def plot_dco_values(ax, values, color="k"):
interpol1 = {"temperature": values["temperature"][0:7], "values": values["values"][0:7]}
fit1 = pylab.polyfit(interpol1["temperature"], interpol1["values"], 1)
print "m={} b={}".format(fit1[0], fit1[1])
fit_fn1 = pylab.poly1d(fit1)
interpol2 = {"temperature": values["temperature"][6:14], "values": values["values"][6:14]}
fit2 = pylab.polyfit(interpol2["temperature"], interpol2["values"], 1)
print "m={} b={}".format(fit2[0], fit2[1])
fit_fn2 = pylab.poly1d(fit2)
plot = ax.plot(
interpol1["temperature"],
fit_fn1(interpol1["temperature"]),
"k-",
interpol2["temperature"],
fit_fn2(interpol2["temperature"]),
"k-",
# values['temperature'], values['values'], '{}-'.format(color),
values["temperature"],
values["values"],
"{}o".format(color),
markersize=5,
)
pylab.setp(plot[0], linewidth=2)
pylab.setp(plot[1], linewidth=2)
return plot
def regression(x, y, df, ax=None):
"""
Trace x contre y et calcul la fonction de régression
Paramètres:
x: Nom de la colonne des x
y: Nom de la colonne des y
df: Dataframe contenant les valeurs
ax: Axes où tracer le qqplot, sinon en créait un
Retourne: ax, eq, corr
ax: Axes (graphique)
eq: equation de regression linéaire de premier degré
corr: coefficient de corrélation
"""
if ax is None:
ax = _new_default_axe('defaut')
_x = df[x]
_y = df[y]
fit = plb.polyfit(_x, _y, 1)
fit_fn = plb.poly1d(fit) # fit_fn is now a function which takes in x and returns an estimate for y
plt.plot(_x, _y, 'yo', _x, fit_fn(_x), '--k', axes=ax)
try:
ax = plt.gca()
ax.set_xlabel(x)
ax.set_ylabel(y)
plt.draw()
except:
pass
eq = plb.poly1d(fit_fn)
corr = stats.correlation(_y, _x)
return ax, eq, corr
def __init__(self, coeff_list):
# NB: we adopt the weak-term-first convention for inputs
self.coeff_list = coeff_list
self.q = pylab.poly1d(coeff_list[::-1])
self.qd = pylab.polyder(self.q)
self.qdd = pylab.polyder(self.qd)
self.degree = self.q.order
def analyze(self):
logging.info('Analyze and plot results')
with tb.open_file(self.output_filename + '.h5', 'r+') as in_file_h5:
data = in_file_h5.root.plsr_dac_data[:]
# Calculate mean PlsrDAC transfer function
mean_data = np.zeros(shape=(len(self.scan_parameter_steps), ), dtype=[(self.scan_parameter, np.int32), ('voltage_mean', np.float), ('voltage_rms', np.float)])
for index, parameter in enumerate(self.scan_parameter_steps):
mean_data[self.scan_parameter][index] = parameter
mean_data['voltage_mean'][index] = data['voltage'][data[self.scan_parameter] == parameter].mean()
mean_data['voltage_rms'][index] = data['voltage'][data[self.scan_parameter] == parameter].std()
plt.errorbar(self.scan_parameter_steps, mean_data['voltage_mean'], mean_data['voltage_rms'])
# Plot and fit result
x, y, y_err = np.array(self.scan_parameter_steps), mean_data['voltage_mean'], mean_data['voltage_rms']
fit = polyfit(x[np.logical_and(x >= self.fit_range[0], x <= self.fit_range[1])], y[np.logical_and(x >= self.fit_range[0], x <= self.fit_range[1])], 1)
fit_fn = poly1d(fit)
plt.clf()
plt.errorbar(x, y, y_err, label='data')
plt.plot(x, fit_fn(x), '--k', label=str(fit_fn))
plt.title(self.scan_parameter + ' calibration')
plt.xlabel(self.scan_parameter)
plt.ylabel('Voltage [V]')
plt.grid(True)
plt.legend(loc=0)
plt.savefig(self.output_filename + '.pdf')
# Store result in file
self.register.calibration_parameters['Vcal_Coeff_0'] = fit[1] * 1000. # store in mV
self.register.calibration_parameters['Vcal_Coeff_1'] = fit[0] * 1000. # store in mV/DAC
def scatterPlot(actual,predicted,dataset_name=''):
'''
Demonstrate Scatter Plot generation of the actual labels vs predicted labels
This requires Matplotlib installed on the local system.
Inputs:
=======
actual: (list) a list of actual values for a label column
predicted: (list) a list of predicted values for a label column
Outputs:
========
Displays the scatter plot in a window
'''
try:
import matplotlib, pylab
from pylab import poly1d, polyfit, plot
except ImportError:
print 'Matplotlib/Pylab does not exist, skipping Scatter Plot Demo'
return
if(not actual or not predicted) :
return
#Line of best fit
fit = polyfit(actual,predicted,1)
fit_func = poly1d(fit)
#
matplotlib.pyplot.scatter(actual,predicted, facecolors='none', edgecolors=COLOR_LIGHT_RED, s=50, linewidth=2)
plot(actual,fit_func(actual),'k')
pylab.title('Scatter plot of Actual Vs Predicted values for dataset : {dataset_name}'.format(dataset_name=dataset_name), weight='bold')
pylab.xlabel('Actual', weight='bold')
pylab.ylabel('Predicted', weight='bold')
pylab.show()
def __init__(self, data, name, polyfit_degree=7):
self.data = data
reflectance_curve = pylab.polyfit([wavelength for wavelength, reflectance in data],
[reflectance for wavelength, reflectance in data],
polyfit_degree)
self.reflectance_curve = pylab.poly1d(reflectance_curve)
self.name = name
def plot_variances(datasets, labels, xlabel=None, ylabel=None, title=None):
fig, ax = plt.subplots()
variances = [np.log(np.var(d)) for d in datasets]
num_labels = [float(l) for l in labels]
fit = pylab.polyfit(num_labels, variances, 1)
fit_fn = pylab.poly1d(fit)
lin_approx = fit_fn(num_labels)
r_sq = r_squared(variances, lin_approx)
secondary_num_labels = [num_labels[0] - num_labels[1]] + num_labels + [num_labels[-1] + num_labels[1]]
ax.plot(
num_labels, variances, 'yo',
secondary_num_labels, fit_fn(secondary_num_labels), '--k'
)
ax.annotate(
'$(r^2 = {0:.2f})$'.format(r_sq),
(0.90, 0.495),
xycoords='axes fraction'
)
plt.xlim([secondary_num_labels[0], secondary_num_labels[-1]])
if xlabel is not None:
plt.xlabel(xlabel, labelpad=20)
if ylabel is not None:
plt.ylabel(ylabel, labelpad=20)
if title is not None:
plt.title(title)
plt.show()
def main():
#get list of files
files = filelist("files")
#set up plotting
# run through each channel
for ch in range(32):
means = np.zeros(len(files)/2)
varis = np.zeros(len(files)/2)
#process pairs of images
for i in range(0, len(files), 2):
im1 = readfile(files[i])[:,ch*64:ch*64+64]
im2 = readfile(files[i+1])[:,ch*64:ch*64+64]
print "processing: ", files[i], files[i+1]
#get sum of means
som = summean(im1, im2)
#get variance of difference)
vod = diffvar(im1, im2)
means[i/2] = som
varis[i/2] = vod
print 'means:', means
print 'variances: ', varis
fit = pl.polyfit(varis, means, 1)
fit_fn = pl.poly1d(fit)
print "e-/ADU = ", fit[0]
plt.plot(varis, means, 'o', varis, fit_fn(varis), '--')
plt.legend(['Channel '+str(ch), str(round(fit[0],3))+' e-/ADU'], loc=9)
plt.xlabel('Variance')
plt.ylabel('Mean')
plt.title('Conversion Gain')
plt.savefig('ch'+str(ch).zfill(2)+'.png', bbox_inches='tight')
plt.clf()
return 0
def visualise(fileName, title="", linearFit=False):
""" Draw graph representing the result. A bit verbose as that seem to be the only way to make borders gray.
fileName = path and name of the pickled results
"""
with open(fileName, "rb") as f:
data = pickle.load(f)
fig = plt.figure()
p = fig.add_subplot(111)
if not linearFit:
p.plot(data[0], data[1], 'bo-', label="sentiment")
p.plot([data[0][0], data[0][-1]], [data[1][0], data[1][-1]],
'g', label="straight line through first and last point")
else:
fit = polyfit(data[0], data[1], 1)
fitFunc = poly1d(fit)
p.plot(data[0], data[1], 'ro', label='sentiment')
p.plot(data[0], fitFunc(data[0]), "--k", label="linear fit")
p.legend(prop={'size': 10}, frameon=False)
plt.ylabel("Average happiness")
plt.xlabel("Rating")
for e in ['bottom', 'top', 'left', 'right']:
p.spines[e].set_color('gray')
if title:
plt.title(title)
plt.show()
def qqplot(x, y, df, interval=0.5, ax=None):
"""
Trace un QQPlot de x contre y, avec un interval de confiance autour de la
droite de régression.
Paramètres:
x: Nom de la colonne des x
y: Nom de la colonne des y
df: Dataframe contenant les valeurs
nb: Nombre de quantiles à calculer dans la plage [0, 1.]
interval: (défaut=0.5) [0-1.], interval de confiance autour de la droite
de régression
ax: Axes où tracer le qqplot, sinon en créait un
"""
if ax is None:
ax = _new_default_axe('defaut')
_x = df[x]
_y = df[y]
fit = plb.polyfit(_x, _y, 1)
fit_fn = plb.poly1d(fit) # fit_fn is now a function which takes in x and returns an estimate for y
# Trace les points et la droite de régression
fit_y = fit_fn(_x)
plt.plot(_x, _y, 'yo', _x, fit_y, '--k', axes=ax)
ax.set_xlabel(x)
ax.set_ylabel(y)
# Puis trace les lignes d'interval autour de la droite de régression
plt.plot(_x, fit_y + fit_y * interval, '-r')
plt.plot(_x, fit_y - fit_y * interval, '-r')
return ax
def linear_reg_plotter(group,x,y):
plt.axes(axisbg="#777777")
fit = pylab.polyfit(x,y,1)
fit_fn = pylab.poly1d(fit)
plt.scatter(x,y,color=node_color[group])
plt.plot(x, fit_fn(x),color=node_color[group])
def polyfit_anno(x, y, order=1, color='k'): fit = pl.polyfit(x.values(), y.values(), order) fit_fn = pl.poly1d(fit) pl.plot(x.values(), fit_fn(x.values()), color) midpoint = (min(x.values()) + max(x.values()))/2 minpointy = min(y.values())/1.5 # about arrow properties: http://matplotlib.org/1.3.1/users/annotations_guide.html ax.annotate(fit_fn, xy=(midpoint, fit_fn(midpoint)), xytext=(1.2*midpoint, minpointy), arrowprops=dict(arrowstyle="->", ec=color))
def age_vs_matches():
#opens excel file and allows for python to plot
import matplotlib.pyplot as graph
from pylab import polyfit
from pylab import poly1d
from xlrd import open_workbook
from xlutils.copy import copy
book = open_workbook('genecomparison2.xls', formatting_info=True)
wbook = copy(book)
sheet8= wbook.get_sheet(7)
genecompare = open('genecomparison.txt', 'r')
ages_matches = []
ages = []
matches = []
genecompare.readline()
k = 1
#determines the number of matches per sample, and creates a list of the ages and number of matches for that age
for line in genecompare:
match = 0
genes = line[2:]
cols = line.split('\t')
ages.append(int(cols[1]))
for i in genes:
if i == 'y':
match = match + 1
ages_matches.append([int(cols[1]), match])
matches.append(match)
print ages, len(ages)
print matches, len(matches)
print ages_matches, len(matches)
#plots a scatter plot of each age and total matches for that age in python
graph.scatter(ages,matches)
graph.show()
#creates a trendline for a scatter plot
fit = polyfit(ages,matches,1)
fit_fn = poly1d(fit) # fit_fn is now a function which takes in a age and returns an estimate for matches
graph.plot(ages,matches, 'yo', ages, fit_fn(ages), '--k')
graph.show()
data = (ages,matches)
graph.boxplot(ages,matches,vert = False)
graph.ylabel('Number of Cancer Gene Mutations')
graph.xlabel('Age')
graph.title('Total number of Cancer Gene Mutations per age ')
graph.plot(ages, fit_fn(ages), '--k')
graph.show()
sheet8.write(0,0, 'age')
sheet8.write(0,1,'total number of cancer gene mutations')
#writes a list of ages and matches so can also plot in excel and have tabulated data
for i in ages_matches:
sheet8.write(k,0,i[0])
sheet8.write(k,1,i[1])
k = k + 1
wbook.save('genecomparison2.xls')
def vizualizationClusters(clusters, pr, vol, Name=0, picFormat = "png", withLabels = False):
numberOfClusters = len(clusters)
colors = ['r', 'b', 'g', 'c', 'k', 'm' ,'y','w', 'r', 'b', 'g', 'c', 'k', 'm' ,'y','w', 'r', 'b', 'g', 'c', 'k', 'm' ,'y','w', 'r', 'b', 'g', 'c', 'k', 'm' ,'y','w', 'r', 'b', 'g', 'c', 'k', 'm' ,'y','w', 'r', 'b', 'g', 'c', 'k', 'm' ,'y','w', 'r', 'b', 'g', 'c', 'k', 'm' ,'y','w', 'r', 'b', 'g', 'c', 'k', 'm' ,'y','w', 'r', 'b', 'g', 'c', 'k', 'm' ,'y','w''r', 'b', 'g', 'c', 'k', 'm' ,'y','w', 'r', 'b', 'g', 'c', 'k', 'm' ,'y','w', 'r', 'b']
fig = pl.figure()
R2 = {}
R2all = 0
labels = []
xall =[]
yall = []
for i in range(numberOfClusters):
xNoTest = []
yNoTest = []
xTest = []
yTest = []
for j in range(clusters[i].getLength()):
dateAndHour = str(clusters[i].getElement(j))
labels.append(dateAndHour)
if (clusters[i].points[j].isTest==0):
xNoTest.append(vol[dateAndHour])
yNoTest.append(pr[dateAndHour])
else:
xTest.append(vol[dateAndHour])
yTest.append(pr[dateAndHour])
fit = pl.polyfit(xNoTest, yNoTest, 1)
fit_fn = pl.poly1d(fit)
R2[i] = evaluationOfR2(xTest,fit_fn,yTest)
R2all = R2all + R2[i]
pl.plot(xNoTest,yNoTest, 'yo', xNoTest, fit_fn(xNoTest), '--k', color = colors[i])
pl.plot(xTest,yTest, 'y*', color = colors[i])
pl.xlabel("Volume, MWh")
pl.ylabel("Price, Rubles per MWh")
xall = xall + xNoTest + xTest
yall = yall + yNoTest + yTest
# annotation
if withLabels:
for i, lab in enumerate(labels):
pl.annotate(lab, xy = (xall[i], yall[i]), xytext = (-5, 5), textcoords = 'offset points', ha = 'right', va = 'bottom' , fontsize = 5)
pl.savefig("./pic/"+str(pathToPic) + "/" + str(Name)+"test"+str(i)+"."+picFormat, format=picFormat)
pl.close(fig)
return R2all
def create_curve(data, polyfit_degree=7):
"""This function finds a best-fit curve for a list of data points.
This function accepts a list argument 'data' as input and an integer
polynomial degree of fit. The 'data' argument is a list of 2 item
tuples (x, y). The polynomial degree of fit defaults to a 7th degree
polynomial. The function returns the equation for the polynomial
fit line."""
reflectance_curve = pylab.polyfit([wavelength for wavelength,
reflectance in data],
[reflectance for wavelength,
reflectance in data],
polyfit_degree)
return pylab.poly1d(reflectance_curve)
def plot_table_values(ax, values, color="k", pol=1):
fit = pylab.polyfit(values["temperature"], values["values"], pol)
fit_fn = pylab.poly1d(fit)
plot = ax.plot(
values["temperature"],
fit_fn(values["temperature"]),
"{}-".format(color),
values["temperature"],
values["values"],
"{}o".format(color),
markersize=5,
)
pylab.setp(plot[0], linewidth=2)
return plot
def makeGraph(X,Y, xName, yName, name="NoName"):
fig = plt.figure()
ax = fig.add_subplot(111)
superName = "Comparison of {} and {}".format(xName,yName)
outname = "{} from {}.png".format(superName,name)
fig.suptitle(superName)
ax.scatter(X,Y)
fit = polyfit(X,Y,1)
fit_fn = poly1d(fit) # fit_fn is now a function which takes in x and returns an estimate for y
ax.plot(X,Y, 'yo', X, fit_fn(X), '--k')
ax.set_xlabel('Size of MCS found by {}'.format(xName))
ax.set_ylabel('Size of MCS found by {}'.format(yName))
ax.text(1, 1, "y = {}*x + {}".format(fit[0], fit[1]))
fig.savefig(outname)
def plot_linear_regression_values(ax, values, color="k"):
fit = pylab.polyfit(values["temperature"], values["values"], 1)
print "m={} b={}".format(fit[0], fit[1])
fit_fn = pylab.poly1d(fit)
plot = ax.plot(
values["temperature"],
fit_fn(values["temperature"]),
"{}-".format(color),
values["temperature"],
values["values"],
"{}o".format(color),
markersize=5,
)
pylab.setp(plot[0], linewidth=2)
return plot
def analyze(self):
logging.info('Analyze and plot results')
x = self.data[:, 0]
y = self.data[:, 1]
fit = polyfit(x[np.logical_and(x >= self.fit_range[0], x <= self.fit_range[1])], y[np.logical_and(x >= self.fit_range[0], x <= self.fit_range[1])], 1)
fit_fn = poly1d(fit)
plt.plot(x, y, 'o-', label='data')
plt.plot(x, fit_fn(x), '--k', label=str(fit_fn))
plt.title(self.scan_parameter + ' calibration')
plt.xlabel(self.scan_parameter)
plt.ylabel('Voltage [V]')
plt.grid(True)
plt.legend(loc=0)
plt.savefig(self.output_filename + '.pdf')
# Store result in file
self.register.calibration_parameters['Vcal_Coeff_0'] = fit[1] * 1000. # store in mV
self.register.calibration_parameters['Vcal_Coeff_1'] = fit[0] * 1000. # store in mV/DAC
def make_plot(x,y):
m1 = 10
m2 = 100
fit = pyl.polyfit(np.log(x[m1:m2]), np.log(y[m1:m2]),1)
fit_fun = pyl.poly1d(fit)
power_fit = lambda x: math.exp(fit_fun(math.log(x)))
vec_power_fit = np.vectorize(power_fit)
plt.plot(x, y, 'yo')
plt.plot(x,vec_power_fit(x),'b')
ax = plt.axes()
ax.set_yscale('log')
ax.set_xscale('log')
ax.text(0.3, 0.07,
'log(y(x)) = %.3f log(x) + %.3f' % (fit[1], fit[0]),
fontsize=14,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
plt.show()
def get_fit(which):
f = open("final_position.txt","r")
data = pl.genfromtxt(f,comments = "L")
if which=="x":
datnum = 2
if which=="vx":
datnum = 0
x = pl.array([])
y = pl.array([])
for i,j in enumerate(data[:-7,datnum]):
if i%2 == 0:
x = pl.append(x,data[i,4])
y = pl.append(y,j)
fit = pl.polyfit(x,y,2)
fitted = pl.poly1d(fit)
return fitted
def estimate_bo_params(p1=-0.05):
data = np.loadtxt("bo.dat")
data = data.transpose()
r = data[0]
bo = data[1]
x = np.log(r)
y = np.log(np.log(bo)/p1)
fit = polyfit(x, y, 1)
fit_fn = poly1d(fit)
p2 = fit[0]
p1 = p1
r0 = math.exp(-fit[1]/p2)
print "r0 = ", r0
print "pbo1 = ", p1
print "pbo2 = ", p2
plt.plot(x, y, x,fit_fn(x), '--k')
plt.show()
def plot_time_histogram(by_times, by_nodes, nodes, time, suffix):
ranking = [by_times[time][v] for v in by_times[time]]
ranking.sort()
Y = ranking
minY = min(Y)
nBins = 10
bins = [minY + int(numpy.power(2, i)) for i in range(nBins)]
print bins
N, tempbins, temppatches = pylab.hist(Y, bins)
H = [[bins[i+1], float(N[i])/sum(N)] for i in range(len(N)) if N[i]>0]
print N
pylab.close()
X = [h[0] for h in H]
Y1 = [h[1] for h in H]
fit = pylab.polyfit(numpy.log(X), numpy.log(Y1), 1)
fit_fn = pylab.poly1d(fit)
Y2 = numpy.exp(fit_fn(numpy.log(X)))
output_filename = constants.CHARTS_FOLDER_NAME + 'time_hist' + '_' + suffix
pylab.figure(figsize=(5, 4))
pylab.rcParams.update({'font.size': 20})
pylab.xscale('log')
pylab.yscale('log')
pylab.scatter(X, Y1)
pylab.plot(X, Y2, '--')
#pylab.xlabel('# of edges')
#pylab.ylabel('Probability')
#pylab.title(output_filename)
pylab.savefig(output_filename + '.pdf')
pylab.close()
def start(self,
solve=True,
username="",
password="",
gameType="Prisoner Meeting"):
""" Start the experiments and graph the result at the end.
Parameters:
solve -- True if we should solve the policies here. False if we should instead load them.
username -- The NEOS server username.
password -- The NEOS server password.
gameType -- The type of game: "Prisoner Meeting" or "Battle Meeting".
"""
for numNodes in self.numControllerNodes:
values = [list(), list(), list()]
standardError = [list(), list(), list()]
computeFinalValues = [list(), list(), list()]
# For each slack term, create the MCC, solve it, and compute the value.
for slack in self.slackValues:
print(
"----- Starting Configuration [Num Nodes: %i, Slack %.1f] -----"
% (numNodes, slack))
# Create the MCC, FSCs, etc. Then solve the MCC. Then load the policies.
mcc = MCC(gameType)
aliceFSC = FSC(mcc, "Alice", numNodes)
bobFSC = FSC(mcc, "Bob", numNodes)
fscVector = FSCVector([aliceFSC, bobFSC])
if solve:
# Note: This overwrites the FSCs and re-saves them for later use, if you want.
mccSolve = MCCSolve(mcc,
fscVector,
maxNumSteps=self.maxNumSteps,
delta=slack)
totalTime, individualTimes = mccSolve.solve(
username,
password,
resolve=(slack == self.slackValues[0]))
print(
"Individual Times: [R0: %.2fs, R1: %.2fs, R2: %.2fs]" %
(individualTimes[0], individualTimes[1],
individualTimes[2]))
print("Total Time: %.2f seconds" % (totalTime))
print("")
# We can also use the output files to compute the actual values and make another graph!
computeFinalValuesResult = self._compute_final_values()
computeFinalValues[0] += [computeFinalValuesResult[0]]
computeFinalValues[1] += [computeFinalValuesResult[1]]
computeFinalValues[2] += [computeFinalValuesResult[2]]
else:
aliceFSC.load("%i_%i" % (numNodes, int(slack)))
bobFSC.load("%i_%i" % (numNodes, int(slack)))
# Compute the average value following this FSC policy.
data = [list(), list(), list()]
averages = np.array([0.0, 0.0, 0.0])
for i in range(self.numTrials):
belief = mcc.get_initial_belief()
state = None
currentValue = 0.0
targetValue = random.random()
for s in mcc.states:
try:
currentValue += belief[s]
if currentValue >= targetValue:
state = s
break
except:
continue
aliceState = aliceFSC.get_initial_state()
bobState = bobFSC.get_initial_state()
trialValues = [0.0, 0.0, 0.0]
compoundedGamma = 1.0
for t in range(self.horizon):
action = (aliceFSC.get_action(aliceState),
bobFSC.get_action(bobState))
trialValues[0] += compoundedGamma * mcc.R0(
state, action)
trialValues[1] += compoundedGamma * mcc.Ri(
"Alice", state, action)
trialValues[2] += compoundedGamma * mcc.Ri(
"Bob", state, action)
compoundedGamma *= mcc.gamma
successor = mcc.get_successor(state, action)
observation = mcc.get_observation(action, successor)
state = successor
aliceState = aliceFSC.get_successor(
aliceState, action[0], observation[0])
bobState = bobFSC.get_successor(
bobState, action[1], observation[1])
for j in range(len(averages)):
data[j] += [trialValues[j]]
averages[j] = float(i * averages[j] +
trialValues[j]) / float(i + 1.0)
# Record the value and compute standard error.
for i in range(len(values)):
values[i] += [averages[i]]
standardError[i] += [
math.sqrt(
sum([
pow(data[i][j] - averages[i], 2)
for j in range(len(data[i]))
]) / float(len(data[i]) - 1.0))
]
# Compute some final things and make adjustments.
for i in range(len(values)):
values[i] = np.array(values[i])
if solve:
for i in range(len(computeFinalValues)):
computeFinalValues[i] = np.array(computeFinalValues[i])
minV = min([min(v) for v in values])
maxV = max([max(v) for v in values])
if gameType == "Battle Meeting":
minV = -2.0
maxV = 35.0
elif gameType == "Prisoner Meeting":
minV = 0.0
maxV = 50.0
# Plot the result, providing beautiful paper-worthy labels.
if self.graphRegionValues:
labels = ["V0", "Vi Min", "Vi Max", "Vi Trend", "(V1+V2)/2"]
else:
labels = ["V0", "V1", "V2", "Trend", "(V1+V2)/2"]
linestyles = ["-", "--", ":", "-", "-"]
markers = ["o", "s", "^", "", ""]
colors = ["r", "g", "b", "k", "k"]
minSlack = min(self.slackValues)
maxSlack = max(self.slackValues)
pylab.rcParams.update({'font.size': 18})
pylab.title("%s: ADR vs. Slack (Num Nodes = %i)" %
(gameType, numNodes))
pylab.hold(True)
pylab.xlabel("Slack")
pylab.xticks(np.arange(minSlack, maxSlack + 5.0, 5.0))
pylab.xlim([minSlack, maxSlack])
pylab.ylabel("Average Discounted Reward")
pylab.yticks(np.arange(0.0, int(maxV) + 5.0, 5.0))
pylab.ylim([0.0, int(maxV)])
pylab.hlines(np.arange(0.0,
int(maxV) + 1.0, 5.0),
minSlack - 1.0,
maxSlack + 1.0,
colors=[(0.6, 0.6, 0.6)])
if self.graphRegionValues:
for i in range(len(self.slackValues)):
if values[1][i] > values[2][i]:
tmp = values[1][i]
values[1][i] = values[2][i]
values[2][i] = tmp
tmp = standardError[1][i]
standardError[1][i] = standardError[2][i]
standardError[2][i] = tmp
pylab.fill_between(self.slackValues,
values[1],
values[2],
facecolor=(0.85, 0.85, 0.85))
for i in range(len(values)):
pylab.errorbar(self.slackValues,
values[i],
yerr=standardError[i],
linestyle=linestyles[i],
linewidth=3,
marker=markers[i],
markersize=18,
color=colors[i])
pylab.plot(self.slackValues,
values[i],
label=labels[i],
linestyle=linestyles[i],
linewidth=8,
marker=markers[i],
markersize=18,
color=colors[i])
# Special: Print a trend line for the individual objectives.
if self.graphTrendLine:
trendLineZ = pylab.polyfit(
self.slackValues + self.slackValues,
pylab.concatenate((values[1], values[2]), axis=0), 1)
trendLinePoly = pylab.poly1d(trendLineZ)
trendLineValues = [
trendLinePoly(slackValue)
for slackValue in self.slackValues
]
pylab.plot(self.slackValues,
trendLineValues,
label=labels[3],
linestyle=linestyles[3],
linewidth=8,
marker=markers[3],
markersize=18,
color=colors[3])
# Special: Print the average of the individual objectives.
if self.graphAverageLine:
pylab.plot(self.slackValues,
[(values[1][i] + values[2][i]) / 2.0
for i in range(len(self.slackValues))],
label=labels[4],
linestyle=linestyles[4],
linewidth=8,
marker=markers[4],
markersize=18,
color=colors[4])
pylab.rcParams.update({'font.size': 14})
if gameType == "Battle Meeting":
pylab.legend(loc=1) # Upper Right
elif gameType == "Prisoner Meeting":
pylab.legend(loc=3) # Lower Left
pylab.show()
pylab.rcParams.update({'font.size': 18})
# Special: If we just solved for these, then we have the actual values! Plot these results too!
if solve:
labels = ["V0", "V1", "V2"]
linestyles = ["-", "--", ":"]
markers = ["o", "s", "^"]
colors = ["r", "g", "b"]
minSlack = min(self.slackValues)
maxSlack = max(self.slackValues)
pylab.title("%s: Computed Values vs. Slack (Num Nodes = %i)" %
(gameType, numNodes))
pylab.hold(True)
pylab.xlabel("Slack")
pylab.xticks(np.arange(minSlack, maxSlack + 5.0, 5.0))
pylab.xlim([minSlack - 0.1, maxSlack + 0.1])
pylab.ylabel("Computed Values")
pylab.yticks(np.arange(int(minV), int(maxV) + 5.0, 5.0))
pylab.ylim([minV - 0.1, int(maxV) + 1.1])
pylab.hlines(np.arange(int(minV) - 1.0,
int(maxV) + 1.0, 5.0),
minSlack - 1.0,
maxSlack + 1.0,
colors=[(0.6, 0.6, 0.6)])
for i in range(len(computeFinalValues)):
pylab.plot(self.slackValues,
computeFinalValues[i],
label=labels[i],
linestyle=linestyles[i],
linewidth=8,
marker=markers[i],
markersize=18,
color=colors[i])
pylab.rcParams.update({'font.size': 14})
if gameType == "Prisoner Meeting":
pylab.legend(loc=3) # Lower Left
elif gameType == "Battle Meeting":
pylab.legend(loc=1) # Upper Right
pylab.show()
toM = 1e-5
toCM = 1e-1
toCM5 = 1e4
data = np.loadtxt("msd.dat")
data = data.transpose()
start = int(0.2*len(data[0]))
end = int(0.8*len(data[0]))
x = data[0] * ts
y = data[1]
fx = x[start:end]
fy = y[start:end]
fit = polyfit(fx, fy, 1)
fit_fn = poly1d(fit)
print "%.4e"%(fit[0]/6*toM)
print "%.4e"%(fit[0]/6*toCM)
print "%.4e 10^-5cm^2/s"%(fit[0]/6*toCM5)
plt.plot(x, y)
plt.plot(fx, fit_fn(fx), '--k', lw=2)
plt.xlabel("Simulation Time (fs)")
plt.ylabel("MSD ($\AA^2$)")
plt.show()
def plot_shifts(by_times, by_nodes, nodes, suffix):
shifts = []
times = by_times.keys()
times.sort()
prev_state = None
for time in times:
#get rid of saturdays and sundays
#if time%7==1 or time%7==2:
#if time%7==1:
# continue
state = calc_network_state(by_times, nodes, time)
if prev_state!=None:
shift = diff_network(prev_state, state, len(by_nodes))
shifts.append( shift )
prev_state = state
print len(nodes)
print len(shifts)
##############################
# ordered
##############################
###test
const = numpy.median(shifts)
shifts = [numpy.abs(s-const) for s in shifts]
###
values = [s for s in shifts]
values.sort(reverse=True)
plot_values(values, 'XXX')
###'''
##############################
# bins 15
##############################
Y = shifts
minY = min(Y)
maxY = max(Y)
nBins = 15
binSize = (maxY-minY)/float(nBins)
bins = [minY + binSize*float(i) for i in range(nBins)]
print bins
N, tempbins, temppatches = pylab.hist(Y, bins)
pylab.close()
N = [float(n)/float(sum(N)) for n in N]
N = [n for n in N]
print N
X = [bins[i+1] for i in range(len(N)) if bins[i+1]>0 and N[i]>0]
Y1 = [N[i] for i in range(len(N)) if bins[i+1]>0 and N[i]>0]
fit = pylab.polyfit(numpy.log(X), numpy.log(Y1), 1)
fit_fn = pylab.poly1d(fit)
Y2 = numpy.exp(fit_fn(numpy.log(X)))
output_filename = constants.CHARTS_FOLDER_NAME + 'shifts_bins_15' + '_' + suffix
pylab.figure(figsize=(9, 4))
pylab.xscale('log')
pylab.yscale('log')
pylab.scatter(X, Y1)
pylab.plot(X, Y2, '--')
#pylab.xlabel('Distance between following network states')
#pylab.ylabel('Probability')
#pylab.title(output_filename)
pylab.savefig(output_filename + '.pdf')
pylab.close()
#'''
##############################
# bins 30
##############################
Y = shifts
minY = min(Y)
maxY = max(Y)
nBins = 30
binSize = (maxY-minY)/float(nBins)
bins = [minY + binSize*float(i) for i in range(nBins)]
print bins
N, tempbins, temppatches = pylab.hist(Y, bins)
pylab.close()
N = [float(n)/float(sum(N)) for n in N]
N = [n for n in N]
print N
X = [bins[i+1] for i in range(len(N)) if bins[i+1]>0 and N[i]>0]
Y1 = [N[i] for i in range(len(N)) if bins[i+1]>0 and N[i]>0]
fit = pylab.polyfit(numpy.log(X), numpy.log(Y1), 1)
fit_fn = pylab.poly1d(fit)
Y2 = numpy.exp(fit_fn(numpy.log(X)))
output_filename = constants.CHARTS_FOLDER_NAME + 'shifts_bins_30' + '_' + suffix
pylab.figure(figsize=(9, 4))
pylab.xscale('log')
pylab.yscale('log')
pylab.scatter(X, Y1)
pylab.plot(X, Y2, '--')
#pylab.xlabel('Distance between following network states')
#pylab.ylabel('Probability')
#pylab.title(output_filename)
pylab.savefig(output_filename + '.pdf')
pylab.close()
def analyze(self):
logging.info('Analysing the PlsrDAC waveforms')
with tb.open_file(self.output_filename + '.h5', 'r') as in_file_h5:
data = in_file_h5.root.PlsrDACwaveforms[:]
times = np.array(in_file_h5.root.PlsrDACwaveforms._v_attrs.times)
scan_parameter_values = in_file_h5.root.PlsrDACwaveforms._v_attrs.scan_parameter_values
trigger_levels = in_file_h5.root.PlsrDACwaveforms._v_attrs.trigger_levels
fit_range = ast.literal_eval(
in_file_h5.root.configuration.run_conf[:][np.where(
in_file_h5.root.configuration.run_conf[:]['name'] ==
'fit_range')]['value'][0])
fit_range_step = ast.literal_eval(
in_file_h5.root.configuration.run_conf[:][np.where(
in_file_h5.root.configuration.run_conf[:]['name'] ==
'fit_range_step')]['value'][0])
progress_bar = progressbar.ProgressBar(widgets=[
'',
progressbar.Percentage(), ' ',
progressbar.Bar(marker='*', left='|', right='|'), ' ',
progressbar.AdaptiveETA()
],
maxval=data.shape[0],
term_width=80)
with tb.open_file(self.output_filename + '_interpreted.h5',
'w') as out_file_h5:
description = [('PlsrDAC', np.uint32),
('voltage_step', np.float)
] # output data table description
data_array = np.zeros((data.shape[0], ), dtype=description)
data_table = out_file_h5.create_table(
out_file_h5.root,
name='plsr_dac_data',
description=np.zeros((1, ), dtype=description).dtype,
title=
'Voltage steps from transient PlsrDAC calibration scan')
with PdfPages(self.output_filename +
'_interpreted.pdf') as output_pdf:
progress_bar.start()
for index in range(data.shape[0]):
voltages = data[index]
trigger_level = trigger_levels[index]
plsr_dac = scan_parameter_values[index]
if trigger_level < 0.005:
logging.warning(
'The trigger threshold for PlsrDAC %d is with %d mV too low. Thus this setting is omitted in the analysis!',
plsr_dac, trigger_level * 1000.)
data_array['voltage_step'][index] = np.NaN
continue
step_index = np.where(
np.abs(voltages - trigger_level) == np.amin(
np.abs(voltages - trigger_level)))[0][0]
left_step_fit_range = (step_index +
fit_range_step[0][0],
step_index +
fit_range_step[0][1])
right_step_fit_range = (step_index +
fit_range_step[1][0],
step_index +
fit_range_step[1][1])
# Error handling if selected fit range exeeds limits
if left_step_fit_range[0] < 0 or left_step_fit_range[
1] < 0 or right_step_fit_range[0] >= data.shape[
1] or right_step_fit_range[1] >= data.shape[
1] or left_step_fit_range[
0] >= left_step_fit_range[
1] or right_step_fit_range[
0] >= right_step_fit_range[
1]:
logging.warning(
'The step fit limits for PlsrDAC %d are out of bounds. Omit this data!',
plsr_dac)
data_array['voltage_step'][index] = np.NaN
continue
times_left_step, voltage_left_step = times[
left_step_fit_range[0]:
left_step_fit_range[1]], voltages[
left_step_fit_range[0]:left_step_fit_range[1]]
times_right_step, voltage_right_step = times[
right_step_fit_range[0]:right_step_fit_range[
1]], voltages[right_step_fit_range[0]:
right_step_fit_range[1]]
median_left_step = np.median(voltage_left_step)
median_right_step = np.median(voltage_right_step)
data_array['PlsrDAC'][index] = plsr_dac
data_array['voltage_step'][
index] = median_left_step - median_right_step
# Plot waveform + fit
plt.clf()
plt.ylim(0, 1500)
plt.grid()
plt.plot(times * 1e9, voltages * 1e3, label='Data')
plt.plot(times * 1e9,
np.repeat([trigger_level * 1e3], len(times)),
'--',
label='Trigger (%d mV)' %
(trigger_level * 1000))
plt.plot(times_left_step * 1e9,
np.repeat(median_left_step * 1e3,
times_left_step.shape[0]),
'-',
linewidth=2,
label='Left of step constant fit')
plt.plot(times_right_step * 1e9,
np.repeat(median_right_step * 1e3,
times_right_step.shape[0]),
'-',
linewidth=2,
label='Right of step constant fit')
plt.title('PulserDAC %d waveform' % plsr_dac)
plt.xlabel('Time [ns]')
plt.ylabel('Voltage [mV]')
plt.legend(loc=0)
output_pdf.savefig()
progress_bar.update(index)
data_table.append(data_array[np.isfinite(
data_array['voltage_step'])]) # store valid data
# Plot, fit and store linear PlsrDAC transfer function
x, y = data_array[np.isfinite(
data_array['voltage_step'])]['PlsrDAC'], data_array[
np.isfinite(
data_array['voltage_step'])]['voltage_step']
fit = polyfit(
x[np.logical_and(x >= fit_range[0],
x <= fit_range[1])],
y[np.logical_and(x >= fit_range[0],
x <= fit_range[1])], 1)
fit_fn = poly1d(fit)
plt.clf()
plt.plot(x, y, '.-', label='data')
plt.plot(x, fit_fn(x), '--k', label=str(fit_fn))
plt.title('PlsrDAC calibration')
plt.xlabel('PlsrDAC')
plt.ylabel('Voltage step [V]')
plt.grid(True)
plt.legend(loc=0)
output_pdf.savefig()
# Store result in file
self.register.calibration_parameters[
'Vcal_Coeff_0'] = fit[1] * 1000. # store in mV
self.register.calibration_parameters[
'Vcal_Coeff_1'] = fit[0] * 1000. # store in mV/DAC
progress_bar.finish()
#Plot 5
csv = open("data/g19_lab09data_random.csv")
step_sum_random = 0.0
step_times_random = []
i = 1
for line in csv:
values = line.split(",")
step_sum_random += float(values[2])
if i % 15 == 0:
step_sum_random = step_sum_random / 15
step_times_random.append(step_sum_random)
step_sum_random = 0.0
i += 1
step_line = pl.poly1d(pl.polyfit(x, np.array(step_times), 1))
step_line_random = pl.poly1d(pl.polyfit(x, np.array(step_times_random), 1))
pl.plot(x,
np.array(step_times),
"b.",
x,
step_line(x),
"b-",
label="Average Step Times")
pl.plot(x,
np.array(step_times_random),
"r.",
x,
step_line_random(x),
"r-",
label="Average Random Step Times")
def conversionGainFrame(imglist, quadrant):
"""Determine the conversion gain for a given quadrant. A spatial analysis is done,
using all pixels in a quadrant.
The mean-variance difference method is used, i.e. the differences between four pairs
of flat images are used, each pairs exposure time is twice that of the previous pair.
Variance = 2*Nread**2 + Signal1 + Signal2
Will plot both linear and log plots including mean sum vs. variance difference,
mean sum vs variance difference - readnoise**2 and a fit to slop for gain in e-/ADU
see http://www.noao.edu/kpno/manuals/whirc/WHIRC_VI_Mean-variance_101026.pdf
by Dick Joyce, 2010
Args:
imglist (list): File names of image pairs of increasing exposure time
quadrant (int): Quandrant being analyzed.
Returns:
gain, readnoise (float): Gain in e-/ADU and readnoise in e-
Example:
g, rn = detector.conversionGainFrame([q1_0s, q1_0s, q1_2s, q1_2s, q1_4s, q1_4s], 3)
"""
# run through each channel
means = np.zeros(len(imglist) / 2)
varis = np.zeros(len(imglist) / 2)
#process pairs of images
for i in range(0, len(imglist), 2):
im1 = imglist[i]
im2 = imglist[i + 1]
#get sum of means
som = summean(im1, im2)
#get variance of difference)
vod = diffvar(im1, im2)
means[i / 2] = som
varis[i / 2] = vod
print 'means:', means
print 'variances: ', varis
fit = pl.polyfit(means, varis, 1)
print fit
fit_fn = pl.poly1d(fit)
print 'y intercept', fit_fn(0.0)
eADU = 1.0 / fit[0]
print "e-/ADU = {0}".format(str(eADU))
fig1 = plt.figure(figsize=(10, 10))
plt.loglog(means, varis, 'o', means, fit_fn(means), '--')
plt.legend(['Channel ' + str(quadrant),
str(round(eADU, 3)) + ' e-/ADU'],
loc=9)
plt.xlabel('Mean')
plt.ylabel('Variance')
plt.title('Conversion Gain, Quadrant {0}'.format(str(quadrant)))
plt.show()
fig2 = plt.figure(figsize=(10, 10))
plt.plot(means, varis, 'o', means, fit_fn(means), '--')
plt.legend(['Channel ' + str(quadrant),
str(round(eADU, 3)) + ' e-/ADU'],
loc=9)
plt.xlabel('Mean')
plt.ylabel('Variance')
plt.title('Conversion Gain, Quadrant {0}'.format(str(quadrant)))
plt.show()
def plot_model_model(dataframe, **kwargs):
"""
Create the LIE model scatter plot
"""
settings = {'tollerance': 5, 'color': 'red'}
settings.update(kwargs)
ax = settings.get('ax', None)
combine_plots = False
if ax:
combine_plots = True
# Plot the training set
trainset = dataframe.trainset
label = 'train'
if combine_plots:
label = None
ax = trainset.plot(kind='scatter', x='ref_affinity', y='dg_calc', color=settings['color'], label=label, s=25, ax=ax)
ax.set_aspect('equal')
# Plot datalabels if needed
if settings.get('plot_labels', False):
for i, point in trainset.iterrows():
ax.text(point['ref_affinity'], point['dg_calc'], "{0:.0f}".format(point['case']), fontsize=8)
# Force X and Y axis to have the same data range
axis_min = 10 * round(min([trainset['ref_affinity'].min(), trainset['dg_calc'].min()]) / 10)
axis_max = 10 * round(max([trainset['ref_affinity'].max(), trainset['dg_calc'].max()]) / 10)
# Give it a bit more space
ax.set_xlim(axis_min - 10, axis_max + 10)
ax.set_ylim(axis_min - 10, axis_max + 10)
# Plot the regression line
if settings.get('plot_regline', False):
ref = trainset['ref_affinity'].values
fitx = polyfit(ref, trainset['dg_calc'].values, 1)
fit_fnx = poly1d(fitx)
ax.plot(ref, fit_fnx(ref), 'r-', label="fit", linewidth=0.5)
# Add diagonal and error margins
if not combine_plots:
xlim = ax.get_xlim()
ylim = ax.get_ylim()
ax.plot(xlim, ylim, 'k-', linewidth=0.5)
ax.plot((xlim[0], xlim[1] - settings['tollerance']), (ylim[0] + settings['tollerance'], ylim[1]), 'k--')
ax.plot((xlim[0] + settings['tollerance'], xlim[1]), (ylim[0], ylim[1] - settings['tollerance']), 'k--')
# Plot the test set if any
testset = dataframe.testset
if not testset.empty and settings.get('plot_test', True):
label = 'test'
if combine_plots:
label = None
ax = testset.plot(kind='scatter', x='ref_affinity', y='dg_calc', label=label, s=20, ax=ax)
# Plot datalabels if needed
if settings.get('plot_labels', False):
for i, point in testset.iterrows():
ax.text(point['ref_affinity'], point['dg_calc'], "{0:.0f}".format(point['case']), fontsize=8)
ax.set_xlabel(r'$\Delta$$G_{Ref}$ (kJ/mol)', fontsize=15)
ax.set_ylabel(r'$\Delta$$G_{Calc}$ (kJ/mol)', fontsize=15)
ax.legend(loc="best", frameon=False)
return ax
import pylab
from scipy import stats
# our data
nosleep = [ 39, 18, 48, 24, 46, 35, 30, 34, 42 ]
mistakes = [ 6, 8, 13, 5, 17, 6, 15, 8, 2 ]
# calculate the regression equation
m, b, r, p, std_err = stats.linregress( nosleep, mistakes )
fit = pylab.polyfit( nosleep, mistakes, 1 )
fit_fn = pylab.poly1d( fit )
# plot scatter and regression line
pylab.plot( nosleep, mistakes, 'o', nosleep, fit_fn ( nosleep) )
# add the (written) equation to the plot
equation = "y' = %.3f x %+.3f" % ( m, b )
#pylab.figtext( 0.5, 0.4, equation )
# adjust the graph's details
pylab.xlabel("Hours without sleep")
pylab.ylabel("Numer of Mistakes")
pylab.xlim([10,50])
pylab.ylim([0,14])
pylab.show()
def Elliptic():
rho_list = pl.logspace(-1, 0, 2)
Roell_list = pl.logspace(pl.log10(0.1), pl.log10(50), 20)
markers = ["bo-", "gv-", "r>-"]
# Elastic limit
pl.plot([Roell_list[0], Roell_list[-1]], [1 / 3, 1 / 3],
"k",
linewidth=3.0,
alpha=0.2)
for i, rho in enumerate(rho_list):
tf_list = []
te_list = []
for Roell in Roell_list:
ell = 1 / Roell
te, tf = find_data(
["material.ell", "problem.rho", "problem.hsize"],
[ell, rho, 1e-3])
te_list.append(te)
tf_list.append(tf)
pl.savetxt("Rolist%s.txt" % i, Roell_list)
pl.savetxt("tf_list%s.txt" % i, tf_list)
pl.savetxt("te_list%s.txt" % i, te_list)
pl.figure(1)
pl.loglog(Roell_list, tf_list, markers[i], label=r"$\rho=%g$" % (rho))
pl.savefig("elliptic_tf_%s.pdf" % i)
pl.figure(2)
pl.loglog(Roell_list, te_list, markers[i], label=r"$\rho=%g$" % (rho))
pl.savefig("elliptic_te_%s.pdf" % i)
if near(rho, 0.1):
pl.figure(1)
ind = 12
coeff = pl.polyfit(pl.log(Roell_list[ind:]), pl.log(tf_list[ind:]),
1)
poly = pl.poly1d(coeff)
fit = pl.exp(poly(pl.log(Roell_list[ind:])))
print("The slope = %.2e" % (coeff[0]))
pl.loglog(Roell_list[ind:], fit, "k-", linewidth=3.0, alpha=0.2)
pl.figure(1)
pl.xlabel(r"Relative defect size $a/\ell$")
pl.ylabel("$\sigma/\sigma_0$ at fracture")
pl.legend(loc="best")
pl.xlim([9e-2, 1e2])
pl.grid(True)
pl.savefig("elliptic_tf.pdf")
pl.figure(2)
pl.xlabel(r"Relative defect size $a/\ell$")
pl.ylabel("$\sigma/\sigma_0$ at loss of elasticity")
pl.legend(loc="best")
pl.xlim([9e-2, 1e2])
pl.grid(True)
pl.savefig("elliptic_te.pdf")
pl.figure(3)
rho_list = pl.linspace(0.1, 1, 10)
te_list = []
for i, rho in enumerate(rho_list):
te, tf = find_data(["material.ell", "problem.rho", "problem.hsize"],
[1.0, rho, 1e-3])
te_list.append(te)
pl.plot(rho_list, te_list, "o", label="Num.")
pl.savetxt("rho_list.txt", rho_list)
pl.savetxt("rho_te_list.txt", te_list)
rho_list = pl.linspace(0.1, 1, 1000)
pl.plot(rho_list,
rho_list / (rho_list + 2),
"k",
label="Theo.",
linewidth=3.0,
alpha=0.2)
pl.xlabel(r"Ellipticity $\rho$")
pl.ylabel("$\sigma/\sigma_0$ at loss of elasticity")
pl.legend(loc="best")
pl.grid(True)
pl.savefig("elliptic_te_rho.pdf", bbox_inches='tight')
def asym_quantum_factor(J,b):
"""
This takes the places of K^2 in calculating the energy levels
for asymmetric rotators. Townes and Schawlow, Ch. 4. For
J > 6 this returns an empty tuple. Note that it doesn't matter which version of
b is used since b_prolate(kappa) = b_oblate(-kappa) and the equations are
symmetric in b or depend on b**2.
"""
roots = ()
if J == 0:
roots = (0,)
elif J == 1:
roots = (0., 1+b, 1-b)
elif J == 2:
roots = ( 4., 1-3*b, 1+3*b)
p = poly1d([1, -4, -12*b**2])
roots = roots + tuple(p.r)
elif J == 3:
roots = (4.,)
p = poly1d([1, -4, -60*b**2])
roots = roots + tuple(p.r)
p = poly1d([1, -10+6*b, 9-54*b-15*b**2])
roots = roots + tuple(p.r)
p = poly1d([1, -10-6*b, 9+54*b-15*b**2])
roots = roots + tuple(p.r)
elif J == 4:
p = poly1d([1, -10*(1-b), 9-90*b-63*b**2])
roots = tuple(p.r)
p = poly1d([1, -10*(1+b), 9+90*b-63*b**2])
roots = roots + tuple(p.r)
p = poly1d([1, -20, 64-28*b**2])
roots = roots + tuple(p.r)
p = poly1d([1, -20, 64-208*b**2, 2880*b**2])
roots = roots + tuple(p.r)
elif J == 5:
p = poly1d([1, -20, 64-108*b**2])
roots = tuple(p.r)
p = poly1d([1, -20, 64-528*b**2,6720*b**2])
roots = roots + tuple(p.r)
p = poly1d([1, -35+15*b, 259-510*b-213*b**2, -225+3375*b+4245*b**2-675*b**3])
roots = roots + tuple(p.r)
p = poly1d([1, -35-15*b, 259+510*b-213*b**2, -225-3375*b+4245*b**2+675*b**3])
roots = roots + tuple(p.r)
elif J == 6:
p = poly1d([1, -35+21*b, 259-714*b-525*b**2, -225+4725*b+9165*b**2-3465*b**3])
roots = tuple(p.r)
p = poly1d([1, -35-21*b, 259+714*b-525*b**2, -225-4725*b+9165*b**2+3465*b**3])
roots = roots + tuple(p.r)
p = poly1d([1, -56, 784-336*b**2, -2304+9984*b**2])
roots = roots + tuple(p.r)
p = poly1d([1, -56, 784-1176*b**2, -2304+53664*b**2, -483840*b**2+55440*b**4])
roots = roots + tuple(p.r)
else:
roots = ()
return roots
def tafel(cycle,
base=None,
limit_current_range=(0.15, 0.20),
catalyst_mass=None,
area_real=None,
activity_potential=0.9,
shift=1.5,
rpm=1600,
report='area mass',
sweep_rate=20,
graph=True,
copy=False,
verb=False,
area_geometric=1,
**kwargs):
# unzip data
iL_lower, iL_upper = limit_current_range
potential = np.array(cycle[0], dtype=float)
current = np.array(cycle[1], dtype=float)
current /= area_geometric
verb > 2 and print(f' cycle <{cycle.shape}>')
if graph > 1:
plt.figure(f'ORR - Tafel - {rpm} - positive sweep')
plt.plot(potential, current, label='Raw data')
# cut for useful data
# TODO: add another filter
rang = iL_lower < potential
potential = potential[rang]
current = current[rang]
verb > 2 and print(f' cut down to <{len(current)}>')
# TODO: interpolate base to ignore it's size
if base is not None:
xB, yB = base
# extra_data_before = len(yB) - len(rang)
# yB = yB[extra_data_before:]
yB = yB[rang]
assert len(current) == len(
yB
), f'cycle<{len(current)}> and base<{len(base)}> have different length.'
else:
yB = np.empty(len(current), dtype=float)
yB[:] = current[-1]
# yB = array([current[-1] for i in range(len(current))])
# remove baseline
current -= yB
if graph > 1:
plt.plot(potential, current, label='Corrected data')
# current to specific density [A / cm^2 Pt]
current_density = current / area_real
# get diffusion controlled current JL
# TODO: auto cut
JLrang = (potential > iL_lower) & (potential < iL_upper)
JL = np.average(current[JLrang])
verb > 2 and print(f' JL <{JLrang.sum()}> = {JL}')
if graph > 1:
plt.plot(potential[JLrang],
current[JLrang],
label='Diffusion limited region')
plt.plot([iL_lower, iL_upper], [JL, JL], 'k')
plt.legend()
# correction 2 get Jk, cut @ upper limit for noise
# TODO: auto cut
rang = (potential > iL_upper) & (current != JL) & (current != 0)
potential = potential[rang]
current = current[rang]
verb > 2 and print(f' cut down to <{len(current)} to match>')
Jk = current * JL / (JL - current)
# TODO: get Ik @ 0.9V for acts
# tafel slopes calcs
# to log scale
logJk = np.log10(abs(Jk))
# TODO: calc tafel rangs
# get points cn pend neg
negS = np.diff(logJk) < 0
potential = potential[1:][negS]
current = current[1:][negS]
logJk = logJk[1:][negS]
lowCh = logJk[-1] + shift # arbitrario TODO: calc
##
lowRang = (logJk > lowCh) & (logJk < lowCh + 1)
highRang = (logJk > lowCh + 1) & (logJk < lowCh + 2)
# TOpatch: start ~0.92V
verb > 2 and print(f' negative slope <{len(current)}>')
lowJk = logJk[lowRang]
highJk = logJk[highRang]
lowfit = polyfit(potential[lowRang], lowJk, 1)
lowFit = poly1d(lowfit)
highfit = polyfit(potential[highRang], highJk, 1)
highFit = poly1d(highfit)
factor_area = area_geometric
factor_mass = area_geometric
if area_real is not None:
factor_area /= area_real
if catalyst_mass is not None:
factor_mass /= 1e-3 * catalyst_mass
# get acts
low_current = 10**lowFit(activity_potential)
high_current = 10**highFit(activity_potential)
# TODO: report slopes
tafel_slope_low = 1 / lowfit[0]
tafel_slope_high = 1 / highfit[0]
act_low = Activities(mass_act=low_current * factor_mass,
area_act=low_current * factor_area,
tafel_slope=tafel_slope_low)
act_high = Activities(mass_act=high_current * factor_mass,
area_act=high_current * factor_area,
tafel_slope=tafel_slope_high)
# copy to excel
if copy:
d = OrderedDict([('potential', potential), ('log Jk', logJk)])
df = pd.DataFrame(data=d)
save_to_excel(df, 'results.xlsx', 'Tafel', index=False)
d = OrderedDict([('potential', potential[lowRang]),
('low overpotential\nJk', lowJk)])
df = pd.DataFrame(data=d)
save_to_excel(df, 'results.xlsx', 'Tafel', 3, index=False)
d = OrderedDict([('potential', potential[highRang]),
('high overpotential\nJk', highJk)])
df = pd.DataFrame(data=d)
save_to_excel(df, 'results.xlsx', 'Tafel', 5, index=False)
# plot
if graph: # graph:
plt.figure('ORR - Tafel')
plt.plot(potential, logJk, ":")
plt.plot(potential[lowRang], lowFit(potential[lowRang]))
# plt.plot(potential[highRang], highFit(potential[highRang]))
# plt.plot(highJk, potential[highRang])
plt.xlabel('Potential [V$_{NHE}$]')
plt.ylabel('log J$_k$ [A/cm$^2_{Pt,Pd}$]')
plt.title("Tafel")
# plt.show()
return act_low, act_high
def trendline( x, y, polyfit_degree ):
tl = np.polyfit( x, y, polyfit_degree )
tlp = pl.poly1d( tl )
return x, tlp(x)
y = [4, 8, 7, 7, 7, 4, 11, 9, 10, 3]
# x tick labels
labels = [
'Feb-19', 'Mar-19', 'Apr-19', 'Jun-19', 'Aug-19', 'Sep-19', 'Oct-19',
'Nov-19', 'Dec-19', 'Feb-20'
]
fig = plt.figure()
# plotting the graph
plt.plot(x, y)
plt.scatter(x, y, color='black')
plt.xticks(x, labels, rotation=90) # substitutes x tick labels for x numbers
# add regression line
from pylab import polyfit, poly1d
fit = polyfit(x, y, 2)
fit_fn = poly1d(fit)
plt.plot(x, fit_fn(x), color='red')
# naming the x axis
plt.xlabel('Month')
# naming the y axis
plt.ylabel('Volunteers')
# giving a title to my graph
plt.title('Volunteers per Month in 2019')
# function to show the plot
plt.show()
#fig.savefig('lineplot.pdf')
def conversionGainFrame(imglist, quadrant):
"""Determine the conversion gain for a given quadrant. A spatial analysis is done,
using all pixels in a quadrant.
The mean-variance difference method is used, i.e. the differences between four pairs
of flat images are used, each pairs exposure time is twice that of the previous pair.
Variance = 2*Nread**2 + Signal1 + Signal2
Will plot both linear and log plots including mean sum vs. variance difference,
mean sum vs variance difference - readnoise**2 and a fit to slop for gain in e-/ADU
see http://www.noao.edu/kpno/manuals/whirc/WHIRC_VI_Mean-variance_101026.pdf
by Dick Joyce, 2010
Args:
imglist (list): File names of image pairs of increasing exposure time
quadrant (int): Quandrant being analyzed.
Returns:
gain, readnoise (float): Gain in e-/ADU and readnoise in e-
Example:
g, rn = detector.conversionGainFrame([q1_0s, q1_0s, q1_2s, q1_2s, q1_4s, q1_4s], 3)
"""
# run through each channel
means = np.zeros(len(imglist)/2)
varis = np.zeros(len(imglist)/2)
#process pairs of images
for i in range(0, len(imglist), 2):
im1 = imglist[i]
im2 = imglist[i+1]
#get sum of means
som = summean(im1, im2)
#get variance of difference)
vod = diffvar(im1, im2)
means[i/2] = som
varis[i/2] = vod
print 'means:', means
print 'variances: ', varis
fit = pl.polyfit(means, varis, 1)
print fit
fit_fn = pl.poly1d(fit)
print 'y intercept', fit_fn(0.0)
eADU = 1.0/fit[0]
print "e-/ADU = {0}".format(str(eADU))
fig1 = plt.figure(figsize=(10,10))
plt.loglog(means, varis, 'o', means, fit_fn(means), '--')
plt.legend(['Channel '+str(quadrant), str(round(eADU,3))+' e-/ADU'], loc=9)
plt.xlabel('Mean')
plt.ylabel('Variance')
plt.title('Conversion Gain, Quadrant {0}'.format(str(quadrant)))
plt.show()
fig2 = plt.figure(figsize=(10,10))
plt.plot(means, varis, 'o', means, fit_fn(means), '--')
plt.legend(['Channel '+str(quadrant), str(round(eADU,3))+' e-/ADU'], loc=9)
plt.xlabel('Mean')
plt.ylabel('Variance')
plt.title('Conversion Gain, Quadrant {0}'.format(str(quadrant)))
plt.show()
def plot(s, filepath):
print("Plotting schedule.")
print(s.to_pretty_string())
for line in s.runs:
plots = []
runs = s.runs_by_date(line)
fig = plt.figure()
ax = fig.add_subplot(111)
# To make a stacked bar graph, we need to create an
# array for each i batch
num_batches_per_run = []
for r in runs:
num_batches_per_run.append(len(r.batches))
max_batches = max(num_batches_per_run)
batches_to_plot = []
# init array with empty lists which will be filled with batch info
for i in range(0,max_batches):
batches_to_plot.append([])
for i in range(0, max_batches):
for r in runs:
if i >= len(r.batches):
# pad with zeros if there is no entry
batches_to_plot[i].extend([0])
continue
# add ith batch of r to ith row in batches_to_plot
b = r.batches[i]
qty = int(b.expected_quantity)
batches_to_plot[i].extend([qty]) # qty must be a list because extend tries to iterate over elements, and ints are not iterable
#set up formatting
N = len(runs) # max days recorded per schedule
x_pos = np.arange(N) # column placing
width = 0.6 # how wide the bars will appear
cols = ['r', 'b', 'g','y', '#D4D4D4', '#551A8B', '#EE82EE', '#FF6103', '#FFCC11', '#CDD704']
col = 1 # represents the colour of the batches
xticks = dates_to_weekday(s, line) # convert the dates to weekdays
plt.xticks(x_pos+width/2., np.asarray(xticks))
plt.ylabel('Run Total')
plt.xlabel('Date of Production')
plt.title("Production Schedule for "+ line + " -- "+s.date)
bottom = np.zeros(N,)
# build graph and print data
print("Building graph ("+line+")")
bars = []
batch_labels = [value for sublist in batches_to_plot for value in sublist] # turns lsit of lists into a flat lsit of values
for i, b in enumerate(batches_to_plot):
bars.append(ax.bar(x_pos, np.asarray(b), width, bottom=bottom, color = cols[i % len(cols)]))
# add the batch value to the offset of the bar positions
bottom += b
# add labels
for j in range(len(bars)):
for i, bar in enumerate(bars[j].get_children()):
bl = bar.get_xy()
x = 0.5*bar.get_width() + bl[0]
y = 0.5*bar.get_height() + bl[1]
if not batches_to_plot[j][i] == 0:
ax.text(x,y, "%d" % (batches_to_plot[j][i]), ha='center', va = 'center')
# trend line
x = x_pos
y = np.array([r.expected_total for r in s.runs[line]])
fit = polyfit(x,y,1)
fit_fn = poly1d(fit)
trendline = plt.plot(x,y, 'ro', x, fit_fn(x), '--k', linewidth=2)
# save figures to appropriate directories
if not os.path.exists(filepath):
os.makedirs(filepath)
filename = line + '.pdf'
if os.path.exists(filepath + filename):
choice = input(filename + " already exists. Overwrite? (y/n) ")
if choice == 'y':
os.remove(filepath+filename)
print("Overwriting...")
else:
continue
plt.savefig(filepath + filename)
print("Report complete ({0})".format(s.date))
def Circular():
fig = pl.figure()
ax = fig.add_subplot(111)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlim([9e-2, 1e2])
# Elastic limit
Roell_list = pl.logspace(pl.log10(0.1), pl.log10(50), 20)
ax.plot([Roell_list[0], Roell_list[-1]], [1 / 3, 1 / 3],
"k",
linewidth=3.0,
alpha=0.2)
# Numerical results
tf_list = []
te_list = []
for Roell in Roell_list:
ell = 1 / Roell
te, tf = find_data(["material.ell", "problem.rho", "problem.hsize"],
[ell, 1, 1e-3])
te_list.append(te)
tf_list.append(tf)
ax.plot(Roell_list, tf_list, "bo-") # label="Num."
ind = int(len(Roell_list) / 2.5)
coeff = pl.polyfit(pl.log(Roell_list[ind:-ind]), pl.log(tf_list[ind:-ind]),
1)
poly = pl.poly1d(coeff)
fit = pl.exp(poly(pl.log(Roell_list[ind:-ind])))
print("The slope = %.2e" % (coeff[0]))
pl.loglog(Roell_list[ind:-ind], fit, "k-", linewidth=3.0, alpha=0.2)
# Experimental results
E = 3e3
sigc = 72
Gc = 290e-3
ell = 3 / 8 * Gc * E / sigc**2
print ell
ell = 26e-3
data = pl.loadtxt("literature/exp.csv", delimiter=",")
Roell_exp = data[:, 0] / (2 * ell)
Roell_exp[0] = Roell_list[0]
sig_center = (pl.amin(data[:, 1:], 1) + pl.amax(data[:, 1:], 1)) / 2
err1 = -(pl.amin(data[:, 1:], 1) - sig_center) / sigc
err2 = (pl.amax(data[:, 1:], 1) - sig_center) / sigc
pl.errorbar(Roell_exp,
sig_center / sigc,
yerr=[err1, err2],
label="Exp.",
fmt="g.")
# Numerical results from C. Kuhn
# data = loadtxt("literature/Kuhn.csv", delimiter=",")
# # ell = 0.00885
# Roell_Kuhn = data[:, 0]/ell
# ax.plot(Roell_Kuhn, data[:, 1], "r>-", label="Kuhn")
pl.ylim([1.0 / 4.0, 1.1])
pl.xlabel("Relative hole size $R/\ell$")
pl.ylabel("$\sigma/\sigma_0$ at fracture")
pl.legend(loc="best")
pl.savefig("circular_tf.pdf", bbox_inches='tight')
def plot(s, filepath):
print("Plotting schedule.")
print(s.to_pretty_string())
for line in s.runs:
plots = []
runs = s.runs_by_date(line)
fig = plt.figure()
ax = fig.add_subplot(111)
# To make a stacked bar graph, we need to create an
# array for each i batch
num_batches_per_run = []
for r in runs:
num_batches_per_run.append(len(r.batches))
max_batches = max(num_batches_per_run)
batches_to_plot = []
# init array with empty lists which will be filled with batch info
for i in range(0, max_batches):
batches_to_plot.append([])
for i in range(0, max_batches):
for r in runs:
if i >= len(r.batches):
# pad with zeros if there is no entry
batches_to_plot[i].extend([0])
continue
# add ith batch of r to ith row in batches_to_plot
b = r.batches[i]
qty = int(b.expected_quantity)
batches_to_plot[i].extend(
[qty]
) # qty must be a list because extend tries to iterate over elements, and ints are not iterable
#set up formatting
N = len(runs) # max days recorded per schedule
x_pos = np.arange(N) # column placing
width = 0.6 # how wide the bars will appear
cols = [
'r', 'b', 'g', 'y', '#D4D4D4', '#551A8B', '#EE82EE', '#FF6103',
'#FFCC11', '#CDD704'
]
col = 1 # represents the colour of the batches
xticks = dates_to_weekday(s, line) # convert the dates to weekdays
plt.xticks(x_pos + width / 2., np.asarray(xticks))
plt.ylabel('Run Total')
plt.xlabel('Date of Production')
plt.title("Production Schedule for " + line + " -- " + s.date)
bottom = np.zeros(N, )
# build graph and print data
print("Building graph (" + line + ")")
bars = []
batch_labels = [
value for sublist in batches_to_plot for value in sublist
] # turns lsit of lists into a flat lsit of values
for i, b in enumerate(batches_to_plot):
bars.append(
ax.bar(x_pos,
np.asarray(b),
width,
bottom=bottom,
color=cols[i % len(cols)]))
# add the batch value to the offset of the bar positions
bottom += b
# add labels
for j in range(len(bars)):
for i, bar in enumerate(bars[j].get_children()):
bl = bar.get_xy()
x = 0.5 * bar.get_width() + bl[0]
y = 0.5 * bar.get_height() + bl[1]
if not batches_to_plot[j][i] == 0:
ax.text(x,
y,
"%d" % (batches_to_plot[j][i]),
ha='center',
va='center')
# trend line
x = x_pos
y = np.array([r.expected_total for r in s.runs[line]])
fit = polyfit(x, y, 1)
fit_fn = poly1d(fit)
trendline = plt.plot(x, y, 'ro', x, fit_fn(x), '--k', linewidth=2)
# save figures to appropriate directories
if not os.path.exists(filepath):
os.makedirs(filepath)
filename = line + '.pdf'
if os.path.exists(filepath + filename):
choice = input(filename + " already exists. Overwrite? (y/n) ")
if choice == 'y':
os.remove(filepath + filename)
print("Overwriting...")
else:
continue
plt.savefig(filepath + filename)
print("Report complete ({0})".format(s.date))
def plot3():
"""
Compute and plot data for Plot 3.
"""
global pool
g = "grid2"
X = numpy.arange(1e-12, 8.1, 1 if DEBUG else 0.1)
E = []
Eerr = []
EL = []
V = []
VL = []
S = []
for d in X:
# We will skip Eppstein on large values because it takes way too long
if DEBUG and d >= 4:
d = 5.1
if d < 5.1:
data = plot3F((g, "e:f", d))
E.append(data[0])
EL.append(data[2])
else:
E.append(float('inf'))
EL.append(float('inf'))
data = pool.map_async(
plot3F, map(lambda _: (g, "r:f:c:0.15", d),
xrange(RUNS))).get(99999999)
v, suc, vl = zip(*data)
VL.append(max(vl))
V.append(numpy.mean(v))
S.append(numpy.mean(suc))
matplotlib.pyplot.clf()
axTime = matplotlib.pyplot.subplots()[1]
axLength = axTime.twinx()
l1, = axTime.plot(X, E, "b-.")
V = numpy.array(V)
l2, = axTime.plot(X, V, "g--")
ELi = EL.index(float('inf'))
ELfit = pylab.poly1d(pylab.polyfit(X[:ELi], EL[:ELi], 1))(X)
l3, = axLength.plot(X, EL, "m.")
axLength.plot(X, ELfit, "m")
VLfit = pylab.poly1d(pylab.polyfit(X, VL, 1))(X)
l4, = axLength.plot(X, VL, "r+")
axLength.plot(X, VLfit, "r")
axTime.set_ylabel("Time (s)")
axTime.set_ylim([0, 25])
axLength.set_ylabel("Length")
axLength.set_ylim([0, 7])
axTime.set_xlabel("Minimum Diversity Required")
matplotlib.pyplot.xlim([0, 8])
matplotlib.pyplot.legend(
(l1, l2, l3, l4), ('Eppstein time', 'Voss time', 'Eppstein max length',
'Voss max length'), 'upper left')
matplotlib.pyplot.savefig("plot3.png")
return
def main():
# make sure we have a valid file to use
if( len(sys.argv) <= 1 ):
print("usage: %s data.csv threads_per_block"%sys.argv[0])
exit()
try:
dataFile = open( sys.argv[1] )
except IOError:
print("Error opening file %s"%sys.argv[1])
exit()
title = ''
averages = []
pcs = []
line = dataFile.readline().strip().split(',')
title = line[0]
blocks = map(int, line[1:-1])
threads = []
for line in dataFile.xreadlines():
data = line.strip().split(',')
averages.append([])
threads.append(int(data[0]))
for val in data[1:-1]:
averages[-1].append(np.float64(val))
fit0_m = []
fit0_b = []
fit1_m = []
fit1_b = []
fit1_c = []
fit2_m = []
fit2_b = []
fit2_c = []
for threads_per_block_to_plot in range( 64, 1024+1, 32 ):
print("Fitting: %i"%(threads_per_block_to_plot))
avg_idx = threads.index(threads_per_block_to_plot)
# Linear fit for < drop
drop_idx = next(idx for idx,val in enumerate(blocks) if val >= (2**(14.00))/threads_per_block_to_plot)
rise_idx = next(idx for idx,val in enumerate(blocks) if val >= (2**(16.56))/threads_per_block_to_plot)
fit0_x = blocks[:drop_idx]
m0,b0 = polyfit( fit0_x, averages[avg_idx][:drop_idx], 1 )
fit0_y = poly1d( (m0,b0) )(fit0_x)
# Log fit for drop < x < rise
fit1_x = np.array([ float(x) for x in blocks[drop_idx:rise_idx] ])
#generate weights for x
weight1 = [ (x if x>1 else 1) for x in [ (blocks[i] - blocks[i-1])/10.0 for i in range(drop_idx, rise_idx) ] ]
# fit to log
C1 = drop_idx-1
B1 = 1
fit1_x_log = [ np.log(B1*(x-C1)) for x in fit1_x ]
m1,b1 = polyfit( fit1_x_log, averages[avg_idx][drop_idx:rise_idx], 1, w=weight1 )
fit1_y = poly1d( (m1,b1) )(fit1_x_log)
# Log fit for > rise
fit2_x = np.array([ float(x) for x in blocks[rise_idx:] ])
#generate weights for x
weight2 = [ (x if x>1 else 1) for x in [ (blocks[i] - blocks[i-1])/10.0 for i in range(rise_idx, len(blocks)) ] ]
# fit to log
C2 = rise_idx-1
B2 = 1
fit2_x_log = [ np.log(B2*(x-C2)) for x in fit2_x ]
m2,b2 = polyfit( fit2_x_log, averages[avg_idx][rise_idx:], 1, w=weight2 )
fit2_y = poly1d( (m2,b2) )(fit2_x_log)
# save fit data
fit0_m.append(m0)
fit0_b.append(b0)
fit1_m.append(m1)
fit1_b.append(b1)
fit1_c.append(C1)
fit2_m.append(m2)
fit2_b.append(b2)
fit2_c.append(C2)
# create a new plot to use
fig = plt.figure(figsize=figure_size)
ax = fig.add_subplot( 111 )
# line at 16*1024 threads
ax.axvline(x=drop_idx, label='2^14 threads')
ax.axvline(x=rise_idx, label='2^16.56 threads')
# scatter plot
ax.scatter( blocks, averages[avg_idx] )
# fits
if show_eqs:
ax.plot( fit0_x, fit0_y, label="16384 threads fit: %f*x+%f"%(m0,b0) )
ax.plot( fit1_x, fit1_y, label="Log fit: %f*ln(x-%f)+%f"%(m1, C1, b1) )
ax.plot( fit2_x, fit2_y, label="Log fit: %f*ln(x-%f)+%f"%(m2, C2, b2) )
ax.legend(loc='lower right')
else:
ax.plot( fit0_x, fit0_y )
ax.plot( fit1_x, fit1_y )
ax.plot( fit2_x, fit2_y )
ax.xaxis.label.set_size(font_size)
ax.yaxis.label.set_size(font_size)
ax.legend(loc='lower right', prop={'size':font_size})
ax.tick_params(axis='both', which='major', labelsize=font_size)
ax.tick_params(axis='both', which='minor', labelsize=font_size)
ax.legend(loc='lower right', prop={'size':30})
ax.set_xlabel( 'Number of Blocks' )
ax.set_ylabel( 'Average Sync Cost' )
ax.set_ylim( [ 0, y_lim ] ) #ax.get_ylim()[1] ] )
ax.set_xlim( [ 0, 2000 ] )
fig.savefig("%s_%i.png" % (sys.argv[1][:-4], threads_per_block_to_plot))
#plt.show(block=False)
#plt.pause(0.5)
# Now we have the data for each fit, so fit that
# Linear fit
fig = plt.figure(figsize=figure_size)
ax = fig.add_subplot( 111 )
ax.scatter( threads, fit0_m )
ax.set_xlabel( 'Number of Threads/Blocks' )
ax.set_ylabel( 'Slope for first linear fit' )
fig.savefig("%s_%s%i.png" % (sys.argv[1][:-4], "m", 0))
fig = plt.figure(figsize=figure_size)
ax = fig.add_subplot( 111 )
ax.scatter( threads, fit0_b )
ax.set_xlabel( 'Number of Threads/Blocks' )
ax.set_ylabel( 'Intercept for first linear fit' )
fig.savefig("%s_%s%i.png" % (sys.argv[1][:-4], "b", 0))
# Log fit
fig = plt.figure(figsize=figure_size)
ax = fig.add_subplot( 111 )
ax.scatter( threads, fit1_m )
ax.set_xlabel( 'Number of Threads/Blocks' )
ax.set_ylabel( 'Slope for log fit' )
fig.savefig("%s_%s%i.png" % (sys.argv[1][:-4], "m", 1))
fig = plt.figure(figsize=figure_size)
ax = fig.add_subplot( 111 )
ax.scatter( threads, fit1_b )
ax.set_xlabel( 'Number of Threads/Blocks' )
ax.set_ylabel( 'Intercept for log fit' )
fig.savefig("%s_%s%i.png" % (sys.argv[1][:-4], "b", 1))
fig = plt.figure(figsize=figure_size)
ax = fig.add_subplot( 111 )
ax.scatter( threads, fit1_c )
ax.set_xlabel( 'Number of Threads/Blocks' )
ax.set_ylabel( 'L1size for log fit' )
# Log fit
fig = plt.figure(figsize=figure_size)
ax = fig.add_subplot( 111 )
ax.scatter( threads, fit2_m )
ax.set_xlabel( 'Number of Threads/Blocks' )
ax.set_ylabel( 'Slope for log fit' )
fig.savefig("%s_%s%i.png" % (sys.argv[1][:-4], "m", 2))
fig = plt.figure(figsize=figure_size)
ax = fig.add_subplot( 111 )
ax.scatter( threads, fit2_b )
ax.set_xlabel( 'Number of Threads/Blocks' )
ax.set_ylabel( 'Intercept for log fit' )
fig.savefig("%s_%s%i.png" % (sys.argv[1][:-4], "b", 2))
fig = plt.figure(figsize=figure_size)
ax = fig.add_subplot( 111 )
ax.scatter( threads, fit2_c )
ax.set_xlabel( 'Number of Threads/Blocks' )
ax.set_ylabel( 'L1size for log fit' )
fig.savefig("%s_%s%i.png" % (sys.argv[1][:-4], "c", 2))
if not y_scores_recall.has_key(k):
y_scores_recall[k] = []
y_scores_recall[k].append(v.mean())
for k,v in all_f1[key].items():
if not y_scores_f1.has_key(k):
y_scores_f1[k] = []
y_scores_f1[k].append(v.mean())
x_entropy_all = x_entropy['all']
x_entropy.pop('all')
x_entropy_std.pop('all')
# 感觉熵与精度之间存在线性相关
fit = pylab.polyfit(x_entropy_all,y_scores_all,1)
fit_fn = pylab.poly1d(fit)
print "熵与总体分类精度之间的相关系数：", pearsonr(x_entropy_all, y_scores_all)
from scipy.optimize import curve_fit
def func(x,a,b):
return a*np.log(b*x)
popt,pcov = curve_fit(func, x_size, x_entropy_all)
#y_fit = np.exp(popt[0]*x)
# 整体上
plt.figure()
plt.subplot(132)
plt.plot(x_size, y_scores_all,'o-')
plt.xlabel('Sample Size')
plt.ylabel('Total Accuracy')
