added per-miner graphs

[p2pool.git] / p2pool / util / math.py

diff --git a/p2pool/util/math.py b/p2pool/util/math.py
index 0d66c78..aa5ea8a 100644 (file)
--- a/p2pool/util/math.py
+++ b/p2pool/util/math.py
@@ -3,6 +3,7 @@ from __future__ import absolute_import, division
 import __builtin__
 import math
 import random
+import time
 
 def median(x, use_float=True):
     # there exist better algorithms...
@@ -64,6 +65,8 @@ def add_dicts(*dicts):
             res[k] = res.get(k, 0) + v
     return dict((k, v) for k, v in res.iteritems() if v)
 
+mult_dict = lambda c, x: dict((k, c*v) for k, v in x.iteritems())
+
 def format(x):
     prefixes = 'kMGTPEZY'
     count = 0
@@ -73,6 +76,12 @@ def format(x):
     s = '' if count == 0 else prefixes[count - 1]
     return '%i' % (x,) + s
 
+def format_dt(dt):
+    for value, name in [(60*60*24, 'days'), (60*60, 'hours'), (60, 'minutes'), (1, 'seconds')]:
+        if dt > value:
+            break
+    return '%.01f %s' % (dt/value, name)
+
 perfect_round = lambda x: int(x + random.random())
 
 def erf(x):
@@ -111,11 +120,11 @@ def ierf(z):
 try:
     from scipy import special
 except ImportError:
-    print 'Install SciPy for more accurate confidence intervals!'
     def binomial_conf_interval(x, n, conf=0.95):
         assert 0 <= x <= n and 0 <= conf < 1
         if n == 0:
             left = random.random()*(1 - conf)
+            return left, left + conf
         # approximate - Wilson score interval
         z = math.sqrt(2)*ierf(conf)
         p = x/n
@@ -138,21 +147,13 @@ else:
         left_a = find_root(f, (1-conf)/2, bounds=(0, 1-conf))
         return float(special.betaincinv(x+1, n-x+1, left_a)), float(special.betaincinv(x+1, n-x+1, left_a + conf))
 
-def binomial_conf_center_radius(x, n, conf=0.95):
-    assert 0 <= x <= n and 0 <= conf < 1
-    left, right = binomial_conf_interval(x, n, conf)
-    if n == 0:
-        return (left+right)/2, (right-left)/2
-    p = x/n
-    return p, max(p - left, right - p)
-
 minmax = lambda x: (min(x), max(x))
 
 def format_binomial_conf(x, n, conf=0.95, f=lambda x: x):
     if n == 0:
         return '???'
     left, right = minmax(map(f, binomial_conf_interval(x, n, conf)))
-    return '~%.1f%% (%.f-%.f%%)' % (100*f(x/n), 100*left-1/2, 100*right+1/2)
+    return '~%.1f%% (%.f-%.f%%)' % (100*f(x/n), math.floor(100*left), math.ceil(100*right))
 
 def reversed(x):
     try:
@@ -212,6 +213,35 @@ def string_to_natural(s, alphabet=None):
         assert not s.startswith(alphabet[0])
         return sum(alphabet.index(char) * len(alphabet)**i for i, char in enumerate(reversed(s)))
 
+class RateMonitor(object):
+    def __init__(self, max_lookback_time):
+        self.max_lookback_time = max_lookback_time
+        
+        self.datums = []
+        self.first_timestamp = None
+    
+    def _prune(self):
+        start_time = time.time() - self.max_lookback_time
+        for i, (ts, datum) in enumerate(self.datums):
+            if ts > start_time:
+                self.datums[:] = self.datums[i:]
+                return
+    
+    def get_datums_in_last(self, dt=None):
+        if dt is None:
+            dt = self.max_lookback_time
+        assert dt <= self.max_lookback_time
+        self._prune()
+        now = time.time()
+        return [datum for ts, datum in self.datums if ts > now - dt], min(dt, now - self.first_timestamp) if self.first_timestamp is not None else 0
+    
+    def add_datum(self, datum):
+        self._prune()
+        t = time.time()
+        self.datums.append((t, datum))
+        if self.first_timestamp is None:
+            self.first_timestamp = t
+
 if __name__ == '__main__':
     import random
     a = 1