# these 4 are from exer1
# for comparison
X1 = np.polyfit(layer_x, ytrack1, 1)[1]
X2 = np.polyfit(layer_x, ytrack2, 1)[1]
Xav = (X1 + X2) / 2 - X0
histXav[i] = Xav
# convert units to micrometer
histConst *= 10**4
# histPull *= 10**4
histXav *= 10**4
# set up bins and scale after observation
binEdges = np.linspace(-500, 500, 100)
ax[0].hist(histConst, bins=binEdges, align="left", label="Constrained fit")
statBox(ax[0], histConst, binEdges, name="Constrain")
ax[0].set_title("X0 - Xf with constrain")
ax[0].set(xlabel="$\Delta$ X ($\mu m$)")
# ax[1].hist(histPull, bins=binEdges, log=True, align="left")
pullbinEdges = np.linspace(-5, 5, 100)
ax[1].hist(histPull, bins=pullbinEdges, log=True, align="left")
ax[1].set_title("Pull of constrained fit")
ax[1].set(xlabel="$\Delta$ X ($\mu m$)")
# ----------------un-comment and change line 7 to compare to average method ---
# ax[2].hist(histXav, bins=binEdges, align="left")
# ax[2].set_title("Xav - X0")
# # label everything
# ax[2].set(xlabel="$\Delta$ X ($\mu m$)")
# # add claudio's box
# statBox(ax[2], histXav, binEdges, name="Average")
import matplotlib.pyplot as plt
from ccHistStuff import statBox, plotErr
#dataSet = np.load("dataSet.npy")#Loads numpy data set
# need to load a txt file, simply a list of numbers!! not too bad hopefully
dataSet = np.loadtxt('mass.txt')
#print(dataSet)
dataArray = np.array(dataSet) #creats a 1D array populated with dataSet data
bins = 17#
binEdges = [0,20]
# Plot histogram of data and log scale on y axis.
fig = plt.figure() # create a plot object
ax = fig.add_subplot(1,1,1) # Add a plot space (in this case the whole window)
hist = plt.hist(dataArray, bins = bins, normed=True) # histogram plot
ax.set_yscale('log') #linear to log on y (allows us to visualize distribution better)
statBox(ax, dataArray, binEdges) # adds statistics box to graph
plt.show() # have to tell python to show the plot!!
## Problem: this is not ploting a histogram at the moment,
# Good: Numpy array is imported correctly
# Good: a figure is being created
# Bad: histo bars are not showinup on graph
#------------------------------------------------------------
# Conner Addison 8984874
# Physics 129L
#------------------------------------------------------------
# Homework 4, Exercise 1
import numpy as np
import matplotlib.pyplot as plt
import math as m
from ccHistStuff import statBox
# Loading data
x = np.load('dataSet.npy')
# Setting up figure
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_yscale('log')
ax.set(title='Data set array frequency, semi-log',
xlabel='Value of x',
ylabel='Frequency of value, log scale')
# Finishing fig
ax.hist(x, m.floor(max(x) * 3))
statBox(ax, x, x)
plt.savefig('datavis.png')
plt.show()
intercept1 = popt1[1]
diff0 = intercept0 - x0
diff1 = intercept1 - x0
diffav = np.mean([intercept0,intercept1]) - x0
difference0.append(diff0)
difference1.append(diff1)
differenceav.append(diffav)
#Plotting
fig, ax = plt.subplots()
binEdges = np.linspace(-500,500,100)
ax.hist([difference0,difference1],bins=binEdges,label=['X1-X0', 'X2-X0'])
cc.statBox(ax, difference0, binEdges, x=.21, y=.98,label='Track 1')
cc.statBox(ax, difference1, binEdges, x=.98, y=.98,label='Track 2')
plt.legend(loc='center right')
plt.xlabel('Intercept Difference [micrometers]')
plt.ylabel('Number of Occurences')
fig.show()
input('Press <Enter> to continue')
fig, ax = plt.subplots()
binEdges = np.linspace(-500,500,100)
ax.hist(differenceav,bins=binEdges,label='Xav-X0',edgecolor='black')
cc.statBox(ax,differenceav,binEdges,label='Average of Tracks')
plt.legend(loc='center right')
plt.xlabel('Average Intercept Difference [micrometers]')
plt.ylabel('Number of Occurences')
fig.show()
# Since we are just making a plot it does not matter.
# If we were to use x for some real work we would
# reshuffle the elements with np.random.shuffle
#------------------------------------------------
# Plot the histogram and the line
#------------------------------------------------
fig, ax, = plt.subplots()
# the histogram
contents, binEdges, _ = ax.hist(x,
np.linspace(0., 1., nbins + 1),
histtype='step',
log=False,
color='blue')
cc.statBox(ax, x, binEdges, x=0.3)
# cc.plotErr(ax, contents, binEdges, color='blue')
# the line (mind the normalization!!!!)
xl = np.array([0., 1.]) # straight line, 2 points are enough!!!
yl = (1. / 3) * (1. + 4 * xl) * (n / nbins)
ax.plot(xl, yl, color='orange', linestyle='dashed')
# make it prettier
ax.set_xlim(binEdges[0], binEdges[-1])
ax.tick_params("both", direction='in', length=10, right=True)
ax.set_xticks(binEdges, minor=True)
ax.tick_params("both", direction='in', length=7, right=True, which='minor')
ax.set_xlabel("Linear random variable")
ax.set_ylabel("Number of entries")
#------------------------------------------------------------
# Conner Addison 8984874
# Physics 129L
#------------------------------------------------------------
# Homework 5, Exercise 3
import math
import numpy as np
import matplotlib.pyplot as plt
from ccHistStuff import statBox
# Loading data
txt_file = 'mass.txt'
bins = 20
data = np.loadtxt(txt_file)
# Setting up figure
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(data, bins)
ax.set(title='Mass frequency distribution',
xlabel='Mass',
ylabel='Frequency of value')
statBox(ax, data, data)
plt.show()
step4 = np.matmul(step3, At)
step5 = np.matmul(step4, W)
dp = np.matmul(step5, dy)
# Reassigning guess, must have lost negative sign somewhere because this only works with the minus
p[0] -= dp[0][0]
p[1] -= dp[1][0]
print(f"---------- ITERATION {j + 1} ----------")
print(f" p = {p}")
print("")
# Printing results
print("")
print("-------------------- FINAL RESULTS (BLUE) --------------------")
print(f"p = {p}")
y_f = np.array([exponential_fit(i, p) for i in x])
chi = chiSquared(y_f, y0)
print(f"Chi squared = {chi}")
print("")
# Finishing fig
ax.set_yscale('log')
ax.set(title='Log data fit, Gaussian regime',
xlabel='X',
ylabel='Count',
xlim=(bin_centers[0], bin_centers[N - 1]))
cc.statBox(ax, x, bins)
ax.plot(x, y, color='b')
#plt.savefig('chifit.png')
plt.show()
#!/usr/bin/python3
#Zipeng Wang
#3909934
#read data from .npy file and plot a semilog histogram
import ccHistStuff as cc
import numpy as np
import matplotlib.pyplot as plt
data = np.load('dataSet.npy')#import data
print(min(data),max(data)) #for test purposes
#histogram
nbins = 24
bins = np.linspace(0,24,nbins+1)#get these numbers from trial
fig,ax = plt.subplots()
contents,binEdges,_, = ax.hist(data,bins,log = True,color = 'b',rwidth = 0.85)
cc.statBox(ax,data,binEdges)# add cc's stat box
#Fig config stuffs
ax.set_xlim(binEdges[0],binEdges[-1])
ax.set_xticks(binEdges,minor = True)
ax.tick_params("both",direction = 'in')
ax.set_xlabel('x')
ax.set_ylabel('count')
plt.show()
xlist.append(xlist[-1])
# put list into array removing the burn in part
xr = np.array(xlist[nBurn:])
# now plot it...go out to 4 sigma
low = mean - 4 * sigma
high = mean + 4 * sigma
nbins = 100
bins = np.linspace(low, high, nbins + 1)
fig, ax = plt.subplots()
contents, bins, _ = ax.hist(xr, bins, histtype='step', color='blue')
# Add a stats box at the top right edge (CUSTOM CODE)
cc.statBox(ax, xr, bins)
# Add errors (CUSTOM CODE)
# cc.plotErr(ax, contents, bins, color='blue')
# The default is to *not* have the x axis run from
# the lowest bin edge to the highest bin edge.
# This seems pretty silly, so override this
ax.set_xlim(bins[0], bins[-1])
# I do not like the defaults....
# I want the ticks to be into the plot, also on the right of the plot
ax.tick_params("both", direction='in', length=10, right=True)
# Also show a gaussian curve of correct mean,sigma.
# Properly normalized given number of entries and binsize.
fig = plt.figure(figsize=(14, 6))
plot = fig.add_subplot(131)
fig.subplots_adjust(wspace=.5)
for n in range(10):
plot.plot(data[n][0][1], data[n][1][0], color='.6', linestyle='--')
plot.plot(data[n][1][1], data[n][1][0], color='.6', linestyle='--')
plot.set(title='First 10 collisions',
xlabel=r'Sensor $x$ position',
ylabel=r'Sensor $y$ position')
# Adding histogram of error
hist1 = fig.add_subplot(132)
hist2 = fig.add_subplot(133)
(n1, bins1, patches1) = hist1.hist((x1_x0, x2_x0), 20, stacked=True)
(n2, bins2, patches2) = hist2.hist(xa_x0, 20)
hist1.set(xlim=(-.05, .05),
title=r'$(x_1/x_2 - x_0 )$',
xlabel=r'$\Delta x$',
ylabel='Counts')
hist2.set(xlim=(-.05, .05),
title=r'$x_a - x_0$',
xlabel=r'$\Delta x$',
ylabel='Counts')
# Adding stat box
cc.statBox(hist1, x1_x0, bins1)
cc.statBox(hist2, xa_x0, bins2)
# Saving/showing figure
#plt.savefig('tracks.png')
plt.show()
print(" ")
population[i] = blah
#-------------------------------------------------------------------
# extract the first digit, as a number, from the population string
#------------------------------------------------------------------
digit = np.array([])
for entry in population:
digit = np.append(digit, int(entry[0]))
#------------------------------------------------
# Plot the digit now
#------------------------------------------------
fig, ax, = plt.subplots()
contents, binEdges, _ = ax.hist(digit,
np.linspace(0.5, 9.5, 10),
histtype='step',
log=False,
color='black')
# Make it prettier, add the stat box
cc.statBox(ax, digit, binEdges)
# cc.plotErr(ax, contents, binEdges, color='blue')
ax.set_xlim(binEdges[0], binEdges[-1])
# ax.set_ylim(0. , 1.4*np.max(contents))
ax.tick_params("both", direction='in', length=10, right=True)
ax.set_xticks(binEdges, minor=True)
# ax.tick_params("both", direction='in', length=7, right=True, which='minor')
fig.show()
input("Press any key to continue")
####################################### end of fit 3
print('end of fit 3')
# Histogram configuration
x1 = 100.
x2 = 200.
nb = 20
bins = np.linspace(x1, x2, nb+1)
# plot the histogram
# plot fitted curve1
f1, a1 = plt.subplots()
c, b, _ = a1.hist(mass, bins, histtype='step')
cc.statBox(a1, mass, b)
a1.set_xlim(b[0], b[-1])
a1.tick_params("both", direction='in', length=10, right=True)
a1.set_xticks(b, minor=True)
a1.tick_params("both", direction='in', length=7, right=True, which='minor')
plot_x = np.linspace(100,200,1001)
plot_y = fittedB1*B_1(plot_x,fitteda)+fittedS1*S_pdf(plot_x)
a1.plot(plot_x,plot_y*5,label='exponential fit') # have this *5 factor because the
#area of the
# histogram is not 200 but 200*5(bin width)
a1.set_xlabel('mass[GeV]')
a1.set_ylabel('count')
a1.legend(loc = 'upper left')
# plot fitted curve2
#Chi-Squared Calculation (if desired)
y0_theory = popt[0]*(x_data - intercept)
y1_theory = popt[1]*(x_data - intercept)
y_theory = np.concatenate((y0_theory,y1_theory))
y_data = np.concatenate((y0_data,y1_data))
chi = chisquare(y_data, y_theory)[0]
chi_squ.append(chi)
#Plotting
fig, ax = plt.subplots()
binEdges = np.linspace(-500,500,100)
ax.hist(difference, bins=binEdges, label='Difference', edgecolor='black')
cc.statBox(ax, difference, binEdges, label='Constrained Fit')
plt.legend(loc='center right')
plt.xlabel('Intercept Difference [micrometers]')
plt.ylabel('Number of Occurences')
fig.show()
input('Press <Enter> to continue')
fig, ax = plt.subplots()
binEdges = np.linspace(-.02,.02,100)
ax.hist(pull_values, bins=binEdges, label='Pull', edgecolor='black',log=True)
cc.statBox(ax, pull_values, binEdges, label='Pull')
plt.legend(loc='center right')
plt.xlabel('Pull')
plt.ylabel('Number of Occurences')
fig.show()
input('Press <Enter> to continue')
# Plotting stuff (default: one subplot in x and one in y)
thisFigure, thisAxes = plt.subplots()
# the bin edges (nbins + 1 because of lower and upper edge)
nbins = 40
bins = np.linspace(0, 20, nbins+1)
# the histogram
fig, ax = plt.subplots()
contents, binEdges, _ = ax.hist(data, bins, histtype='step', log=True, color='black',label='Raw Data')
# We were asked to add labels and stat box
ax.set_xlabel('X')
ax.set_ylabel('Entries per 0.5')
cc.statBox(ax, data, binEdges)
# This is purely esthetics (personal preference)
ax.tick_params("both", direction='in', length=10, right=True)
ax.set_xticks(binEdges, minor=True)
ax.tick_params("both", direction='in', length=7, right=True, which='minor')
ax.set_xlim(0, 20)
plt.plot(data, f(data,p_0,p_1),label='Chi-Squared Fit' )
plt.legend(loc='center left')
fig.show()
input('Press <Enter> to continue')
from scipy import special
import ccHistStuff as cc
# Read the masses into an array
mass = np.loadtxt("mass.txt")
# How many do we have, what is the max and min
# print( len(mass) )
# m1 = np.amax(mass)
# m2 = np.amin(mass)
# print(m1,m2)
# From te tests above, looks like 200 entries
# between 100 and 200?
# A reasonable binning may be 20 bisn
x1 = 100.
x2 = 200.
nb = 20
bins = np.linspace(x1, x2, nb + 1)
# plot now
f, a = plt.subplots()
c, b, _ = a.hist(mass, bins, histtype='step')
cc.statBox(a, mass, b)
a.set_xlim(b[0], b[-1])
a.tick_params("both", direction='in', length=10, right=True)
a.set_xticks(b, minor=True)
a.tick_params("both", direction='in', length=7, right=True, which='minor')
f.show()
input("Carriage return to continue....")
X0 = np.array(X0)
X1 = np.array(X1)
X2 = np.array(X2)
low = -500*10**(-4)
high = -low
nbins = 100
bins = np.linspace(low,high,nbins+1)
fig1,ax1 = plt.subplots()
c1,b1,_, = ax1.hist((X1-X0),bins, histtype = 'step',color= 'b',label = 'X1-X0')
c2,b2,_, = ax1.hist((X2-X0),bins,histtype = 'step',color = 'r',label = 'X2-X0')
#cc.statBox(ax1,(X1-X0),b1) #ccStatBoxes will stack together if multiples are in the
#graph. Only show (X2-X0) since they are more or less the same
cc.statBox(ax1,(X2-X0),b1)
ax1.set_xlim(b1[0],b1[-1])
ax1.legend(loc = 'upper left')
ax1.set_xlabel('X_fitted - X0[cm]')
ax1.set_ylabel('count')
fig2,ax2 = plt.subplots()
Xav = (X1+X2)/2
c3,b3,_, = ax2.hist((Xav-X0),bins,histtype = 'step',color = 'k',label = 'Xav-X0')
cc.statBox(ax2,(Xav-X0),b1)
ax2.set_xlim(b1[0],b1[-1])
ax2.legend(loc = 'upper left')
ax2.set_xlabel('X_fitted - X0[cm]')
ax2.set_ylabel('count')
chisq = ((N - y0)**2 / N).sum()
print(' ')
print('interation No.', iteration)
print('Chisq is', chisq)
print('p0=', p[0])
print('p1=', p[1])
#print final parameters and chisq
print('')
print('final values')
print('Chisq is', chisq)
print('p0=', p[0])
print('p1=', p[1])
#plot fitted functions (extend to the full range of graph)
y_long = f(x_long, p[0], p[1])
ax.plot(x_long, y_long, '--r')
# We were asked to add labels and stat box
ax.set_xlabel('X')
ax.set_ylabel('Entries per 0.5')
cc.statBox(ax, X, binEdges)
# This is purely esthetics (personal preference)
ax.tick_params("both", direction='in', length=10, right=True)
ax.set_xticks(binEdges, minor=True)
ax.tick_params("both", direction='in', length=7, right=True, which='minor')
ax.set_xlim(0, 20)
plt.show()
# Extract the arguments
seed = args['seed']
N = args['nEvents']
tau = args['tau']
nbins = args['bins']
tmax = args['maxHist']*tau
# Initialize random number
np.random.seed(seed)
# the times (could have used np.random.exponential instead)
times = -tau * np.log(np.random.rand(N))
# Plotting stuff (default: one subplot in x and one in y)
fig, ax = plt.subplots(1,1)
# the bin edges (nbins + 1 because of lower and upper edge)
bins = np.linspace(0, tmax, nbins+1)
# the histogram
contents, binEdges, _ = ax.hist(times, bins, histtype='step', log=True, color='black')
cc.statBox(ax, times, binEdges)
ax.set_xlim(binEdges[0], binEdges[-1])
ax.tick_params("both", direction='in', length=10, right=True)
# show the figure
fig.show()
# plt.show() (this would pause automatically)
input("Press any key to continue")
