def sed_to_group_current(fine_time, fine_stress, tau1, tau2, k1, mode, groups):
"""
Convert fine stress to current in all groups of Merkel Cells.
Parameters
----------
fine_sed : 1d-array
Interpolated SED.
groups : 1d-array
Groups of Merkel cells.
Example: [8, 5, 3, 1] has 4 groups, 8, 5, 3, and 1 cell in each group.
Returns
-------
group_current : nd-array
Generator current generated by stress in groups.
n = the size of groups.
"""
mc_size = groups.shape[0]
stress_size = fine_stress.shape[0]
group_current = np.zeros([stress_size, mc_size])
for i in range(mc_size):
group_current[:, i] = stress_to_current(fine_time, fine_stress, tau1,
tau2, k1, mode) * groups[i]
return group_current
def plot_current_and_spike(key,
val,
fig,
axs,
fine_time=fine_time,
fine_stress=fine_stress,
fitApproach=fitApproach,
**plotkws):
# Initialization
lmpars = copy.deepcopy(fitApproach.ref_mean_lmpars)
lmpars[key].value = val
params_dict_new = lmpars_to_params(lmpars)
# Formatting data
if key == 'k2':
lmpars['k4'].value = k4_list[k2_list.index(val)]
params_dict_new = lmpars_to_params(lmpars)
keyname = 'kmc1'
params_paper = get_params_paper(lmpars)
intval = int(params_paper[keyname] * 100)
elif key == 'tau1':
keyname = 'tau_nr'
intval = int(val)
elif key == 'tau2':
keyname = 'tau_mc'
intval = int(val)
# Calculation
single_current = stress_to_current(fine_time, fine_stress,
**params_dict_new).sum(axis=1)
spike_time, fr_inst = get_mod_spike(
lmpars,
**fitApproach.data_dicts_dicts[animal][stim]['mod_data_dict'])
# Plotting
axs[0].plot(fine_time,
single_current,
'-',
label='%s=%d' % (keyname, intval),
**plotkws)
axs[1].plot(spike_time,
fr_inst * 1e3,
'.',
label='%s=%d' % (keyname, intval),
**plotkws)
# Saving data
np.savetxt('./data/GregCSVs/ParamTuning/current_%s_%d.csv' %
(keyname, intval),
single_current,
delimiter=',')
np.savetxt('./data/GregCSVs/ParamTuning/time_%s_%d.csv' %
(keyname, intval),
fine_time,
delimiter=',')
np.savetxt('./data/GregCSVs/ParamTuning/fr_inst_%s_%d.csv' %
(keyname, intval),
fr_inst,
delimiter=',')
np.savetxt('./data/GregCSVs/ParamTuning/spike_time_%s_%d.csv' %
(keyname, intval),
spike_time,
delimiter=',')
return fig, axs
def plot_current_and_spike(key, val, fig, axs,
fine_time=fine_time, fine_stress=fine_stress,
fitApproach=fitApproach,
**plotkws):
# Initialization
lmpars = copy.deepcopy(fitApproach.ref_mean_lmpars)
lmpars[key].value = val
params_dict_new = lmpars_to_params(lmpars)
# Formatting data
if key == 'k2':
lmpars['k4'].value = k4_list[k2_list.index(val)]
params_dict_new = lmpars_to_params(lmpars)
keyname = 'kmc1'
params_paper = get_params_paper(lmpars)
intval = int(params_paper[keyname] * 100)
elif key == 'tau1':
keyname = 'tau_nr'
intval = int(val)
elif key == 'tau2':
keyname = 'tau_mc'
intval = int(val)
# Calculation
single_current = stress_to_current(fine_time, fine_stress,
**params_dict_new).sum(axis=1)
spike_time, fr_inst = get_mod_spike(
lmpars,
**fitApproach.data_dicts_dicts[animal][stim]['mod_data_dict'])
# Plotting
axs[0].plot(fine_time, single_current, '-',
label='%s=%d' % (keyname, intval),
**plotkws)
axs[1].plot(spike_time, fr_inst * 1e3, '.',
label='%s=%d' % (keyname, intval),
**plotkws)
# Saving data
np.savetxt('./data/GregCSVs/ParamTuning/current_%s_%d.csv' %
(keyname, intval), single_current, delimiter=',')
np.savetxt('./data/GregCSVs/ParamTuning/time_%s_%d.csv' %
(keyname, intval), fine_time, delimiter=',')
np.savetxt('./data/GregCSVs/ParamTuning/fr_inst_%s_%d.csv' %
(keyname, intval), fr_inst, delimiter=',')
np.savetxt('./data/GregCSVs/ParamTuning/spike_time_%s_%d.csv' %
(keyname, intval), spike_time, delimiter=',')
return fig, axs
def stress_to_group_current(fine_time, fine_stress, groups, **params):
"""
Convert fine stress to current in all groups of Merkel Cells.
Parameters
----------
fine_time : 1xM array
Interpolated time.
fine_stress : 1xM array
Interpolated stress.
groups : 1xN array
Groups of Merkel cells.
Example: [8, 5, 3, 1] has 4 groups, 8, 5, 3, and 1 cell in each group.
Returns
-------
group_gen_current : MxN array
Generator current generated by stress in groups.
"""
single_gen_current = stress_to_current(
fine_time, fine_stress, **params).sum(axis=1)
group_gen_current = np.multiply(single_gen_current[None].T, groups)
return group_gen_current
def stress_to_group_current(fine_time, fine_stress, groups, **params):
"""
Convert fine stress to current in all groups of Merkel Cells.
Parameters
----------
fine_time : 1xM array
Interpolated time.
fine_stress : 1xM array
Interpolated stress.
groups : 1xN array
Groups of Merkel cells.
Example: [8, 5, 3, 1] has 4 groups, 8, 5, 3, and 1 cell in each group.
Returns
-------
group_gen_current : MxN array
Generator current generated by stress in groups.
"""
single_gen_current = stress_to_current(fine_time, fine_stress,
**params).sum(axis=1)
group_gen_current = np.multiply(single_gen_current[None].T, groups)
return group_gen_current
def setup_gen_function(data):
# Generator function decay parameters
data['current_arr'] = stress_to_current(
data['fine_time'], data['fine_stress'], **params)
def test_stress_to_current(load_data):
current_arr = stress_to_current(load_data['fine_time'],
load_data['fine_stress'], **params)
assert np.allclose(current_arr, load_data['current_arr'])
fig, axs = plt.subplots(3, 3, figsize=(7, 6))
k_scale_dict_dict = {
'Piezo2CONT': {},
'Piezo2CKO': {'k4': 0},
'Atoh1CKO': {'k2': 0, 'k4': 0}}
for i, animal in enumerate(ANIMAL_LIST):
lmpars_cko = get_lmpars_cko(fitApproach.ref_mean_lmpars,
k_scale_dict_dict[animal])
# Plot current
for stim in REF_STIM_LIST:
fine_time = fitApproach.data_dicts_dicts[animal][stim][
'mod_data_dict']['time']
fine_stress = fitApproach.data_dicts_dicts[animal][stim][
'mod_data_dict']['stress']
params_dict = lmpars_to_params(lmpars_cko)
single_current = stress_to_current(fine_time, fine_stress,
**params_dict).sum(axis=1)
axs[0, i].plot(fine_time, -single_current, color=COLOR_LIST[stim])
axs[0, i].set_xlabel('Time (msec)')
axs[0, i].set_ylabel('Current (pA)')
axs[0, i].set_ylim(-20, 0)
# Add the bar on top
mod_peak_time = fine_time[single_current.argmax()]
axs[0, i].plot([mod_peak_time, 5000],
[2 - stim * .5, 2 - stim * .5], '-',
lw=4, c='.5', clip_on=False)
axs[0, i].plot([0, mod_peak_time],
[2 - stim * .5, 2 - stim * .5], '-',
lw=4, color='k', clip_on=False)
axs[0, i].set_xlim(0, 5000)
# Plot firing rate
fitApproach.plot_cko_customized(
def setup_gen_function(data):
# Generator function decay parameters
data['current_arr'] = stress_to_current(data['fine_time'],
data['fine_stress'], **params)
'Atoh1CKO': {
'k2': 0,
'k4': 0
}
}
for i, animal in enumerate(ANIMAL_LIST):
lmpars_cko = get_lmpars_cko(fitApproach.ref_mean_lmpars,
k_scale_dict_dict[animal])
# Plot current
for stim in REF_STIM_LIST:
fine_time = fitApproach.data_dicts_dicts[animal][stim][
'mod_data_dict']['time']
fine_stress = fitApproach.data_dicts_dicts[animal][stim][
'mod_data_dict']['stress']
params_dict = lmpars_to_params(lmpars_cko)
single_current = stress_to_current(fine_time, fine_stress,
**params_dict).sum(axis=1)
axs[0, i].plot(fine_time, -single_current, color=COLOR_LIST[stim])
axs[0, i].set_xlabel('Time (msec)')
axs[0, i].set_ylabel('Current (pA)')
axs[0, i].set_ylim(-20, 0)
# Add the bar on top
mod_peak_time = fine_time[single_current.argmax()]
axs[0, i].plot([mod_peak_time, 5000],
[2 - stim * .5, 2 - stim * .5],
'-',
lw=4,
c='.5',
clip_on=False)
axs[0, i].plot([0, mod_peak_time], [2 - stim * .5, 2 - stim * .5],
'-',
lw=4,
def test_stress_to_current(load_data):
current_arr = stress_to_current(
load_data['fine_time'], load_data['fine_stress'], **params)
assert np.allclose(current_arr, load_data['current_arr'])