diff --git a/ohmpi/hardware_system.py b/ohmpi/hardware_system.py
index d789576de562d113d362eb01d45620b88cf10c42..d69b8ca072a91ba89df660ad6ffa7aea4a06c391 100644
--- a/ohmpi/hardware_system.py
+++ b/ohmpi/hardware_system.py
@@ -317,6 +317,43 @@ class OhmPiHardware:
sp = np.mean(mean_vmn[np.ix_(polarity == 1)] + mean_vmn[np.ix_(polarity == -1)]) / 2
return sp
+ def _find_vab(self, vab, iab, vmn, p_max, vab_max, vmn_max):
+ iab_mean = np.mean(iab)
+ iab_std = np.std(iab)
+ vmn_mean = np.mean(vmn)
+ vmn_std = np.std(vmn)
+ print(f'iab: ({iab_mean:.5f}, {iab_std:5f}), vmn: ({vmn_mean:.4f}, {vmn_std:.4f})')
+ # bounds on iab
+ iab_upper_bound = iab_mean + 2 * iab_std
+ iab_lower_bound = np.max([0.00001, iab_mean - 2 * iab_std])
+ # bounds on vmn
+ vmn_upper_bound = vmn_mean + 2 * vmn_std
+ vmn_lower_bound = np.max([0.000001, vmn_mean - 2 * vmn_std])
+ # ax.plot([vab[k]] * 2, [vmn_lower_bound, vmn_upper_bound], '-b')
+ # ax.plot([0, vab_max], [0, vmn_upper_bound * vab_max / vab[k]], '-r', alpha=(k + 1) / n_steps)
+ # ax.plot([0, vab_max], [0, vmn_lower_bound * vab_max / vab[k]], '-g', alpha=(k + 1) / n_steps)
+ # bounds on rab
+ rab_lower_bound = np.max([0.1, np.abs(vab / iab_upper_bound)])
+ rab_upper_bound = np.max([0.1, np.abs(vab / iab_lower_bound)])
+ # bounds on r
+ r_lower_bound = np.max([0.1, np.abs(vmn_lower_bound / iab_upper_bound)])
+ r_upper_bound = np.max([0.1, np.abs(vmn_upper_bound / iab_lower_bound)])
+ # conditions for vab update
+ cond_vmn_max = rab_lower_bound / r_upper_bound * vmn_max
+ cond_p_max = np.sqrt(p_max * rab_lower_bound)
+ print(f'Rab: [{rab_lower_bound:.1f}, {rab_upper_bound:.1f}], R: [{r_lower_bound:.1f},{r_upper_bound:.1f}]')
+ print(f'{k}: [{vab_max:.1f}, {cond_vmn_max:.1f}, {cond_p_max:.1f}]')
+ new_vab = np.min([vab_max, cond_vmn_max, cond_p_max])
+ if new_vab == vab_max:
+ print('Vab bounded by Vab max')
+ elif new_vab == cond_p_max:
+ print('Vab bounded by Pmax')
+ else:
+ assert new_vab == cond_vmn_max
+ print('Vab bounded by Vmn max')
+
+ return new_vab
+
def _compute_tx_volt(self, pulse_duration=0.1, strategy='vmax', tx_volt=5., vab_max=voltage_max,
iab_max=current_max, vmn_max=5., vmn_min=voltage_min, polarities=(1, -1), delay=0.050):
# TODO: Optimise how to pass iab_max, vab_max, vmn_min
@@ -393,56 +430,15 @@ class OhmPiHardware:
diff_vab = np.inf
while (k < n_steps) and (diff_vab > diff_vab_lim):
vabs = []
- for pol in polarities:
- # self.tx.polarity = pol
- # set gains automatically
- injection = Thread(target=self._inject, kwargs={'injection_duration': 0.2, 'polarity': pol})
- readings = Thread(target=self._read_values)
- injection.start()
- self.tx_sync.wait()
- readings.start()
- readings.join()
- injection.join()
- v = np.where((self.readings[:, 0] > delay) & (self.readings[:, 2] != 0))[0] # NOTE : discard data aquired in the first x ms
+ self._vab_pulses(vab_list[k], durations=[0.2, 0.2], polarities=[1, -1])
+ for pol in range(2):
+ v = np.where((self.readings[:, 0] > delay) & (self.readings[:, 2] != 0) & (self.readings[:, pol]))[0] # NOTE : discard data aquired in the first x ms
iab = self.readings[v, 3]/1000.
vmn = np.abs(self.readings[v, 4]/1000. * self.readings[v, 2])
- iab_mean = np.mean(iab)
- iab_std = np.std(iab)
- vmn_mean = np.mean(vmn)
- vmn_std = np.std(vmn)
- print(f'iab: ({iab_mean:.5f}, {iab_std:5f}), vmn: ({vmn_mean:.4f}, {vmn_std:.4f})')
- # bounds on iab
- iab_upper_bound = iab_mean + 2 * iab_std
- iab_lower_bound = np.max([0.00001, iab_mean - 2 * iab_std])
- # bounds on vmn
- vmn_upper_bound = vmn_mean + 2 * vmn_std
- vmn_lower_bound = np.max([0.000001, vmn_mean - 2 * vmn_std])
- # ax.plot([vab[k]] * 2, [vmn_lower_bound, vmn_upper_bound], '-b')
- # ax.plot([0, vab_max], [0, vmn_upper_bound * vab_max / vab[k]], '-r', alpha=(k + 1) / n_steps)
- # ax.plot([0, vab_max], [0, vmn_lower_bound * vab_max / vab[k]], '-g', alpha=(k + 1) / n_steps)
- # bounds on rab
- rab_lower_bound = np.max([0.1, np.abs(vab_list[k] / iab_upper_bound)])
- rab_upper_bound = np.max([0.1, np.abs(vab_list[k] / iab_lower_bound)])
- # bounds on r
- r_lower_bound = np.max([0.1, np.abs(vmn_lower_bound / iab_upper_bound)])
- r_upper_bound = np.max([0.1, np.abs(vmn_upper_bound / iab_lower_bound)])
- # conditions for vab update
- cond_vmn_max = rab_lower_bound / r_upper_bound * vmn_max
- cond_p_max = np.sqrt(p_max * rab_lower_bound)
- new_vab = np.min([vab_max, cond_vmn_max, cond_p_max])
+ new_vab = self._find_vab(vab_list[k], iab, vmn, )
diff_vab = np.abs(new_vab - vab_list[k])
- print(f'Rab: [{rab_lower_bound:.1f}, {rab_upper_bound:.1f}], R: [{r_lower_bound:.1f},{r_upper_bound:.1f}]')
- print(
- f'{k}: [{vab_max:.1f}, {cond_vmn_max:.1f}, {cond_p_max:.1f}], new_vab: {new_vab}, diff_vab: {diff_vab}\n')
- if new_vab == vab_max:
- print('Vab bounded by Vab max')
- elif new_vab == cond_p_max:
- print('Vab bounded by Pmax')
- elif new_vab == cond_vmn_max:
- print('Vab bounded by Vmn max')
- else:
- print('Next step')
vabs.append(new_vab)
+ print(f'new_vab: {new_vab}, diff_vab: {diff_vab}\n')
if diff_vab < diff_vab_lim:
print('stopped on vab increase too small')
else: