9 +11,9 @@ s = Stats.load(files[0]) for f in files[1:]: s.merge(f) -#s.analyze_speed() +s.analyze_speed() print("\n" + "-"*40 + "\n") -#s.analyze_visible_window(True) +s.analyze_visible_window(False) for i in ["split cell", "ejected mass", "virus"]: s.analyze_deviations(i) print("") diff --git a/reversing_game_mechanics/stuff.txt b/reversing_game_mechanics/stuff.txt new file mode 100644 index 0000000..29a5248 --- /dev/null +++ b/reversing_game_mechanics/stuff.txt @@ -0,0 +1,105 @@ +as of 2015-08-30 + +CELL SIZE VS SPEED + + size**0.45 * speed = 86.05616001328154 + + + + +SIZE VS VIEWPORT + + with 1 cells, depending on sum(size) + median ratio = 1.7025611175785798 + diag / size**0.33 = 608.971483054539 + + with 2 cells, depending on sum(size) + median ratio = 1.6963503649635037 + diag / size**0.33 = 585.5509541758322 + + with 3 cells, depending on sum(size) + median ratio = 1.6898326898326899 + diag / size**0.58 = 170.29929514108093 + + with 4 cells, depending on sum(size) + median ratio = 1.650784427658338 + diag / size**0.0 = 3158.567553889486 + + with 1 cells, depending on sum(mass) + median ratio = 1.7025611175785798 + diag / size**0.17 = 1270.6199859482824 + + with 2 cells, depending on sum(mass) + median ratio = 1.6972934472934473 + diag / size**0.16 = 1407.4522241811242 + + with 3 cells, depending on sum(mass) + median ratio = 1.6975546975546976 + diag / size**0.28 = 910.623966202271 + + with 4 cells, depending on sum(mass) + median ratio = 1.6625734116390818 + diag / size**0.0 = 3141.1700855829763 + + + + +EJECT/SPLIT DIRECTIONS + + split cell eject/split direction deviations: mean = 0.0009390500296252917, stddev=0.31212271930689983, ndata=621 + 75% of the splits had a deviation smaller than 0.02 rad = 1.19 deg + + ejected mass eject/split direction deviations: mean = -0.021378484138331356, stddev=0.730695490707546, ndata=1585 + 75% of the splits had a deviation smaller than 0.39 rad = 22.16 deg + + + + +EJECT/SPLIT DISTANCES + + split cell eject/split distances: mean = 378.6264099585539, stddev =214.15995855502896, ndata=1226 + split cell meann = 23.37846655791191, stddevn =17.23260859398865 + 75% of the distances lie in the interval 370.30 plusminus 218.60 + 80% of the distances lie in the interval 370.30 plusminus 262.32 + max = 1205.46 + 75% of the flight lengths lie in the interval 20.00 plusminus 9.00 + 78% of the flight lengths lie in the interval 20.00 plusminus 10.80 + + ejected mass eject/split distances: mean = 473.3307839719213, stddev =159.4625848157587, ndata=1121 + ejected mass meann = 42.015165031222125, stddevn =8.5656796143937 + 75% of the distances lie in the interval 534.64 plusminus 2.10 + 77% of the distances lie in the interval 534.64 plusminus 2.52 + max = 637.28 + 75% of the flight lengths lie in the interval 44.00 plusminus 1.00 + 79% of the flight lengths lie in the interval 44.00 plusminus 1.20 + + virus eject/split distances: mean = 396.47928995805, stddev =219.79929069475193, ndata=9 + virus meann = 42.666666666666664, stddevn =6.879922480183431 + 75% of the distances lie in the interval 510.53 plusminus 363.80 + 88% of the distances lie in the interval 510.53 plusminus 436.56 + max = 580.08 + 75% of the flight lengths lie in the interval 45.00 plusminus 10.00 + 77% of the flight lengths lie in the interval 45.00 plusminus 12.00 + + + + +VIRUS SIZES + + I've seen the following 7 virus sizes: + 100: 386018 times + 106: 124015 times + 113: 72084 times + 119: 41825 times + 125: 24954 times + 131: 373398 times + 136: 11550 times + + + + +REMERGES + + 75% of the remerge durations lie at 32.00 plusminus 30.00 frames + 75% of the remerges were started after 767.00 plusminus 20.00 frames + @@ -26,6 +26,8 @@ def quantile(values, q): return 0 def find_smallest_q_confidence_area(values, q): + """Calculates the (mid, delta) with the smallest delta, such that at least q * len(values) + lie within the interval [mid-delta, mid+delta].""" try: mid = min(values, key = lambda value : quantile(list(map(lambda x : abs(x-value), values)), q)) deviation = quantile(list(map(lambda x : abs(x-mid), values)),q) @@ -34,6 +36,14 @@ def find_smallest_q_confidence_area(values, q): except: return 0,0 +def get_delta_confidence(values, mid, delta): + #"""Calculates which fraction of the values lie within [mid-delta, mid+delta]""" + #try: + return len(list(filter(lambda v : (mid-delta <= v and v <= mid+delta), values))) / len(values) + #except: + # raise + # return 0 + def avg(values): if not isinstance(values, dict): return sum(values)/len(values) @@ -359,11 +369,17 @@ class Stats: for ncells in sorted(self.data.size_vs_visible_window.keys()): if len(self.data.size_vs_visible_window[ncells]) > 0: print("\nwith "+str(ncells)+" cells, depending on sum(size)") - self.analyze_visible_window_helper(self.data.size_vs_visible_window[ncells], verbose) + try: + self.analyze_visible_window_helper(self.data.size_vs_visible_window[ncells], verbose) + except ZeroDivisionError: + print("\toops.") for ncells in sorted(self.data.mass_vs_visible_window.keys()): if len(self.data.mass_vs_visible_window[ncells]) > 0: print("\nwith "+str(ncells)+" cells, depending on sum(mass)") - self.analyze_visible_window_helper(self.data.mass_vs_visible_window[ncells], verbose) + try: + self.analyze_visible_window_helper(self.data.mass_vs_visible_window[ncells], verbose) + except ZeroDivisionError: + print("\toops.") def analyze_deviations(self, celltype): ds = self.data.eject_deviations[celltype] @@ -412,8 +428,13 @@ class Stats: #quant = quantile(list(map(lambda v : abs(v-midpoints[maxidx]), ds)), q/100) #print("\t"+str(q)+"% of values lie have a distance of at most "+str(quant)+" from the maximum") - print("\t75%% of the distances lie in the interval %.2f plusminus %.2f" % find_smallest_q_confidence_area(ds, 0.75)) - print("\t75%% of the flight lengths lie in the interval %.2f plusminus %.2f" % find_smallest_q_confidence_area(ns, 0.75)) + mid, delta = find_smallest_q_confidence_area(ds, 0.75) + print("\t75%% of the distances lie in the interval %.2f plusminus %.2f" % (mid,delta)) + print("\t%2d%% of the distances lie in the interval %.2f plusminus %.2f" % (100*get_delta_confidence(ds, mid, delta*1.2), mid, delta*1.2) ) + print("\tmax = %.2f" % (max(ds))) + mid, delta = find_smallest_q_confidence_area(ns, 0.75) + print("\t75%% of the flight lengths lie in the interval %.2f plusminus %.2f" % (mid,delta)) + print("\t%2d%% of the flight lengths lie in the interval %.2f plusminus %.2f" % (100*get_delta_confidence(ns,mid,delta*1.2),mid,delta*1.2)) print("") def analyze_virus_sizes(self): @@ -424,7 +445,7 @@ class Stats: def analyze_remerge(self): relevant = list(filter(lambda r : r.is_parent_child, self.data.remerging.values())) durations = list(map(lambda r : r.end_time - r.begin_time, relevant)) - print(fit_gaussian(durations)) + print("75%% of the remerge durations lie at %.2f plusminus %.2f frames" % find_smallest_q_confidence_area(durations,0.75)) waittimes = list(map(lambda r : r.begin_time - max(r.birth1, r.birth2), relevant)) - print(fit_gaussian(waittimes)) - + print("75%% of the remerges were started after %.2f plusminus %.2f frames" % find_smallest_q_confidence_area(waittimes,0.75)) + |