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")
def plots(values, timelist, Cell_number, value_type, Stim_List, dirs, parameters): """ Plots all calculated values, saves plots to generated directory, returns plot scale. """ Mean_plot = 0 # Flatten nested lists (normalized lists are not nested). if value_type == "Normalized aFRET mean": values_concat = [ values[i:i+Cell_number] for i in range(0, (len(values)), Cell_number) ] Mean_sd = [ standard_deviation(values_concat[i]) for i in range(len(values_concat)) ] Mean_sd = [item for sublist in Mean_sd for item in sublist] Mean_plot = 1 elif value_type == "Normalized dFRET mean": values_concat = [ values[i:i+Cell_number] for i in range(0, (len(values)), Cell_number) ] Mean_sd = [ standard_deviation(values_concat[i]) for i in range(len(values_concat)) ] Mean_sd = [item for sublist in Mean_sd for item in sublist] Mean_plot = 1 else: if "Normalized" not in value_type: values = [item for sublist in values for item in sublist] #Repeats list items x cell_number (match timepoints with # of cells). timelist = [x for item in timelist for x in repeat(item, Cell_number)] # Scaling of plots. max_Y = 1 if max(values) > 3: if not isinstance(values[0], list): max_Y = max(values)*1.3 elif max(values) > 2.5: max_Y = 3.3 elif max(values) > 2: max_Y = 2.7 elif max(values) > 1.5: max_Y = 2.2 elif max(values) > 1.3: max_Y = 1.7 elif max(values) > 1: max_Y = 1.4 min_Y = 0 if min(values) > 2: min_Y = min(values)*0.8 elif min(values) > 1.5: min_Y = 1.5 elif min(values) > 1: min_Y = 1 elif min(values) > 0.5: min_Y = 0.2 elif min(values) < -0.5: min_Y = min(values)*1.3 elif min(values) < -0.2: min_Y = -0.3 elif min(values) < -0.1: min_Y = -0.15 elif min(values) < -0.08: min_Y = -0.1 elif min(values) < -0.05: min_Y = -0.08 elif min(values) < -0.01: min_Y = -0.06 # Scaling of normalized plots.. if "Normalized" in value_type: min_Y, max_Y = float(parameters["p_min_n"]), float(parameters["p_max_n"]) if value_type == "dFRET": max_Y = float(parameters["p_max"]) min_y = float(parameters["p_min"]) elif value_type =="aFRET": max_Y = float(parameters["p_max"]) min_y = float(parameters["p_min"]) # Call plot, set scale. plot = Plot(Title, "Time (minutes)", value_type) if len(timelist) > 1: plot.setLimits(min(timelist), max(timelist), min_Y, max_Y) else: plot.setLimits(-1, 1, min_Y, max_Y) # Retrieve colors. Colors, Colors_old = colorlist() # Set colors, plot points. if Mean_plot == 0: for i in range(Cell_number): if i < 19: plot.setColor(Color(*Colors[i][0:3])) elif i >= 19: plot.setColor(eval(Colors_old[i])) print "Out of fancy colors, using java.awt.color defaults" elif i > 28: print "29 color limit exceeded" return plot.setLineWidth(1.5) plot.addPoints(timelist[i :: Cell_number], values[i :: Cell_number], Plot.LINE) plot.setLineWidth(1) # Comment in to define color + fillcolor for circles. plot.setColor(Color(*Colors[i][0:3]), Color(*Colors[i][0:3])) #plot.addPoints(timelist[i :: Cell_number], values[i :: Cell_number], Plot.CIRCLE) else: min_Y, max_Y = 0.6, 1.6 if len(timelist) > 1: plot.setLimits(min(timelist), max(timelist), min_Y, max_Y) else: plot.setLimits(-1, 1, min_Y, max_Y) plot.setColor("Color.BLACK") plot.setLineWidth(1.5) plot.addPoints(timelist[0 :: Cell_number], Mean_sd[0::2], Plot.LINE) plot.setLineWidth(1) plot.setColor("Color.BLACK", "Color.BLACK") plot.addPoints(timelist[0 :: Cell_number], Mean_sd[0::2], Plot.CIRCLE) plot.setColor(Color(*Colors[6][0:3])) plot.addErrorBars(Mean_sd[1::2]) # Get's stim name from input. if not Stim_List == False: text = [ sublist[i] for sublist in Stim_List for i in range(len(Stim_List)) ] Stim_List = [ sublist[1:] for sublist in Stim_List ] # Plot stimulation markers. plot.setLineWidth(2) for sublist in Stim_List: plot.setColor("Color.GRAY") plot.drawLine(sublist[0], min_Y+((max_Y-min_Y) * 0.82), sublist[1], min_Y+((max_Y-min_Y) * 0.82)) plot.drawDottedLine(sublist[0], min_Y+((max_Y-min_Y) * 0.82), sublist[0], -1, 4) plot.drawDottedLine(sublist[1], min_Y+((max_Y-min_Y) * 0.82), sublist[1], -1, 4) plot.setFont(Font.BOLD, 16) plot.addText(text[0], sublist[0], min_Y+((max_Y-min_Y) * 0.82)) cell_num = 0 if "concentration" not in value_type: testfile = open(os.path.join(dirs["Tables"], value_type + ".txt"), "w") data = plot.getResultsTable() headings = data.getHeadings() datadict = {} for heading in headings: index = data.getColumnIndex(heading) if "Y" in heading: column = { "Cell "+str(cell_num).zfill(2) : [round(float(i), 4) for i in data.getColumn(index)] } elif "X" in heading: column = {"X" : [round(float(i), 4) for i in data.getColumn(index)] } cell_num += 1 datadict.update(column) sorted_data = [] for row in zip(*([key] + value for key, value in sorted(datadict.items()))): sorted_data.append(row) testfile.write("\t\t".join(sorted_data[0])) # Prints output in columns, copy paste directly to sigma/prisma/excel etc. for cell in range (1, len(sorted_data), 1): testfile.write("\n") for times in range(len(sorted_data[cell])): testfile.write(str(sorted_data[cell][times]) + "\t\t") # Dumps sorted data to JSON format, for use in eg. matplotlib. with open(os.path.join(dirs["Tables"], value_type + ".json"), "w") as outfile: datadict["Stim"] = Stim_List json.dump(datadict, outfile, sort_keys=True) testfile.close() # Generate High-res plot with anti-aliasing (Scale x 1). plot = plot.makeHighResolution(Title, 1, True, True) #PlotWindow.noGridLines = True # Save plot with appropriate title. IJ.saveAs(plot, "PNG", os.path.join(dirs["Plots"], str(Title)+str(value_type))) # (For ratiometric image-generator) return max_Y, min_Y