def ImageFromSpikes(input_spikes,
file_path="./latest.png",
show=True,
save=True,
xmin=False,
xmax=False):
'''Input: get converted to:
spikes = list of lists (neurons) of ms
ie. [[1, 3, 5], [2, 4, 5]] # 2 neurons, 3 spikes/neuron, ms 1, 3...
'''
global sp
from plugins.importer import spikesDictToArray
spikes = spikesDictToArray(input_spikes)
if xmax or xmin: #TODO: Use numpy (faster) v1.0
new_spikes = []
for i in spikes:
i = [l for l in i if l < xmax and l > xmin]
new_spikes.append(i)
spikes = new_spikes
if save:
cprint("\t*\tSaving image to %s" % file_path, 'info')
sp.set_fig_name(file_path)
sp.set_figsize((24, 18))
sp.set_linestyle('-')
sp.set_markerscale(0.8)
sp.plot_spikes(spikes, draw=show, savefig=save)
# del sp #fixes memory leak? Seems not
del spikes
del input_spikes
# Debug code
# from subprocess import call
# call(["feh", file_path])
return True
def POST(self):
global Work
global client_port
cprint("Data received, saving!", 'okgreen')
save = open("MultiHeaded-analisys.csv", 'a')
save.write(web.data())
save.close()
cprint("Sending next!", 'okgreen')
work_append(
web.ctx['ip'], client_port, Work.pop(1,1))
work_start(web.ctx['ip'], client_port)
def main_loop(content, remote, dummy):
i = 0
total = len(content)
for values in content:
filename = "./%s/%s_%s.png" % (values[1], values[2], values[3])
cprint("[%s/%s] %s match and is missing, generating image\r" % (i, total, filename), 'info')
#TODO:allow compressed
data = import_history("%s/%s_%s_output.gz" % (values[0], values[2], values[3]), compressed=True)
state = ImageIO.ImageFromSpikes(
data["NeMo"][1],
file_path = filename,
save = True,
show = False
)
del state
del data #Memory leak again?
i += 1
def info_print(total, core_number, print_only = False):
from libs.IO import cprint
if not total:
cprint("No simulations to analyze, exiting")
exit()
cprint("Total number of analysis to be run: %s" % (total), 'okblue')
cprint("And we are going to use %s%s %s"
%
( #Style :D
"even " if core_number >= 8 \
else "well " if core_number >= 4 \
else "just " if core_number <= 2 \
else ""
, core_number
, "thread" if core_number == 1 else "threads"
) #Style again XD
, 'okgreen' if core_number >= 4 \
else 'okblue' if core_number >= 3 \
else 'warning' if core_number >= 2 \
else 'red'
)
if print_only: #Print simulations number and exit!
exit()
def showImageFile(image_path):
try:
img = Image.open(image_path)
img.show()
except IOError:
cprint("ERROR: cannot load image (Image file does not exists)", 'fail')
#!/usr/bin/env python2
from PIL import Image
from libs.IO import cprint
from libs.IO import COLORS
try:
from neuronpy.graphics import spikeplot
except ImportError:
cprint("You need nepuronpy to create spikes images\n\
(as root) pip2 install neuronpy; pip2 install matplotlib", 'fail')
sp = spikeplot.SpikePlot(savefig=True)
def showImageFile(image_path):
try:
img = Image.open(image_path)
img.show()
except IOError:
cprint("ERROR: cannot load image (Image file does not exists)", 'fail')
def saveRawImage(img, name, close=True):
try:
img.save(name)
except AttributeError:
try:
img.savefig(name)
if close:
img.close()
except:
raise
return True
Work = workQueue(list_all(args.path, args.start_from, args.end_to))
if __name__ == "__main__":
from libs.IO import cprint
from time import sleep
app = Worker(urls, globals())
info = {}
#Get info needed as core number etc
for client in clients:
info[client] = {}
success = False
while not success:
try:
response = requests.get(ip_port(client,client_port)+"/cores").text
success = True
except requests.exceptions.ConnectionError:
cprint("Cannot connect to %s" % (client),'fail')
cprint("Retry in 1 sec", 'info')
sleep(1)
info[client]["cores"] = json.loads(response)["cores"]
info[client]["multiplier"] = json.loads(response)["multiplier"]
#Add work to queue. Will be started when everyone get it's own
#(that way we can start the server that receive the results in time)
#Append a work for every core of every client
for client_ip in info:
cprint("Loading data for %s" % client_ip, 'info')
work_append(
client_ip, client_port, Work.pop(
info[client_ip]["cores"]
, info[client_ip]["multiplier"]))
)
parser.add_argument('--cores',
help = 'Number of cores to use. All by default'
, dest = 'cores'
, default = get_cores()
)
args = parser.parse_args()
path = args.path
#Read ANALYSIS file
fname = args.analysis_file
with open(fname) as f:
content = f.readlines()
cprint("Total files provided: %s; Number of cores: %s" % (len(content), args.cores), 'info')
is_folder(args.images_path)
real_content = []
# existing = listdir(args.images_path) #Slower than isfile
for line in content:
file_data = line.split(",")
general = file_data[0]
config = file_data[1]
if args.run_all or (file_data[2] == "1" == file_data[7]):
filename = "%s_%s.png" % (general, config)
if not isfile("./%s" % args.images_path + filename):
# if filename not in existing: #Slower than isfile O.o
real_content.append((args.path, args.images_path, general, config))
# else: existing.remove(filename)
commands = substituteRanges([args], [])
#fix parentheses:
commands = [ i for i in commands if not missing(i) ]
#set just to be sure no duplicate runs
real_commands = set(commands)
#TODO:
#FIXME: remove "--show-image" etc to prevent different hashes of same config
session_hash = hashDict(real_commands)
#Save commands list
is_folder("./commands")
commands_file = "./commands/" + session_hash + "_commands"
cprint ("Saving %s commands to file: %s" % (len(real_commands), commands_file), 'info')
saveKey(commands_file, commands)
# Debug: re-enable in release
# cprint("Running in 5s", "warning")
# time.sleep(5)
#Start
is_folder (output_dir)
cprint("We'll use %s as output dir" % (output_dir), 'info')
#Let's run the simulations (surely not the best way, but does the job)
#TODO: add the loop inside Runner?
start = time.time()
try:
run = import_history(output_dir + "/" + session_hash + "_batch")
def return_analysis_output(f, neurons_info, input_conf, steps):
try:
to_write = "%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\n" % (
f.split("_")[0] #Save somewhere to save time, used multimple times #1
, f.split("_")[1]
, neurons_info[4]["mode"]
, neurons_info[4]["on_time"]
, neurons_info[4]["off_time"]
, neurons_info[4]["burst_freq"]
, neurons_info[4]["not_burst_freq"]
, neurons_info[5]["mode"]
, neurons_info[5]["on_time"]
, neurons_info[5]["off_time"]
, neurons_info[5]["burst_freq"]
, neurons_info[5]["not_burst_freq"]
, input_conf["cerebellum_ctrl_a"][0]
, input_conf["cerebellum_ctrl_a"][1]
, input_conf["cerebellum_ctrl_a"][2]
, input_conf["cerebellum_ctrl_b"][0]
, input_conf["cerebellum_ctrl_b"][1]
, input_conf["cerebellum_ctrl_b"][2]
, input_conf["_stim_a"]
, input_conf["_stim_b"]
, input_conf["_start_a"]
, input_conf["_start_b"]
, input_conf["_stop_a"]
, input_conf["_stop_b"]
, input_conf["CIN"][0][0]
, input_conf["CIN"][0][1]
, input_conf["CIN"][0][2]
, input_conf["CIN"][0][3]
, input_conf["CIN"][0][4]
, input_conf["CIN"][0][5]
, input_conf["CIN"][0][6]
, input_conf["EIN"][0][0]
, input_conf["EIN"][0][1]
, input_conf["EIN"][0][2]
, input_conf["EIN"][0][3]
, input_conf["EIN"][0][4]
, input_conf["EIN"][0][5]
, input_conf["EIN"][0][6]
, input_conf["synapses"][0][2][0] #delay
, input_conf["synapses"][0][2][1] #int
, input_conf["synapses"][1][2][0] #delay
, input_conf["synapses"][1][2][1] #int
, input_conf["synapses"][2][2][0] #delay
, input_conf["synapses"][2][2][1] #int
, input_conf["synapses"][3][2][0] #delay
, input_conf["synapses"][3][2][1] #int
, input_conf["synapses"][4][2][0] #delay
, input_conf["synapses"][4][2][1] #int
, input_conf["synapses"][5][2][0] #delay
, input_conf["synapses"][5][2][1] #int
, steps #38
)
except KeyError:
to_write = "%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\n" % (
f.split("_")[0] #Save somewhere to save time, used multimple times #1
, f.split("_")[1]
, 3
, 0
, 0
, 0
, 0
, 3
, 0
, 0
, 0
, 0
, input_conf["cerebellum_ctrl_a"][0]
, input_conf["cerebellum_ctrl_a"][1]
, input_conf["cerebellum_ctrl_a"][2]
, input_conf["cerebellum_ctrl_b"][0]
, input_conf["cerebellum_ctrl_b"][1]
, input_conf["cerebellum_ctrl_b"][2]
, input_conf["_stim_a"]
, input_conf["_stim_b"]
, input_conf["_start_a"]
, input_conf["_start_b"]
, input_conf["_stop_a"]
, input_conf["_stop_b"]
, input_conf["CIN"][0][0]
, input_conf["CIN"][0][1]
, input_conf["CIN"][0][2]
, input_conf["CIN"][0][3]
, input_conf["CIN"][0][4]
, input_conf["CIN"][0][5]
, input_conf["CIN"][0][6]
, input_conf["EIN"][0][0]
, input_conf["EIN"][0][1]
, input_conf["EIN"][0][2]
, input_conf["EIN"][0][3]
, input_conf["EIN"][0][4]
, input_conf["EIN"][0][5]
, input_conf["EIN"][0][6]
, input_conf["synapses"][0][2][0] #delay
, input_conf["synapses"][0][2][1] #int
, input_conf["synapses"][1][2][0] #delay
, input_conf["synapses"][1][2][1] #int
, input_conf["synapses"][2][2][0] #delay
, input_conf["synapses"][2][2][1] #int
, input_conf["synapses"][3][2][0] #delay
, input_conf["synapses"][3][2][1] #int
, input_conf["synapses"][4][2][0] #delay
, input_conf["synapses"][4][2][1] #int
, input_conf["synapses"][5][2][0] #delay
, input_conf["synapses"][5][2][1] #int
, steps #29
)
cprint("Key error!","fail")
return to_write