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_general_lapaz(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_lapaz(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_log_custom(d_sum,
v_max,
v_min,
map_fun=fa.get_ax_bolivia):
ax = map_fun(fig_args={'figsize': (10, 8)})
p_args_ = {
'norm': mpl_colors.LogNorm(vmin=v_min, vmax=v_max)
}
fa.logpolar_plot(d_sum, ax=ax, perM=v_max, perm=v_min,
quantile=False,
patch_args=p_args_,
drop_zeros=False,
)
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_clust_in_lapaz(_n, dscc):
for _f in range(_n):
ax = fa.get_ax_lapaz()
ax.set_title(str(_f))
_ds = dscc.loc[{co.CLUS_LENGTH_DIM: _n}][
{co.R_CENTER: slice(1, 23)}]
_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])
if _ds[co.CONC].max().item() != 0:
fa.logpolar_plot(_ds, ax=ax, patch_args={'cmap': _cm},
colorbar=False)
fa.add_chc_lpb(ax)
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
# %%
# %%
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])})
# %%
i = 0
ax = fa.get_ax_lapaz()
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.CCPer].mean(dim=[co.RL])
fa.logpolar_plot(ar,
# %%
from matplotlib.patches import Circle, Wedge, Polygon
from matplotlib.collections import PatchCollection
# %%
conc = nds/nds[fa.GA]
conc.name = 'conc'
# %%
# %%
ax = fa.get_ax_bolivia()
# fig, ax = plt.subplots()
ax = fa.logpolar_plot(conc.where(conc>0),ax,name='conc',perM=.8,perm=.1,
colorbar=True,
patch_args={'norm':matplotlib.colors.LogNorm(),'cmap':'viridis'}
)
# %%
ax = fa.get_ax_bolivia()
# fig, ax = plt.subplots()
ax = fa.logpolar_plot(conc.where(conc>0),ax,name='conc',perM=.8,perm=.1,
colorbar=True,
# patch_args={'norm':matplotlib.colors.LogNorm()}
)
# %%
ax = fa.get_ax_bolivia()
# fig, ax = plt.subplots()
ax = fa.logpolar_plot(nds,ax,perM=.99,perm=.1,
colorbar=True
# self = FLP.FlexLogPol(path,concat=False)
self = FLP.FlexLogPol(
path,
# concat=True,
concat=False,
get_clusters=False,
open_merged=True,
clusters_avail=False)
# %%
# %%
com = fa.get_dims_complement(self.merged_ds, [co.R_CENTER, co.TH_CENTER])
sds = self.merged_ds.sum(com)
ax = fa.get_ax_bolivia()
fa.logpolar_plot(sds[co.CONC], name=co.CONC, ax=ax, drop_zeros=False)
# %%
ax = fa.get_ax_bolivia()
fa.logpolar_plot(self.merged_ds[co.TOPO],
name=co.TOPO,
ax=ax,
drop_zeros=False)
# %%
self.reset_z_levels()
# %%
com = fa.get_dims_complement(self.merged_ds, [co.R_CENTER, co.TH_CENTER])
sds = self.merged_ds.sum(com)
ax = fa.get_ax_bolivia()
# %%
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')
# %%
# %%
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')
# %% [markdown]
# # new part
# %%
dsZ = dsSM
# %%
# %%
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),
# norm = mpl.colors.SymLogNorm(linthresh=10),
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