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getLogDistributions.py
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getLogDistributions.py
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from pylab import load, save, loglog, show
import scipy
import scipy.stats
from scipy import array
import numpy as np
import itertools
def checkIfVoid(listValues):
# Check the list of Values
if not len(listValues):
raise "Please pass a list of values"
else:
return True
def getLogBins(first_point, last_point, log_step):
"""
get the bin in log scale and the center bin value
Parameters:
----------------
first_point, last_point : number
First and last point of the x-axis
log_step : number
Required log-distance between x-points
Returns:
-----------
xbins : array of the x values at the center (in log-scale) of the bin
bins : array of the x values of the bins
"""
log_first_point = scipy.log10(first_point)
log_last_point = scipy.log10(last_point)
# Calculate the bins as required by the histogram function, i.e. the bins edges including the rightmost one
N_log_steps = scipy.floor((log_last_point-log_first_point)/log_step) + 1.
llp = N_log_steps * log_step + log_first_point
bins_in_log_scale = np.linspace(log_first_point, llp, N_log_steps+1)
bins = 10**bins_in_log_scale
center_of_bins_log_scale = bins_in_log_scale[:-1] + log_step/2.
xbins = 10**center_of_bins_log_scale
return xbins, bins
def logDistribution(listValues, log_step=0.2, first_point=None, last_point=None, normed=True):
"""
Calculate the distribution in log scale from a list of values
"""
# Check the list of Values
if not checkIfVoid(listValues):
print("Error")
if not first_point:
first_point = scipy.amin(listValues)
if not last_point:
last_point = scipy.amax(listValues)
xbins, bins = getLogBins(first_point, last_point, log_step)
yhist = scipy.stats.stats.histogram2(listValues, bins)
deltas = bins[1:]-bins[:-1]
yhist = yhist[:-1]/deltas
if normed:
yhist = yhist/scipy.sum(yhist)
return xbins, yhist
def averageLogDistribution(values, log_step=0.2, first_point=None, last_point=None):
"""
calculates the <values> vs. xVariable in log scale
Parameters:
---------------
values : dict
A dictionary where the keys are the xValues
and each element contains an array-like sequence of data
Example:
[1: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
2: array([1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2]),
values : ndarray
Two columns array of xValues and yValues,
to be rearranged as above
Returns:
center point of the bin, average value within the bin
"""
# Check the list of Values
if not checkIfVoid(values):
print("Error")
if isinstance(values, dict):
xValues = np.asarray(values.keys())
yValues = np.asarray(values.values())
elif isinstance(values, np.ndarray):
xValues = np.unique(values[:, 0])
yValues = []
for xVal in xValues:
index = values[:,0] == xVal
yValues.append(values[index, 1])
yValues = scipy.array(yValues)
else:
print("Values shape not recognized")
return
if not first_point:
first_point = scipy.amin(xValues)*0.99
if not last_point:
last_point = scipy.amax(xValues)*1.01
xbins, bins = getLogBins(first_point, last_point, log_step)
yAverage = []
for i, j in zip(bins[:-1],bins[1:]):
q1, q2 = np.greater_equal(xValues, i), np.less(xValues, j)
q = np.logical_and(q1, q2)
if sum(q) == 0:
averageValue = np.NaN
else:
allElements = [val for val in itertools.chain(*yValues[q])]
averageValue = sum(allElements)/float(len(allElements))
#print averageValue, allElements
yAverage.append(averageValue)
yAverage = np.asanyarray(yAverage)
# Check if there are NaN values
iNan = np.isnan(yAverage)
x = xbins[~iNan]
y = yAverage[~iNan]
return x, y
if __name__ == "__main__":
#listValues = scipy.rand(100)*230
#listValues = scipy.loadtxt("/home/gf/Python/Moke/wtm_sizes.dat")
#xbins, yhist = logDistribution(listValues)
#loglog(xbins,yhist,'o')
#show()
#x,y = averageLogDistribution(N_cluster)
#loglog(x,y, 'bo')
#show()
q = np.array([[ 1, 1],
[ 1, 1],
[ 2, 2],
[ 5, 1],
[ 1, 1],
[ 6, 3],
[32258, 84],
[22689, 458],
[ 520, 25],
[ 510, 16],
[ 1215, 6],
[ 20, 10],
[ 38, 12],
[ 1324, 9],
[ 104, 14],
[ 44, 12],
[ 512, 8],
[ 56, 15],
[ 4989, 3],
[ 17, 9],
[ 11, 5],
[ 19, 6],
[ 271, 1],
[ 7, 2],
[ 4, 2],
[ 77, 3],
[ 1, 1],
[ 389, 4],
[ 49, 11],
[ 2921, 5],
[ 7, 4],
[ 8, 3],
[ 2, 1],
[ 285, 3],
[ 1, 1],
[ 1, 1],
[ 55, 2],
[ 8, 6],
[ 2346, 5],
[ 127, 27],
[ 201, 13],
[ 742, 10],
[ 22, 8],
[ 14, 7],
[ 1439, 6],
[ 17, 8],
[ 3, 3],
[ 28, 12],
[ 1167, 2],
[ 67, 9],
[ 47, 5],
[ 1, 1],
[ 1, 1],
[ 92, 1],
[ 3, 1],
[ 7, 1],
[ 7, 3],
[ 19, 2],
[ 2, 1],
[ 97, 1],
[ 6, 1],
[ 3, 1],
[ 1, 1],
[ 47, 7],
[ 354, 5],
[ 3, 2],
[ 2, 1],
[ 3, 1],
[ 1182, 1],
[ 1, 1],
[ 1, 1],
[ 12, 1],
[ 2, 1],
[ 17, 3],
[ 4, 1],
[ 12, 1],
[ 1, 1],
[ 83, 7],
[ 3, 1],
[ 156, 2],
[ 1, 1],
[ 167, 2],
[ 114, 3],
[ 2, 1],
[ 105, 2],
[ 3, 1],
[ 79, 4],
[ 162, 16],
[ 876, 7],
[ 27, 6],
[ 6, 3],
[ 275, 5],
[ 75, 8],
[ 4, 1],
[ 1, 1],
[ 74, 1],
[ 10, 3],
[ 11, 4],
[ 1019, 1],
[ 13, 6],
[ 12, 4],
[ 6, 2],
[ 6, 4],
[ 483, 5],
[ 377, 3],
[ 1346, 6],
[ 1071, 5],
[ 163, 6],
[ 3, 2],
[ 143, 2],
[ 24, 6],
[ 1500, 15],
[ 712, 39],
[ 28, 8],
[ 5, 3],
[ 4, 3],
[ 63, 6],
[ 726, 1],
[ 71, 1],
[ 5492, 5],
[ 20, 3],
[ 3, 1],
[ 3, 1],
[ 19, 2],
[ 3, 1],
[ 1, 1],
[ 1, 1],
[ 125, 1],
[ 3, 1],
[ 7, 3],
[ 247, 1],
[ 3, 1],
[ 2, 1],
[ 11, 1],
[ 15, 3],
[ 3, 1],
[ 2, 2],
[ 7, 3],
[ 279, 6],
[ 131, 6],
[ 5, 1],
[ 1565, 2],
[ 12, 1],
[ 296, 1],
[ 21, 3],
[ 118, 3],
[ 13, 1],
[ 53, 3],
[ 57, 11],
[ 75, 8],
[ 2, 1],
[ 89, 1],
[ 1, 1],
[ 57, 6],
[ 96, 4],
[ 23, 10],
[ 181, 3],
[ 2, 1],
[ 1, 1],
[ 10, 1],
[ 2, 1],
[ 26, 4],
[ 4, 2],
[ 41, 1],
[ 463, 4],
[ 1, 1],
[ 134, 1],
[ 15, 6],
[ 4452, 6],
[ 564, 35],
[ 2090, 18],
[13804, 10],
[ 1050, 4],
[ 71, 4],
[ 964, 11],
[ 7471, 5],
[ 216, 6],
[ 103, 13],
[ 41, 13],
[ 793, 8],
[ 764, 6],
[ 463, 6],
[ 10, 5],
[ 467, 12],
[ 904, 6],
[ 77, 7],
[ 3, 1],
[ 169, 11],
[ 234, 8],
[ 16, 2],
[ 4, 2],
[ 1432, 3],
[ 1, 1],
[ 141, 2],
[ 64, 17],
[ 203, 4],
[ 102, 7],
[ 51, 7],
[ 2, 1],
[ 2, 1],
[ 3445, 2],
[ 52, 2],
[ 2, 1],
[ 180, 1],
[ 6, 1],
[ 1017, 2],
[ 1, 1],
[ 904, 4],
[ 4740, 4],
[ 482, 24],
[ 165, 1],
[ 1, 1],
[ 228, 1],
[ 7, 1],
[ 11, 3],
[ 807, 2],
[ 1, 1],
[ 1, 1],
[ 34, 2],
[ 41, 1],
[ 1, 1],
[ 2, 1],
[ 2, 1],
[ 2, 1],
[ 16, 5],
[ 30, 2],
[ 1, 1],
[ 2, 2],
[37088, 3],
[ 80, 16],
[ 17, 7],
[ 35, 9],
[ 235, 3],
[ 82, 3],
[ 2, 1],
[ 660, 8],
[ 131, 17],
[ 4, 1],
[ 303, 3],
[ 16, 6],
[ 340, 5],
[ 201, 13],
[25939, 14],
[17963, 7],
[ 3, 3],
[ 7, 4],
[ 8, 4],
[ 159, 7],
[ 1810, 2],
[ 4, 1],
[ 1, 1],
[ 57, 3],
[ 954, 2],
[ 18, 4],
[ 6, 5],
[ 100, 2],
[ 1, 1],
[ 2, 1],
[ 1, 1],
[ 287, 1],
[ 142, 1],
[ 8, 3],
[ 9, 6],
[ 124, 5],
[ 2, 1],
[ 629, 2],
[ 3, 1],
[ 4, 1],
[ 2, 1],
[ 9, 1],
[ 2, 1],
[ 2, 1],
[ 1378, 2],
[ 9836, 1],
[ 29, 5],
[ 38, 1],
[ 6, 2],
[ 170, 1],
[ 38, 4],
[ 21, 7],
[ 7, 4],
[ 2, 2],
[ 5825, 1],
[ 2, 1],
[ 5, 1],
[ 1, 1],
[ 3, 1],
[ 1, 1],
[ 2, 1]])
import random
import matplotlib.pylab as plt
imageSize = 500**2
N = 100
sizes = np.random.uniform(1, imageSize, N)
nCluster = scipy.array([int(s**0.5)*(1+random.randint(-5,5)/100.) for s in sizes])
#q = np.array(zip(sizes, nCluster))
x,y = averageLogDistribution(q)
plt.loglog(x,y, 'bo')
plt.grid()
plt.show()