def plot_cno_saline():
data: pd.DataFrame = pd.read_csv(
analysis_folder.joinpath("decoder_cno.csv"),
usecols=[1, 2, 3],
index_col=[2, 1]).sort_index() # type: ignore
group_strs = ('saline', 'cno')
paired_data = [data.loc[treat, 'mutual_info'] for treat in group_strs]
p_value = wilcoxon(*paired_data).pvalue
res = ["median: "] + str(data.groupby("treat").median()).split('\n')[2:]
print_stats("saline vs. cno in Gq", res + [f"paired wilcox: p={p_value}"])
with Figure(fig_folder.joinpath("decoder-pair-cno.svg"),
figsize=(6, 9)) as axes:
boxplots = plots.boxplot(axes[0],
paired_data,
whis=(10., 90.),
zorder=1,
showfliers=False,
colors=colors[2:4],
widths=0.65)
plots.dots(axes[0], paired_data, zorder=3, s=24)
axes[0].set_xticklabels(["Gq Saline", "Gq CNO"])
plots.annotate_boxplot(axes[0], boxplots, 24, 1.2, [((0, 1), p_value)])
[
axes[0].plot([1, 2], x, color='gray')
for x in np.array(paired_data).T
]
def temp(data, ax, name, jitter=0.1):
res = ["median: "]
res.extend(str(data.groupby("group").median()).split("\n")[1:-1])
annotation = list()
for (idx, x), (idy, y) in combinations(enumerate(groups), 2):
p = mannwhitneyu(data.loc[x, ], data.loc[y, ]).pvalue
if p < 0.05:
res.append(f"mann u {x} v. {y}, p={p}")
annotation.append(((idx, idy), p))
print_stats(name, res)
values = [data.loc[x, ].tolist() for x in groups]
boxplots = plots.boxplot(ax,
values,
zorder=1,
whis=(10., 90.),
showfliers=False,
colors=colors)
plots.dots(ax, values, zorder=3, s=24, jitter=jitter)
plots.annotate_boxplot(ax, boxplots, 24, 1.2, annotation)
ax.set_xticklabels(group_strs)
ax.set_ylabel(name)
def temp(data, ax, name):
res = ["median: "] + str(
data.groupby('group').median()).split('\n')[2:]
group_names = ('wt', 'glt1', 'dredd')
group_strs = ["WT", "GLT1", "Gq"]
annotation = list()
for (idx, x), (idy, y) in combinations(enumerate(group_names), 2):
p = mannwhitneyu(data.loc[x, ], data.loc[y, ], True,
'two-sided').pvalue
res.append(f"mann u, {x} v. {y} p={p}")
if p < 0.05:
annotation.append(((idx, idy), p))
print_stats(name, res)
values = [data.loc[x, "mutual_info"] for x in group_names]
boxplots = plots.boxplot(ax,
values,
whis=(10., 90.),
zorder=1,
showfliers=False,
colors=colors)
plots.dots(ax, values, zorder=3, s=24, jitter=0.02)
ax.set_xticklabels(group_strs)
plots.annotate_boxplot(ax, boxplots, 24, 1.2, annotation)
def draw_diagnal():
data = pd.read_csv(proj_folder.joinpath("data", "analysis", "classifier_power_validated.csv"))
means = data.groupby(["id", "session", "group", "type"]).mean()
colors = {'wt': '#00272B', 'glt1': '#099963', 'dredd': '#F94040', "gcamp6f": "#619CFF"}
xs, ys = zip(*[(means.query(f"type=='corr' and group=='{group}'")['precision'].values,
means.query(f"type=='mean' and group=='{group}'")['precision'].values)
for group in ('wt', 'gcamp6f', 'glt1', 'dredd')])
# customize Figure generation
fig = plt.figure(figsize=(9, 9))
ax1 = fig.add_subplot()
pair(xs, ys, [colors[x] for x in ('wt', 'gcamp6f', 'glt1', 'dredd')], ax1,
density_params={"bw": 0.5}, scatter_params={"alpha": 0.7})
plt.savefig(fig_folder.joinpath("classifier-scatterplot.svg"))
fig2 = plt.figure(figsize=(4, 9))
ax2 = fig2.add_subplot(1, 1, 1, sharey=ax1)
data_mean = data[data.type == "mean"]
data_mean = data_mean.set_index(["group", "id", "session"])
values_mean = [data_mean.loc[group_str, 'precision'].values for group_str in ('wt', 'glt1', 'dredd')]
boxplot = plots.boxplot(ax2, values_mean, whis=(10., 90.), showfliers=False, colors=colors.values())
plots.annotate_boxplot(ax2, boxplot, 16, 1.0, p_values=[((0, 1), 0.334), ((0, 2), 0.964)])
ax2.set_xticklabels(["WT", 'gcamp6s', "GLT1", "Gq"])
ax2.set_ylim(*ax1.get_ylim())
plt.savefig(fig_folder.joinpath("classifier-edge.svg"))
plt.show()