def sign_barplot(df, val_col, group_col, test="HSD"):
if test == "HSD":
result_df = tukey_hsd(df, val_col, group_col)
if test == "tukey":
result_df = sp.posthoc_tukey(df, val_col, group_col)
if test == "ttest":
result_df = sp.posthoc_ttest(df, val_col, group_col)
if test == "scheffe":
result_df = sp.posthoc_scheffe(df, val_col, group_col)
if test == "dscf":
result_df = sp.posthoc_dscf(df, val_col, group_col)
if test == "conover":
result_df = sp.posthoc_conover(df, val_col, group_col)
#マッピングのプロファイル
fig, ax = plt.subplots(1, 2, figsize=(10, 6))
cmap = ['1', '#fb6a4a', '#08306b', '#4292c6', '#c6dbef']
heatmap_args = {
'cmap': cmap,
'linewidths': 0.25,
'linecolor': '0.5',
'clip_on': False,
'square': True
}
sp.sign_plot(result_df, ax=ax[1], **heatmap_args) #検定結果を描画
sns.barplot(data=df, x=group_col, y=val_col, capsize=0.1,
ax=ax[0]) #使ったデータを描画
plt.show()
def sign_plot(self, df, x, y):
p = self.posthoc(df, x, y)
heatmap_args = {
'linewidths': 0.25,
'linecolor': '0.5',
'clip_on': False,
'square': True,
'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]
}
sp.sign_plot(p, **heatmap_args)
def heatmap_plot(self, p_values, axes=None, symmetric=False):
"""Draws heatmap plot for visualizing statistical test results.
If |symmetric| is enabled, it masks out the upper triangle of the
p-value table (as it is redundant with the lower triangle).
"""
if symmetric:
mask = np.zeros_like(p_values)
mask[np.triu_indices_from(p_values)] = True
heatmap_args = {
'linewidths': 0.5,
'linecolor': '0.5',
'clip_on': False,
'square': True,
'cbar_ax_bbox': [0.85, 0.35, 0.04, 0.3],
'mask': mask if symmetric else None
}
sp.sign_plot(p_values, ax=axes, **heatmap_args)
def SignificancePlot(self, methods=None, metric='MAE'):
# -- Method(s)
if methods == None:
methods = self.methods
else:
if set(methods) <= set(self.methods):
raise ("Some method is wrong!")
else:
self.methods = methods
# -- set metric
self.metric = metric
self.mag = self.metricSort[metric]
# -- get data from dataset(s)
if self.multidataset:
Y = self.__getData()
else:
Y = self.__getDataMono()
# -- Significance plot, a heatmap of p values
methodNames = [x.upper() for x in self.methods]
Ypd = pd.DataFrame(Y, columns=methodNames)
ph = sp.posthoc_nemenyi_friedman(Ypd)
cmap = ['1', '#fb6a4a', '#08306b', '#4292c6', '#c6dbef']
heatmap_args = {
'cmap': cmap,
'linewidths': 0.25,
'linecolor': '0.5',
'clip_on': False,
'square': True,
'cbar_ax_bbox': [0.85, 0.35, 0.04, 0.3]
}
plt.figure(figsize=(5, 4))
sp.sign_plot(ph, cbar=True, **heatmap_args)
plt.title('p-vals')
fname = 'SP_' + self.metric + '.pdf'
plt.savefig(fname)
plt.show()
def heatmap_plot(p_values, axes=None, symmetric=False, **kwargs):
"""
Heatmap for p_values
"""
if symmetric:
mask = np.zeros_like(p_values)
mask[np.triu_indices_from(p_values)] = True
heatmap_args = {
'linewidths': 0.5,
'linecolor': '0.5',
'clip_on': False,
'square': True,
'cbar_ax_bbox': [0.85, 0.35, 0.04, 0.3],
'mask': mask if symmetric else None,
}
heatmap_args.update(kwargs)
return sp.sign_plot(p_values, ax=axes, **heatmap_args)
def field_longitudinal(fn, thres):
pp = PdfPages('figures/field_longitudinal_gamma={}_thres={}.pdf'.format(
gamma, thres))
years = ["1996", "2001", "2007", "2012"]
def ind(yr):
return years.index(yr)
fig, ax = plt.subplots(1, 1)
ax.grid(False)
# color-blind spectrum: http://personal.sron.nl/~pault/colourschemes.pdf
colors = [
"#88ccee", "#44aa99", "#999933", "#DDCC77", "#CC6677", "#882255",
"#AA4499"
]
colors = colors[0:len(krange)]
bars = []
rows = []
# Crude
hm = {v: [] for v in krange}
sm = 0
total = {ind(y): 0 for y in years}
byyear = {ind(y): [] for y in years}
def process_vc(tup):
x, yr, sample = tup
# Run the misfits calculation
mf = s.misfits(x, krange, gamma=gamma)
sr = {k: s.compute(x, k, gamma=gamma) for k in krange}
return x, mf, yr, sample, sr
cached_process = memory.cache(process_vc)
stf = open('figures/strains.out', 'w')
for year in tqdm(years):
fdata = Parallel(n_jobs=num_cores)(
delayed(cached_process)(tup)
for tup in read_field_data_year(fn, [year]))
for x, mf, yr, sample, sr in fdata:
mf = np.array(mf)
# Find first drop below threshold
tqdm.write("Length:{}. Interesting bases:{}. Misfits: {}".format(
len(x), len([xx for xx in x if 1.0 - 1e-6 > xx > 1e-6]), mf))
best = sum(mf > thres)
if best >= len(mf):
continue
best += 1
if best not in hm:
hm[best] = []
byyear[ind(year)] += [best]
hm[best] += [ind(year)]
total[ind(year)] += 1
# Print out the strain sequences of at least 5% proportion in a sample
assert best in sr
print(sr[best][1], max(sr[best][1]))
stf.write("DOMSTR\t{}\t{:.2f}%\n".format(yr, max(sr[best][1])))
for i, f in enumerate(sr[best][1]):
if f >= 0.05:
stf.write("STRAIN\t{}\t{}\t{}\t{:.2f}%\t{}\n".format(
sample, yr, i, 100.0 * sr[best][1][i], "".join([
"{}".format(int(z)) if not np.isnan(z) else "N"
for z in sr[best][0][i]
])))
stf.close()
of = open(
'figures/field_longitudinal_gamma={}_thres={}.txt'.format(
gamma, thres), 'w')
for year in years:
of.write("Year %s\t" % year + "\t".join([
"%d=%d" % (v, len(list(filter(lambda y: y == ind(year), hm[v]))))
for v in krange
]))
of.write("\tAverage (including 5+):\t" + "%2.4f" %
(np.mean(byyear[ind(year)])) + "\tAverage (excluding 5):\t" +
"%2.4f" % (np.mean([sc
for sc in byyear[ind(year)] if sc < 5])))
of.write("\tMedian:\t" + "%2.4f" % (np.median(byyear[ind(year)])) +
"\n")
m = [hm[v] for v in krange]
plt.ylabel('% samples')
plt.xlabel('Survey year')
plt.xticks(np.arange(len(years)), years)
plt.ylim([0, 100])
weights = np.array([[100.0 / float(total[int(y)]) for y in hm[v]]
for v in krange])
bins = np.arange(len(years) + 1) - 0.5
hatch = '/'
_, _, patches = plt.hist(
m,
bins=bins,
histtype='bar',
stacked=True,
weights=weights,
rwidth=0.5,
color=colors,
label=[
"%s%d strain%s" % ("=" if v != krange[-1] else "$\geq$", v,
"s" if v != krange[0] else "") for v in krange
]) #, hatch=hatch)
plt.legend(
bbox_to_anchor=(1.04, 0.5),
loc="center left",
borderaxespad=0,
prop={'size': 10},
)
mm = np.array(m)
lk = {
year: {
v: len(list(filter(lambda y: y == ind(year), hm[v])))
for v in krange
}
for year in years
}
for j, bc in enumerate(patches):
for i, p in enumerate(bc):
#l = np.sum(np.array(byyear[i]) == len(patches)-j-1)
l = lk[years[i]][krange[j]]
if l == 0:
continue
h1 = p.get_height()
print("{} {}".format(p, l))
z = 100.0 * l / float(sum(lk[years[i]].values()))
ax.text(p.get_x() + p.get_width() / 2.,
p.get_y() + h1 / 2.,
"%d%%" % int(z),
ha="center",
va="center",
color="black",
fontsize=12,
fontweight="bold")
pp.savefig(bbox_inches="tight")
pp.close()
for y in years:
of.write("%s: length %d\n" % (y, len(byyear[ind(y)])))
of.write("{}\n".format(byyear[ind("1996")]))
of.write("H1\t{}\t1996 vs 2001:\t{}\n".format(
thres, sts.mannwhitneyu(byyear[ind("1996")], byyear[ind("2001")])))
of.write("H2\t{}\t2007 vs 2012:\t{}\n".format(
thres, sts.mannwhitneyu(byyear[ind("2007")], byyear[ind("2012")])))
x = [byyear[ind(y)] for y in years]
#pc = sp.posthoc_conover(x, val_col='values', group_col='groups', p_adjust='holm')
kr = sts.kruskal(*x)
of.write("Kruskal-Willis:\n{}\n".format(kr))
pc = sp.posthoc_conover(x,
val_col='values',
group_col='groups',
p_adjust='fdr_tsbky')
of.write("Conover:\n{}\n".format(pc))
# Format: diagonal, non-significant, p<0.001, p<0.01, p<0.05
cmap = ['1', '#fb6a4a', '#08306b', '#4292c6', '#c6dbef']
heatmap_args = {
'cmap': cmap,
'linewidths': 0.25,
'linecolor': '0.5',
'clip_on': False,
'square': True,
'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]
}
sp.sign_plot(pc, **heatmap_args)
of.close()
print('statistic: ' + str(t))
print('pvalue: ' + str(p))
print(' ')
pc = sp.posthoc_nemenyi_friedman(data_MAE_df)
cmap = ['1', '#fb6a4a', '#08306b', '#4292c6', '#c6dbef']
heatmap_args = {
'cmap': cmap,
'linewidths': 0.25,
'linecolor': '0.5',
'clip_on': False,
'square': True,
'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]
}
plt.figure()
sp.sign_plot(pc, **heatmap_args)
plt.title('Nemenyi Test MAE')
data_CC_df = pd.DataFrame(data_CC, columns=all_methods)
print('\nFriedman Test CC:')
#print(ss.friedmanchisquare(*data_CC.T))
#print(' ')
t, p, ranks_cc, piv_cc = ft(data_CC[:, 0], data_CC[:, 1], data_CC[:, 2],
data_CC[:, 3], data_CC[:, 4], data_CC[:, 5],
data_CC[:, 6], data_CC[:, 7])
avranksCC = list(np.divide(ranks_cc, n_datasets))
print('statistic: ' + str(t))
print('pvalue: ' + str(p))
print(' ')
pc = sp.posthoc_nemenyi_friedman(data_CC_df)
from denn import *
from scipy.stats import kruskal
import scikit_posthocs as sp
import pylustrator
pylustrator.start()
path = Path('../../data/results/experiment4')
# fitness plots
no_nn = pd.read_csv(path/'no_nn_mof.csv')
nn_normal_rand = pd.read_csv(path/'nn-normal-random_mof.csv')
nn_dist_rand = pd.read_csv(path/'nn-distribution-random_mof.csv')
nn_dropout_rand= pd.read_csv(path/'nn-dropout-random_mof.csv')
labels = ['no_nn', 'nn_normal_rand', 'nn_dist_rand', 'nn_drop_rand']
x=np.array([no_nn.mof, nn_normal_rand.mof, nn_dist_rand.mof,nn_dropout_rand.mof])
stat, p = kruskal(no_nn,nn_normal_rand,nn_dist_rand,nn_dropout_rand)
pc=sp.posthoc_conover(x, p_adjust='holm', val_col='values', group_col='groups')
print('Statistics=%.3f, p=%.3f' % (stat, p))
print(pc)
heatmap_args = {'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True, 'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]}
ax,cbar = sp.sign_plot(pc, **heatmap_args)
ax.set_xticklabels(labels)
ax.set_yticklabels(labels)
plt.show()
def plot_test_results(results):
heatmap_args = {'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True,
'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]}
return sp.sign_plot(results, **heatmap_args)
def compareBases(mainFolders, folders, tp, rep, lastGen, plotType, saveFile):
logCol = 1
file = 'evolution'
if ((tp == 'evol') or (tp == 'evolBase')):
logCol = 1
file = 'evolution'
variable = 'Fitness'
elif ((tp == 'nModules') or (tp == 'nModulesBase')):
logCol = 2
file = 'bestFeatures'
variable = 'Number of Modules'
elif ((tp == 'brokenConn') or (tp == 'brokenConnBase')):
logCol = 11
file = 'meanFeatures'
variable = 'Number of Broken Connections'
elif ((tp == 'nConn') or (tp == 'nConnBase')):
logCol = 19
file = 'bestFeatures'
variable = 'Average Connections per Module'
dfAll = pd.DataFrame()
data = []
for l in range(0, len(mainFolders)):
dfBase = pd.DataFrame()
for k in range(0, len(folders)):
#nGenerations = minGenerationCount(mainFolders[l],folders[k],rep)
data.clear()
for i in range(0, rep):
csv_file = open('./' + mainFolders[l] + '/' + folders[k] +
'xL/' + str(i + 1) + '/log/' + file + '.txt')
csv_reader = csv.reader(csv_file)
oldRows = list(csv_reader)
rows = []
for row in oldRows:
rows.append(row[0].split(" - "))
#print(rows)
#print(nGenerations)
if (lastGen):
data.append(float(rows[-1][logCol]))
else:
line_count = 0
for row in rows:
#print(row[logCol])
data.append(float(row[logCol]))
line_count = line_count + 1
#if line_count >= nGenerations:
# break
dfPartial = pd.DataFrame(data, columns=[variable])
dfPartial['Length'] = folders[k]
#print(dfPartial)
dfBase = dfBase.append(dfPartial, ignore_index=True)
#ax1.set_title('Length x'+folders[k])
dfBase['Base'] = mainFolders[l]
dfAll = dfAll.append(dfBase, ignore_index=True)
#print(dfAll)
#dfAll.boxplot(column='Fitness',by='Length',ax=ax1,grid=False,notch=False)
#dfAll.groupby('Length',sort=True).boxplot()
#print(dfAll)
#print([group['Fitness'].values for name,group in dfAll.groupby(['Length','Base'])])
if ((tp != 'nModulesBase') and (tp != 'brokenConnBase')
and (tp != 'evolBase') and (tp != 'nConnBase')):
print(
scp_stats.kruskal(*[
group[variable].values
for name, group in dfAll.groupby(['Length', 'Base'])
]))
else:
print(
scp_stats.kruskal(*[
group[variable].values
for name, group in dfAll.groupby(['Base'])
]))
if ((tp != 'brokenConn') and (tp != 'nModulesBase')
and (tp != 'brokenConnBase') and (tp != 'evolBase')
and (tp != 'nConnBase')):
#Connover
postHoc = sp.posthoc_conover([
group[variable].values
for name, group in dfAll.groupby(['Length', 'Base'])
])
#print(postHoc)
#Mann-Whitney
#postHoc = sp.posthoc_mannwhitney([group['Fitness'].values for name,group in dfAll.groupby(['Length','Base'])])
#print(postHoc)
heatmap_args = {
'linewidths': 0.25,
'linecolor': '0.5',
'clip_on': False,
'square': True,
'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]
}
sp.sign_plot(postHoc, **heatmap_args)
fig = plt.figure(figsize=(15, 10))
y = variable
if ((tp == 'nModulesBase') or (tp == 'brokenConnBase')
or (tp == 'evolBase') or (tp == 'nConnBase')):
x = 'Base'
order = mainFolders
if (plotType == 'box'):
#ax = sns.boxplot(data=dfAll, x=x, y=y,order=order,showfliers=False)
ax = sns.boxplot(data=dfAll, x=x, y=y, order=order)
elif (plotType == 'swarm'):
ax = sns.swarmplot(data=dfAll, x=x, y=y, order=order)
elif (plotType == 'strip'):
ax = sns.stripplot(data=dfAll, x=x, y=y, order=order)
elif (plotType == 'violin'):
ax = sns.violinplot(data=dfAll, x=x, y=y, order=order)
else:
x = "Length"
hue = "Base"
order = folders
if (plotType == 'box'):
#ax = sns.boxplot(data=dfAll, x=x, y=y,order=order,hue = hue,showfliers=False)
ax = sns.boxplot(data=dfAll, x=x, y=y, order=order, hue=hue)
elif (plotType == 'swarm'):
ax = sns.swarmplot(data=dfAll, x=x, y=y, order=order, hue=hue)
elif (plotType == 'strip'):
ax = sns.stripplot(data=dfAll, x=x, y=y, order=order, hue=hue)
elif (plotType == 'violin'):
ax = sns.violinplot(data=dfAll, x=x, y=y, order=order, hue=hue)
#dfAll.boxplot(column='Fitness',by=['Length','Base'],ax=ax1,grid=False,notch=False)
#if(tp=='evol'):
#ax.set_ylim(-0.1,11)
plt.savefig(saveFile + tp + plotType + '.eps', bbox_inches="tight")
plt.show()
def boxplotResults(mainFolder, folders, tp, rep, indiv, lastGen, plotType,
saveFile, annotatePairs):
logCol = 1
file = 'evolution'
if (tp == 'evol'):
logCol = 1
file = 'evolution'
variable = 'Fitness'
elif (tp == 'nModules'):
logCol = 2
file = 'bestFeatures'
variable = 'Number of Modules'
elif (tp == 'brokenConn'):
logCol = 11
file = 'meanFeatures'
variable = 'Number of Broken Connections'
elif (tp == 'nConn'):
logCol = 19
file = 'bestFeatures'
variable = 'Average Connections per Module'
#dfAll = pd.DataFrame(columns=folders)
dfAll = pd.DataFrame()
if (not indiv):
fig = plt.figure(figsize=(15, 10))
ax1 = fig.gca()
data = []
for k in range(0, len(folders)):
if (indiv):
fig = plt.figure()
ax1 = fig.gca()
#nGenerations = minGenerationCount(mainFolder,folders[k],rep)
data.clear()
for i in range(0, rep):
csv_file = open('./' + mainFolder + '/' + folders[k] + 'xL/' +
str(i + 1) + '/log/' + file + '.txt')
csv_reader = csv.reader(csv_file)
oldRows = list(csv_reader)
rows = []
for row in oldRows:
rows.append(row[0].split(" - "))
#print(row)
#print(rows)
#print(nGenerations)
if (lastGen):
data.append(float(rows[-1][logCol]))
else:
line_count = 0
for row in rows:
#print(row[logCol])
data.append(float(row[logCol]))
line_count = line_count + 1
#if line_count >= nGenerations:
# break
dfPartial = pd.DataFrame(data, columns=[variable])
dfPartial['Length'] = folders[k]
#print(dfPartial)
dfAll = dfAll.append(dfPartial, ignore_index=True)
#ax1.set_title('Length x'+folders[k])
#print(dfAll)
#print(dfAll)
#dfAll.boxplot(column='Fitness',by='Length',ax=ax1,grid=False,notch=False)
#dfAll.groupby('Length',sort=True).boxplot()
#if(tp=='evol'):
# ax1.set_ylim(-0.1,11)
x = "Length"
y = variable
order = folders
if (plotType == 'box'):
#ax = sns.boxplot(data=dfAll, x=x, y=y,order=order,showfliers=False)
ax = sns.boxplot(data=dfAll, x=x, y=y, order=order)
elif (plotType == 'swarm'):
ax = sns.swarmplot(data=dfAll, x=x, y=y, order=order)
elif (plotType == 'strip'):
ax = sns.stripplot(data=dfAll, x=x, y=y, order=order)
elif (plotType == 'violin'):
ax = sns.violinplot(data=dfAll, x=x, y=y, order=order)
if (tp != 'brokenConn'):
add_stat_annotation(ax,
data=dfAll,
x=x,
y=y,
order=order,
box_pairs=annotatePairs,
test='Mann-Whitney',
text_format='star',
loc='outside',
verbose=2)
plt.savefig(saveFile + tp + plotType + '.eps', bbox_inches="tight")
plt.show()
print(
scp_stats.kruskal(*[
group[variable].values for name, group in dfAll.groupby('Length')
]))
#Connover
#postHoc = sp.posthoc_conover(dfAll,val_col='Fitness',group_col='Length')
#print(postHoc)
if (tp != 'brokenConn'):
#Mann-Whitney
postHoc = sp.posthoc_mannwhitney(dfAll,
val_col=variable,
group_col='Length')
#print(postHoc)
heatmap_args = {
'linewidths': 0.25,
'linecolor': '0.5',
'clip_on': False,
'square': True,
'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]
}
sp.sign_plot(postHoc, **heatmap_args)
for system_order in ['finite', 'infinite']:
print(system_order.upper())
for eval in range(len(function_evals_of_interest)):
algorithms_at_fes = []
for algorithm in ['sa', 'acfsa', 'pso', 'aiwpso', 'acor', 'baacor']:
print(algorithm)
# Load test costs of a given metaheuristic for a given system, considering some number of objective function evaluations
base_filename = './results/' + algorithm + '_' + system_order
test_costs_mat = np.load(base_filename + '_test_costs.npy')
test_costs_of_interest = test_costs_mat[:, evals_mask]
costs_fe = test_costs_of_interest[:, eval]
algorithms_at_fes.append(list(costs_fe))
print(
str(function_evals_of_interest[eval]) + ': \t' +
str(np.mean(costs_fe)))
algorithms_at_fes = np.array(algorithms_at_fes)
print('\n Statistical significance')
print(np.shape(algorithms_at_fes))
print('Friedman p-val = ' +
str(scipy.stats.friedmanchisquare(*algorithms_at_fes)[1]) +
'\n\n')
nm_posthoc = sp.posthoc_nemenyi_friedman(algorithms_at_fes.T)
plt.figure()
sp.sign_plot(nm_posthoc, **heatmap_args)
plt.show()
print('\n')
# prepare the pairwise plots
pc_voc = sp.posthoc_conover(df_voc, val_col='score', group_col='method')
pc_acc = sp.posthoc_conover(df_acc, val_col='score', group_col='method')
f = plt.figure(figsize=(10, 10))
# Format: diagonal, non-significant, p<0.001, p<0.01, p<0.05
cmap = ['1', '#ff2626', '#ffffff', '#fcbdbd', '#ff7272']
heatmap_args = {
'cmap': cmap,
'linewidths': 0.25,
'linecolor': '0.5',
'clip_on': False,
'square': True,
'cbar_ax_bbox': [0.90, 0.35, 0.04, 0.3]
}
sp.sign_plot(pc_voc, **heatmap_args)
f.tight_layout()
f.savefig("pairwise_vocals.pdf", bbox_inches='tight', dpi=300)
f = plt.figure(figsize=(8.709677419, 8.709677419))
# Format: diagonal, non-significant, p<0.001, p<0.01, p<0.05
cmap = ['1', '#ff2626', '#ffffff', '#fcbdbd', '#ff7272']
heatmap_args = {
'cmap': cmap,
'linewidths': 0.25,
'linecolor': '0.5',
'clip_on': False,
'square': True,
'cbar_ax_bbox': [0.90, 0.35, 0.04, 0.3]
def plot(scores, ax):
# Pretty plot of significance
heatmap_args = {'linewidths': 1, 'linecolor': '0.5', 'square': True,
'cbar_ax_bbox': [0.82, 0.35, 0.04, 0.3]}
sp.sign_plot(scores, ax=ax, **heatmap_args)
project_path = os.path.join(os.path.join('Research', 'BCBL', 'SOCIALCON '))
data_path = os.path.join(project_path, '2_empirical', '1_fMRI', '1_decoding',
'1_binomial', '0_data')
df_likable_Co = pd.read_csv('postHocTests_likableness_conceptCV.csv',
sep=';',
header=None)
ROIs = ['LTL', 'IFG', 'Prec', 'ATL', 'aPFC', 'Ins', 'ACC', 'PCC', 'V1']
# Set up styling presets
sns.set_context("poster", font_scale=1, rc={"lines.linewidth": 3})
sns.set_style('white')
# Create the figure
fig, ax = plt.subplots(figsize=(15, 8))
# cmap = ['1', '#FFFFFF', '#B4171F', '#E5323B', '#EE767C']
# cmap = ['1', '#FFFFFF', '#C13944', '#D5727A', '#E7ADB2']
# cmap = ['1', '#FFFFFF', '#131A21', '#2E4052', '#496683']
cmap = ['1', '#FFFFFF', '#43494F', '#666F79', '#8D959F']
heatmap_args = {
'cmap': cmap,
'linewidths': 3,
'linecolor': '0.05',
'clip_on': False,
'square': True,
'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]
}
sp.sign_plot(df_likable_Co, **heatmap_args)
plt.show()
