Repo created

tools/python/city_radius.py

@@ -0,0 +1,158 @@
+import sys, os, math
+import matplotlib.pyplot as plt
+from optparse import OptionParser
+
+
+cities = []
+
+def strip(s):
+    return s.strip('\t\n ')
+
+def load_data(path):
+    
+    global cities
+
+    f = open(path, 'r')
+    lines = f.readlines()
+    f.close();
+
+    for l in lines:
+
+        if l.startswith('#'):
+            continue
+
+        data = l.split('|')
+
+        if len(data) < 6:
+            continue
+
+        item = {}
+
+        item['name'] = strip(data[0])
+        item['population'] = int(strip(data[1]))
+        item['region'] = strip(data[2])
+        item['width'] = float(strip(data[3]))
+        item['height'] = float(strip(data[4]))
+
+        item['square'] = float(data[5])
+
+        cities.append(item)
+
+    # build plot
+    print "Cities count: %d" % len(cities)
+
+def formula(popul, base = 32, mult = 0.5):
+    #return math.exp(math.log(popul, base)) * mult
+    return math.pow(popul, 1 / base) * mult
+
+def avgDistance(approx, data):
+    dist = 0
+    for x in xrange(len(data)):
+        dist += math.fabs(approx[x] - data[x])
+    return dist / float(len(data))  
+
+def findBest(popul, data, minBase = 5, maxBase = 100, stepBase = 0.1, minMult = 0.01, maxMult = 1, stepMult = 0.01):
+
+    # try to find best parameters
+    base = minBase
+
+    minDist = -1
+    bestMult = minMult
+    bestBase = base
+
+    while base <= maxBase:
+        print "%.02f%% best mult: %f, best base: %f, best dist: %f" % (100 * (base - minBase) / (maxBase - minBase), bestMult, bestBase, minDist)
+        mult = minMult
+        
+        while mult <= maxMult:
+            approx = []
+
+            for p in popul:
+                approx.append(formula(p, base, mult))
+
+            dist = avgDistance(approx, data)
+
+            if minDist < 0 or minDist > dist:
+                minDist = dist
+                bestBase = base
+                bestMult = mult
+
+            mult += stepMult
+
+        base += stepBase
+
+    return (bestBase, bestMult)
+
+def process_data(steps_count, base, mult, bestFind = False, dataFlag = 0):
+    avgData = []
+    maxData = []
+    sqrData = []
+    population = []
+    maxPopulation = 0
+    minPopulation = -1
+    for city in cities:
+        p = city['population']
+        w = city['width']
+        h = city['height']
+        s = city['square']
+        population.append(p)
+        if p > maxPopulation:
+            maxPopulation = p
+        if minPopulation < 0 or p < minPopulation:
+            minPopulation = p
+
+        maxData.append(max([w, h]))
+        avgData.append((w + h) * 0.5)
+        sqrData.append(math.sqrt(s))
+
+
+    bestBase = base
+    bestMult = mult
+    if bestFind:
+        d = maxData
+        if dataFlag == 1:
+            d = avgData
+        elif dataFlag == 2:
+            d = sqrData
+        bestBase, bestMult = findBest(population, d)
+
+    print "Finished\n\nBest mult: %f, Best base: %f" % (bestMult, bestBase)
+
+    approx = []
+    population2 = []
+    v = minPopulation
+    step = (maxPopulation - minPopulation) / float(steps_count)
+    for i in xrange(0, steps_count):
+        approx.append(formula(v, bestBase, bestMult))
+        population2.append(v)
+        v += step
+
+    plt.plot(population, avgData, 'bo', population, maxData, 'ro', population, sqrData, 'go', population2, approx, 'y')
+    plt.axis([minPopulation, maxPopulation, 0, 100])
+    plt.xscale('log')
+    plt.show()
+
+if __name__ == "__main__":
+
+    if len(sys.argv) < 3:
+        print 'city_radius.py <data_file> <steps>'
+    
+    parser = OptionParser()
+    parser.add_option("-f", "--file", dest="filename", default="city_popul_sqr.data",
+                    help="source data file", metavar="path")
+    parser.add_option("-s", "--scan",
+                    dest="best", default=False, action="store_true",
+                    help="scan best values of mult and base")
+    parser.add_option('-m', "--mult",
+                    dest='mult', default=1,
+                    help='multiplier value')
+    parser.add_option('-b', '--base',
+                    dest='base', default=3.6,
+                    help="base value")
+    parser.add_option('-d', '--data', 
+                    default=0, dest='data',
+                    help="Dataset to use on best values scan: 0 - max, 1 - avg, 2 - sqr")
+
+    (options, args) = parser.parse_args()
+    load_data(options.filename)
+    process_data(1000, float(options.base), float(options.mult), options.best, int(options.data))