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generate data to analyse MXTuning/19_01_29

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zamofing_t
2019-01-29 17:40:06 +01:00
parent 38888e02fd
commit ad5e162d73
2 changed files with 76 additions and 46 deletions
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120
python/MXTuning.py
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@@ -9,13 +9,14 @@
tuning functions for ESB-MX
Modes:
bit 0=1: record/plot current step
bit 1=2: custom chirp record/plot for IdCmd->ActPos transfer function
bit 1=4: custom chirp record/plot for DesPos->ActPos transfer function
bit 2=8: plot the full bode recording
bit 3=16: plot the full bode recording with an approximation model
bit 4=32: plot all raw acquired data files
bit 5=64: generate observer code (after files generated with matlab)
bit 0=1: record/plot current step
bit 1=2: custom chirp record/plot for IdCmd->ActPos transfer function
bit 2=4: custom chirp record/plot for DesPos->ActPos transfer function
bit 3=8: custom chirp record/plot for various transfer function
bit 4=16: plot the full bode recording
bit 5=32: plot the full bode recording with an approximation model
bit 6=64: plot all raw acquired data files
bit 7=128: generate observer code (after files generated with matlab)
-> check https://github.com/klauer/ppmac for fast data gathering server which supports
phase gathering -> not yet compiling: /home/zamofing_t/Documents/prj/SwissFEL/PowerBrickInspector/ppmac/fast_gather
@@ -44,9 +45,10 @@ class MXTuning(Tuning):
self.homed=False
def init_stage(self,fn=''):
def init_stage(self,mot=3,fn=''):
print(fn)
comm=self.comm
if comm is None or self.homed or os.path.isfile(fn):
if comm is None or (self.homed&mot)==mot or os.path.isfile(fn):
return
gpascii=comm.gpascii
@@ -60,7 +62,7 @@ class MXTuning(Tuning):
break
sys.stdout.write('.');sys.stdout.flush()
time.sleep(.2)
self.homed=True
self.homed=3
def bode_model_plot(self, mot):
self.bode_full_plot(mot,self.baseDir)
@@ -347,14 +349,17 @@ EXPORT_SYMBOL(obsvr_servo_ctrl_{motid});'''.format(motid=motid)
def run(self,mode):
#plt.ion()
self.homed=0
if mode&1: # full recording current step
plt.close('all')
self.homed=False
for mot in (1, 2):
fn=os.path.join(self.baseDir, 'curr_step%d.npz' % mot)
self.init_stage(fn)
self.init_stage(mot,fn)
plt.close('all')
self.bode_current(motor=mot, magMove=1000, magPhase=500, dwell=10, file=fn)
self.homed&=~mot
plt.show(block=False)
save_figs(fn)
f=np.load(fn)
fn=fn[:-3]+'mat'
import scipy.io
@@ -362,8 +367,6 @@ EXPORT_SYMBOL(obsvr_servo_ctrl_{motid});'''.format(motid=motid)
print('save to matlab file:'+fn)
if mode&2:
plt.close('all')
self.homed=False
motLst = (1, 2) # (2,)#
#recType:
# IA 0 IqCmd,ActPos (for plant transfer function)
@@ -374,25 +377,30 @@ EXPORT_SYMBOL(obsvr_servo_ctrl_{motid});'''.format(motid=motid)
#>>>>> IqCmd->ActPos transfer function (plant) using closed loop for low frequencies
for mot in motLst:
fn = os.path.join(self.baseDir, 'chirp_IA_%da.npz' % mot)
self.init_stage(fn)
self.custom_chirp(motor=mot, minFrq=1, maxFrq=15, amp=1000, tSec=15, recType=0,openLoop=False, file=fn)
self.homed=False
self.init_stage(mot,fn)
plt.close('all')
self.custom_chirp(motor=mot, minFrq=1, maxFrq=15, amp=1000, tSec=15, recType=0,mode=1, file=fn)
self.homed &= ~mot
plt.show(block=False)
save_figs(fn)
#>>>>> IqCmd->ActPos transfer function (plant) using open loop for high frequencies
for ext,amp,minFrq,maxFrq,tSec in (('b', 10, 10, 100*1.5, 30),
('c', 50, 100, 300*1.5, 30),
('d', 50, 300, 1000*1.5, 10),
('e', 100, 1000, 2000, 10)):
self.homed=False
for mot in motLst:
fn = os.path.join(self.baseDir, 'chirp_IA_%d%s.npz' % (mot,ext))
self.init_stage(fn)
self.custom_chirp(motor=mot, minFrq=minFrq, maxFrq=maxFrq, amp=amp, tSec=tSec, recType=0,openLoop=True, file=fn)
self.init_stage(mot,fn)
plt.close('all')
self.custom_chirp(motor=mot, minFrq=minFrq, maxFrq=maxFrq, amp=amp, tSec=tSec, recType=0,mode=0, file=fn)
self.homed&=~mot
plt.show(block=False)
save_figs(fn)
if mode&4:
plt.close('all')
self.homed = False
motLst = (1, 2) # (2,)#
motLst = (1, 2) #(2,)#
#>>>>> desPos->actPos transfer function (regulation) using closed loop
#motor1: 0dB at 20.4 Hz
#motor2: 0dB at 11.3 Hz
@@ -408,49 +416,59 @@ EXPORT_SYMBOL(obsvr_servo_ctrl_{motid});'''.format(motid=motid)
('b', 20, 30, 75*1.5, 15),
('c', 5, 75, 150*1.5, 5),
('d', 1, 150, 750, 5)):
self.homed=False
for mot in motLst:
fn = os.path.join(self.baseDir, 'chirp_DA_%d%s.npz' % (mot,ext))
self.init_stage(fn)
self.custom_chirp(motor=mot, minFrq=minFrq, maxFrq=maxFrq, amp=amp, tSec=tSec, recType=1,openLoop=False, file=fn)
self.init_stage(mot,fn)
plt.close('all')
self.custom_chirp(motor=mot, minFrq=minFrq, maxFrq=maxFrq, amp=amp, tSec=tSec, recType=1,mode=1, file=fn)
self.homed&=~mot
plt.show(block=False)
save_figs(fn)
if mode & 8:
#>>>>> all data for different transfer function
for ext,amp,minFrq,maxFrq,tSec in (('a', 5, 10, 250, 10),):
self.homed=False
for mot in motLst:
fn = os.path.join(self.baseDir, 'chirp_all_%d%s.npz' % (mot,ext))
self.init_stage(fn)
self.custom_chirp(motor=mot, minFrq=minFrq, maxFrq=maxFrq, amp=amp, tSec=tSec, recType=2,openLoop=False, file=fn)
#using closed loop with 1/w^2 lower amplitude
for ext,amp,minFrq,maxFrq,tSec,crpMd,mot in (('a', 800, 10, 250, 20, 2, 1),
('a', 800, 10, 250, 20, 2, 2),
('b', 5, 10, 220, 20, 1, 1),
('b', 5, 10, 220, 20, 1, 2),
):
fn = os.path.join(self.baseDir, 'chirp_all_%d%s.npz' % (mot,ext))
self.init_stage(mot,fn)
plt.close('all')
self.custom_chirp(motor=mot, minFrq=minFrq, maxFrq=maxFrq, amp=amp, tSec=tSec, recType=2,mode=crpMd, file=fn)
self.homed&=~mot
plt.show(block=False)
save_figs(fn)
if os.path.isfile(fn):
f = np.load(fn)
data = f['data']
meta = f['meta'].item()
meta['file'] = file
if len(meta['address']) == 4:
for xy in ((0, 1), (0, 3), (2, 3)):
self.bode_plot(data, xy=xy, mode=25, **meta)
if mode&8: #plot the full bode recording
if mode&16: #plot the full bode recording
plt.close('all')
self.bode_full_plot(mot=1,base=self.baseDir)
self.bode_full_plot(mot=2,base=self.baseDir)
plt.show(block=False)
save_figs(os.path.join(self.baseDir,'bode_full_plot'))
if mode&16: #plot the full bode recording with an approximation model
if mode&32: #plot the full bode recording with an approximation model
plt.close('all')
self.bode_model_plot(mot=1)
self.bode_model_plot(mot=2)
plt.show(block=False)
save_figs(os.path.join(self.baseDir,'bode_model_plot'))
if mode&32: # plot all raw acquired data files
if mode&64: # plot all raw acquired data files
# display bode plots
import glob
for fn in glob.glob(os.path.join(self.baseDir,'*.npz')):
print(fn)
fh = np.load(fn)
meta = fh['meta'].item()
data = fh['data']
self.bode_plot(data, mode=25, **meta)
plt.close('all')
self.bode_plot(data, mode=25, kwargs=meta)
plt.show(block=False)
save_figs(fn)
if mode&64: #generater code
if mode&128: #generater code
#before this can be done, the observer controller has to be designed with matlab:
#s.a.ESB_MX/matlab/Readme.md
#clear;
@@ -487,6 +505,18 @@ EXPORT_SYMBOL(obsvr_servo_ctrl_{motid});'''.format(motid=motid)
print('done')
plt.show()
def save_figs(fn):
figures = [manager.canvas.figure
for manager in mpl._pylab_helpers.Gcf.get_all_fig_managers()]
#print(figures)
fnBase=fn.rsplit('.')[0]
(a,b)=os.path.split(fnBase)
fnBase=os.path.join(a,'img',b)
for i, figure in enumerate(figures):
#figure.savefig('figure%d.png' % i)
fn=fnBase+'%d.png' % i
figure.savefig(fn)
#figure.savefig(fn.rsplit('.')[0]+'%d.eps' % i)
def bode(mdl):