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
