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# 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))



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

Load directories



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root_directory = 'D:/github/ekostat_calculator'#"../" #os.getcwd()
workspace_directory = root_directory + '/workspaces' 
resource_directory = root_directory + '/resources'
#alias = 'lena'
user_id = 'test_user' #kanske ska vara off_line user?
workspace_alias = 'Arnold_1'

Initiate EventHandler



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



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

Load existing workspace



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##### BEHÖVS BARA FÖRSTA GÅNGEN FÖR ATT SKAPA WORKSPACE #######
ekos.copy_workspace(source_uuid='default_workspace', target_alias=workspace_alias)



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ekos.print_workspaces()



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



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workspace_alias = ekos.get_alias_for_unique_id(workspace_unique_id = workspace_uuid)



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ekos.load_workspace(unique_id = workspace_uuid)



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ekos.import_default_data(workspace_alias = workspace_alias)

Load all data in workspace



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

INNAN DU LADDAR DATA FÖRSTA GÅNGEN BEHÖVER DU SÄTTA STATUS 1 PÅ DE FILER SOM SKA LADDAS I FILEN:

workspaces/my_workspace/input_data/datatype_settings.txt



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ekos.load_data(unique_id = workspace_uuid)



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w = ekos.get_workspace(unique_id = workspace_uuid)
len(w.data_handler.get_all_column_data_df())
### Om "rätt" DATA så bör len bli 10694

Step 0



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len(w.data_handler.all_data.VISS_EU_CD.unique())
#403

Set station datafilter to empty list (includes all stations)



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# Only need to set filter first time you run workspace. Afterwards only if yo want to change it
w.get_data_filter_object(step=0).set_include_list_filter(filter_name = 'STATN', filter_list = [])

Apply first data filter



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w.get_data_filter_object(step=0).include_list_filter



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w.get_data_filter_object(step=0).exclude_list_filter



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

Step 1 Set subset filter



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ekos.copy_subset(workspace_uuid=workspace_uuid, 
                    subset_source_uuid='default_subset', 
                    subset_target_alias='B')

apply filters in step 1



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subset_uuid = ekos.get_unique_id_for_alias(workspace_alias = workspace_alias, subset_alias = 'B')
print('My subsets:', w.get_subset_list())

f1 = w.get_data_filter_object(subset = subset_uuid, step=1) 
print('Apply filters:', f1.include_list_filter)



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

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



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print(w.get_filtered_data(step = 1, subset = subset_uuid).columns)

Step 2



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#### check that ref settings are in place
w.get_step_object(step = 2, subset = subset_uuid).indicator_ref_settings



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indicator_list = w.get_available_indicators(subset= subset_uuid, step=2)



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indicator_list



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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)))



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test_wb = ['SE584340-174401',
 'SE581700-113000',
 'SE654470-222700',
 'SE570900-121060',
 'SE633000-195000',
 'SE625000-180075',
 'SE601440-184000',
 'SE612791-171130',
 'SE572072-115880',
 'SE582147-111771',
 'SE572227-115662',
 'SE580688-114860',
 'SE575500-113750',
 'SE591400-183200','SE575370-164220', 'SE573940-163560', 'SE565400-163600', 'SE570080-163430', 'SE565800-163000', 'SE570340-163710', 'SE570500-163750']

Apply indicator filter



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#for wb in wb_list:
#    try:
#        print(w.mapping_objects['water_body'][wb])
#        print('*************************************')
#    except AttributeError:
#        print('no match for {}'.format(wb))
#        print('*************************************')
#        continue



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



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w.get_filtered_data(subset = subset_uuid, step= 2, water_body = 'SE581700-113000', indicator = 'din_winter').head()



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w.index_handler.booleans['step_0'][subset_uuid]['step_1']['step_2'][test_wb[0]].keys()#['SE584340-174401'].keys()



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wb = test_wb[0]#'SE583926-161744' #typomr 22
#wb = 'SE654470-222700' #typomr 13
type_area = '2'#'01s - Västkustens inre kustvatten'
indicator = 'din_winter'
#w.index_handler.booleans['step_0'][subset_uuid]['step_1']['step_2'][type_area]['din_winter']['boolean']



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



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w.get_filtered_data(step = 2, subset = subset_uuid, indicator = 'secchi', water_body = 'SE591400-183200').dropna(subset = ['SECCHI']).drop_duplicates(subset = ['SDATE', 'VISS_EU_CD', 'SECCHI'])[['SDATE','VISS_EU_CD','SECCHI','DEPH']]



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

Step 3 Load Indicator objects step 3....



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



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w.get_step_object(step = 3, subset = subset_uuid).mapping_objects['water_body'].get_type_area_for_water_body('SE574050-114780', include_suffix=True)



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w.get_step_object(step = 3, subset = subset_uuid).calculate_status(indicator_list = ['secchi'])