From 354c5dd29bbd31630ca2ccca996daa70bbe05b15 Mon Sep 17 00:00:00 2001
From: su530201 <olivier.kaufmann@umons.ac.be>
Date: Wed, 18 Oct 2023 22:44:24 +0200
Subject: [PATCH] Fixes bug in _compute_tx_volt
---
ohmpi/hardware_system.py | 17 +++++++++--------
1 file changed, 9 insertions(+), 8 deletions(-)
diff --git a/ohmpi/hardware_system.py b/ohmpi/hardware_system.py
index 234048d7..e55efe3e 100644
--- a/ohmpi/hardware_system.py
+++ b/ohmpi/hardware_system.py
@@ -458,8 +458,9 @@ class OhmPiHardware:
vab_max = np.abs(vab_max)
vmn_min = np.abs(vmn_min)
k = 0
- vab = np.zeros(n_steps + 1) * np.nan
- vab[k] = np.min([np.abs(tx_volt), vab_max])
+ vab_list = np.zeros(n_steps + 1) * np.nan
+ vab = np.min([np.abs(tx_volt), vab_max])
+ vab_list[k] = vab
# self.tx.turn_on()
switch_pwr_off, switch_tx_pwr_off = False, False # TODO: check if these should be moved in kwargs
if self.tx.pwr_state == 'off':
@@ -491,7 +492,7 @@ class OhmPiHardware:
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
+ v = np.where((self.readings[:, 0] > delay) & (self.readings[:, 2] != 0))[0] # NOTE : discard data aquired in the first x ms
iab = self.readings[v, 3]
vmn = self.readings[v, 4] * self.readings[v, 2]
iab_mean = np.mean(iab)
@@ -509,8 +510,8 @@ class OhmPiHardware:
# 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 = vab[k] / iab_upper_bound
- rab_upper_bound = vab[k] / iab_lower_bound
+ rab_lower_bound = vab_list[k] / iab_upper_bound
+ rab_upper_bound = vab_list[k] / iab_lower_bound
# bounds on r
r_lower_bound = vmn_lower_bound / iab_upper_bound
r_upper_bound = vmn_upper_bound / iab_lower_bound
@@ -518,7 +519,7 @@ class OhmPiHardware:
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])
- diff_vab = np.abs(new_vab - vab[k])
+ 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')
@@ -536,8 +537,8 @@ class OhmPiHardware:
else:
print('stopped on maximum number of steps reached')
k = k + 1
- vab[k] = np.min(vabs)
- vab_opt = vab[k-1]
+ vab_list[k] = np.min(vabs)
+ vab_opt = vab_list[k-1]
print(f'Selected Vab: {vab_opt:.2f}')
# if strategy == 'vmax':
--
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