def do():
duration_in_ms = 10 * 1000
rate_in_ms = 100 / 1000
"""
Generate spikes for 10s (or longer if you want better statistics) using a
Poisson spike generator with a constant rate of 100Hz, and record their
times of occurrence.
"""
spikes = generator.short_way(duration_in_ms, rate_in_ms)
"""
Compute the coefficient of variation of the interspike intervals,
"""
coefficient_of_variation = analysis.coefficient_of_variation(spikes)
print(f'Coefficient of Variation: %1.3f' % coefficient_of_variation)
"""
and the Fano factor for spike counts obtained over counting intervals
ranging from 1 to 100ms.
"""
windows = [1, 10, 25, 50, 100]
for window in windows:
fano_factor = analysis.fano_factor(window, duration_in_ms, spikes)
print(f'Fano factor: %1.3f' % fano_factor)
"""
Plot the interspike interval histogram.
"""
window = 10
bins = np.linspace(window, duration_in_ms, int(duration_in_ms / window))
values, _ = np.histogram(spikes, bins)
plotter.show(spikes, bins, f'Bucket size of {window}ms')
def main():
training_data_file = open(argv[1], 'r')
test_data_file = open(argv[2], 'r')
algorithm_number = int(argv[3])
training_data = []
for line in training_data_file.readlines():
split_line = line.split()
target_class = int(split_line[-1])
training_data.append((tuple(float(x) for x in split_line[:-1]), target_class))
test_data = []
for line in test_data_file.readlines():
test_data.append(tuple(float(x) for x in line.split()))
if algorithm_number == 1:
class_count = int(argv[4])
perceptron = Perceptron(vector_size=2, class_count=class_count)
else:
class_count = 2
perceptron = AlternativePerceptron(vector_size=2)
has_classification_errors = True
while has_classification_errors:
has_classification_errors = perceptron.train(training_data)
classified_data = []
for vector in test_data:
classified_data.append((vector, perceptron.guess(vector)))
colors = [random_color() for _ in range(class_count)]
plotter.draw_classes(training_data, 'Perceptron Classifier: Training Data', 1, colors)
plotter.draw_classes(classified_data, 'Perceptron Classifier: Classified Data', 2, colors)
plotter.show()
def input_graph():
global graph
from sphereofinfluence import SoIGraph
graph = SoIGraph.randomize(0, 100, 100)
plotter.start_gui(graph)
# plotter.show()
plotter.plt.ion()
plotter.plot_graph(graph)
plotter.plot_arrows(graph)
plotter.show()
# breadth_first_search(graph)
# depth_first_search(graph)
dijkstra(graph)
for i in range(len(x)):
vetor.append([x[i], y[i]])
plot_dot(vetor, c='m')
corr = correlacao(x, y)
reg = regressao(x, y)
plot_line(calc(reg, x, y),
c='r',
label=" Correlação = " + str(corr) + "\n ß0 = " + str(reg[0]) +
"\n ß1 = " + str(reg[1]))
x1 = [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5]
y1 = [8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68]
x2 = [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5]
y2 = [9.14, 8.14, 8.47, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74]
x3 = [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 19]
y3 = [6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 5.56, 7.91, 6.89, 12.50]
plot(x1, y1)
show()
plot(x2, y2)
show()
plot(x3, y3)
show()
import compute
import loader
import plotter
dataset = loader.load_dataset('patient_1')
data = dataset["data"]
mask = dataset["mask"]
threshold = 95.0
scaled = compute.to_hounsfield_units(data)
masked = list(map(lambda i: compute.apply_mask(i, mask, threshold), scaled))
gradients = compute.compute_gradients(masked)
masked_gradients = list(
map(lambda i: compute.apply_mask(i, mask, threshold), gradients))
def slicer(arr):
return arr[30, :, :]
scaled_slices = list(map(slicer, scaled))
masked_slices = list(map(slicer, masked))
gradient_slices = list(map(slicer, masked_gradients))
plotter.plot(scaled_slices, masked_slices, gradient_slices)
plotter.show()
def main():
choices = ['func1', 'func2', 'func3', 'func4', 'func5', 'func6', 'func7']
parser = argparse.ArgumentParser()
parser.add_argument('fn', help='function to plot', choices=choices)
parser.add_argument('vars', nargs='*', type=int, help='function variables')
parser.add_argument('-a',
'--anim',
action='store_true',
help='animate plot')
args = parser.parse_args()
if args.fn == 'func1':
nt, a, b, c = args.vars
func = functions.func1
z = np.array([
np.exp(2 * np.pi * 1j * func(n, a, b, c))
for n in range(1, nt + 1)
])
if args.fn == 'func2':
nt, a = args.vars
func = functions.func2
z = np.array(
[np.exp(2 * np.pi * 1j * func(n, a)) for n in range(1, nt + 1)])
if args.fn == 'func3':
nt, = args.vars
func = functions.func3
z = np.array(
[np.exp(2 * np.pi * 1j * func(n)) for n in range(1, nt + 1)])
if args.fn == 'func4':
nt, a = args.vars
func = functions.func4
z = np.array(
[np.exp(2 * np.pi * 1j * func(n, a)) for n in range(1, nt + 1)])
if args.fn == 'func5':
nt, a, b = args.vars
func = functions.func5
z = np.array(
[np.exp(2 * np.pi * 1j * func(n, a, b)) for n in range(1, nt + 1)])
if args.fn == 'func6':
nt, a = args.vars
func = functions.func6
z = np.array(
[np.exp(2 * np.pi * 1j * func(n, a)) for n in range(1, nt + 1)])
if args.fn == 'func7':
nt, a = args.vars
func = functions.func7
z = np.array(
[np.exp(2 * np.pi * 1j * func(n, a)) for n in range(1, nt + 1)])
z = z.cumsum()
plotter.plot_expsum(z)
if args.anim:
plotter.animate_expsum(z)
plotter.show()
import sys
sys.path.insert(1, '../')
import plotter as plt
p1 = plt.plot([1, 2, 3], [1, 3, 2],
'r-o', [1, 2, 2.5, 3], [3, 2, 1.5, 1],
'b-o',
new=True,
position=[2, 1, 1])
p2 = plt.plot([1, 2, 3], [1, 3, 2],
'r-o', [1, 2, 3], [3, 2, 1],
'b-o',
new=True,
position=[2, 1, 2])
#print p1._datapairs[0].clipPath()
plt.show()
#plt.plot([1,2,3],[1,3,2], '')
#plt.show()
#
#plt.show()
#
#plt.plot([1,2,3],[1,3,2], '')
#plt.plot([1,2,3,4],[1,3,2,4], '')
#plt.show()
def preParse(file, tagAndProbe):
# Versuche die Datei zu öffnen
try:
f = open(file,"r")
except IOError:
print "Datei wurde nicht gefunden."
exit(0)
# optional: die Darstellung für den Fortschritt
# (Initialisierung)
lineNums = getLen(file)/2 # 1 Event = 2 Zeilen
#lineNums = 4967428
currentLine = 0
currentPercent = 0
startTime = int(time.time())
lastNumber = 0
lastSeconds = startTime
print "Beginne. Parse %i Events"%lineNums
fh = plotter.FilterHisto()
diagrams = ((0, "Ladungskriterium", (0,0)), (1, "Richtungskriterium", (0,1)),
(2, "Spurqualität", (0,2)), (3, "Detektorkritierum", (1,0)),
(4, "Kammernzahl", (1,1)), (5, "Rapiditätskriterium", (1,2)),
(6, "Impulskriterium", (2,0)), (7, "Isolationskriterium", (2,1)),
(8, "Massenfilter", (2,2)))
for i in diagrams:
fh.initSubdiagram(str(i[0]), i[1], i[2])
spektrum = plotter.Histo("Spektrum")
detaildiagram = plotter.DetailDiagram("Gefilterte Ereignisse",
MIN_M_INV, MAX_M_INV, NBINS)
# Kopf der Datei wegspalten
l = f.readline()
while l[0] == "#":
l = f.readline()
# DiagramMassList
# Generiere eine Liste mit 11 leeren Listen als Inhalt, die nachher
# die invarianten Massen der gefilterten Variablen (die Filter und
# auch die Filter passierenden) invarianten Massen.
# In dieser Implementierung gibt es folgende Reihenfolge:
# 1-9 Filter
# 1. gleiche Ladung
# 2. gleicher Jet
# 3. Spurqualität
# 4. Spurendetektor und Pixeldetektor-Hits vorhanden
# 5. mindestens 10 Kammern
# 6. Pseudorapidität kleiner 2,1
# 7. Mindestransversalimpuls 20 GeV
# 8. Isolationsfaktor des Myons
# 9. Massenfilter (nur bei Tag+Probe)
# 10. volles Spektrum
# 11. gefiltertes Spektrum
while l:
# Fortschrittsanzeige - Optional
currentLine += 1
if int(float(currentLine)*100/lineNums) != round(currentPercent):
curRate = (currentLine - lastNumber)/(time.time()-lastSeconds)/1000
avgRate = (currentLine)/(time.time()-startTime)/1000
lastNumber = currentLine
lastSeconds = time.time()
currentPercent = round(float(currentLine)*100/lineNums)
totalRate = currentLine/(time.time()-startTime)
estimated = round((lineNums-currentLine)/totalRate)
print "Habe %i Prozent geschafft. Current Rate: %.3f kHz, Avg: %.3f kHz, estimated remaining time: %is"%(currentPercent, curRate, avgRate, estimated)
if currentPercent == 1 and False:
break
# Zeile benennen und nächste Zeile auslesen
l1 = l
l2 = f.readline()
# Generiert einen Fehler, falls die Dateiinhalte nicht Modulo2-Teilbar sind
if not l2:
raise IndexError("Inhalt der Eingabedatei ungültig. Zeilenanzahl muss Modulo3-Teilbar sein")
# Erstelle Teilchen aus den Eingabezeilen
m1 = particleFromFileLine(l1)
m2 = particleFromFileLine(l2)
# Filtere die Myonen nach den implementierten Filtern
if tagAndProbe:
tagAndProbeFill(fh, spektrum, detaildiagram, m1, m2)
else:
normalFill(fh, spektrum, detaildiagram, m1, m2)
# Nächste Zeile lesen
l = f.readline()
#for f in eventsList:
# f.close()
# Statistik
print "Parsing beendet. Benötigte Zeit: %i Sekunden. Avg Rate: %.3f kHz"%(int(time.time()-startTime), (currentLine)/(time.time()-startTime)/1000)
print "Zeichne Plots ..."
t = time.time()
binContents = detaildiagram.getBinContents()
detaildiagram.drawErrors(binContents, np.sqrt(binContents))
print "Zeichnen beendet. Benötigte Zeit: %i Sekunden"%int(time.time()-t)
fh.plot()
spektrum.addLabels()
spektrum.plot()
fp = Fitpanel(detaildiagram, binContents,
MIN_M_INV, MAX_M_INV, NBINS, np.sqrt(binContents), '../diagrams-2/fits.txt')
detaildiagram.plot()
#detaildiagram.save("../diagrams-2/zoomed.png")
plotter.show()
X = load(filename)
flowWEA = X[:, 2]
flowNSA = X[:, 3]
avgDelayWEA = X[:, 4]
avgDelayNSA = X[:, 5]
[X, Y] = meshgrid(range(300, 1300, 100), range(300, 1300, 100))
Z = griddata(flowWEA, flowNSA, avgDelayWEA, X, Y)
delayFCWE = Z[0]
Z = griddata(flowWEA, flowNSA, avgDelayNSA, X, Y)
delayFCNS = Z[0]
figure(figsize=(12, 6))
subplot(1, 2, 1)
plot(flow, delayFCWE, flow, delayVAWE)
ylim(0, 60)
xlabel("Flow")
ylabel("Average Delay WE")
legend(("FC", "VA"), loc='upper left')
subplot(1, 2, 2)
plot(flow, delayFCNS, flow, delayVANS)
ylim(0, 60)
xlabel("Flow")
ylabel("Average Delay NS")
legend(("FC", "VA"), loc='upper left')
show()
#!/usr/bin/python2 import sys sys.path.insert(1, '../') import plotter as plt p1 = plt.plot([1,2,3],[1,3,2], 'r-o', [1,2,2.5,3], [3,2,1.5,1], 'b-o', new=True, position=[2,1,1]) p2 = plt.plot([1,2,3],[1,3,2], 'r-o', [1,2,3], [3,2,1], 'b-o', new=True, position=[2,1,2]) #print p1._datapairs[0].clipPath() plt.show() #plt.plot([1,2,3],[1,3,2], '') #plt.show() # #plt.show() # #plt.plot([1,2,3],[1,3,2], '') #plt.plot([1,2,3,4],[1,3,2,4], '') #plt.show()