def integration_export():
global INT_EXP_OPTIONS
path = selectSaveFolder()
if path == None:
return
dss = __get_selected_files__()
if len(dss) == 0:
print 'Error: please select at least one data file.'
prog_bar.max = len(dss) + 1
prog_bar.selection = 0
if len(dss) == 0:
return
fi = File(path)
if not fi.exists():
if not fi.mkdir():
print 'Error: failed to make directory: ' + path
return
if eff_corr_enabled.value and eff_map.value != None \
and len(eff_map.value.strip()) > 0:
map = lib.make_eff_map(
df,
get_calibration_path() + '/' + str(eff_map.value))
else:
map = None
dss_idx = 0
rfs = []
for dinfo in dss:
dss_idx += 1
prog_bar.selection = dss_idx
df.datasets.clear()
log('exporting ' + dinfo)
ds = df[str(dinfo)]
rds = silent_reduce(ds, map)
masks = []
if exp_mask.value == INT_EXP_OPTIONS[0]:
if reg_enabled.value:
try:
masks = Plot1.get_masks()
except:
pass
if len(masks) == 0:
# if reg_list.value != None and reg_list.value.strip() != '':
# masks = str2mask(reg_list.value)
masks = str2mask(get_mask_str())
c_masks = []
for m in masks:
c_m = RectangleMask(True, -180, m.minY, 360, m.maxY - m.minY)
c_masks.append(c_m)
vi = lib.v_intg(rds, c_masks)
vi.location = ds.location
rf = lib.v_export(vi, path)
rfs.append(rf)
elif exp_mask.value == INT_EXP_OPTIONS[1]:
vi = lib.v_intg(rds, masks)
vi.location = ds.location
rf = lib.v_export(vi, path)
rfs.append(rf)
else:
idx = INT_EXP_OPTIONS.index(exp_mask.value)
mask_group = make_mask_group(rds, idx)
for mask in mask_group:
vi = lib.v_intg(rds, [mask])
vi.location = ds.location
rf = lib.v_export(vi, path)
rfs.append(rf)
prog_bar.selection = dss_idx + 1
prog_bar.selection = 0
# set_pref_value(SAVED_MASK_PRFN , str(reg_list.value))
set_pref_value(SAVED_MASK_PRFN, get_mask_str())
set_pref_value(SAVED_EFFICIENCY_FILENAME_PRFN, str(eff_map.value))
save_pref()
# sname = 'Kowari_reduced_' + str(int(time.time() * 1000))[2:] + '.zip'
# print 'compressing result in ' + sname
# f_out = zipfile.ZipFile(path + '/' + sname, mode='w')
# for rfn in rfs:
# try:
# f_out.write(rfn, arcname = rfn[rfn.rindex('/') + 1 :])
# except:
# print 'failed to zip'
# f_out.close()
# f_out.close()
# report_file(sname)
zip_files(rfs, '/Kowari_rd_' + str(int(time.time()))[2:] + '.zip')
print 'Done'
def integration_export():
global INT_EXP_OPTIONS
path = selectSaveFolder()
if path == None:
return
dss = __get_selected_files__()
if len(dss) == 0:
print 'Error: please select at least one data file.'
prog_bar.max = len(dss) + 1
prog_bar.selection = 0
if len(dss) == 0:
return
fi = File(path)
if not fi.exists():
if not fi.mkdir():
print 'Error: failed to make directory: ' + path
return
if eff_corr_enabled.value and eff_map.value != None \
and len(eff_map.value.strip()) > 0:
map = lib.make_eff_map(df, get_calibration_path() + '/' + str(eff_map.value))
else:
map = None
dss_idx = 0
rfs = []
for dinfo in dss:
dss_idx += 1
prog_bar.selection = dss_idx
df.datasets.clear()
log('exporting ' + dinfo)
ds = df[str(dinfo)]
rds = silent_reduce(ds, map)
masks = []
if exp_mask.value == INT_EXP_OPTIONS[0]:
if reg_enabled.value :
try:
masks = Plot1.get_masks()
except:
pass
if len(masks) == 0:
# if reg_list.value != None and reg_list.value.strip() != '':
# masks = str2mask(reg_list.value)
masks = str2mask(get_mask_str())
c_masks = []
for m in masks:
c_m = RectangleMask(True, -180, m.minY, 360, m.maxY - m.minY)
c_masks.append(c_m)
vi = lib.v_intg(rds, c_masks)
vi.location = ds.location
rf = lib.v_export(vi, path)
rfs.append(rf)
elif exp_mask.value == INT_EXP_OPTIONS[1]:
vi = lib.v_intg(rds, masks)
vi.location = ds.location
rf = lib.v_export(vi, path)
rfs.append(rf)
else :
idx = INT_EXP_OPTIONS.index(exp_mask.value)
mask_group = make_mask_group(rds, idx)
for mask in mask_group :
vi = lib.v_intg(rds, [mask])
vi.location = ds.location
rf = lib.v_export(vi, path)
rfs.append(rf)
prog_bar.selection = dss_idx + 1
prog_bar.selection = 0
# set_pref_value(SAVED_MASK_PRFN , str(reg_list.value))
set_pref_value(SAVED_MASK_PRFN , get_mask_str())
set_pref_value(SAVED_EFFICIENCY_FILENAME_PRFN , str(eff_map.value))
save_pref()
# sname = 'Kowari_reduced_' + str(int(time.time() * 1000))[2:] + '.zip'
# print 'compressing result in ' + sname
# f_out = zipfile.ZipFile(path + '/' + sname, mode='w')
# for rfn in rfs:
# try:
# f_out.write(rfn, arcname = rfn[rfn.rindex('/') + 1 :])
# except:
# print 'failed to zip'
# f_out.close()
# f_out.close()
# report_file(sname)
zip_files(rfs, '/Kowari_rd_' + str(int(time.time()))[2:] + '.zip')
print 'Done'
def integration_export():
global INT_EXP_OPTIONS
path = selectSaveFolder()
if path == None:
return
dss = __DATASOURCE__.getSelectedDatasets()
if len(dss) == 0:
print 'Error: please select at least one data file.'
prog_bar.max = len(dss) + 1
prog_bar.selection = 0
if len(dss) == 0:
return
fi = File(path)
if not fi.exists():
if not fi.mkdir():
print 'Error: failed to make directory: ' + path
return
if eff_corr_enabled.value and eff_map.value != None \
and len(eff_map.value.strip()) > 0:
map = lib.make_eff_map(df, str(eff_map.value))
else:
map = None
dss_idx = 0
for dinfo in dss:
dss_idx += 1
prog_bar.selection = dss_idx
df.datasets.clear()
log('exporting ' + dinfo.location)
ds = df[str(dinfo.location)]
rds = silent_reduce(ds, map)
masks = []
if exp_mask.value == INT_EXP_OPTIONS[0]:
if reg_enabled.value :
try:
masks = Plot1.get_masks()
except:
pass
if len(masks) == 0:
if reg_list.value != None and reg_list.value.strip() != '':
masks = str2mask(reg_list.value)
c_masks = []
for m in masks:
c_m = RectangleMask(True, -180, m.minY, 360, m.maxY - m.minY)
c_masks.append(c_m)
vi = lib.v_intg(rds, c_masks)
vi.location = ds.location
lib.v_export(vi, path)
elif exp_mask.value == INT_EXP_OPTIONS[1]:
vi = lib.v_intg(rds, masks)
vi.location = ds.location
lib.v_export(vi, path)
else :
idx = INT_EXP_OPTIONS.index(exp_mask.value)
mask_group = make_mask_group(rds, idx)
for mask in mask_group :
vi = lib.v_intg(rds, [mask])
vi.location = ds.location
lib.v_export(vi, path)
prog_bar.selection = dss_idx + 1
prog_bar.selection = 0
set_prof_value(SAVED_MASK_PRFN , str(reg_list.value))
set_prof_value(SAVED_EFFICIENCY_FILENAME_PRFN , str(eff_map.value))
save_pref()
print 'Done'