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 [3]:
import core
import importlib
importlib.reload(core)
import logging
importlib.reload(core)
try:
logging.shutdown()
importlib.reload(logging)
except:
pass
import pandas as pd
import numpy as np
import json
from event_handler import EventHandler
print(core.__file__)
pd.__version__
..\core\__init__.py
Out[3]:
'0.19.2'
In [4]:
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)
In [5]:
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?
In [77]:
ekos = EventHandler(root_directory)
2018-04-03 19:01:57,435 event_handler.py 64 __init__ DEBUG Start EventHandler: event_handler
2018-04-03 19:01:57,449 event_handler.py 65 __init__ DEBUG
2018-04-03 19:01:57,453 event_handler.py 66 __init__ INFO TEST info logger
2018-04-03 19:01:57,455 event_handler.py 67 __init__ WARNING TEST warning logger
2018-04-03 19:01:57,458 event_handler.py 68 __init__ ERROR TEST error logger
2018-04-03 19:01:57,480 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
In [77]:
# Remove all workspaces belonging to test user
# ekos.remove_test_user_workspaces()
In [7]:
# remove selected workspace
workspace_uuid = ekos.get_unique_id_for_alias(user_id, 'lena_indicator')
#ekos.delete_workspace(user_id = user_id, unique_id = workspace_uuid, permanently=True)
In [48]:
#default_workspace = core.WorkSpace(alias = 'default_workspace',
# unique_id = 'default_workspace',
# parent_directory=workspace_directory,
# resource_directory=resource_directory,
# user_id = 'default')
In [49]:
#default_workspace.step_0.print_all_paths()
In [50]:
#default_workspace.import_default_data()
In [52]:
#ekos.copy_workspace(user_id = user_id, source_alias = 'default_workspace', target_alias = 'lena_indicator')
In [8]:
workspace_alias = 'lena_indicator'
In [78]:
ekos.load_workspace(user_id, alias = workspace_alias)
#Här får jag ibland felmeddelande core has no attribute ParameterMapping
2018-04-03 19:02:05,608 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[78]:
True
In [10]:
workspace_uuid = ekos.get_unique_id_for_alias(user_id, workspace_alias)
print(workspace_uuid)
147f5d47-773c-43f0-b337-57208718d0cf
In [15]:
#ekos.import_default_data(user_id, workspace_alias = workspace_alias)
2018-03-26 15:07:46,967 event_handler.py 938 import_default_data DEBUG Trying to load default data in workspace "147f5d47-773c-43f0-b337-57208718d0cf" with alias "lena_indicator"
2018-03-26 15:07:48,013 workspaces.py 972 import_default_data DEBUG Default data has been copied to workspace raw data folder.
In [79]:
ekos.load_data(user_id = user_id, unique_id = workspace_uuid)
w = ekos.get_workspace(user_id, unique_id = workspace_uuid, alias = workspace_alias)
len(w.data_handler.get_all_column_data_df())
2018-04-03 19:02:14,237 workspaces.py 1395 load_all_data DEBUG data has been loaded from existing all_data.txt file.
2018-04-03 19:02:14,237 event_handler.py 919 get_workspace DEBUG Getting workspace "147f5d47-773c-43f0-b337-57208718d0cf" with alias "lena_indicator"
Out[79]:
101465
In [80]:
f0 = w.get_data_filter_object(step=0)
f0.include_list_filter
#include_WB = ['Norrbottens skärgårds kustvatten']#,
#'N S M Bottenhavets kustvatten']
include_stations = []
#exclude_WB = ['Norrbottens skärgårds kustvatten']
include_years = []
#w.set_data_filter(step=0, filter_type='include_list', filter_name='WATERBODY_NAME', data=include_WB)
w.set_data_filter(step=0, filter_type='include_list', filter_name='STATN', data=include_stations)
#w.set_data_filter(step=0, filter_type='exclude_list', filter_name='WATERBODY_NAME', data=exclude_WB)
w.set_data_filter(step=0, filter_type='include_list', filter_name='MYEAR', data=include_years)
f0.include_list_filter
Out[80]:
{'MYEAR': [], 'STATN': []}
In [81]:
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
In [82]:
data_after_first_filter = w.get_filtered_data(step=0) # level=0 means first filter
print('{} rows matching the filter criteria'.format(len(data_after_first_filter)))
2018-04-03 19:02:21,963 workspaces.py 1271 get_filtered_data DEBUG STEP: step_0
101465 rows matching the filter criteria
In [53]:
#ekos.copy_subset(user_id,
# workspace_alias=workspace_alias,
# workspace_uuid=None,
# subset_source_alias='default_subset',
# subset_source_uuid='default_subset',
# subset_target_alias='A')
In [83]:
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)
2018-04-03 19:02:25,776 workspaces.py 1271 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'],
dtype='object')
In [84]:
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[84]:
{'BQI': <core.filters.SettingsDataFilter at 0xb785080>,
'din_winter': <core.filters.SettingsDataFilter at 0xb785828>,
'dip_winter': <core.filters.SettingsDataFilter at 0xb785518>,
'ntot_summer': <core.filters.SettingsDataFilter at 0xb785da0>,
'ntot_winter': <core.filters.SettingsDataFilter at 0xb785668>,
'ptot_summer': <core.filters.SettingsDataFilter at 0x14357358>,
'ptot_winter': <core.filters.SettingsDataFilter at 0x991ad68>}
In [53]:
#ref_set = w.get_step_object(step = 2, subset = subset_uuid).indicator_ref_settings['din_winter']
#ref_set.settings.ref_columns
#ref_set.settings.df[ref_set.settings.ref_columns]
In [85]:
dinw_filter_set = w.get_step_object(step = 2, subset = subset_uuid).get_indicator_data_filter_settings('din_winter')
dinw_filter_set.settings.df
Out[85]:
TYPE_AREA_NUMBER
TYPE_AREA_SUFFIX
DEPH_INTERVAL
MIN_NR_YEARS
MIN_NR_VALUES
TIME_DELTA_TOLERANCE
POS_RADIUS_TOLERANCE
DEPH_TOLERANCE
MONTH_LIST
REF_VALUE_LIMIT
HG_VALUE_LIMIT
GM_VALUE_LIMIT
MP_VALUE_LIMIT
PB_VALUE_LIMIT
HG_EQR_LIMIT
GM_EQR_LIMIT
MP_EQR_LIMIT
PB_EQR_LIMIT
SALINITY MAX
0
1
n
0-10
3
3
3
0.1
5
12;1;2;3
-0.51852*s+20
-0.64815*s+25
-0.77778*s+30
-1.1667*s+45
-1.8148*s+70
0.8
0.66
0.44
0.28
27
1
1
s
0-10
3
3
3
0.1
5
12;1;2;3
-0.525*s+15
-0.656*s+18.75
-0.7875*s+22.5
-1.1813*s+33.75
-1.838*s+52.5
0.8
0.67
0.44
0.29
20
2
2
0-10
3
3
3
0.1
5
12;1;2;3
-0.51852*s+20
-0.64815*s+25
-0.77778*s+30
-1.1667*s+45
-1.8148*s+70
0.8
0.66
0.44
0.28
27
3
3
0-10
3
3
3
0.1
5
12;1;2;3
-0.51852*s+20
-0.64815*s+25
-0.77778*s+30
-1.1667*s+45
-1.8148*s+70
0.8
0.66
0.44
0.28
27
4
4
0-10
3
3
3
0.1
5
12;1;2;3
-0.525*s+15
-0.656*s+18.75
-0.7875*s+22.5
-1.1813*s+33.75
-1.838*s+52.5
0.8
0.67
0.44
0.29
20
5
5
0-10
3
3
3
0.1
5
12;1;2;3
0.125*s+1.5
0.1563*s+1.88
0.1875*s+2.25
0.2813*s+3.375
0.4375*s+5.25
0.8
0.67
0.44
0.29
20
6
6
0-10
3
3
3
0.1
5
12;1;2;3
0.125*s+1.5
0.1563*s+1.88
0.1875*s+2.25
0.2813*s+3.375
0.4375*s+5.25
0.8
0.67
0.44
0.29
20
7
7
0-10
3
3
3
0.1
5
12;1;2
-4.928*s+37
-6.1618*s+46.2
-7.3929*s+55.5
-11.089*s+83.25
-17.25*s+130
0.8
0.67
0.45
0.29
7
8
8
0-10
3
3
3
0.1
5
12;1;2
-4.928*s+37
-6.1618*s+46.2
-7.3929*s+55.5
-11.089*s+83.25
-17.25*s+130
0.8
0.67
0.45
0.29
7
9
9
0-10
3
3
3
0.1
5
12;1;2
-4.928*s+37
-6.1618*s+46.2
-7.3929*s+55.5
-11.089*s+83.25
-17.25*s+130
0.8
0.67
0.45
0.29
7
10
10
0-10
3
3
3
0.1
5
12;1;2
0*s+2.5
0*s+3.125
0*s+3.75
0*s+5.625
0*s+8.75
0.81
0.66
0.45
0.28
7
11
11
0-10
3
3
3
0.1
5
12;1;2
0*s+2.5
0*s+3.125
0*s+3.75
0*s+5.625
0*s+8.75
0.81
0.66
0.45
0.28
7
12
12
s
0-10
3
3
3
0.1
5
12;1;2
-1.0833*s+9
-1.354*s+11.25
-1.625*s+13.5
-2.4375*s+20.25
-3.792*s+31.5
0.8
0.66
0.44
0.29
6
13
12
n
0-10
3
3
3
0.1
5
12;1;2
-0.75*s+7
-0.9375*s+8.75
-1.125*s+10.5
-1.6875*s+15.75
-2.625*s+24.5
0.8
0.67
0.44
0.29
6
14
13
0-10
3
3
3
0.1
5
12;1;2
-1.0833*s+9
-1.354*s+11.25
-1.625*s+13.5
-2.4375*s+20.25
-3.792*s+31.5
0.8
0.66
0.44
0.29
6
15
14
0-10
3
3
3
0.1
5
12;1;2
-1.0833*s+9
-1.354*s+11.25
-1.625*s+13.5
-2.4375*s+20.25
-3.792*s+31.5
0.8
0.66
0.44
0.29
6
16
15
0-10
3
3
3
0.1
5
12;1;2
-0.75*s+7
-0.9375*s+8.75
-1.125*s+10.5
-1.6875*s+15.75
-2.625*s+24.5
0.8
0.67
0.44
0.29
6
17
16
0-10
3
3
3
0.1
5
11;12;1;2
-0.4*s+5
-0.5*s+6.25
-0.6*s+7.5
-0.9*s+11.25
-1.4*s+17.5
0.8
0.67
0.44
0.29
5
18
17
0-10
3
3
3
0.1
5
11;12;1;2
-0.4*s+5
-0.5*s+6.25
-0.6*s+7.5
-0.9*s+11.25
-1.4*s+17.5
0.8
0.67
0.44
0.29
5
19
18
0-10
3
3
3
0.1
5
11;12;1;2
-0.2*s+5
-0.25*s+6.25
-0.3*s+7.5
-0.45*s+11.25
-0.7*s+17.5
0.8
0.66
0.44
0.28
5
20
19
0-10
3
3
3
0.1
5
11;12;1;2
-0.2*s+5
-0.25*s+6.25
-0.3*s+7.5
-0.45*s+11.25
-0.7*s+17.5
0.8
0.66
0.44
0.28
5
21
20
0-10
3
3
3
0.1
5
11;12;1;2
-0.76*s+8
-0.95*s+10
-1.14*s+12
-1.71*s+18
-2.66*s+28
0.8
0.67
0.44
0.29
5
22
21
0-10
3
3
3
0.1
5
11;12;1;2
-0.76*s+8
-0.95*s+10
-1.14*s+12
-1.71*s+18
-2.66*s+28
0.8
0.67
0.44
0.29
5
23
22
0-10
3
3
3
0.1
5
11;12;1;2
-1.333*s+9
-1.667*s+11.25
-2*s+13.5
-3*s+20.25
-4.667*s+31.5
0.8
0.67
0.44
0.29
3
24
23
0-10
3
3
3
0.1
5
11;12;1;2
-1.333*s+9
-1.667*s+11.25
-2*s+13.5
-3*s+20.25
-4.667*s+31.5
0.8
0.67
0.44
0.29
3
25
24
0-10
3
3
3
0.1
5
12;1;2
-0.75*s+7
-0.9375*s+8.75
-1.125*s+10.5
-1.6875*s+15.75
-2.625*s+24.5
0.8
0.67
0.44
0.29
6
26
25
0-10
3
3
3
0.1
5
12;1;2;3
-0.525*s+15
-0.656*s+18.75
-0.7875*s+22.5
-1.1813*s+33.75
-1.838*s+52.5
0.8
0.67
0.44
0.29
20
In [96]:
dinw_filter_set = w.get_step_object(step = 2, subset = subset_uuid).get_indicator_ref_settings('din_winter')
dinw_filter_set.allowed_variables
Out[96]:
TYPE_AREA_NUMBER
TYPE_AREA_SUFFIX
DEPH_INTERVAL
MIN_NR_YEARS
MIN_NR_VALUES
TIME_DELTA_TOLERANCE
POS_RADIUS_TOLERANCE
DEPH_TOLERANCE
MONTH_LIST
REF_VALUE_LIMIT
HG_VALUE_LIMIT
GM_VALUE_LIMIT
MP_VALUE_LIMIT
PB_VALUE_LIMIT
HG_EQR_LIMIT
GM_EQR_LIMIT
MP_EQR_LIMIT
PB_EQR_LIMIT
SALINITY MAX
0
1
n
0-10
3
3
3
0.1
5
12;1;2;3
-0.51852*s+20
-0.64815*s+25
-0.77778*s+30
-1.1667*s+45
-1.8148*s+70
0.8
0.66
0.44
0.28
27
1
1
s
0-10
3
3
3
0.1
5
12;1;2;3
-0.525*s+15
-0.656*s+18.75
-0.7875*s+22.5
-1.1813*s+33.75
-1.838*s+52.5
0.8
0.67
0.44
0.29
20
2
2
0-10
3
3
3
0.1
5
12;1;2;3
-0.51852*s+20
-0.64815*s+25
-0.77778*s+30
-1.1667*s+45
-1.8148*s+70
0.8
0.66
0.44
0.28
27
3
3
0-10
3
3
3
0.1
5
12;1;2;3
-0.51852*s+20
-0.64815*s+25
-0.77778*s+30
-1.1667*s+45
-1.8148*s+70
0.8
0.66
0.44
0.28
27
4
4
0-10
3
3
3
0.1
5
12;1;2;3
-0.525*s+15
-0.656*s+18.75
-0.7875*s+22.5
-1.1813*s+33.75
-1.838*s+52.5
0.8
0.67
0.44
0.29
20
5
5
0-10
3
3
3
0.1
5
12;1;2;3
0.125*s+1.5
0.1563*s+1.88
0.1875*s+2.25
0.2813*s+3.375
0.4375*s+5.25
0.8
0.67
0.44
0.29
20
6
6
0-10
3
3
3
0.1
5
12;1;2;3
0.125*s+1.5
0.1563*s+1.88
0.1875*s+2.25
0.2813*s+3.375
0.4375*s+5.25
0.8
0.67
0.44
0.29
20
7
7
0-10
3
3
3
0.1
5
12;1;2
-4.928*s+37
-6.1618*s+46.2
-7.3929*s+55.5
-11.089*s+83.25
-17.25*s+130
0.8
0.67
0.45
0.29
7
8
8
0-10
3
3
3
0.1
5
12;1;2
-4.928*s+37
-6.1618*s+46.2
-7.3929*s+55.5
-11.089*s+83.25
-17.25*s+130
0.8
0.67
0.45
0.29
7
9
9
0-10
3
3
3
0.1
5
12;1;2
-4.928*s+37
-6.1618*s+46.2
-7.3929*s+55.5
-11.089*s+83.25
-17.25*s+130
0.8
0.67
0.45
0.29
7
10
10
0-10
3
3
3
0.1
5
12;1;2
0*s+2.5
0*s+3.125
0*s+3.75
0*s+5.625
0*s+8.75
0.81
0.66
0.45
0.28
7
11
11
0-10
3
3
3
0.1
5
12;1;2
0*s+2.5
0*s+3.125
0*s+3.75
0*s+5.625
0*s+8.75
0.81
0.66
0.45
0.28
7
12
12
s
0-10
3
3
3
0.1
5
12;1;2
-1.0833*s+9
-1.354*s+11.25
-1.625*s+13.5
-2.4375*s+20.25
-3.792*s+31.5
0.8
0.66
0.44
0.29
6
13
12
n
0-10
3
3
3
0.1
5
12;1;2
-0.75*s+7
-0.9375*s+8.75
-1.125*s+10.5
-1.6875*s+15.75
-2.625*s+24.5
0.8
0.67
0.44
0.29
6
14
13
0-10
3
3
3
0.1
5
12;1;2
-1.0833*s+9
-1.354*s+11.25
-1.625*s+13.5
-2.4375*s+20.25
-3.792*s+31.5
0.8
0.66
0.44
0.29
6
15
14
0-10
3
3
3
0.1
5
12;1;2
-1.0833*s+9
-1.354*s+11.25
-1.625*s+13.5
-2.4375*s+20.25
-3.792*s+31.5
0.8
0.66
0.44
0.29
6
16
15
0-10
3
3
3
0.1
5
12;1;2
-0.75*s+7
-0.9375*s+8.75
-1.125*s+10.5
-1.6875*s+15.75
-2.625*s+24.5
0.8
0.67
0.44
0.29
6
17
16
0-10
3
3
3
0.1
5
11;12;1;2
-0.4*s+5
-0.5*s+6.25
-0.6*s+7.5
-0.9*s+11.25
-1.4*s+17.5
0.8
0.67
0.44
0.29
5
18
17
0-10
3
3
3
0.1
5
11;12;1;2
-0.4*s+5
-0.5*s+6.25
-0.6*s+7.5
-0.9*s+11.25
-1.4*s+17.5
0.8
0.67
0.44
0.29
5
19
18
0-10
3
3
3
0.1
5
11;12;1;2
-0.2*s+5
-0.25*s+6.25
-0.3*s+7.5
-0.45*s+11.25
-0.7*s+17.5
0.8
0.66
0.44
0.28
5
20
19
0-10
3
3
3
0.1
5
11;12;1;2
-0.2*s+5
-0.25*s+6.25
-0.3*s+7.5
-0.45*s+11.25
-0.7*s+17.5
0.8
0.66
0.44
0.28
5
21
20
0-10
3
3
3
0.1
5
11;12;1;2
-0.76*s+8
-0.95*s+10
-1.14*s+12
-1.71*s+18
-2.66*s+28
0.8
0.67
0.44
0.29
5
22
21
0-10
3
3
3
0.1
5
11;12;1;2
-0.76*s+8
-0.95*s+10
-1.14*s+12
-1.71*s+18
-2.66*s+28
0.8
0.67
0.44
0.29
5
23
22
0-10
3
3
3
0.1
5
11;12;1;2
-1.333*s+9
-1.667*s+11.25
-2*s+13.5
-3*s+20.25
-4.667*s+31.5
0.8
0.67
0.44
0.29
3
24
23
0-10
3
3
3
0.1
5
11;12;1;2
-1.333*s+9
-1.667*s+11.25
-2*s+13.5
-3*s+20.25
-4.667*s+31.5
0.8
0.67
0.44
0.29
3
25
24
0-10
3
3
3
0.1
5
12;1;2
-0.75*s+7
-0.9375*s+8.75
-1.125*s+10.5
-1.6875*s+15.75
-2.625*s+24.5
0.8
0.67
0.44
0.29
6
26
25
0-10
3
3
3
0.1
5
12;1;2;3
-0.525*s+15
-0.656*s+18.75
-0.7875*s+22.5
-1.1813*s+33.75
-1.838*s+52.5
0.8
0.67
0.44
0.29
20
In [86]:
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()))
number of waterbodies in step 1: 310
number of type areas in step 1: 27
In [87]:
for type_area in typeA_list:
w.apply_indicator_data_filter(step = 2,
subset = subset_uuid,
indicator = 'din_winter',
type_area = type_area)
add filter for step: step_2, type area: 14, indicator: din_winter, level: None
Water body None
14
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 14
RESULT [[0, 10]]
14
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 14
add filter for step: step_2, type area: 2, indicator: din_winter, level: None
Water body None
2
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 2
RESULT [[0, 10]]
2
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 2
add filter for step: step_2, type area: 9, indicator: din_winter, level: None
Water body None
9
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 9
RESULT [[0, 10]]
9
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 9
add filter for step: step_2, type area: 22, indicator: din_winter, level: None
Water body None
22
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 22
RESULT [[0, 10]]
22
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 22
add filter for step: step_2, type area: 20, indicator: din_winter, level: None
Water body None
20
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 20
RESULT [[0, 10]]
20
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 20
add filter for step: step_2, type area: 18, indicator: din_winter, level: None
Water body None
18
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 18
RESULT [[0, 10]]
18
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 18
add filter for step: step_2, type area: 12n, indicator: din_winter, level: None
Water body None
12n
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 12n
RESULT [[0, 10]]
12n
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 12n
add filter for step: step_2, type area: 13, indicator: din_winter, level: None
Water body None
13
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 13
RESULT [[0, 10]]
13
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 13
add filter for step: step_2, type area: 12s, indicator: din_winter, level: None
Water body None
12s
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 12s
RESULT [[0, 10]]
12s
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 12s
add filter for step: step_2, type area: 1n, indicator: din_winter, level: None
Water body None
1n
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 1n
RESULT [[0, 10]]
1n
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 1n
add filter for step: step_2, type area: 25, indicator: din_winter, level: None
Water body None
25
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 25
RESULT [[0, 10]]
25
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 25
add filter for step: step_2, type area: 21, indicator: din_winter, level: None
Water body None
21
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 21
RESULT [[0, 10]]
21
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 21
add filter for step: step_2, type area: 7, indicator: din_winter, level: None
Water body None
7
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 7
RESULT [[0, 10]]
7
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 7
add filter for step: step_2, type area: 5, indicator: din_winter, level: None
Water body None
5
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 5
RESULT [[0, 10]]
5
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 5
add filter for step: step_2, type area: 16, indicator: din_winter, level: None
Water body None
16
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 16
RESULT [[0, 10]]
16
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 16
add filter for step: step_2, type area: 23, indicator: din_winter, level: None
Water body None
23
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 23
RESULT [[0, 10]]
23
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 23
add filter for step: step_2, type area: 3, indicator: din_winter, level: None
Water body None
3
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 3
RESULT [[0, 10]]
3
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 3
add filter for step: step_2, type area: 1s, indicator: din_winter, level: None
Water body None
1s
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 1s
RESULT [[0, 10]]
1s
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 1s
add filter for step: step_2, type area: 4, indicator: din_winter, level: None
Water body None
4
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 4
RESULT [[0, 10]]
4
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 4
add filter for step: step_2, type area: 6, indicator: din_winter, level: None
Water body None
6
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 6
RESULT [[0, 10]]
6
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 6
add filter for step: step_2, type area: 8, indicator: din_winter, level: None
Water body None
8
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 8
RESULT [[0, 10]]
8
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 8
add filter for step: step_2, type area: 10, indicator: din_winter, level: None
Water body None
10
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 10
RESULT [[0, 10]]
10
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 10
add filter for step: step_2, type area: 24, indicator: din_winter, level: None
Water body None
24
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 24
RESULT [[0, 10]]
24
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 24
add filter for step: step_2, type area: 17, indicator: din_winter, level: None
Water body None
17
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 17
RESULT [[0, 10]]
17
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 17
add filter for step: step_2, type area: 15, indicator: din_winter, level: None
Water body None
15
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 15
RESULT [[0, 10]]
15
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 15
add filter for step: step_2, type area: 19, indicator: din_winter, level: None
Water body None
19
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 19
RESULT [[0, 10]]
19
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 19
add filter for step: step_2, type area: 11, indicator: din_winter, level: None
Water body None
11
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 11
RESULT [[0, 10]]
11
Index(['TYPE_AREA_NUMBER', 'TYPE_AREA_SUFFIX', 'DEPH_INTERVAL', 'MIN_NR_YEARS',
'MIN_NR_VALUES', 'TIME_DELTA_TOLERANCE', 'POS_RADIUS_TOLERANCE',
'DEPH_TOLERANCE', 'MONTH_LIST', 'REF_VALUE_LIMIT', 'HG_VALUE_LIMIT',
'GM_VALUE_LIMIT', 'MP_VALUE_LIMIT', 'PB_VALUE_LIMIT', 'HG_EQR_LIMIT',
'GM_EQR_LIMIT', 'MP_EQR_LIMIT', 'PB_EQR_LIMIT', 'SALINITY MAX'],
dtype='object')
type_area_type_area 11
In [83]:
#print(len(w.index_handler.booleans['step_0'][subset_uuid]['step_1']['step_2'].keys()))
#w.index_handler.booleans['step_0'][subset_uuid]['step_1']['step_2'].keys()
In [88]:
wb = '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 [89]:
#temp_df = w.get_filtered_data(step = 2, subset = subset_uuid, indicator = 'din_winter', water_body = 'SE654470-222700')
#temp_df.loc[(temp_df['MONTH'].isin([11, 12, 1, 2])) & (temp_df['VISS_EU_CD'].isin(['SE654470-222700']))][['MONTH', 'WATER_BODY_NAME', 'VISS_EU_CD', 'WATER_TYPE_AREA']]
print(w.get_filtered_data(step = 2, subset = subset_uuid, type_area = type_area, indicator = 'din_winter').MONTH.unique())
#[['MONTH', 'WATER_BODY_NAME', 'VISS_EU_CD']]
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()
2018-04-03 19:02:43,614 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
2018-04-03 19:02:43,629 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
2018-04-03 19:02:43,630 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
2018-04-03 19:02:43,630 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
[12 3 2 1]
0.0 10.0
Out[89]:
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 [ ]:
In [90]:
water_body = wb
temp_df = w.get_filtered_data(step = 2, subset = subset_uuid, indicator = 'din_winter', type_area = type_area)[['SDATE','MONTH', 'WATER_BODY_NAME', 'VISS_EU_CD', 'WATER_TYPE_AREA', 'DIN','SALT_CTD', 'SALT_BTL']].dropna(thresh=7)
print('Waterbodys left: {}'.format(temp_df['WATER_BODY_NAME'].unique()))
temp_df.loc[temp_df['WATER_BODY_NAME'].isin(['Gullmarn centralbassäng'])]
2018-04-03 19:02:46,960 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
Waterbodys left: ['Kungshamn s skärgård' 'Rivö fjord' 'Yttre Brofjorden'
'Gullmarn centralbassäng' 'Byfjorden' 'Halsefjorden' 'Havstensfjorden'
'Älgöfjorden' 'Koljö fjord' 'Dana fjord' 'S Kosterfjorden'
'Onsala kustvatten' 'Marstrandsfjorden' 'S Kalmarsunds utsjövatten'
'N m Öresunds kustvatten' 'Askims fjord' 'Stridsfjorden'
'Råssö-Resöfjorden sek namn' 'Färlevfjorden' 'Sannäsfjorden sek namn'
'Tanumskilen' 'Dynekilen' 'Strömstadsfjorden' 'Inre Tjärnöarkipelagen'
'Kalvöfjorden' 'Stigfjorden' 'Askeröfjorden' 'Ellösefjorden'
'Snäckedjupet' 'Brofjorden' 'Fjällbacka inre skärgård'
'Hunnebostrand skärgård' 'Bottnefjorden' 'Grebbestad inre skärgård'
'Trälebergskile' 'Åbyfjorden' 'N m Hallands kustvatten' 'Skälderviken'
'Sotefjorden' 'Lommabukten' 'S m Öresunds kustvatten' 'Inre Gamlebyviken'
'Vivassen' 'Västrumsfjärden' 'M n Kalmarsunds utsjövatten'
'Mönsteråsområdet sek namn' 'Lövöområdet sek namn'
'Ödänglaområdet sek namn' 'Emområdet sek namn' 'Gåsfjärden' 'Västra sjön'
'S n Kalmarsund' 'Edsviken' 'Krabbfjärden' 'Fågelöfjärden'
'Inre Oskarshamnsområdet' 'Simpevarpsområdet' 'Pampusfjärden'
'Yttre Bråviken' 'Inre Bråviken' 'Arkösund' 'Ronnebyfjärden'
'Torhamnsfjärden' 'Östra fjärden' 'Yttre redden' 'Hallarumsviken'
'Västra fjärden' 'Gussöfjärden' 'S v s Kalmarsunds kustvatten'
'Inre Slätbaken' 'Trännöfjärden' 'Kärrfjärden' 'Orren'
'Inre Pukaviksbukten' 'Tjäröfjärden'
'Västra Blekinge skärgårds kustvatten' 'Karlshamnsfjärden'
'Sölvesborgsviken' 'Inre Valdemarsviken' 'Yttre Valdemarsviken'
'Halsöfjärden' 'Kaggebofjärden' 'V Hanöbuktens kustvatten' 'Gaviksfjärden'
'Örefjärden' 'Rånefjärden' 'Norafjärden' 'Sörleviken' 'Bäckfjärden'
'Idbyfjärden' 'Lilla Värtan' 'Skäldervikens kustvatten'
'N Öresunds kustvatten' 'Lundåkrabukten' 'S Öresunds kustvatten'
'Björnöfjärden' 'Nämdöfjärden' 'Älgöfjärden' 'Sollenkrokafjärden'
'Laholmsbukten' 'Inre Kungsbackafjorden' 'Yttre Kungsbackafjorden'
'Repskärsfjärden' 'Bollstafjärden' 'Storfjärden' 'Ramöfjärden sek namn'
'Norra sundet' 'Södra Sundet' 'Kramforsfjärden sek namn'
'Yttre Österfjärden' 'Holmsund' 'S n Kvarkens kustvatten'
'Fjärdgrundsområdet sek namn' 'Mjältöfjärden sek namn' 'Sörbrändöfjärden'
'Sandöfjärden' 'Yttre Lulefjärden' 'Inrefjärden' 'Vargödraget'
'Yttrefjärden' 'Ö sydkustens kustvatten' 'Gårdsfjärden'
'Agöfjärden sek namn' 'S Höga kustens kustvatten' 'Alnösundet'
'Sundsvallsfjärden' 'Klingerfjärden' 'Draget' 'Svartviksfjärden'
'Hudiksvallsfjärden' 'Njutångersfjärden' 'Siviksfjärden' 'Enångersfjärden'
'Stora Värtan' 'Kapellskärs hamnområde' 'Norrtäljeviken' 'Björköfjärden'
'Ortalaviken' 'Galtfjärden' 'Bergshamraviken'
'Norrbottens skärgårds kustvatten' 'Lillfjärden' 'Baggholmsdraget'
'Göteborgs s skärgårds kustvatten' 'Möröfjärden' 'Ostnäsfjärden'
'Svensbyfjärden' 'Ersnäsfjärden' 'Granöfjärden' 'Mulöviken'
'Måttsundsfjärden' 'Boviksfjärden' 'Burefjärden' 'Inre Lövselefjärden'
'Kågefjärden' 'Hemsösundet sek namn' 'Älandsfjärden' 'Österlångslädan'
'Västerfjärden' 'Megrundsområdet' 'N n Kalmarsunds utsjövatten'
'Nordmalingsfjärden' 'Raggavaviken' 'Tavlefjärden'
'Laholmsbuktens kustvatten']
Out[90]:
SDATE
MONTH
WATER_BODY_NAME
VISS_EU_CD
WATER_TYPE_AREA
DIN
SALT_CTD
SALT_BTL
5220
2017-12-05
12
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
15.76
18.350
NaN
5221
2017-12-05
12
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
8.59
24.190
NaN
5222
2017-12-05
12
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.4
25.220
NaN
5223
2017-12-05
12
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.46
27.980
NaN
6451
2017-03-12
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
0.37
24.320
NaN
6452
2017-03-12
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
0.4
24.840
NaN
6453
2017-03-12
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
1.36
26.000
NaN
6474
2017-03-07
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
10.88
30.820
29.640
6475
2017-03-07
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
8.63
31.810
31.470
6476
2017-03-07
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.45
33.000
33.040
6477
2017-03-07
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.22
33.220
33.210
6565
2017-02-13
2
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
4.06
20.830
NaN
6566
2017-02-13
2
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
4.24
20.890
NaN
6567
2017-02-13
2
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
5.13
22.050
NaN
6612
2017-02-06
2
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
7.47
25.170
24.860
6613
2017-02-06
2
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
7.37
25.170
24.910
6614
2017-02-06
2
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.78
25.190
25.240
6615
2017-02-06
2
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
7.25
26.260
26.800
6783
2017-01-12
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
4.65
30.770
NaN
6784
2017-01-12
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
4.66
30.770
NaN
6785
2017-01-12
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
4.71
30.830
NaN
6872
2017-01-09
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
5.87
31.140
NaN
6873
2017-01-09
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
5.57
31.150
NaN
6874
2017-01-09
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
5.44
31.320
NaN
6875
2017-01-09
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
5.35
31.320
NaN
7002
2016-12-11
12
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
4.97
31.310
NaN
7003
2016-12-11
12
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
4.4
32.390
NaN
7117
2016-12-06
12
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
5.46
30.500
30.475
7118
2016-12-06
12
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.04
32.340
32.306
7119
2016-12-06
12
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
4.01
33.350
33.400
...
...
...
...
...
...
...
...
...
27062
2013-12-03
12
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.32
29.480
NaN
27063
2013-12-03
12
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.29
29.480
NaN
27064
2013-12-03
12
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.1
29.770
NaN
27065
2013-12-03
12
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
5.95
30.000
NaN
32869
2013-03-25
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
0.3
23.255
NaN
32870
2013-03-25
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
0.3
23.536
23.275
32871
2013-03-25
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
0.39
23.728
25.430
33223
2013-03-06
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
8.83
19.210
NaN
33224
2013-03-06
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
7.09
21.120
NaN
33225
2013-03-06
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.89
21.980
NaN
33226
2013-03-06
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
8.09
26.450
NaN
33234
2013-03-06
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.18
23.670
NaN
33235
2013-03-06
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
5.88
23.900
NaN
33236
2013-03-06
3
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.31
24.260
NaN
33571
2013-02-26
2
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.28
21.250
21.992
33572
2013-02-26
2
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.7
21.366
22.908
33573
2013-02-26
2
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
7.1
24.407
25.012
34007
2013-02-07
2
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
8.88
24.020
NaN
34008
2013-02-07
2
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
7.99
32.380
NaN
34009
2013-02-07
2
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
8.12
33.360
NaN
34117
2013-01-28
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
6.05
21.651
21.755
34118
2013-01-28
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
5.7
21.644
21.869
34119
2013-01-28
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
5.78
21.706
21.953
34437
2013-01-08
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
11.68
6.530
NaN
34438
2013-01-08
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
7.77
20.720
NaN
34439
2013-01-08
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
7.59
23.660
NaN
34440
2013-01-08
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
7.34
25.180
NaN
34490
2013-01-08
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
9.55
24.050
24.004
34491
2013-01-08
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
9.42
24.700
24.696
34492
2013-01-08
1
Gullmarn centralbassäng
SE581700-113000
02 - Västkustens fjordar
8.67
26.440
26.733
157 rows × 8 columns
In [91]:
w.get_available_indicators(subset= 'A', step=2)
2018-04-03 19:02:48,893 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
2018-04-03 19:02:48,975 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
2018-04-03 19:02:49,077 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
subset A
subset A
subset A
2018-04-03 19:02:49,184 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
subset A
2018-04-03 19:02:49,452 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
2018-04-03 19:02:49,532 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
2018-04-03 19:02:49,621 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
subset A
subset A
"['TOT_COVER_ALL' 'SALT'] not in index"
subset A
2018-04-03 19:02:49,737 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
2018-04-03 19:02:49,851 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
subset A
subset A
2018-04-03 19:02:49,956 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
2018-04-03 19:02:50,067 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
subset A
subset A
2018-04-03 19:02:50,192 workspaces.py 1271 get_filtered_data DEBUG STEP: step_2
subset A
Out[91]:
['biov',
'bqi',
'chl',
'din_winter',
'dip_winter',
'ntot_summer',
'ntot_winter',
'o2',
'ptot_summer',
'ptot_winter',
'secchi']
In [92]:
w.cfg['indicators']
[item.strip() for item in w.cfg['indicators'].loc['din_winter'][0].split(', ')]
Out[92]:
['DIN', 'SALT_CTD']
In [93]:
w.get_step_object(step = 3, subset = subset_uuid).calculate_indicator_status(subset_unique_id = subset_uuid, indicator_list = ['din_winter'])
In [94]:
w.get_step_object(step = 3, subset = subset_uuid).indicator_objects['din_winter'].get_water_body_indicator_df(water_body = wb)
---------------------------------------------------------------------------
UserWarning Traceback (most recent call last)
<ipython-input-94-d8c6914d608a> in <module>()
----> 1 w.get_step_object(step = 3, subset = subset_uuid).indicator_objects['din_winter'].get_water_body_indicator_df(water_body = wb)
D:\github\ekostat_calculator\core\indicators.py in get_water_body_indicator_df(self, water_body, level)
91 """
92 type_area = self.mapping_objects['water_body'].get_type_area_for_water_body(water_body, include_suffix=True)
---> 93 df = self.get_filtered_data(subset = self.subset, step = self.step, type_area = type_area, indicator = self.name)
94 df = df[self.column_list]
95 try:
D:\github\ekostat_calculator\core\indicators.py in get_filtered_data(self, subset, step, type_area, indicator)
74 """
75
---> 76 return self.index_handler.get_filtered_data(subset, step, type_area, indicator)
77
78 #==========================================================================
D:\github\ekostat_calculator\core\index_handler.py in get_filtered_data(self, subset, step, type_area, indicator)
322 """
323
--> 324 step_0, step_1, step_2 = self._get_steps(step=step)
325
326 boolean = self._get_boolean(step_0, subset, step_1, step_2, type_area, indicator)
D:\github\ekostat_calculator\core\index_handler.py in _get_steps(self, step)
186 return 'step_0', 'step_1', step
187 else:
--> 188 raise UserWarning('Step definition is incorrect. Acceptable step is step_0, step_1 or step_2')
189
190
UserWarning: Step definition is incorrect. Acceptable step is step_0, step_1 or step_2
In [95]:
w.get_step_object(step = 3, subset = subset_uuid).indicator_objects['din_winter'].get_ref_value(type_area = '1s', salinity = 25)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-95-2382cea2d78d> in <module>()
----> 1 w.get_step_object(step = 3, subset = subset_uuid).indicator_objects['din_winter'].get_ref_value(type_area = '1s', salinity = 25)
D:\github\ekostat_calculator\core\indicators.py in get_ref_value(self, type_area, salinity)
138
139 """
--> 140 return self.ref_settings.get_ref_value(type_area, salinity)
141
142 #==========================================================================
D:\github\ekostat_calculator\core\filters.py in get_ref_value(self, type_area, salinity)
866 Updated 20180328 by Lena Viktorsson
867 """
--> 868 ref_value = self.get_value(variable = self.refvalue_column[0], type_area = type_area)
869
870 if ref_value is float:
AttributeError: 'SettingsRef' object has no attribute 'refvalue_column'
In [67]:
indicator= 'din_winter'
w.get_step_object(step = 3, subset = subset_uuid).get_indicator_data_filter_settings(indicator)
Out[67]:
False
In [242]:
s = w.get_subset_object(subset_uuid).indicator_objects['din_winter']
s.get_filtered_data(subset = subset_uuid, step = 'step_2')
Out[242]:
VISS_EU_ID
SDATE
DEPH
SEA_AREA_NAME
SALT_BTL
DOXY_BTL
PHOS
NTRA
AMON
SECCHI
NTOT
PTOT
DIN
MONTH
0
SE652400-223501
1985-01-01
0.00
Norrbottens skärgårds kustvatten
3.000
8.081
0.500
3.003
0.100
6.8
18.401
0.619
3.1
1
1
SE652400-223501
1985-01-01
0.25
Norrbottens skärgårds kustvatten
3.000
8.081
0.500
3.003
0.100
6.8
18.401
0.619
3.1
1
2
SE652400-223501
1985-01-01
0.75
Norrbottens skärgårds kustvatten
3.000
8.074
0.500
3.003
0.100
6.8
18.430
0.621
3.1
1
3
SE652400-223501
1985-01-01
1.25
Norrbottens skärgårds kustvatten
3.000
8.070
0.500
3.003
0.100
6.8
18.430
0.621
3.1
1
4
SE652400-223501
1985-01-01
1.75
Norrbottens skärgårds kustvatten
3.000
8.067
0.500
3.003
0.100
6.8
18.430
0.621
3.1
1
5
SE652400-223501
1985-01-01
2.25
Norrbottens skärgårds kustvatten
3.000
8.065
0.500
3.003
0.100
6.8
18.430
0.621
3.1
1
6
SE652400-223501
1985-01-01
2.75
Norrbottens skärgårds kustvatten
3.000
8.063
0.500
3.003
0.100
6.8
18.430
0.621
3.1
1
7
SE652400-223501
1985-01-01
3.25
Norrbottens skärgårds kustvatten
3.000
8.062
0.500
3.003
0.100
6.8
18.430
0.621
3.1
1
8
SE652400-223501
1985-01-01
3.75
Norrbottens skärgårds kustvatten
3.000
8.061
0.500
3.003
0.100
6.8
18.430
0.621
3.1
1
9
SE652400-223501
1985-01-01
4.50
Norrbottens skärgårds kustvatten
3.000
8.059
0.500
3.003
0.100
6.8
18.430
0.621
3.1
1
10
SE652400-223501
1985-01-01
5.50
Norrbottens skärgårds kustvatten
3.000
8.057
0.500
3.003
0.100
6.8
18.430
0.621
3.1
1
11
SE652400-223501
1985-01-01
6.50
Norrbottens skärgårds kustvatten
3.000
8.055
0.500
3.003
0.100
6.8
18.430
0.621
3.1
1
12
SE652400-223501
1985-01-01
7.50
Norrbottens skärgårds kustvatten
3.000
8.053
0.500
3.003
0.100
6.8
18.430
0.621
3.1
1
13
SE652400-223501
1985-01-01
8.50
Norrbottens skärgårds kustvatten
3.000
8.052
0.500
3.003
0.100
6.8
18.431
0.621
3.1
1
14
SE652400-223501
1985-01-01
9.50
Norrbottens skärgårds kustvatten
3.000
8.050
0.500
3.003
0.100
6.8
18.431
0.621
3.1
1
15
SE652400-223501
1985-01-01
112.50
Norrbottens skärgårds kustvatten
3.000
7.994
0.501
3.017
0.100
6.8
18.444
0.621
3.12
1
16
SE652400-223501
1985-01-01
115.00
Norrbottens skärgårds kustvatten
3.000
7.994
0.501
3.017
0.100
6.8
18.444
0.621
3.12
1
17
SE652400-223501
1985-01-02
0.00
Norrbottens skärgårds kustvatten
3.000
8.183
0.501
3.015
0.100
6.8
18.403
0.619
3.12
1
18
SE652400-223501
1985-01-02
0.25
Norrbottens skärgårds kustvatten
3.000
8.183
0.501
3.015
0.100
6.8
18.403
0.619
3.12
1
19
SE652400-223501
1985-01-02
0.75
Norrbottens skärgårds kustvatten
3.000
8.177
0.501
3.015
0.100
6.8
18.430
0.620
3.12
1
20
SE652400-223501
1985-01-02
1.25
Norrbottens skärgårds kustvatten
3.000
8.174
0.501
3.015
0.100
6.8
18.430
0.620
3.12
1
21
SE652400-223501
1985-01-02
1.75
Norrbottens skärgårds kustvatten
3.000
8.172
0.501
3.015
0.100
6.8
18.430
0.620
3.12
1
22
SE652400-223501
1985-01-02
2.25
Norrbottens skärgårds kustvatten
3.000
8.170
0.501
3.015
0.100
6.8
18.430
0.620
3.12
1
23
SE652400-223501
1985-01-02
2.75
Norrbottens skärgårds kustvatten
3.000
8.169
0.501
3.015
0.100
6.8
18.430
0.620
3.12
1
24
SE652400-223501
1985-01-02
3.25
Norrbottens skärgårds kustvatten
3.000
8.167
0.501
3.015
0.100
6.8
18.430
0.620
3.12
1
25
SE652400-223501
1985-01-02
3.75
Norrbottens skärgårds kustvatten
3.000
8.166
0.501
3.015
0.100
6.8
18.430
0.620
3.12
1
26
SE652400-223501
1985-01-02
4.50
Norrbottens skärgårds kustvatten
3.000
8.165
0.501
3.015
0.100
6.8
18.430
0.620
3.12
1
27
SE652400-223501
1985-01-02
5.50
Norrbottens skärgårds kustvatten
3.000
8.163
0.501
3.015
0.100
6.8
18.430
0.620
3.12
1
28
SE652400-223501
1985-01-02
6.50
Norrbottens skärgårds kustvatten
3.000
8.162
0.501
3.015
0.100
6.8
18.431
0.620
3.12
1
29
SE652400-223501
1985-01-02
7.50
Norrbottens skärgårds kustvatten
3.000
8.160
0.501
3.015
0.100
6.8
18.431
0.620
3.12
1
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
106
SE652400-223501
1985-01-07
1.75
Norrbottens skärgårds kustvatten
3.039
8.552
0.453
3.213
0.113
7.2
18.111
0.553
3.33
1
107
SE652400-223501
1985-01-07
2.25
Norrbottens skärgårds kustvatten
3.045
8.547
0.449
3.215
0.111
7.2
18.092
0.549
3.33
1
108
SE652400-223501
1985-01-07
2.75
Norrbottens skärgårds kustvatten
3.050
8.542
0.446
3.217
0.109
7.2
18.077
0.546
3.33
1
109
SE652400-223501
1985-01-07
3.25
Norrbottens skärgårds kustvatten
3.054
8.538
0.443
3.219
0.107
7.2
18.065
0.543
3.33
1
110
SE652400-223501
1985-01-07
3.75
Norrbottens skärgårds kustvatten
3.058
8.534
0.441
3.221
0.106
7.2
18.055
0.541
3.33
1
111
SE652400-223501
1985-01-07
4.50
Norrbottens skärgårds kustvatten
3.062
8.528
0.438
3.223
0.104
7.2
18.045
0.538
3.33
1
112
SE652400-223501
1985-01-07
5.50
Norrbottens skärgårds kustvatten
3.066
8.521
0.435
3.226
0.103
7.2
18.039
0.536
3.33
1
113
SE652400-223501
1985-01-07
6.50
Norrbottens skärgårds kustvatten
3.068
8.515
0.433
3.229
0.102
7.2
18.039
0.534
3.33
1
114
SE652400-223501
1985-01-07
7.50
Norrbottens skärgårds kustvatten
3.070
8.510
0.432
3.233
0.101
7.2
18.043
0.534
3.33
1
115
SE652400-223501
1985-01-07
8.50
Norrbottens skärgårds kustvatten
3.071
8.506
0.432
3.236
0.101
7.2
18.052
0.534
3.34
1
116
SE652400-223501
1985-01-07
9.50
Norrbottens skärgårds kustvatten
3.072
8.503
0.431
3.239
0.100
7.2
18.061
0.534
3.34
1
117
SE652400-223501
1985-01-07
112.50
Norrbottens skärgårds kustvatten
3.262
7.961
0.426
4.257
0.099
7.2
17.547
0.538
4.36
1
118
SE652400-223501
1985-01-07
115.00
Norrbottens skärgårds kustvatten
3.262
7.961
0.426
4.257
0.099
7.2
17.547
0.538
4.36
1
119
SE652400-223501
1985-01-08
0.00
Norrbottens skärgårds kustvatten
3.034
8.571
0.451
3.248
0.119
7.2
18.108
0.549
3.37
1
120
SE652400-223501
1985-01-08
0.25
Norrbottens skärgårds kustvatten
3.034
8.571
0.451
3.248
0.119
7.2
18.108
0.549
3.37
1
121
SE652400-223501
1985-01-08
0.75
Norrbottens skärgårds kustvatten
3.033
8.571
0.451
3.248
0.119
7.2
18.113
0.549
3.37
1
122
SE652400-223501
1985-01-08
1.25
Norrbottens skärgårds kustvatten
3.035
8.571
0.450
3.249
0.118
7.2
18.108
0.549
3.37
1
123
SE652400-223501
1985-01-08
1.75
Norrbottens skärgårds kustvatten
3.039
8.569
0.448
3.250
0.116
7.2
18.095
0.546
3.37
1
124
SE652400-223501
1985-01-08
2.25
Norrbottens skärgårds kustvatten
3.046
8.565
0.444
3.252
0.114
7.2
18.075
0.541
3.37
1
125
SE652400-223501
1985-01-08
2.75
Norrbottens skärgårds kustvatten
3.052
8.561
0.440
3.255
0.112
7.2
18.058
0.538
3.37
1
126
SE652400-223501
1985-01-08
3.25
Norrbottens skärgårds kustvatten
3.058
8.558
0.437
3.257
0.110
7.2
18.043
0.534
3.37
1
127
SE652400-223501
1985-01-08
3.75
Norrbottens skärgårds kustvatten
3.062
8.554
0.434
3.259
0.109
7.2
18.031
0.531
3.37
1
128
SE652400-223501
1985-01-08
4.50
Norrbottens skärgårds kustvatten
3.068
8.548
0.430
3.262
0.106
7.2
18.015
0.528
3.37
1
129
SE652400-223501
1985-01-08
5.50
Norrbottens skärgårds kustvatten
3.073
8.541
0.427
3.266
0.104
7.2
18.002
0.524
3.37
1
130
SE652400-223501
1985-01-08
6.50
Norrbottens skärgårds kustvatten
3.077
8.535
0.424
3.270
0.103
7.2
17.996
0.522
3.37
1
131
SE652400-223501
1985-01-08
7.50
Norrbottens skärgårds kustvatten
3.080
8.530
0.423
3.274
0.102
7.2
17.996
0.521
3.38
1
132
SE652400-223501
1985-01-08
8.50
Norrbottens skärgårds kustvatten
3.081
8.525
0.421
3.278
0.101
7.2
18.000
0.520
3.38
1
133
SE652400-223501
1985-01-08
9.50
Norrbottens skärgårds kustvatten
3.082
8.521
0.421
3.282
0.101
7.2
18.007
0.520
3.38
1
134
SE652400-223501
1985-01-08
112.50
Norrbottens skärgårds kustvatten
3.300
7.911
0.419
4.454
0.100
7.2
17.461
0.531
4.55
1
135
SE652400-223501
1985-01-08
115.00
Norrbottens skärgårds kustvatten
3.300
7.911
0.419
4.454
0.100
7.2
17.461
0.531
4.55
1
136 rows × 14 columns
In [120]:
B2_NTOT_WINTER_SETTINGS = lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_ref_settings['ntot_winter']
lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_ref_settings['ntot_winter'].allowed_variables
# gör om till
# lv_workspace.get_indicator_ref_settings(step = , subset = , indicator = , waterbody/type)
# ger samma resultat som:
#lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_ref_settings['ntot_winter'].settings.ref_columns
lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_ref_settings['ntot_winter'].settings.get_value('EK G/M', 22)
#print(B2_NTOT_WINTER_SETTINGS)
#B2_NTOT_WINTER_SETTINGS.get_value('2', 'DEPTH_INTERVAL')
Out[120]:
'0.85'
In [124]:
av = lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_data_filter_settings['ntot_winter'].allowed_variables
lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_data_filter_settings['ntot_winter'].settings.df[av]
Out[124]:
DEPH_INTERVAL
MONTH_LIST
0
0-10
1;2;3;12
1
0-10
1;2;3;12
2
0-10
1;2;3;12
3
0-10
1;2;3;12
4
0-10
1;2;3;12
5
0-10
1;2;3;12
6
0-10
1;2;3;12
7
0-10
1;2;12
8
0-10
1;2;12
9
0-10
1;2;12
10
0-10
1;2;12
11
0-10
1;2;12
12
0-10
1;2;12
13
0-10
1;2;12
14
0-10
1;2;12
15
0-10
1;2;12
16
0-10
1;2;12
17
0-10
1;2;11;12
18
0-10
1;2;11;12
19
0-10
1;2;11;12
20
0-10
1;2;11;12
21
0-10
1;2;11;12
22
0-10
1;2;11;12
23
0-10
1;2;11;12
24
0-10
1;2;11;12
25
0-10
1;2;12
26
0-10
1;2;3;12
In [119]:
lv_workspace.get_subset_object('B').get_step_object('step_2').indicator_data_filter_settings['ntot_winter'].settings.df
Out[119]:
TYPE_AREA_NUMBER
TYPE_AREA_SUFFIX
DEPH_INTERVAL
MIN_NR_YEARS
MIN_NR_VALUES
TIME_DELTA_TOLERANCE
POS_RADIUS_TOLERANCE
DEPH_TOLERANCE
MONTH_LIST
EKV REF
EKV H/G
EKV G/M
EKV M/O
EKV O/D
EK H/G
EK G/M
EK M/O
EK O/D
SALINITY MAX
0
1
n
0-10
3
3
3
0.1
5
1;2;3;12
-0.630*s+36
-0.715*s+40.86
-0.799*s+45.72
-1.0546*s+60.3
-1.480*s+84.6
0.88
0.79
0.6
0.43
27
1
1
s
0-10
3
3
3
0.1
5
1;2;3;12
-0.65*s+30
-0.738*s+34.05
-0.8255*s+38.1
-1.0888*s+50.25
-1.528*s+70.5
0.88
0.79
0.6
0.43
20
2
2
NaN
0-10
3
3
3
0.1
5
1;2;3;12
-0.630*s+36
-0.715*s+40.86
-0.799*s+45.72
-1.0546*s+60.3
-1.480*s+84.6
0.88
0.79
0.6
0.43
27
3
3
NaN
0-10
3
3
3
0.1
5
1;2;3;12
-0.630*s+36
-0.715*s+40.86
-0.799*s+45.72
-1.0546*s+60.3
-1.480*s+84.6
0.88
0.79
0.6
0.43
27
4
4
NaN
0-10
3
3
3
0.1
5
1;2;3;12
-0.65*s+30
-0.738*s+34.05
-0.8255*s+38.1
-1.0888*s+50.25
-1.528*s+70.5
0.88
0.79
0.6
0.43
20
5
5
NaN
0-10
3
3
3
0.1
5
1;2;3;12
0*s+17
0*s+19.295
0*s+21.59
0*s+28.475
0*s+39.95
0.89
0.77
0.61
0.43
20
6
6
NaN
0-10
3
3
3
0.1
5
1;2;3;12
0*s+17
0*s+19.295
0*s+21.59
0*s+28.475
0*s+39.95
0.89
0.77
0.61
0.43
20
7
7
NaN
0-10
3
3
3
0.1
5
1;2;12
-6*s+59
-6.6*s+64.9
-7.2*s+70.8
-9*s+88.5
-12*s+118
0.91
0.84
0.67
0.5
7
8
8
NaN
0-10
3
3
3
0.1
5
1;2;12
-6*s+59
-6.6*s+64.9
-7.2*s+70.8
-9*s+88.5
-12*s+118
0.91
0.84
0.67
0.5
7
9
9
NaN
0-10
3
3
3
0.1
5
1;2;12
-6*s+59
-6.6*s+64.9
-7.2*s+70.8
-9*s+88.5
-12*s+118
0.91
0.84
0.67
0.5
7
10
10
NaN
0-10
3
3
3
0.1
5
1;2;12
0*s+17
0*s+18.7
0*s+20.4
0*s+25.5
0*s+34
0.89
0.85
0.65
0.5
7
11
11
NaN
0-10
3
3
3
0.1
5
1;2;12
0*s+17
0*s+18.7
0*s+20.4
0*s+25.5
0*s+34
0.89
0.85
0.65
0.5
7
12
12
s
0-10
3
3
3
0.1
5
1;2;12
-2.833*s+34
-3.1167*s+37.4
-3.4*s+40.8
-4.25*s+51
-5.6667*s+68
0.91
0.83
0.66
0.5
6
13
12
n
0-10
3
3
3
0.1
5
1;2;12
-1*s+23
-1.1*s+25.3
-1.2*s+27.6
-1.5*s+34.5
-2*s+46
0.93
0.85
0.68
0.51
6
14
13
NaN
0-10
3
3
3
0.1
5
1;2;12
-2.833*s+34
-3.1167*s+37.4
-3.4*s+40.8
-4.25*s+51
-5.6667*s+68
0.91
0.83
0.66
0.5
6
15
14
NaN
0-10
3
3
3
0.1
5
1;2;12
-2.833*s+34
-3.1167*s+37.4
-3.4*s+40.8
-4.25*s+51
-5.6667*s+68
0.91
0.83
0.66
0.5
6
16
15
NaN
0-10
3
3
3
0.1
5
1;2;12
-1*s+23
-1.1*s+25.3
-1.2*s+27.6
-1.5*s+34.5
-2*s+46
0.93
0.85
0.68
0.51
6
17
16
NaN
0-10
3
3
3
0.1
5
1;2;11;12
-1*s+23
-1.1*s+25.3
-1.2*s+27.6
-1.5*s+34.5
-2*s+46
0.93
0.85
0.68
0.51
5
18
17
NaN
0-10
3
3
3
0.1
5
1;2;11;12
-1*s+23
-1.1*s+25.3
-1.2*s+27.6
-1.5*s+34.5
-2*s+46
0.93
0.85
0.68
0.51
5
19
18
NaN
0-10
3
3
3
0.1
5
1;2;11;12
-0.4*s+20
-0.44*s+22
-0.48*s+24
-0.6*s+30
-0.8*s+40
0.91
0.83
0.66
0.5
5
20
19
NaN
0-10
3
3
3
0.1
5
1;2;11;12
-0.4*s+20
-0.44*s+22
-0.48*s+24
-0.6*s+30
-0.8*s+40
0.91
0.83
0.66
0.5
5
21
20
NaN
0-10
3
3
3
0.1
5
1;2;11;12
-0.6*s+21
-0.66*s+23.1
-0.72*s+25.2
-0.9*s+31.5
-1.2*s+42
0.91
0.83
0.67
0.5
5
22
21
NaN
0-10
3
3
3
0.1
5
1;2;11;12
-0.6*s+21
-0.66*s+23.1
-0.72*s+25.2
-0.9*s+31.5
-1.2*s+42
0.91
0.83
0.67
0.5
5
23
22
NaN
0-10
3
3
3
0.1
5
1;2;11;12
-1*s+21
-1.09*s+22.89
-1.18*s+24.78
-1.45*s+30.45
-1.9*s+39.9
0.93
0.85
0.68
0.51
3
24
23
NaN
0-10
3
3
3
0.1
5
1;2;11;12
-1*s+21
-1.09*s+22.89
-1.18*s+24.78
-1.45*s+30.45
-1.9*s+39.9
0.93
0.85
0.68
0.51
3
25
24
NaN
0-10
3
3
3
0.1
5
1;2;12
-1*s+23
-1.1*s+25.3
-1.2*s+27.6
-1.5*s+34.5
-2*s+46
0.93
0.85
0.68
0.51
6
26
25
NaN
0-10
3
3
3
0.1
5
1;2;3;12
-0.65*s+30
-0.738*s+34.05
-0.8255*s+38.1
-1.0888*s+50.25
-1.528*s+70.5
0.88
0.79
0.6
0.43
20
In [67]:
B2_NTOT_WINTER_SETTINGS.settings.mapping_water_body['N m Bottenvikens kustvatten']
Out[67]:
{'BASIN_NUMBER': '110002.0',
'CENTER_LAT': '64° 42\' 35,349"',
'CENTER_LON': '21° 24\' 5,383"',
'EU_CD': 'SE648760-213140',
'HID': '649640-214530',
'TYPE_AREA_NUMBER': '23',
'TYPE_AREA_SUFFIX': '',
'URL_VISS': 'http://www.viss.lansstyrelsen.se/waters.aspx?waterEUID=SE648760-213140'}
In [35]:
f1_A = lv_workspace.get_data_filter_object(step=1, subset='A')
f1_A.include_list_filter
Out[35]:
{'MYEAR': ['2016', '2017'],
'SEA_AREA_NAME': ['Gullmarn centralbassäng', 'Rivö fjord'],
'STATN': ['BJÖRKHOLMEN']}
In [136]:
lv_workspace.get_data_filter_info(step=1, subset='A')
Out[136]:
{'exclude_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN'],
'include_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN']}
In [36]:
f1_A.exclude_list_filter
Out[36]:
{'MYEAR': ['2015', '2016'], 'SEA_AREA_NAME': [], 'STATN': ['SLÄGGÖ']}
In [21]:
f0.include_list_filter
Out[21]:
{'MYEAR': ['2016', '2017'],
'SEA_AREA_NAME': ['Byfjorden',
'Gullmarn centralbassäng',
'Havstensfjorden',
'Rivö fjord'],
'STATN': []}
In [22]:
lv_workspace.apply_subset_filter(subset='A') # Not handled properly by the IndexHandler
Out[22]:
True
In [23]:
data_after_subset_filter = lv_workspace.get_filtered_data(level=1, subset='A') # level=0 means first filter
print('{} rows mathing the filter criteria'.format(len(data_after_subset_filter)))
data_after_subset_filter.head()
198 rows mathing the filter criteria
Out[23]:
AMON
BQIm
CPHL
DEPH
DOXY_BTL
DOXY_CTD
LATIT_DD
LONGI_DD
MNDEP
MXDEP
...
SERNO
SHARKID_MD5
SHIPC
STATN
STIME
TEMP_BTL
TEMP_CTD
WATER_DISTRICT
WATER_TYPE_AREA
WLTYP
2628
0.76
NaN
0.6
0.0
7.05
58.38767
11.62667
NaN
NaN
...
8.0
NaN
77SN
BJÖRKHOLMEN
17:30
4.96
4.84
Västerhavets vattendistrikt
02 - Västkustens fjordar
2 - Havsområde innanför 1 NM
2629
0.72
NaN
0.5
2.0
7.12
58.38767
11.62667
NaN
NaN
...
8.0
NaN
77SN
BJÖRKHOLMEN
17:30
4.93
4.84
Västerhavets vattendistrikt
02 - Västkustens fjordar
2 - Havsområde innanför 1 NM
2630
0.74
NaN
0.6
5.0
7.16
58.38767
11.62667
NaN
NaN
...
8.0
NaN
77SN
BJÖRKHOLMEN
17:30
4.88
4.84
Västerhavets vattendistrikt
02 - Västkustens fjordar
2 - Havsområde innanför 1 NM
2631
0.65
NaN
0.5
10.0
7.11
58.38767
11.62667
NaN
NaN
...
8.0
NaN
77SN
BJÖRKHOLMEN
17:30
5.12
4.86
Västerhavets vattendistrikt
02 - Västkustens fjordar
2 - Havsområde innanför 1 NM
2632
0.46
NaN
0.3
15.0
6.86
58.38767
11.62667
NaN
NaN
...
8.0
NaN
77SN
BJÖRKHOLMEN
17:30
5.52
5.10
Västerhavets vattendistrikt
02 - Västkustens fjordar
2 - Havsområde innanför 1 NM
5 rows × 46 columns
In [24]:
# show available waterbodies
lst = data_after_subset_filter.SEA_AREA_NAME.unique()
print('Waterbodies in subset:\n{}'.format('\n'.join(lst)))
Waterbodies in subset:
Gullmarn centralbassäng
In [24]:
import numpy as np
np.where(lv_workspace.index_handler.subset_filter)
Out[24]:
(array([2629, 2630, 2631, 2632, 2633, 2634, 2635, 2636, 2637, 2638, 2639,
2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764,
2870, 2871, 2872, 2873, 2874, 2875, 2876, 2877, 2878, 2879, 2880,
2952, 2953, 2954, 2955, 2956, 2957, 2958, 2959, 2960, 2961, 2962,
3077, 3078, 3079, 3080, 3081, 3082, 3083, 3084, 3085, 3086, 3087,
3185, 3186, 3187, 3188, 3189, 3190, 3191, 3192, 3193, 3194, 3195,
3282, 3283, 3284, 3285, 3286, 3287, 3288, 3289, 3290, 3291, 3292,
3409, 3410, 3411, 3412, 3413, 3414, 3415, 3416, 3417, 3418, 3419,
3493, 3494, 3495, 3496, 3497, 3498, 3499, 3500, 3501, 3502, 3503,
3624, 3625, 3626, 3627, 3628, 3629, 3630, 3631, 3632, 3633, 3634,
3717, 3718, 3719, 3720, 3721, 3722, 3723, 3724, 3725, 3726, 3727,
3859, 3860, 3861, 3862, 3863, 3864, 3865, 3866, 3867, 3868, 3869,
3979, 3980, 3981, 3982, 3983, 3984, 3985, 3986, 3987, 3988, 3989,
4110, 4111, 4112, 4113, 4114, 4115, 4116, 4117, 4118, 4119, 4120,
4265, 4266, 4267, 4268, 4269, 4270, 4271, 4272, 4273, 4274, 4275,
4354, 4355, 4356, 4357, 4358, 4359, 4360, 4361, 4362, 4363, 4364,
4502, 4503, 4504, 4505, 4506, 4507, 4508, 4509, 4510, 4511, 4512,
4806, 4807, 4808, 4809, 4810, 4811, 4812, 4813, 4814, 4815, 4816], dtype=int64),)
In [25]:
f = lv_workspace.get_data_filter_object(step=1, subset='A')
In [26]:
f.all_filters
Out[26]:
{'exclude_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN'],
'include_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN']}
In [27]:
f.exclude_list_filter
Out[27]:
{'MYEAR': ['2015', '2016'], 'SEA_AREA_NAME': [], 'STATN': ['SLÄGGÖ']}
In [28]:
f.include_list_filter
Out[28]:
{'MYEAR': ['2016', '2017'],
'SEA_AREA_NAME': ['Gullmarn centralbassäng', 'Rivö fjord'],
'STATN': ['BJÖRKHOLMEN']}
In [29]:
s = lv_workspace.get_step_1_object('A')
In [30]:
s.data_filter.all_filters
Out[30]:
{'exclude_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN'],
'include_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN']}
In [31]:
f0 = lv_workspace.get_data_filter_object(step=0)
In [32]:
f0.exclude_list_filter
Out[32]:
{'MYEAR': ['2015', '2016'], 'SEA_AREA_NAME': [], 'STATN': []}
In [33]:
f0.include_list_filter
Out[33]:
{'MYEAR': ['2016', '2017'],
'SEA_AREA_NAME': ['Byfjorden',
'Gullmarn centralbassäng',
'Havstensfjorden',
'Rivö fjord'],
'STATN': []}
In [ ]:
In [ ]:
lv_workspace.initiate_quality_factors()
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
Content source: ekostat/ekostat_calculator
Similar notebooks: