def testPandasNamedColumns(self): ampl = self.ampl try: import pandas as pd except ImportError: return df_unindexed = pd.DataFrame( [['Apple', 'Red', 3, 1.29], ['Apple', 'Green', 9, 0.99], ['Pear', 'Red', 25, 2.59], ['Pear', 'Green', 26, 2.79], ['Lime', 'Green', 99, 0.39]], columns=['Fruit', 'Color', 'Count', 'Price']) # RangeIndex self.assertEqual( DataFrame.fromPandas(df_unindexed).getHeaders(), ('index0', 'Fruit', 'Color', 'Count', 'Price')) # MultiIndex df_indexed = df_unindexed.set_index(['Fruit', 'Color']) self.assertEqual( DataFrame.fromPandas(df_indexed, index_names=['Fruit', 'Color']).getHeaders(), ('Fruit', 'Color', 'Count', 'Price')) # Index without name df = pd.DataFrame([[1, 2, 3, 4, 5], [6, 7, 8, 9, 0]], index=['First', 'Second'], columns=[1, 2, 3, 4, 5]) self.assertEqual( DataFrame.fromPandas(df.stack()).getHeaders(), ('index0', 'index1', '0'))
def testPandas(self): ampl = self.ampl try: import pandas as pd except ImportError: return df = pd.DataFrame({"a": [1, 2], "b": [3.5, 4]}, index=["x", "y"]) ampl.eval(""" set S; param a{S}; param b{S}; """) ampl.setData(df, "S") self.assertEqual(list(ampl.set["S"].members()), ["x", "y"]) self.assertEqual(ampl.param["a"]["x"], 1) self.assertEqual(ampl.param["b"]["y"], 4) df2 = pd.DataFrame( { "a": [10, 20, 30], }, index=["x", "y", "z"], ) df3 = pd.DataFrame({}, index=["xx", "yy"]) df = DataFrame.fromPandas(df) df2 = DataFrame.fromPandas(df2) df3 = DataFrame.fromPandas(df3) self.assertTrue(isinstance(df.toDict(), dict)) self.assertTrue(isinstance(df.toList(), list)) self.assertTrue(isinstance(df.toPandas(), pd.DataFrame)) self.assertEqual(df.toList()[0][1:], (1, 3.5)) self.assertEqual(df2.toList()[0], ("x", 10)) self.assertEqual(df3.toList()[0], "xx") self.assertEqual(set(df.toDict().keys()), set(["x", "y"])) self.assertEqual(set(df2.toDict().keys()), set(["x", "y", "z"])) self.assertEqual(set(df3.toDict().keys()), set(["xx", "yy"])) self.assertEqual(df.toDict()["x"], (1, 3.5)) self.assertEqual(df2.toDict()["x"], 10) self.assertEqual(df3.toDict()["xx"], None) csv_file = os.path.join(os.path.dirname(__file__), "data.csv") p_df = pd.read_csv(csv_file, sep=";", index_col=0) df = DataFrame.fromPandas(p_df) self.assertTrue(isinstance(df.toDict(), dict)) self.assertEqual(set(df.toDict().keys()), set([1.0, 2.0, 3.0])) self.assertEqual(set(df.toList()[0]), set([1.0, 0.01])) self.assertEqual(set(df.toList()[1]), set([2.0, 0.02])) self.assertEqual(set(df.toList()[2]), set([3.0, 0.03]))
def testPandas(self): ampl = self.ampl try: import pandas as pd except ImportError: return df = pd.DataFrame({'a': [1, 2], 'b': [3.5, 4]}, index=['x', 'y']) ampl.eval(''' set S; param a{S}; param b{S}; ''') ampl.setData(df, 'S') self.assertEqual(list(ampl.set['S'].members()), ['x', 'y']) self.assertEqual(ampl.param['a']['x'], 1) self.assertEqual(ampl.param['a']['y'], 2) df2 = pd.DataFrame({ 'a': [10, 20, 30], }, index=['x', 'y', 'z']) df3 = pd.DataFrame({}, index=['xx', 'yy']) df = DataFrame.fromPandas(df) df2 = DataFrame.fromPandas(df2) df3 = DataFrame.fromPandas(df3) self.assertTrue(isinstance(df.toDict(), dict)) self.assertTrue(isinstance(df.toList(), list)) self.assertTrue(isinstance(df.toPandas(), pd.DataFrame)) self.assertEqual(df.toList()[0][1:], (1, 3.5)) self.assertEqual(df2.toList()[0], ('x', 10)) self.assertEqual(df3.toList()[0], 'xx') self.assertEqual(set(df.toDict().keys()), set(['x', 'y'])) self.assertEqual(set(df2.toDict().keys()), set(['x', 'y', 'z'])) self.assertEqual(set(df3.toDict().keys()), set(['xx', 'yy'])) self.assertEqual(df.toDict()['x'], (1, 3.5)) self.assertEqual(df2.toDict()['x'], 10) self.assertEqual(df3.toDict()['xx'], None) csv_file = os.path.join(os.path.dirname(__file__), 'data.csv') p_df = pd.read_table(csv_file, sep=';', index_col=0) df = DataFrame.fromPandas(p_df) self.assertTrue(isinstance(df.toDict(), dict)) self.assertEqual(set(df.toDict().keys()), set([1.0, 2.0, 3.0])) self.assertEqual(set(df.toList()[0]), set([1.0, 0.01])) self.assertEqual(set(df.toList()[1]), set([2.0, 0.02])) self.assertEqual(set(df.toList()[2]), set([3.0, 0.03]))
def testPandasNamedColumns(self): ampl = self.ampl try: import pandas as pd except ImportError: return df_unindexed = pd.DataFrame( [ ["Apple", "Red", 3, 1.29], ["Apple", "Green", 9, 0.99], ["Pear", "Red", 25, 2.59], ["Pear", "Green", 26, 2.79], ["Lime", "Green", 99, 0.39], ], columns=["Fruit", "Color", "Count", "Price"], ) # RangeIndex self.assertEqual( DataFrame.fromPandas(df_unindexed).getHeaders(), ("index0", "Fruit", "Color", "Count", "Price"), ) # MultiIndex df_indexed = df_unindexed.set_index(["Fruit", "Color"]) self.assertEqual( DataFrame.fromPandas(df_indexed, index_names=["Fruit", "Color"]).getHeaders(), ("Fruit", "Color", "Count", "Price"), ) # Index without name df = pd.DataFrame( [[1, 2, 3, 4, 5], [6, 7, 8, 9, 0]], index=["First", "Second"], columns=[1, 2, 3, 4, 5], ) self.assertEqual( DataFrame.fromPandas(df.stack()).getHeaders(), ("index0", "index1", "0"))
assign_set_data('DFFR_PRICE', dffr.DFFR_PRICE.values) assign_set_data('DA_PRICE', da.DA_PRICE.unique()) ## Assign parameter data ## ampl.getParameter('Cost').set(0) ampl.getParameter('Ramp').set(999999) ampl.getParameter('Ramp_DFFR').set(9999999) ampl.getParameter('P_MAX').set(2) n_DFFR_PRICE = len(dffr.DFFR_PRICE.unique()) n_DA_PRICE = len(da.DA_PRICE.unique()) n_INTERVAL = len(intervals) df = pd.DataFrame([1/n_DFFR_PRICE] * n_DFFR_PRICE, columns=['p_R'], index=dffr.DFFR_PRICE.values) ampl.setData(DataFrame.fromPandas(df)) df = pd.DataFrame([1/n_DA_PRICE] * n_DA_PRICE, columns=['p_DA'], index=da.DA_PRICE.unique()) ampl.setData(DataFrame.fromPandas(df)) df = dffr.set_index('DFFR_PRICE') ampl.setData(DataFrame.fromPandas(df)) df = da.set_index(['DA_PRICE','INTERVALS']) ampl.setData(DataFrame.fromPandas(df)) ## Solve the model ## # Set ampl options settings = { 'solver' : 'cplexamp',
def prodalloc(RID, SID, shared_ns=None, f_out=None, f_log=None): from amplpy import AMPL, DataFrame if shared_ns == None: (df_demand, wup_12mavg, ppp_sum12, df_scenario, sw_avail, df_penfunc, df_relcost) = pull_data(RID, SID) # ======================================= # instantiate AMPL class and set AMPL options ampl = AMPL() # set options ampl.setOption('presolve', False) ampl.setOption('solver', 'gurobi_ampl') # ampl.setOption('solver', 'cbc') ampl.setOption('gurobi_options', 'iisfind=1 iismethod=1 lpmethod=4 mipgap=1e-6 warmstart=1') ampl.setOption('reset_initial_guesses', True) ampl.setOption('solver_msg', True) # real model from model file d_cur = os.getcwd() f_model = os.path.join(d_cur, 'model.amp') ampl.read(f_model) if shared_ns != None: df_demand = shared_ns.df_demand.query( f'RealizationID == {RID}').loc[:, ['wpda', 'dates', 'Demand']] dates = df_demand.loc[:, ['dates']].values df_demand.loc[:, 'dates'] = [ pd.to_datetime(d).strftime('%Y-%b') for d in dates ] df_demand.set_index(keys=['wpda', 'dates'], inplace=True) wpda = sorted(list(set([w for (w, m) in df_demand.index]))) monyr = df_demand.loc[('COT', ), ].index.values nyears = int(len(monyr) / 12.0) # lambda for determine number of days in a month dates = dates[0:(12 * nyears)] f_ndays_mo = lambda aday: (aday + dt.timedelta(days=32)).replace(day=1 ) - aday ndays_mo = map(f_ndays_mo, [pd.to_datetime(d[0]) for d in dates]) ndays_mo = [i.days for i in ndays_mo] # add data to sets -- Demand ampl.getParameter('nyears').value = nyears ampl.getSet('monyr').setValues(monyr) ampl.getSet('wpda').setValues(wpda) ampl.getParameter('demand').setValues(DataFrame.fromPandas(df_demand)) ampl.getParameter('ndays_mo').setValues(ndays_mo) # index to year number yearno = [(i // 12) + 1 for i in range(len(dates))] ampl.getParameter('yearno').setValues(np.asarray(yearno)) monthno = [pd.to_datetime(d[0]).month for d in dates] ampl.getParameter('monthno').setValues(np.asarray(monthno)) # --------------------------------------- # WUP and preferred ranges if shared_ns != None: wup_12mavg = shared_ns.wup_12mavg ampl.getParameter('wup_12mavg').setValues( DataFrame.fromPandas(wup_12mavg.loc[:, ['wup_12mavg']])) ampl.getParameter('prod_range_lo').setValues( DataFrame.fromPandas(wup_12mavg.loc[:, ['prod_range_lo']])) ampl.getParameter('prod_range_hi').setValues( DataFrame.fromPandas(wup_12mavg.loc[:, ['prod_range_hi']])) # add ppp_sum12 if shared_ns != None: ppp_sum12 = shared_ns.ppp_sum12 ppp_sum12.loc[:, 'monyr'] = [i for i in range(1, 12)] * 3 ppp_sum12.set_index(keys=['WF', 'monyr'], inplace=True) ampl.getParameter('ppp_sum12').setValues(DataFrame.fromPandas(ppp_sum12)) # Relative cost of water per a million gallon if shared_ns != None: df_relcost = shared_ns.df_relcost ampl.getParameter('relcost').setValues(DataFrame.fromPandas(df_relcost)) # --------------------------------------- # Scenario's data if shared_ns != None: df_scenario = shared_ns.df_scenario.query( f'ScenarioID=={SID}').loc[:, ['ParameterName', 'MonthNo', 'Value']] AVAIL_PCTILE = df_scenario.query(f"ParameterName == 'AVAIL_PCTILE'") AVAIL_PCTILE = AVAIL_PCTILE.loc[AVAIL_PCTILE.index, 'Value'].values[0] RES_INIT = df_scenario.query(f"ParameterName == 'RES_INIT'") RES_INIT = RES_INIT.loc[RES_INIT.index, 'Value'].values[0] # Surface Water Availability Data by month repeated for nyears ampl.getParameter('avail_pctile').value = AVAIL_PCTILE if shared_ns != None: sw_avail = shared_ns.sw_avail.query( f'Percentile == {AVAIL_PCTILE}' ).loc[:, ['source', 'monthno', 'value']] # sw_avail = temp.copy() # if nyears > 1: # for i in range(1, nyears): # sw_avail = sw_avail.append(temp) # srcs = sw_avail.loc[:, 'source'].unique() # for j in srcs: # sw_avail.loc[sw_avail['source']==j,'monthno'] = [i+1 for i in range(len(monyr))] # sw_avail.set_index(keys=['source', 'monthno'], inplace=True) # ampl.getParameter('ngw_avail').setValues(DataFrame.fromPandas(sw_avail)) sw_avail.set_index(keys=['source', 'monthno'], inplace=True) ampl.getParameter('ngw_avail').setValues(DataFrame.fromPandas(sw_avail)) # --------------------------------------- # Penalty functions for under utilization if shared_ns != None: df_penfunc = shared_ns.df_penfunc ampl.getParameter('penfunc_x').setValues( DataFrame.fromPandas(df_penfunc.loc[:, ['under_limit']])) ampl.getParameter('penfunc_r').setValues( DataFrame.fromPandas(df_penfunc.loc[:, ['penalty_rate']])) ''' # ======================================= # Read fixed allocation from spreadsheet # f_excel = os.path.join( # d_cur, 'WY 2019 monthly delivery and supply for budget InitialDraft.xlsx') sheet_names = ['WY 2019','WY 2020','WY 2021','WY 2022','WY 2023','WY 2024'] # ch_poc: Central Hills delivery (row 16) # reg_lithia: Regional to Lithia (row 20) # reg_cot: Regional to City of Tampa (row 26) # reg_thic: THIC intertie purchase (row 37) # crw_prod: Carrollwood WF production (row 40) # eag_prod: Production for Eagle Well (row 41) # row index is zero based, minus one header row = -2 row_offset = -2 ch_poc, reg_lithia, reg_cot, reg_thic, crw_prod, eag_prod = [], [], [], [], [], [] # These DV is fixed or receives values from other optimizer bud_fix, ds_fix, swtp_fix=[], [], [] for i in range(nyears): df_excel = pd.read_excel(f_excel, sheet_names[i], usecols='C:N', nrows=41) ch_poc .extend(list(df_excel.loc[16 + row_offset, :].values)) reg_lithia.extend(list(df_excel.loc[20 + row_offset, :].values)) reg_cot .extend(list(df_excel.loc[26 + row_offset, :].values)) reg_thic .extend(list(df_excel.loc[37 + row_offset, :].values)) crw_prod .extend(list(df_excel.loc[40 + row_offset, :].values)) eag_prod .extend(list(df_excel.loc[41 + row_offset, :].values)) bud_fix .extend(list(df_excel.loc[22 + row_offset, :].values)) ds_fix .extend(list(df_excel.loc[36 + row_offset, :].values)) swtp_fix.extend(list(df_excel.loc[38 + row_offset, :].values)) ''' ch_poc = df_scenario[df_scenario.ParameterName == 'ch_poc'].Value reg_lithia = df_scenario[df_scenario.ParameterName == 'reg_lithia'].Value reg_cot = df_scenario[df_scenario.ParameterName == 'reg_cot'].Value reg_thic = df_scenario[df_scenario.ParameterName == 'reg_thic'].Value crw_prod = df_scenario[df_scenario.ParameterName == 'crw_prod'].Value eag_prod = df_scenario[df_scenario.ParameterName == 'eag_prod'].Value ampl.getParameter('ch_poc').setValues(np.asarray(ch_poc, dtype=np.float32)) ampl.getParameter('reg_lithia').setValues( np.asarray(reg_lithia, dtype=np.float32)) ampl.getParameter('reg_cot').setValues( np.asarray(reg_cot, dtype=np.float32)) ampl.getParameter('reg_thic').setValues( np.asarray(reg_thic, dtype=np.float32)) ampl.getParameter('crw_prod').setValues( np.asarray(crw_prod, dtype=np.float32)) ampl.getParameter('eag_prod').setValues( np.asarray(eag_prod, dtype=np.float32)) # overloaded function 'VariableInstance_fix' need float64 bud_fix = np.asarray( df_scenario[df_scenario.ParameterName == 'bud_fix'].Value, dtype=np.float64) ds_fix = np.asarray( df_scenario[df_scenario.ParameterName == 'ds_fix'].Value, dtype=np.float64) swtp_fix = np.asarray( df_scenario[df_scenario.ParameterName == 'swtp_fix'].Value, dtype=np.float64) ampl.getParameter('bud_fix').setValues( np.asarray(bud_fix, dtype=np.float32)) ampl.getParameter('ds_fix').setValues(np.asarray(ds_fix, dtype=np.float32)) ampl.getParameter('swtp_fix').setValues( np.asarray(swtp_fix, dtype=np.float32)) # --------------------------------------- # initialize/fix variable values ampl.getParameter('res_init').value = RES_INIT res_vol = ampl.getVariable('res_vol') res_vol[0].fix(RES_INIT) gw_prod = ampl.getVariable('gw_prod') bud_prod = [ gw_prod[j, i] for ((j, i), k) in gw_prod.instances() if j == 'BUD' ] for i in range(len(bud_prod)): bud_prod[i].fix(bud_fix[i]) ds_prod = ampl.getVariable('ds_prod') for i in range(ds_prod.numInstances()): ds_prod[i + 1].fix(ds_fix[i]) swtp_prod = ampl.getVariable('swtp_prod') for i in range(swtp_prod.numInstances()): swtp_prod[i + 1].fix(swtp_fix[i]) # --------------------------------------- # dump data with open('dump.dat', 'w') as f: with stdout_redirected(f): ampl.display('wpda,monyr') ampl.display('demand') ampl.display('nyears') ampl.display('ndays_mo,yearno,monthno') # ampl.display('years') # ampl.display('dem_total') ampl.display( 'ch_poc,reg_lithia,reg_cot,reg_thic,crw_prod,eag_prod,bud_fix,ds_fix' ) ampl.display('wup_12mavg') ampl.display('ppp_sum12') ampl.display('ngw_avail') ampl.display('prod_range_lo,prod_range_hi') ampl.display('relcost') ampl.display('penfunc_x,penfunc_r') # ======================================= # silence solver with open('nul', 'w') as f: with stdout_redirected(f): ampl.solve() if f_out != None: with open(f_out, 'w') as f: print(r'# *** SOURCE ALLOCATION MODEL ****', file=f) print(r'# Monthly Delivery and Supply for Budgeting', file=f) print('\n# Objective: {}'.format( ampl.getObjective('mip_obj').value()), file=f) if (f_log != None) & (ampl.getObjective('mip_obj').result() == 'solved'): with open(f_log, "w") as f: print('\n\nDump Variable and Constraint Values', file=f) with stdout_redirected(f): write_log(ampl) if ampl.getObjective('mip_obj').result() == 'infeasible': if f_log != None: with open(f_log, "w") as f: with stdout_redirected(f): write_iis(ampl) else: write_iis(ampl) # ======================================= # print output # groundwater production temp = ampl.getVariable('gw_prod').getValues().toPandas().join( ampl.getVariable('gw_under').getValues().toPandas()).join( ampl.getVariable('gw_over').getValues().toPandas()) temp.columns = [i.replace('.val', '') for i in temp.columns] # pivoting df by source cwup_prod = temp.loc[[i for i in temp.index if i[0] == 'CWUP'], :].assign( index=monyr).set_index('index').rename(columns={ 'gw_prod': 'cwup_prod', 'gw_under': 'cwup_under', 'gw_over': 'cwup_over' }) bud_prod = temp.loc[[i for i in temp.index if i[0] == 'BUD'], :].assign( index=monyr).set_index('index').rename(columns={ 'gw_prod': 'bud_prod', 'gw_under': 'bud_under', 'gw_over': 'bud_over' }) sch_prod = temp.loc[[i for i in temp.index if i[0] == 'SCH'], :].assign( index=monyr).set_index('index').rename(columns={ 'gw_prod': 'sch_prod', 'gw_under': 'sch_under', 'gw_over': 'sch_over' }) temp = cwup_prod.join(sch_prod.join(bud_prod)) gw_results = temp temp_avg = temp.groupby(by=yearno).mean().reset_index() temp_avg = temp_avg.loc[:, [i for i in temp_avg.columns[1:temp_avg.shape[1]]]] if f_out != None: with open(f_out, 'a') as f: # print heading print('\n\n# Monthly Groudwater Production', file=f) for l in range(len(temp)): if (l % 12) == 0: print(('\n%10s' % 'Yr-Month') + ('%11s' * len(temp.columns) % tuple(temp.columns)), file=f) print(('%10s' % monyr[l]) + ('%11.3f' * len(temp.columns) % tuple(temp.iloc[l, :].values)), file=f) if (l + 1) % 12 == 0: print(('%10s' % 'Average') + ('%11.3f' * len(temp_avg.columns) % tuple(temp_avg.iloc[l // 12, :].values)), file=f) print( ('\n%10s' % 'Total Avg') + ('%11.3f' * len(temp_avg.columns) % tuple(temp_avg.mean().values)), file=f) # --------------------------------------- # SWTP Production to_swtp = ampl.getVariable('to_swtp').getValues().toPandas() to_res = ampl.getVariable('to_res').getValues().toPandas() idx = to_swtp.index temp = ampl.getVariable('swtp_prod').getValues().toPandas() temp = temp.assign(tbc_swtp=to_swtp.loc[[i for i in idx if i[0] == 'TBC']].values) temp = temp.assign( alf_swtp=to_swtp.loc[[i for i in idx if i[0] == 'Alafia']].values) temp = temp.join(ampl.getVariable('res_eff').getValues().toPandas()) temp = temp.assign(tbc_res=to_res.loc[[i for i in idx if i[0] == 'TBC']].values) temp = temp.assign(alf_res=to_res.loc[[i for i in idx if i[0] == 'Alafia']].values) temp = temp.join(ampl.getVariable('res_inf').getValues().toPandas()).join( ampl.getVariable('res_vol').getValues().toPandas()) # Add availability columns temp = temp.assign(tbc_avail=sw_avail.loc[[('TBC', i) for i in monthno], ['value']].values) temp = temp.assign(alf_avail=sw_avail.loc[[('Alafia', i) for i in monthno], ['value']].values) # Add SW withdraws df1 = ampl.getVariable('sw_withdraw').getValues().toPandas() idx = temp.index temp = temp.assign(tbc_wthdr=df1.loc[[('TBC', i) for i in idx], :].values) temp = temp.assign(alf_wthdr=df1.loc[[('Alafia', i) for i in idx], :].values) temp.columns = [i.replace('.val', '') for i in temp.columns] sw_results = temp # Compute annual average temp_avg = temp.groupby(by=yearno).mean().reset_index() temp_avg = temp_avg.loc[:, [i for i in temp_avg.columns[1:temp_avg.shape[1]]]] if f_out != None: with open(f_out, 'a') as f: # print heading print('\n\n# Monthly Surface Water Production', file=f) for l in range(len(temp)): if (l % 12) == 0: print(('\n%10s' % 'Yr-Month') + ('%10s' * len(temp.columns) % tuple(temp.columns)), file=f) print(('%10s' % monyr[l]) + ('%10.3f' * len(temp.columns) % tuple(temp.loc[float(l + 1), :])), file=f) if ((l + 1) % 12) == 0: print(('%10s' % 'Average') + ('%10.3f' * len(temp_avg.columns) % tuple(temp_avg.loc[l // 12, :])), file=f) print( ('\n%10s' % 'Total Avg') + ('%10.3f' * len(temp_avg.columns) % tuple(temp_avg.mean().values)), file=f) # --------------------------------------- # print multi objective values temp = ampl.getVariable('prodcost_avg').getValues().toPandas() idx = [i for i in temp.index] temp = temp.assign( prod_avg=temp.loc[:, 'prodcost_avg.val'] * 1e-3 / np.asarray([df_relcost.loc[i[0], 'relcost'] for i in idx])) temp = temp.join( ampl.getVariable('uu_penalty').getValues().toPandas()).join( ampl.getVariable('uu_avg').getValues().toPandas()) temp.columns = [i.replace('.val', '') for i in temp.columns] temp_avg = temp.groupby([i[0] for i in temp.index]).mean().reset_index() if f_out != None: with open(f_out, 'a') as f: print( '\n\n# Annual Production and Under Utilization (Opportunity) Costs', file=f) for l in range(len(temp)): if (l % nyears) == 0: print(('\n%10s%10s' % ('YearNo', 'Source')) + ('%15s' * len(temp.columns) % tuple(temp.columns)), file=f) print(('%10d%10s' % (idx[l][1], idx[l][0])) + ('%15.3f' * len(temp.columns) % tuple(temp.loc[[idx[l]], :].values[0])), file=f) if ((l + 1) % nyears) == 0: print(('%10s' % 'Average') + (('%10s' + '%15.3f' * len(temp.columns)) % tuple(temp_avg.iloc[l // nyears, :])), file=f) ampl.close() # prepare data for ploting if f_out != None: df_plotdata = df_demand.groupby(level=1).sum().join(df_demand.loc[( 'COT', ), ['Demand']].rename(columns={'Demand': 'COT'})).assign( TBW_Demand=lambda x: x.Demand - x.COT).loc[:, ['TBW_Demand']].join( gw_results.join(sw_results.set_index(gw_results.index))) monyr = [pd.Timestamp(i + '-01') for i in df_plotdata.index] df_plotdata = df_plotdata.assign( Dates=monyr).set_index('Dates').sort_index() df_plotdata = df_plotdata.assign(ndays_mo=ndays_mo) plot_results(SID, AVAIL_PCTILE, df_plotdata, f_out)