def plot_bolivia_elevation(PROJ, ax2, bar_legend, grid_style, lala, levels,
lola_extent, lolo, lon_large_ticks, ncmap, topo_ds,
white_grid_color):
ax = fa.get_ax_bolivia(chc_lp_legend=False,
ax=ax2,
plot_cities=False,
lola_extent=lola_extent,
plot_ocean=True,
lola_ticks=[lolo, lala],
draw_labels=False,
grid_style=grid_style,
grid_color=white_grid_color,
grid_alpha=1,
map_res='110m',
xlab_bot=False,
ylab_left=False)
# ax.set_yticks([-20,0],crs=ccrs.PlateCarree())
ax.set_ylabel(None)
ax.set_xticks(lon_large_ticks, crs=PROJ)
ax.set_xlabel(None)
slice_ = {'lat': slice(None, None, 10), 'lon': slice(None, None, 10)}
band_ = topo_ds['Band1'][slice_] / 1000
band_ = band_.where(band_ > 0)
band_.name = bar_legend
band_.plot(
cmap=ncmap,
ax=ax,
levels=levels,
subplot_kws={'transform': PROJ},
# center=2000,
norm=Normalize(0, 5, clip=False),
add_colorbar=False,
rasterized=True)
ax.set_xlabel(None)
ax.set_ylabel(None)
def plot_cluster_map(
self,
i,
log_plot_dic=None,
map_dic=None,
par_to_plot=co.COL,
):
if map_dic is None:
map_dic = {}
if log_plot_dic is None:
log_plot_dic = {}
clus = self.cluster_flags[i]
boo = self.merged_ds[co.ClusFlag] == clus
ax = fa.get_ax_bolivia(**map_dic)
# fig,ax = plt.subplots()
warnings.simplefilter('ignore')
ar = self.merged_ds.where(boo)[par_to_plot]
com = fa.get_dims_complement(ar, [co.R_CENTER, co.TH_CENTER])
ar = ar.sum(dim=com)
if par_to_plot is co.CPer:
ar = ar / self.merged_ds[par_to_plot].sum() * 100
cmap = fa.get_custom_cmap(self.colors[i])
def_dic = dict(ax=ax,
name=par_to_plot,
perM=.95,
quantile=True,
colorbar=False,
patch_args={'cmap': cmap})
com_dic = {**def_dic, **log_plot_dic}
fa.logpolar_plot(ar, **com_dic)
def plot_bolivia(ax, da_srl, name, r, t0, t1, tlen):
da_th_r = da_srl.sum([co.ZM, co.RL])
ax: plt.Axes = fa.get_ax_bolivia(ax=ax)
patch = fa.logpolar_plot(da_th_r.to_dataset(),
ax=ax,
name=co.CONC,
return_patch=True,
colorbar=False)
ax.set_title(
f'{name}:{r[name]} | {r[t0]} - {r[t1]} | {tlen}:{int(r[tlen])}')
return patch
def plot_general(ds1):
cl = co.CPer
c1 = ds1[cl].sum([co.RL, co.ZM])
c2 = ds1[cl].sum([co.ZM, co.RL, co.TH_CENTER])
ar = c1 / c2
# ar = c1
ar = ar.isel(**{co.R_CENTER: slice(0, -3)})
ax = fa.get_ax_bolivia(fig_args={'figsize': (5, 5)})
fa.logpolar_plot(ar, name=co.CPer, ax=ax, perM=.95, perm=.01)
ax.set_xlim(-75, -60)
ax.set_ylim(-25, -7)
return ax
def plot_clust_in_bolivia(_n, dscc):
ax = fa.get_ax_bolivia()
_f = 2
for _f in range(_n):
_ds = dscc.loc[{co.CLUS_LENGTH_DIM: _n}]
_ds = _ds.drop(co.KMEAN_OBJ)
_ds = _ds.where(_ds[co.FLAG] == _f)
_ds = _ds.sum([co.RL, co.ZM])
_ds = _ds[[co.CONC]]
_cm = fa.get_custom_cmap(ucp.cc[_f][:3])
fa.logpolar_plot(_ds, ax=ax, patch_args={'cmap': _cm}, colorbar=False)
def plot_clust_bolivia_individual(_n, dscc):
for _f in range(_n):
ax = fa.get_ax_bolivia()
ax.set_title(str(_f))
_ds = dscc.loc[{co.CLUS_LENGTH_DIM: _n}]
_ds = _ds.drop(co.KMEAN_OBJ)
_ds = _ds.where(_ds[co.FLAG] == _f)
_ds = _ds.sum([co.RL, co.ZM])
_ds = _ds[[co.CONC]]
_cm = fa.get_custom_cmap([*ucp.cc, *ucp.cc, *ucp.cc][_f][:3])
fa.logpolar_plot(_ds, ax=ax, patch_args={'cmap': _cm}, colorbar=False)
def plot_bolivia_domains(PROJ, ax1, dark_grid_color, grid_style, lala,
lat_large_ticks, lola_extent, lolo, lon_large_ticks):
ax = fa.get_ax_bolivia(ax=ax1,
chc_lp_legend=False,
lola_extent=lola_extent,
plot_cities=False,
lola_ticks=[lolo, lala],
draw_labels=False,
grid_style=grid_style,
grid_color=dark_grid_color,
grid_alpha=1,
map_res='110m',
xlab_bot=False,
ylab_left=False)
ax.set_yticks(lat_large_ticks, crs=PROJ)
ax.set_ylabel(None)
ax.set_xticks(lon_large_ticks, crs=PROJ)
ax.set_xlabel(None)
add_doms_bolivia(PROJ, ax1)
def get_topoline(zline=3900):
import scipy.ndimage
topo_ds = xr.open_dataset(pjoin(co.tmp_data_path, 'etopo1_bedrock.nc'))
ax = fa.get_ax_bolivia()
band_: xr.DataArray = topo_ds['Band1']
band_.values = scipy.ndimage.gaussian_filter(band_.values,
sigma=5,
truncate=10)
res = band_.plot.contour(levels=[0, zline], ax=ax)
ax.figure.clear()
segs = res.allsegs[1]
data_ser = pd.DataFrame(segs, columns=['data'])
data_ser['l'] = data_ser['data'].apply(lambda l: len(l))
data_ser = data_ser.sort_values('l', ascending=False).iloc[0]['data']
seg = data_ser.T
return seg
axfig.set_ylabel('sdfsdf')
axfig.set_xticks([])
axfig.set_yticks([])
ax.figure
# %%
lab = '(# particles [%] * (residence time))'
fig = self.plot_clusters_inlfuence(ylab='')
fig.text(0.5, 0.1, 'release time (UTC)', ha='center', va='center')
fig.text(0.1, 0.5, lab, ha='center', va='center', rotation='vertical')
# %%
# %%
i = 0
ax = fa.get_ax_bolivia()
for i in range(len_clus):
clus = self.cluster_flags[i]
boo = self.merged_ds[co.ClusFlag] == clus
# fig,ax = plt.subplots()
import warnings
warnings.simplefilter('ignore')
ar = self.merged_ds.where(boo)[co.CPer].mean(dim=[co.RL])
fa.logpolar_plot(ar,
ax,
name=co.CPer,
perM=.15,
quantile=False,
colorbar=False,
patch_args={'cmap': fa.get_custom_cmap(FLP.COLORS[i])})
dsF= self.filter_hours_with_few_mea()
# %%
dsSM = ds1 = FLP.smooth_merged_ds(
dsF
)
# %%
cl = co.CPer
c1 =ds1[cl].sum([co.RL,co.ZM])
c2 = ds1[cl].sum([co.ZM,co.RL,co.TH_CENTER])
ar = c1/c2
# ar = c1
ar = ar.isel(**{co.R_CENTER:slice(0,-3)})
ax = fa.get_ax_bolivia(fig_args={'figsize':(5,5)})
fa.logpolar_plot(ar,name=co.CPer,ax=ax,perM=.95,perm=.01)
ax.set_xlim(-75,-60)
ax.set_ylim(-25,-7)
# ax = fa.get_ax_lapaz()
# fa.logpolar_plot(ar,name=co.CPer,ax=ax,perM=.95)
# %%
ax.figure.savefig('/tmp/map.pdf')
# %%
# %%
ds1.sum([co.ZM,co.RL,co.TH_CENTER])[co.CPer].plot(marker='.',xscale='log')
def plot_ecoregion(
ax_,
extent,
mega1,
plot_box=False,
xticks=None,
yticks=None,
xd=-.3,
edgecolor='r',
left_lab=False,
right_lab=False,
top_lab=False,
botttom_lab=False,
red_rect_extent=None,
remove_label_inside_box=False,
):
if yticks is None:
yticks = [-10, -20, -30]
if xticks is None:
xticks = [-80, -70, -60]
ax = fa.get_ax_bolivia(
lola_extent=extent,
fig_args={
'figsize': (3.125, 3.125),
'dpi': 300
},
draw_labels=False,
map_line_alpha=.3,
plot_cities=False,
# chc_lp_legend=False,
ax=ax_,
xlab_bot=False,
ylab_left=False,
grid_alpha=0)
lo2, lo1, la2, la1 = extent
rec = shapely.geometry.Polygon([[lo1, la1], [lo1, la2], [lo2, la2],
[lo2, la1], [lo1, la1]])
def _f(p):
p1: shapely.geometry.MultiPolygon = p['geometry']
print(p['name'])
try:
res = p1.intersection(rec)
except:
try:
res = p1.buffer(.01)
res = res.intersection(rec)
except:
res = np.nan
return res
mm = mega1.copy()
mm['geometry'] = mm.apply(_f, axis=1)
mm = mm[mm.area > 0]
mm['rp'] = mm.representative_point()
ax.legend().remove()
# plt.show()
res = mm.plot(ax=ax, color=mm['color'])
if plot_box:
add_red_box(ax, edgecolor, red_rect_extent)
def _lab(r):
if remove_label_inside_box:
print('removing')
if r["inf_index"] in [10, 21, 36, 24]:
return 'pass'
t = ax.annotate(f'•{r["inf_index"]}',
[r['rp'].x + r['xd'], r['rp'].y + r['yd']],
color='k',
weight='medium',
size=5,
zorder=100)
# t.set_bbox(dict(
# facecolor='white', alpha=0.2, edgecolor='none'))
return t
mm = mm[mm.area > .5]
mm = mm[mm['concur_area'] > .1]
mm['xd'] = xd
mm['yd'] = 0
b1 = mm['inf_index'].isin([35, 38])
mm = mm[~b1]
mm['lab'] = mm.apply(_lab, axis=1)
ax.set_xticks(xticks)
ax.set_yticks(yticks)
left = left_lab
right = right_lab
top = top_lab,
bottom = botttom_lab
ax.tick_params(
labelleft=left,
left=left,
labelright=right,
right=right,
labeltop=top,
top=top,
labelbottom=bottom,
bottom=bottom,
)
def plot_pathways(geo_df,
lon_range=15,
lat_range=13,
centroid_to_plot=None,
chc_lp_legend=False,
ax_bolivia=True,
ax=None,
min_font_size=5,
labs_to_plot='all',
background_alpha=0):
npdf = geo_df
if centroid_to_plot is None:
centroid_to_plot = ['LR', 'MR']
if ax is None:
ucp.set_dpi(300)
f_width = 7.25
figsize = (f_width, f_width / 1.6)
_, ax = plt.subplots(subplot_kw=dict(projection=crt.crs.PlateCarree()),
figsize=figsize)
ax: plt.Axes
if ax_bolivia:
fa.get_ax_bolivia(
ax=ax,
plot_cities=False,
map_res='50m',
lake_face_color='none',
map_line_alpha=.5,
chc_lp_legend=chc_lp_legend,
grid_alpha=0,
draw_labels=False,
xlab_top=False,
xlab_bot=False,
ylab_left=False,
ylab_right=False,
)
else:
fa.get_ax_lapaz(ax=ax,
plot_cities=False,
lake_face_color='none',
map_line_alpha=.5,
chc_lp_legend=chc_lp_legend,
grid_alpha=0,
draw_labels=False,
y_left_lab=False)
npdf['text_xy'] = [[[0, 0]]] * len(npdf)
npdf.loc['07_LR', 'text_xy'] = [[[-5, -12]]]
npdf.loc['08_LR', 'text_xy'] = [[[-5, 7]]]
npdf.loc['10_LR', 'text_xy'] = [[[8, 0]]]
npdf.loc['11_MR', 'text_xy'] = [[[-13, 7]]]
npdf.loc['02_MR', 'text_xy'] = [[[-10, 7]]]
npdf.loc['04_MR', 'text_xy'] = [[[-10, 7]]]
npdf.loc['05_MR', 'text_xy'] = [[[5, 0]]]
npdf.loc['09_MR', 'text_xy'] = [[[-25, -8]]]
npdf.loc['08_SM', 'text_xy'] = [[[-25, -10]]]
npdf.loc['12_SM', 'text_xy'] = [[[-3, 7]]]
npdf.loc['03_SM', 'text_xy'] = [[[5, -4]]]
npdf.loc['06_SM', 'text_xy'] = [[[-7, -10]]]
npdf.loc['11_SR', 'text_xy'] = [[[-10, 7]]]
npdf.loc['02_SR', 'text_xy'] = [[[-3, 7]]]
npdf.loc['12_SR', 'text_xy'] = [[[-12, 7]]]
npdf.loc['04_SR', 'text_xy'] = [[[4, -10]]]
npdf.loc['07_SR', 'text_xy'] = [[[-17, -12]]]
npdf.loc['10_SR', 'text_xy'] = [[[-10, 5]]]
lrpdf = npdf[npdf['range'].isin(centroid_to_plot)]
for l, r in lrpdf.iterrows():
ax.scatter(r['lon'],
r['lat'],
s=40,
c=[r['range_color']],
marker=r['range_marker'],
zorder=20,
edgecolors=['k'],
linewidth=1)
if labs_to_plot == 'all':
lab_lrpdf = lrpdf
else:
lab_lrpdf = npdf[npdf['range'].isin(labs_to_plot)]
for l, r in lab_lrpdf.iterrows():
ax.annotate(
r.name,
xy=r[['lon', 'lat']],
xytext=r['text_xy'][0],
textcoords='offset points',
zorder=12,
backgroundcolor=[1, 1, 1, background_alpha],
# bbox=dict(boxstyle="round",
# alpha=0,
# linewidth=0,
# facecolor='w'
# ),
alpha=.5,
fontsize=min_font_size,
arrowprops=dict(arrowstyle='-', alpha=.5))
# ax.set_ylim(-15,15LR)
import cartopy.mpl.geoaxes
ax: crt.mpl.geoaxes.GeoAxesSubplot
_xx, _yy = lon_range, lat_range
extent = get_extent(_xx, _yy)
ax.set_extent(extent, crt.crs.PlateCarree())
# plt.show()
return ax
def plot_pathways(npdf,
dac_sum,
conc_name,
lon_range=15,
lat_range=13,
centroid_to_plot=None,
chc_lp_legend=False,
q_level=.8,
ax_bolivia=True,
ax=None,
min_fs=5,
add_patch=True,
labs_to_plot='all',
background_alpha=0):
if centroid_to_plot is None:
centroid_to_plot = ['LR', 'MR']
if ax is None:
ucp.set_dpi(300)
f_width = 7.25
figsize = (f_width, f_width / 1.6)
_, ax = plt.subplots(subplot_kw=dict(projection=crt.crs.PlateCarree()),
figsize=figsize)
ax: plt.Axes
if ax_bolivia:
fa.get_ax_bolivia(
ax=ax,
plot_cities=False,
map_res='50m',
lake_face_color='none',
map_line_alpha=.5,
chc_lp_legend=chc_lp_legend,
grid_alpha=0,
draw_labels=False,
)
else:
fa.get_ax_lapaz(ax=ax,
plot_cities=False,
lake_face_color='none',
map_line_alpha=.5,
chc_lp_legend=chc_lp_legend,
grid_alpha=0,
draw_labels=False,
y_left_lab=False)
# gpdf = npdf.groupby('range')
for l, r in npdf.iterrows():
_q = q_level
l = r['cluster_i']
res = get_res_quan(dac_sum, l, _q, conc_name=conc_name)
pols = res.apply(lambda r: sh_polygon_from_row(r), axis=1)
u = cascaded_union(pols)
patch = PolygonPatch(u,
alpha=.7,
facecolor=r['c6_colors'],
edgecolor=r['c6_colors'],
linewidth=1,
zorder=11)
if add_patch:
ax.add_patch(patch)
npdf['text_xy'] = [[[0, 0]]] * len(npdf)
npdf.loc['07_LR', 'text_xy'] = [[[-5, -12]]]
npdf.loc['08_LR', 'text_xy'] = [[[-5, 7]]]
npdf.loc['10_LR', 'text_xy'] = [[[8, 0]]]
npdf.loc['11_MR', 'text_xy'] = [[[-13, 7]]]
npdf.loc['02_MR', 'text_xy'] = [[[-10, 7]]]
npdf.loc['04_MR', 'text_xy'] = [[[-10, 7]]]
npdf.loc['05_MR', 'text_xy'] = [[[5, -10]]]
npdf.loc['09_MR', 'text_xy'] = [[[-25, -8]]]
npdf.loc['08_SM', 'text_xy'] = [[[-23, -15]]]
npdf.loc['12_SM', 'text_xy'] = [[[-3, 7]]]
npdf.loc['03_SM', 'text_xy'] = [[[5, -4]]]
npdf.loc['06_SM', 'text_xy'] = [[[-7, -10]]]
npdf.loc['11_SR', 'text_xy'] = [[[-10, 7]]]
npdf.loc['02_SR', 'text_xy'] = [[[-3, 7]]]
npdf.loc['12_SR', 'text_xy'] = [[[-12, 7]]]
npdf.loc['04_SR', 'text_xy'] = [[[4, -10]]]
npdf.loc['07_SR', 'text_xy'] = [[[-17, -12]]]
npdf.loc['10_SR', 'text_xy'] = [[[-30, 0]]]
_x = npdf[co.R_CENTER] * np.sin(npdf[co.TH_CENTER])
_y = npdf[co.R_CENTER] * np.cos(npdf[co.TH_CENTER])
npdf['lat'] = _y + co.CHC_LAT
npdf['lon'] = _x + co.CHC_LON
lrpdf = npdf[npdf['range'].isin(centroid_to_plot)]
for l, r in lrpdf.iterrows():
ax.scatter(r['lon'],
r['lat'],
s=40,
c=[r['range_color']],
marker=r['range_marker'],
zorder=20,
edgecolors=['k'],
linewidth=1)
if labs_to_plot == 'all':
lab_lrpdf = lrpdf
else:
lab_lrpdf = npdf[npdf['range'].isin(labs_to_plot)]
for l, r in lab_lrpdf.iterrows():
ax.annotate(
r.name,
xy=r[['lon', 'lat']],
xytext=r['text_xy'][0],
textcoords='offset points',
zorder=12,
backgroundcolor=[1, 1, 1, background_alpha],
# bbox=dict(boxstyle="round",
# alpha=0,
# linewidth=0,
# facecolor='w'
# ),
alpha=.5,
fontsize=min_fs,
arrowprops=dict(arrowstyle='-', alpha=.5))
# ax.set_ylim(-15,15LR)
import cartopy.mpl.geoaxes
ax: crt.mpl.geoaxes.GeoAxesSubplot
_xx, _yy = lon_range, lat_range
extent = get_extent(_xx, _yy)
ax.set_extent(extent, crt.crs.PlateCarree())
# plt.show()
return ax
pat = Path(co.tmp_data_path) / 'new_log_pol_ds_asl_v01.nc'
# %%
ds = xr.open_dataset(pat)
# %%
ds
# %%
ds['CONC'][{co.RL: 10}].sum(co.ZM).plot(y=co.R_CENTER, x=co.TH_CENTER)
# %%
da = ds.loc[{co.RL: '2018-01-10 15'}]
# %%
ax = fa.get_ax_bolivia()
fa.logpolar_plot(da.sum(co.ZM), ax=ax)
# %%
da
# %%
da
# %%
da = ds.loc[{co.RL: slice('2018-01-10 21', '2018-01-11 06')}]
da['dis'] = da[co.R_CENTER] * 100
da = da.assign_coords(dis=da['dis'])
da['CONC'].sum(co.TH_CENTER).\
plot(
x='dis',figsize=(20,5),
def clus_plot(
i_lab, ds: xr.Dataset,
conc_lab=None,
ds_lab=None,
figure_size=15,
dpi=400,
fig_title: str = None,
conc_lab_ts=None,
df_prop=None
):
if conc_lab is None:
conc_lab = ds.attrs['chosen_smooth']
if conc_lab_ts is None:
conc_lab_ts = conc_lab
r_dis_km: xr.DataArray = ds[co.R_CENTER] * 100
r_dis_km = r_dis_km.assign_attrs({
'units': 'km',
'long_name': 'radial distance from CHC'
})
# return r_dis_km
ds = ds.assign_coords(**{R_DIS_KM: r_dis_km})
if ds_lab is None:
ds_lab = ds[[conc_lab_ts, conc_lab]].where(ds['lab'] == i_lab)
ds_lab = ds_lab.assign_coords(**{R_DIS_KM: r_dis_km})
# print(ds_lab.attrs)
cols = 10
rows = 10
f_len = figure_size
f: plt.Figure = plt.figure(
figsize=(f_len, cols / rows * f_len),
dpi=dpi
)
xn = 2
yn = 4
xp = .07
yp = .05
ww = (1 - xp * 3) / xn
hh = (1 - yp * 5) / yn
# yy = .29
# xx = .4
# xxL = .55
# ypos = .35
l_lpb = ((2 * xp) + ww, (2 * yp) + hh, .8 * ww, hh)
l_bol = ((2 * xp) + ww, yp, .8 * ww, hh)
l_ts = (xp, 1 - yp - hh, 1 - 2 * xp, hh)
l_per_inf = (xp, (3 * yp) + (2 * hh), ww, hh)
l_far = (xp, yp, 1.2 * ww, hh)
l_zoom = (xp, 2 * yp + hh, 1.2 * ww, hh)
ax_vertical_far_: plt.Axes = f.add_axes(l_far)
da_lab = ds_lab[conc_lab]
da_lab.load()
plot_vertical_ax(ax_vertical_far_, conc_lab, ds, da_lab)
ax_vertical_far_.scatter(0, 5240, c='blue', label='CHC')
ax_vertical_far_.legend()
ax_vertical_far_.grid(True)
ax_vertical_far_.set_axisbelow(False)
ax_vertical_zoom = f.add_axes(l_zoom)
plot_vertical_ax(ax_vertical_zoom, conc_lab, ds, da_lab,
xlim=(0, 200))
ax_vertical_zoom.scatter(0, 5240, c='blue', label='CHC')
ax_vertical_zoom.legend()
ax_vertical_zoom.grid(True)
ax_vertical_zoom.set_axisbelow(False)
carree = crt.crs.PlateCarree()
ax_lpb: cartopy.mpl.geoaxes.GeoAxesSubplot = \
f.add_axes(l_lpb, projection=carree)
cc_ = da_lab.sum([co.RL, co.ZM], keep_attrs=True)
ax_lpb = fa.get_ax_lapaz(ax=ax_lpb)
print(cc_.attrs)
fa.logpolar_plot(cc_, name=conc_lab, ax=ax_lpb)
ax_bol = f.add_axes(l_bol, projection=carree)
lo_la_bol_range = 15
lo_la_limits = [
co.CHC_LON - lo_la_bol_range,
co.CHC_LON + lo_la_bol_range,
co.CHC_LAT - lo_la_bol_range,
co.CHC_LAT + lo_la_bol_range,
]
ax_bol = fa.get_ax_bolivia(ax=ax_bol, lola_extent=lo_la_limits)
fa.logpolar_plot(cc_, name=conc_lab, ax=ax_bol)
# noinspection PyUnusedLocal
ax_ts = f.add_axes(l_ts)
plot_lab_time_series(da_lab=ds_lab[conc_lab_ts], ax_ts=ax_ts)
ax_per_inf = f.add_axes(l_per_inf)
if df_prop is not None:
plot_cluster_summary_figure(
df_prop,
'SRR [%]',
'distance from CHC [km]',
# save_fig=True,
fig_save_name='dis_vs_srr_influence.pdf',
xy_locs=([-30, 5], [0, 9], [500, 11], [950, 9]),
y_range=(0, 13),
add_vertical_lines=True,
add_cluster_group_label=False,
ax=ax_per_inf
)
y = df_prop.set_index('cluster_i')['SRR [%]'][i_lab]
x = df_prop.set_index('cluster_i')['distance from CHC [km]'][i_lab]
# ax_per_inf.scatter(x,y,c='k',s=10,marker='o')
ax_per_inf.scatter(x, y, s=200, marker='o', facecolors='none',
edgecolors='k')
# todo fix this
else:
plot_concentration_influence_div_by_cluster(
ds=ds, conc_lab=conc_lab, i_lab=i_lab,
ax=ax_per_inf
)
plt.show()
if fig_title is None:
f.suptitle(f'cluster: {i_lab}', y=.95)
else:
f.suptitle(fig_title, y=1)
f.tight_layout()
log.ger.debug('show plot')
f.show()
return f
