def plot_share_delay_histogram(packets):
"""
Takes a list of PacketInfo tuples as input and plots the PDF of the intershare delay.
"""
seq_num_to_ts = defaultdict(list)
for p_i in packets:
seq_num_to_ts[p_i.seq_num].append(p_i.timestamp)
inter_share_delays = []
for seq_num, ts in seq_num_to_ts.items():
inter_share_delay = max(ts) - min(ts)
inter_share_delays.append(inter_share_delay)
plt.histogram(inter_share_delays, density=True)
helpers.save_figure("share-delay-pdf.pdf")
def hist(self):
pylt.histogram(self.nums)
import numpy as np
import matplotlib.pyplot as plt
data = np.loadtxt('X.dat')
plt.figure()
plt.histogram(data)
plt.show()
import matplotlib.pyplot as plt X = [590, 540, 740, 130, 810, 300, 320, 230, 470, 620, 770, 250] Y = [32, 36, 39, 52, 61, 72, 77, 75, 68, 57, 48, 48] plt.histogram(X, Y) plt.show()
def make_plot_observed(fname, bins=None, norm=None):
d = pl.genfromtxt(fname, names=True, invalid_raise=False)
# observed quantities
Z = pl.array([chargeNumber(id) for id in d['ID'].astype(int)]) # element
A = pl.array([massNumber(id)
for id in d['ID'].astype(int)]) # atomic mass number
lE = pl.log10(d['E']) + 18 # energy in log10(E/eV))
if bins == None:
lEbins = pl.arange(18, 20.51, 0.01) # logarithmic bins
lEcens = (lEbins[1:] + lEbins[:-1]) / 2 # logarithmic bin centers
else:
lEbins = pl.arange(17.5, 20.2, 0.1) # logarithmic bins
lEcens = (lEbins[1:] + lEbins[:-1]) / 2 # logarithmic bin centers
dE = 10**lEbins[1:] - 10**lEbins[:-1] # bin widths
# identify mass groups
EE = (10.0**lEcens)**3
J1 = EE * pl.histogram(lE, bins=lEbins)[0] / dE
J = np.zeros_like(J1)
Jn = np.zeros([len(J1, 58)])
for iz in range(1, 57):
idx = (Z == iz)
J += pl.histogram(lE[idx], bins=lEbins)[0] / dE
# idx1 = A == 1
# idx2 = (A > 1) * (A < 5)
# idx3 = (A > 4) * (A < 23)
# idx4 = (A > 22) * (A < 39)
# # calculate spectrum: J(E) = dN/dE, we want E^3 J(E)
# EE = (10.0 ** lEcens) ** 3
#lum = np.sum(J * dE)
# J = EE * pl.histogram(lE, bins=lEbins)[0] / dE
# J1 = EE * pl.histogram(lE[idx1], bins=lEbins)[0] / dE
# J2 = EE * pl.histogram(lE[idx2], bins=lEbins)[0] / dE
# J3 = EE * pl.histogram(lE[idx3], bins=lEbins)[0] / dE
# J4 = EE * pl.histogram(lE[idx4], bins=lEbins)[0] / dE
# normalize the histograms
# if norm == None:
# norm = 1.0 / J[0]
# else:
# norm = norm / J[10]
norm = 1.0 / J[0]
J1 *= norm
# J2 *= norm
# J3 *= norm
# J4 *= norm
J *= norm
pl.plot(lEcens, smooth(J), color='k', linewidth=3, label="Total")
pl.plot(lEcens, smooth(J1), color='#e84118', label='A = 1')
pl.plot(lEcens, smooth(J2), color="#7f8fa6", label='A = 2-4')
pl.plot(lEcens, smooth(J3), color="#44bd32", label='A = 5-22')
pl.plot(lEcens, smooth(J4), color="#00a8ff", label='A = 23-38')
pl.legend(fontsize=20, frameon=True, loc=3)
pl.semilogy()
#pl.ylim(1e-5)
pl.grid()
pl.ylabel('$E^3 J(E)$ [a.u.]')
pl.xlabel('$\log_{10}$(E/eV)')
def plot_folds(linear_filename, bbox):
import fracpaq as fpq
import matplotlib.pylab as plt
import sys
import numpy as np
linear = gpd.read_file(linear_filename)
folds = linear[linear['feature'].str.contains("Fold")]
folds = folds.dropna(subset=['geometry'])
f = open('fold.txt', 'w')
for inf, fold in folds.iterrows():
for coords in fold['geometry'].coords:
ostr = "{}\t{}\t".format(coords[0], coords[1])
f.write(ostr)
f.write('\n')
f.close()
fn = 'fold.txt'
# defaults
CM2INCHES = 0.3937
bGrid = False
bEqualArea = False
nBinWidth = 5
sColour = 'C0'
xSize = 15.0 * CM2INCHES
ySize = 15.0 * CM2INCHES
# get nodes and calculate angles from nodes
nodelist = fpq.getNodes(fn)
xnodelist = nodelist[0]
ynodelist = nodelist[1]
segangle = fpq.getSegAngles(xnodelist, ynodelist)
nSegs = len(segangle)
if (nSegs > 0):
# bin the data and find maximum per bin
nBins = int(round(360 / nBinWidth))
segangleDoubled = np.zeros(len(segangle))
segangleDoubled = np.copy(segangle)
segangleDoubled = np.concatenate(
[segangleDoubled, segangleDoubled + 180.0])
n, b = plt.histogram(segangleDoubled, nBins)
nMax = max(n)
# plot the segment angle distribution
plt.figure(figsize=(xSize, ySize))
plt.subplot(111, projection='polar')
coll = fpq.rose(segangle,
bins=nBins,
bidirectional=True,
eqarea=True,
color=sColour)
plt.xticks(np.radians(range(0, 360, 45)),
['0', '45', '90', '135', '180', '215', '270', '315'])
if (nMax > 6):
plt.rgrids(range(0, int(round(nMax * 1.1)),
int(round((nMax * 1.1) / 5))),
angle=330)
plt.ylim(0, int(round(nMax * 1.1)))
plt.title('Segment strikes, n=%i' % nSegs)
plt.savefig("folds_rose.pdf")
#folds.plot(figsize=(7,7),edgecolor='#ff0000',linewidth=0.3)
#print('Plotted %5d segments & angles' % nSegs)
#plt.savefig('folds.pdf')
plt.title('Fold axial traces')
plt.show()
def plot():
plt.histogram(sale_price)
plt.axvline(x=sale_price.mean())
plt.axvline(x=sale_price.median())
plt.axvline(x=sale_price.mode())