def plot_distance_height_chc(_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.TH_CENTER])
_ds = _ds[[co.CONC]]
_cm = fa.get_custom_cmap([*ucp.cc, *ucp.cc, *ucp.cc][_f][:3])
_km = _ds[co.R_CENTER] * 100
DIS = 'Distance [km]'
_km.name = DIS
_ds = _ds.assign_coords(**{DIS: _km})
_, ax = plt.subplots()
_ds[co.CONC].plot(x=DIS, cmap=_cm, ax=ax)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlim(10, 2e3)
ax.set_ylim(25e1, 2e4)
ax.set_title(str(_f))
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_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_clust_height(self,
ax: plt.Axes,
i,
perM,
drop_zero=True,
par_to_plot=co.COL):
clus = self.cluster_flags[i]
boo = self.merged_ds[co.ClusFlag] == clus
ar = self.merged_ds[par_to_plot].where(boo)
com = fa.get_dims_complement(ar, [co.R_CENTER, co.ZM])
ar = ar.sum(dim=com)
if par_to_plot is co.CPer:
ar = ar / self.merged_ds[par_to_plot].sum() * 100
if drop_zero:
ar = ar.where(ar > 0)
q = ar.quantile(perM)
lab = "km from CHC"
ar[lab] = ar[co.R_CENTER] * 100
ar = ar.swap_dims({co.R_CENTER: lab})
try:
ar.name = co.PLOT_LABS[par_to_plot]
except AssertionError as error:
log.ger.error(error)
pass
ar.plot(
cmap=fa.get_custom_cmap(self.colors[i]),
vmin=0,
vmax=q,
ax=ax,
x=lab,
)
ax.set_yscale('log')
ax.set_ylim(100, 20000)
ax.set_xscale('log')
ax.set_xlim(.05 * 100, 30 * 100)
ax.grid(True, 'major', axis='y')
ax.grid(True, 'both', axis='x')
ax.set_ylabel(co.PLOT_LABS[co.ZM])
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,
ax,
name=co.CCPer,
perM=.05,
# patch_args={'cmap':cmap}
# )
# ax.set_xlim(-75,-60)
# ax.set_title(ii)
# yl=ax.set_ylim(-25,-7)
# %%
ax.figure.savefig('/tmp/clus_res.pdf')
# %%
ii=5
ds_fl = dsZ[co.CPer].where(dsZ[co.ClusFlag]==ii)
# %%
ax = fa.get_ax_bolivia()
cmap = fa.get_custom_cmap(self.colors[ii])
fa.logpolar_plot(ds_fl.sum([co.RL,co.ZM]),
name=co.CPer,ax=ax,
patch_args={'cmap':cmap}
)
ax.set_xlim(-75,-60)
yl=ax.set_ylim(-25,-7)
# %%
import simplekml
# %%
import simplekml
kml = simplekml.Kml()
# %%
def plot_absolute_height(self,
i,
ax=None,
perM=.95,
drop_zero=True,
par_to_plit=co.COL):
mer = self.merged_ds.copy()
# i = 6
clus = self.cluster_flags[i]
H = co.H
fla = co.FLAGS
HC = 'H*CONC'
ver_area = 'VER_AREA'
log_center = np.log(mer[co.R_CENTER])
dis = log_center - \
log_center.shift({co.R_CENTER: 1})
dis = dis.median()
l1 = log_center - dis / 2
l2 = log_center + dis / 2
l1 = np.e**l1
l2 = np.e**l2
r_dis = (l2 - l1) * 100000
zlen = 500
ar = np.arange(zlen / 2, 20000, zlen)
mer = mer[[co.CONC, fla, co.TOPO]]
# return merged_ds
mer = mer.where(mer[fla] == clus, 0)
mer['c/v'] = mer[co.CONC] / mer[co.VOL]
mer = mer.interp(**{co.ZM: ar})
mer[co.VOL] = mer[co.GA] * zlen
mer[co.CONC] = mer['c/v'] * mer[co.VOL]
mer[H] = mer[co.TOPO] + mer[co.ZM]
mer[HC] = mer[H] * mer[co.CONC]
var = [co.CONC, HC]
com = fa.get_dims_complement(mer, [co.R_CENTER, co.ZM])
ms = mer[var].sum(com)
ms[ver_area] = ms[co.ZLM] * r_dis
# ms:xr.DataArray = ms/ms[ver_area]
ms[co.CONC] = ms[co.CONC].where(ms[co.CONC] > 0, 0)
# ms = ms*zlen*r_dis
ms[H] = ms[HC] / ms[co.CONC]
def find_nearest(value):
# ar = self.get_z_lin()
array = np.asarray(ar)
idx = (np.abs(array - value)).argmin()
return array[idx]
ms[H] = xr.apply_ufunc(find_nearest, ms[H], vectorize=True)
hs = ms.to_dataframe().groupby([H, co.R_CENTER
]).sum()[co.CONC].to_xarray()
lab = "km from CHC"
hs[lab] = hs[co.R_CENTER] * 100
hs = hs.swap_dims({co.R_CENTER: lab})
hs1 = hs.interp(**{H: ar})
hs1 = hs1.combine_first(hs)
if drop_zero:
hs1 = hs1.where(hs1 > 0)
if ax is None:
fig, ax = plt.subplots(figsize=(10, 5))
hs1 = hs1 / self.merged_ds[co.CONC].sum() * 100
hs1.name = co.CPer
q = hs1.quantile(perM)
hs1.name = co.PLOT_LABS[co.CPer]
hs1.plot(
cmap=fa.get_custom_cmap(self.colors[i]),
vmin=0,
vmax=q,
ax=ax,
x=lab,
)
ax.set_ylim(100, 20000)
ax.set_xscale('log')
ax.set_xlim(.05 * 100, 30 * 100)
ax.grid(True, 'major', axis='y')
ax.grid(True, 'both', axis='x')
ax.set_ylabel(co.PLOT_LABS[co.H])
def plot_dis_height_quantiles_chc_single(_f, _n, dsF, dscc, axs=None):
if axs is None:
_, ax = plt.subplots()
else:
if type(axs) == np.ndarray:
ax = axs[_f]
else:
ax = axs
_cm = fa.get_custom_cmap([*ucp.cc, *ucp.cc, *ucp.cc][_f][:3])
_ds = dscc.loc[{co.CLUS_LENGTH_DIM: _n}]
_ds = _ds.drop(co.KMEAN_OBJ)
_ds = _ds.sum([co.RL])
_ds1 = xr.merge([_ds, dsF[[co.TOPO]]]).where(
_ds[co.FLAG] == _f)
_ds1 = _ds1.swap_dims({co.R_CENTER: co.DIS})
_ds2 = _ds1 / dsF[co.ZLM]
_ds3 = _ds2[co.CONC]
zmin, zmax = _ds2[co.ZM].quantile([0, 1])
zz = np.arange(zmin, zmax, zmin)
zl = zz[1] - zz[0]
_ds4 = _ds3.interp(**{co.ZM: zz}) * zl
ZREAL = 'ZREAL'
_ds4[ZREAL] = _ds4 * 0 + _ds4[co.ZM] + (
(_ds4[co.TOPO] / zl).round() * zl)
_dims = set(_ds4.dims)
_keep = _dims.union({ZREAL})
# noinspection SpellCheckingInspection
_coor = set(_ds4.coords)
_drop = list(_coor - _keep)
_ds5 = _ds4.drop(_drop).to_dataframe()
_ds6 = \
_ds5.reset_index().groupby([ZREAL, co.DIS, co.TH_CENTER]).sum()[
[co.CONC]].to_xarray()
_ds6.sum(co.TH_CENTER)[co.CONC].plot(xscale='log', vmin=0, ax=ax,
cmap=_cm)
HEIGHT = 'HEIGHT'
_ds1[HEIGHT] = (_ds1[co.TOPO] + _ds1[co.ZM])
_dg = _ds1[[HEIGHT, co.CONC]].to_dataframe()[[HEIGHT, co.CONC]]
_dg = _dg.groupby(co.DIS)
_dh = (_ds1[co.CONC] * (_ds1[co.TOPO] + _ds1[co.ZM])).mean(
[co.TH_CENTER, co.ZM])
_dh = _dh / (_ds1[co.CONC].mean([co.TH_CENTER, co.ZM]))
ax.set_xlim(10, 2e3)
_dh = (_ds1[co.CONC] * (_ds1[co.TOPO])).mean([co.TH_CENTER, co.ZM])
_dh = _dh / (_ds1[co.CONC].mean([co.TH_CENTER, co.ZM]))
_dh.plot(x=co.DIS, color='k', ax=ax)
ax.set_xscale('log')
ax.set_xlim(5, 2e3)
ax.set_ylim(0, 1.5e4)
ax.set_title(str(_f))
ax.set_xlabel('Radial distance from CHC [km]')
ax.set_ylabel('Height [masl]')
ax.grid(color='k', alpha=.3, linestyle='--')
ax.set_axisbelow(False)
ax.set_facecolor('white')
ax: plt.Axes = ax
ax.scatter(5.2, 5200, color='red')
ax.text(6, 5200, 'CHC')