notebook.community



In [1]:

    
# 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))









    



D:\github\ekostat_calculator



In [2]:

    
import pandas as pd
import numpy as np
import json
import timeit

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[2]:





'0.19.2'



In [3]:

    
def print_workspaces():
    request = {'user_id': user_id} 
    respons = ekos.request_workspace_list(request)
    print('')
    print('='*100)
    print('Workspaces for user: {}'.format(user_id)) 
    print('')
    for item in respons['workspaces']:
        print('-'*100)
        for key in sorted(item.keys()):
            print('{}:\t{}'.format(key, item[key]))
        print('')
    print('='*100)
    
    
def print_json(data): 
    json_string = json.dumps(data, indent=2, sort_keys=True)
    print(json_string)

Load directories



In [4]:

    
root_directory = "../" #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'

Initiate EventHandler



In [144]:

    
ekos = EventHandler(root_directory)









    



2018-04-20 16:51:31,444	event_handler.py	64	__init__	DEBUG	Start EventHandler: event_handler
2018-04-20 16:51:31,449	event_handler.py	65	__init__	DEBUG	
2018-04-20 16:51:31,458	event_handler.py	66	__init__	INFO	TEST info logger
2018-04-20 16:51:31,462	event_handler.py	67	__init__	WARNING	TEST warning logger
2018-04-20 16:51:31,472	event_handler.py	68	__init__	ERROR	TEST error logger
2018-04-20 16:51:31,476	event_handler.py	69	__init__	DEBUG	TEST debug logger






    



¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
dict_keys(['main_event_handler', 'subset_30062c90-2a60-4ee1-9944-f00329db1174', 'subset_default_subset', 'workspace_147f5d47-773c-43f0-b337-57208718d0cf'])
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
event_handler _ main_event_handler

Load existing workspace



In [145]:

    
ekos.load_workspace(user_id, alias = workspace_alias)
# Här får jag ofta felmeddelande:
# AttributeError: module 'core' has no attribute 'ParameterMapping'









    



2018-04-20 16:51:34,559	event_handler.py	1483	load_workspace	DEBUG	Trying to load workspace "147f5d47-773c-43f0-b337-57208718d0cf" with alias "lena_indicator"






    



====================================================================================================
Initiating WorkSpace: ..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf
Parent directory is: ..//workspaces
Resource directory is: ..//resources
=== 30062c90-2a60-4ee1-9944-f00329db1174
status ['editable', 'readable', 'deleted']
!!! A
!!! 30062c90-2a60-4ee1-9944-f00329db1174
!!! ..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets
----------------------------------------------------------------------------------------------------
Initiating Subset: ..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/30062c90-2a60-4ee1-9944-f00329db1174
===
..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/30062c90-2a60-4ee1-9944-f00329db1174
load_water_body_station_filter
Initiating WorkStep: ..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/30062c90-2a60-4ee1-9944-f00329db1174/step_1
load_water_body_station_filter
Initiating WorkStep: ..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/30062c90-2a60-4ee1-9944-f00329db1174/step_2
load_water_body_station_filter
Initiating WorkStep: ..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/30062c90-2a60-4ee1-9944-f00329db1174/step_3
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
dict_keys(['main_event_handler', 'subset_30062c90-2a60-4ee1-9944-f00329db1174', 'subset_default_subset', 'workspace_147f5d47-773c-43f0-b337-57208718d0cf'])
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
30062c90-2a60-4ee1-9944-f00329db1174 _ main_event_handler
30062c90-2a60-4ee1-9944-f00329db1174 _ subset_30062c90-2a60-4ee1-9944-f00329db1174
=== default_subset
!!! default_subset
!!! default_subset
!!! ..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets
----------------------------------------------------------------------------------------------------
Initiating Subset: ..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/default_subset
===
..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/default_subset
load_water_body_station_filter
Initiating WorkStep: ..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/default_subset/step_1
load_water_body_station_filter
Initiating WorkStep: ..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/default_subset/step_2
load_water_body_station_filter
Initiating WorkStep: ..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/subsets/default_subset/step_3
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
dict_keys(['main_event_handler', 'subset_30062c90-2a60-4ee1-9944-f00329db1174', 'subset_default_subset', 'workspace_147f5d47-773c-43f0-b337-57208718d0cf'])
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
default_subset _ main_event_handler
default_subset _ subset_30062c90-2a60-4ee1-9944-f00329db1174
default_subset _ subset_default_subset
load_water_body_station_filter
Initiating WorkStep: ..//workspaces/147f5d47-773c-43f0-b337-57208718d0cf/step_0
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
dict_keys(['main_event_handler', 'subset_30062c90-2a60-4ee1-9944-f00329db1174', 'subset_default_subset', 'workspace_147f5d47-773c-43f0-b337-57208718d0cf'])
¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤
147f5d47-773c-43f0-b337-57208718d0cf _ main_event_handler
147f5d47-773c-43f0-b337-57208718d0cf _ subset_30062c90-2a60-4ee1-9944-f00329db1174
147f5d47-773c-43f0-b337-57208718d0cf _ subset_default_subset
147f5d47-773c-43f0-b337-57208718d0cf _ workspace_147f5d47-773c-43f0-b337-57208718d0cf






    Out[145]:





True



In [146]:

    
workspace_uuid = ekos.get_unique_id_for_alias(user_id, workspace_alias)
print(workspace_uuid)









    



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



In [8]:

    
#ekos.import_default_data(user_id, workspace_alias = workspace_alias, force = True)

Load all data in workspace



In [9]:

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



In [147]:

    
#%%timeit
ekos.load_data(user_id = user_id, unique_id = workspace_uuid, force = True)
w = ekos.get_workspace(user_id, unique_id = workspace_uuid, alias = workspace_alias)
len(w.data_handler.get_all_column_data_df())









    



2018-04-20 16:51:40,438	workspaces.py	1417	load_all_data	DEBUG	data has been loaded from existing all_data.pickle file.
2018-04-20 16:51:40,438	event_handler.py	919	get_workspace	DEBUG	Getting workspace "147f5d47-773c-43f0-b337-57208718d0cf" with alias "lena_indicator"






    Out[147]:





101465

Step 0

Apply first data filter



In [148]:

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









    



add filter for step: step_0, type area: None, indicator: None, level: None






    



---------------------------------------------------------------------------
UserWarning                               Traceback (most recent call last)
<ipython-input-148-15c502314a78> in <module>()
----> 1 w.apply_data_filter(step = 0) # This sets the first level of data filter in the IndexHandler

D:\github\ekostat_calculator\core\workspaces.py in apply_data_filter(self, step, subset)
   1029             filter_object = step_object.get_data_filter_object()
   1030 
-> 1031         all_ok = self.index_handler.add_filter(filter_object=filter_object, step=step, subset=subset)
   1032         return all_ok
   1033 

D:\github\ekostat_calculator\core\index_handler.py in add_filter(self, filter_object, subset, step, type_area, indicator, level)
    299         self._add_boolean_to_dict(step_0, subset, step_1, step_2, type_area, indicator, level,
    300                                   filter_object=filter_object,
--> 301                                   df=df)
    302 
    303 

D:\github\ekostat_calculator\core\index_handler.py in _add_boolean_to_dict(self, step_0, subset, step_1, step_2, type_area, indicator, level, filter_object, df)
    122         for key in boolean_dict_keys:
    123             if not key in use_keys.get('keys_in_booleans'):
--> 124                 raise UserWarning(key,'should be included in IndexHandler, perhaps we are jumping to far ahead?')
    125 
    126             if key in use_keys.get('keys')[0]:

UserWarning: ('step_0', 'should be included in IndexHandler, perhaps we are jumping to far ahead?')

Step 1 Set subset filter



In [12]:

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

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'])









    



2018-04-20 12:42:32,694	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_1






    



['30062c90-2a60-4ee1-9944-f00329db1174', 'default_subset']
{'MYEAR': ['2013', '2014', '2015', '2016', '2017', '2018', '2019'], 'STATN': [], 'WATER_BODY': []}
add filter for step: step_1, type area: None, indicator: None, level: None
Index(['Unnamed: 0', 'AMON', 'BIOV_CONC_ALL', 'BQIm', 'CPHL_INTEG', 'DEPH',
       'DIN', 'DOXY_BTL', 'DOXY_CTD', 'LATIT_DD', 'LONGI_DD', 'MNDEP', 'MXDEP',
       'MYEAR', 'NTOT', 'NTRA', 'NTRI', 'NTRZ', 'PHOS', 'PTOT', 'Q_AMON',
       'Q_BIOV_CONC_ALL', 'Q_BQIm', 'Q_CPHL_INTEG', 'Q_DOXY_BTL', 'Q_DOXY_CTD',
       'Q_NTOT', 'Q_NTRA', 'Q_NTRI', 'Q_NTRZ', 'Q_PHOS', 'Q_PTOT',
       'Q_SALT_BTL', 'Q_SALT_CTD', 'Q_SECCHI', 'Q_TEMP_BTL', 'Q_TEMP_CTD',
       'SALT_BTL', 'SALT_CTD', 'SDATE', 'SEA_BASIN', 'SECCHI', 'SERNO',
       'SHARKID_MD5', 'SHIPC', 'STATN', 'STIME', 'TEMP_BTL', 'TEMP_CTD',
       'VISS_EU_CD', 'WATER_BODY_NAME', 'WATER_DISTRICT', 'WATER_TYPE_AREA',
       'WLTYP', 'MONTH', 'YEAR', 'POSITION'],
      dtype='object')






    Out[12]:






  
    
      
      SDATE
      YEAR
      MONTH
      POSITION
      VISS_EU_CD
      WATER_TYPE_AREA
      DEPH
      MNDEP
      MXDEP
      BQIm
    
  
  
    
      94754
      2017-06-28
      2017
      6
      59.01_11.44
      SE590020-114520
      02 - Västkustens fjordar
      NaN
      9.6
      9.6
      6.861810
    
    
      94755
      2017-06-28
      2017
      6
      59.01_11.44
      SE590020-114520
      02 - Västkustens fjordar
      NaN
      9.6
      9.6
      4.985970
    
    
      94756
      2017-06-28
      2017
      6
      59.03_11.42
      SE590860-113810
      02 - Västkustens fjordar
      NaN
      21.6
      21.6
      2.844030
    
    
      94757
      2017-06-28
      2017
      6
      59.03_11.42
      SE590860-113810
      02 - Västkustens fjordar
      NaN
      21.6
      21.6
      2.316120
    
    
      94758
      2017-06-28
      2017
      6
      59.07_11.39
      SE590860-113810
      02 - Västkustens fjordar
      NaN
      35.7
      35.7
      0.667618
    
    
      94759
      2017-06-28
      2017
      6
      59.11_11.31
      SE590860-113810
      02 - Västkustens fjordar
      NaN
      33.6
      33.6
      12.692900
    
    
      94760
      2017-06-28
      2017
      6
      59.11_11.31
      SE590860-113810
      02 - Västkustens fjordar
      NaN
      33.3
      33.3
      11.458700
    
    
      94761
      2017-06-28
      2017
      6
      59.08_11.22
      SE590900-112300
      02 - Västkustens fjordar
      NaN
      31.2
      31.2
      11.670900
    
    
      94762
      2017-06-28
      2017
      6
      59.08_11.22
      SE590900-112300
      02 - Västkustens fjordar
      NaN
      31.2
      31.2
      12.202500
    
    
      94763
      2017-06-28
      2017
      6
      59.09_11.22
      SE590900-112300
      02 - Västkustens fjordar
      NaN
      34
      34
      13.570800
    
    
      94764
      2017-06-28
      2017
      6
      59.09_11.22
      SE590900-112300
      02 - Västkustens fjordar
      NaN
      34
      34
      13.522100
    
    
      94765
      2017-06-28
      2017
      6
      58.97_11.09
      SE590670-111380
      01n - Västkustens inre kustvatten
      NaN
      61
      61
      12.179000
    
    
      94766
      2017-06-28
      2017
      6
      58.97_11.09
      SE590670-111380
      01n - Västkustens inre kustvatten
      NaN
      61
      61
      11.601800
    
    
      94767
      2017-06-02
      2017
      6
      56.56_12.82
      SE563330-124600
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      16
      16
      4.857090
    
    
      94768
      2017-06-02
      2017
      6
      56.56_12.82
      SE563330-124600
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      16
      16
      6.748780
    
    
      94769
      2017-06-02
      2017
      6
      56.56_12.82
      SE563330-124600
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      16
      16
      5.344290
    
    
      94770
      2017-06-02
      2017
      6
      56.56_12.82
      SE563330-124600
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      16
      16
      7.503390
    
    
      94771
      2017-06-02
      2017
      6
      56.56_12.82
      SE563330-124600
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      16
      16
      4.536430
    
    
      94772
      2017-06-01
      2017
      6
      59.60_18.94
      SE593500-190000
      12n - Östergötlands och Stockholms skärgård. M...
      NaN
      24
      24
      11.270000
    
    
      94773
      2017-05-31
      2017
      5
      59.54_18.96
      SE593500-190000
      12n - Östergötlands och Stockholms skärgård. M...
      NaN
      21
      21
      7.381660
    
    
      94774
      2017-05-31
      2017
      5
      59.51_19.00
      SE593500-190000
      12n - Östergötlands och Stockholms skärgård. M...
      NaN
      59.5
      59.5
      9.612610
    
    
      94775
      2017-05-30
      2017
      5
      56.57_12.61
      SE563000-123351
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      22
      22
      8.638710
    
    
      94776
      2017-05-30
      2017
      5
      56.57_12.61
      SE563000-123351
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      22
      22
      8.557110
    
    
      94777
      2017-05-30
      2017
      5
      56.57_12.61
      SE563000-123351
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      22
      22
      8.848160
    
    
      94778
      2017-05-30
      2017
      5
      56.57_12.61
      SE563000-123351
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      22
      22
      8.042580
    
    
      94779
      2017-05-30
      2017
      5
      56.57_12.61
      SE563000-123351
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      22
      22
      10.343300
    
    
      94780
      2017-05-30
      2017
      5
      56.56_12.72
      SE563330-124600
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      20
      20
      9.926600
    
    
      94781
      2017-05-30
      2017
      5
      56.56_12.72
      SE563330-124600
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      20
      20
      9.912360
    
    
      94782
      2017-05-30
      2017
      5
      56.56_12.72
      SE563330-124600
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      20
      20
      9.426740
    
    
      94783
      2017-05-30
      2017
      5
      56.56_12.72
      SE563330-124600
      05 - Södra Hallands och norra Öresunds kustvatten
      NaN
      20
      20
      10.446600
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      96866
      2013-05-06
      2013
      5
      56.03_12.69
      SE562290-124131
      06 - Öresunds kustvatten
      NaN
      29
      29
      12.613500
    
    
      96867
      2013-05-06
      2013
      5
      56.03_12.69
      SE562290-124131
      06 - Öresunds kustvatten
      NaN
      29
      29
      12.071600
    
    
      96868
      2013-05-06
      2013
      5
      56.03_12.69
      SE562290-124131
      06 - Öresunds kustvatten
      NaN
      29
      29
      12.137100
    
    
      96869
      2013-05-03
      2013
      5
      58.93_11.14
      SE585600-110880
      01n - Västkustens inre kustvatten
      NaN
      27
      27
      12.169300
    
    
      96870
      2013-05-03
      2013
      5
      58.93_11.14
      SE585600-110880
      01n - Västkustens inre kustvatten
      NaN
      27
      27
      13.209700
    
    
      96871
      2013-05-03
      2013
      5
      58.93_11.14
      SE585600-110880
      01n - Västkustens inre kustvatten
      NaN
      27
      27
      13.109400
    
    
      96872
      2013-05-02
      2013
      5
      58.35_11.40
      SE582000-112350
      01n - Västkustens inre kustvatten
      NaN
      32
      32
      11.794600
    
    
      96873
      2013-05-02
      2013
      5
      58.35_11.40
      SE582000-112350
      01n - Västkustens inre kustvatten
      NaN
      32
      32
      11.704300
    
    
      96874
      2013-05-02
      2013
      5
      58.35_11.40
      SE582000-112350
      01n - Västkustens inre kustvatten
      NaN
      32
      32
      11.474500
    
    
      96875
      2013-05-02
      2013
      5
      58.97_11.09
      SE590670-111380
      01n - Västkustens inre kustvatten
      NaN
      61
      61
      12.435400
    
    
      96876
      2013-05-02
      2013
      5
      58.97_11.09
      SE590670-111380
      01n - Västkustens inre kustvatten
      NaN
      61
      61
      11.012300
    
    
      96877
      2013-05-02
      2013
      5
      58.97_11.09
      SE590670-111380
      01n - Västkustens inre kustvatten
      NaN
      61
      61
      12.547400
    
    
      96878
      2013-05-02
      2013
      5
      58.80_11.16
      SE584725-111050
      01n - Västkustens inre kustvatten
      NaN
      46
      46
      11.486100
    
    
      96879
      2013-05-02
      2013
      5
      58.80_11.16
      SE584725-111050
      01n - Västkustens inre kustvatten
      NaN
      46
      46
      10.438500
    
    
      96880
      2013-05-02
      2013
      5
      58.80_11.16
      SE584725-111050
      01n - Västkustens inre kustvatten
      NaN
      46
      46
      11.613500
    
    
      96881
      2013-05-02
      2013
      5
      58.70_11.18
      SE584200-105901
      03 - Västkustens yttre kustvatten. Skagerrak
      NaN
      41
      41
      12.999100
    
    
      96882
      2013-05-02
      2013
      5
      58.70_11.18
      SE584200-105901
      03 - Västkustens yttre kustvatten. Skagerrak
      NaN
      41
      41
      13.338500
    
    
      96883
      2013-05-02
      2013
      5
      58.70_11.18
      SE584200-105901
      03 - Västkustens yttre kustvatten. Skagerrak
      NaN
      41
      41
      13.439500
    
    
      96884
      2013-05-02
      2013
      5
      58.73_11.17
      SE584400-116000
      01n - Västkustens inre kustvatten
      NaN
      31
      31
      12.741700
    
    
      96885
      2013-05-02
      2013
      5
      58.73_11.17
      SE584400-116000
      01n - Västkustens inre kustvatten
      NaN
      31
      31
      13.293900
    
    
      96886
      2013-05-02
      2013
      5
      58.73_11.17
      SE584400-116000
      01n - Västkustens inre kustvatten
      NaN
      31
      31
      13.076300
    
    
      96887
      2013-05-02
      2013
      5
      58.38_11.15
      SE582700-110451
      03 - Västkustens yttre kustvatten. Skagerrak
      NaN
      48
      48
      12.520100
    
    
      96888
      2013-05-02
      2013
      5
      58.38_11.15
      SE582700-110451
      03 - Västkustens yttre kustvatten. Skagerrak
      NaN
      48
      48
      12.158700
    
    
      96889
      2013-05-02
      2013
      5
      58.38_11.15
      SE582700-110451
      03 - Västkustens yttre kustvatten. Skagerrak
      NaN
      48
      48
      12.023600
    
    
      96890
      2013-05-02
      2013
      5
      58.34_11.36
      SE580500-111801
      03 - Västkustens yttre kustvatten. Skagerrak
      NaN
      28
      28
      10.701500
    
    
      96891
      2013-05-02
      2013
      5
      58.34_11.36
      SE580500-111801
      03 - Västkustens yttre kustvatten. Skagerrak
      NaN
      28
      28
      11.404400
    
    
      96892
      2013-05-02
      2013
      5
      58.34_11.36
      SE580500-111801
      03 - Västkustens yttre kustvatten. Skagerrak
      NaN
      28
      28
      10.524400
    
    
      96893
      2013-05-02
      2013
      5
      58.61_11.14
      SE584200-105901
      03 - Västkustens yttre kustvatten. Skagerrak
      NaN
      48
      48
      10.848100
    
    
      96894
      2013-05-02
      2013
      5
      58.61_11.14
      SE584200-105901
      03 - Västkustens yttre kustvatten. Skagerrak
      NaN
      48
      48
      7.732150
    
    
      96895
      2013-05-02
      2013
      5
      58.61_11.14
      SE584200-105901
      03 - Västkustens yttre kustvatten. Skagerrak
      NaN
      48
      48
      10.384400
    
  

2142 rows × 10 columns

Step 2



In [13]:

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

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









    Out[13]:





{'biov': <core.filters.SettingsDataFilter at 0x487dd68>,
 'bqi': <core.filters.SettingsDataFilter at 0x4885a58>,
 'chl': <core.filters.SettingsDataFilter at 0x4885b00>,
 'din_winter': <core.filters.SettingsDataFilter at 0x4885b38>,
 'dip_winter': <core.filters.SettingsDataFilter at 0x4885ba8>,
 'ntot_summer': <core.filters.SettingsDataFilter at 0x4885be0>,
 'ntot_winter': <core.filters.SettingsDataFilter at 0x4885c50>,
 'oxygen': <core.filters.SettingsDataFilter at 0x4885630>,
 'ptot_summer': <core.filters.SettingsDataFilter at 0x4885780>,
 'ptot_winter': <core.filters.SettingsDataFilter at 0x4885748>,
 'secchi': <core.filters.SettingsDataFilter at 0x48856a0>}



In [14]:

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









    Out[14]:





{'biov': <core.filters.SettingsRef at 0x48856d8>,
 'bqi': <core.filters.SettingsRef at 0x4885668>,
 'chl': <core.filters.SettingsRef at 0x48857b8>,
 'din_winter': <core.filters.SettingsRef at 0x4885828>,
 'dip_winter': <core.filters.SettingsRef at 0x4885860>,
 'ntot_summer': <core.filters.SettingsRef at 0x4885898>,
 'ntot_winter': <core.filters.SettingsRef at 0x48858d0>,
 'oxygen': <core.filters.SettingsRef at 0x4885940>,
 'ptot_summer': <core.filters.SettingsRef at 0x48859b0>,
 'ptot_winter': <core.filters.SettingsRef at 0x4885978>,
 'secchi': <core.filters.SettingsRef at 0x4885a20>}



In [15]:

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









    Out[15]:





<core.filters.SettingsFile at 0xa511ac8>



In [16]:

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









    



2018-04-20 12:42:37,728	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2
2018-04-20 12:42:37,829	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2






    



subset A
subset A






    



2018-04-20 12:42:37,934	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2
2018-04-20 12:42:38,040	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2






    



subset A
subset A






    



2018-04-20 12:42:38,194	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2
2018-04-20 12:42:38,375	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2






    



subset A
subset A






    



2018-04-20 12:42:38,462	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2
2018-04-20 12:42:38,578	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2






    



subset A
subset A






    



2018-04-20 12:42:38,675	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2
2018-04-20 12:42:38,811	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2






    



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






    



2018-04-20 12:42:38,944	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2
2018-04-20 12:42:39,068	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2






    



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

Apply indicator filter



In [17]:

    
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)))

#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:
    for type_area in typeA_list:
        w.apply_indicator_data_filter(step = 2, 
                              subset = subset_uuid, 
                              indicator = indicator,
                              type_area = type_area)









    



number of waterbodies in step 1: 310
number of type areas in step 1: 27
add filter for step: step_2, type area: 14, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 2, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 9, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 22, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 20, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 18, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 12n, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 13, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 12s, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 1n, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 25, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 21, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 7, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 5, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 16, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 23, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 3, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 1s, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 4, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 6, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 8, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 10, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 24, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 17, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 15, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 19, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 11, indicator: oxygen, level: None
Water body None
add filter for step: step_2, type area: 14, indicator: bqi, level: None
Water body None
RESULT [5, 60]
add filter for step: step_2, type area: 2, indicator: bqi, level: None
Water body None
RESULT [[5, 20], [20, 500]]
add filter for step: step_2, type area: 9, indicator: bqi, level: None
Water body None
RESULT [5, 60]
add filter for step: step_2, type area: 22, indicator: bqi, level: None
Water body None
RESULT [5, 500]
add filter for step: step_2, type area: 20, indicator: bqi, level: None
Water body None
RESULT [5, 500]
add filter for step: step_2, type area: 18, indicator: bqi, level: None
Water body None
RESULT [5, 500]
add filter for step: step_2, type area: 12n, indicator: bqi, level: None
Water body None
RESULT []
add filter for step: step_2, type area: 13, indicator: bqi, level: None
Water body None
RESULT [5, 60]
add filter for step: step_2, type area: 12s, indicator: bqi, level: None
Water body None
RESULT []
add filter for step: step_2, type area: 1n, indicator: bqi, level: None
Water body None
RESULT []
add filter for step: step_2, type area: 25, indicator: bqi, level: None
Water body None
RESULT [[5, 20], [20, 500]]
add filter for step: step_2, type area: 21, indicator: bqi, level: None
Water body None
RESULT [5, 500]
add filter for step: step_2, type area: 7, indicator: bqi, level: None
Water body None
RESULT [5, 60]
add filter for step: step_2, type area: 5, indicator: bqi, level: None
Water body None
RESULT [[5, 20], [20, 500]]
add filter for step: step_2, type area: 16, indicator: bqi, level: None
Water body None
RESULT [5, 500]
add filter for step: step_2, type area: 23, indicator: bqi, level: None
Water body None
RESULT [5, 500]
add filter for step: step_2, type area: 3, indicator: bqi, level: None
Water body None
RESULT [[5, 20], [20, 500]]
add filter for step: step_2, type area: 1s, indicator: bqi, level: None
Water body None
RESULT []
add filter for step: step_2, type area: 4, indicator: bqi, level: None
Water body None
RESULT [[5, 20], [20, 500]]
add filter for step: step_2, type area: 6, indicator: bqi, level: None
Water body None
RESULT [[5, 20], [20, 500]]
add filter for step: step_2, type area: 8, indicator: bqi, level: None
Water body None
RESULT [5, 60]
add filter for step: step_2, type area: 10, indicator: bqi, level: None
Water body None
RESULT [5, 60]
add filter for step: step_2, type area: 24, indicator: bqi, level: None
Water body None
RESULT [5, 60]
add filter for step: step_2, type area: 17, indicator: bqi, level: None
Water body None
RESULT [5, 500]
add filter for step: step_2, type area: 15, indicator: bqi, level: None
Water body None
RESULT [5, 60]
add filter for step: step_2, type area: 19, indicator: bqi, level: None
Water body None
RESULT [5, 500]
add filter for step: step_2, type area: 11, indicator: bqi, level: None
Water body None
RESULT [5, 60]
add filter for step: step_2, type area: 14, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 2, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 9, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 22, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 20, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 18, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 12n, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 13, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 12s, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 1n, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 25, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 21, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 7, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 5, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 16, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 23, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 3, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 1s, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 4, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 6, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 8, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 10, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 24, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 17, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 15, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 19, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 11, indicator: din_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 14, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 2, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 9, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 22, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 20, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 18, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 12n, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 13, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 12s, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 1n, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 25, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 21, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 7, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 5, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 16, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 23, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 3, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 1s, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 4, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 6, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 8, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 10, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 24, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 17, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 15, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 19, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 11, indicator: ntot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 14, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 2, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 9, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 22, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 20, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 18, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 12n, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 13, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 12s, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 1n, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 25, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 21, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 7, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 5, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 16, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 23, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 3, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 1s, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 4, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 6, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 8, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 10, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 24, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 17, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 15, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 19, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 11, indicator: ntot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 14, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 2, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 9, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 22, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 20, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 18, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 12n, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 13, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 12s, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 1n, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 25, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 21, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 7, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 5, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 16, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 23, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 3, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 1s, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 4, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 6, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 8, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 10, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 24, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 17, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 15, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 19, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 11, indicator: dip_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 14, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 2, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 9, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 22, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 20, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 18, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 12n, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 13, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 12s, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 1n, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 25, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 21, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 7, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 5, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 16, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 23, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 3, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 1s, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 4, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 6, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 8, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 10, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 24, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 17, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 15, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 19, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 11, indicator: ptot_summer, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 14, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 2, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 9, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 22, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 20, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 18, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 12n, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 13, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 12s, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 1n, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 25, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 21, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 7, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 5, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 16, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 23, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 3, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 1s, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 4, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 6, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 8, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 10, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 24, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 17, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 15, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 19, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 11, indicator: ptot_winter, level: None
Water body None
RESULT [0, 10]
add filter for step: step_2, type area: 14, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 2, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 9, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 22, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 20, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 18, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 12n, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 13, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 12s, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 1n, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 25, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 21, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 7, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 5, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 16, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 23, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 3, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 1s, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 4, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 6, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 8, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 10, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 24, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 17, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 15, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 19, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 11, indicator: biov, level: None
Water body None
add filter for step: step_2, type area: 14, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 2, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 9, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 22, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 20, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 18, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 12n, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 13, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 12s, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 1n, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 25, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 21, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 7, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 5, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 16, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 23, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 3, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 1s, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 4, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 6, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 8, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 10, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 24, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 17, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 15, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 19, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 11, indicator: chl, level: None
Water body None
add filter for step: step_2, type area: 14, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 2, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 9, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 22, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 20, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 18, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 12n, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 13, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 12s, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 1n, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 25, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 21, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 7, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 5, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 16, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 23, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 3, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 1s, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 4, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 6, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 8, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 10, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 24, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 17, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 15, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 19, indicator: secchi, level: None
Water body None
add filter for step: step_2, type area: 11, indicator: secchi, level: None
Water body None



In [122]:

    
wb_list









    Out[122]:





array(['SE584340-174401', 'SE581700-113000', 'SE561400-161201',
       'SE654470-222700', 'SE633000-195000', 'SE625180-181655',
       'SE654820-222660', 'SE625000-180075', 'SE628750-183300',
       'SE631346-184241', 'SE631646-185280', 'SE583730-164501',
       'SE582705-163350', 'SE582050-165820', 'SE582147-111771',
       'SE580688-114860', 'SE575500-113750', 'SE574050-114780',
       'SE625900-174360', 'SE630383-183500', 'SE633550-200700',
       'SE636570-203590', 'SE633043-193300', 'SE552170-130626',
       'SE562000-123800', 'SE591400-183200', 'SE591200-183600',
       'SE592040-184000', 'SE592245-184400', 'SE600740-183460',
       'SE601070-182870', 'SE631610-184500', 'SE625500-175153',
       'SE622126-172430', 'SE622339-172190', 'SE652920-222650',
       'SE656620-222480', 'SE656300-222750', 'SE655120-220380',
       'SE655180-218660', 'SE636150-199220', 'SE635040-204196',
       'SE637070-204260', 'SE636910-204040', 'SE602120-181610',
       'SE582000-115270', 'SE575700-114240', 'SE581740-114820',
       'SE563330-124600', 'SE654860-219880', 'SE647050-213980',
       'SE575340-113000', 'SE654500-232000', 'SE652400-223501',
       'SE653900-223280', 'SE582000-112350', 'SE581260-113220',
       'SE574000-114230', 'SE585100-110600', 'SE572540-114801',
       'SE555545-124332', 'SE561005-150250', 'SE580610-113615',
       'SE580325-113500', 'SE580500-114725', 'SE581120-112680',
       'SE581365-112910', 'SE584750-111185', 'SE584890-110950',
       'SE582630-113515', 'SE584450-111445', 'SE584670-111300',
       'SE582150-112530', 'SE585600-110880', 'SE585200-111140',
       'SE583710-111535', 'SE582665-111706', 'SE582850-111760',
       'SE584030-111400', 'SE581853-112736', 'SE582230-112255',
       'SE573500-115150', 'SE571000-184001', 'SE585990-111125',
       'SE570000-120701', 'SE582700-110451', 'SE584200-105901',
       'SE554040-125750', 'SE554500-125001', 'SE575150-162700',
       'SE575370-164220', 'SE573940-163560', 'SE565400-163600',
       'SE570080-163430', 'SE565800-163000', 'SE570340-163710',
       'SE570500-163750', 'SE573500-163500', 'SE563825-161810',
       'SE564250-162500', 'SE659024-162417', 'SE572205-163500',
       'SE571552-162848', 'SE572500-164500', 'SE583718-161687',
       'SE583926-161744', 'SE583000-165600', 'SE560940-151740',
       'SE560500-154880', 'SE560810-153980', 'SE560780-153500',
       'SE560795-154730', 'SE560775-153055', 'SE560700-155801',
       'SE582460-164500', 'SE581520-165000', 'SE560825-144215',
       'SE621688-144133', 'SE560900-145280', 'SE560205-143545',
       'SE581000-164020', 'SE580585-164720', 'SE580735-165296',
       'SE580000-164060', 'SE554800-142001', 'SE562450-122751',
       'SE602400-183190', 'SE603870-181301', 'SE601300-182880',
       'SE603190-174000', 'SE601660-183550', 'SE594100-185690',
       'SE593750-184900', 'SE593820-185500', 'SE593300-183600',
       'SE592420-182210', 'SE590400-174090', 'SE590990-174015',
       'SE658507-162696', 'SE585200-174000', 'SE585345-174950',
       'SE590550-174540', 'SE583970-170280', 'SE584430-170665',
       'SE584227-171600', 'SE584390-172085', 'SE584400-172270',
       'SE584600-173200', 'SE584820-172920', 'SE584905-172980',
       'SE585075-173130', 'SE585040-173535', 'SE584870-174310',
       'SE583875-170270', 'SE584085-171600', 'SE658352-163189',
       'SE591090-182300', 'SE591160-182400', 'SE590385-180890',
       'SE584960-175280', 'SE591050-182740', 'SE590000-183000',
       'SE591300-182800', 'SE594200-192000', 'SE592640-184500',
       'SE593000-190500', 'SE593180-191280', 'SE594845-191240',
       'SE594800-190655', 'SE591655-183200', 'SE591745-182250',
       'SE594670-185500', 'SE594800-190220', 'SE601310-183700',
       'SE601000-183510', 'SE600565-184600', 'SE601300-184180',
       'SE585797-181090', 'SE574931-113131', 'SE561030-122821',
       'SE554810-125240', 'SE552500-124461', 'SE572472-120302',
       'SE572135-120141', 'SE570900-164501', 'SE644150-211000',
       'SE594340-190448', 'SE592400-180800', 'SE593750-183962',
       'SE634110-201920', 'SE634210-202020', 'SE643700-211940',
       'SE645000-212100', 'SE651818-212790', 'SE651475-214300',
       'SE651800-214740', 'SE651500-213108', 'SE645500-212000',
       'SE653176-222000', 'SE728806-179329', 'SE623890-178030',
       'SE622500-172430', 'SE622860-173000', 'SE622000-172300',
       'SE624870-175500', 'SE624615-180500', 'SE624335-180000',
       'SE623810-180350', 'SE623340-175556', 'SE634950-202940',
       'SE590740-174135', 'SE631840-191130', 'SE614165-171500',
       'SE613760-171000', 'SE613500-172500', 'SE613591-171000',
       'SE613500-171000', 'SE613240-171000', 'SE635660-199490',
       'SE574160-113351', 'SE580240-112501', 'SE573300-113801',
       'SE657412-164249', 'SE657608-164193', 'SE595000-185600',
       'SE612303-171075', 'SE622795-174565', 'SE623300-176210',
       'SE630210-187470', 'SE592600-181600', 'SE592575-181770',
       'SE623980-175600', 'SE645000-213500', 'SE604200-174400',
       'SE592790-183000', 'SE637640-204160', 'SE652020-211930',
       'SE644730-210650', 'SE641840-211540', 'SE634230-201605',
       'SE563000-123351', 'SE604250-173000', 'SE611676-171000',
       'SE580000-164500', 'SE590020-114520', 'SE590860-113810',
       'SE590900-112300', 'SE590670-111380', 'SE593500-190000',
       'SE571720-120640', 'SE635300-205251', 'SE582500-113890',
       'SE637310-204860', 'SE634740-203020', 'SE646360-213700',
       'SE643920-211500', 'SE584725-111050', 'SE650280-213110',
       'SE654640-233190', 'SE654575-234250', 'SE562290-124131',
       'SE592000-190500', 'SE570900-121060', 'SE571240-121000',
       'SE564500-122601', 'SE601440-184000', 'SE612791-171130',
       'SE572072-115880', 'SE573322-115478', 'SE572227-115662',
       'SE573044-115355', 'SE572308-115550', 'SE580500-111801',
       'SE574170-190001', 'SE584400-116000', 'SE583625-111300',
       'SE572000-180001', 'SE572350-180930', 'SE583121-171401',
       'SE633400-195000', 'SE611000-171500', 'SE611213-171063',
       'SE611600-171500', 'SE581280-170070', 'SE581240-165220',
       'SE581820-165500', 'SE619690-175690', 'SE612520-172080',
       'SE605660-172380', 'SE622011-146303', 'SE561480-148220',
       'SE561150-147620', 'SE560740-144375', 'SE603000-181500',
       'SE561000-153320', 'SE561000-150390', 'SE560385-154500',
       'SE560200-143175', 'SE654200-222920', 'SE654000-222430',
       'SE650750-213500', 'SE650460-213400', 'SE625416-182696',
       'SE628480-183070', 'SE593500-191660', 'SE593330-192540',
       'SE593000-192000', 'SE611766-171305', 'SE575150-190400',
       'SE654291-224000'], dtype=object)



In [124]:

    
wb = 'SE572072-115880' 'SE654470-222700'
type_area = '2'#'01s - Västkustens inre kustvatten'
#w.index_handler.booleans['step_0'][subset_uuid]['step_1']['step_2'][type_area]['din_winter']['boolean']



In [125]:

    
print(w.get_filtered_data(step = 2, subset = subset_uuid, type_area = type_area, indicator = 'din_winter').MONTH.unique())
print(w.get_filtered_data(step = 2, subset = subset_uuid, type_area = type_area, indicator = 'din_winter').DEPH.min(),
        w.get_filtered_data(step = 2, subset = subset_uuid, type_area = type_area, indicator = 'din_winter').DEPH.max())
w.get_filtered_data(step = 2, subset = subset_uuid, type_area = type_area).WATER_TYPE_AREA.unique()









    



2018-04-20 15:09:56,452	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2
2018-04-20 15:09:56,466	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2
2018-04-20 15:09:56,503	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2
2018-04-20 15:09:56,555	workspaces.py	1304	get_filtered_data	DEBUG	STEP: step_2






    



[12 11 10  9  8  7  6  5  4  3  2  1]
0.0 246.0






    Out[125]:





array(['14 - Östergötlands yttre kustvatten', '02 - Västkustens fjordar',
       '09 - Blekinge skärgård och Kalmarsund. Yttre kustvatten',
       '22 - Norra Bottenviken. Inre kustvatten',
       '20 - Norra Kvarkens inre kustvatten',
       '18 - Norra Bottenhavet. Höga kusten. Inre kustvatten',
       '12n - Östergötlands och Stockholms skärgård. Mellankustvatten',
       '13 - Östergötlands inre kustvatten',
       '12s - Östergötlands och Stockholms skärgård. Mellankustvatten',
       '01n - Västkustens inre kustvatten',
       '25 - Göta älvs- och Nordre älvs estuarie',
       '21 - Norra Kvarkens yttre kustvatten', '07 - Skånes kustvatten',
       '05 - Södra Hallands och norra Öresunds kustvatten',
       '16 - Södra Bottenhavet. Inre kustvatten',
       '23 - Norra Bottenviken. Yttre kustvatten',
       '03 - Västkustens yttre kustvatten. Skagerrak',
       '01s - Västkustens inre kustvatten',
       '04 - Västkustens yttre kustvatten. Kattegatt',
       '06 - Öresunds kustvatten',
       '08 - Blekinge skärgård och Kalmarsund. Inre kustvatten',
       '10 - Ölands och Gotlands kustvatten',
       '24 - Stockholms inre skärgård och Hallsfjärden',
       '17 - Södra Bottenhavet. Yttre kustvatten',
       '15 - Stockholms skärgård. Yttre kustvatten',
       '19 - Norra Bottenhavet. Höga kusten. Yttre kustvatten',
       '11 - Gotlands nordvästra kustvatten'], dtype=object)



In [126]:

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









    Out[126]:





['BQIm']

Step 3 Load Indicator objects step 3....



In [127]:

    
w.get_step_object(step = 2, subset = subset_uuid).indicator_tolerance_settings['bqi']









    Out[127]:





<core.filters.SettingsTolerance at 0x487df98>



In [128]:

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









    



IndicatorNutrients
<class 'core.indicators.IndicatorNutrients'>
din_winter
IndicatorNutrients
<class 'core.indicators.IndicatorNutrients'>
ntot_summer
IndicatorNutrients
<class 'core.indicators.IndicatorNutrients'>
ntot_winter
IndicatorNutrients
<class 'core.indicators.IndicatorNutrients'>
dip_winter
IndicatorNutrients
<class 'core.indicators.IndicatorNutrients'>
ptot_summer
IndicatorNutrients
<class 'core.indicators.IndicatorNutrients'>
ptot_winter
IndicatorBQI
<class 'core.indicators.IndicatorBQI'>
bqi
IndicatorOxygen
<class 'core.indicators.IndicatorOxygen'>
oxygen
IndicatorPhytoplankton
<class 'core.indicators.IndicatorPhytoplankton'>
biov
IndicatorSecchi
<class 'core.indicators.IndicatorSecchi'>
secchi



In [129]:

    
w.get_step_object(step = 3, subset = subset_uuid).indicator_objects[indicator].get_ref_value(type_area = '1s', salinity = 25.0)









    Out[129]:





False



In [130]:

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



In [131]:

    
dw_obj.get_water_body_indicator_df(wb)



In [132]:

    
dw_obj.column_list









    Out[132]:





['SDATE',
 'YEAR',
 'MONTH',
 'POSITION',
 'VISS_EU_CD',
 'WATER_TYPE_AREA',
 'BQIm',
 'MNDEP',
 'MXDEP']



In [137]:

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









    Out[137]:






  
    
      
      SDATE
      YEAR
      MONTH
      POSITION
      VISS_EU_CD
      WATER_TYPE_AREA
      BQIm
      MNDEP
      MXDEP
    
  
  
    
      95125
      2016-05-25
      2016
      5
      57.36_11.98
      SE572072-115880
      01s - Västkustens inre kustvatten
      7.54714
      8.5
      8.5
    
    
      95126
      2016-05-25
      2016
      5
      57.36_11.98
      SE572072-115880
      01s - Västkustens inre kustvatten
      4.31313
      8.5
      8.5
    
    
      95127
      2016-05-25
      2016
      5
      57.36_11.98
      SE572072-115880
      01s - Västkustens inre kustvatten
      5.53224
      8.5
      8.5
    
    
      95128
      2016-05-25
      2016
      5
      57.36_11.98
      SE572072-115880
      01s - Västkustens inre kustvatten
      5.15111
      8.5
      8.5
    
    
      95129
      2016-05-25
      2016
      5
      57.36_11.98
      SE572072-115880
      01s - Västkustens inre kustvatten
      5.14814
      8.5
      8.5
    
    
      95150
      2016-05-25
      2016
      5
      57.36_11.99
      SE572072-115880
      01s - Västkustens inre kustvatten
      3.11854
      6
      6
    
    
      95151
      2016-05-25
      2016
      5
      57.34_11.98
      SE572072-115880
      01s - Västkustens inre kustvatten
      10.05430
      21
      21
    
    
      95152
      2016-05-25
      2016
      5
      57.34_11.98
      SE572072-115880
      01s - Västkustens inre kustvatten
      10.04500
      21
      21
    
    
      95153
      2016-05-25
      2016
      5
      57.35_11.96
      SE572072-115880
      01s - Västkustens inre kustvatten
      5.57512
      25
      25
    
    
      95154
      2016-05-25
      2016
      5
      57.35_11.97
      SE572072-115880
      01s - Västkustens inre kustvatten
      8.28729
      17.7
      17.7
    
    
      95155
      2016-05-25
      2016
      5
      57.36_11.98
      SE572072-115880
      01s - Västkustens inre kustvatten
      3.64912
      8.3
      8.3
    
    
      95156
      2016-05-25
      2016
      5
      57.36_11.98
      SE572072-115880
      01s - Västkustens inre kustvatten
      4.67920
      9.1
      9.1
    
    
      95157
      2016-05-25
      2016
      5
      57.35_11.97
      SE572072-115880
      01s - Västkustens inre kustvatten
      9.31441
      21.2
      21.2
    
    
      95158
      2016-05-25
      2016
      5
      57.34_11.97
      SE572072-115880
      01s - Västkustens inre kustvatten
      6.72661
      21.4
      21.4



In [134]:

    
water_body = wb
type_area = dw_obj.mapping_objects['water_body'].get_type_area_for_water_body(water_body, include_suffix=True)
parameter = dw_obj.indicator_parameter        
by_year_pos = df.groupby(['YEAR', 'POSITION'])[dw_obj.indicator_parameter].agg(['count', 'min', 'max', 'mean']).reset_index()
by_year_pos.rename(columns={'mean':'position_mean', 'count': 'position_count', 'min': 'position_min', 'max': 'station_max'}, inplace=True)
by_year_pos.columns









    Out[134]:





Index(['YEAR', 'POSITION', 'position_count', 'position_min', 'station_max',
       'position_mean'],
      dtype='object')



In [ ]:



In [138]:

    
year = 2016
by_year_pos.loc[by_year_pos.YEAR == year]['position_mean'].dropna().sample(frac = 1, replace = True).mean()









    Out[138]:





7.386736666666668



In [139]:

    
by_year = by_year_pos.groupby(['YEAR']).position_mean.agg(['count', 'min', 'max', 'mean']).reset_index()
for ix, value in by_year.YEAR.items():
    print(value)



In [140]:

    
# Random selection with replacement of as many values as there are station means (frac = 1)
n = 9999
BQIsim_year = []
for ix, year in by_year.YEAR.items():
    BQIsim = []
    print('number of stations in year ({}): {}'.format(year,len(by_year_pos['position_mean'])))
    for x in range(n):
        BQIsim.append(by_year_pos.loc[by_year_pos.YEAR == year]['position_mean'].sample(frac = 1, replace = True).mean())
    BQIsim_year.append(np.percentile(BQIsim, 0.2))









    



number of stations in year (2016): 6



In [141]:

    
periodmean = np.mean(BQIsim_year)
periodmean









    Out[141]:





4.0655602971428566



In [32]:

    
%%timeit
BQIsim = []
for x in range(n):
        BQIsim.append(by_year_pos.loc[by_year_pos.YEAR == year]['position_mean'].sample(frac = 1, replace = True).mean())









    



1 loop, best of 3: 8.4 s per loop



In [33]:

    
%%timeit
BQIsim = []
[BQIsim.append(by_year_pos.loc[by_year_pos.YEAR == year]['position_mean'].sample(frac = 1, replace = True).mean()) for line in range(n)]









    



1 loop, best of 3: 7.99 s per loop



In [111]:

    
%%timeit
df_list = [by_year_pos.loc[by_year_pos.YEAR == year]['position_mean'].dropna()]*n
def bootstrap(df):
    return df.sample(frac = 1, replace = True).mean()









    



1000 loops, best of 3: 1.16 ms per loop



In [79]:

    
BQIsim = map(bootstrap, df_list)









    



1000 loops, best of 3: 703 µs per loop



In [117]:

    
%%timeit
BQIsim = (bootstrap(by_year_pos.loc[by_year_pos.YEAR == year]['position_mean'].dropna()) for x in range(n))
type(BQIsim)









    



1 loop, best of 3: 13.2 s per loop



In [119]:

    
%%timeit
BQIsim = (bootstrap(by_year_pos.loc[by_year_pos.YEAR == year]['position_mean'].dropna()) for x in range(n))
type(BQIsim)









    



The slowest run took 5.03 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 928 ns per loop



In [83]:

    
%%timeit
BQIsim = map(bootstrap, df_list)









    



The slowest run took 8.93 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 261 ns per loop



In [100]:

    
def tolist(mapobj):
    return [x for x in mapobj]



In [104]:

    
%%timeit   
BQIsim = tolist(map(bootstrap, df_list))
percentile = np.percentile(BQIsim, 0.2)









    



1 loop, best of 3: 1.62 s per loop



In [107]:

    
%%timeit   
BQIsim = np.fromiter(map(bootstrap, df_list), dtype = np.float)
percentile = np.percentile(BQIsim, 0.2)









    



1 loop, best of 3: 1.65 s per loop



In [84]:

    
%%timeit   
BQIsim = [*map(bootstrap, df_list)]
percentile = np.percentile(BQIsim, 0.2)









    



1 loop, best of 3: 1.64 s per loop



In [85]:

    
%%timeit
BQIsim = map(bootstrap, df_list)
BQIsim = list(BQIsim)
percentile = np.percentile(BQIsim, 0.2)









    



1 loop, best of 3: 1.64 s per loop



In [86]:

    
%%timeit
BQIsim = map(bootstrap, df_list)
BQIsim = [x for x in BQIsim]
percentile = np.percentile(BQIsim, 0.2)









    



1 loop, best of 3: 1.65 s per loop



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 [158]:

    
#dw_obj.tolerance_settings.get_value(variable = 'MIN_NR_YEARS', type_area = '22')
#dw_obj.tolerance_settings.get_min_nr_years(type_area = '22')



In [142]:

    
#%%timeit
BQI_sim2 = dw_obj.calculate_status_value(wb)









    



number of stations in year (2016): 6
0     6.726610
1    10.049650
2     5.575120
3     8.800850
4     5.145726
5     3.118540
Name: position_mean, dtype: float64
SE572072-115880
4.12902873714
[4.129028737142856]



In [143]:

    
BQI_sim2









    Out[143]:





4.129028737142856



In [118]:

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



In [32]:

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









    



dict_keys(['parameter', 'salt_parameter', 'water_body', 'all_data', 'all_ok', 'water_body_status', 'mean_EQR_by_year_pos', 'mean_EQR_by_year', 'mean_EQR_by_period', 'number_of_years', 'numerical_class', 'status'])
**************************************
parameter
BIOV_CONC_ALL
**************************************
salt_parameter
SALT_CTD
**************************************
water_body
SE654470-222700
**************************************
all_ok
mean_ek_value    False
Name: all_ok, dtype: bool
**************************************
water_body_status
None
**************************************
mean_EQR_by_year_pos
    YEAR     POSITION  number_of_dates       min  max  mean_ek_value
0   2013  65.73_22.43                0       NaN  NaN            NaN
1   2013  65.73_22.45               10  0.040906  1.0       0.638793
2   2013  65.75_22.40                0       NaN  NaN            NaN
3   2013  65.76_22.48                0       NaN  NaN            NaN
4   2013  65.77_22.50                0       NaN  NaN            NaN
5   2013  65.77_22.53                0       NaN  NaN            NaN
6   2013  65.78_22.43                0       NaN  NaN            NaN
7   2014  65.73_22.43                0       NaN  NaN            NaN
8   2014  65.73_22.44                0       NaN  NaN            NaN
9   2014  65.73_22.45               10  0.060073  1.0       0.614813
10  2014  65.75_22.40                0       NaN  NaN            NaN
11  2014  65.78_22.43                0       NaN  NaN            NaN
12  2015  65.73_22.44                0       NaN  NaN            NaN
13  2015  65.73_22.45                9  0.043947  1.0       0.619119
14  2015  65.75_22.40                0       NaN  NaN            NaN
15  2015  65.76_22.48                0       NaN  NaN            NaN
16  2015  65.77_22.53                0       NaN  NaN            NaN
17  2015  65.78_22.43                0       NaN  NaN            NaN
18  2016  65.73_22.44                0       NaN  NaN            NaN
19  2016  65.73_22.45               10  0.056598  1.0       0.687118
20  2016  65.75_22.40                0       NaN  NaN            NaN
21  2016  65.76_22.48                0       NaN  NaN            NaN
22  2016  65.77_22.53                0       NaN  NaN            NaN
23  2016  65.78_22.43                0       NaN  NaN            NaN
**************************************
mean_EQR_by_year
      number_of_dates       min       max  mean_ek_value
YEAR                                                    
2013                1  0.638793  0.638793       0.638793
2014                1  0.614813  0.614813       0.614813
2015                1  0.619119  0.619119       0.619119
2016                1  0.687118  0.687118       0.687118
**************************************
mean_EQR_by_period
0.639960720229
**************************************
number_of_years
4.0
**************************************
numerical_class
3.99981295347
**************************************
status
GOOD



In [36]:

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









    Out[36]:






  
    
      
      YEAR
      POSITION
      number_of_dates
      min
      max
      mean_ek_value
    
  
  
    
      1
      2013
      65.73_22.45
      10
      0.040906
      1.0
      0.638793
    
    
      9
      2014
      65.73_22.45
      10
      0.060073
      1.0
      0.614813
    
    
      13
      2015
      65.73_22.45
      9
      0.043947
      1.0
      0.619119
    
    
      19
      2016
      65.73_22.45
      10
      0.056598
      1.0
      0.687118



In [22]:

    
dw_obj.water_body_indicator_df[wb]









    Out[22]:






  
    
      
      SDATE
      YEAR
      MONTH
      VISS_EU_CD
      WATER_TYPE_AREA
      DEPH
      DIN
      SALT_CTD
      REFERENCE_VALUE
    
  
  
    
      7057
      2016-12-07
      2016
      12
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      1.0
      6.35
      2.000
      6.334000
    
    
      7058
      2016-12-07
      2016
      12
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      5.0
      6.41
      2.000
      6.334000
    
    
      7059
      2016-12-07
      2016
      12
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      10.0
      6.81
      2.000
      6.334000
    
    
      7349
      2016-11-09
      2016
      11
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      1.0
      7.56
      2.000
      6.334000
    
    
      7350
      2016-11-09
      2016
      11
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      5.0
      5.40
      2.000
      6.334000
    
    
      7351
      2016-11-09
      2016
      11
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      10.0
      5.67
      2.000
      6.334000
    
    
      10343
      2016-01-27
      2016
      1
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      1.0
      11.37
      2.000
      6.334000
    
    
      10344
      2016-01-27
      2016
      1
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      5.0
      7.53
      2.279
      5.962093
    
    
      10345
      2016-01-27
      2016
      1
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      10.0
      7.26
      2.345
      5.874115
    
    
      11065
      2015-12-02
      2015
      12
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      1.0
      5.86
      1.945
      6.407315
    
    
      11066
      2015-12-02
      2015
      12
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      5.0
      6.01
      1.941
      6.412647
    
    
      11067
      2015-12-02
      2015
      12
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      10.0
      5.84
      1.943
      6.409981
    
    
      17831
      2015-02-11
      2015
      2
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      1.0
      9.05
      0.660
      8.120220
    
    
      17832
      2015-02-11
      2015
      2
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      5.0
      6.98
      1.730
      6.693910
    
    
      17833
      2015-02-11
      2015
      2
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      10.0
      7.29
      2.020
      6.307340
    
    
      18397
      2015-01-14
      2015
      1
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      1.0
      6.09
      NaN
      NaN
    
    
      18398
      2015-01-14
      2015
      1
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      5.0
      6.22
      NaN
      NaN
    
    
      18399
      2015-01-14
      2015
      1
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      10.0
      6.74
      NaN
      NaN
    
    
      18916
      2014-12-03
      2014
      12
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      1.0
      5.07
      2.285
      5.954095
    
    
      18917
      2014-12-03
      2014
      12
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      5.0
      5.05
      2.273
      5.970091
    
    
      18918
      2014-12-03
      2014
      12
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      10.0
      5.18
      2.310
      5.920770
    
    
      26382
      2014-01-16
      2014
      1
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      1.0
      9.76
      0.679
      8.094893
    
    
      26383
      2014-01-16
      2014
      1
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      5.0
      7.35
      1.542
      6.944514
    
    
      26384
      2014-01-16
      2014
      1
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      10.0
      9.37
      1.958
      6.389986
    
    
      27000
      2013-12-04
      2013
      12
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      1.0
      5.57
      2.373
      5.836791
    
    
      27001
      2013-12-04
      2013
      12
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      5.0
      5.66
      2.373
      5.836791
    
    
      27002
      2013-12-04
      2013
      12
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      10.0
      5.75
      2.373
      5.836791
    
    
      34344
      2013-01-15
      2013
      1
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      1.0
      6.49
      1.384
      7.155128
    
    
      34345
      2013-01-15
      2013
      1
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      5.0
      7.81
      2.207
      6.058069
    
    
      34346
      2013-01-15
      2013
      1
      SE654470-222700
      22 - Norra Bottenviken. Inre kustvatten
      10.0
      7.96
      2.721
      5.372907



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()



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'}

Set subset time and area filter



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': []}

Apply subset filter



In [22]:

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









    Out[22]:





True

Extract filtered data



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': []}



In [ ]:

Quality factor Nutrients



In [ ]:

    
lv_workspace.initiate_quality_factors()



In [ ]:



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In [ ]:

	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

	SDATE	YEAR	MONTH	POSITION	VISS_EU_CD	WATER_TYPE_AREA	DEPH	MNDEP	MXDEP	BQIm
94754	2017-06-28	2017	6	59.01_11.44	SE590020-114520	02 - Västkustens fjordar	NaN	9.6	9.6	6.861810
94755	2017-06-28	2017	6	59.01_11.44	SE590020-114520	02 - Västkustens fjordar	NaN	9.6	9.6	4.985970
94756	2017-06-28	2017	6	59.03_11.42	SE590860-113810	02 - Västkustens fjordar	NaN	21.6	21.6	2.844030
94757	2017-06-28	2017	6	59.03_11.42	SE590860-113810	02 - Västkustens fjordar	NaN	21.6	21.6	2.316120
94758	2017-06-28	2017	6	59.07_11.39	SE590860-113810	02 - Västkustens fjordar	NaN	35.7	35.7	0.667618
94759	2017-06-28	2017	6	59.11_11.31	SE590860-113810	02 - Västkustens fjordar	NaN	33.6	33.6	12.692900
94760	2017-06-28	2017	6	59.11_11.31	SE590860-113810	02 - Västkustens fjordar	NaN	33.3	33.3	11.458700
94761	2017-06-28	2017	6	59.08_11.22	SE590900-112300	02 - Västkustens fjordar	NaN	31.2	31.2	11.670900
94762	2017-06-28	2017	6	59.08_11.22	SE590900-112300	02 - Västkustens fjordar	NaN	31.2	31.2	12.202500
94763	2017-06-28	2017	6	59.09_11.22	SE590900-112300	02 - Västkustens fjordar	NaN	34	34	13.570800
94764	2017-06-28	2017	6	59.09_11.22	SE590900-112300	02 - Västkustens fjordar	NaN	34	34	13.522100
94765	2017-06-28	2017	6	58.97_11.09	SE590670-111380	01n - Västkustens inre kustvatten	NaN	61	61	12.179000
94766	2017-06-28	2017	6	58.97_11.09	SE590670-111380	01n - Västkustens inre kustvatten	NaN	61	61	11.601800
94767	2017-06-02	2017	6	56.56_12.82	SE563330-124600	05 - Södra Hallands och norra Öresunds kustvatten	NaN	16	16	4.857090
94768	2017-06-02	2017	6	56.56_12.82	SE563330-124600	05 - Södra Hallands och norra Öresunds kustvatten	NaN	16	16	6.748780
94769	2017-06-02	2017	6	56.56_12.82	SE563330-124600	05 - Södra Hallands och norra Öresunds kustvatten	NaN	16	16	5.344290
94770	2017-06-02	2017	6	56.56_12.82	SE563330-124600	05 - Södra Hallands och norra Öresunds kustvatten	NaN	16	16	7.503390
94771	2017-06-02	2017	6	56.56_12.82	SE563330-124600	05 - Södra Hallands och norra Öresunds kustvatten	NaN	16	16	4.536430
94772	2017-06-01	2017	6	59.60_18.94	SE593500-190000	12n - Östergötlands och Stockholms skärgård. M...	NaN	24	24	11.270000
94773	2017-05-31	2017	5	59.54_18.96	SE593500-190000	12n - Östergötlands och Stockholms skärgård. M...	NaN	21	21	7.381660
94774	2017-05-31	2017	5	59.51_19.00	SE593500-190000	12n - Östergötlands och Stockholms skärgård. M...	NaN	59.5	59.5	9.612610
94775	2017-05-30	2017	5	56.57_12.61	SE563000-123351	05 - Södra Hallands och norra Öresunds kustvatten	NaN	22	22	8.638710
94776	2017-05-30	2017	5	56.57_12.61	SE563000-123351	05 - Södra Hallands och norra Öresunds kustvatten	NaN	22	22	8.557110
94777	2017-05-30	2017	5	56.57_12.61	SE563000-123351	05 - Södra Hallands och norra Öresunds kustvatten	NaN	22	22	8.848160
94778	2017-05-30	2017	5	56.57_12.61	SE563000-123351	05 - Södra Hallands och norra Öresunds kustvatten	NaN	22	22	8.042580
94779	2017-05-30	2017	5	56.57_12.61	SE563000-123351	05 - Södra Hallands och norra Öresunds kustvatten	NaN	22	22	10.343300
94780	2017-05-30	2017	5	56.56_12.72	SE563330-124600	05 - Södra Hallands och norra Öresunds kustvatten	NaN	20	20	9.926600
94781	2017-05-30	2017	5	56.56_12.72	SE563330-124600	05 - Södra Hallands och norra Öresunds kustvatten	NaN	20	20	9.912360
94782	2017-05-30	2017	5	56.56_12.72	SE563330-124600	05 - Södra Hallands och norra Öresunds kustvatten	NaN	20	20	9.426740
94783	2017-05-30	2017	5	56.56_12.72	SE563330-124600	05 - Södra Hallands och norra Öresunds kustvatten	NaN	20	20	10.446600
...	...	...	...	...	...	...	...	...	...	...
96866	2013-05-06	2013	5	56.03_12.69	SE562290-124131	06 - Öresunds kustvatten	NaN	29	29	12.613500
96867	2013-05-06	2013	5	56.03_12.69	SE562290-124131	06 - Öresunds kustvatten	NaN	29	29	12.071600
96868	2013-05-06	2013	5	56.03_12.69	SE562290-124131	06 - Öresunds kustvatten	NaN	29	29	12.137100
96869	2013-05-03	2013	5	58.93_11.14	SE585600-110880	01n - Västkustens inre kustvatten	NaN	27	27	12.169300
96870	2013-05-03	2013	5	58.93_11.14	SE585600-110880	01n - Västkustens inre kustvatten	NaN	27	27	13.209700
96871	2013-05-03	2013	5	58.93_11.14	SE585600-110880	01n - Västkustens inre kustvatten	NaN	27	27	13.109400
96872	2013-05-02	2013	5	58.35_11.40	SE582000-112350	01n - Västkustens inre kustvatten	NaN	32	32	11.794600
96873	2013-05-02	2013	5	58.35_11.40	SE582000-112350	01n - Västkustens inre kustvatten	NaN	32	32	11.704300
96874	2013-05-02	2013	5	58.35_11.40	SE582000-112350	01n - Västkustens inre kustvatten	NaN	32	32	11.474500
96875	2013-05-02	2013	5	58.97_11.09	SE590670-111380	01n - Västkustens inre kustvatten	NaN	61	61	12.435400
96876	2013-05-02	2013	5	58.97_11.09	SE590670-111380	01n - Västkustens inre kustvatten	NaN	61	61	11.012300
96877	2013-05-02	2013	5	58.97_11.09	SE590670-111380	01n - Västkustens inre kustvatten	NaN	61	61	12.547400
96878	2013-05-02	2013	5	58.80_11.16	SE584725-111050	01n - Västkustens inre kustvatten	NaN	46	46	11.486100
96879	2013-05-02	2013	5	58.80_11.16	SE584725-111050	01n - Västkustens inre kustvatten	NaN	46	46	10.438500
96880	2013-05-02	2013	5	58.80_11.16	SE584725-111050	01n - Västkustens inre kustvatten	NaN	46	46	11.613500
96881	2013-05-02	2013	5	58.70_11.18	SE584200-105901	03 - Västkustens yttre kustvatten. Skagerrak	NaN	41	41	12.999100
96882	2013-05-02	2013	5	58.70_11.18	SE584200-105901	03 - Västkustens yttre kustvatten. Skagerrak	NaN	41	41	13.338500
96883	2013-05-02	2013	5	58.70_11.18	SE584200-105901	03 - Västkustens yttre kustvatten. Skagerrak	NaN	41	41	13.439500
96884	2013-05-02	2013	5	58.73_11.17	SE584400-116000	01n - Västkustens inre kustvatten	NaN	31	31	12.741700
96885	2013-05-02	2013	5	58.73_11.17	SE584400-116000	01n - Västkustens inre kustvatten	NaN	31	31	13.293900
96886	2013-05-02	2013	5	58.73_11.17	SE584400-116000	01n - Västkustens inre kustvatten	NaN	31	31	13.076300
96887	2013-05-02	2013	5	58.38_11.15	SE582700-110451	03 - Västkustens yttre kustvatten. Skagerrak	NaN	48	48	12.520100
96888	2013-05-02	2013	5	58.38_11.15	SE582700-110451	03 - Västkustens yttre kustvatten. Skagerrak	NaN	48	48	12.158700
96889	2013-05-02	2013	5	58.38_11.15	SE582700-110451	03 - Västkustens yttre kustvatten. Skagerrak	NaN	48	48	12.023600
96890	2013-05-02	2013	5	58.34_11.36	SE580500-111801	03 - Västkustens yttre kustvatten. Skagerrak	NaN	28	28	10.701500
96891	2013-05-02	2013	5	58.34_11.36	SE580500-111801	03 - Västkustens yttre kustvatten. Skagerrak	NaN	28	28	11.404400
96892	2013-05-02	2013	5	58.34_11.36	SE580500-111801	03 - Västkustens yttre kustvatten. Skagerrak	NaN	28	28	10.524400
96893	2013-05-02	2013	5	58.61_11.14	SE584200-105901	03 - Västkustens yttre kustvatten. Skagerrak	NaN	48	48	10.848100
96894	2013-05-02	2013	5	58.61_11.14	SE584200-105901	03 - Västkustens yttre kustvatten. Skagerrak	NaN	48	48	7.732150
96895	2013-05-02	2013	5	58.61_11.14	SE584200-105901	03 - Västkustens yttre kustvatten. Skagerrak	NaN	48	48	10.384400

	SDATE	YEAR	MONTH	POSITION	VISS_EU_CD	WATER_TYPE_AREA	BQIm	MNDEP	MXDEP
95125	2016-05-25	2016	5	57.36_11.98	SE572072-115880	01s - Västkustens inre kustvatten	7.54714	8.5	8.5
95126	2016-05-25	2016	5	57.36_11.98	SE572072-115880	01s - Västkustens inre kustvatten	4.31313	8.5	8.5
95127	2016-05-25	2016	5	57.36_11.98	SE572072-115880	01s - Västkustens inre kustvatten	5.53224	8.5	8.5
95128	2016-05-25	2016	5	57.36_11.98	SE572072-115880	01s - Västkustens inre kustvatten	5.15111	8.5	8.5
95129	2016-05-25	2016	5	57.36_11.98	SE572072-115880	01s - Västkustens inre kustvatten	5.14814	8.5	8.5
95150	2016-05-25	2016	5	57.36_11.99	SE572072-115880	01s - Västkustens inre kustvatten	3.11854	6	6
95151	2016-05-25	2016	5	57.34_11.98	SE572072-115880	01s - Västkustens inre kustvatten	10.05430	21	21
95152	2016-05-25	2016	5	57.34_11.98	SE572072-115880	01s - Västkustens inre kustvatten	10.04500	21	21
95153	2016-05-25	2016	5	57.35_11.96	SE572072-115880	01s - Västkustens inre kustvatten	5.57512	25	25
95154	2016-05-25	2016	5	57.35_11.97	SE572072-115880	01s - Västkustens inre kustvatten	8.28729	17.7	17.7
95155	2016-05-25	2016	5	57.36_11.98	SE572072-115880	01s - Västkustens inre kustvatten	3.64912	8.3	8.3
95156	2016-05-25	2016	5	57.36_11.98	SE572072-115880	01s - Västkustens inre kustvatten	4.67920	9.1	9.1
95157	2016-05-25	2016	5	57.35_11.97	SE572072-115880	01s - Västkustens inre kustvatten	9.31441	21.2	21.2
95158	2016-05-25	2016	5	57.34_11.97	SE572072-115880	01s - Västkustens inre kustvatten	6.72661	21.4	21.4

	YEAR	POSITION	number_of_dates	min	max	mean_ek_value
1	2013	65.73_22.45	10	0.040906	1.0	0.638793
9	2014	65.73_22.45	10	0.060073	1.0	0.614813
13	2015	65.73_22.45	9	0.043947	1.0	0.619119
19	2016	65.73_22.45	10	0.056598	1.0	0.687118

	SDATE	YEAR	MONTH	VISS_EU_CD	WATER_TYPE_AREA	DEPH	DIN	SALT_CTD	REFERENCE_VALUE
7057	2016-12-07	2016	12	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	1.0	6.35	2.000	6.334000
7058	2016-12-07	2016	12	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	5.0	6.41	2.000	6.334000
7059	2016-12-07	2016	12	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	10.0	6.81	2.000	6.334000
7349	2016-11-09	2016	11	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	1.0	7.56	2.000	6.334000
7350	2016-11-09	2016	11	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	5.0	5.40	2.000	6.334000
7351	2016-11-09	2016	11	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	10.0	5.67	2.000	6.334000
10343	2016-01-27	2016	1	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	1.0	11.37	2.000	6.334000
10344	2016-01-27	2016	1	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	5.0	7.53	2.279	5.962093
10345	2016-01-27	2016	1	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	10.0	7.26	2.345	5.874115
11065	2015-12-02	2015	12	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	1.0	5.86	1.945	6.407315
11066	2015-12-02	2015	12	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	5.0	6.01	1.941	6.412647
11067	2015-12-02	2015	12	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	10.0	5.84	1.943	6.409981
17831	2015-02-11	2015	2	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	1.0	9.05	0.660	8.120220
17832	2015-02-11	2015	2	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	5.0	6.98	1.730	6.693910
17833	2015-02-11	2015	2	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	10.0	7.29	2.020	6.307340
18397	2015-01-14	2015	1	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	1.0	6.09	NaN	NaN
18398	2015-01-14	2015	1	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	5.0	6.22	NaN	NaN
18399	2015-01-14	2015	1	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	10.0	6.74	NaN	NaN
18916	2014-12-03	2014	12	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	1.0	5.07	2.285	5.954095
18917	2014-12-03	2014	12	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	5.0	5.05	2.273	5.970091
18918	2014-12-03	2014	12	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	10.0	5.18	2.310	5.920770
26382	2014-01-16	2014	1	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	1.0	9.76	0.679	8.094893
26383	2014-01-16	2014	1	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	5.0	7.35	1.542	6.944514
26384	2014-01-16	2014	1	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	10.0	9.37	1.958	6.389986
27000	2013-12-04	2013	12	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	1.0	5.57	2.373	5.836791
27001	2013-12-04	2013	12	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	5.0	5.66	2.373	5.836791
27002	2013-12-04	2013	12	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	10.0	5.75	2.373	5.836791
34344	2013-01-15	2013	1	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	1.0	6.49	1.384	7.155128
34345	2013-01-15	2013	1	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	5.0	7.81	2.207	6.058069
34346	2013-01-15	2013	1	SE654470-222700	22 - Norra Bottenviken. Inre kustvatten	10.0	7.96	2.721	5.372907

	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

	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

	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

	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

	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

	AMON	BQIm	CPHL	DEPH	DOXY_BTL	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