def plot_bleb_evolution(ts, prop_to_plots, title):
"""Plot evolution of a given bleb property over time"""
title = title.replace("\u00B5", "u")
plt = Plot((title + " against time"), "Time", title)
plt.add("line", ts, prop_to_plots)
plt.show()
plot_data = [(t, d) for t, d in zip(ts, prop_to_plots)]
return plt.getImagePlus(), plot_data
def createAngleTable(self, roi):
polygon = roi.getPolygon()
xPoints = polygon.xpoints
yPoints = polygon.ypoints
nPoints = polygon.npoints
table = ResultsTable()
firstAngle = Math.atan2(yPoints[0], xPoints[0])
plot = Plot(str(roi.getName()) + " Angle", "--", "angle")
angles = []
derivative = []
derivativeSign = []
posDerivative = 0
negDerivative = 0
for i in range(nPoints):
x = xPoints[i]
y = yPoints[i]
angle = Math.atan2(y, x)
angles.append(angle - firstAngle)
if i == 0:
continue
derivative.append(angle - angles[-2])
derivativeSign.append(Math.signum(derivative[-1]))
if derivativeSign[-1] > 0:
posDerivative = posDerivative + 1
else:
negDerivative = negDerivative + 1
maxSign = max(posDerivative, negDerivative)
minSign = min(posDerivative, negDerivative)
print("--" + str(roi.getName()))
signRatio = float(minSign) / float(maxSign)
print("Min Sign = " + str(minSign))
print("Max Sign = " + str(maxSign))
print("Sign Ratio = " + str(signRatio))
plot.add("filled", derivativeSign)
plot.show()
if signRatio < 1.1:
# table.show(str( roi.getName())+" Angle")
return True
return False
from ij.gui import Plot
from java.awt import Color
import jarray
import os
# start clean
IJ.run("Close All")
# create example data arrays
xa = [1, 2, 3, 4]
ya = [3, 3.5, 4, 4.5]
# convert to java array
jxa = jarray.array(xa, 'd')
jya = jarray.array(ya, 'd')
# Create filled plot
plt = Plot("Line plot", "X", "Y")
plt.setLimits(0, 5, 0, 5)
plt.setFrameSize(600, 300)
plt.setColor("blue", "#ccccff")
# the circles are small. Can't figure out how to make
# them larger... These 2 calls are equivalent...
# plt.addPoints(jxa,jya, Plot.CIRCLE)
plt.add("circles", jxa, jya)
plt.setColor(Color.RED)
plt.setLineWidth(1)
plt.drawLine(0.0, 2.5, 4.0, 4.5)
plt.setXYLabels("X", "Y")
plt.show()
# Find the best linear fit of mean gray level vs camera exposure
cf = CurveFitter(exposures, list(levels))
cf.doFit(CurveFitter.STRAIGHT_LINE)
fitParams = cf.getParams()
slope = fitParams[1]
intercept = fitParams[0]
rSqr = cf.getRSquared()
print("slope=", slope, " ; intercept=", intercept, " ; rSquared=", rSqr)
# Plot the data and the regression line
newPlotFlags = Plot.TRIANGLE + Plot.X_GRID + Plot.X_NUMBERS + Plot.Y_GRID + Plot.Y_NUMBERS
newPlot = Plot("DARK NOISE", "EXPOSURE, ms", "MEAN GRAY LEVEL", newPlotFlags)
newPlot.setLineWidth(2)
newPlot.setColor("red")
newPlot.add("triangle", exposures, list(levels))
newPlot.setLineWidth(1)
newPlot.setColor("black")
newPlot.drawLine(exposures[0], cf.f(exposures[0]), exposures[-1],
cf.f(exposures[-1]))
newPlot.setColor("blue")
newPlot.setFontSize(20)
newPlot.addText("y = a+bx", 100.0, 13000.0)
newPlot.addText("a = " + str(round(intercept, 2)), 100.0, 12250.0)
newPlot.addText("b = " + str(round(slope, 2)), 100.0, 11500.0)
newPlot.addText("R squared = " + str(round(rSqr, 3)), 100.0, 10750.0)
newPlot.show()
# Place the plot data into a ResultsTable
rt = newPlot.getResultsTable()
rt.show("Dark Noise Results")
from ij.gui import Plot
from math import sin, cos, radians
# title, X label, Y label
plot = Plot("My data", "time", "value")
# Series 1
plot.setColor("blue")
plot.add("circle", [50 + sin(radians(i * 5)) * 50 for i in xrange(100)])
# Series 2
plot.setColor("magenta")
plot.add("diamond", [50 + cos(radians(i * 5)) * 50 for i in xrange(100)])
# Series 3
plot.setColor("black")
plot.add("line", [50 + cos(-1.0 + radians(i * 5)) * 50 for i in xrange(100)])
plot.show()
from ij.gui import Plot
from math import sin, radians
plot = Plot("My data", "time", "value")
plot.setColor("blue")
plot.add("circle", [sin(radians(i)) for i in xrange(1000)])
plot.show()
stack_crop = stack.crop(x_val, y_val, 0, width, height, NSlices)
time1 = timeit.default_timer()
print roi
print stack
print stack_crop
modal_vals = []
for idx in range(0, stack_crop.size()):
#Determine Mode of each Slice
sel_frame = stack_crop.getProcessor(idx + 1) #1-based indexing
frame_hist = sel_frame.getHistogram()
hist_set = sorted(set(frame_hist))
modal_count = hist_set[-1]
modal_val = frame_hist.index(modal_count)
if modal_val == 0:
modal_count = hist_set[-2]
modal_val = frame_hist.index(modal_count)
modal_vals.append(modal_val)
if idx % 1000 == 0:
print(idx, timeit.default_timer() - time1)
print sorted(set(modal_vals))
print timeit.default_timer() - time1
final_plot = Plot("Modal Values", "Stack", "Value")
final_plot.add("line", modal_vals)
final_plot.show()
print timeit.default_timer() - time1