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Commit b60bf0de authored by Arnaud WATLET's avatar Arnaud WATLET
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Updates compute_tx_volt kwargs

parent 986ac206
master 144-create-testing-framework-for-tests-automation code_refactor dev/dummy v2024_rc v2025_alpha v2024.0.35 v2024.0.34 v2024.0.33 v2024.0.32 v2024.0.31 v2024.0.30 v2024.0.29 v2024.0.28 v2024.0.27 v2024.0.26 v2024.0.25 v2024.0.24 v2024.0.23 v2024.0.22 v2024.0.21 v2024.0.20 v2024.0.19 v2024.0.18 v2024.0.17 v2024.0.16 v2024.0.15 v2024.0.14 v2024.0.13 v2024.0.12 v2024.0.11 v2024.0.10 v2024.0.9 v2024.0.8 v2024.0.7 v2024.0.0
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ohmpi/hardware_system.py
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......@@ -403,8 +403,9 @@ class OhmPiHardware:
# polarity = 1
# return vab, polarity, rab
def _compute_tx_volt(self, pulse_duration=0.1, strategy='vmax', tx_volt=5.,
vab_max=voltage_max, vmn_min=voltage_min, polarities=(1, -1), delay=0.050):
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
"""Estimates best Tx voltage based on different strategies.
At first a half-cycle is made for a short duration with a fixed
known voltage. This gives us Iab and Rab. We also measure Vmn.
......@@ -443,127 +444,126 @@ class OhmPiHardware:
rab : float
Resistance between injection electrodes
"""
# Get those values from components
p_max = 2.5
vmn_max = 5.
# define a sill
diff_vab_lim = 2.5
n_steps = 4
# Set gain at min
self.rx.reset_gain()
vab_max = np.abs(vab_max)
vmn_min = np.abs(vmn_min)
vab = np.min([np.abs(tx_volt), vab_max])
# 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':
self.tx.pwr_state = 'on'
switch_tx_pwr_off = True
if self.tx.pwr.pwr_state == 'off':
self.tx.pwr.pwr_state = 'on'
switch_pwr_off = True
if 1. / self.rx.sampling_rate > pulse_duration:
sampling_rate = 1. / pulse_duration # TODO: check this...
else:
sampling_rate = self.tx.sampling_rate
current, voltage = 0., 0.
if self.tx.pwr.voltage_adjustable:
self.tx.pwr.voltage = vab
if self.tx.pwr.pwr_state == 'off':
self.tx.pwr.pwr_state = 'on'
switch_pwr_off = True
k = 0
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
iab = self.readings[v, 3]
vmn = self.readings[v, 4] * 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 = vab[k] / iab_upper_bound
rab_upper_bound = vab[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
# 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])
diff_vab = np.abs(new_vab - vab[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')
vab[k + 1] = new_vab
k = k + 1
vabs.append(new_vab)
if diff_vab < diff_vab_lim:
print('stopped on vab increase too small')
else:
print('stopped on maximum number of steps reached')
# Get those values from components
p_max = vab_max * iab_max
# define a sill
diff_vab_lim = 2.5
n_steps = 4
# Set gain at min
self.rx.reset_gain()
vab_max = np.abs(vab_max)
vmn_min = np.abs(vmn_min)
vab = np.min([np.abs(tx_volt), vab_max])
# 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':
self.tx.pwr_state = 'on'
switch_tx_pwr_off = True
if self.tx.pwr.pwr_state == 'off':
self.tx.pwr.pwr_state = 'on'
switch_pwr_off = True
if 1. / self.rx.sampling_rate > pulse_duration:
sampling_rate = 1. / pulse_duration # TODO: check this...
else:
sampling_rate = self.tx.sampling_rate
current, voltage = 0., 0.
if self.tx.pwr.voltage_adjustable:
self.tx.pwr.voltage = vab
if self.tx.pwr.pwr_state == 'off':
self.tx.pwr.pwr_state = 'on'
switch_pwr_off = True
k = 0
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
iab = self.readings[v, 3]
vmn = self.readings[v, 4] * 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 = vab[k] / iab_upper_bound
rab_upper_bound = vab[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
# 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])
diff_vab = np.abs(new_vab - vab[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')
vab[k + 1] = new_vab
k = k + 1
vabs.append(new_vab)
if diff_vab < diff_vab_lim:
print('stopped on vab increase too small')
else:
print('stopped on maximum number of steps reached')
print(f'Selected Vab: {vab:.2f}')
vab = np.min(vabs)
print(f'Selected Vab: {vab:.2f}')
vab = np.min(vabs)
print(f'Selected Vab: {vab:.2f}')
# if strategy == 'vmax':
# # implement different strategies
# if vab < vab_max and iab < current_max:
# vab = vab * np.min([0.9 * vab_max / vab, 0.9 * current_max / iab]) # TODO: check if setting at 90% of max as a safety margin is OK
# self.tx.exec_logger.debug(f'vmax strategy: setting VAB to {vab} V.')
# elif strategy == 'vmin':
# if vab <= vab_max and iab < current_max:
# vab = vab * np.min([0.9 * vab_max / vab, vmn_min / np.abs(vmn), 0.9 * current_max / iab]) # TODO: check if setting at 90% of max as a safety margin is OK
# elif strategy != 'constant':
# self.tx.exec_logger.warning(f'Unknown strategy {strategy} for setting VAB! Using {vab} V')
# else:
# self.tx.exec_logger.debug(f'Constant strategy for setting VAB, using {vab} V')
# # self.tx.turn_off()
# if switch_pwr_off:
# self.tx.pwr.pwr_state = 'off'
# if switch_tx_pwr_off:
# self.tx.pwr_state = 'off'
# rab = (np.abs(vab) * 1000.) / iab
# self.exec_logger.debug(f'RAB = {rab:.2f} Ohms')
# if vmn < 0:
# polarity = -1 # TODO: check if we really need to return polarity
# else:
# polarity = 1
return vab, None, None
# if strategy == 'vmax':
# # implement different strategies
# if vab < vab_max and iab < current_max:
# vab = vab * np.min([0.9 * vab_max / vab, 0.9 * current_max / iab]) # TODO: check if setting at 90% of max as a safety margin is OK
# self.tx.exec_logger.debug(f'vmax strategy: setting VAB to {vab} V.')
# elif strategy == 'vmin':
# if vab <= vab_max and iab < current_max:
# vab = vab * np.min([0.9 * vab_max / vab, vmn_min / np.abs(vmn), 0.9 * current_max / iab]) # TODO: check if setting at 90% of max as a safety margin is OK
# elif strategy != 'constant':
# self.tx.exec_logger.warning(f'Unknown strategy {strategy} for setting VAB! Using {vab} V')
# else:
# self.tx.exec_logger.debug(f'Constant strategy for setting VAB, using {vab} V')
# # self.tx.turn_off()
# if switch_pwr_off:
# self.tx.pwr.pwr_state = 'off'
# if switch_tx_pwr_off:
# self.tx.pwr_state = 'off'
# rab = (np.abs(vab) * 1000.) / iab
# self.exec_logger.debug(f'RAB = {rab:.2f} Ohms')
# if vmn < 0:
# polarity = -1 # TODO: check if we really need to return polarity
# else:
# polarity = 1
return vab
def _plot_readings(self, save_fig=False):
# Plot graphs
......
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