def draw_L(type_dist):
numHorSec = 48
noBins = 50
bins = np.linspace(-13, 20, noBins + 1) #divide N scale
logPoints = np.linspace(0, 4, numHorSec) #divide Lmax scale
Z = [] #no.hits 2D array - we draw this
horSec = np.linspace(0, 4, numHorSec)
glajenje = 0.2
'''
for fileNo in horSec:
array = readData("inf"+str(fileNo))
Z+=array
'''
Z = readData("infL" + str(horSec[-1]))
Z = list(filter(lambda x: x <= np.random.normal(9.5, glajenje), Z))
#out = fl.gaussian_filter(Z, 1)
nV, binsV, patchesV = plt.hist(Z, 200)
out = fl.gaussian_filter(nV, 2)
m = np.where(out == out.max())
m1 = binsV[m][0]
Z = [10**x for x in Z]
avg = sum(Z) / len(Z)
mediana = np.median(Z)
#print('Največja verjetnost: L = '+ str(10**m1))
#print("Povprečje: L = " + str(10 ** avg))
plt.cla()
plt.plot(binsV[0:-1], out, 'red')
plt.ylabel('frequency')
plt.xlabel('Log(L)')
#plt.legend(loc=1)
plt.title('Model 3 L(N), last slice, median: ' +
str(np.round(mediana, 2)) + " avg: " + str(np.round(avg, 2)))
#plt.annotate('max', (m1, 0), annotation_clip=False)
#plt.axvline(m1, color ='r', alpha = 0.5)
#plt.axvline(avg, color='r', alpha=0.5)
plt.show()
def draw_L(type_dist):
numHorSec = 48
noBins = 50
bins = np.linspace(-13, 20, noBins + 1) #devide N scale
logPoints = np.linspace(0, 4, numHorSec) #devide Lmax scale
Z = [] #no.hits 2D array - we draw this
horSec = np.linspace(0, 4, numHorSec)
#for fileNo in horSec:
array = readData("inf_l_" + str(horSec[-1]))
Z += array
#out = fl.gaussian_filter(Z, 1)
median = 10**np.median(Z)
print("Median: " + str(median))
nV, binsV, patchesV = plt.hist(Z, 200)
# out = fl.gaussian_filter(nV, 2)
m = np.where(nV == nV.max())
m1 = binsV[m][0]
Z1 = [10**i for i in Z]
avg = sum(Z1) / len(Z1)
print('Največja verjetnost: L = ' + str(10**m1))
print("Povprečje: L = " + str(avg))
plt.cla()
plt.plot(binsV[0:-1], nV, 'red')
plt.ylabel('frequency')
plt.xlabel('Log(L)')
#plt.legend(loc=1)
plt.title('Model 1, L(N). Median: {0}, average: {1}'.format(
round(median, 2), round(avg, 2)))
#plt.annotate('max', (m1, 0), annotation_clip=False)
#plt.axvline(m1, color ='r', alpha = 0.5)
#plt.axvline(avg, color='r', alpha=0.5)
plt.show()
from mpl_toolkits.mplot3d import Axes3D
#import scipy.ndimage.filters as fl
#from libraries.StandardizeDistribution import StandardizeDistributionW
numHorSec = 20
noBins = 50
bins = np.linspace(-2, 5, noBins + 1) #devide N scale
horSec = np.linspace(2, 10, numHorSec) # devide Lmax scale
Z = [None] * numHorSec # no.hits 2D array - we draw this
i = 0
# go through the files
for fileNo in horSec:
array = readData("/inf" + str(fileNo.round(11)))
# array = array[0:4664]
#array = list(filter(lambda x: 0 <= x < 4, array))
Z[i], _ = np.histogram(array, bins)
# Z[i] = np.multiply(Z[i], 2.0/float(fileNo), out=Z[i], casting="unsafe") # decay factor is 2/fileNo = 2/maxL
i += 1
X, Y = np.meshgrid(bins[0:-1], horSec)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X = np.array(X)
Y = np.array(Y)
Z = np.array(Z)
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import scipy.ndimage.filters as fl
from libraries.StandardizeDistribution import StandardizeDistributionW
i = 0
numHorSec = 48
noBins = 100
bins = np.linspace(0, 10**(4), noBins + 1) #devide N scale
horSec = np.linspace(2, 10, numHorSec) # devide Lmax scale
Z = [None] * numHorSec # no.hits 2D array - we draw this
# go through the files
for fileNo in horSec:
array = readData("/inf" + str(fileNo))
# array = array[0:4664]
array = [10**i for i in filter(lambda x: 4 > x > i, array)]
Z[i], _ = np.histogram(array, bins)
# Z[i] = np.multiply(Z[i], 2.0/float(fileNo), out=Z[i], casting="unsafe") # decay factor is 2/fileNo = 2/maxL
X, Y = np.meshgrid(bins[0:-1], horSec)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X = np.array(X)
Y = np.array(Y)
Z = np.array(Z)
# saveData(Z, "no_hits") old
if True:
bin_no = 100
start = 0
end = 10000
fixed_n = [1, 10, 100, 1000, 10000]
colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple']
bounds = [500000, 50000, 500000, 5000000, 50000000]
bounds = list(map(lambda x: x / 2, bounds))
bins = np.linspace(start, end, bin_no)
for i, maxN in enumerate(fixed_n):
array = readData("inf_l_" + str(maxN))
array = list(map(lambda x: 10**x, array))
med = np.median(array)
std = np.std(array)
title = 'N = {:5}, median = {:8.0f}'.format(fixed_n[i], med)
# plt.figure(title)
plt.subplot(2, 3, i + 1)
plt.title(title)
bins = np.linspace(0, bounds[i], bin_no)
# y, bins = np.histogram(array, )
y, bins = np.histogram(array, bins=bins)
#y = plt.hist(array, bins=bins, color=colors[i])
y = y / max(y)
# adj_bins = bins + (bins[1] - bins[0]) / 2
# ax = plt.subplot(111)
plt.plot(bins[:-1], y, colors[i])
from libraries.IO import readData
from libraries.IO import saveData
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import scipy.ndimage.filters as fl
from libraries.StandardizeDistribution import StandardizeDistributionW
numHorSec = 20
start = 0
end = -4
noBins = 100
range_bin = (start, 10**end)
array = readData("/N_Sandberg_no_cut")
# val, edges = np.histogram(array, bins, range)
n, bins, patches = plt.hist(array, noBins, range_bin)
plt.title("No cut, range: " + str(start) + " - " + str(10**end) +
" bin size: " + str(bins[1] - bins[0]))
plt.xlabel("N")
plt.ylabel("log(maxL)")
plt.show()
print("done")
from mpl_toolkits.mplot3d import Axes3D
import scipy.ndimage.filters as fl
from libraries.StandardizeDistribution import StandardizeDistributionW
from numpy.random import randn
from scipy.ndimage import gaussian_filter
numHorSec = 48
# numHorSec = 5
noBins = 60
bins = np.linspace(-3, 11, noBins + 1)
horSec = np.linspace(2, 10, numHorSec)
Z = [None] * numHorSec
i = 0
for fileNo in horSec:
array = readData("inf_loguniform_" + str(fileNo))
#array = list(filter(lambda x: 0 <= x, array))
Z[i], _ = np.histogram(array, bins)
print(str(int(10**fileNo)) + " - " + str(bins[np.argmax(Z[i])]))
i += 1
#Z = gaussian_filter(Z, sigma=1)
X, Y = np.meshgrid(bins[0:-1], horSec)
X = np.array(X)
Y = np.array(Y)
Z = np.array(Z)
#Z = np.flip(Z, 0)
#horSec = horSec[::-1]
# fig = plt.figure()
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import scipy.ndimage.filters as fl
from libraries.StandardizeDistribution import StandardizeDistributionW
numHorSec = 48
#numHorSec = 5
noBins = 60
bins = np.linspace(-2, 13, noBins + 1)
bins = np.linspace(0, 10**13, noBins + 1)
horSec = np.linspace(0, 4, numHorSec)
Z = [None] * numHorSec
i = 0
for fileNo in horSec:
array = readData("inf_l_" + str(fileNo))
Z[i], _ = np.histogram(array, bins)
# Z[i] = np.multiply(Z[i], 2.0/float(fileNo), out=Z[i], casting="unsafe") # decay factor is 2/fileNo = 2/maxL
#print(np.argmax(Z[0]))
print(str(int(10**fileNo)) + " - " + str(bins[np.argmax(Z[i])]))
i += 1
Z = 10**Z
X, Y = np.meshgrid(bins[0:-1], horSec)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X = np.array(X)
Y = np.array(Y)
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import scipy.ndimage.filters as fl
from libraries.StandardizeDistribution import StandardizeDistributionW
numHorSec = 48
#numHorSec = 5
noBins = 60
bins = np.linspace(-2, 13, noBins + 1)
horSec = np.linspace(0, 4, numHorSec)
Z = [None] * numHorSec
i = 0
for fileNo in horSec:
array = readData("infL" + str(fileNo))
Z[i], _ = np.histogram(array, bins)
# Z[i] = np.multiply(Z[i], 2.0/float(fileNo), out=Z[i], casting="unsafe") # decay factor is 2/fileNo = 2/maxL
#print(np.argmax(Z[0]))
print(str(int(10**fileNo)) + " - " + str(bins[np.argmax(Z[i])]))
i += 1
X, Y = np.meshgrid(bins[0:-1], horSec)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X = np.array(X)
Y = np.array(Y)
Z = np.array(Z)
