def test(process_input_list, cpu_count):
scheduler_list = []
# fcfs = FCFS(process_input_list, cpu_count)
scheduler_list.append(RR(process_input_list, cpu_count, 2)) # quantum = 2
scheduler_list.append(RR(process_input_list, cpu_count, 3)) # quantum = 3
# scheduler_list.append(HRRN(process_input_list, cpu_count))
# ...
for test_idx, scheduler in enumerate(scheduler_list):
print(
"[ TEST {0} - {1}]=======================================".format(
test_idx + 1,
type(scheduler).__name__))
scheduler.run()
print_process_time_table(scheduler.processes)
def calculate(self):
if not (self.SJF_btn.isChecked() or self.FCFS_btn.isChecked()
or self.RR_btn.isChecked() or self.SRTF_btn.isChecked()):
self.show_popup("Enter algorithm to execute")
return
if len(self.processes) == 0:
self.show_popup(
"There must be at least one process to execute this algorithm")
return
self.sortDict()
if self.SJF_btn.isChecked():
res = SJF(self.processes)
elif self.FCFS_btn.isChecked():
res = FCFS(self.processes)
elif self.RR_btn.isChecked():
if self.quantum_time.text() == "":
self.show_popup("Enter value for Quantum time first")
return
else:
res = RR(self.processes, int(self.quantum_time.text()))
else:
res = SRTF(self.processes)
self.lcdNumber.display(res[0])
self.lcdNumber_2.display(res[1])
def __init__(self):
super().__init__()
tabwidget = QTabWidget()
tabwidget.addTab(RR.RRClass(), "RR")
tabwidget.addTab(FCFS.FCFSClass(), "FCFS")
tabwidget.addTab(SJF_Preemptive.SJFPreemptiveClass(), "SJF Preemptive")
tabwidget.addTab(SJF_NonPreemptive.SJFNonPreemptiveClass(),
"SJF Non Preemptive")
tabwidget.addTab(Priority_Preemptive.PriorityPreemptiveClass(),
"Priority Preemptive")
tabwidget.addTab(Priority_NonPreemptive.PriorityNonPreemptiveClass(),
"Priority Non Preemptive")
vboxLayout = QVBoxLayout()
vboxLayout.addWidget(tabwidget)
self.setLayout(vboxLayout)
def ScheduleAlgo(self):
InputFile = open(self.ProcessFile, "r")
Lines = InputFile.readlines()
ProcessNum = int(Lines[0])
AT = []
BT = []
ID = []
PT = []
for i in range(1,ProcessNum+1):
temp = Lines[i].split(' ')
ID.append(int(temp[0]))
AT.append(float(temp[1]))
BT.append(float(temp[2]))
PT.append(int(temp[3]))
select = self.var.get()
if( select == 1):
Scheduler = HPF(AT,BT,ID,ProcessNum,int(self.ContextTime.get()),PT)
print("hpf")
elif( select == 2):
Scheduler = FCFS(AT,BT,ID,ProcessNum,int(self.ContextTime.get()))
print("fcfs")
elif( select == 3):
print("rr")
Scheduler = RR(AT,BT,ID,ProcessNum,int(self.ContextTime.get()),int(self.QuantamTime.get()))
else:
print("srtn")
Scheduler = SRTN(AT,BT,ID,ProcessNum,int(self.ContextTime.get()))
Scheduler.Schedule()
AvgTT,AvgWTT = Scheduler.TurnaroundTime()
Processes = Scheduler.Processes
self.OutFile("ProcessInfo2",Processes,AvgTT,AvgWTT)
Scheduler.DisplayProcesses()
ufreqs = np.unique(rr_stimparams[:, 0])
urrs = np.unique(rr_stimparams[:, 1])
freq_played, freq_ix_played, _ = misc.closest(ufreqs, cf, log = True)
rr_bins = np.arange(0, 6, 0.05)
rr_psth_stim, usp = Spikes.calc_psth_by_stim(rr_rast, rr_stimparams, bins = rr_bins)
cf_psth = rr_psth_stim[freq_ix_played, :, :]
noise_psth = rr_psth_stim[0, :, :]
fig = plt.figure()
nrrs = cf_psth.shape[0]
ax = []
for i in range(nrrs):
ax.append(fig.add_subplot(nrrs, 1, i+1))
ax[-1].plot(rr_bins[:-1], cf_psth[i, :])
RR.plot_tone_pips(urrs[i], 6, 0.05, 0.025, ax = ax[-1], color = 'r')
if i>0:
ax[-1].set_xticklabels('')
misc.sameyaxis(ax)
if len(voc_path) > 0:
voc_file = h5py.File(voc_path, 'r')
voc_rast = voc_file['rast'].value
voc_stimparams = voc_file['stimID'].value
voc_file.close()
# voc_rast = voc_rast[1:, :]
# voc_stimparams = voc_stimparams[:-1, :]
voc_stimparams = voc_stimparams[:, 0]
rast = frr['rast'].value stimparams = frr['stimID'].value frr.close() # if not np.isnan(spktimes[0]): cf = cfs[cfs[:, 0]==unitnum, 1][0] cf_hz = ix2freq[20:][int(cf)] freqs = stimparams[:, 0] rrs = stimparams[:, 1] ufreqs = np.unique(freqs) urrs = np.unique(rrs) nrrs = urrs.size # now we determine which of the frequencies we played is closest to this neuron's CF thisfreq, thisfreq_ix, thisfreq_err = misc.closest(ufreqs, cf_hz, log = True) if np.abs(thisfreq_err) > 0.2: print 'No close frequency found!' thisfreq = ufreqs[thisfreq_ix] # isolate the parts of the raster for this frequency and build a psth for each RR ix = RF.get_trials(stimparams, np.array([thisfreq, np.nan])) thisrast = rast[ix, :1050] thisstims = stimparams[ix, :] psths, ustims = Spikes.calc_psth_by_stim(thisrast, thisstims) rrtf = RR.calc_rrtf_all(thisrast, thisstims, thisfreq, urrs) db.resize(db.size+1) db[-1] = np.array((gen, exp, sess, unitnum, cf_hz, rrtf, urrs), dtype = dtype) np.savez(savepath, db)
def rr_make_contactsheets():
'''
loop through all the sessions and plot the rrtfs
'''
fig = plt.figure(figsize = (30, 18));
txt_suptitle = fig.suptitle('')
ax_cfrrtf = fig.add_axes((0.76, 0.76, 0.24, 0.23));
ax_cfvs = ax_cfrrtf.twinx();
ax_cfcircpsthall = fig.add_axes((0.62, (11/14.)-0.02, 0.1, (1/7.)+0.04), polar = True)
ax_cfcircpsthall.set_xticklabels(''); ax_cfcircpsthall.set_yticklabels('');
ax_rf = fig.add_axes((0.67, 0.51, 0.33, 0.23));
ax_rfrast = fig.add_axes((0.67, 0.25, 0.33, 0.24));
ax_rfrast.set_xticklabels('');
ax_rfpsth = fig.add_axes((0.67, 0.01, 0.33, 0.24));
ax_cfrr = [fig.add_axes((0.03, 1-((i+1)/7.), 0.35, 1/7.)) for i in np.arange(nrrs)]
ax_cfalignedpsth = [fig.add_axes((0.38, 1-((i+1)/7.), 0.17, 1/7.)) for i in np.arange(nrrs)]
ax_cfcircpsth = [fig.add_axes((0.53, 1-((i+1)/7.), 0.1, 1/7.), polar = True) for i in np.arange(nrrs)]
# ax_noiserr = [fig.add_subplot(nrrs, 3, i) for i in np.arange(1, 3*nrrs, 3)]
for sessionpath in sessionpaths:
session = os.path.split(sessionpath)[1]
unitinfos = fileconversion.get_session_unitinfo(sessionpath, onlycomplete = ('RF', 'RR', 'VOC'))
for unitkey in unitinfos.keys():
txt_suptitle.set_text('%s %s' % (session, unitkey))
unitinfo = unitinfos[unitkey]
rf_ix = unitinfo['stimtype'].index('RF')
f_rf = h5py.File(unitinfo['fpath'][rf_ix], 'r')
rf_rast = f_rf['rast'].value
rf_stimparams = f_rf['stimID'].value
cf_ix = f_rf['cf'].value
f_rf.close()
cf = ix2freq[20:][int(cf_ix)]
''' calculate and plot RF, psth, and sorted raster'''
rf = RF.calc_rf(rf_rast, rf_stimparams)
rf_psth = Spikes.calc_psth(rf_rast)
RF.plot_rf(rf, cf = cf_ix, axes_on = False, ax = ax_rf) # plot RF
ax_rf.axvline(cf_ix, color = 'r', lw = 1.5)
Spikes.plot_sorted_raster(rf_rast, rf_stimparams, ax = ax_rfrast) # plot raster
ax_rfpsth.plot(t_rf, Spikes.exp_smoo(rf_psth, tau = 0.005)) # plot PSTH
''' calcualte and plot RRTFs for CF and noise stimuli '''
rr_ix = unitinfo['stimtype'].index('RR')
f_rr = h5py.File(unitinfo['fpath'][rr_ix], 'r')
rr_rast = f_rr['rast'].value
rr_stimparams = f_rr['stimID'].value
f_rr.close()
# find the played CF
rr_ufreqs = np.unique(rr_stimparams[:, 0])
urrs = np.unique(rr_stimparams[:, 1])
npips = (urrs*4).astype(int)
rr_freq, rr_ufreq_ix, _ = misc.closest(rr_ufreqs, cf, log = True)
ax_rf.axvline(RF.calc_freq2ix(rr_freq), color = 'g', lw = 1.5)
# calculate the PSTHs for each repetition rate
tmp = Spikes.calc_psth_by_stim(rr_rast, rr_stimparams)
rr_cfpth = tmp[0][rr_ufreq_ix, :, :]
# rrtf_noisepsth = tmp[0][0, :, :]
# plot the aligned psths
RR.aligned_psth_separate_all(rr_rast, rr_stimparams, rr_freq, npips, axs = ax_cfalignedpsth)
[a.set_yticklabels('') for a in ax_cfalignedpsth]
[a.set_xticklabels('') for a in ax_cfalignedpsth[:-1]]
# plot circular psths
r, V, theta = RR.circ_psth_all(rr_rast, rr_stimparams, rr_freq, npips, axs = ax_cfcircpsth)
[a.set_yticklabels('') for a in ax_cfcircpsth]
[a.set_xticklabels('') for a in ax_cfcircpsth]
# plot all circular summed vector strengths
ax_cfcircpsthall.plot(theta, V, '.-')
[ax_cfcircpsthall.plot([0, th], [0, v], color = 'b', alpha = 1-(i/10.)) for i, (th, v) in enumerate(zip(theta, V))]
# plot RRTF
rrtf = RR.calc_rrtf_all(rr_rast, rr_stimparams, rr_freq, urrs, npips)
ax_cfrrtf.plot(rrtf, '.-', ms = 10)
ax_cfvs.plot(V*np.cos(theta), 'g.-', ms = 10)
for tick in ax_cfvs.yaxis.get_major_ticks():
tick.set_pad(-5)
tick.label2.set_horizontalalignment('right')
# plot repetition rate PSTHs
for i in xrange(nrrs):
# RR.plot_rrtf(t_rrtf, rrtf_noisepsth[i, :], urrs[i], int(4*urrs[i]), onset = 0.05, duration = 0.025, ax = ax_noiserr[i])
RR.plot_rrtf(t_rrtf, rr_cfpth[i, :], urrs[i], int(4*urrs[i]), onset = 0.05, duration = 0.025, ax = ax_cfrr[i])
# ax_noiserr[0].set_title('Noise RRTFs')
ax_cfrr[0].set_title('CF RRTFs (%.0f kHz)' % (cf/1000))
# [a.set_xlim(0, 4.5) for a in ax_noiserr]
[a.set_xlim(0, 4.5) for a in ax_cfrr]
misc.sameyaxis(ax_cfrr+ax_cfalignedpsth)
figsavepath = os.path.join(studydir, 'Sheets', 'RRTFs', '%s_%s_RRTF.png' % (session, unitkey))
print figsavepath
fig.savefig(figsavepath)
[a.cla() for a in fig.get_axes()] # clear all axes
def analyze():
gens = []
exps = []
sesss = []
unitnums = []
cfs = []
noise_rrtfs = []
cf_rrtfs = []
for session in sessions:
_, gen, exp, _ = session.split("_")
unitinfos = fileconversion.get_session_unitinfo(session, onlycomplete=("RF", "RR", "VOC"))
for unitkey in unitinfos.iterkeys():
unitinfo = unitinfos[unitkey]
print unitinfo["session"], unitinfo["unitnum"]
rr_ix = unitinfo["stimtype"].index("RR")
f = h5py.File(unitinfo["fpath"][rr_ix], "r")
rast = f["rast"].value
stimparams = f["stimID"].value
cf_ix = f["cf"].value
f.close()
ufreqs = np.unique(stimparams[:, 0])
nfreqs = ufreqs.size
urrs = np.unique(stimparams[:, 1])
cf = RF.ix2freq[20:][np.round(cf_ix).astype(int)]
_, ix, err = misc.closest(ufreqs, cf, log=True)
if np.abs(err) > 0.5:
print "Repetition rate stimuli not present for this unit (nearest tone %2.2f octaves away)" % err
continue
ufreq_played = ufreqs[ix]
npips = urrs * 4
noise_rrtf = RR.calc_rrtf_all(rast, stimparams, 0.0, urrs, npips=npips)
cf_rrtf = RR.calc_rrtf_all(rast, stimparams, ufreq_played, urrs, npips=npips)
gens.append(gen)
exps.append(exp)
sesss.append(session)
unitnums.append(unitinfo["unitnum"])
cfs.append(cf_ix)
noise_rrtfs.append(noise_rrtf)
cf_rrtfs.append(cf_rrtf)
df = pd.DataFrame(
dict(gen=gens, exp=exps, sess=sesss, unit=unitnums, cf=cfs, noise_rrtf=noise_rrtfs, cf_rrtf=cf_rrtfs)
)
d = pd.HDFStore(os.path.join(studydir, "Analysis", "rrtf_data.h5"))
d["df"] = df
d.close()
return df
print("write the scheduling algorithm name(RR,FCFS,HPF,SRTN): ")
algo = input()
if algo == "HPF":
proc, contxtPeriods = HPF(processes)
elif algo == "FCFS":
proc, contxtPeriods = FCFS(processes)
elif algo == "SRTN":
print("Enter Context Switching Time: ")
cntxt = input()
proc, contxtPeriods = SRTN(processes, float(cntxt))
elif algo == "RR":
print("Enter Context Switching Time: ")
cntxt = input()
print("Enter quantum: ")
quantum = input()
proc, contxtPeriods = RR(processes, int(quantum), float(cntxt))
# getting lists
x, y = getLists(proc, contxtPeriods)
plt.plot(x, y)
plt.ylabel("pid")
plt.xlabel("time steps")
plt.title(algo)
plt.grid()
plt.show()
# to be removed::
# for i in range (0,len(proc)):
# print(proc[i].id, proc[i].startT, proc[i].finishT, proc[i].TAT)