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# coding: utf-8
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import os
import sys
path = "../"
path = "D:/github/w_vattenstatus/ekostat_calculator"
sys.path.append(path)
#os.path.abspath("../")
print(os.path.abspath(path))
# In[2]:
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__
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root_directory = 'D:/github/w_vattenstatus/ekostat_calculator'#"../" #os.getcwd()
workspace_directory = root_directory + '/workspaces'
resource_directory = root_directory + '/resources'
#alias = 'lena'
user_id = 'test_user' #kanske ska vara off_line user?
workspace_alias = 'lena_indicator'
# ## Initiate EventHandler
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',
'cache_directory': 'D:/github/w_vattenstatus/cache'}
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))
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###############################################################################################################################
# ### Make a new workspace
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# ekos.copy_workspace(source_uuid='default_workspace', target_alias='kustzonsmodellen_3daydata')
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# ### See existing workspaces and choose workspace name to load
ekos.print_workspaces()
workspace_uuid = ekos.get_unique_id_for_alias(workspace_alias = workspace_alias) #'kuszonsmodellen' lena_indicator
print(workspace_uuid)
workspace_alias = ekos.get_alias_for_unique_id(workspace_uuid = workspace_uuid)
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###############################################################################################################################
# ### Load existing workspace
ekos.load_workspace(unique_id = workspace_uuid)
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###############################################################################################################################
# ### import data
# ekos.import_default_data(workspace_alias = workspace_alias)
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###############################################################################################################################
# ### Load all data in workspace
# #### if there is old data that you want to remove
ekos.get_workspace(workspace_uuid = workspace_uuid).delete_alldata_export()
ekos.get_workspace(workspace_uuid = workspace_uuid).delete_all_export_data()
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###############################################################################################################################
# #### to just load existing data in workspace
ekos.load_data(workspace_uuid = workspace_uuid)
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###############################################################################################################################
# ### check workspace data length
w = ekos.get_workspace(workspace_uuid = workspace_uuid)
len(w.data_handler.get_all_column_data_df())
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###############################################################################################################################
# ### see subsets in data
for subset_uuid in w.get_subset_list():
print('uuid {} alias {}'.format(subset_uuid, w.uuid_mapping.get_alias(unique_id=subset_uuid)))
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###############################################################################################################################
# # Step 0
print(w.data_handler.all_data.columns)
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###############################################################################################################################
# ### Apply first data filter
w.apply_data_filter(step = 0) # This sets the first level of data filter in the IndexHandler
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###############################################################################################################################
# # Step 1
# ### make new subset
# w.copy_subset(source_uuid='default_subset', target_alias='test_kustzon')
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###############################################################################################################################
# ### Choose subset name to load
subset_alias = 'test_subset'
subset_uuid = ekos.get_unique_id_for_alias(workspace_alias = workspace_alias, subset_alias = subset_alias)
print('subset_alias', subset_alias, 'subset_uuid', subset_uuid)
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# #### year filter
w.set_data_filter(subset = subset_uuid, step=1,
filter_type='include_list',
filter_name='MYEAR',
data=[2007,2008,2009,2010,2011,2012])#['2011', '2012', '2013']) #, 2014, 2015, 2016
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###############################################################################################################################
# #### waterbody filter
w.set_data_filter(subset = subset_uuid, step=1,
filter_type='include_list',
filter_name='viss_eu_cd', data = []) #'SE584340-174401', 'SE581700-113000', 'SE654470-222700', 'SE633000-195000', 'SE625180-181655'
# data=['SE584340-174401', 'SE581700-113000', 'SE654470-222700', 'SE633000-195000', 'SE625180-181655'])
# wb with no data for din 'SE591400-182320'
f1 = w.get_data_filter_object(subset = subset_uuid, step=1)
print(f1.include_list_filter)
print('subset_alias:', subset_alias, '\nsubset uuid:', subset_uuid)
f1 = w.get_data_filter_object(subset = subset_uuid, step=1)
print(f1.include_list_filter)
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###############################################################################################################################
# ## Apply step 1 datafilter to subset
w.apply_data_filter(subset = subset_uuid, step = 1)
filtered_data = w.get_filtered_data(step = 1, subset = subset_uuid)
#print(filtered_data['VISS_EU_CD'].unique())
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filtered_data[['AMON','NTRA','DIN','CPHL_INTEG_CALC','DEPH']].head()
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### Load indicator settings filter
w.get_step_object(step = 2, subset = subset_uuid).load_indicator_settings_filters()
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###############################################################################################################################
### set available indicators
w.get_available_indicators(subset= subset_uuid, step=2)
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###############################################################################################################################
# ### choose indicators
#list(zip(typeA_list, df_step1.WATER_TYPE_AREA.unique()))
# indicator_list = ['oxygen','din_winter','ntot_summer', 'ntot_winter', 'dip_winter', 'ptot_summer', 'ptot_winter','bqi', 'biov', 'chl', 'secchi']
# indicator_list = ['din_winter','ntot_summer', 'ntot_winter', 'dip_winter', 'ptot_summer', 'ptot_winter']
#indicator_list = ['biov', 'chl']
# indicator_list = ['bqi', 'biov', 'chl', 'secchi']
#indicator_list = ['bqi', 'secchi'] + ['biov', 'chl'] + ['din_winter']
# indicator_list = ['din_winter','ntot_summer']
# indicator_list = ['indicator_' + indicator for indicator in indicator_list]
indicator_list = w.available_indicators
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###############################################################################################################################
# ### Apply indicator data filter
print('apply indicator data filter to {}'.format(indicator_list))
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('*************************************')
#df = w.get_filtered_data(subset = subset_uuid, step = 'step_2', water_body = 'SE625180-181655', indicator = 'indicator_din_winter').dropna(subset = ['DIN'])
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# ### Set up indicator objects
print('indicator set up to {}'.format(indicator_list))
w.get_step_object(step = 3, subset = subset_uuid).indicator_setup(indicator_list = indicator_list)
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###############################################################################################################################
# ### CALCULATE STATUS
print('CALCULATE STATUS to {}'.format(indicator_list))
w.get_step_object(step = 3, subset = subset_uuid).calculate_status(indicator_list = indicator_list)
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###############################################################################################################################
# ### CALCULATE QUALITY ELEMENTS
w.get_step_object(step = 3, subset = subset_uuid).calculate_quality_element(quality_element = 'nutrients')
# w.get_step_object(step = 3, subset = subset_uuid).calculate_quality_element(quality_element = 'phytoplankton')
# w.get_step_object(step = 3, subset = subset_uuid).calculate_quality_element(quality_element = 'bottomfauna')
# w.get_step_object(step = 3, subset = subset_uuid).calculate_quality_element(quality_element = 'oxygen')
# w.get_step_object(step = 3, subset = subset_uuid).calculate_quality_element(quality_element = 'secchi')
# w.get_step_object(step = 3, subset = subset_uuid).calculate_quality_element(subset_unique_id = subset_uuid, quality_element = 'Phytoplankton')