def load_case(rain=True):
cases_name = 't_model' if rain else 'feb15'
scheme_id = conf.SCHEME_ID_MELT if rain else conf.SCHEME_ID_SNOW
cases = multicase.read_cases(cases_name)
c = cases.case[0]
c.load_classification(scheme_id)
return c
def init_cases(cases_id=None, season=''):
"""initialize cases data"""
if cases_id is None:
if season == 'snow':
cases_id = CASES_SNOW
elif season == 'rain':
cases_id = CASES_RAIN
cases = multicase.read_cases(cases_id)
if cases.ml.astype(bool).all():
cases = cases[cases['ml_ok'].fillna(0).astype(bool)]
return cases
def init_data(cases_id, scheme_id, has_ml=False, use_cache=False, **kws):
"""initialize cases data"""
if use_cache:
cases_fname = 'cases_{}{}.pkl'.format(cases_id, scheme_id)
cases_file = path.join(CACHE_DIR, cases_fname)
cc_fname = 'cc_{}{}.pkl'.format(cases_id, scheme_id)
cc_file = path.join(CACHE_DIR, cc_fname)
if path.exists(cases_file) and path.exists(cc_file):
print('Using cached profile data.')
return pkl_load(cases_file), pkl_load(cc_file)
cases = multicase.read_cases(cases_id)
if has_ml:
cases = cases[cases.ml_ok.astype(bool)]
for i, c in cases.case.iteritems():
c.load_classification(scheme_id)
cases, cc = recase.combine_cases_t_thresh(cases)
#cc = multicase.MultiCase.by_combining(cases, has_ml=has_ml, **kws)
cc.load_classification(scheme_id)
for i, c in cases.case.iteritems():
c.load_classification(scheme_id)
if use_cache:
pkl_dump(cases, cases_file)
pkl_dump(cc, cc_file)
return cases, cc
# coding: utf-8
import matplotlib.pyplot as plt
from os import path
from radcomp.vertical import multicase
from scr_erad_cases import savedir
if __name__ == '__main__':
plt.close('all')
cases = multicase.read_cases('feb21')
c = cases.case.iloc[0]
fig, _ = c.plot(plot_fr=False, plot_t=False, plot_azs=False)
fig.savefig(path.join(savedir, 'feb21.png'), bbox_inches='tight')
# coding: utf-8
from os import path
import matplotlib.pyplot as plt
from radcomp.tools import cloudnet
from radcomp.vertical import multicase
import conf
if __name__ == '__main__':
plt.ion()
plt.close('all')
cases = multicase.read_cases('t_model')
c = cases.case[0]
c.load_model_temperature()
#c.load_classification(conf.SCHEME_ID_MELT)
c.load_model_data(variable='rh')
params = ['zh', 'zdr', 'kdp', 'rh']
fig, axarr = c.plot(plot_fr=False,
plot_t=False,
plot_azs=False,
t_contour_ax_ind=[0, 1, 2],
cmap='viridis',
params=params,
t_levels=(-20, -8, 0))
import matplotlib.pyplot as plt
from os import path
from radcomp.vertical import multicase #, RESULTS_DIR
#from j24 import ensure_join
import conf
case_set_r = 'erad18_rain'
case_set_s = 'erad18_snow'
scheme_id_r = conf.SCHEME_ID_MELT
scheme_id_s = conf.SCHEME_ID_SNOW
savedir = conf.P1_FIG_DIR
if __name__ == '__main__':
plt.close('all')
plt.ion()
cases_r = multicase.read_cases(case_set_r)
cases_s = multicase.read_cases(case_set_s)
for i, c in cases_r.case.iteritems():
c.load_classification(scheme_id_r)
fig, axarr = c.plot(n_extra_ax=0, plot_silh=False) #, cmap='viridis')
fig.set_size_inches(2.5, 4)
axarr[-1].set_xticks(axarr[-1].get_xticks()[1::2])
fig.savefig(path.join(savedir, 'hm_case.png'), bbox_inches='tight')
for i, c in cases_s.case.iteritems():
c.load_classification(scheme_id_s)
fig, axarr = c.plot(n_extra_ax=0,
plot_silh=False,
plot_fr=False,
plot_azs=False)
fig.set_size_inches(3.5, 4)
#axarr[-1].set_xticks(axarr[-1].get_xticks()[0::2])
def cases_from_cases_set():
"""setup cases based on a cases list"""
return multicase.read_cases('debug')
# coding: utf-8
"""script for debugging ML"""
import matplotlib.pyplot as plt
from radcomp.vertical import multicase
from conf import SCHEME_ID_MELT
name = 'mlt2_3eig10clus_pca'
#name = SCHEME_ID_MELT
if __name__ == '__main__':
plt.close('all')
cases = multicase.read_cases('ml_debug')
c = cases.case[0]
c.load_classification(name)
c.plot(params=['ZH', 'zdr', 'RHO', 'MLI'], cmap='viridis')
def workflow(c, vpc_params, plot_kws={}, **kws):
"""training workflow"""
vpc = classification.VPC(**vpc_params)
c.vpc = vpc
training(c, **kws)
make_plots(vpc, **plot_kws)
if __name__ == '__main__':
bracketing = False
plt.ion()
plt.close('all')
cases_id = 'rain'
#
rain_season = cases_id in ('rain', )
cases = multicase.read_cases(cases_id)
if rain_season:
cases = cases[cases['ml_ok'].fillna(0).astype(bool)]
vpc_params = VPC_PARAMS_RAIN if rain_season else VPC_PARAMS_SNOW
c = multicase.MultiCase.by_combining(cases, has_ml=rain_season)
if bracketing:
for n_clusters in np.arange(14, 20):
vpc_params.update({'n_clusters': n_clusters})
plot_kws = dict(plt_silh=False,
plt_sca=False,
plt_top=False,
save_plots=False)
print(vpc_params)
workflow(c, vpc_params, plot_kws=plot_kws)
else:
kws = dict(save_plots=True)