)
if args.show_membrane:
ImageIO.ImageFromMembranes(output["NeMo"][0])
if args.angle_images:
ImageIO.ImageFromAngles(
(output["YARP"]["read"],
output["YARP"]["wrote"]), #FIXME: should be pyspike
file_path=output_dir + "/" + uniqueId + "_angles.png")
#Analysis:
if args.analyze_spikes_frequency: #TODO: write all conditions etc
from plugins.analysis import spikes
from plugins.importer import spikesDictToArray
neuron_number = 0
for i in spikesDictToArray(output["NeMo"][1]):
# print "ANALYSIS not working yet"
#Fixme: changed function names, doesn't match anymore
freq = spikes.getFreq(spikes.neuronSpikesToSquare(i, split_range=(5000, 30000)))
duty_cycle = float(freq[1]) / (freq[0] + freq[1])
# for on, p in enumerate(freq[3]):
# for l in p.items():
# f.write("%s,%s,%s\n" % (l[0], l[1], on))
if freq:
print ("Neuron: %s, duty cycle: %f, osc-freq: %f"
%
(neuron_number, duty_cycle, 1000./(freq[0] + freq[1])))
neuron_number += 1
print "All done, thanks!"
def main_loop(outputs, master_ip = False, in_data = False, split_range = (False, False)):
from plugins.analysis import spikes
from plugins.importer import spikesDictToArray
#???? Depends on kind of analysis, what to do??
neurons_to_analyze = [4, 5] #FIXME: read from cli
#TODO: add loop count
idx = 0
total = len(outputs)
for f in outputs:
if not master_ip: #Locale, read file
data, input_conf = read_output(f, args.path, idx, total)
else: #Already read file from host
data, input_conf = in_data[idx][0], in_data[idx][1]
idx += 1
if not data:
continue
# FIXME: re-enable when dispatch works
# if args.save_images:
# cprint("Saving image",'info')
# ImageIO.ImageFromSpikes(
# data["NeMo"][1]
# , file_path = "./latest.png" #FIXME: set wright path
# , save = True
# , show = False #Quite useless if True, right?
# )
# if args.images_only:
# continue
neuron_number = 0
neurons_info = {}
all_neurons_spikes_list = spikesDictToArray(data["NeMo"][1])
if len(all_neurons_spikes_list) < max(neurons_to_analyze) -1:
neurons_info={}
for n in neurons_to_analyze:
neurons_info[n] = {}
neurons_info[n]["on_time"] = 0
neurons_info[n]["off_time"] = 0
neurons_info[n]["mode"] = 3 #Dead neuron
neurons_info[n]["not_burst_freq"] = 0
neurons_info[n]["burst_freq"] = 0
else:
for i in all_neurons_spikes_list:
#What's the pythonic way to do this?
if neuron_number not in neurons_to_analyze:
neuron_number +=1
continue
raw, thresholded = spikes.neuronSpikesToSquare(
i,
data["ran_steps"],
split_range=split_range)
off_time, on_time, osc, periods = spikes.getFreq(
(raw, thresholded), data["ran_steps"])
not_burst_freq, burst_freq = spikes.getBurstFreq(raw, thresholded)
if not osc: #Not oscillating
state = max(off_time, on_time)
if state == off_time:
mode = 0 #Neuron is stable OFF
else:
mode = 2 #Neuron is stable ON
else: #Neuron IS oscillating
mode = 1 #Neuron is both ON and OFF
neurons_info[neuron_number] = {}
neurons_info[neuron_number]["on_time"] = on_time
neurons_info[neuron_number]["off_time"] = off_time
neurons_info[neuron_number]["mode"] = mode
neurons_info[neuron_number]["not_burst_freq"] = not_burst_freq
neurons_info[neuron_number]["burst_freq"] = burst_freq
neuron_number += 1
to_write = return_analysis_output(f,
neurons_info,
input_conf,
data["ran_steps"])
general_config, config_hash = f.split("_")[0:2]
with open("ANALYSIS_period.csv", 'a') as filewriter:
for on, p in enumerate(periods):
for l in p.items():
filewriter.write("%s,%s,%s,%s,%s\n" % (
general_config, config_hash,
l[0], l[1], on))
if not master_ip:
# is_folder("analysis") TODO: better dir organization
save = open("ANALYSIS.csv", 'a')#We could open this before
save.write(to_write)
save.close() #And close it after, to speed up
else:
response_request(to_write, master_ip, "10665")