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'