def retrieving_max_shift():
ref = gdx_to_df(gdx_file, 'DEM_RES_FP')
new = gdx_to_df(gdx_file, 'demand_new_res')
old_indexr = ref.index.names
old_indexn = new.index.names
ref['C'] = [zone_dict[z] for z in ref.index.get_level_values('Z')]
new['C'] = [zone_dict[z] for z in new.index.get_level_values('Z')]
ref.set_index('C', append=True, inplace=True)
new.set_index('C', append=True, inplace=True)
ref = ref.reorder_levels(['C'] + old_indexr)
new = new.reorder_levels(['C'] + old_indexn)
shiftedtot=0
for i in ref.index:
value1 = float(ref.get_value(i,'DEM_RES_FP'))
value2 = float(new.get_value((i[0],i[1],i[2],i[3],'L'),'demand_new_res'))
shifted = (value1-value2)
# shifted = abs(shifted)
# print value1,value2,shifted
ref.set_value(i,'DEM_RES_FP',shifted)
shiftedtot = shiftedtot + shifted
ref.reset_index(inplace=True)
new.reset_index(inplace=True)
maxshift = pivot_table(ref, 'DEM_RES_FP', index=['Z'], columns=['C'], aggfunc=np.max)
# print shiftedtot
return maxshift
def retrieving_stor_cap_c():
stor = gdx_to_df(gdx_file, 'p_cap_c')
old_index = stor.index.names
stor['C'] = [zone_dict[z] for z in stor.index.get_level_values('Z')]
stor.set_index('C', append=True, inplace=True)
stor = stor.reorder_levels(['C'] + old_index)
stor = pivot_table(stor.reset_index(), 'p_cap_c', index=['Y','Z','S'], columns=['C'], aggfunc=np.sum)
return stor
def retrieving_cap():
cap = gdx_to_df(gdx_file, 'cap')
old_index = cap.index.names
cap['C'] = [zone_dict[z] for z in cap.index.get_level_values('Z')]
cap.set_index('C', append=True, inplace=True)
cap = cap.reorder_levels(['C'] + old_index)
cap.reset_index(inplace=True)
cap = pivot_table(cap, 'cap', index=['Y', 'Z', 'G'], columns=['C'], aggfunc=np.sum)
return cap
def retrieving_edr_cost():
costgDR = gdx_to_df(gdx_file, 'costgDR')
old_index = costgDR.index.names
costgDR['C'] = [zone_dict[z] for z in costgDR.index.get_level_values('Z')]
costgDR.set_index('C', append=True, inplace=True)
costgDR = costgDR.reorder_levels(['C'] + old_index)
costgDR.reset_index(inplace=True)
costgDR = pivot_table(costgDR, 'costgDR', index=['Z'], columns=['C'], aggfunc=np.sum)
return costgDR
def retrieving_res_s():
res_s = gdx_to_df(gdx_file, 'res_s')
old_index = res_s.index.names
# print old_index
res_s['C'] = [storage[z] for z in res_s.index.get_level_values('Z')]
res_s.set_index('C', append=True, inplace=True)
res_s = res_s.reorder_levels(['C'] + old_index)
for i in res_s.index:
value = float(res_s.get_value(i,'res_s'))
res_s.set_value(i,'res_s',value)
res_s = pivot_table(res_s.reset_index(), 'res_s', index=['Y','R'], columns=['C'], aggfunc=np.sum)
return res_s
def retrieving_stor_d():
stord = gdx_to_df(gdx_file, 'p_d')
old_index = stord.index.names
stord['C'] = [zone_dict[z] for z in stord.index.get_level_values('Z')]
stord.set_index('C', append=True, inplace=True)
stord = stord.reorder_levels(['C'] + old_index)
for i in stord.index:
value = float(stord.get_value(i,'p_d'))*weight[i[2]-1]
stord.set_value(i,'p_d',value)
stord.reset_index(inplace=True)
stord = pivot_table(stord, 'p_d', index=['Y'], columns=['C'], aggfunc=np.sum)
return stord
def retrieving_gen():
gen = gdx_to_df(gdx_file, 'gen')
old_index = gen.index.names
gen['C'] = [zone_dict[z] for z in gen.index.get_level_values('Z')]
gen.set_index('C', append=True, inplace=True)
gen = gen.reorder_levels(['C'] + old_index)
for i in gen.index:
value = float(gen.get_value(i,'gen'))*weight[i[2]-1]
gen.set_value(i,'gen',value)
gen.reset_index(inplace=True)
gen = pivot_table(gen, 'gen', index=['Y', 'G'], columns=['C'], aggfunc=np.sum)
return gen
def retrieving_res_DR():
res_DR = gdx_to_df(gdx_file, 'res_DR')
old_index = res_DR.index.names
# print old_index
res_DR['C'] = [resDR[z] for z in res_DR.index.get_level_values('Z')]
res_DR.set_index('C', append=True, inplace=True)
res_DR = res_DR.reorder_levels(['C'] + old_index)
for i in res_DR.index:
value = float(res_DR.get_value(i,'res_DR'))*weight[i[2]-1]
res_DR.set_value(i,'res_DR',value)
res_DR = pivot_table(res_DR.reset_index(), 'res_DR', index=['Y','R'], columns=['C'],aggfunc=np.sum)
return res_DR
def test_read_out_parameters(self):
gdx_file = os.path.join(os.getcwd(), "tests", "test_database.gdx")
create_test_database(gdx_file)
df_param_s = ga.gdx_to_df(gdx_file, "Param_S")
self.assertEqual(len(df_param_s), 10)
self.assertEqual(sum(df_param_s["Param_S"]), 60.0)
self.assertEqual(df_param_s.index.names, ["S"])
self.assertEqual(df_param_s.columns, ["Param_S"])
df_param_s_s = ga.gdx_to_df(gdx_file, "Param_S_S")
self.assertEqual(len(df_param_s_s), 100)
self.assertEqual(sum(df_param_s_s["Param_S_S"]), 0)
self.assertEqual(df_param_s_s.index.names, ["S", "SS"])
self.assertEqual(df_param_s_s.columns, ["Param_S_S"])
df_param_s_i = ga.gdx_to_df(gdx_file, "Param_S_I")
self.assertEqual(len(df_param_s_i), 100)
self.assertEqual(sum(df_param_s_i["Param_S_I"]), 0)
self.assertEqual(df_param_s_i.index.names, ["S", "I"])
self.assertEqual(df_param_s_i.columns, ["Param_S_I"])
def test_read_out_sets(self):
gdx_file = os.path.join(os.getcwd(), "tests", "test_database.gdx")
create_test_database(gdx_file)
df_s = ga.gdx_to_df(gdx_file, "S")
self.assertEqual(len(df_s), 10)
df_subs = ga.gdx_to_df(gdx_file, "SubS")
self.assertEqual(len(df_subs), 10)
self.assertEqual(len(df_subs[df_subs["SubS"]]), 5)
df_i = ga.gdx_to_df(gdx_file, "I")
self.assertEqual(len(df_i), 10)
df_subi = ga.gdx_to_df(gdx_file, "SubI")
self.assertEqual(len(df_subi), 10)
self.assertEqual(len(df_subi[df_subi["SubI"]]), 5)
df_subsi = ga.gdx_to_df(gdx_file, "SubSI")
self.assertEqual(len(df_subsi), 100)
self.assertEqual(len(df_subsi[df_subsi["SubSI"]]), 25)
df_subs_empty = ga.gdx_to_df(gdx_file, "SubSEmpty")
self.assertEqual(len(df_subs_empty), 10)
self.assertEqual(len(df_subs_empty[df_subs_empty["SubSEmpty"]]), 0)
def retrieving_curt():
curt = gdx_to_df(gdx_file, 'curt')
old_index = curt.index.names
curt['C'] = [curtail[z] for z in curt.index.get_level_values('Z')]
curt.set_index('C', append=True, inplace=True)
curt = curt.reorder_levels(['C'] + old_index)
# print curt.index.names
for i in curt.index:
value = float(curt.get_value(i,'curt'))*weight[i[2]-1]
curt.set_value(i,'curt',value)
curt.reset_index(inplace=True)
curt = pivot_table(curt, 'curt', index=['Y'], columns=['C'], aggfunc=np.sum)
return curt
def retrieving_demand():
new = gdx_to_df(gdx_file, 'demand_unit')
old_index = new.index.names
new['C'] = [zone_dict[z] for z in new.index.get_level_values('Z')]
new.set_index('C', append=True, inplace=True)
new = new.reorder_levels(['C'] + old_index)
print new.index.names
for i in new.index:
value = float(new.get_value(i,'demand_unit'))*weight[i[1]-1]
new.set_value(i,'demand_unit',value)
new.reset_index(inplace=True)
new = pivot_table(new, 'demand_unit', index=['Z'], columns=['C'], aggfunc=np.sum)
# print shiftedtot
return new
from gams_addon import gdx_to_df, DomainInfo
from openpyxl.styles import Style, Border, Alignment, Protection, Font, colors
file = 'results\out_db_40_DR.gdx'
gdx_file = os.path.join(os.getcwd(), '%s' % file)
writefile = os.getcwd() + '\\' + 'excel\output_elasticity_model.xlsx'
writer = ExcelWriter(writefile)
print gdx_file
zone_dict = dict()
zone_dict['BEL_Z'] = 'BEL'
print 'Retrieving price_unit_clone'
price_unit = gdx_to_df(gdx_file, 'price_unit_clone')
old_index = price_unit.index.names
price_unit['C'] = [zone_dict[z] for z in price_unit.index.get_level_values('Z')]
price_unit.set_index('C', append=True, inplace=True)
price_unit = price_unit.reorder_levels(['C'] + old_index)
price_unit.reset_index(inplace=True)
price_unit = pivot_table(price_unit, 'price_unit_clone', index=['P', 'T','Z'], columns=['C'], aggfunc=np.sum)
print 'Retrieving demand_unit'
demand_unit = gdx_to_df(gdx_file, 'demand_unit')
old_index = demand_unit.index.names
demand_unit['C'] = [zone_dict[z] for z in demand_unit.index.get_level_values('Z')]
demand_unit.set_index('C', append=True, inplace=True)
demand_unit = demand_unit.reorder_levels(['C'] + old_index)
demand_unit.reset_index(inplace=True)
demand_unit = pivot_table(demand_unit, 'demand_unit', index=['P','T','Z'], columns=['C'], aggfunc=np.sum)
