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notebook_test
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Add errorbars

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imperator 1 year ago
parent 0f2a06a74b
commit 660342aab0
1 changed files with 66 additions and 25 deletions
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91
utils.py
Unescape Escape View File

@ -1,6 +1,7 @@
from itertools import product
import numpy as np
from scipy.integrate import trapezoid
from scipy.stats import sem
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from IPython.display import display
@ -36,6 +37,22 @@ def draw_figure(fig):
plt.show()
def update_errorbar(err_container, x, y, yerr):
err_container.lines[0].set_data(x, y)
linecol = err_container.lines[2][0]
segments = []
for xi, yi, yerri in zip(x, y, yerr):
segments.append([[xi, yi - yerri], [xi, yi + yerri]])
linecol.set_segments(segments)
if len(err_container.lines[1]) == 2:
lower_caps, upper_caps = err_container.lines[1]
lower_caps.set_data(x, y - yerr)
upper_caps.set_data(x, y + yerr)
def maybe_setup(setup_fun, state):
if not is_colab:
return
@ -202,17 +219,6 @@ def plot_sims(C_size=11, num_sims=30 if not is_colab else 5):
interact(update_plot, **sliders)
def update_errorbar(err_container, x, y, yerr):
err_container.lines[0].set_data(x, y)
linecol = err_container.lines[2][0]
segments = []
for xi, yi, yerri in zip(x, y, yerr):
segments.append([[xi, yi - yerri], [xi, yi + yerri]])
linecol.set_segments(segments)
def plot_model_free_analysis_conditions(C, ks, num_sims_per_condition=2_000):
setup_matplotlib_magic()
@ -437,13 +443,15 @@ def plot_single_neuron(mat_data):
def setup():
fig, axes = plt.subplots(figsize=(6.5, 4.5))
neuron_line = axes.plot([], [])[0]
neuron_line = axes.errorbar([np.nan], [np.nan], yerr=[np.nan], capsize=4., label='mean firing rate with 95% CI')
axes.set(
ylabel=r'$\sqrt{N_\mathrm{spikes}}$',
xlabel='time [ms]',
xlim=(0, 800)
)
axes.legend(loc='upper right')
return {'fig': fig, 'axes': axes, 'neuron_line': neuron_line}
@ -451,8 +459,13 @@ def plot_single_neuron(mat_data):
def update_plot(neuron_idx):
maybe_setup(setup, state)
state['neuron_line'].set_data(time, binned_spike_matrix.mean(axis=0)[:, neuron_idx])
update_errorbar(
state['neuron_line'],
time,
binned_spike_matrix[:, :, neuron_idx].mean(axis=0),
yerr=sem(binned_spike_matrix[:, :, neuron_idx], axis=0) * 1.96
)
state['axes'].relim()
state['axes'].autoscale(axis='y')
@ -478,15 +491,15 @@ def plot_neuron_by_choice(mat_data):
def setup():
fig, axes = plt.subplots(1, 2, figsize=(8, 4), sharex=True)
choices = ['right choice', 'left choice']
choices = ['right choice (95% CI)', 'left choice (95% CI)']
correct_lines = []
for choice in choices:
correct_line = axes[0].plot([], [], label=choice)[0]
correct_line = axes[0].errorbar([np.nan], [np.nan], yerr=[np.nan], capsize=4., label=choice)
correct_lines += [correct_line]
incorrect_lines = []
for choice in choices:
incorrect_line = axes[1].plot([], [], label=choice)[0]
incorrect_line = axes[1].errorbar([np.nan], [np.nan], yerr=[np.nan], capsize=4., label=choice)
incorrect_lines += [incorrect_line]
axes[0].set(
@ -509,10 +522,30 @@ def plot_neuron_by_choice(mat_data):
def update_plot(neuron_idx):
maybe_setup(setup, state)
state['correct_lines'][0].set_data(time, binned_spike_matrix[correct_trials_mask & right_choice].mean(axis=0)[:, neuron_idx])
state['correct_lines'][1].set_data(time, binned_spike_matrix[correct_trials_mask & ~right_choice].mean(axis=0)[:, neuron_idx])
state['incorrect_lines'][0].set_data(time, binned_spike_matrix[~correct_trials_mask & right_choice].mean(axis=0)[:, neuron_idx])
state['incorrect_lines'][1].set_data(time, binned_spike_matrix[~correct_trials_mask & ~right_choice].mean(axis=0)[:, neuron_idx])
update_errorbar(
state['correct_lines'][0],
time,
binned_spike_matrix[correct_trials_mask & right_choice][:, :, neuron_idx].mean(axis=0),
sem(binned_spike_matrix[correct_trials_mask & right_choice][:, :, neuron_idx], axis=0) * 1.96
)
update_errorbar(
state['correct_lines'][1],
time,
binned_spike_matrix[correct_trials_mask & ~right_choice][:, :, neuron_idx].mean(axis=0),
sem(binned_spike_matrix[correct_trials_mask & ~right_choice][:, :, neuron_idx], axis=0) * 1.96
)
update_errorbar(
state['incorrect_lines'][0],
time,
binned_spike_matrix[~correct_trials_mask & right_choice][:, :, neuron_idx].mean(axis=0),
sem(binned_spike_matrix[~correct_trials_mask & right_choice][:, :, neuron_idx], axis=0) * 1.96
)
update_errorbar(
state['incorrect_lines'][1],
time,
binned_spike_matrix[~correct_trials_mask & ~right_choice][:, :, neuron_idx].mean(axis=0),
sem(binned_spike_matrix[~correct_trials_mask & ~right_choice][:, :, neuron_idx], axis=0) * 1.96
)
state['axes'][0].relim()
state['axes'][1].relim()
@ -546,12 +579,12 @@ def plot_neuron_by_coherence(mat_data):
choices = ['right choice', 'left choice']
correct_lines = []
for coherence in coherences:
correct_line = axes[0].plot([], [], label=f'{coherence = :.1%}')[0]
correct_line = axes[0].plot([np.nan], [np.nan], yerr=[np.nan], capsize=4., label=f'{coherence = :.1%} (95% CI)')
correct_lines += [correct_line]
incorrect_lines = []
for coherence in coherences:
incorrect_line = axes[1].plot([], [], label=f'{coherence = :.1%}')[0]
incorrect_line = axes[1].plot([np.nan], [np.nan], yerr=[np.nan], capsize=4., label=f'{coherence = :.1%} (95% CI)')
incorrect_lines += [incorrect_line]
axes[0].set(
@ -577,8 +610,16 @@ def plot_neuron_by_coherence(mat_data):
for i, coherence in enumerate(coherences):
coherence_mask = (mat_data['dot_coh'].flatten() == coherence)
state['correct_lines'][i].set_data(time, binned_spike_matrix[correct_trials_mask & coherence_mask].mean(axis=0)[:, neuron_idx])
state['incorrect_lines'][i].set_data(time, binned_spike_matrix[~correct_trials_mask & coherence_mask].mean(axis=0)[:, neuron_idx])
update_errorbar(
state['correct_lines'][i],
time,
binned_spike_matrix[correct_trials_mask & coherence_mask][:, :, neuron_idx].mean(axis=0)
)
update_errorbar(
state['incorrect_lines'][i],
time,
sem(binned_spike_matrix[~correct_trials_mask & coherence_mask][:, :, neuron_idx], axis=0) * 1.96
)
state['axes'][0].relim()
state['axes'][1].relim()

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