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simulation_protocols.py
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216 lines (192 loc) · 11.6 KB
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# This file contains simulation protocols for threshold search of the enumerated phrenicus experiments.
# Parameters in the function:
# - x-offset of the nerve shape
# - z-offset of the nerve shape
# - x-offset and z-offset of the nerve shape
# Possible outputs:
# - Thresholds (either in E-field amplitude or coil current)
# - AFs, E-field along axon, potential along axon
# Output format: csf (via Pandas dataframe)
#
# Other parameters, set in the GUI: E-field, Smooth field, Nerve_shape, Stimulus, Axon-diameters
# Created: 25.01.2023
# Author: J Rapp
x_search = False
z_search = False
shift = False
class ExperimentProtocol:
def __init__(self, main_widget, interpol_rad, nerve_shape_step, shift=True, threshold=False):
super(ExperimentProtocol, self).__init__()
self.master_widget = main_widget
def threshold_search(self):
# Transfer to main file
if not self.nerve_widget.nerve_dict:
return
selected_nerve = self.nerve_widget.nerve_dict[self.nerve_widget.nerve_combo_box.currentText()]
if not selected_nerve.axon_list:
return
export_dict = {'Diameter': []}
for axon in selected_nerve.axon_list:
export_dict['Diameter'].append(axon.diameter)
if x_search:
self.run_x_search(axon,export_dict)
elif z_search:
elif x_search and z_search:
elif shift:
shift_search(x_search, z_search)
# Set position parameter
# z-offset only
# z-offset and x-offset (different e-fields)
z_offset = np.arange(-25000, 26000, 1000)
export_dict['z-offset'] = z_offset
for filename in sorted(field_path):
print(str(os.path.basename(filename)))
with open(filename, 'rb') as e:
self.e_field_widget.e_field = pickle.load(e)
export_dict[os.path.basename(filename)] = []
self.e_field_widget.smooth_e_field()
self.e_field_widget.nerve_shape.z = self.e_field_widget.nerve_shape.z + z
if self.e_field_widget.state == self.e_field_widget.E_FIELD_ONLY:
neuron_sim = ns.NeuronSimEField(self.e_field_widget.e_field, interpolation_radius_index,
axon, self.time_axis, self.stimulus, self.total_time)
elif self.e_field_widget.state == self.e_field_widget.NERVE_SHAPE_ONLY:
neuron_sim = ns.NeuronSimNerveShape(self.e_field_widget.nerve_shape, nerve_shape_step_size,
axon, self.time_axis, self.stimulus, self.total_time)
elif self.e_field_widget.state == self.e_field_widget.E_FIELD_WITH_NERVE_SHAPE:
neuron_sim = ns.NeuronSimEFieldWithNerveShape(self.e_field_widget.e_field,
interpolation_radius_index,
self.e_field_widget.nerve_shape, nerve_shape_step_size,
axon, self.time_axis, self.stimulus, self.total_time)
neuron_sim.quasipot()
threshold = neuron_sim.threshold_simulation(self.threshold_widget)
self.threshold_label.setText(str(threshold))
current = 6000 * threshold
self.e_field_widget.nerve_shape.z = self.e_field_widget.nerve_shape.z - z
export_dict[z].append(current)
df = pd.DataFrame(export_dict)
today = date.today()
df.to_csv(str(today) + 'phrenic_fo8_diam_vs_z_offset_x_8_RECT.csv', index=False, header=True)
print('Finished!')
def threshold_search(self):
if not self.nerve_widget.nerve_dict:
return
selected_nerve = self.nerve_widget.nerve_dict[self.nerve_widget.nerve_combo_box.currentText()]
if not selected_nerve.axon_list:
return
# Dict -----------------------------------------------------------------
export_dict = {'Diameter': []}
for axon in selected_nerve.axon_list:
export_dict['Diameter'].append(axon.diameter)
z_offset = np.arange(-25000, 26000, 1000)
for z in z_offset:
if z not in export_dict:
export_dict[z] = []
self.e_field_widget.nerve_shape.z = self.e_field_widget.nerve_shape.z + z
if self.e_field_widget.state == self.e_field_widget.E_FIELD_ONLY:
neuron_sim = ns.NeuronSimEField(self.e_field_widget.e_field, interpolation_radius_index,
axon, self.time_axis, self.stimulus, self.total_time)
elif self.e_field_widget.state == self.e_field_widget.NERVE_SHAPE_ONLY:
neuron_sim = ns.NeuronSimNerveShape(self.e_field_widget.nerve_shape, nerve_shape_step_size,
axon, self.time_axis, self.stimulus, self.total_time)
elif self.e_field_widget.state == self.e_field_widget.E_FIELD_WITH_NERVE_SHAPE:
neuron_sim = ns.NeuronSimEFieldWithNerveShape(self.e_field_widget.e_field,
interpolation_radius_index,
self.e_field_widget.nerve_shape, nerve_shape_step_size,
axon, self.time_axis, self.stimulus, self.total_time)
neuron_sim.quasipot()
threshold = neuron_sim.threshold_simulation(self.threshold_widget)
self.threshold_label.setText(str(threshold))
current = 6000 * threshold
self.e_field_widget.nerve_shape.z = self.e_field_widget.nerve_shape.z - z
export_dict[z].append(current)
df = pd.DataFrame(export_dict)
today = date.today()
df.to_csv(str(today) + 'phrenic_fo8_diam_vs_z_offset_x_8_RECT.csv', index=False, header=True)
print('Finished!')
def threshold_search_with_shift(self):
if not self.nerve_widget.nerve_dict:
return
selected_nerve = self.nerve_widget.nerve_dict[self.nerve_widget.nerve_combo_box.currentText()]
if not selected_nerve.axon_list:
return
# Dict -----------------------------------------------------------------
offset = [0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700]
exp_name = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r']
for l, e in zip(offset, exp_name):
export_dict = {'Diameter': []}
for axon in selected_nerve.axon_list:
export_dict['Diameter'].append(axon.diameter)
z_offset = np.arange(-25000, 26000, 1000)
for z in z_offset:
if z not in export_dict:
export_dict[z] = []
nerve_shape = deepcopy(self.e_field_widget.nerve_shape)
new_x, new_y, new_z = mf.generate_new_start_point(nerve_shape.x[0], nerve_shape.y[0],
nerve_shape.z[0], nerve_shape.x[28],
nerve_shape.y[28], nerve_shape.z[28], l)
nerve_shape.x[0] = new_x
nerve_shape.y[0] = new_y
nerve_shape.z[0] = new_z
nerve_shape.z = nerve_shape.z + z
if self.e_field_widget.state == self.e_field_widget.E_FIELD_ONLY:
neuron_sim = ns.NeuronSimEField(self.e_field_widget.e_field, interpolation_radius_index,
axon, self.time_axis, self.stimulus, self.total_time)
elif self.e_field_widget.state == self.e_field_widget.NERVE_SHAPE_ONLY:
neuron_sim = ns.NeuronSimNerveShape(nerve_shape, nerve_shape_step_size,
axon, self.time_axis, self.stimulus, self.total_time)
elif self.e_field_widget.state == self.e_field_widget.E_FIELD_WITH_NERVE_SHAPE:
neuron_sim = ns.NeuronSimEFieldWithNerveShape(self.e_field_widget.e_field,
interpolation_radius_index, nerve_shape,
nerve_shape_step_size,
axon, self.time_axis, self.stimulus, self.total_time)
neuron_sim.quasipot()
threshold = neuron_sim.threshold_simulation(self.threshold_widget)
self.threshold_label.setText(str(threshold))
current = 6000 * threshold
nerve_shape.z = nerve_shape.z - z
export_dict[z].append(current)
df = pd.DataFrame(export_dict)
today = date.today()
df.to_csv('020_' + e + e + '.csv', index=False, header=True)
print('Finished!')
def run_x_search(self, axon, exportdict):
master = self.master_widget
export_dict = exportdict
x_offset = np.arange(-25000, 26000, 1000)
for x in x_offset:
if x not in export_dict:
export_dict[x] = []
master.input_data_widget.nerve_shape.z = master.input_data_widget.nerve_shape.z + z
self.simulate(axon)
threshold = neuron_sim.threshold_simulation(self.threshold_widget)
self.threshold_label.setText(str(threshold))
current = 6000 * threshold
self.e_field_widget.nerve_shape.z = self.e_field_widget.nerve_shape.z - z
export_dict[z].append(current)
return # some list of offset th currents
def run_z_search(diam):
z_offset = np.arange(-25000, 26000, 1000)
for z in z_offset:
if z not in export_dict:
export_dict[z] = []
def simulate(self, axon, threshold=False):
master = self.master_widget
if master.input_data_widget.state == master.input_data_widget.NERVE_SHAPE_ONLY:
neuron_sim = ns.NeuronSimNerveShape(master.input_data_widget.nerve_shape, nerve_shape_step_size,
axon, master.time_axis, master.stimulus, master.total_time)
elif master.input_data_widget.state == master.input_data_widget.E_FIELD_WITH_NERVE_SHAPE:
neuron_sim = ns.NeuronSimEFieldWithNerveShape(master.input_data_widget.e_field,
interpolation_radius_index,
master.input_data_widget.nerve_shape, nerve_shape_step_size,
axon, master.time_axis, master.stimulus, master.total_time)
else:
neuron_sim = ns.NeuronSimEField(master.input_data_widget.e_field, interpolation_radius_index,
axon, master.time_axis, master.stimulus, master.total_time)
neuron_sim.quasipot()
if threshold:
threshold = neuron_sim.threshold_simulation(master.threshold_widget)
return threshold
else:
return neuron_sim.mdf, neuron_sim.axon.e_field_along_axon, neuron_sim.axon.potential_along_axon
def shift_search(xsearch,zsearch):
return