def _plot_external_validation(ui,ax):
keys = ('wgms','wgms_calibrated')
cb = {'wgms':ui.cb_id070,
'wgms_calibrated':ui.cb_id071}
col_le = {'wgms':ui.le_id202,
'wgms_calibrated':ui.le_id205}
ls_le = {'wgms':ui.cob_id034,
'wgms_calibrated':ui.cob_id035}
label_le = {'wgms':ui.le_id203,
'wgms_calibrated':ui.le_id206}
cmd_le = {'wgms':ui.le_id204,
'wgms_calibrated':ui.le_id207}
for key in keys:
# load external validation data
file_path = os.path.join(project_dir,project_name,'validation','external','regression','%s.txt' % key)
mjd, kg, mjd_use, mjd_regression, kg_use, kg_smooth, kg_adjusted, kg_regression, outliers = _load_data(file_path)
# original data
if cb[key].isChecked():
x = mjd
y = kg
xo = mjd_use
yo = outliers
ix = np.isnan(xo)
xo = xo[~ix]
yo = yo[~ix]
color = col_le[key].text()
if not color:
color = tuple(np.random.random(3))
linestyle = ls_le[key].currentText()
if not linestyle:
linestyle = '-'
label = label_le[key].text()
if not label:
label = key.upper()
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if cmd_le[key].text():
eval(cmd_le[key].text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
if not np.all(np.isnan(outliers)):
xo = [date_functions.mjd2datetimenumber(xj) for xj in xo]
ax.plot_date(xo,yo,color=color,linestyle='',marker='o',markerfacecolor='w',label=label+' (outliers)')
def test_mjd2datetimenumber():
"""
Test if the function `mjd2datetimenumber` correctly converts from the
Modified Julian date format to the Python datetime format.
"""
mjd = [43826, 47273]
dt_true = [datetime(1978, 11, 14), datetime(1988, 4, 22)]
dt_test = date_functions.mjd2datetimenumber(mjd)
assert dt_test[0] == dt_true[0]
assert dt_test[1] == dt_true[1]
def _plot_gia(ui,ax):
keys = ('a','ice5','ice6','ice6_grace','klemann')
cb = {'a':ui.cb_id061,
'ice5':ui.cb_id062,
'ice6':ui.cb_id063,
'ice6_grace':ui.cb_id064,
'klemann':ui.cb_id065}
col_le = {'a':ui.le_id175,
'ice5':ui.le_id178,
'ice6':ui.le_id181,
'ice6_grace':ui.le_id184,
'klemann':ui.le_id187}
ls_le = {'a':ui.cob_id025,
'ice5':ui.cob_id026,
'ice6':ui.cob_id027,
'ice6_grace':ui.cob_id028,
'klemann':ui.cob_id029}
label_le = {'a':ui.le_id176,
'ice5':ui.le_id179,
'ice6':ui.le_id182,
'ice6_grace':ui.le_id185,
'klemann':ui.le_id188}
cmd_le = {'a':ui.le_id177,
'ice5':ui.le_id180,
'ice6':ui.le_id183,
'ice6_grace':ui.le_id186,
'klemann':ui.le_id189}
for key in keys:
# load correction data
file_path = os.path.join(project_dir,project_name,'corrections',key,'regression','%s.txt' % key)
mjd, kg, mjd_use, mjd_regression, kg_use, kg_smooth, kg_adjusted, kg_regression, outliers = _load_data(file_path)
# original data
if cb[key].isChecked():
x = mjd
y = kg
color = col_le[key].text()
if not color:
color = tuple(np.random.random(3))
linestyle = ls_le[key].currentText()
if not linestyle:
linestyle = '-'
label = label_le[key].text()
if not label:
label = key.upper()
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if cmd_le[key].text():
eval(cmd_le[key].text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
def _plot_internal_validation(ui,ax):
keys = ('m1','m1_syn')
cb = {'m1':(ui.cb_id066,None,None),
'm1_syn':(ui.cb_id067,ui.cb_id068,ui.cb_id069)}
col_le = {'m1':(ui.le_id190,None,None),
'm1_syn':(ui.le_id193,ui.le_id196,ui.le_id199)}
ls_le = {'m1':(ui.cob_id030,None,None),
'm1_syn':(ui.cob_id031,ui.cob_id032,ui.cob_id033)}
label_le = {'m1':(ui.le_id191,None,None),
'm1_syn':(ui.le_id194,ui.le_id197,ui.le_id200)}
cmd_le = {'m1':(ui.le_id192,None,None),
'm1_syn':(ui.le_id195,ui.le_id198,ui.le_id201)}
for key in keys:
# load validation data
file_path = os.path.join(project_dir,project_name,'validation','internal','regression','%s.txt' % key)
mjd, kg, mjd_use, mjd_regression, kg_use, kg_smooth, kg_adjusted, kg_regression, outliers = _load_data(file_path)
# original data
if cb[key][0].isChecked():
x = mjd
y = kg
xo = mjd_use
yo = outliers
ix = np.isnan(xo)
xo = xo[~ix]
yo = yo[~ix]
color = col_le[key][0].text()
if not color:
color = tuple(np.random.random(3))
linestyle = ls_le[key][0].currentText()
if not linestyle:
linestyle = '-'
label = label_le[key][0].text()
if not label:
label = key.upper()
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if cmd_le[key][0].text():
eval(cmd_le[key][0].text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
if not np.all(np.isnan(outliers)):
xo = [date_functions.mjd2datetimenumber(xj) for xj in xo]
ax.plot_date(xo,yo,color=color,linestyle='',marker='o',markerfacecolor='w',label=label+' (outliers)')
# smoothed data
if not cb[key][1]:
continue
if cb[key][1].isChecked():
x = mjd_use
y = kg_smooth
color = col_le[key][1].text()
if not color:
color = tuple(np.random.random(3))
linestyle = ls_le[key][1].currentText()
if not linestyle:
linestyle = '-'
label = label_le[key][1].text()
if not label:
label = key.upper() + ' smoothed'
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if cmd_le[key][1].text():
eval(cmd_le[key][1].text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
# regression model
if cb[key][2].isChecked():
x = mjd_regression
y = kg_regression
color = col_le[key][2].text()
if not color:
color = tuple(np.random.random(3))
linestyle = ls_le[key][2].currentText()
if not linestyle:
linestyle = '-'
label = label_le[key][2].text()
if not label:
label = key.upper() + ' regression'
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if cmd_le[key][2].text():
eval(cmd_le[key][2].text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
def _plot_hydrology(ui,ax):
keys = ('gldas','lsdm','wghm','promet')
cb = {'gldas':(ui.cb_id049,ui.cb_id050,ui.cb_id051),
'lsdm':(ui.cb_id052,ui.cb_id053,ui.cb_id054),
'wghm':(ui.cb_id055,ui.cb_id056,ui.cb_id057),
'promet':(ui.cb_id058,ui.cb_id059,ui.cb_id060)}
col_le = {'gldas':(ui.le_id139,ui.le_id142,ui.le_id145),
'lsdm':(ui.le_id148,ui.le_id151,ui.le_id154),
'wghm':(ui.le_id157,ui.le_id160,ui.le_id163),
'promet':(ui.le_id166,ui.le_id169,ui.le_id172)}
ls_le = {'gldas':(ui.cob_id013,ui.cob_id014,ui.cob_id015),
'lsdm':(ui.cob_id016,ui.cob_id017,ui.cob_id018),
'wghm':(ui.cob_id019,ui.cob_id020,ui.cob_id021),
'promet':(ui.cob_id022,ui.cob_id023,ui.cob_id024)}
label_le = {'gldas':(ui.le_id140,ui.le_id143,ui.le_id146),
'lsdm':(ui.le_id149,ui.le_id152,ui.le_id155),
'wghm':(ui.le_id158,ui.le_id161,ui.le_id164),
'promet':(ui.le_id167,ui.le_id170,ui.le_id173)}
cmd_le = {'gldas':(ui.le_id141,ui.le_id144,ui.le_id147),
'lsdm':(ui.le_id150,ui.le_id153,ui.le_id156),
'wghm':(ui.le_id159,ui.le_id162,ui.le_id165),
'promet':(ui.le_id168,ui.le_id171,ui.le_id174)}
for key in keys:
# load correction data
file_path = os.path.join(project_dir,project_name,'corrections',key,'regression','%s.txt' % key)
mjd, kg, mjd_use, mjd_regression, kg_use, kg_smooth, kg_adjusted, kg_regression, outliers = _load_data(file_path)
# original data
if cb[key][0].isChecked():
x = mjd
y = kg
xo = mjd_use
yo = outliers
ix = np.isnan(xo)
xo = xo[~ix]
yo = yo[~ix]
color = col_le[key][0].text()
if not color:
color = tuple(np.random.random(3))
linestyle = ls_le[key][0].currentText()
if not linestyle:
linestyle = '-'
label = label_le[key][0].text()
if not label:
label = key.upper()
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if cmd_le[key][0].text():
eval(cmd_le[key][0].text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
if not np.all(np.isnan(outliers)):
xo = [date_functions.mjd2datetimenumber(xj) for xj in xo]
ax.plot_date(xo,yo,color=color,linestyle='',marker='o',markerfacecolor='w',label=label+' (outliers)')
# smoothed data
if cb[key][1].isChecked():
x = mjd_use
y = kg_smooth
color = col_le[key][1].text()
if not color:
color = tuple(np.random.random(3))
linestyle = ls_le[key][1].currentText()
if not linestyle:
linestyle = '-'
label = label_le[key][1].text()
if not label:
label = key.upper() + ' smoothed'
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if cmd_le[key][1].text():
eval(cmd_le[key][1].text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
# regression model
if cb[key][2].isChecked():
x = mjd_regression
y = kg_regression
color = col_le[key][2].text()
if not color:
color = tuple(np.random.random(3))
linestyle = ls_le[key][2].currentText()
if not linestyle:
linestyle = '-'
label = label_le[key][2].text()
if not label:
label = key.upper() + ' regression'
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if cmd_le[key][2].text():
eval(cmd_le[key][2].text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
def _plot_goce(ui,ax):
if not (ui.cb_id080.isChecked() or ui.cb_id081.isChecked() or ui.cb_id082.isChecked()):
return
# load goce data
key = 'goce'
file_path = os.path.join(project_dir,project_name,'grace','single_solution','regression','%s.txt' % key)
mjd, kg, mjd_use, mjd_regression, kg_use, kg_smooth, kg_adjusted, kg_regression, outliers = _load_data(file_path)
# original data
if ui.cb_id080.isChecked():
x = mjd
y = kg
xo = mjd_use
yo = outliers
ix = np.isnan(xo)
xo = xo[~ix]
yo = yo[~ix]
color = ui.le_id227.text()
if not color:
color = tuple(np.random.random(3))
linestyle = ui.cob_id010.currentText()
if not linestyle:
linestyle = '-'
label = ui.le_id228.text()
if not label:
label = 'GOCE'
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if ui.le_id229.text():
eval(ui.le_id229.text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
if not np.all(np.isnan(outliers)):
xo = [date_functions.mjd2datetimenumber(xj) for xj in xo]
ax.plot_date(xo,yo,color=color,linestyle='',marker='o',markerfacecolor='w',label=label+' (outliers)')
# smoothed data
if ui.cb_id081.isChecked():
x = mjd_use
y = kg_smooth
color = ui.le_id230.text()
if not color:
color = tuple(np.random.random(3))
linestyle = ui.cob_id011.currentText()
if not linestyle:
linestyle = '-'
label = ui.le_id231.text()
if not label:
label = 'GOCE smoothed'
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if ui.le_id232.text():
eval(ui.le_id232.text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
# regression model
if ui.cb_id082.isChecked():
x = mjd_regression
y = kg_regression
color = ui.le_id233.text()
if not color:
color = tuple(np.random.random(3))
linestyle = ui.cob_id012.currentText()
if not linestyle:
linestyle = '-'
label = ui.le_id234.text()
if not label:
label = 'GOCE regression'
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if ui.le_id235.text():
eval(ui.le_id235.text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
def _plot_grace(ui,ax):
if not (ui.cb_id046.isChecked() or ui.cb_id047.isChecked() or ui.cb_id048.isChecked() or ui.cb_id045.isChecked()):
return
# load grace+goco data
key = 'grace'
file_path = os.path.join(project_dir,project_name,'grace','single_solution','regression','%s.txt' % key)
mjd, kg, mjd_use, mjd_regression, kg_use, kg_smooth, kg_adjusted, kg_regression, outliers = _load_data(file_path)
mjd_ga, kg_ga, mjd_use_ga, mjd_regression_ga, kg_use_ga, kg_smooth_ga, kg_adjusted_ga, kg_regression_ga, outliers_ga = (), (), (), (), (), (), (), (), ()
pop_size = ui.sb_id009.value()
for ix in list(range(pop_size)):
key_i = '%s_individual%d' % (key,ix)
file_path = os.path.join(project_dir,project_name,'grace','genetic_algorithm','regression','%s.txt' % key_i)
mjd_i, kg_i, mjd_use_i, mjd_regression_i, kg_use_i, kg_smooth_i, kg_adjusted_i, kg_regression_i, outliers_i = _load_data(file_path)
mjd_ga += (mjd_i,)
kg_ga += (kg_i,)
mjd_use_ga += (mjd_use_i,)
mjd_regression_ga += (mjd_regression_i,)
kg_use_ga += (kg_use_i,)
kg_smooth_ga += (kg_smooth_i,)
kg_adjusted_ga += (kg_adjusted_i,)
kg_regression_ga += (kg_regression_i,)
outliers_ga += (outliers_i,)
# original data
if ui.cb_id046.isChecked():
x = mjd
y = kg
xo = mjd_use
yo = outliers
ix = np.isnan(xo)
xo = xo[~ix]
yo = yo[~ix]
color = ui.le_id209.text()
if not color:
color = tuple(np.random.random(3))
linestyle = ui.cob_id010.currentText()
if not linestyle:
linestyle = '-'
label = ui.le_id210.text()
if not label:
label = 'GRACE'
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if ui.le_id211.text():
eval(ui.le_id211.text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
if not np.all(np.isnan(outliers)):
xo = [date_functions.mjd2datetimenumber(xj) for xj in xo]
ax.plot_date(xo,yo,color=color,linestyle='',marker='o',markerfacecolor='w',label=label+' (outliers)')
# smoothed data
if ui.cb_id047.isChecked():
x = mjd_use
y = kg_smooth
color = ui.le_id212.text()
if not color:
color = tuple(np.random.random(3))
linestyle = ui.cob_id011.currentText()
if not linestyle:
linestyle = '-'
label = ui.le_id213.text()
if not label:
label = 'GRACE smoothed'
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if ui.le_id214.text():
eval(ui.le_id214.text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
# regression model
if ui.cb_id048.isChecked():
x = mjd_regression
y = kg_regression
color = ui.le_id215.text()
if not color:
color = tuple(np.random.random(3))
linestyle = ui.cob_id012.currentText()
if not linestyle:
linestyle = '-'
label = ui.le_id216.text()
if not label:
label = 'GRACE regression'
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if ui.le_id217.text():
eval(ui.le_id217.text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
# GA population
if ui.cb_id045.isChecked():
color = ui.le_id136.text()
if not color:
color = tuple(np.random.random(3))
linestyle = ui.cob_id009.currentText()
if not linestyle:
linestyle = '-'
label = ui.le_id137.text()
if not label:
label = 'GRACE GA population'
for ix,(x,y) in enumerate(zip(mjd_ga,kg_ga)):
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if ix>0:
label = '_nolegend_'
if ui.le_id138.text():
eval(ui.le_id138.text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
def make_plot(ui,skip_try=False):
if not skip_try:
logging.info('New plot...')
try:
make_plot(ui,skip_try=True)
except:
logging.info('Could not create plots. Check settings and result files.')
return
# paths
global project_name
global project_dir
project_name = str(ui.le_id001.text())
project_dir = str(ui.le_id002.text())
# upadte rc params
_update_rc_params(ui)
# initiate figure
fig,ax = plt.subplots()
# plot data
_plot_grace_goce(ui,ax)
_plot_grace(ui,ax)
_plot_goce(ui,ax)
_plot_hydrology(ui,ax)
_plot_gia(ui,ax)
_plot_internal_validation(ui,ax)
_plot_external_validation(ui,ax)
# ax
title = ui.le_id124.text()
if title:
ax.set_title(title)
xlabel = ui.le_id125.text()
if xlabel:
ax.set_xlabel(xlabel)
ylabel = ui.le_id126.text()
if ylabel:
ax.set_ylabel(ylabel)
if ui.rb_id097.isChecked():
xl = date_functions.mjd2datetimenumber(ui.dsb_id032.value())
xr = date_functions.mjd2datetimenumber(ui.dsb_id033.value())
ax.set_xlim(left=xl,right=xr)
else:
date_begin = ui.de_id003.date().toString("yyyy-MM-dd")
date_end = ui.de_id004.date().toString("yyyy-MM-dd")
mjd_start = date_functions.ymstring2mjd(date_begin)
tmp = date_functions.ymstring2mjd(date_end)
_, mjd_end = date_functions.mjd2mjdmrange(tmp)
xl = date_functions.mjd2datetimenumber(mjd_start)
xr = date_functions.mjd2datetimenumber(mjd_end)
ax.set_xlim(left=xl,right=xr)
if ui.rb_id099.isChecked():
ax.set_ylim(bottom=ui.dsb_id034.value(),top=ui.dsb_id035.value())
if ui.rb_id102.isChecked():
ax.set_yscale('linear')
elif ui.rb_id103.isChecked():
ax.set_yscale('log')
ax.legend()
fig.autofmt_xdate()
# export plot data
file_path = os.path.join(project_dir,project_name,'plot_data.txt')
f = open(file_path,'w')
w = csv.writer(f)
for line in ax.lines:
x = line.get_xdata()
x = date_functions.datetimenumber2mjd(x)
x = date_functions.mjd2ymstring(x,'%Y-%m-%d')
y = line.get_ydata()
l = line.get_label()
w.writerow(['%s_x' % l,x])
w.writerow(['%s_y' % l,y])
f.close()
# show figure
fig.show()
def _plot_grace_goce(ui,ax):
if not (ui.cb_id042.isChecked() or ui.cb_id043.isChecked() or ui.cb_id044.isChecked()):
return
# load grace+goco data
key = 'grace_goce'
file_path = os.path.join(project_dir,project_name,'grace','single_solution','regression','%s.txt' % key)
mjd, kg, mjd_use, mjd_regression, kg_use, kg_smooth, kg_adjusted, kg_regression, outliers = _load_data(file_path)
key = 'grace'
file_path = os.path.join(project_dir,project_name,'grace','single_solution','regression','%s.txt' % key)
mjd_grace, kg_grace, mjd_use_grace, mjd_regression_grace, kg_use_grace, kg_smooth_grace, kg_adjusted_grace, kg_regression_grace, outliers_grace = _load_data(file_path)
key = 'goco'
file_path = os.path.join(project_dir,project_name,'corrections',key,'regression','%s.txt' % key)
mjd_goco, kg_goco, mjd_use_goco, mjd_regression_goco, kg_use_goco, kg_smooth_goco, kg_adjusted_goco, kg_regression_goco, outliers_goco = _load_data(file_path)
# original data
if ui.cb_id042.isChecked():
x = np.unique(np.concatenate((mjd,mjd_grace,mjd_goco)))
y_gg, y_g, y_go, y = np.ones(x.size)*np.nan, np.ones(x.size)*np.nan, np.ones(x.size)*np.nan, np.ones(x.size)*np.nan
for ix,xi in enumerate(x):
if np.isnan(xi):
continue
jx = mjd==xi
if kg[jx]:
y_gg[ix] = kg[jx]
jx = mjd_grace==xi
if kg_grace[jx]:
y_g[ix] = kg_grace[jx]
jx = mjd_goco==xi
if kg_goco[jx]:
y_go[ix] = kg_goco[jx]
for ix,xi in enumerate(x):
if np.isnan(y_gg[ix]):
y[ix] = y_go[ix] + y_g[ix]
else:
y[ix] = y_go[ix] + y_gg[ix]
color = ui.le_id127.text()
if not color:
color = tuple(np.random.random(3))
linestyle = ui.cob_id006.currentText()
if not linestyle:
linestyle = '-'
label = ui.le_id128.text()
if not label:
label = 'GRACE+GOCO (+GOCE)'
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if ui.le_id129.text():
eval(ui.le_id129.text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
# smoothed data
if ui.cb_id043.isChecked():
x = np.unique(np.concatenate((mjd_use,mjd_use_grace,mjd_use_goco)))
y_gg, y_g, y_go, y = np.ones(x.size)*np.nan, np.ones(x.size)*np.nan, np.ones(x.size)*np.nan, np.ones(x.size)*np.nan
for ix,xi in enumerate(x):
if np.isnan(xi):
continue
jx = mjd_use==xi
if kg_smooth[jx]:
y_gg[ix] = kg_smooth[jx]
jx = mjd_use_grace==xi
if kg_smooth_grace[jx]:
y_g[ix] = kg_smooth_grace[jx]
jx = mjd_use_goco==xi
if kg_smooth_goco[jx]:
y_go[ix] = kg_smooth_goco[jx]
for ix,xi in enumerate(x):
if np.isnan(y_gg[ix]):
y[ix] = y_go[ix] + y_g[ix]
else:
y[ix] = y_go[ix] + y_gg[ix]
color = ui.le_id130.text()
if not color:
color = tuple(np.random.random(3))
linestyle = ui.cob_id007.currentText()
if not linestyle:
linestyle = '-'
label = ui.le_id131.text()
if not label:
label = 'GRACE+GOCO (+GOCE) smoothed'
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if ui.le_id132.text():
eval(ui.le_id132.text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
# regression model
if ui.cb_id044.isChecked():
x = np.unique(np.concatenate((mjd_regression,mjd_regression_grace,mjd_regression_goco)))
y_gg, y_g, y_go, y = np.ones(x.size)*np.nan, np.ones(x.size)*np.nan, np.ones(x.size)*np.nan, np.ones(x.size)*np.nan
for ix,xi in enumerate(x):
if np.isnan(xi):
continue
jx = mjd_regression==xi
if kg_regression[jx]:
y_gg[ix] = kg_regression[jx]
jx = mjd_regression_grace==xi
if kg_regression_grace[jx]:
y_g[ix] = kg_regression_grace[jx]
jx = mjd_regression_goco==xi
if kg_regression_goco[jx]:
y_go[ix] = kg_regression_goco[jx]
for ix,xi in enumerate(x):
if np.isnan(y_gg[ix]):
y[ix] = y_go[ix] + y_g[ix]
else:
y[ix] = y_go[ix] + y_gg[ix]
color = ui.le_id133.text()
if not color:
color = tuple(np.random.random(3))
linestyle = ui.cob_id008.currentText()
if not linestyle:
linestyle = '-'
label = ui.le_id134.text()
if not label:
label = 'GRACE+GOCO (+GOCE) regression'
x = [date_functions.mjd2datetimenumber(xj) for xj in x]
if ui.le_id135.text():
eval(ui.le_id135.text())
else:
ax.plot_date(x,y,color=color,linestyle=linestyle,marker=None,label=label)
