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)