yl="Probability\ncorrect (%)",
ymin=44,
ymax=91,
yticks=[50, 70, 90])
print(df_correctness_as_function_of_coherence)
myfig.Line(p0,
x=[0, 25, 50, 100],
y=df_correctness_as_function_of_coherence.values,
lc="black",
zorder=1)
myfig.Scatter(p0,
x=[0, 25, 50, 100],
y=df_correctness_as_function_of_coherence.values,
lc='black',
pt='o',
lw=0.5,
ps=9.8,
pc='white',
zorder=2)
####
# Interbout interval as function of coherence
p0 = myfig.Plot(fig,
num='',
xpos=5,
ypos=20.5,
plot_height=1.25,
plot_width=1.25,
lw=1,
pc='white',
)[f"fraction_of_time_spent"]
fraction_of_time_spent_control = df1.query(
"experiment_name == 'virtual_valley_stimulus_control_drosolarva' and region_bin == @region_bin and time_bin == @time_bin"
)[f"fraction_of_time_spent"]
p_time_spent = ttest_ind(fraction_of_time_spent_experiment,
fraction_of_time_spent_control,
nan_policy='omit',
equal_var=False)[1]
myfig.Scatter(
p0,
x=np.random.random(len(fraction_of_time_spent_experiment)) *
0.1 - 0.05 + x_pos - 0.2,
y=fraction_of_time_spent_experiment,
lc=color,
pt='o',
lw=0.5,
ps=2,
pc='white',
zorder=2,
alpha=0.5)
myfig.Scatter(
p0,
x=np.random.random(len(fraction_of_time_spent_control)) * 0.1 -
0.05 + x_pos + 0.2,
y=fraction_of_time_spent_control,
lc='gray',
pt='o',
lw=0.5,
ps=2,
pc='white',
zticks=[-50, -25, 0, 14, 25, 50],
zl=
"Performance index\n(% change relative to control\nfor fraction of time\nspent in the dark ring)"
)
for i in range(5):
for j in range(5):
print(df.index)
run_length_multiplier = [0.25, 0.5, 1, 2, 4][i]
turn_amplitude_multiplier = [0.25, 0.5, 1, 2, 4][j]
myfig.Scatter(p0,
x=[i],
y=[j],
lc='none',
pt='s',
pc=cmap(
norm(df.loc[run_length_multiplier,
turn_amplitude_multiplier])),
ps=65,
vmin=-40,
vmax=40)
fig.savepdf(root_path / f"figure3_gridsearch", open_pdf=True)
sdfsfsf
# sdf
# print(df.loc[0.5, 0.5])
#
# asd
# pl.plot(df["phototaxis_index_mean"].values)
# #m = [df.loc[]]
angle_change0 = df.query("experiment_name == @experiment_name")[f"angle_change_at_current_turn_event_if_dark_at_current_turn_event"]
angle_change1 = df.query("experiment_name == @experiment_name")[f"angle_change_at_current_turn_event_if_bright_at_current_turn_event"]
if i == 1:
angle_change0 = df.query("experiment_name == @experiment_name")[f"angle_change_at_current_turn_event_if_darkening_since_previous_turn_event"]
angle_change1 = df.query("experiment_name == @experiment_name")[f"angle_change_at_current_turn_event_if_brightening_since_previous_turn_event"]
for j in range(len(angle_change0)):
x1 = np.random.random() * 0.2 - 0.1 + [0, 1, 2.5, 3.5, 6, 7, 8.5, 9.5, 11, 12, 13.5, 14.5, 16, 17, 19.5, 20.5, 22, 23][i * 2]
x2 = np.random.random() * 0.2 - 0.1 + [0, 1, 2.5, 3.5, 6, 7, 8.5, 9.5, 11, 12, 13.5, 14.5, 16, 17, 19.5, 20.5, 22, 23][i * 2 + 1]
y1 = angle_change0[j]
y2 = angle_change1[j]
myfig.Line(p0, x=[x1, x2], y=[y1, y2], lc=color, lw=0.25, zorder=1, alpha=0.5)
myfig.Scatter(p0, x=[x1], y=[y1], lc=color, pt='o', lw=0.25, ps=2, pc='white', zorder=2, alpha=0.5)
myfig.Scatter(p0, x=[x2], y=[y2], lc=color, pt='o', lw=0.25, ps=2, pc='white', zorder=2, alpha=0.5)
x1 = [0, 1, 2.5, 3.5][i * 2]
x2 = [0, 1, 2.5, 3.5][i * 2 + 1]
y1 = np.mean(angle_change0)
y2 = np.mean(angle_change1)
myfig.Line(p0, x=[x1, x2], y=[y1, y2], lc=color, lw=1, zorder=3, alpha=0.9)
myfig.Line(p0, x=[x1, x2], y=[y1, y2], lc=color, lw=1, zorder=3, alpha=0.9)
myfig.Scatter(p0, x=[x1, x2], y=[y1, y2], lc=color, pt='o', lw=0.25, ps=2, pc='white', zorder=4, alpha=0.9)
p = ttest_1samp(angle_change0 - angle_change1, 0, nan_policy='omit')[1]
print("Angle change statistical comparison", i, "Experiment", experiment_name, ": p = ", p, np.mean(angle_change0 - angle_change1), "n = ", len(angle_change0 - angle_change1))
myfig.Line(p0, x=[x1 + 0.1, x2 - 0.1], y=[55, 55], lc='black', lw=0.75)
ymax=91,
yticks=[50, 70, 90],
vlines=[0]) for optimized_i in range(7)
]
# Show the target experimental data
myfig.Line(p1_experiment,
x=[0, 25, 50, 100],
y=target_correctness_as_function_of_coherence.values,
lc="black",
zorder=1)
myfig.Scatter(p1_experiment,
x=[0, 25, 50, 100],
y=target_correctness_as_function_of_coherence.values,
lc='black',
pt='o',
lw=0.5,
ps=9.8,
pc='white',
zorder=2)
myfig.Line(
p2_experiment,
x=[0, 25, 50, 100],
y=target_inter_bout_interval_as_function_of_coherence.values,
lc="black",
zorder=1)
myfig.Scatter(
p2_experiment,
x=[0, 25, 50, 100],
y=target_inter_bout_interval_as_function_of_coherence.values,
luminance_change = df.query(
"experiment_name == @experiment_name"
)[f"luminance_change_over_last_2s_before_current_turn_event"]
if i == 4:
luminance_change = df.query(
"experiment_name == @experiment_name"
)[f"luminance_change_over_last_1s_before_current_turn_event"]
x = np.random.random() * 0.2 - 0.1 + i
y = luminance_change[j]
myfig.Scatter(p0,
x=[x],
y=[y],
lc=color,
pt='o',
lw=0.5,
ps=2,
pc='white',
zorder=2,
alpha=0.5)
if i > 0:
myfig.Line(p0,
x=[x, previous_x],
y=[y, previous_y],
lc=color,
lw=0.5,
zorder=1,
alpha=0.5)
previous_x = x
hlines=[0],
xl="",
xmin=-0.5,
xmax=1.5,
xticks=[0, 1],
yl="Speed (cm/s)" if i == 0 else None,
ymin=-0.1,
ymax=0.51,
yticks=[0, 0.25, 0.5] if i == 0 else None)
myfig.Scatter(p0,
x=[0] * 12,
y=np.nanmean(speed_over_time_wt[:, int(80):int(100)],
axis=1),
lc='black',
pt='o',
lw=0.5,
ps=5,
pc='white',
zorder=2,
alpha=0.5)
myfig.Scatter(p0,
x=[0],
y=np.median(
np.nanmean(speed_over_time_wt[:, int(80):int(100)],
axis=1)),
lc='black',
pt='o',
lw=1,
ps=10,
binned_same_direction_mutant_mean = df_extracted_binned_features_same_direction_mutant.query("genotype == @mutant_name").groupby(["bin"]).mean()
binned_same_direction_mutant_sem = df_extracted_binned_features_same_direction_mutant.query("genotype == @mutant_name").groupby(["bin"]).sem()
histogram_heading_angle_change_mutant_mean = df_extracted_binned_features_heading_angle_change_histograms_mutant.query("genotype == @mutant_name").groupby(["stim", "bin"]).mean()
histogram_heading_angle_change_mutant_sem = df_extracted_binned_features_heading_angle_change_histograms_mutant.query("genotype == @mutant_name").groupby(["stim", "bin"]).sem()
#####
# Correctness as function of coherence
p0 = myfig.Plot(fig, num='b1', xpos=10, ypos=ypos, plot_height=1.25, plot_width=1.25, title="Wildtype / mutant",
lw=1, pc='white', errorbar_area=False,
xmin=-15, xmax=115, hlines=[50], xticks=[0, 25, 50, 100], xticklabels=[""]*4,
yl="Probability\ncorrect (%)", ymin=44, ymax=91, yticks=[50, 70, 90])
myfig.Line(p0, x=[0, 25, 50, 100], y=correctness_as_function_of_coherence_wt_mean, yerr=correctness_as_function_of_coherence_wt_sem, lc="black", zorder=1)
myfig.Scatter(p0, x=[0, 25, 50, 100], y=correctness_as_function_of_coherence_wt_mean, lc='black', pt='o', lw=0.5, ps=9.8, pc='white', zorder=2)
myfig.Line(p0, x=[0, 25, 50, 100], y=correctness_as_function_of_coherence_mutant_mean, yerr=correctness_as_function_of_coherence_mutant_sem, lc=basecolor, zorder=1)
myfig.Scatter(p0, x=[0, 25, 50, 100], y=correctness_as_function_of_coherence_mutant_mean, lc=basecolor, pt='o', lw=0.5, ps=9.8, pc='white', zorder=2)
####
# Interbout interval as function of coherence
p0 = myfig.Plot(fig, num='b2', xpos=10, ypos=ypos - 1.5, plot_height=1.25, plot_width=1.25,
lw=1, pc='white', errorbar_area=False,
xl="Coherence (%)", xmin=-15, xmax=115, xticks=[0, 25, 50, 100], hlines=[50],
yl="Interbout\ninterval (s)", ymin=0.6, ymax=1.9, yticks=[0.7, 1.2, 1.7])
myfig.Line(p0, x=[0, 25, 50, 100], y=interbout_interval_as_function_of_coherence_wt_mean, yerr=interbout_interval_as_function_of_coherence_wt_sem, lc="black", zorder=1)
myfig.Scatter(p0, x=[0, 25, 50, 100], y=interbout_interval_as_function_of_coherence_wt_mean, lc='black', pt='o', lw=0.5, ps=9.8, pc='white', zorder=2)
myfig.Line(p0, x=[0, 25, 50, 100], y=interbout_interval_as_function_of_coherence_mutant_mean, yerr=interbout_interval_as_function_of_coherence_mutant_sem, lc=basecolor, zorder=1)
p0 = myfig.Plot(fig, num='', xpos=1.5, ypos=y_pos, plot_height=2.5, plot_width=2.5,
lw=1, pc='white', errorbar_area=False,
xl=errornames[error_index0], xmin=xmin, xmax=xmax, xticks=xticks,
yl=errornames[error_index1], ymin=ymin, ymax=ymax,yticks=yticks, hlines=[0], vlines=[0])
print(F[0][:, error_index0].max())
print(F[0][:, error_index1].max())
res = NonDominatedSorting().do(F[0])
x = F[0][res[0]][:, error_index0]
y = F[0][res[0]][:, error_index1]
ind = np.where((x < xmax) & (y < ymax))
x = x[ind]
y = y[ind]
myfig.Scatter(p0, x,y, pt='.', ps=8, lw=0, pc='blue',alpha=0.8)
res = NonDominatedSorting().do(F[-1])
x = F[-1][res[0]][:, error_index0]
y = F[-1][res[0]][:, error_index1]
ind = np.where((x < xmax) & (y < ymax))
x = x[ind]
y = y[ind]
myfig.Scatter(p0, x, y, pt='.', ps=8, lw=0, pc='red',alpha=0.3)
y_pos-= 4
tsne = TSNE(n_components=2, random_state=1)
res_initial = NonDominatedSorting().do(F[0])
for i, age in enumerate([7, 14, 21]):
group_polarization_rel_chance = np.load(
root_path /
f"group_polarization_rel_chance_{genotype}_age{age}dpf.npy")
group_spacing_rel_chance = np.load(
root_path / f"group_spacing_rel_chance_{genotype}_age{age}dpf.npy"
) #[:,::10] / 100
group_speed = np.load(
root_path / f"group_speed_{genotype}_age{age}dpf.npy") #[:, :] / 100
myfig.Scatter(p0,
x=[i] * group_speed.shape[0],
y=group_speed,
lc='black',
pt='o',
lw=0.5,
ps=5,
pc='white',
zorder=2,
alpha=0.5)
myfig.Scatter(p0,
x=[i],
y=np.median(group_speed),
lc='black',
pt='o',
lw=1,
ps=10,
pc='white',
zorder=2)
cs_next_shuffled.append(c_next_shuffled)
cs_second_next.append(c_second_next)
cs_second_next_shuffled.append(c_second_next_shuffled)
cs_third_next.append(c_third_next)
cs_third_next_shuffled.append(c_third_next_shuffled)
all_first_bouts = np.array(all_first_bouts)
all_next_bouts = np.array(all_next_bouts)
all_first_bouts_shuffled = np.array(all_first_bouts_shuffled)
all_next_bouts_shuffled = np.array(all_next_bouts_shuffled)
ind1 = np.where((all_first_bouts < 2) & (all_next_bouts < 2))
ind2 = np.where((all_first_bouts_shuffled < 2) & (all_next_bouts_shuffled < 2))
myfig.Scatter(p0, x=all_first_bouts[ind1], y=all_next_bouts[ind1], lc='C3', pt='.', alpha=0.25, ps=1, lw=0.25, rasterized=True)
myfig.Scatter(p1, x=all_first_bouts_shuffled[ind2], y=all_next_bouts_shuffled[ind2], lc='gray', pt='.', alpha=0.25, ps=1, lw=0.25, rasterized=True)
myfig.Scatter(p2, x=[0], y=[c_next], lc='C3', pt='o', ps=1.5, zorder=2)
myfig.Scatter(p2, x=[1], y=[c_next_shuffled], lc='gray', pt='o', ps=1.5, zorder=2)
myfig.Line(p2, x=[0,1], y=[c_next, c_next_shuffled], lc='black', lw=0.5, zorder=1)
myfig.Scatter(p3, x=[0], y=[c_second_next], lc='C3', pt='o', ps=1.5, zorder=2)
myfig.Scatter(p3, x=[1], y=[c_second_next_shuffled], lc='gray', pt='o', ps=1.5, zorder=2)
myfig.Line(p3, x=[0, 1], y=[c_second_next, c_second_next_shuffled], lc='black', lw=0.5, zorder=1)
myfig.Scatter(p4, x=[0], y=[c_third_next], lc='C3', pt='o', ps=1.5, zorder=2)
myfig.Scatter(p4, x=[1], y=[c_third_next_shuffled], lc='gray', pt='o', ps=1.5, zorder=2)
myfig.Line(p4, x=[0, 1], y=[c_third_next, c_third_next_shuffled], lc='black', lw=0.5, zorder=1)