In [42]:

    

# Reload when code changed:
%load_ext autoreload
%autoreload 2
%pwd
import os 
import sys
path = "../"
sys.path.append(path)
#os.path.abspath("../")
print(os.path.abspath(path))


    

    




The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload
D:\github\ekostat_calculator

In [43]:

    

import pandas as pd
import numpy as np
import json
import timeit
import time
import core
import importlib
importlib.reload(core)
import logging
importlib.reload(core) 
try:
    logging.shutdown()
    importlib.reload(logging)
except:
    pass
from event_handler import EventHandler
print(core.__file__)
pd.__version__


    

    




..\core\__init__.py






    
Out[43]:





'0.19.2'

In [88]:

    

root_directory = 'D:/github/ekostat_calculator'#"../" #os.getcwd()
workspace_directory = root_directory + '/workspaces' 
resource_directory = root_directory + '/resources'
#alias = 'lena'
user_id = 'test_user' #kanske ska vara off_line user?
workspace_alias = 'lena_indicator'


    

In [89]:

    

print(root_directory)
paths = {'user_id': user_id, 
         'workspace_directory': root_directory + '/workspaces', 
         'resource_directory': root_directory + '/resources', 
         'log_directory': 'D:/github' + '/log', 
         'test_data_directory': 'D:/github' + '/test_data'}


    

    




D:/github/ekostat_calculator

In [140]:

    

t0 = time.time()
ekos = EventHandler(**paths)
#request = ekos.test_requests['request_workspace_list']
#response = ekos.request_workspace_list(request) 
#ekos.write_test_response('request_workspace_list', response)
print('-'*50)
print('Time for request: {}'.format(time.time()-t0))
# OLD: ekos = EventHandler(root_directory)


    

    




2018-06-01 14:43:32,592	event_handler.py	74	__init__	DEBUG	Start EventHandler: event_handler






    




--------------------------------------------------
Time for request: 4.503200054168701

In [91]:

    

#ekos.copy_workspace(source_uuid='default_workspace', target_alias='lena_1')


    

In [141]:

    

ekos.print_workspaces()


    

    




====================================================================================================
Current workspaces for user are:

uuid                                    alias                         status                        
----------------------------------------------------------------------------------------------------
default_workspace                       default_workspace             readable                      
ddc27979-76f8-471c-a0b4-3bc773c6ecbf    lena                          editable                      
9eea0d00-c024-410f-912a-980eed55acae    lena_newdata                  editable                      
147f5d47-773c-43f0-b337-57208718d0cf    lena_indicator                editable                      
6fe05a39-2018-4cba-9dd4-27057578ff23    lena_1                        editable                      
====================================================================================================

In [169]:

    

workspace_uuid = ekos.get_unique_id_for_alias(workspace_alias = 'lena_indicator')
print(workspace_uuid)


    

    




147f5d47-773c-43f0-b337-57208718d0cf

In [170]:

    

workspace_alias = ekos.get_alias_for_unique_id(workspace_unique_id = workspace_uuid)


    

In [171]:

    

ekos.load_workspace(unique_id = workspace_uuid)
# Här får jag ofta felmeddelande:
# AttributeError: module 'core' has no attribute 'ParameterMapping'


    

    




2018-06-01 14:53:17,949	event_handler.py	1656	load_workspace	DEBUG	Trying to load new workspace "147f5d47-773c-43f0-b337-57208718d0cf" with alias "lena_indicator"






    




====================================================================================================
Initiating WorkSpace: D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf
Parent directory is: D:/github/ekostat_calculator/workspaces
Resource directory is: D:/github/ekostat_calculator/resources
=== 30062c90-2a60-4ee1-9944-f00329db1174
!!! A
!!! 30062c90-2a60-4ee1-9944-f00329db1174
!!! D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets
----------------------------------------------------------------------------------------------------
Initiating Subset: D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/30062c90-2a60-4ee1-9944-f00329db1174
===
D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/30062c90-2a60-4ee1-9944-f00329db1174
load_water_body_station_filter
Initiating WorkStep: D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/30062c90-2a60-4ee1-9944-f00329db1174/step_1
load_water_body_station_filter
Initiating WorkStep: D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/30062c90-2a60-4ee1-9944-f00329db1174/step_2
load_water_body_station_filter
Initiating WorkStep: D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/30062c90-2a60-4ee1-9944-f00329db1174/step_3
=== default_subset
!!! default_subset
!!! default_subset
!!! D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets
----------------------------------------------------------------------------------------------------
Initiating Subset: D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/default_subset
===
D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/default_subset
load_water_body_station_filter
Initiating WorkStep: D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/default_subset/step_1
load_water_body_station_filter
Initiating WorkStep: D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/default_subset/step_2
load_water_body_station_filter
Initiating WorkStep: D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/default_subset/step_3
load_water_body_station_filter
Initiating WorkStep: D:/github/ekostat_calculator/workspaces/147f5d47-773c-43f0-b337-57208718d0cf/step_0






    




2018-06-01 14:53:18,262	event_handler.py	1674	load_workspace	INFO	Workspace "147f5d47-773c-43f0-b337-57208718d0cf" with alias "lena_indicator loaded."






    
Out[171]:





True

In [103]:

    

ekos.import_default_data(workspace_alias = workspace_alias)


    

    




2018-06-01 13:43:03,753	event_handler.py	1024	import_default_data	DEBUG	Trying to load default data in workspace "6fe05a39-2018-4cba-9dd4-27057578ff23" with alias "lena_1"
2018-06-01 13:43:03,770	workspaces.py	1006	import_default_data	DEBUG	Default data file has been copied to workspace raw data folder: chlorophyll_integrated_2015_2016_row_format.txt
2018-06-01 13:43:03,792	workspaces.py	1006	import_default_data	DEBUG	Default data file has been copied to workspace raw data folder: chlorophyll_sharkweb_data_chlorophyll_wb_2007-2017_20180320.txt
2018-06-01 13:43:03,805	workspaces.py	1006	import_default_data	DEBUG	Default data file has been copied to workspace raw data folder: physicalchemicalmodel_110001_PROFILER_alldepths_SE652400-223501_toolbox.dat
2018-06-01 13:43:03,829	workspaces.py	1006	import_default_data	DEBUG	Default data file has been copied to workspace raw data folder: physicalchemicalmodel_120004_PROFILER_alldepths_SE612520-172080_toolbox.dat






    




File already added
File already added
File already added
File already added






    




2018-06-01 13:43:04,047	workspaces.py	1006	import_default_data	DEBUG	Default data file has been copied to workspace raw data folder: physicalchemical_sharkweb_data_fyskem_wb_2007-2017_20180320.txt
2018-06-01 13:43:04,146	workspaces.py	1006	import_default_data	DEBUG	Default data file has been copied to workspace raw data folder: phytoplankton_2016_row_format.txt






    




File already added
File already added






    




2018-06-01 13:43:04,407	workspaces.py	1006	import_default_data	DEBUG	Default data file has been copied to workspace raw data folder: phytoplankton_sharkweb_data_biovolume_wb_2007-2012_20180320.txt
2018-06-01 13:43:04,572	workspaces.py	1006	import_default_data	DEBUG	Default data file has been copied to workspace raw data folder: phytoplankton_sharkweb_data_biovolume_wb_2013-2017_20180320.txt






    




File already added
File already added






    




2018-06-01 13:43:04,638	workspaces.py	1006	import_default_data	DEBUG	Default data file has been copied to workspace raw data folder: zoobenthos_2016_row_format_2.txt
2018-06-01 13:43:04,670	workspaces.py	1006	import_default_data	DEBUG	Default data file has been copied to workspace raw data folder: zoobenthos_sharkweb_data_BQIm_wb_2007-2017_20180320.txt






    




File already added
File already added

In [172]:

    

#ekos.get_workspace(unique_id = workspace_uuid, alias = workspace_alias).delete_alldata_export()


    

    




2018-06-01 14:53:26,607	event_handler.py	1003	get_workspace	DEBUG	Getting workspace "147f5d47-773c-43f0-b337-57208718d0cf" with alias "None"






    
Out[172]:





<core.workspaces.WorkSpace at 0x9014550>

In [178]:

    

#%%timeit
ekos.load_data(unique_id = workspace_uuid)


    

    




2018-06-01 15:04:08,785	workspaces.py	1645	load_datatype_data	DEBUG	Data has been loaded from existing file: column_format_physicalchemical_data.pickle
2018-06-01 15:04:08,803	workspaces.py	1645	load_datatype_data	DEBUG	Data has been loaded from existing file: column_format_physicalchemicalmodel_data.pickle
2018-06-01 15:04:08,833	workspaces.py	1645	load_datatype_data	DEBUG	Data has been loaded from existing file: column_format_chlorophyll_data.pickle
2018-06-01 15:04:08,876	workspaces.py	1645	load_datatype_data	DEBUG	Data has been loaded from existing file: column_format_phytoplankton_data.pickle
2018-06-01 15:04:08,915	workspaces.py	1645	load_datatype_data	DEBUG	Data has been loaded from existing file: column_format_zoobenthos_data.pickle






    




dict_keys(['..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/input_data/raw_data/sharkweb_data_fyskem_wb_2007-2017_20180320.txt'])
dict_keys([])
dict_keys(['..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/input_data/raw_data/sharkweb_data_chlorophyll_wb_2007-2017_20180320.txt'])
dict_keys(['..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/input_data/raw_data/sharkweb_data_biovolume_wb_2007-2012_20180320.txt', '..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/input_data/raw_data/sharkweb_data_biovolume_wb_2013-2017_20180320.txt'])
dict_keys(['..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/input_data/raw_data/sharkweb_data_BQIm_wb_2007-2017_20180320.txt'])
chlorophyll
{}
physicalchemical
{}
physicalchemicalmodel
{}
phytoplankton
{}
zoobenthos
{}
No data available after "merge_all_data"!

In [146]:

    

w = ekos.get_workspace(unique_id = workspace_uuid)
len(w.data_handler.get_all_column_data_df())


    

    




2018-06-01 14:44:07,667	event_handler.py	1003	get_workspace	DEBUG	Getting workspace "6fe05a39-2018-4cba-9dd4-27057578ff23" with alias "None"






    
Out[146]:





106094

In [147]:

    

w.apply_data_filter(step = 0) # This sets the first level of data filter in the IndexHandler


    

In [129]:

    

#w.copy_subset(source_uuid='default_subset', target_alias='A')


    

    




2018-06-01 14:27:35,142	logger.py	85	add_log	DEBUG	
2018-06-01 14:27:35,142	logger.py	86	add_log	DEBUG	========================================================================================================================
2018-06-01 14:27:35,142	logger.py	87	add_log	DEBUG	### Log added for log_id "fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1" at locaton: D:\github\ekostat_calculator\workspaces\6fe05a39-2018-4cba-9dd4-27057578ff23\log\subset_fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1.log
2018-06-01 14:27:35,142	logger.py	88	add_log	DEBUG	------------------------------------------------------------------------------------------------------------------------






    




¤ A
target_uuid fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1
source_subset_path: D:/github/ekostat_calculator/workspaces/6fe05a39-2018-4cba-9dd4-27057578ff23/subsets/default_subset
target_subset_path: D:/github/ekostat_calculator/workspaces/6fe05a39-2018-4cba-9dd4-27057578ff23/subsets/fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1
=== fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1
!!! A
!!! fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1
!!! D:/github/ekostat_calculator/workspaces/6fe05a39-2018-4cba-9dd4-27057578ff23/subsets
----------------------------------------------------------------------------------------------------
Initiating Subset: D:/github/ekostat_calculator/workspaces/6fe05a39-2018-4cba-9dd4-27057578ff23/subsets/fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1
===
D:/github/ekostat_calculator/workspaces/6fe05a39-2018-4cba-9dd4-27057578ff23/subsets/fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1
load_water_body_station_filter
Initiating WorkStep: D:/github/ekostat_calculator/workspaces/6fe05a39-2018-4cba-9dd4-27057578ff23/subsets/fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1/step_1
load_water_body_station_filter
Initiating WorkStep: D:/github/ekostat_calculator/workspaces/6fe05a39-2018-4cba-9dd4-27057578ff23/subsets/fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1/step_2
load_water_body_station_filter
Initiating WorkStep: D:/github/ekostat_calculator/workspaces/6fe05a39-2018-4cba-9dd4-27057578ff23/subsets/fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1/step_3
====================================================================================================
fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1
D:/github/ekostat_calculator/workspaces/6fe05a39-2018-4cba-9dd4-27057578ff23/log
subset
----------------------------------------------------------------------------------------------------






    
Out[129]:





{'alias': 'A',
 'status': 'editable',
 'subset_uuid': 'fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1'}

In [148]:

    

subset_uuid = ekos.get_unique_id_for_alias(workspace_alias = workspace_alias, subset_alias = 'A')
print(w.get_subset_list(), subset_uuid)

f1 = w.get_data_filter_object(subset = subset_uuid, step=1) 
print(f1.include_list_filter)

w.apply_data_filter(subset = subset_uuid, step = 1)

df_step1 = w.get_filtered_data(step = 1, subset = subset_uuid)
#print(df_step1.columns)
#df_step1[['SDATE', 'YEAR', 'MONTH', 'POSITION', 'VISS_EU_CD', 'WATER_TYPE_AREA', 'DEPH', 'MNDEP', 'MXDEP','BQIm']].dropna(subset = ['BQIm'])


    

    




['default_subset', 'fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1'] fb1db7b1-b6bd-4ef9-9ee7-84520b9be8c1
{'MYEAR': ['2013', '2014', '2015', '2016', '2017', '2018', '2019'], 'STATN': [], 'WATER_BODY': []}






    




2018-06-01 14:44:14,283	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_1

In [149]:

    

water_body = 'SE574000-114230' # type_area 1-s,
w.get_step_object(step = 2, subset = subset_uuid).load_indicator_settings_filters()

temp = w.get_step_object(step = 2, subset = subset_uuid).indicator_data_filter_settings['oxygen']
temp.get_value(variable='REF_VALUE', type_area='1', water_body = water_body)
#temp.settings.df


    

    




SE574000-114230
['unspecified' 'SE581740-114820' 'SE581260-113220' 'SE581700-113000'
 'SE582000-115270' 'SE563000-123351' 'SE561030-122821' 'SE562450-122751'
 'SE562000-123800' 'SE555545-124332' 'SE592000-184700' 'SE658352-163189'
 'SE591800-181360' 'SE592290-181600']
. . . . .
Series([], Name: REF_VALUE, dtype: object)
0
. . . . .
----
water_body SE574000-114230, type_area 1-s, variable REF_VALUE
num 1, suf s, suf? ['s' 'n']
0    10
Name: REF_VALUE, dtype: object <class 'pandas.core.series.Series'>
['10'] <class 'numpy.ndarray'>
value 10 in refvalue_column
['10']






    
Out[149]:





'10'

In [150]:

    

w.get_step_object(step = 2, subset = subset_uuid).get_indicator_data_filter_settings('oxygen')


    

    
Out[150]:





<core.filters.SettingsDataFilter at 0x11531240>

In [151]:

    

w.get_step_object(step = 2, subset = subset_uuid).indicator_ref_settings


    

    
Out[151]:





{'biov': <core.filters.SettingsRef at 0x115312e8>,
 'bqi': <core.filters.SettingsRef at 0x11531278>,
 'chl': <core.filters.SettingsRef at 0x115316d8>,
 'din_winter': <core.filters.SettingsRef at 0x115317b8>,
 'dip_winter': <core.filters.SettingsRef at 0x11531898>,
 'ntot_summer': <core.filters.SettingsRef at 0x115318d0>,
 'ntot_winter': <core.filters.SettingsRef at 0x11531908>,
 'oxygen': <core.filters.SettingsRef at 0x11531978>,
 'ptot_summer': <core.filters.SettingsRef at 0x115319e8>,
 'ptot_winter': <core.filters.SettingsRef at 0x115319b0>,
 'secchi': <core.filters.SettingsRef at 0x11531ac8>}

In [152]:

    

w.get_step_object(step = 2, subset = subset_uuid)._indicator_setting_files['oxygen']


    

    
Out[152]:





<core.filters.SettingsFile at 0x13766fd0>

In [153]:

    

indicator_list = w.get_available_indicators(subset= 'A', step=2)


    

    




2018-06-01 14:44:29,443	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 14:44:29,464	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 14:44:29,478	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 14:44:29,485	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 14:44:29,492	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 14:44:29,500	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 14:44:29,509	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 14:44:29,517	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 14:44:29,526	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 14:44:29,538	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 14:44:29,551	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 14:44:29,566	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2






    




subset A
subset A
subset A
subset A
subset A
subset A
subset A
subset A
"['TOT_COVER_ALL'] not in index"
subset A
subset A
subset A
subset A

In [154]:

    

wb_list = df_step1.VISS_EU_CD.unique()
print('number of waterbodies in step 1: {}'.format(len(wb_list)))
typeA_list = [row.split('-')[0].strip().lstrip('0') for row in df_step1.WATER_TYPE_AREA.unique()]
print('number of type areas in step 1: {}'.format(len(typeA_list)))


    

    




number of waterbodies in step 1: 3
number of type areas in step 1: 3

In [155]:

    

wb_list


    

    
Out[155]:





array(['SE574050-114780', 'SE582000-112350', 'SE574000-114230'], dtype=object)

In [63]:

    

#list(zip(typeA_list, df_step1.WATER_TYPE_AREA.unique()))
indicator_list = ['oxygen','bqi','din_winter','ntot_summer', 'ntot_winter', 'dip_winter', 'ptot_summer', 'ptot_winter', 'biov', 'chl', 'secchi']
for indicator in indicator_list:
    w.apply_indicator_data_filter(step = 2, 
                          subset = subset_uuid, 
                          indicator = indicator)


    

    




2018-06-01 11:33:55,617	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2






    




Water body SE584340-174401
Water body SE581700-113000
Water body SE561400-161201
Water body SE654470-222700
Water body SE633000-195000
Water body SE625180-181655
Water body SE654820-222660
Water body SE625000-180075
Water body SE628750-183300
Water body SE631346-184241
Water body SE631646-185280
Water body SE583730-164501
Water body SE582705-163350
Water body SE582050-165820
Water body SE582147-111771
Water body SE580688-114860
Water body SE575500-113750
Water body SE574050-114780
Water body SE625900-174360
Water body SE630383-183500
Water body SE633550-200700
Water body SE636570-203590
Water body SE633043-193300
Water body SE552170-130626
Water body SE562000-123800
Water body SE591400-183200
Water body SE591200-183600
Water body SE592040-184000
Water body SE592245-184400
Water body SE600740-183460
Water body SE601070-182870
Water body SE631610-184500
Water body SE625500-175153
Water body SE622126-172430
Water body SE622339-172190
Water body SE652920-222650
Water body SE656620-222480
Water body SE656300-222750
Water body SE655120-220380
Water body SE655180-218660
Water body SE636150-199220
Water body SE635040-204196
Water body SE637070-204260
Water body SE636910-204040
Water body SE602120-181610
Water body SE582000-115270
Water body SE575700-114240
Water body SE581740-114820
Water body SE563330-124600
Water body SE654860-219880
Water body SE647050-213980
Water body SE575340-113000
Water body SE654500-232000
Water body SE652400-223501
Water body SE653900-223280
Water body SE582000-112350
Water body SE581260-113220
Water body SE574000-114230
Water body SE585100-110600
Water body SE572540-114801
Water body SE555545-124332
Water body SE561005-150250
Water body SE580610-113615
Water body SE580325-113500
Water body SE580500-114725
Water body SE581120-112680
Water body SE581365-112910
Water body SE584750-111185
Water body SE584890-110950
Water body SE582630-113515
Water body SE584450-111445
Water body SE584670-111300
Water body SE582150-112530
Water body SE585600-110880
Water body SE585200-111140
Water body SE583710-111535
Water body SE582665-111706
Water body SE582850-111760
Water body SE584030-111400
Water body SE581853-112736
Water body SE582230-112255
Water body SE573500-115150
Water body SE571000-184001
Water body SE585990-111125
Water body SE570000-120701
Water body SE582700-110451
Water body SE584200-105901
Water body SE554040-125750
Water body SE554500-125001
Water body SE575150-162700
Water body SE575370-164220
Water body SE573940-163560
Water body SE565400-163600
Water body SE570080-163430
Water body SE565800-163000
Water body SE570340-163710
Water body SE570500-163750
Water body SE573500-163500
Water body SE563825-161810
Water body SE564250-162500
Water body SE659024-162417
Water body SE572205-163500
Water body SE571552-162848
Water body SE572500-164500
Water body SE583718-161687
Water body SE583926-161744
Water body SE583000-165600
Water body SE560940-151740
Water body SE560500-154880
Water body SE560810-153980
Water body SE560780-153500
Water body SE560795-154730
Water body SE560775-153055
Water body SE560700-155801
Water body SE582460-164500
Water body SE581520-165000
Water body SE560825-144215
Water body SE621688-144133
Water body SE560900-145280
Water body SE560205-143545
Water body SE581000-164020
Water body SE580585-164720
Water body SE580735-165296
Water body SE580000-164060
Water body SE554800-142001
Water body SE562450-122751
Water body SE602400-183190
Water body SE603870-181301
Water body SE601300-182880
Water body SE603190-174000
Water body SE601660-183550
Water body SE594100-185690
Water body SE593750-184900
Water body SE593820-185500
Water body SE593300-183600
Water body SE592420-182210
Water body SE590400-174090
Water body SE590990-174015
Water body SE658507-162696
Water body SE585200-174000
Water body SE585345-174950
Water body SE590550-174540
Water body SE583970-170280
Water body SE584430-170665
Water body SE584227-171600
Water body SE584390-172085
Water body SE584400-172270
Water body SE584600-173200
Water body SE584820-172920
Water body SE584905-172980
Water body SE585075-173130
Water body SE585040-173535
Water body SE584870-174310
Water body SE583875-170270
Water body SE584085-171600
Water body SE658352-163189
Water body SE591090-182300
Water body SE591160-182400
Water body SE590385-180890
Water body SE584960-175280
Water body SE591050-182740
Water body SE590000-183000
Water body SE591300-182800
Water body SE594200-192000
Water body SE592640-184500
Water body SE593000-190500
Water body SE593180-191280
Water body SE594845-191240
Water body SE594800-190655
Water body SE591655-183200
Water body SE591745-182250
Water body SE594670-185500
Water body SE594800-190220
Water body SE601310-183700
Water body SE601000-183510
Water body SE600565-184600
Water body SE601300-184180
Water body SE585797-181090
Water body SE574931-113131
Water body SE561030-122821
Water body SE554810-125240
Water body SE552500-124461
Water body SE572472-120302
Water body SE572135-120141
Water body SE570900-164501
Water body SE644150-211000
Water body SE594340-190448
Water body SE592400-180800
Water body SE593750-183962
Water body SE634110-201920
Water body SE634210-202020
Water body SE643700-211940
Water body SE645000-212100
Water body SE651818-212790
Water body SE651475-214300
Water body SE651800-214740
Water body SE651500-213108
Water body SE645500-212000
Water body SE653176-222000
Water body SE728806-179329
Water body SE623890-178030
Water body SE622500-172430
Water body SE622860-173000
Water body SE622000-172300
Water body SE624870-175500
Water body SE624615-180500
Water body SE624335-180000
Water body SE623810-180350
Water body SE623340-175556
Water body SE634950-202940
Water body SE590740-174135
Water body SE631840-191130
Water body SE614165-171500
Water body SE613760-171000
Water body SE613500-172500
Water body SE613591-171000
Water body SE613500-171000
Water body SE613240-171000
Water body SE635660-199490
Water body SE574160-113351
Water body SE580240-112501
Water body SE573300-113801
Water body SE657412-164249
Water body SE657608-164193
Water body SE595000-185600
Water body SE612303-171075
Water body SE622795-174565
Water body SE623300-176210
Water body SE630210-187470
Water body SE592600-181600
Water body SE592575-181770
Water body SE623980-175600
Water body SE645000-213500
Water body SE604200-174400
Water body SE592790-183000
Water body SE637640-204160
Water body SE652020-211930
Water body SE644730-210650
Water body SE641840-211540
Water body SE634230-201605
Water body SE563000-123351
Water body SE604250-173000
Water body SE611676-171000
Water body SE580000-164500
Water body SE590020-114520
Water body SE590860-113810
Water body SE590900-112300
Water body SE590670-111380
Water body SE593500-190000
Water body SE571720-120640
Water body SE635300-205251
Water body SE582500-113890
Water body SE637310-204860
Water body SE634740-203020
Water body SE646360-213700
Water body SE643920-211500
Water body SE584725-111050
Water body SE650280-213110
Water body SE654640-233190
Water body SE654575-234250
Water body SE562290-124131
Water body SE592000-190500
Water body SE570900-121060
Water body SE571240-121000
Water body SE564500-122601
Water body SE601440-184000
Water body SE612791-171130
Water body SE572072-115880
Water body SE573322-115478
Water body SE572227-115662
Water body SE573044-115355
Water body SE572308-115550
Water body SE580500-111801
Water body SE574170-190001
Water body SE584400-116000
Water body SE583625-111300
Water body SE572000-180001
Water body SE572350-180930
Water body SE583121-171401
Water body SE633400-195000
Water body SE611000-171500
Water body SE611213-171063
Water body SE611600-171500
Water body SE581280-170070
Water body SE581240-165220
Water body SE581820-165500
Water body SE619690-175690
Water body SE612520-172080
Water body SE605660-172380
Water body SE622011-146303
Water body SE561480-148220
Water body SE561150-147620
Water body SE560740-144375
Water body SE603000-181500
Water body SE561000-153320
Water body SE561000-150390
Water body SE560385-154500
Water body SE560200-143175
Water body SE654200-222920
Water body SE654000-222430
Water body SE650750-213500
Water body SE650460-213400
Water body SE625416-182696
Water body SE628480-183070
Water body SE593500-191660
Water body SE593330-192540
Water body SE593000-192000





    




2018-06-01 11:34:07,753	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2






    




Water body SE611766-171305
Water body SE575150-190400
Water body SE654291-224000
Water body SE584340-174401
SE584340-174401
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584340-174401, type_area 14, variable MONTH_LIST
num 14, suf , suf? ['']
19    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE581700-113000
SE581700-113000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE581700-113000, type_area 2, variable MONTH_LIST
num 2, suf , suf? ['' '']
2    5;6
3    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE561400-161201
SE561400-161201
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE561400-161201, type_area 9, variable MONTH_LIST
num 9, suf , suf? ['']
14    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE654470-222700
SE654470-222700
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE654470-222700, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE633000-195000
SE633000-195000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE633000-195000, type_area 20, variable MONTH_LIST
num 20, suf , suf? ['']
25    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE625180-181655
SE625180-181655
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE625180-181655, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE654820-222660
SE654820-222660
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE654820-222660, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE625000-180075
SE625000-180075
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE625000-180075, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE628750-183300
SE628750-183300
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE628750-183300, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE631346-184241
SE631346-184241
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE631346-184241, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE631646-185280
SE631646-185280
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE631646-185280, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE583730-164501
SE583730-164501
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE583730-164501, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE582705-163350
SE582705-163350
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE582705-163350, type_area 13, variable MONTH_LIST
num 13, suf , suf? ['']
18    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE582050-165820
SE582050-165820
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE582050-165820, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE582147-111771
SE582147-111771
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE582147-111771, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE580688-114860
SE580688-114860
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE580688-114860, type_area 2, variable MONTH_LIST
num 2, suf , suf? ['' '']
2    5;6
3    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE575500-113750
SE575500-113750
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE575500-113750, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE574050-114780
'WaterBody' object has no attribute 'SESE574050-114780'
waterbody matching file does not recognise water body with VISS_EU_CD SE574050-114780
RESULT False
Water body SE625900-174360
SE625900-174360
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE625900-174360, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE630383-183500
SE630383-183500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE630383-183500, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE633550-200700
SE633550-200700
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE633550-200700, type_area 21, variable MONTH_LIST
num 21, suf , suf? ['']
26    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE636570-203590
SE636570-203590
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE636570-203590, type_area 20, variable MONTH_LIST
num 20, suf , suf? ['']
25    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE633043-193300
SE633043-193300
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE633043-193300, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE552170-130626
SE552170-130626
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE552170-130626, type_area 7, variable MONTH_LIST
num 7, suf , suf? ['']
12    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE562000-123800
SE562000-123800
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE562000-123800, type_area 5, variable MONTH_LIST
num 5, suf , suf? ['' '']
8    5;6
9    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE591400-183200
SE591400-183200
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE591400-183200, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE591200-183600
SE591200-183600
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE591200-183600, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE592040-184000
SE592040-184000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE592040-184000, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE592245-184400
SE592245-184400
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE592245-184400, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE600740-183460
SE600740-183460
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE600740-183460, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE601070-182870
SE601070-182870
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE601070-182870, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE631610-184500
SE631610-184500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE631610-184500, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE625500-175153
SE625500-175153
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE625500-175153, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE622126-172430
SE622126-172430
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE622126-172430, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE622339-172190
SE622339-172190
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE622339-172190, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE652920-222650
SE652920-222650
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE652920-222650, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE656620-222480
SE656620-222480
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE656620-222480, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE656300-222750
SE656300-222750
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE656300-222750, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE655120-220380
SE655120-220380
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE655120-220380, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE655180-218660
SE655180-218660
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE655180-218660, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE636150-199220
SE636150-199220
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE636150-199220, type_area 20, variable MONTH_LIST
num 20, suf , suf? ['']
25    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE635040-204196
SE635040-204196
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE635040-204196, type_area 20, variable MONTH_LIST
num 20, suf , suf? ['']
25    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE637070-204260
SE637070-204260
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE637070-204260, type_area 20, variable MONTH_LIST
num 20, suf , suf? ['']
25    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE636910-204040
SE636910-204040
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE636910-204040, type_area 20, variable MONTH_LIST
num 20, suf , suf? ['']
25    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE602120-181610
SE602120-181610
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE602120-181610, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE582000-115270
SE582000-115270
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE582000-115270, type_area 2, variable MONTH_LIST
num 2, suf , suf? ['' '']
2    5;6
3    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE575700-114240
SE575700-114240
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE575700-114240, type_area 2, variable MONTH_LIST
num 2, suf , suf? ['' '']
2    5;6
3    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE581740-114820
SE581740-114820
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE581740-114820, type_area 2, variable MONTH_LIST
num 2, suf , suf? ['' '']
2    5;6
3    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE563330-124600
SE563330-124600
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE563330-124600, type_area 5, variable MONTH_LIST
num 5, suf , suf? ['' '']
8    5;6
9    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE654860-219880
SE654860-219880
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE654860-219880, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE647050-213980
'WaterBody' object has no attribute 'SESE647050-213980'
waterbody matching file does not recognise water body with VISS_EU_CD SE647050-213980
RESULT False
Water body SE575340-113000
SE575340-113000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE575340-113000, type_area 3, variable MONTH_LIST
num 3, suf , suf? ['' '']
4    5;6
5    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE654500-232000
SE654500-232000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE654500-232000, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE652400-223501
SE652400-223501
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE652400-223501, type_area 23, variable MONTH_LIST
num 23, suf , suf? ['']
28    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE653900-223280
SE653900-223280
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE653900-223280, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE582000-112350
SE582000-112350
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE582000-112350, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE581260-113220
SE581260-113220
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE581260-113220, type_area 2, variable MONTH_LIST
num 2, suf , suf? ['' '']
2    5;6
3    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE574000-114230
SE574000-114230
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE574000-114230, type_area 1-s, variable MONTH_LIST
num 1, suf s, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE585100-110600
SE585100-110600
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE585100-110600, type_area 3, variable MONTH_LIST
num 3, suf , suf? ['' '']
4    5;6
5    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE572540-114801
SE572540-114801
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE572540-114801, type_area 4, variable MONTH_LIST
num 4, suf , suf? ['' '']
6    5;6
7    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE555545-124332
SE555545-124332
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE555545-124332, type_area 6, variable MONTH_LIST
num 6, suf , suf? ['' '']
10    5;6
11    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE561005-150250
SE561005-150250
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE561005-150250, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE580610-113615
SE580610-113615
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE580610-113615, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE580325-113500
SE580325-113500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE580325-113500, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE580500-114725
SE580500-114725
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE580500-114725, type_area 2, variable MONTH_LIST
num 2, suf , suf? ['' '']
2    5;6
3    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE581120-112680
SE581120-112680
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE581120-112680, type_area 2, variable MONTH_LIST
num 2, suf , suf? ['' '']
2    5;6
3    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE581365-112910
SE581365-112910
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE581365-112910, type_area 2, variable MONTH_LIST
num 2, suf , suf? ['' '']
2    5;6
3    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE584750-111185
SE584750-111185
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584750-111185, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE584890-110950
SE584890-110950
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584890-110950, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE582630-113515
SE582630-113515
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE582630-113515, type_area 2, variable MONTH_LIST
num 2, suf , suf? ['' '']
2    5;6
3    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE584450-111445
SE584450-111445
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584450-111445, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE584670-111300
SE584670-111300
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584670-111300, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE582150-112530
SE582150-112530
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE582150-112530, type_area 2, variable MONTH_LIST
num 2, suf , suf? ['' '']
2    5;6
3    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE585600-110880
SE585600-110880
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE585600-110880, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE585200-111140
SE585200-111140
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE585200-111140, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE583710-111535
SE583710-111535
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE583710-111535, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE582665-111706
SE582665-111706
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE582665-111706, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE582850-111760
SE582850-111760
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE582850-111760, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE584030-111400
SE584030-111400
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584030-111400, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE581853-112736
SE581853-112736
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE581853-112736, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE582230-112255
SE582230-112255
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE582230-112255, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE573500-115150
SE573500-115150
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE573500-115150, type_area 1-s, variable MONTH_LIST
num 1, suf s, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE571000-184001
SE571000-184001
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE571000-184001, type_area 10, variable MONTH_LIST
num 10, suf , suf? ['']
15    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE585990-111125
SE585990-111125
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE585990-111125, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE570000-120701
SE570000-120701
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE570000-120701, type_area 4, variable MONTH_LIST
num 4, suf , suf? ['' '']
6    5;6
7    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE582700-110451
SE582700-110451
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE582700-110451, type_area 3, variable MONTH_LIST
num 3, suf , suf? ['' '']
4    5;6
5    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE584200-105901
SE584200-105901
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584200-105901, type_area 3, variable MONTH_LIST
num 3, suf , suf? ['' '']
4    5;6
5    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE554040-125750
SE554040-125750
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE554040-125750, type_area 6, variable MONTH_LIST
num 6, suf , suf? ['' '']
10    5;6
11    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE554500-125001
SE554500-125001
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE554500-125001, type_area 6, variable MONTH_LIST
num 6, suf , suf? ['' '']
10    5;6
11    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE575150-162700
SE575150-162700
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE575150-162700, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE575370-164220
SE575370-164220
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE575370-164220, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE573940-163560
SE573940-163560
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE573940-163560, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE565400-163600
SE565400-163600
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE565400-163600, type_area 9, variable MONTH_LIST
num 9, suf , suf? ['']
14    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE570080-163430
SE570080-163430
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE570080-163430, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE565800-163000
SE565800-163000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE565800-163000, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE570340-163710
SE570340-163710
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE570340-163710, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE570500-163750
SE570500-163750
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE570500-163750, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE573500-163500
SE573500-163500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE573500-163500, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE563825-161810
SE563825-161810
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE563825-161810, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE564250-162500
SE564250-162500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE564250-162500, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE659024-162417
SE659024-162417
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE659024-162417, type_area 24, variable MONTH_LIST
num 24, suf , suf? ['']
29    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE572205-163500
SE572205-163500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE572205-163500, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE571552-162848
SE571552-162848
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE571552-162848, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE572500-164500
SE572500-164500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE572500-164500, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE583718-161687
SE583718-161687
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE583718-161687, type_area 13, variable MONTH_LIST
num 13, suf , suf? ['']
18    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE583926-161744
SE583926-161744
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE583926-161744, type_area 13, variable MONTH_LIST
num 13, suf , suf? ['']
18    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE583000-165600
SE583000-165600
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE583000-165600, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE560940-151740
SE560940-151740
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE560940-151740, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE560500-154880
SE560500-154880
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE560500-154880, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE560810-153980
SE560810-153980
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE560810-153980, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE560780-153500
SE560780-153500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE560780-153500, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE560795-154730
SE560795-154730
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE560795-154730, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE560775-153055
SE560775-153055
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE560775-153055, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE560700-155801
SE560700-155801
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE560700-155801, type_area 9, variable MONTH_LIST
num 9, suf , suf? ['']
14    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE582460-164500
SE582460-164500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE582460-164500, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE581520-165000
SE581520-165000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE581520-165000, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE560825-144215
SE560825-144215
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE560825-144215, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE621688-144133
SE621688-144133
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE621688-144133, type_area 9, variable MONTH_LIST
num 9, suf , suf? ['']
14    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE560900-145280
SE560900-145280
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE560900-145280, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE560205-143545
SE560205-143545
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE560205-143545, type_area 7, variable MONTH_LIST
num 7, suf , suf? ['']
12    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE581000-164020
SE581000-164020
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE581000-164020, type_area 13, variable MONTH_LIST
num 13, suf , suf? ['']
18    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE580585-164720
SE580585-164720
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE580585-164720, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE580735-165296
SE580735-165296
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE580735-165296, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE580000-164060
SE580000-164060
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE580000-164060, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE554800-142001
SE554800-142001
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE554800-142001, type_area 7, variable MONTH_LIST
num 7, suf , suf? ['']
12    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE562450-122751
SE562450-122751
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE562450-122751, type_area 5, variable MONTH_LIST
num 5, suf , suf? ['' '']
8    5;6
9    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE602400-183190
SE602400-183190
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE602400-183190, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE603870-181301
SE603870-181301
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE603870-181301, type_area 17, variable MONTH_LIST
num 17, suf , suf? ['']
22    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE601300-182880
SE601300-182880
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE601300-182880, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE603190-174000
SE603190-174000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE603190-174000, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE601660-183550
SE601660-183550
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE601660-183550, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE594100-185690
SE594100-185690
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE594100-185690, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE593750-184900
SE593750-184900
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE593750-184900, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE593820-185500
SE593820-185500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE593820-185500, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE593300-183600
SE593300-183600
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE593300-183600, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE592420-182210
SE592420-182210
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE592420-182210, type_area 24, variable MONTH_LIST
num 24, suf , suf? ['']
29    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE590400-174090
SE590400-174090
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE590400-174090, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE590990-174015
SE590990-174015
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE590990-174015, type_area 24, variable MONTH_LIST
num 24, suf , suf? ['']
29    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE658507-162696
SE658507-162696
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE658507-162696, type_area 24, variable MONTH_LIST
num 24, suf , suf? ['']
29    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE585200-174000
SE585200-174000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE585200-174000, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE585345-174950
SE585345-174950
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE585345-174950, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE590550-174540
SE590550-174540
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE590550-174540, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE583970-170280
SE583970-170280
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE583970-170280, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE584430-170665
SE584430-170665
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584430-170665, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE584227-171600
SE584227-171600
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584227-171600, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE584390-172085
SE584390-172085
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584390-172085, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE584400-172270
SE584400-172270
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584400-172270, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE584600-173200
SE584600-173200
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584600-173200, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE584820-172920
SE584820-172920
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584820-172920, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE584905-172980
SE584905-172980
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584905-172980, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE585075-173130
SE585075-173130
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE585075-173130, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE585040-173535
SE585040-173535
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE585040-173535, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE584870-174310
SE584870-174310
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584870-174310, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE583875-170270
SE583875-170270
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE583875-170270, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE584085-171600
SE584085-171600
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584085-171600, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE658352-163189
SE658352-163189
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE658352-163189, type_area 24, variable MONTH_LIST
num 24, suf , suf? ['']
29    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE591090-182300
SE591090-182300
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE591090-182300, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE591160-182400
SE591160-182400
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE591160-182400, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE590385-180890
SE590385-180890
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE590385-180890, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE584960-175280
SE584960-175280
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584960-175280, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE591050-182740
SE591050-182740
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE591050-182740, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE590000-183000
SE590000-183000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE590000-183000, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE591300-182800
SE591300-182800
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE591300-182800, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE594200-192000
SE594200-192000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE594200-192000, type_area 15, variable MONTH_LIST
num 15, suf , suf? ['']
20    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE592640-184500
SE592640-184500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE592640-184500, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE593000-190500
SE593000-190500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE593000-190500, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE593180-191280
SE593180-191280
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE593180-191280, type_area 15, variable MONTH_LIST
num 15, suf , suf? ['']
20    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE594845-191240
SE594845-191240
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE594845-191240, type_area 15, variable MONTH_LIST
num 15, suf , suf? ['']
20    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE594800-190655
SE594800-190655
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE594800-190655, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE591655-183200
SE591655-183200
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE591655-183200, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE591745-182250
SE591745-182250
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE591745-182250, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE594670-185500
SE594670-185500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE594670-185500, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE594800-190220
SE594800-190220
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE594800-190220, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE601310-183700
SE601310-183700
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE601310-183700, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE601000-183510
SE601000-183510
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE601000-183510, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE600565-184600
SE600565-184600
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE600565-184600, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE601300-184180
SE601300-184180
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE601300-184180, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE585797-181090
SE585797-181090
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE585797-181090, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE574931-113131
SE574931-113131
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE574931-113131, type_area 4, variable MONTH_LIST
num 4, suf , suf? ['' '']
6    5;6
7    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE561030-122821
SE561030-122821
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE561030-122821, type_area 5, variable MONTH_LIST
num 5, suf , suf? ['' '']
8    5;6
9    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE554810-125240
SE554810-125240
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE554810-125240, type_area 6, variable MONTH_LIST
num 6, suf , suf? ['' '']
10    5;6
11    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE552500-124461
SE552500-124461
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE552500-124461, type_area 7, variable MONTH_LIST
num 7, suf , suf? ['']
12    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE572472-120302
SE572472-120302
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE572472-120302, type_area 1-s, variable MONTH_LIST
num 1, suf s, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE572135-120141
SE572135-120141
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE572135-120141, type_area 1-s, variable MONTH_LIST
num 1, suf s, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE570900-164501
SE570900-164501
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE570900-164501, type_area 9, variable MONTH_LIST
num 9, suf , suf? ['']
14    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE644150-211000
SE644150-211000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE644150-211000, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE594340-190448
SE594340-190448
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE594340-190448, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE592400-180800
SE592400-180800
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE592400-180800, type_area 24, variable MONTH_LIST
num 24, suf , suf? ['']
29    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE593750-183962
SE593750-183962
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE593750-183962, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE634110-201920
'WaterBody' object has no attribute 'SESE634110-201920'
waterbody matching file does not recognise water body with VISS_EU_CD SE634110-201920
RESULT False
Water body SE634210-202020
'WaterBody' object has no attribute 'SESE634210-202020'
waterbody matching file does not recognise water body with VISS_EU_CD SE634210-202020
RESULT False
Water body SE643700-211940
SE643700-211940
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE643700-211940, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE645000-212100
SE645000-212100
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE645000-212100, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE651818-212790
SE651818-212790
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE651818-212790, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE651475-214300
SE651475-214300
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE651475-214300, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE651800-214740
SE651800-214740
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE651800-214740, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE651500-213108
SE651500-213108
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE651500-213108, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE645500-212000
SE645500-212000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE645500-212000, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE653176-222000
SE653176-222000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE653176-222000, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE728806-179329
SE728806-179329
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE728806-179329, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE623890-178030
SE623890-178030
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE623890-178030, type_area 19, variable MONTH_LIST
num 19, suf , suf? ['']
24    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE622500-172430
SE622500-172430
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE622500-172430, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE622860-173000
SE622860-173000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE622860-173000, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE622000-172300
SE622000-172300
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE622000-172300, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE624870-175500
SE624870-175500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE624870-175500, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE624615-180500
SE624615-180500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE624615-180500, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE624335-180000
SE624335-180000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE624335-180000, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE623810-180350
SE623810-180350
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE623810-180350, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE623340-175556
SE623340-175556
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE623340-175556, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE634950-202940
SE634950-202940
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE634950-202940, type_area 20, variable MONTH_LIST
num 20, suf , suf? ['']
25    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE590740-174135
SE590740-174135
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE590740-174135, type_area 24, variable MONTH_LIST
num 24, suf , suf? ['']
29    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE631840-191130
SE631840-191130
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE631840-191130, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE614165-171500
SE614165-171500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE614165-171500, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE613760-171000
SE613760-171000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE613760-171000, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE613500-172500
SE613500-172500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE613500-172500, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE613591-171000
SE613591-171000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE613591-171000, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE613500-171000
SE613500-171000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE613500-171000, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE613240-171000
SE613240-171000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE613240-171000, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE635660-199490
SE635660-199490
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE635660-199490, type_area 20, variable MONTH_LIST
num 20, suf , suf? ['']
25    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE574160-113351
SE574160-113351
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE574160-113351, type_area 4, variable MONTH_LIST
num 4, suf , suf? ['' '']
6    5;6
7    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE580240-112501
SE580240-112501
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE580240-112501, type_area 3, variable MONTH_LIST
num 3, suf , suf? ['' '']
4    5;6
5    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE573300-113801
SE573300-113801
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE573300-113801, type_area 4, variable MONTH_LIST
num 4, suf , suf? ['' '']
6    5;6
7    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE657412-164249
SE657412-164249
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE657412-164249, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE657608-164193
SE657608-164193
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE657608-164193, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE595000-185600
SE595000-185600
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE595000-185600, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE612303-171075
SE612303-171075
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE612303-171075, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE622795-174565
SE622795-174565
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE622795-174565, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE623300-176210
SE623300-176210
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE623300-176210, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE630210-187470
SE630210-187470
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE630210-187470, type_area 19, variable MONTH_LIST
num 19, suf , suf? ['']
24    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE592600-181600
SE592600-181600
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE592600-181600, type_area 24, variable MONTH_LIST
num 24, suf , suf? ['']
29    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE592575-181770
SE592575-181770
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE592575-181770, type_area 24, variable MONTH_LIST
num 24, suf , suf? ['']
29    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE623980-175600
SE623980-175600
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE623980-175600, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE645000-213500
SE645000-213500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE645000-213500, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE604200-174400
SE604200-174400
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE604200-174400, type_area 17, variable MONTH_LIST
num 17, suf , suf? ['']
22    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE592790-183000
SE592790-183000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE592790-183000, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE637640-204160
SE637640-204160
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE637640-204160, type_area 20, variable MONTH_LIST
num 20, suf , suf? ['']
25    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE652020-211930
'WaterBody' object has no attribute 'SESE652020-211930'
waterbody matching file does not recognise water body with VISS_EU_CD SE652020-211930
RESULT False
Water body SE644730-210650
SE644730-210650
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE644730-210650, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE641840-211540
'WaterBody' object has no attribute 'SESE641840-211540'
waterbody matching file does not recognise water body with VISS_EU_CD SE641840-211540
RESULT False
Water body SE634230-201605
SE634230-201605
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE634230-201605, type_area 20, variable MONTH_LIST
num 20, suf , suf? ['']
25    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE563000-123351
SE563000-123351
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE563000-123351, type_area 5, variable MONTH_LIST
num 5, suf , suf? ['' '']
8    5;6
9    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE604250-173000
SE604250-173000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE604250-173000, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE611676-171000
SE611676-171000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE611676-171000, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE580000-164500
SE580000-164500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE580000-164500, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE590020-114520
'WaterBody' object has no attribute 'SESE590020-114520'
waterbody matching file does not recognise water body with VISS_EU_CD SE590020-114520
RESULT False
Water body SE590860-113810
'WaterBody' object has no attribute 'SESE590860-113810'
waterbody matching file does not recognise water body with VISS_EU_CD SE590860-113810
RESULT False
Water body SE590900-112300
'WaterBody' object has no attribute 'SESE590900-112300'
waterbody matching file does not recognise water body with VISS_EU_CD SE590900-112300
RESULT False
Water body SE590670-111380
'WaterBody' object has no attribute 'SESE590670-111380'
waterbody matching file does not recognise water body with VISS_EU_CD SE590670-111380
RESULT False
Water body SE593500-190000
SE593500-190000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE593500-190000, type_area 12-n, variable MONTH_LIST
num 12, suf n, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE571720-120640
SE571720-120640
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE571720-120640, type_area 1-s, variable MONTH_LIST
num 1, suf s, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE635300-205251
SE635300-205251
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE635300-205251, type_area 21, variable MONTH_LIST
num 21, suf , suf? ['']
26    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE582500-113890
SE582500-113890
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE582500-113890, type_area 2, variable MONTH_LIST
num 2, suf , suf? ['' '']
2    5;6
3    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE637310-204860
SE637310-204860
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE637310-204860, type_area 20, variable MONTH_LIST
num 20, suf , suf? ['']
25    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE634740-203020
SE634740-203020
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE634740-203020, type_area 20, variable MONTH_LIST
num 20, suf , suf? ['']
25    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE646360-213700
SE646360-213700
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE646360-213700, type_area 23, variable MONTH_LIST
num 23, suf , suf? ['']
28    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE643920-211500
SE643920-211500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE643920-211500, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE584725-111050
SE584725-111050
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584725-111050, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE650280-213110
SE650280-213110
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE650280-213110, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE654640-233190
'WaterBody' object has no attribute 'SESE654640-233190'
waterbody matching file does not recognise water body with VISS_EU_CD SE654640-233190
RESULT False
Water body SE654575-234250
SE654575-234250
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE654575-234250, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE562290-124131
SE562290-124131
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE562290-124131, type_area 6, variable MONTH_LIST
num 6, suf , suf? ['' '']
10    5;6
11    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE592000-190500
SE592000-190500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE592000-190500, type_area 15, variable MONTH_LIST
num 15, suf , suf? ['']
20    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE570900-121060
SE570900-121060
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE570900-121060, type_area 1-s, variable MONTH_LIST
num 1, suf s, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE571240-121000
SE571240-121000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE571240-121000, type_area 1-s, variable MONTH_LIST
num 1, suf s, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE564500-122601
SE564500-122601
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE564500-122601, type_area 5, variable MONTH_LIST
num 5, suf , suf? ['' '']
8    5;6
9    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE601440-184000
SE601440-184000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE601440-184000, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE612791-171130
SE612791-171130
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE612791-171130, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE572072-115880
SE572072-115880
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE572072-115880, type_area 1-s, variable MONTH_LIST
num 1, suf s, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE573322-115478
SE573322-115478
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE573322-115478, type_area 1-s, variable MONTH_LIST
num 1, suf s, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE572227-115662
SE572227-115662
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE572227-115662, type_area 1-s, variable MONTH_LIST
num 1, suf s, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE573044-115355
SE573044-115355
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE573044-115355, type_area 1-s, variable MONTH_LIST
num 1, suf s, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE572308-115550
SE572308-115550
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE572308-115550, type_area 1-s, variable MONTH_LIST
num 1, suf s, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE580500-111801
SE580500-111801
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE580500-111801, type_area 3, variable MONTH_LIST
num 3, suf , suf? ['' '']
4    5;6
5    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE574170-190001
SE574170-190001
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE574170-190001, type_area 10, variable MONTH_LIST
num 10, suf , suf? ['']
15    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE584400-116000
SE584400-116000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE584400-116000, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE583625-111300
SE583625-111300
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE583625-111300, type_area 1-n, variable MONTH_LIST
num 1, suf n, suf? ['' '']
0    5;6
1    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6' '5;6'] <class 'numpy.ndarray'>
[[5, 6], [5, 6]]
RESULT [5, 6]
Water body SE572000-180001
SE572000-180001
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE572000-180001, type_area 11, variable MONTH_LIST
num 11, suf , suf? ['']
16    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE572350-180930
SE572350-180930
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE572350-180930, type_area 10, variable MONTH_LIST
num 10, suf , suf? ['']
15    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE583121-171401
SE583121-171401
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE583121-171401, type_area 14, variable MONTH_LIST
num 14, suf , suf? ['']
19    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE633400-195000
SE633400-195000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE633400-195000, type_area 19, variable MONTH_LIST
num 19, suf , suf? ['']
24    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE611000-171500
SE611000-171500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE611000-171500, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE611213-171063
SE611213-171063
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE611213-171063, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE611600-171500
SE611600-171500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE611600-171500, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE581280-170070
SE581280-170070
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE581280-170070, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE581240-165220
SE581240-165220
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE581240-165220, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE581820-165500
SE581820-165500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE581820-165500, type_area 12-s, variable MONTH_LIST
num 12, suf s, suf? ['']
17    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE619690-175690
SE619690-175690
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE619690-175690, type_area 19, variable MONTH_LIST
num 19, suf , suf? ['']
24    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE612520-172080
SE612520-172080
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE612520-172080, type_area 17, variable MONTH_LIST
num 17, suf , suf? ['']
22    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE605660-172380
SE605660-172380
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE605660-172380, type_area 17, variable MONTH_LIST
num 17, suf , suf? ['']
22    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE622011-146303
SE622011-146303
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE622011-146303, type_area 9, variable MONTH_LIST
num 9, suf , suf? ['']
14    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE561480-148220
SE561480-148220
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE561480-148220, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE561150-147620
SE561150-147620
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE561150-147620, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE560740-144375
SE560740-144375
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE560740-144375, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE603000-181500
SE603000-181500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE603000-181500, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE561000-153320
SE561000-153320
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE561000-153320, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE561000-150390
SE561000-150390
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE561000-150390, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE560385-154500
SE560385-154500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .





    




2018-06-01 11:34:17,225	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2






    




----
water_body SE560385-154500, type_area 8, variable MONTH_LIST
num 8, suf , suf? ['']
13    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE560200-143175
SE560200-143175
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE560200-143175, type_area 7, variable MONTH_LIST
num 7, suf , suf? ['']
12    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE654200-222920
SE654200-222920
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE654200-222920, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE654000-222430
SE654000-222430
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE654000-222430, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE650750-213500
SE650750-213500
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE650750-213500, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE650460-213400
SE650460-213400
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE650460-213400, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE625416-182696
SE625416-182696
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE625416-182696, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE628480-183070
SE628480-183070
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE628480-183070, type_area 18, variable MONTH_LIST
num 18, suf , suf? ['']
23    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE593500-191660
SE593500-191660
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE593500-191660, type_area 15, variable MONTH_LIST
num 15, suf , suf? ['']
20    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE593330-192540
SE593330-192540
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE593330-192540, type_area 15, variable MONTH_LIST
num 15, suf , suf? ['']
20    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE593000-192000
SE593000-192000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE593000-192000, type_area 15, variable MONTH_LIST
num 15, suf , suf? ['']
20    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE611766-171305
SE611766-171305
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE611766-171305, type_area 16, variable MONTH_LIST
num 16, suf , suf? ['']
21    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE575150-190400
SE575150-190400
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE575150-190400, type_area 10, variable MONTH_LIST
num 10, suf , suf? ['']
15    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE654291-224000
SE654291-224000
['unspecified']
. . . . .
Series([], Name: MONTH_LIST, dtype: object)
0
. . . . .
----
water_body SE654291-224000, type_area 22, variable MONTH_LIST
num 22, suf , suf? ['']
27    5;6
Name: MONTH_LIST, dtype: object <class 'pandas.core.series.Series'>
['5;6'] <class 'numpy.ndarray'>
[[5, 6]]
RESULT [5, 6]
Water body SE584340-174401
SE584340-174401
['unspecified']
. . . . .
Series([], Name: DEPH_INTERVAL, dtype: object)
0
. . . . .
----
water_body SE584340-174401, type_area 14, variable DEPH_INTERVAL
num 14, suf , suf? ['']
15    0-10
Name: DEPH_INTERVAL, dtype: object <class 'pandas.core.series.Series'>
['0-10'] <class 'numpy.ndarray'>
[[0, 10]]






    




---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-63-84208641f686> in <module>()
      4     w.apply_indicator_data_filter(step = 2, 
      5                           subset = subset_uuid,
----> 6                           indicator = indicator)

D:\github\ekostat_calculator\core\workspaces.py in apply_indicator_data_filter(self, subset, indicator, step)
   1125                 self.index_handler.add_filter(filter_object=water_body_filter_object, step=step, subset=subset, water_body = water_body)
   1126 
-> 1127             self.index_handler.add_filter(filter_object=settings_filter_object, step=step, subset=subset, indicator=indicator, water_body = water_body)
   1128 
   1129 

D:\github\ekostat_calculator\core\index_handler.py in add_filter(self, filter_object, subset, step, water_body, indicator)
    342         self._add_boolean_to_dict(step_0, subset, step_1, step_2, water_body, indicator,
    343                                   filter_object=filter_object,
--> 344                                   df=df)
    345     #==========================================================================
    346     def old_add_filter(self, filter_object=None, subset=None, step=None, type_area=None, indicator=None, level=None):

D:\github\ekostat_calculator\core\index_handler.py in _add_boolean_to_dict(self, step_0, subset, step_1, step_2, water_body, indicator, filter_object, df)
    128                 if bool_dict.get('boolean') is not None:
    129                     # Merge boolean from parent with new boolean from filter_object, filter_object is either DataFilter or SettingsDataFilter
--> 130                     bool_dict[key]['boolean'] = bool_dict.get('boolean') & filter_object.get_filter_boolean_for_df(df, water_body = water_body, indicator = indicator)
    131 
    132                 else:

D:\github\ekostat_calculator\core\filters.py in get_filter_boolean_for_df(self, df, water_body, **kwargs)
   1080 #        get_type_area_for_water_body(wb, include_suffix=False)
   1081         return self.settings.get_filter_boolean_for_df(df=df, 
-> 1082                                                        water_body=water_body)
   1083 
   1084 

D:\github\ekostat_calculator\core\filters.py in get_filter_boolean_for_df(self, df, water_body)
    774                 boolean = self._get_boolean_from_interval(df=df,
    775                                                           water_body = water_body,
--> 776                                                           variable=variable)
    777                 self.temp_boolean_interval = boolean
    778             elif variable in self.list_columns:

D:\github\ekostat_calculator\core\filters.py in _get_boolean_from_interval(self, df, type_area, water_body, variable)
    863         parameter = variable.split('_')[0]
    864 
--> 865         return (df[parameter] >= from_value) & (df[parameter] <= to_value)
    866     #==========================================================================
    867     def old_get_boolean_from_interval(self, df=None, type_area=None, variable=None, level=None):

C:\Anaconda3\lib\site-packages\pandas\core\ops.py in wrapper(self, other, axis)
    853 
    854             with np.errstate(all='ignore'):
--> 855                 res = na_op(values, other)
    856             if isscalar(res):
    857                 raise TypeError('Could not compare %s type with Series' %

C:\Anaconda3\lib\site-packages\pandas\core\ops.py in na_op(x, y)
    757 
    758         if is_object_dtype(x.dtype):
--> 759             result = _comp_method_OBJECT_ARRAY(op, x, y)
    760         else:
    761 

C:\Anaconda3\lib\site-packages\pandas\core\ops.py in _comp_method_OBJECT_ARRAY(op, x, y)
    737         result = lib.vec_compare(x, y, op)
    738     else:
--> 739         result = lib.scalar_compare(x, y, op)
    740     return result
    741 

pandas\lib.pyx in pandas.lib.scalar_compare (pandas\lib.c:14847)()

TypeError: '>=' not supported between instances of 'str' and 'int'

In [ ]:

    

w.index_handler.booleans['step_0'][subset_uuid]['step_1']['step_2']['SE584340-174401'].keys()


    

In [66]:

    

wb = 'SE583926-161744' #typomr 22
#wb = 'SE654470-222700' #typomr 13
type_area = '2'#'01s - Västkustens inre kustvatten'
indicator = 'din_winter'
#w.index_handler.booleans['step_0'][subset_uuid]['step_1']['step_2'][type_area]['din_winter']['boolean']


    

In [67]:

    

print(w.get_filtered_data(step = 2, subset = subset_uuid, water_body = wb, indicator = indicator).MONTH.unique())
print(w.get_filtered_data(step = 2, subset = subset_uuid, water_body = wb, indicator = indicator).DEPH.min(),
        w.get_filtered_data(step = 2, subset = subset_uuid, water_body = wb, indicator = indicator).DEPH.max())
print(w.get_filtered_data(step = 2, subset = subset_uuid, water_body = wb, indicator = indicator).VISS_EU_CD.unique())
w.get_filtered_data(step = 2, subset = subset_uuid, water_body = wb).WATER_TYPE_AREA.unique()


    

    




2018-06-01 11:35:40,980	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 11:35:40,991	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 11:35:40,999	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 11:35:41,008	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2
2018-06-01 11:35:41,015	workspaces.py	1410	get_filtered_data	DEBUG	STEP: step_2






    




['12' '10' '8' '7' '6' '2']
 5.0
['SE583926-161744']






    
Out[67]:





array(['13 - Östergötlands inre kustvatten'], dtype=object)

In [68]:

    

w.mapping_objects['quality_element'].cfg['indicators']
[item.strip() for item in w.mapping_objects['quality_element'].cfg['indicators'].loc[indicator][0].split(', ')]


    

    
Out[68]:





['DIN', 'SALT']

In [69]:

    

w.get_step_object(step = 3, subset = subset_uuid).indicator_setup(subset_unique_id = subset_uuid) 
#, indicator_list = ['din_winter', 'dip_winter']


    

    




********
din_winter
----
water_body None, type_area 1s, variable MIN_NR_YEARS
num 1, suf s, suf? ['n' 's']
1    3
Name: MIN_NR_YEARS, dtype: object <class 'pandas.core.series.Series'>
['3'] <class 'numpy.ndarray'>
[3]
----
water_body None, type_area 1s, variable REF_VALUE_LIMIT
num 1, suf s, suf? ['n' 's']
1    -0.525*s+15
Name: REF_VALUE_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['-0.525*s+15'] <class 'numpy.ndarray'>
value -0.525*s+15 in refvalue_column
['-0.525*s+15']
********
ntot_summer
----
water_body None, type_area 1s, variable MIN_NR_YEARS
num 1, suf s, suf? ['n' 's']
1    3
Name: MIN_NR_YEARS, dtype: object <class 'pandas.core.series.Series'>
['3'] <class 'numpy.ndarray'>
[3]
----
water_body None, type_area 1s, variable REF_VALUE_LIMIT
num 1, suf s, suf? ['n' 's']
1    -0.9*s+30
Name: REF_VALUE_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['-0.9*s+30'] <class 'numpy.ndarray'>
value -0.9*s+30 in refvalue_column
['-0.9*s+30']
********
ntot_winter
----
water_body None, type_area 1s, variable MIN_NR_YEARS
num 1, suf s, suf? ['n' 's']
1    3
Name: MIN_NR_YEARS, dtype: object <class 'pandas.core.series.Series'>
['3'] <class 'numpy.ndarray'>
[3]
----
water_body None, type_area 1s, variable REF_VALUE_LIMIT
num 1, suf s, suf? ['n' 's']
1    -0.65*s+30
Name: REF_VALUE_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['-0.65*s+30'] <class 'numpy.ndarray'>
value -0.65*s+30 in refvalue_column
['-0.65*s+30']
********
dip_winter
----
water_body None, type_area 1s, variable MIN_NR_YEARS
num 1, suf s, suf? ['n' 's']
1    3
Name: MIN_NR_YEARS, dtype: object <class 'pandas.core.series.Series'>
['3'] <class 'numpy.ndarray'>
[3]
----
water_body None, type_area 1s, variable REF_VALUE_LIMIT
num 1, suf s, suf? ['n' 's']
1    0.01*s+0.2
Name: REF_VALUE_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['0.01*s+0.2'] <class 'numpy.ndarray'>
value 0.01*s+0.2 in refvalue_column
['0.01*s+0.2']
********
ptot_summer
----
water_body None, type_area 1s, variable MIN_NR_YEARS
num 1, suf s, suf? ['n' 's']
1    3
Name: MIN_NR_YEARS, dtype: object <class 'pandas.core.series.Series'>
['3'] <class 'numpy.ndarray'>
[3]
----
water_body None, type_area 1s, variable REF_VALUE_LIMIT
num 1, suf s, suf? ['n' 's']
1    0*s+0.4
Name: REF_VALUE_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['0*s+0.4'] <class 'numpy.ndarray'>
value 0*s+0.4 in refvalue_column
['0*s+0.4']
********
ptot_winter
----
water_body None, type_area 1s, variable MIN_NR_YEARS
num 1, suf s, suf? ['n' 's']
1    3
Name: MIN_NR_YEARS, dtype: object <class 'pandas.core.series.Series'>
['3'] <class 'numpy.ndarray'>
[3]
----
water_body None, type_area 1s, variable REF_VALUE_LIMIT
num 1, suf s, suf? ['n' 's']
1    0.015*s+0.4
Name: REF_VALUE_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['0.015*s+0.4'] <class 'numpy.ndarray'>
value 0.015*s+0.4 in refvalue_column
['0.015*s+0.4']
********
bqi
----
water_body None, type_area 1s, variable MIN_NR_YEARS
num 1, suf s, suf? ['' '']
Series([], Name: MIN_NR_YEARS, dtype: object) <class 'pandas.core.series.Series'>
[] <class 'numpy.ndarray'>
[]
----
water_body None, type_area 1s, variable HG_VALUE_LIMIT
num 1, suf s, suf? ['' '']
Series([], Name: HG_VALUE_LIMIT, dtype: object) <class 'pandas.core.series.Series'>
[] <class 'numpy.ndarray'>
[]
********
oxygen
----
water_body None, type_area 1s, variable MIN_NR_YEARS
num 1, suf s, suf? ['s' 'n']
0    3
Name: MIN_NR_YEARS, dtype: object <class 'pandas.core.series.Series'>
['3'] <class 'numpy.ndarray'>
[3]
----
water_body None, type_area 1s, variable REF_VALUE
num 1, suf s, suf? ['s' 'n']
0    10
Name: REF_VALUE, dtype: object <class 'pandas.core.series.Series'>
['10'] <class 'numpy.ndarray'>
value 10 in refvalue_column
['10']
********
biov
----
water_body None, type_area 1s, variable MIN_NR_YEARS
num 1, suf s, suf? ['n' 's']
1    3
Name: MIN_NR_YEARS, dtype: object <class 'pandas.core.series.Series'>
['3'] <class 'numpy.ndarray'>
[3]
----
water_body None, type_area 1s, variable REF_VALUE_LIMIT
num 1, suf s, suf? ['n' 's']
1    0.9
Name: REF_VALUE_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['0.9'] <class 'numpy.ndarray'>
value 0.9 in refvalue_column
['0.9']
********
chl
----
water_body None, type_area 1s, variable MIN_NR_YEARS
num 1, suf s, suf? ['n' 's']
1    3
Name: MIN_NR_YEARS, dtype: object <class 'pandas.core.series.Series'>
['3'] <class 'numpy.ndarray'>
[3]
----
water_body None, type_area 1s, variable REF_VALUE_LIMIT
num 1, suf s, suf? ['n' 's']
1    1.6
Name: REF_VALUE_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['1.6'] <class 'numpy.ndarray'>
value 1.6 in refvalue_column
['1.6']
********
secchi
----
water_body None, type_area 1s, variable MIN_NR_YEARS
num 1, suf s, suf? ['n' 's']
1    3
Name: MIN_NR_YEARS, dtype: object <class 'pandas.core.series.Series'>
['3'] <class 'numpy.ndarray'>
[3]
----
water_body None, type_area 1s, variable REF_VALUE_LIMIT
num 1, suf s, suf? ['n' 's']
1    8
Name: REF_VALUE_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['8'] <class 'numpy.ndarray'>
value 8 in refvalue_column
['8']

In [70]:

    

w.get_step_object(step = 3, subset = subset_uuid).indicator_objects[indicator].get_ref_value_type(water_body = wb)


    

    




SE583926-161744
['unspecified']
. . . . .
Series([], Name: REF_VALUE_LIMIT, dtype: object)
0
. . . . .
----
water_body SE583926-161744, type_area 13, variable REF_VALUE_LIMIT
num 13, suf , suf? ['']
14    -1.0833*s+9
Name: REF_VALUE_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['-1.0833*s+9'] <class 'numpy.ndarray'>
value -1.0833*s+9 in refvalue_column
['-1.0833*s+9']






    
Out[70]:





'str'

In [71]:

    

dw_obj = w.get_step_object(step = 3, subset = subset_uuid).indicator_objects[indicator]


    

In [76]:

    

dw_obj.set_water_body_indicator_df(water_body = wb)


    

    




SE583926-161744
SDATE              object
YEAR               object
MONTH              object
POSITION           object
VISS_EU_CD         object
WATER_TYPE_AREA    object
DEPH               object
DIN                object
SALT               object
dtype: object
SE583926-161744 1
SE583926-161744
['unspecified']
. . . . .
Series([], Name: REF_VALUE_LIMIT, dtype: object)
0
. . . . .
----
water_body SE583926-161744, type_area 13, variable REF_VALUE_LIMIT
num 13, suf , suf? ['']
14    -1.0833*s+9
Name: REF_VALUE_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['-1.0833*s+9'] <class 'numpy.ndarray'>
value -1.0833*s+9 in refvalue_column
['-1.0833*s+9']
str
SE583926-161744
['unspecified']
. . . . .
Series([], Name: REF_VALUE_LIMIT, dtype: object)
0
. . . . .
----
water_body SE583926-161744, type_area 13, variable REF_VALUE_LIMIT
num 13, suf , suf? ['']
14    -1.0833*s+9
Name: REF_VALUE_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['-1.0833*s+9'] <class 'numpy.ndarray'>
value -1.0833*s+9 in refvalue_column
['-1.0833*s+9']
ref_value is str
'6.1'
SE583926-161744
['unspecified']
. . . . .
Series([], Name: REF_VALUE_LIMIT, dtype: object)
0
. . . . .
----
water_body SE583926-161744, type_area 13, variable REF_VALUE_LIMIT
num 13, suf , suf? ['']
14    -1.0833*s+9
Name: REF_VALUE_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['-1.0833*s+9'] <class 'numpy.ndarray'>
value -1.0833*s+9 in refvalue_column
['-1.0833*s+9']






    




---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
D:\github\ekostat_calculator\core\filters.py in get_ref_value(self, type_area, water_body, salinity)
   1025                 s = salinity
-> 1026                 ref_value = eval(ref_value)
   1027             except TypeError as e:

D:\github\ekostat_calculator\core\filters.py in <module>()

TypeError: can't multiply sequence by non-int of type 'float'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-76-abb3f026c5f5> in <module>()
----> 1 dw_obj.set_water_body_indicator_df(water_body = wb)

D:\github\ekostat_calculator\core\indicators.py in set_water_body_indicator_df(self, water_body)
    392             df = df.dropna(subset = [self.indicator_parameter])
    393             print(df.dtypes)
--> 394             df = self._add_reference_value_to_df(df, water_body)
    395             self.water_body_indicator_df[water_body] = df
    396         else:

D:\github\ekostat_calculator\core\indicators.py in _add_reference_value_to_df(self, df, water_body)
    114                 salinity = df['SALT'][ix]
    115                 print(repr(salinity))
--> 116                 df['REFERENCE_VALUE'].loc[ix] = self.get_ref_value(water_body = water_body, salinity = salinity)
    117         else:
    118             df['REFERENCE_VALUE'] = self.get_ref_value(water_body)

D:\github\ekostat_calculator\core\indicators.py in get_ref_value(self, type_area, water_body, salinity)
    346 
    347         """
--> 348         return self.ref_settings.get_ref_value(type_area = type_area, water_body = water_body, salinity = salinity)
    349 
    350     #==========================================================================

D:\github\ekostat_calculator\core\filters.py in get_ref_value(self, type_area, water_body, salinity)
   1026                 ref_value = eval(ref_value)
   1027             except TypeError as e:
-> 1028                 raise TypeError('{}\nSalinity TypeError, salinity must be int, float or nan but is {}'.format(e, repr(s)))
   1029                 #TODO: add closes matching salinity somewhere here
   1030         elif not ref_value:

TypeError: can't multiply sequence by non-int of type 'float'
Salinity TypeError, salinity must be int, float or nan but is '6.1'

In [28]:

    

dw_obj.get_water_body_indicator_df(wb)


    

    
Out[28]:






  

    

      

      
SDATE
      
YEAR
      
MONTH
      
POSITION
      
VISS_EU_CD
      
WATER_TYPE_AREA
      
DEPH
      
DIN
      
SALT
      
REFERENCE_VALUE
    
  
  

    

      
7031
      
2016-12-08
      
2016
      
12
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
5.00
      
6.1
      
2.39187
    
    

      
7032
      
2016-12-08
      
2016
      
12
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
4.85
      
6.0
      
2.50020
    
    

      
7033
      
2016-12-08
      
2016
      
12
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
4.92
      
6.0
      
2.50020
    
    

      
10166
      
2016-02-09
      
2016
      
2
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
10.10
      
5.3
      
3.25851
    
    

      
10167
      
2016-02-09
      
2016
      
2
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
9.87
      
5.9
      
2.60853
    
    

      
10168
      
2016-02-09
      
2016
      
2
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
6.95
      
6.5
      
1.95855
    
    

      
10930
      
2015-12-08
      
2015
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
8.38
      
5.5
      
3.04185
    
    

      
10931
      
2015-12-08
      
2015
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
8.13
      
NaN
      
NaN
    
    

      
10932
      
2015-12-08
      
2015
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
7.14
      
6.6
      
1.85022
    
    

      
17901
      
2015-02-09
      
2015
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
13.30
      
5.6
      
2.93352
    
    

      
17902
      
2015-02-09
      
2015
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
9.36
      
NaN
      
NaN
    
    

      
17903
      
2015-02-09
      
2015
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
7.93
      
6.6
      
1.85022
    
    

      
18619
      
2014-12-16
      
2014
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
9.37
      
5.0
      
3.58350
    
    

      
18620
      
2014-12-16
      
2014
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
7.97
      
NaN
      
NaN
    
    

      
18621
      
2014-12-16
      
2014
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
7.19
      
NaN
      
NaN
    
    

      
25719
      
2014-02-25
      
2014
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
25.47
      
2.3
      
6.50841
    
    

      
25720
      
2014-02-25
      
2014
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
15.03
      
NaN
      
NaN
    
    

      
25721
      
2014-02-25
      
2014
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
6.57
      
NaN
      
NaN
    
    

      
26807
      
2013-12-10
      
2013
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
6.93
      
6.4
      
2.06688
    
    

      
26808
      
2013-12-10
      
2013
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
6.15
      
NaN
      
NaN
    
    

      
33959
      
2013-02-08
      
2013
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
41.21
      
0.8
      
8.13336
    
    

      
33960
      
2013-02-08
      
2013
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
14.07
      
NaN
      
NaN
    
  

In [29]:

    

dw_obj.column_list


    

    
Out[29]:





['SDATE',
 'YEAR',
 'MONTH',
 'POSITION',
 'VISS_EU_CD',
 'WATER_TYPE_AREA',
 'DEPH',
 'DIN',
 'SALT']

In [31]:

    

temp = dw_obj.water_body_indicator_df[wb].dropna(subset = ['DIN', 'REFERENCE_VALUE']).copy(deep = True)
temp['ek'] = np.divide(temp.REFERENCE_VALUE,temp.DIN)
temp


    

    
Out[31]:






  

    

      

      
SDATE
      
YEAR
      
MONTH
      
POSITION
      
VISS_EU_CD
      
WATER_TYPE_AREA
      
DEPH
      
DIN
      
SALT
      
REFERENCE_VALUE
      
ek
    
  
  

    

      
7031
      
2016-12-08
      
2016
      
12
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
5.00
      
6.1
      
2.39187
      
0.478374
    
    

      
7032
      
2016-12-08
      
2016
      
12
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
4.85
      
6.0
      
2.50020
      
0.515505
    
    

      
7033
      
2016-12-08
      
2016
      
12
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
4.92
      
6.0
      
2.50020
      
0.508171
    
    

      
10166
      
2016-02-09
      
2016
      
2
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
10.10
      
5.3
      
3.25851
      
0.322625
    
    

      
10167
      
2016-02-09
      
2016
      
2
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
9.87
      
5.9
      
2.60853
      
0.264289
    
    

      
10168
      
2016-02-09
      
2016
      
2
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
6.95
      
6.5
      
1.95855
      
0.281806
    
    

      
10930
      
2015-12-08
      
2015
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
8.38
      
5.5
      
3.04185
      
0.362989
    
    

      
10932
      
2015-12-08
      
2015
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
7.14
      
6.6
      
1.85022
      
0.259134
    
    

      
17901
      
2015-02-09
      
2015
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
13.30
      
5.6
      
2.93352
      
0.220565
    
    

      
17903
      
2015-02-09
      
2015
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
7.93
      
6.6
      
1.85022
      
0.233319
    
    

      
18619
      
2014-12-16
      
2014
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
9.37
      
5.0
      
3.58350
      
0.382444
    
    

      
25719
      
2014-02-25
      
2014
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
25.47
      
2.3
      
6.50841
      
0.255532
    
    

      
26807
      
2013-12-10
      
2013
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
6.93
      
6.4
      
2.06688
      
0.298251
    
    

      
33959
      
2013-02-08
      
2013
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
41.21
      
0.8
      
8.13336
      
0.197364
    
  

In [157]:

    

#dw_obj.ref_settings.get_value(variable = 'HG_EQR_LIMIT', type_area = type_area)
#dw_obj.ref_settings.get_value(variable = 'PB_EQR_LIMIT', type_area = type_area)


    

In [32]:

    

#dw_obj.tolerance_settings.get_value(variable = 'MIN_NR_YEARS', type_area = '22')
#dw_obj.tolerance_settings.get_min_nr_years(type_area = '22')
#dw_obj.mapping_objects['water_body'].keys()


    

    
Out[32]:





['column_name',
 'water_bodies',
 'SE570900-121060',
 'SE571240-121000',
 'SE571720-120640',
 'SE572072-115880',
 'SE572135-120141',
 'SE572227-115662',
 'SE572308-115550',
 'SE572472-120302',
 'SE572838-115515',
 'SE572980-115576',
 'SE573044-115355',
 'SE573100-115580',
 'SE573173-115587',
 'SE573322-115478',
 'SE573500-115150',
 'SE573547-114617',
 'SE573657-114572',
 'SE573797-114618',
 'SE573860-115000',
 'SE574000-114230',
 'SE574330-114000',
 'SE574370-114250',
 'SE574630-113940',
 'SE574870-113795',
 'SE0101010301-C',
 'SE575500-113750',
 'SE575747-113237',
 'SE580025-113168',
 'SE580325-113500',
 'SE580338-112901',
 'SE580500-112970',
 'SE580530-112700',
 'SE580610-113615',
 'SE580650-113000',
 'SE580765-112501',
 'SE580860-114560',
 'SE581338-112332',
 'SE581748-112411',
 'SE581853-112736',
 'SE582000-112350',
 'SE582040-112157',
 'SE582147-111771',
 'SE582210-111880',
 'SE582230-112255',
 'SE582302-111451',
 'SE582420-111370',
 'SE582665-111706',
 'SE582850-111760',
 'SE583050-110650',
 'SE583160-111551',
 'SE583625-111300',
 'SE583710-111535',
 'SE584030-111400',
 'SE584363-110971',
 'SE584400-116000',
 'SE584450-111445',
 'SE584670-111300',
 'SE584725-111050',
 'SE584750-111185',
 'SE584890-110950',
 'SE585160-110880',
 'SE585200-111140',
 'SE585290-110830',
 'SE585600-110880',
 'SE585930-110800',
 'SE585990-111125',
 'SE0101010201-C',
 'SE0101010202-C',
 'SE0101010203-C',
 'SE575700-114240',
 'SE580500-114725',
 'SE580688-114860',
 'SE581120-112680',
 'SE581200-112960',
 'SE581260-113220',
 'SE581260-115280',
 'SE581365-112910',
 'SE581450-113140',
 'SE581520-113750',
 'SE581540-114000',
 'SE581570-113040',
 'SE581700-113000',
 'SE581740-114820',
 'SE582000-115270',
 'SE582150-112530',
 'SE582500-113890',
 'SE582630-113515',
 'SE575340-113000',
 'SE575760-112671',
 'SE580240-112501',
 'SE580500-111801',
 'SE580550-112460',
 'SE582700-110451',
 'SE583450-110750',
 'SE584200-105901',
 'SE585100-110600',
 'SE585400-110400',
 'SE585750-105940',
 'SE570000-120701',
 'SE572540-114801',
 'SE573300-113801',
 'SE574160-113351',
 'SE574931-113131',
 'SE561030-122821',
 'SE562000-123800',
 'SE562450-122751',
 'SE563000-123351',
 'SE563330-124600',
 'SE564500-122601',
 'SE553757-130820',
 'SE554040-125750',
 'SE554500-125001',
 'SE554810-125240',
 'SE555545-124332',
 'SE562290-124131',
 'SE552170-130626',
 'SE552219-130919',
 'SE552220-130920',
 'SE552500-124461',
 'SE552670-142281',
 'SE552800-125430',
 'SE553730-128890',
 'SE554800-142001',
 'SE555685-142290',
 'SE555950-142740',
 'SE560200-143175',
 'SE560205-143545',
 'SE560290-154710',
 'SE560385-154500',
 'SE560500-154435',
 'SE560500-154880',
 'SE560740-144375',
 'SE560740-152650',
 'SE560750-152500',
 'SE560775-153055',
 'SE560780-153500',
 'SE560790-145850',
 'SE560795-154730',
 'SE560810-153980',
 'SE560825-144215',
 'SE560850-150580',
 'SE560895-145500',
 'SE560895-145975',
 'SE560900-145280',
 'SE560900-151260',
 'SE560930-150810',
 'SE560940-151740',
 'SE560950-145810',
 'SE561000-150390',
 'SE561000-152500',
 'SE561000-153320',
 'SE561005-150250',
 'SE561080-153835',
 'SE561150-147620',
 'SE561480-148220',
 'SE563770-161670',
 'SE563825-161810',
 'SE564250-162500',
 'SE565000-162825',
 'SE565460-163000',
 'SE565800-163000',
 'SE570080-163430',
 'SE570340-163710',
 'SE570500-163750',
 'SE570730-163715',
 'SE571450-163320',
 'SE571552-162848',
 'SE572000-163835',
 'SE572205-163500',
 'SE572500-164500',
 'SE622384-147046',
 'SE633846-154163',
 'SE560700-155801',
 'SE561400-161201',
 'SE562000-162271',
 'SE562050-160820',
 'SE563100-161500',
 'SE565400-163600',
 'SE570900-164501',
 'SE621157-148904',
 'SE621688-144133',
 'SE622011-146303',
 'SE562410-164001',
 'SE570000-170351',
 'SE570200-182500',
 'SE570270-181160',
 'SE570450-180651',
 'SE570850-182920',
 'SE571000-184001',
 'SE571800-184300',
 'SE572110-170620',
 'SE572350-180930',
 'SE573200-185701',
 'SE574170-190001',
 'SE575150-190400',
 'SE575300-191801',
 'SE575480-191200',
 'SE575620-191550',
 'SE575920-191650',
 'SE582150-191901',
 'SE582200-191201',
 'SE582350-191651',
 'SE640066-167754',
 'SE572000-180001',
 'SE574520-182151',
 'SE575170-183550',
 'SE575480-184830',
 'SE575675-185101',
 'SE580150-191251',
 'SE572565-164000',
 'SE572650-164000',
 'SE572810-164500',
 'SE573500-163500',
 'SE573500-163900',
 'SE573500-164660',
 'SE573860-164725',
 'SE573865-164160',
 'SE573885-163740',
 'SE573940-163560',
 'SE573972-164250',
 'SE574083-164115',
 'SE574100-164700',
 'SE574160-163610',
 'SE574205-164500',
 'SE574440-164160',
 'SE574500-164500',
 'SE574560-163950',
 'SE574750-164500',
 'SE574820-163550',
 'SE575000-163620',
 'SE575060-164170',
 'SE575095-164630',
 'SE575150-162700',
 'SE575335-165000',
 'SE575370-164220',
 'SE575430-163640',
 'SE575670-163500',
 'SE575782-165143',
 'SE575880-164000',
 'SE580000-164060',
 'SE580000-164500',
 'SE580205-165162',
 'SE580375-164500',
 'SE580585-164720',
 'SE580735-165296',
 'SE580890-165500',
 'SE581240-165220',
 'SE581280-170070',
 'SE581520-165000',
 'SE581660-165710',
 'SE581740-170260',
 'SE581800-170000',
 'SE581815-164320',
 'SE581820-165500',
 'SE581960-164890',
 'SE581975-164500',
 'SE582050-165820',
 'SE582055-165230',
 'SE582070-164820',
 'SE582460-164500',
 'SE582590-165000',
 'SE582600-163810',
 'SE582600-165680',
 'SE582630-165210',
 'SE582820-165920',
 'SE583000-165600',
 'SE583370-165290',
 'SE583730-164501',
 'SE583755-163200',
 'SE583825-163500',
 'SE583875-170270',
 'SE583896-170790',
 'SE583906-170998',
 'SE583960-170700',
 'SE583970-170280',
 'SE584045-170882',
 'SE584067-171125',
 'SE584085-171600',
 'SE584215-170800',
 'SE584227-171600',
 'SE584333-172895',
 'SE584390-172085',
 'SE584400-172270',
 'SE584420-172515',
 'SE584430-170665',
 'SE584434-170260',
 'SE584435-170450',
 'SE584520-172495',
 'SE584600-173200',
 'SE584695-175315',
 'SE584820-172920',
 'SE584840-175400',
 'SE584870-174310',
 'SE584905-172980',
 'SE584960-175280',
 'SE585000-174600',
 'SE585040-173535',
 'SE585075-173130',
 'SE585145-175690',
 'SE585170-175445',
 'SE585200-173430',
 'SE585200-173600',
 'SE585200-174000',
 'SE585345-174950',
 'SE585400-173870',
 'SE585450-175800',
 'SE585797-181090',
 'SE590000-174400',
 'SE590000-183000',
 'SE590200-173765',
 'SE590385-180890',
 'SE590400-174090',
 'SE590500-182000',
 'SE590550-174540',
 'SE590635-182120',
 'SE590730-183763',
 'SE590835-183000',
 'SE591050-182320',
 'SE591050-182740',
 'SE591090-182300',
 'SE591160-182400',
 'SE591200-183600',
 'SE591280-182070',
 'SE591300-182800',
 'SE591330-184225',
 'SE591400-182320',
 'SE591400-183200',
 'SE591655-183200',
 'SE591655-183530',
 'SE591745-182250',
 'SE591755-182800',
 'SE591755-183895',
 'SE591760-181955',
 'SE591815-182670',
 'SE591905-185275',
 'SE592000-184700',
 'SE592040-184000',
 'SE592090-185125',
 'SE592245-184400',
 'SE592280-183550',
 'SE592400-184400',
 'SE592500-185000',
 'SE592547-182720',
 'SE592605-182310',
 'SE592640-184500',
 'SE592650-182815',
 'SE592790-183000',
 'SE593000-190500',
 'SE593080-184500',
 'SE593300-183600',
 'SE593460-184890',
 'SE593500-190000',
 'SE593750-183962',
 'SE593750-184900',
 'SE593820-185500',
 'SE594000-190500',
 'SE594100-185690',
 'SE594250-191040',
 'SE594260-185580',
 'SE594340-190448',
 'SE594350-190530',
 'SE594384-185542',
 'SE594590-190600',
 'SE594670-185500',
 'SE594800-190220',
 'SE594800-190655',
 'SE595000-185600',
 'SE657412-164249',
 'SE657608-164193',
 'SE658180-166649',
 'SE662116-166449',
 'SE580250-164000',
 'SE581000-164020',
 'SE582000-164145',
 'SE582705-163350',
 'SE583718-161687',
 'SE583721-161110',
 'SE583730-162500',
 'SE583926-161744',
 'SE573150-165001',
 'SE574450-165451',
 'SE580380-170001',
 'SE580950-170601',
 'SE581900-171101',
 'SE583121-171401',
 'SE583720-172571',
 'SE584340-174401',
 'SE585350-182001',
 'SE590148-183625',
 'SE590665-184210',
 'SE591175-185000',
 'SE591500-185300',
 'SE591790-185500',
 'SE591910-185600',
 'SE592000-190500',
 'SE592100-192001',
 'SE592500-191750',
 'SE593000-192000',
 'SE593000-193000',
 'SE593180-191280',
 'SE593330-192540',
 'SE593500-191660',
 'SE593500-193255',
 'SE593760-192625',
 'SE593860-192000',
 'SE593920-191440',
 'SE594000-193501',
 'SE594200-192000',
 'SE594845-191240',
 'SE595000-191501',
 'SE600565-184600',
 'SE600740-183460',
 'SE600920-183090',
 'SE601000-183510',
 'SE601000-184030',
 'SE601070-182870',
 'SE601190-182870',
 'SE601204-182670',
 'SE601250-182570',
 'SE601300-182880',
 'SE601300-184180',
 'SE601310-183700',
 'SE601360-182510',
 'SE601440-184000',
 'SE601660-183550',
 'SE602120-181610',
 'SE602400-183190',
 'SE603000-181500',
 'SE603190-174000',
 'SE603650-174500',
 'SE604055-171248',
 'SE604116-171037',
 'SE604200-171765',
 'SE604250-173000',
 'SE604900-171700',
 'SE605140-171674',
 'SE605390-171558',
 'SE605630-179220',
 'SE605760-171000',
 'SE610100-171245',
 'SE610500-171586',
 'SE611000-171500',
 'SE611213-171063',
 'SE611600-171500',
 'SE611676-171000',
 'SE611766-171305',
 'SE612303-171075',
 'SE612791-171130',
 'SE613240-171000',
 'SE613380-171450',
 'SE613500-171000',
 'SE613500-172500',
 'SE613591-171000',
 'SE613760-171000',
 'SE614165-171500',
 'SE595730-185850',
 'SE600590-184933',
 'SE601020-185050',
 'SE603870-181301',
 'SE604200-174400',
 'SE604675-172125',
 'SE605660-172380',
 'SE612520-172080',
 'SE621720-175130',
 'SE621920-175280',
 'SE622000-172300',
 'SE622080-176120',
 'SE622126-172430',
 'SE622339-172190',
 'SE622500-172430',
 'SE622795-174565',
 'SE622860-173000',
 'SE622900-174790',
 'SE623300-176210',
 'SE623340-175556',
 'SE623810-180350',
 'SE623980-175600',
 'SE624335-180000',
 'SE624380-176450',
 'SE624615-180500',
 'SE624800-181030',
 'SE624870-175500',
 'SE625000-180075',
 'SE625180-181655',
 'SE625416-182696',
 'SE625500-175153',
 'SE625710-183000',
 'SE625900-174360',
 'SE628480-183070',
 'SE628750-183300',
 'SE630000-183500',
 'SE630180-182080',
 'SE630203-182615',
 'SE630383-183500',
 'SE630685-184305',
 'SE630760-183315',
 'SE631346-184241',
 'SE631406-185500',
 'SE631450-185200',
 'SE631460-185000',
 'SE631500-190270',
 'SE631610-184500',
 'SE631646-185280',
 'SE631710-188130',
 'SE631840-191130',
 'SE632030-187600',
 'SE632090-189470',
 'SE632090-190370',
 'SE632670-190860',
 'SE632690-193500',
 'SE632760-191300',
 'SE633043-193300',
 'SE634040-193330',
 'SE634850-193570',
 'SE619690-175690',
 'SE621265-173125',
 'SE623890-178030',
 'SE630210-187470',
 'SE633400-195000',
 'SE633000-195000',
 'SE633710-200500',
 'SE634200-202033',
 'SE634230-201605',
 'SE634640-203710',
 'SE634740-203020',
 'SE634950-202940',
 'SE635040-204196',
 'SE635660-199490',
 'SE636150-199220',
 'SE636570-203590',
 'SE636910-204040',
 'SE637070-204260',
 'SE637310-204860',
 'SE637640-204160',
 'SE633550-200700',
 'SE635300-205251',
 'SE640240-205500',
 'SE640400-205770',
 'SE640900-205935',
 'SE641000-210500',
 'SE641250-210560',
 'SE641745-211570',
 'SE641875-212250',
 'SE642035-212600',
 'SE642950-213400',
 'SE643160-212730',
 'SE643550-211920',
 'SE643700-211940',
 'SE643920-211500',
 'SE644030-218500',
 'SE644040-211260',
 'SE644070-211650',
 'SE644150-211000',
 'SE644730-210650',
 'SE645000-212100',
 'SE645000-213500',
 'SE645130-211040',
 'SE645340-211330',
 'SE645500-212000',
 'SE645670-214290',
 'SE645830-212300',
 'SE645950-212650',
 'SE647260-212660',
 'SE650280-213110',
 'SE650460-213400',
 'SE650750-213500',
 'SE651075-213700',
 'SE651475-214300',
 'SE651500-213108',
 'SE651800-214740',
 'SE651818-212790',
 'SE651940-213930',
 'SE652000-213210',
 'SE652000-214000',
 'SE652066-214400',
 'SE652075-213500',
 'SE652150-213000',
 'SE652250-213000',
 'SE652250-213430',
 'SE652385-214180',
 'SE652400-220070',
 'SE652450-222116',
 'SE652465-214080',
 'SE652475-215750',
 'SE652500-213500',
 'SE652686-221500',
 'SE652830-222116',
 'SE652855-224000',
 'SE652920-222650',
 'SE653116-224623',
 'SE653140-224000',
 'SE653176-222000',
 'SE653303-222900',
 'SE653740-222800',
 'SE653900-223280',
 'SE654000-222430',
 'SE654110-224850',
 'SE654200-222920',
 'SE654291-224000',
 'SE654330-222200',
 'SE654360-235780',
 'SE654470-222700',
 'SE654490-220870',
 'SE654500-232000',
 'SE654560-246250',
 'SE654570-225230',
 'SE654575-234250',
 'SE654820-222660',
 'SE654860-219880',
 'SE654990-224540',
 'SE655120-220380',
 'SE655180-218660',
 'SE655260-224280',
 'SE656300-222750',
 'SE656620-222480',
 'SE656840-222800',
 'SE728806-179329',
 'SE729159-179002',
 'SE729849-180191',
 'SE731175-867144',
 'SE731734-867204',
 'SE732081-888706',
 'SE732410-884539',
 'SE641250-211751',
 'SE646360-213700',
 'SE648760-213140',
 'SE652400-223501',
 'SE653840-247900',
 'SE653870-235570',
 'SE654100-234100',
 'SE654130-249500',
 'SE654150-240380',
 'SE590740-174135',
 'SE590990-174015',
 'SE591800-181360',
 'SE591920-180800',
 'SE592135-182700',
 'SE592290-181600',
 'SE592315-182620',
 'SE592400-180800',
 'SE592400-181860',
 'SE592420-182210',
 'SE592435-182400',
 'SE592468-182000',
 'SE592515-182020',
 'SE592575-181770',
 'SE592600-181135',
 'SE592600-181600',
 'SE658352-163189',
 'SE658507-162696',
 'SE659024-162417',
 'SE574650-114360',
 'SE639567-310597',
 'SE639762-309800',
 '1',
 '2',
 '3',
 '4',
 '5',
 '6',
 '7',
 '8',
 '9',
 '10',
 '11',
 '12',
 '13',
 '14',
 '15',
 '16',
 '17',
 '18',
 '19',
 '20',
 '21',
 '22',
 '23',
 '24',
 '25',
 '12n',
 '12s',
 '1n',
 '1s']

In [37]:

    

dw_obj.calculate_ek_value(water_body = wb)


    

    




SE583926-161744
['unspecified']
. . . . .
Series([], Name: MIN_NR_YEARS, dtype: object)
0
. . . . .
----
water_body SE583926-161744, type_area 13, variable MIN_NR_YEARS
num 13, suf , suf? ['']
14    3
Name: MIN_NR_YEARS, dtype: object <class 'pandas.core.series.Series'>
['3'] <class 'numpy.ndarray'>
[3]
			din_winter Ek value Calculated
               count      mean       std       min       25%       50%  \
mean_ek_value    4.0  0.307731  0.065462  0.247807  0.263703  0.293995   

                    75%       max all_ok  
mean_ek_value  0.338023  0.395128  False  
SE583926-161744
['unspecified']
. . . . .
Series([], Name: HG_EQR_LIMIT, dtype: object)
0
. . . . .
----
water_body SE583926-161744, type_area 13, variable HG_EQR_LIMIT
num 13, suf , suf? ['']
14    0.8
Name: HG_EQR_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['0.8'] <class 'numpy.ndarray'>
[0.8]
SE583926-161744
['unspecified']
. . . . .
Series([], Name: GM_EQR_LIMIT, dtype: object)
0
. . . . .
----
water_body SE583926-161744, type_area 13, variable GM_EQR_LIMIT
num 13, suf , suf? ['']
14    0.66
Name: GM_EQR_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['0.66'] <class 'numpy.ndarray'>
[0.66]
SE583926-161744
['unspecified']
. . . . .
Series([], Name: MP_EQR_LIMIT, dtype: object)
0
. . . . .
----
water_body SE583926-161744, type_area 13, variable MP_EQR_LIMIT
num 13, suf , suf? ['']
14    0.44
Name: MP_EQR_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['0.44'] <class 'numpy.ndarray'>
[0.44]
SE583926-161744
['unspecified']
. . . . .
Series([], Name: PB_EQR_LIMIT, dtype: object)
0
. . . . .
----
water_body SE583926-161744, type_area 13, variable PB_EQR_LIMIT
num 13, suf , suf? ['']
14    0.29
Name: PB_EQR_LIMIT, dtype: object <class 'pandas.core.series.Series'>
['0.29'] <class 'numpy.ndarray'>
[0.29]

In [35]:

    

dw_obj = w.get_step_object(step = 3, subset = subset_uuid).indicator_objects[indicator]


    

In [40]:

    

dw_obj.water_body_indicator_df[wb]


    

    
Out[40]:






  

    

      

      
SDATE
      
YEAR
      
MONTH
      
POSITION
      
VISS_EU_CD
      
WATER_TYPE_AREA
      
DEPH
      
DIN
      
SALT
      
REFERENCE_VALUE
      
ek_value
    
  
  

    

      
7031
      
2016-12-08
      
2016
      
12
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
5.00
      
6.1
      
2.39187
      
0.478374
    
    

      
7032
      
2016-12-08
      
2016
      
12
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
4.85
      
6.0
      
2.50020
      
0.515505
    
    

      
7033
      
2016-12-08
      
2016
      
12
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
4.92
      
6.0
      
2.50020
      
0.508171
    
    

      
10166
      
2016-02-09
      
2016
      
2
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
10.10
      
5.3
      
3.25851
      
0.322625
    
    

      
10167
      
2016-02-09
      
2016
      
2
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
9.87
      
5.9
      
2.60853
      
0.264289
    
    

      
10168
      
2016-02-09
      
2016
      
2
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
6.95
      
6.5
      
1.95855
      
0.281806
    
    

      
10930
      
2015-12-08
      
2015
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
8.38
      
5.5
      
3.04185
      
0.362989
    
    

      
10931
      
2015-12-08
      
2015
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
8.13
      
NaN
      
NaN
      
NaN
    
    

      
10932
      
2015-12-08
      
2015
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
7.14
      
6.6
      
1.85022
      
0.259134
    
    

      
17901
      
2015-02-09
      
2015
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
13.30
      
5.6
      
2.93352
      
0.220565
    
    

      
17902
      
2015-02-09
      
2015
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
9.36
      
NaN
      
NaN
      
NaN
    
    

      
17903
      
2015-02-09
      
2015
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
7.93
      
6.6
      
1.85022
      
0.233319
    
    

      
18619
      
2014-12-16
      
2014
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
9.37
      
5.0
      
3.58350
      
0.382444
    
    

      
18620
      
2014-12-16
      
2014
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
7.97
      
NaN
      
NaN
      
NaN
    
    

      
18621
      
2014-12-16
      
2014
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
7.19
      
NaN
      
NaN
      
NaN
    
    

      
25719
      
2014-02-25
      
2014
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
25.47
      
2.3
      
6.50841
      
0.255532
    
    

      
25720
      
2014-02-25
      
2014
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
15.03
      
NaN
      
NaN
      
NaN
    
    

      
25721
      
2014-02-25
      
2014
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
6.57
      
NaN
      
NaN
      
NaN
    
    

      
26807
      
2013-12-10
      
2013
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
6.93
      
6.4
      
2.06688
      
0.298251
    
    

      
26808
      
2013-12-10
      
2013
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
6.15
      
NaN
      
NaN
      
NaN
    
    

      
33959
      
2013-02-08
      
2013
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
41.21
      
0.8
      
8.13336
      
0.197364
    
    

      
33960
      
2013-02-08
      
2013
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
14.07
      
NaN
      
NaN
      
NaN
    
  

In [39]:

    

print(dw_obj.classification_results[wb].keys())
for key, value in dw_obj.classification_results[wb].items():
    if key == 'all_data':
        pass
    else:
        print('**************************************')
        print(key)
        print(value)


    

    




dict_keys(['parameter', 'salt_parameter', 'water_body', 'all_data', 'all_ok', 'water_body_status', 'local_EQR', 'global_EQR', 'status', 'mean_EQR_by_date', 'mean_EQR_by_year', 'mean_EQR_by_period', 'number_of_years', 'local_EQR_by_date', 'local_EQR_by_year', 'local_EQR_by_period'])
**************************************
parameter
DIN
**************************************
salt_parameter
SALT
**************************************
water_body
SE583926-161744
**************************************
all_ok
mean_ek_value    False
Name: all_ok, dtype: bool
**************************************
water_body_status
None
**************************************
local_EQR
0.307731457161
**************************************
global_EQR
0.223641942881
**************************************
status
POOR
**************************************
mean_EQR_by_date
        SDATE  YEAR  number_of_values       min       max  mean_ek_value
0  2013-02-08  2013                 1  0.197364  0.197364       0.197364
1  2013-12-10  2013                 1  0.298251  0.298251       0.298251
2  2014-02-25  2014                 1  0.255532  0.255532       0.255532
3  2014-12-16  2014                 1  0.382444  0.382444       0.382444
4  2015-02-09  2015                 2  0.220565  0.233319       0.226942
5  2015-12-08  2015                 2  0.259134  0.362989       0.311062
6  2016-02-09  2016                 3  0.264289  0.322625       0.289573
7  2016-12-08  2016                 3  0.478374  0.515505       0.500683
**************************************
mean_EQR_by_year
      number_of_dates       min       max  mean_ek_value
YEAR                                                    
2013                2  0.197364  0.298251       0.247807
2014                2  0.255532  0.382444       0.318988
2015                2  0.226942  0.311062       0.269002
2016                2  0.289573  0.500683       0.395128
**************************************
mean_EQR_by_period
0.307731457161
**************************************
number_of_years
4.0
**************************************
local_EQR_by_date
        SDATE  YEAR  number_of_values       min       max  mean_ek_value
0  2013-02-08  2013                 1  0.197364  0.197364       0.197364
1  2013-12-10  2013                 1  0.298251  0.298251       0.298251
2  2014-02-25  2014                 1  0.255532  0.255532       0.255532
3  2014-12-16  2014                 1  0.382444  0.382444       0.382444
4  2015-02-09  2015                 2  0.220565  0.233319       0.226942
5  2015-12-08  2015                 2  0.259134  0.362989       0.311062
6  2016-02-09  2016                 3  0.264289  0.322625       0.289573
7  2016-12-08  2016                 3  0.478374  0.515505       0.500683
**************************************
local_EQR_by_year
      number_of_dates       min       max  mean_ek_value
YEAR                                                    
2013                2  0.197364  0.298251       0.247807
2014                2  0.255532  0.382444       0.318988
2015                2  0.226942  0.311062       0.269002
2016                2  0.289573  0.500683       0.395128
**************************************
local_EQR_by_period
0.307731457161

In [98]:

    

dw_obj.classification_results[wb]['mean_EQR_by_year']#.dropna(subset = ['mean_ek_value'])


    

    
Out[98]:






  

    

      

      
number_of_dates
      
min
      
max
      
mean_ek_value
    
    

      
YEAR
      

      

      

      

    
  
  

    

      
2013
      
2
      
0.197364
      
0.298251
      
0.247807
    
    

      
2014
      
2
      
0.255532
      
0.382444
      
0.318988
    
    

      
2015
      
2
      
0.226942
      
0.311062
      
0.269002
    
    

      
2016
      
2
      
0.289573
      
0.500683
      
0.395128
    
  

In [100]:

    

dw_obj.water_body_indicator_df[wb].dropna(subset = ['REFERENCE_VALUE'])


    

    
Out[100]:






  

    

      

      
SDATE
      
YEAR
      
MONTH
      
POSITION
      
VISS_EU_CD
      
WATER_TYPE_AREA
      
DEPH
      
DIN
      
SALT
      
REFERENCE_VALUE
      
ek_value
    
  
  

    

      
7031
      
2016-12-08
      
2016
      
12
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
5.00
      
6.1
      
2.39187
      
0.478374
    
    

      
7032
      
2016-12-08
      
2016
      
12
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
4.85
      
6.0
      
2.50020
      
0.515505
    
    

      
7033
      
2016-12-08
      
2016
      
12
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
4.92
      
6.0
      
2.50020
      
0.508171
    
    

      
10166
      
2016-02-09
      
2016
      
2
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
10.10
      
5.3
      
3.25851
      
0.322625
    
    

      
10167
      
2016-02-09
      
2016
      
2
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
5.0
      
9.87
      
5.9
      
2.60853
      
0.264289
    
    

      
10168
      
2016-02-09
      
2016
      
2
      
58.64_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
6.95
      
6.5
      
1.95855
      
0.281806
    
    

      
10930
      
2015-12-08
      
2015
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
8.38
      
5.5
      
3.04185
      
0.362989
    
    

      
10932
      
2015-12-08
      
2015
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
7.14
      
6.6
      
1.85022
      
0.259134
    
    

      
17901
      
2015-02-09
      
2015
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
13.30
      
5.6
      
2.93352
      
0.220565
    
    

      
17903
      
2015-02-09
      
2015
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
10.0
      
7.93
      
6.6
      
1.85022
      
0.233319
    
    

      
18619
      
2014-12-16
      
2014
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
9.37
      
5.0
      
3.58350
      
0.382444
    
    

      
25719
      
2014-02-25
      
2014
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
25.47
      
2.3
      
6.50841
      
0.255532
    
    

      
26807
      
2013-12-10
      
2013
      
12
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
6.93
      
6.4
      
2.06688
      
0.298251
    
    

      
33959
      
2013-02-08
      
2013
      
2
      
58.65_16.39
      
SE583926-161744
      
13 - Östergötlands inre kustvatten
      
0.5
      
41.21
      
0.8
      
8.13336
      
0.197364
    
  

In [30]:

    

def get_EK(x):
    y = x.DIN/x.REFERENCE_VALUE
    if y > 1:
        return 1
    else:
        return y

df = dw_obj.water_body_indicator_df[wb]
df['ek_value'] = df.apply(get_EK, axis = 1)


    

In [31]:

    

df[dw_obj.indicator_parameter]/df.REFERENCE_VALUE


    

    
Out[31]:





7057     1.002526
7058     1.011999
7059     1.075150
7349     1.193559
7350     0.852542
7351     0.895169
10343    1.795074
10344    1.262979
10345    1.235931
11065    0.914580
11066    0.937210
11067    0.911079
17831    1.114502
17832    1.042739
17833    1.155796
18397         NaN
18398         NaN
18399         NaN
18916    0.851515
18917    0.845883
18918    0.874886
26382    1.205698
26383    1.058389
26384    1.466357
27000    0.954291
27001    0.969711
27002    0.985130
34344    0.907042
34345    1.289190
34346    1.481507
dtype: float64

In [32]:

    

by_date = df.groupby(['SDATE', 'YEAR'],).ek_value.agg(['count', 'min', 'max', 'mean']).reset_index()
# by_date.to_csv(self.paths['results'] +'/' + self.name + water_body +'by_occation.txt', sep='\t')
by_date.rename(columns={'mean':'mean_ek_value', 'count': 'number_of_values'}, inplace=True) # Cant use "mean" below
by_date


    

    
Out[32]:






  

    

      

      
SDATE
      
YEAR
      
number_of_values
      
min
      
max
      
mean_ek_value
    
  
  

    

      
0
      
2013-01-15
      
2013
      
3
      
0.907042
      
1.000000
      
0.969014
    
    

      
1
      
2013-12-04
      
2013
      
3
      
0.954291
      
0.985130
      
0.969711
    
    

      
2
      
2014-01-16
      
2014
      
3
      
1.000000
      
1.000000
      
1.000000
    
    

      
3
      
2014-12-03
      
2014
      
3
      
0.845883
      
0.874886
      
0.857428
    
    

      
4
      
2015-01-14
      
2015
      
0
      
NaN
      
NaN
      
NaN
    
    

      
5
      
2015-02-11
      
2015
      
3
      
1.000000
      
1.000000
      
1.000000
    
    

      
6
      
2015-12-02
      
2015
      
3
      
0.911079
      
0.937210
      
0.920956
    
    

      
7
      
2016-01-27
      
2016
      
3
      
1.000000
      
1.000000
      
1.000000
    
    

      
8
      
2016-11-09
      
2016
      
3
      
0.852542
      
1.000000
      
0.915904
    
    

      
9
      
2016-12-07
      
2016
      
3
      
1.000000
      
1.000000
      
1.000000
    
  

In [34]:

    

# Remove occations with not enough samples
# Or use count as a flag for what to display for the user?
by_date['all_ok'] = True
ix = by_date.loc[by_date['number_of_values'] < 1, 'all_ok'].index
by_date.set_value(ix, 'all_ok', False)


    

    
Out[34]:






  

    

      

      
SDATE
      
YEAR
      
number_of_values
      
min
      
max
      
mean_ek_value
      
all_ok
    
  
  

    

      
0
      
2013-01-15
      
2013
      
3
      
0.907042
      
1.000000
      
0.969014
      
True
    
    

      
1
      
2013-12-04
      
2013
      
3
      
0.954291
      
0.985130
      
0.969711
      
True
    
    

      
2
      
2014-01-16
      
2014
      
3
      
1.000000
      
1.000000
      
1.000000
      
True
    
    

      
3
      
2014-12-03
      
2014
      
3
      
0.845883
      
0.874886
      
0.857428
      
True
    
    

      
4
      
2015-01-14
      
2015
      
0
      
NaN
      
NaN
      
NaN
      
False
    
    

      
5
      
2015-02-11
      
2015
      
3
      
1.000000
      
1.000000
      
1.000000
      
True
    
    

      
6
      
2015-12-02
      
2015
      
3
      
0.911079
      
0.937210
      
0.920956
      
True
    
    

      
7
      
2016-01-27
      
2016
      
3
      
1.000000
      
1.000000
      
1.000000
      
True
    
    

      
8
      
2016-11-09
      
2016
      
3
      
0.852542
      
1.000000
      
0.915904
      
True
    
    

      
9
      
2016-12-07
      
2016
      
3
      
1.000000
      
1.000000
      
1.000000
      
True
    
  

In [35]:

    

"""
2) Medelvärdet av EK för varje parameter beräknas för varje år.
"""
by_year = by_date.groupby('YEAR').mean_ek_value.agg(['count', 'min', 'max', 'mean']).reset_index()
by_year.rename(columns={'mean':'mean_ek_value', 'count': 'number_of_dates'}, inplace=True)
by_year['all_ok'] = True
by_year.loc[by_year['number_of_dates'] < 1, 'all_ok'] = False
# by_year.to_csv(self.paths['results'] +'/' + self.name + water_body + 'by_year.txt', sep='\t')
by_year


    

    
Out[35]:






  

    

      

      
YEAR
      
number_of_dates
      
min
      
max
      
mean_ek_value
      
all_ok
    
  
  

    

      
0
      
2013
      
2
      
0.969014
      
0.969711
      
0.969362
      
True
    
    

      
1
      
2014
      
2
      
0.857428
      
1.000000
      
0.928714
      
True
    
    

      
2
      
2015
      
2
      
0.920956
      
1.000000
      
0.960478
      
True
    
    

      
3
      
2016
      
3
      
0.915904
      
1.000000
      
0.971968
      
True
    
  

In [36]:

    

by_period = by_year[['mean_ek_value']].describe()
by_period


    

    
Out[36]:






  

    

      

      
mean_ek_value
    
  
  

    

      
count
      
4.000000
    
    

      
mean
      
0.957631
    
    

      
std
      
0.019895
    
    

      
min
      
0.928714
    
    

      
25%
      
0.952537
    
    

      
50%
      
0.964920
    
    

      
75%
      
0.970014
    
    

      
max
      
0.971968
    
  

In [37]:

    

"""
3) Medelvärdet av EK för varje parameter och vattenförekomst (beräknas för minst
en treårsperiod)
"""
by_period = by_year[['mean_ek_value']].describe()#.agg(['count', 'min', 'max', 'mean'])
by_period = by_period.transpose()
#by_period#.loc['mean', 'mean_ek_value']
#
#by_period['count'].get_value('mean_ek_value')
by_period['all_ok']  = True
if by_period['count'].get_value('mean_ek_value') < 3:
    by_period['all_ok'] = False

by_period


    

    
Out[37]:






  

    

      

      
count
      
mean
      
std
      
min
      
25%
      
50%
      
75%
      
max
      
all_ok
    
  
  

    

      
mean_ek_value
      
4.0
      
0.957631
      
0.019895
      
0.928714
      
0.952537
      
0.96492
      
0.970014
      
0.971968
      
True
    
  

In [40]:

    

by_period['mean'].get_value('mean_ek_value')


    

    
Out[40]:





0.95763063655011882

In [29]:

    

temp_df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
...                   index=pd.date_range('1/1/2000', periods=10))
temp_df.iloc[3:7] = np.nan
temp_df.describe()


    

    
Out[29]:






  

    

      

      
A
      
B
      
C
    
  
  

    

      
count
      
6.000000
      
6.000000
      
6.000000
    
    

      
mean
      
0.206290
      
0.471121
      
-0.242431
    
    

      
std
      
0.586035
      
0.679945
      
1.721820
    
    

      
min
      
-0.333494
      
-0.641210
      
-2.997678
    
    

      
25%
      
-0.286442
      
0.214068
      
-1.103137
    
    

      
50%
      
0.056126
      
0.508677
      
0.315088
    
    

      
75%
      
0.657880
      
0.929940
      
0.644624
    
    

      
max
      
1.001151
      
1.264358
      
1.734323
    
  

In [30]:

    

def set_above_one_value(x):
    if x > 1:
        return 1
    else:
        return x
dw_obj.water_body_indicator_df[wb]['EK'] = dw_obj.water_body_indicator_df[wb]['DIN']/dw_obj.water_body_indicator_df[wb]['REFERENCE_VALUE']
dw_obj.water_body_indicator_df[wb]['EK'] = dw_obj.water_body_indicator_df[wb]['EK'].apply(set_above_one_value)
dw_obj.water_body_indicator_df[wb]['EK']


    

    
Out[30]:





7057     1.000000
7058     1.000000
7059     1.000000
7349     1.000000
7350     0.852542
7351     0.895169
10343    1.000000
10344    1.000000
10345    1.000000
11065    0.914580
11066    0.937210
11067    0.911079
17831    1.000000
17832    1.000000
17833    1.000000
18397         NaN
18398         NaN
18399         NaN
18916    0.851515
18917    0.845883
18918    0.874886
26382    1.000000
26383    1.000000
26384    1.000000
27000    0.954291
27001    0.969711
27002    0.985130
34344    0.907042
34345    1.000000
34346    1.000000
Name: EK, dtype: float64

In [31]:

    

dw_obj.get_filtered_data(subset = subset_uuid, step = 'step_2', type_area = 22, indicator = 'din_winter')[['DIN']].dropna()


    

    
Out[31]:






  

    

      

      
DIN
    
  
  

    

      
5199
      
4.68
    
    

      
5200
      
4.55
    
    

      
5201
      
4.54
    
    

      
5202
      
4.23
    
    

      
5203
      
4.42
    
    

      
5204
      
4.19
    
    

      
5205
      
5.59
    
    

      
5206
      
8.84
    
    

      
5207
      
6.77
    
    

      
5208
      
5.68
    
    

      
5209
      
4.39
    
    

      
5210
      
4.27
    
    

      
5211
      
4.63
    
    

      
5212
      
4.59
    
    

      
5213
      
4.59
    
    

      
5214
      
5.08
    
    

      
5215
      
5.34
    
    

      
5216
      
4.60
    
    

      
5217
      
4.31
    
    

      
5218
      
4.19
    
    

      
5219
      
4.60
    
    

      
5220
      
15.76
    
    

      
5221
      
8.59
    
    

      
5222
      
6.40
    
    

      
5223
      
6.46
    
    

      
5224
      
5.67
    
    

      
5225
      
5.34
    
    

      
5226
      
4.62
    
    

      
5227
      
5.18
    
    

      
5228
      
5.18
    
    

      
...
      
...
    
    

      
34530
      
11.72
    
    

      
34531
      
4.89
    
    

      
34532
      
4.85
    
    

      
34533
      
4.39
    
    

      
34534
      
4.22
    
    

      
34535
      
9.33
    
    

      
34536
      
10.92
    
    

      
34537
      
8.57
    
    

      
34538
      
5.36
    
    

      
34539
      
3.93
    
    

      
34540
      
5.71
    
    

      
34541
      
7.14
    
    

      
34542
      
35.71
    
    

      
34543
      
3.93
    
    

      
34544
      
4.14
    
    

      
34545
      
26.35
    
    

      
34546
      
11.60
    
    

      
34547
      
8.32
    
    

      
34548
      
8.46
    
    

      
34549
      
8.60
    
    

      
34550
      
5.55
    
    

      
34551
      
5.39
    
    

      
34552
      
5.42
    
    

      
34553
      
5.40
    
    

      
34554
      
6.17
    
    

      
34555
      
8.48
    
    

      
34556
      
11.34
    
    

      
34557
      
15.83
    
    

      
34558
      
11.80
    
    

      
34559
      
3.85
    
  

18009 rows × 1 columns

In [120]:

    

B2_NTOT_WINTER_SETTINGS = lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_ref_settings['ntot_winter']
lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_ref_settings['ntot_winter'].allowed_variables
# gör om till
# lv_workspace.get_indicator_ref_settings(step = , subset = , indicator = , waterbody/type)
# ger samma resultat som:
#lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_ref_settings['ntot_winter'].settings.ref_columns

lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_ref_settings['ntot_winter'].settings.get_value('EK G/M', 22)
#print(B2_NTOT_WINTER_SETTINGS)
#B2_NTOT_WINTER_SETTINGS.get_value('2', 'DEPTH_INTERVAL')


    

    
Out[120]:





'0.85'

In [124]:

    

av = lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_data_filter_settings['ntot_winter'].allowed_variables
lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_data_filter_settings['ntot_winter'].settings.df[av]


    

    
Out[124]:






  

    

      

      
DEPH_INTERVAL
      
MONTH_LIST
    
  
  

    

      
0
      
0-10
      
1;2;3;12
    
    

      
1
      
0-10
      
1;2;3;12
    
    

      
2
      
0-10
      
1;2;3;12
    
    

      
3
      
0-10
      
1;2;3;12
    
    

      
4
      
0-10
      
1;2;3;12
    
    

      
5
      
0-10
      
1;2;3;12
    
    

      
6
      
0-10
      
1;2;3;12
    
    

      
7
      
0-10
      
1;2;12
    
    

      
8
      
0-10
      
1;2;12
    
    

      
9
      
0-10
      
1;2;12
    
    

      
10
      
0-10
      
1;2;12
    
    

      
11
      
0-10
      
1;2;12
    
    

      
12
      
0-10
      
1;2;12
    
    

      
13
      
0-10
      
1;2;12
    
    

      
14
      
0-10
      
1;2;12
    
    

      
15
      
0-10
      
1;2;12
    
    

      
16
      
0-10
      
1;2;12
    
    

      
17
      
0-10
      
1;2;11;12
    
    

      
18
      
0-10
      
1;2;11;12
    
    

      
19
      
0-10
      
1;2;11;12
    
    

      
20
      
0-10
      
1;2;11;12
    
    

      
21
      
0-10
      
1;2;11;12
    
    

      
22
      
0-10
      
1;2;11;12
    
    

      
23
      
0-10
      
1;2;11;12
    
    

      
24
      
0-10
      
1;2;11;12
    
    

      
25
      
0-10
      
1;2;12
    
    

      
26
      
0-10
      
1;2;3;12
    
  

In [119]:

    

lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_data_filter_settings['ntot_winter'].settings.df


    

    
Out[119]:






  

    

      

      
TYPE_AREA_NUMBER
      
TYPE_AREA_SUFFIX
      
DEPH_INTERVAL
      
MIN_NR_YEARS
      
MIN_NR_VALUES
      
TIME_DELTA_TOLERANCE
      
POS_RADIUS_TOLERANCE
      
DEPH_TOLERANCE
      
MONTH_LIST
      
EKV REF
      
EKV H/G
      
EKV G/M
      
EKV M/O
      
EKV O/D
      
EK H/G
      
EK G/M
      
EK M/O
      
EK O/D
      
SALINITY MAX
    
  
  

    

      
0
      
1
      
n
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;3;12
      
-0.630*s+36
      
-0.715*s+40.86
      
-0.799*s+45.72
      
-1.0546*s+60.3
      
-1.480*s+84.6
      
0.88
      
0.79
      
0.6
      
0.43
      
27
    
    

      
1
      
1
      
s
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;3;12
      
-0.65*s+30
      
-0.738*s+34.05
      
-0.8255*s+38.1
      
-1.0888*s+50.25
      
-1.528*s+70.5
      
0.88
      
0.79
      
0.6
      
0.43
      
20
    
    

      
2
      
2
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;3;12
      
-0.630*s+36
      
-0.715*s+40.86
      
-0.799*s+45.72
      
-1.0546*s+60.3
      
-1.480*s+84.6
      
0.88
      
0.79
      
0.6
      
0.43
      
27
    
    

      
3
      
3
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;3;12
      
-0.630*s+36
      
-0.715*s+40.86
      
-0.799*s+45.72
      
-1.0546*s+60.3
      
-1.480*s+84.6
      
0.88
      
0.79
      
0.6
      
0.43
      
27
    
    

      
4
      
4
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;3;12
      
-0.65*s+30
      
-0.738*s+34.05
      
-0.8255*s+38.1
      
-1.0888*s+50.25
      
-1.528*s+70.5
      
0.88
      
0.79
      
0.6
      
0.43
      
20
    
    

      
5
      
5
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;3;12
      
0*s+17
      
0*s+19.295
      
0*s+21.59
      
0*s+28.475
      
0*s+39.95
      
0.89
      
0.77
      
0.61
      
0.43
      
20
    
    

      
6
      
6
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;3;12
      
0*s+17
      
0*s+19.295
      
0*s+21.59
      
0*s+28.475
      
0*s+39.95
      
0.89
      
0.77
      
0.61
      
0.43
      
20
    
    

      
7
      
7
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;12
      
-6*s+59
      
-6.6*s+64.9
      
-7.2*s+70.8
      
-9*s+88.5
      
-12*s+118
      
0.91
      
0.84
      
0.67
      
0.5
      
7
    
    

      
8
      
8
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;12
      
-6*s+59
      
-6.6*s+64.9
      
-7.2*s+70.8
      
-9*s+88.5
      
-12*s+118
      
0.91
      
0.84
      
0.67
      
0.5
      
7
    
    

      
9
      
9
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;12
      
-6*s+59
      
-6.6*s+64.9
      
-7.2*s+70.8
      
-9*s+88.5
      
-12*s+118
      
0.91
      
0.84
      
0.67
      
0.5
      
7
    
    

      
10
      
10
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;12
      
0*s+17
      
0*s+18.7
      
0*s+20.4
      
0*s+25.5
      
0*s+34
      
0.89
      
0.85
      
0.65
      
0.5
      
7
    
    

      
11
      
11
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;12
      
0*s+17
      
0*s+18.7
      
0*s+20.4
      
0*s+25.5
      
0*s+34
      
0.89
      
0.85
      
0.65
      
0.5
      
7
    
    

      
12
      
12
      
s
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;12
      
-2.833*s+34
      
-3.1167*s+37.4
      
-3.4*s+40.8
      
-4.25*s+51
      
-5.6667*s+68
      
0.91
      
0.83
      
0.66
      
0.5
      
6
    
    

      
13
      
12
      
n
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;12
      
-1*s+23
      
-1.1*s+25.3
      
-1.2*s+27.6
      
-1.5*s+34.5
      
-2*s+46
      
0.93
      
0.85
      
0.68
      
0.51
      
6
    
    

      
14
      
13
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;12
      
-2.833*s+34
      
-3.1167*s+37.4
      
-3.4*s+40.8
      
-4.25*s+51
      
-5.6667*s+68
      
0.91
      
0.83
      
0.66
      
0.5
      
6
    
    

      
15
      
14
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;12
      
-2.833*s+34
      
-3.1167*s+37.4
      
-3.4*s+40.8
      
-4.25*s+51
      
-5.6667*s+68
      
0.91
      
0.83
      
0.66
      
0.5
      
6
    
    

      
16
      
15
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;12
      
-1*s+23
      
-1.1*s+25.3
      
-1.2*s+27.6
      
-1.5*s+34.5
      
-2*s+46
      
0.93
      
0.85
      
0.68
      
0.51
      
6
    
    

      
17
      
16
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;11;12
      
-1*s+23
      
-1.1*s+25.3
      
-1.2*s+27.6
      
-1.5*s+34.5
      
-2*s+46
      
0.93
      
0.85
      
0.68
      
0.51
      
5
    
    

      
18
      
17
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;11;12
      
-1*s+23
      
-1.1*s+25.3
      
-1.2*s+27.6
      
-1.5*s+34.5
      
-2*s+46
      
0.93
      
0.85
      
0.68
      
0.51
      
5
    
    

      
19
      
18
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;11;12
      
-0.4*s+20
      
-0.44*s+22
      
-0.48*s+24
      
-0.6*s+30
      
-0.8*s+40
      
0.91
      
0.83
      
0.66
      
0.5
      
5
    
    

      
20
      
19
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;11;12
      
-0.4*s+20
      
-0.44*s+22
      
-0.48*s+24
      
-0.6*s+30
      
-0.8*s+40
      
0.91
      
0.83
      
0.66
      
0.5
      
5
    
    

      
21
      
20
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;11;12
      
-0.6*s+21
      
-0.66*s+23.1
      
-0.72*s+25.2
      
-0.9*s+31.5
      
-1.2*s+42
      
0.91
      
0.83
      
0.67
      
0.5
      
5
    
    

      
22
      
21
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;11;12
      
-0.6*s+21
      
-0.66*s+23.1
      
-0.72*s+25.2
      
-0.9*s+31.5
      
-1.2*s+42
      
0.91
      
0.83
      
0.67
      
0.5
      
5
    
    

      
23
      
22
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;11;12
      
-1*s+21
      
-1.09*s+22.89
      
-1.18*s+24.78
      
-1.45*s+30.45
      
-1.9*s+39.9
      
0.93
      
0.85
      
0.68
      
0.51
      
3
    
    

      
24
      
23
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;11;12
      
-1*s+21
      
-1.09*s+22.89
      
-1.18*s+24.78
      
-1.45*s+30.45
      
-1.9*s+39.9
      
0.93
      
0.85
      
0.68
      
0.51
      
3
    
    

      
25
      
24
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;12
      
-1*s+23
      
-1.1*s+25.3
      
-1.2*s+27.6
      
-1.5*s+34.5
      
-2*s+46
      
0.93
      
0.85
      
0.68
      
0.51
      
6
    
    

      
26
      
25
      
NaN
      
0-10
      
3
      
3
      
3
      
0.1
      
5
      
1;2;3;12
      
-0.65*s+30
      
-0.738*s+34.05
      
-0.8255*s+38.1
      
-1.0888*s+50.25
      
-1.528*s+70.5
      
0.88
      
0.79
      
0.6
      
0.43
      
20
    
  

In [67]:

    

B2_NTOT_WINTER_SETTINGS.settings.mapping_water_body['N m Bottenvikens kustvatten']


    

    
Out[67]:





{'BASIN_NUMBER': '110002.0',
 'CENTER_LAT': '64° 42\' 35,349"',
 'CENTER_LON': '21° 24\' 5,383"',
 'EU_CD': 'SE648760-213140',
 'HID': '649640-214530',
 'TYPE_AREA_NUMBER': '23',
 'TYPE_AREA_SUFFIX': '',
 'URL_VISS': 'http://www.viss.lansstyrelsen.se/waters.aspx?waterEUID=SE648760-213140'}

In [35]:

    

f1_A = lv_workspace.get_data_filter_object(step=1, subset='A') 
f1_A.include_list_filter


    

    
Out[35]:





{'MYEAR': ['2016', '2017'],
 'SEA_AREA_NAME': ['Gullmarn centralbassäng', 'Rivö fjord'],
 'STATN': ['BJÖRKHOLMEN']}

In [136]:

    

lv_workspace.get_data_filter_info(step=1, subset='A')


    

    
Out[136]:





{'exclude_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN'],
 'include_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN']}

In [36]:

    

f1_A.exclude_list_filter


    

    
Out[36]:





{'MYEAR': ['2015', '2016'], 'SEA_AREA_NAME': [], 'STATN': ['SLÄGGÖ']}

In [21]:

    

f0.include_list_filter


    

    
Out[21]:





{'MYEAR': ['2016', '2017'],
 'SEA_AREA_NAME': ['Byfjorden',
  'Gullmarn centralbassäng',
  'Havstensfjorden',
  'Rivö fjord'],
 'STATN': []}

In [22]:

    

lv_workspace.apply_subset_filter(subset='A') # Not handled properly by the IndexHandler


    

    
Out[22]:





True

In [23]:

    

data_after_subset_filter = lv_workspace.get_filtered_data(level=1, subset='A') # level=0 means first filter 
print('{} rows mathing the filter criteria'.format(len(data_after_subset_filter)))
data_after_subset_filter.head()


    

    




198 rows mathing the filter criteria






    
Out[23]:






  

    

      

      
AMON
      
BQIm
      
CPHL
      
DEPH
      
DOXY_BTL
      
DOXY_CTD
      
LATIT_DD
      
LONGI_DD
      
MNDEP
      
MXDEP
      
...
      
SERNO
      
SHARKID_MD5
      
SHIPC
      
STATN
      
STIME
      
TEMP_BTL
      
TEMP_CTD
      
WATER_DISTRICT
      
WATER_TYPE_AREA
      
WLTYP
    
  
  

    

      
2628
      
0.76
      
NaN
      
0.6
      
0.0
      
7.05
      

      
58.38767
      
11.62667
      
NaN
      
NaN
      
...
      
8.0
      
NaN
      
77SN
      
BJÖRKHOLMEN
      
17:30
      
4.96
      
4.84
      
Västerhavets vattendistrikt
      
02 - Västkustens fjordar
      
2 - Havsområde innanför 1 NM
    
    

      
2629
      
0.72
      
NaN
      
0.5
      
2.0
      
7.12
      

      
58.38767
      
11.62667
      
NaN
      
NaN
      
...
      
8.0
      
NaN
      
77SN
      
BJÖRKHOLMEN
      
17:30
      
4.93
      
4.84
      
Västerhavets vattendistrikt
      
02 - Västkustens fjordar
      
2 - Havsområde innanför 1 NM
    
    

      
2630
      
0.74
      
NaN
      
0.6
      
5.0
      
7.16
      

      
58.38767
      
11.62667
      
NaN
      
NaN
      
...
      
8.0
      
NaN
      
77SN
      
BJÖRKHOLMEN
      
17:30
      
4.88
      
4.84
      
Västerhavets vattendistrikt
      
02 - Västkustens fjordar
      
2 - Havsområde innanför 1 NM
    
    

      
2631
      
0.65
      
NaN
      
0.5
      
10.0
      
7.11
      

      
58.38767
      
11.62667
      
NaN
      
NaN
      
...
      
8.0
      
NaN
      
77SN
      
BJÖRKHOLMEN
      
17:30
      
5.12
      
4.86
      
Västerhavets vattendistrikt
      
02 - Västkustens fjordar
      
2 - Havsområde innanför 1 NM
    
    

      
2632
      
0.46
      
NaN
      
0.3
      
15.0
      
6.86
      

      
58.38767
      
11.62667
      
NaN
      
NaN
      
...
      
8.0
      
NaN
      
77SN
      
BJÖRKHOLMEN
      
17:30
      
5.52
      
5.10
      
Västerhavets vattendistrikt
      
02 - Västkustens fjordar
      
2 - Havsområde innanför 1 NM
    
  

5 rows × 46 columns

In [24]:

    

# show available waterbodies
lst = data_after_subset_filter.SEA_AREA_NAME.unique()
print('Waterbodies in subset:\n{}'.format('\n'.join(lst)))


    

    




Waterbodies in subset:
Gullmarn centralbassäng

In [24]:

    

import numpy as np
np.where(lv_workspace.index_handler.subset_filter)


    

    
Out[24]:





(array([2629, 2630, 2631, 2632, 2633, 2634, 2635, 2636, 2637, 2638, 2639,
        2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764,
        2870, 2871, 2872, 2873, 2874, 2875, 2876, 2877, 2878, 2879, 2880,
        2952, 2953, 2954, 2955, 2956, 2957, 2958, 2959, 2960, 2961, 2962,
        3077, 3078, 3079, 3080, 3081, 3082, 3083, 3084, 3085, 3086, 3087,
        3185, 3186, 3187, 3188, 3189, 3190, 3191, 3192, 3193, 3194, 3195,
        3282, 3283, 3284, 3285, 3286, 3287, 3288, 3289, 3290, 3291, 3292,
        3409, 3410, 3411, 3412, 3413, 3414, 3415, 3416, 3417, 3418, 3419,
        3493, 3494, 3495, 3496, 3497, 3498, 3499, 3500, 3501, 3502, 3503,
        3624, 3625, 3626, 3627, 3628, 3629, 3630, 3631, 3632, 3633, 3634,
        3717, 3718, 3719, 3720, 3721, 3722, 3723, 3724, 3725, 3726, 3727,
        3859, 3860, 3861, 3862, 3863, 3864, 3865, 3866, 3867, 3868, 3869,
        3979, 3980, 3981, 3982, 3983, 3984, 3985, 3986, 3987, 3988, 3989,
        4110, 4111, 4112, 4113, 4114, 4115, 4116, 4117, 4118, 4119, 4120,
        4265, 4266, 4267, 4268, 4269, 4270, 4271, 4272, 4273, 4274, 4275,
        4354, 4355, 4356, 4357, 4358, 4359, 4360, 4361, 4362, 4363, 4364,
        4502, 4503, 4504, 4505, 4506, 4507, 4508, 4509, 4510, 4511, 4512,
        4806, 4807, 4808, 4809, 4810, 4811, 4812, 4813, 4814, 4815, 4816], dtype=int64),)

In [25]:

    

f = lv_workspace.get_data_filter_object(step=1, subset='A')


    

In [26]:

    

f.all_filters


    

    
Out[26]:





{'exclude_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN'],
 'include_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN']}

In [27]:

    

f.exclude_list_filter


    

    
Out[27]:





{'MYEAR': ['2015', '2016'], 'SEA_AREA_NAME': [], 'STATN': ['SLÄGGÖ']}

In [28]:

    

f.include_list_filter


    

    
Out[28]:





{'MYEAR': ['2016', '2017'],
 'SEA_AREA_NAME': ['Gullmarn centralbassäng', 'Rivö fjord'],
 'STATN': ['BJÖRKHOLMEN']}

In [29]:

    

s = lv_workspace.get_step_1_object('A')


    

In [30]:

    

s.data_filter.all_filters


    

    
Out[30]:





{'exclude_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN'],
 'include_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN']}

In [31]:

    

f0 = lv_workspace.get_data_filter_object(step=0)


    

In [32]:

    

f0.exclude_list_filter


    

    
Out[32]:





{'MYEAR': ['2015', '2016'], 'SEA_AREA_NAME': [], 'STATN': []}

In [33]:

    

f0.include_list_filter


    

    
Out[33]:





{'MYEAR': ['2016', '2017'],
 'SEA_AREA_NAME': ['Byfjorden',
  'Gullmarn centralbassäng',
  'Havstensfjorden',
  'Rivö fjord'],
 'STATN': []}

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lv_workspace.initiate_quality_factors()


    

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