Commit 0dbd5e27 authored by Madhur Panwar's avatar Madhur Panwar
Browse files

storage plots removing {max(model.time) + 1}th timestep from charge-discharge... 

storage plots removing {max(model.time) + 1}th timestep from charge-discharge plot, energy_balance plots made

TODO: legend distribution in energy_balance plots, markers to distinguish bw overlapping plots?, kwargs in arguments, docstrings
parent ef910585

pyehub/outputter.py

+15 −8
Original line number Diff line number Diff line
@@ -387,12 +387,13 @@ def plot_storages(results: dict, to_plot: dict = None, size: tuple = (10, 5)) -> 


    for storage in attributes['storages']:

        fig, axes = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=size)

        fig.text(0.524, 0.02, 'Timesteps', ha='center', va='center')

        fig.text(0.524, 0.02, 'Timesteps (-)', ha='center', va='center')

        fig.text(0.01, 0.5, 'Storage state (in %)', ha='center', va='center', rotation='vertical')

        fig.text(0.5, 0.97, storage, ha='center', va='center')

        ax = axes[0]

        ax1 = axes[1]





        ser_storage_state = attributes['storage_level'][storage]

        ser_gross_charge = attributes['energy_to_storage'][storage]

        ser_gross_discharge = -attributes['energy_from_storage'][storage]
@@ -416,24 +417,30 @@ def plot_storages(results: dict, to_plot: dict = None, size: tuple = (10, 5)) -> 
            df = (df.div(capacity)).mul(100)



        if to_plot['pl_state']:

            df['storage state'].plot(kind='bar', xticks=df.index, title=' ', ax=ax, color='deepskyblue', legend=True)

            df['storage state'].plot(kind='bar',  title=' ', ax=ax, color='deepskyblue', legend=True)

        if to_plot['pl_gross_ch']:

            df['charge from stream'].plot(drawstyle='steps-post', xticks=df.index, title=' ', color='lightgreen',

            df['charge from stream'].plot(drawstyle='steps-post',  title=' ', color='lightgreen',

                                          linewidth=3, ax=ax1, legend = True)

        if to_plot['pl_net_ch']:

            df['net charge'].plot(drawstyle='steps-post', xticks=df.index, title=' ', color='green',

            df['net charge'].plot(drawstyle='steps-post',  title=' ', color='green',

                                  linestyle='--', linewidth=2, ax=ax1, legend=True)

        if to_plot['pl_gross_dch']:

            df['discharge to stream'].plot(drawstyle='steps-post', xticks=df.index, title=' ', color='orange',

            df['discharge to stream'].plot(drawstyle='steps-post',  title=' ', color='orange',

                                           linewidth=3, ax=ax1, legend=True)

        if to_plot['pl_net_dch']:

            df['net discharge'].plot(drawstyle='steps-post', xticks=df.index, title=' ', color='red',

            df['net discharge'].plot(drawstyle='steps-post',  title=' ', color='red',

                                     linestyle='--', linewidth=2, ax=ax1, legend=True)

        if to_plot['pl_decay']:

            df['standing loss'].plot(kind='bar', xticks=df.index, title=' ', color='saddlebrown',

            df['standing loss'].plot(kind='bar',  title=' ', color='saddlebrown',

                                     ax=ax, legend=True)

        ax1.set_xticks(df.index[:-1])

        ax.set_xticks(df.index)





        if to_plot['pl_state'] or to_plot['pl_gross_ch'] or to_plot['pl_net_ch'] or to_plot['pl_gross_dch'] \

                or to_plot['pl_net_dch'] or to_plot['pl_decay']:

            plt.legend(loc='best')

            for ax in fig.axes:

                plt.sca(ax)

                plt.xticks(rotation=0)

            plt.show()

pyehub/plots.py

+74 −75
Original line number Diff line number Diff line
@@ -7,106 +7,105 @@ from pyehub.energy_hub.ehub_model import EHubModel 


# sns.set()



def plot_energy_balance(model, results: dict) -> None:

    # solution_section = results['solution']

    # attributes = to_dataframes(solution_section)



def plot_energy_balance(model, results: dict, size: tuple = (8, 5), lw: float = 3, dl: float = 3 ) -> None:

    attributes = results['solution']

    streams_wo_sources = [x for x in model.streams if x not in model.sources]



    list_streams = attributes['streams'] # all the streams

    list_import_streams  = attributes['import_streams'] # streams importing energy from outside world, i.e. 'can import = 1'

    list_export_streams = attributes['export_streams'] # streams exporting energy to the outside world, i.e. 'can export = 1'

    list_LOAD_streams = attributes['demands'] # streams with LOADS



    list_storages = attributes['storages'] # list with all the storage names

    # list_dict_storage = model._data.request['storages']

    dict_storage_stream = {x['name']: x['stream'] for x in model._data._request['storages']}



    # interactions with the outside world (imported and exported energy into/out of streams)

    df_energy_imported = attributes['energy_imported'] #+ df with series' names as ones in list_import_streams,

    # e.g. df_energy_imported[list_import_streams[0]] gives the series of energy imported as 'list_import_streams[0]' stream

    df_energy_exported = attributes['energy_exported'] #- df with series' names as ones in list_export_streams,

    # e.g. df_energy_exported[list_export_streams[0]] gives the series of energy exported as 'list_export_streams[0]' stream



    # interactions with storages

    df_energy_from_storage = attributes['energy_from_storage']  # df with series' names as the ones in list_storages,

    # e.g. df_energy_from_storage[list_storage[0]] gives the energy entering in dict_storage_stream[list_storage[0]]

    # stream from list_storage[0] storage

    df_energy_to_storage = attributes['energy_to_storage']  # df with series' names as the ones in list_storages,

    # e.g. df_energy_to_storage[list_storage[0]] gives the energy leaving from dict_storage_stream[list_storage[0]]

    # stream to list_storage[0] storage



    # plot title 'stream'



    for stream in model.streams:

        stream = 'Irradiation'

        ax = plt.gca()

        # plot title 'stream'

        dict_data = {}

        for t in model.time:

            # get the load for a demand stream

    for stream in streams_wo_sources:

        fig, ax = plt.subplots(nrows=1, ncols=1, figsize=size)



        dict_data_pos = {}

        dict_data_neg = {}



        for t in model.time:

            curr_neg = curr_pos = 0

            if stream in model.demands:

                load = model.LOADS[stream][t]

                curr_neg += (-load)

                place_in_dict(t, model, dict_data, 'LOAD', curr_neg)

                # pick color (initialise from a pallete) , plot load -ve label: 'LOAD'



            if stream in model.sources:

                load = model.LOADS[stream][t]

                curr_neg+=(-load)

                place_in_dict(t, model, dict_data, 'LOAD', curr_neg)

                # pick color (initialise from a pallete) , plot load -ve label: 'LOAD'



            # storage interactions

                place_in_dict(t, model, dict_data_neg, 'LOAD', curr_neg)



            for storage in model._get_storages_from_stream(stream): # storages interacting with current 'stream'

            for storage in model._get_storages_from_stream(stream):

                q_out = float(attributes['energy_to_storage'][t][storage.name])

                curr_neg += (-q_out)

                q_in = float(attributes['energy_from_storage'][t][storage.name])

                curr_pos += q_in

                place_in_dict(t, model, dict_data, 'To '+storage.name+' (S)', curr_neg)

                place_in_dict(t, model, dict_data, 'From '+storage.name+' (S)', curr_pos)

                # next color, style as dashed

                # plot q_out -ve label: 'To 'storage''

                # plot q_in +ve label: 'From 'storage''



                place_in_dict(t, model, dict_data_neg, 'To '+storage.name+' (S)', curr_neg)

                place_in_dict(t, model, dict_data_pos, 'From '+storage.name+' (S)', curr_pos)



            # attributes with float



            for tech in model.technologies:  # _get_techs_from_output_stream(stream):

                # TODO: Account for techs which have two input streams

            for tech in model.technologies:

                conversion_rate = float(attributes['CONVERSION_EFFICIENCY'][tech][stream])

                if conversion_rate < 0:

                    energy_input = float(attributes['energy_input'][t][tech])

                    curr_neg+=(-energy_input)

                    place_in_dict(t, model, dict_data, 'To '+tech+' (C)', curr_neg)

                    # next color

                    # plot energy_input -ve label: 'To 'tech''

                # TODO: Account for CHP which has two output streams

                    place_in_dict(t, model, dict_data_neg, 'To '+tech+' (C)', curr_neg)



                if conversion_rate > 0:

                    energy_input = float(attributes['energy_input'][t][tech])

                    energy_output = energy_input * conversion_rate

                    curr_pos+=energy_output

                    place_in_dict(t, model, dict_data, 'From '+tech+' (C)', curr_pos)

                    # next color

                    # plot energy_output +ve label: 'From 'tech''

                    place_in_dict(t, model, dict_data_pos, 'From '+tech+' (C)', curr_pos)



            if stream in model.export_streams:

                energy_exported = attributes['energy_exported'][t][stream]

                curr_neg += (-energy_exported)

                place_in_dict(t, model, dict_data, 'Exported', curr_neg)

                # next color

                # plot energy_exported -ve label: 'Exported'

                place_in_dict(t, model, dict_data_neg, 'Exported', curr_neg)



            if stream in model.import_streams:

                energy_imported = attributes['energy_imported'][t][stream]

                curr_pos += energy_imported

                place_in_dict(t, model, dict_data, 'Imported', curr_pos)

                # next color

                # plot energy_imported +ve label: 'Imported'

                print()



                place_in_dict(t, model, dict_data_pos, 'Imported', curr_pos)



        x = y = 1

        # mk_index_c = 1

        # mk_index_s = 1

        for label, data in dict_data_neg.items():

            if label.endswith('LOAD'):

                ds = ()

                # mk = 's'

            if label.endswith('(C)'):

                ds = (dl, x)

                x += 1

                # mk = str(mk_index_c % 5)

                # mk_index_c += 1

            if label.endswith('(S)'):

                ds = (dl, y, 1, y)

                y += 1

                # mk = str(mk_index_s % 5)

                # mk_index_s += 1

            if label.endswith('Exported'):

                ds = (dl, 1, 1.5, 1, 1.5, 1, 1.5, 1, 1.5, 1, 1.5, 1)

            if label.endswith('Imported'):

                ds = (dl, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)

            plt.plot(data, label=label, axes=ax, linewidth=lw, dashes=ds)

            plt.legend(loc='best')

            ax.set(title=stream, xticks=model.time, xlabel='Timesteps', ylabel='')



        x = y = 1.2

        for label, data in dict_data_pos.items():

            if label.endswith('LOAD'):

                ds = ()

                # mk = 's'

            if label.endswith('(C)'):

                ds = (dl, x)

                x += 1

                # mk = str(mk_index_c % 5)

                # mk_index_c += 1

            if label.endswith('(S)'):

                ds = (dl, y, 1, y)

                y += 1

                # mk = str(mk_index_s % 5)

                # mk_index_s += 1

            if label.endswith('Exported'):

                ds = (dl, 1, 1.5, 1, 1.5, 1, 1.5, 1, 1.5, 1, 1.5, 1)

            if label.endswith('Imported'):

                ds = (dl, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)

            # plt.plot(data, label=label, axes=ax, linewidth=lw, marker=mk, dashes=ds)

            plt.plot(data, label=label, axes=ax, linewidth=lw, dashes=ds)

            plt.legend(loc='best')

            ax.set(title=stream, xticks=model.time, xlabel='Timesteps (-)', ylabel=stream+' (kWh)')



        plt.show()



def place_in_dict(t, model, dictionary: dict, key: str, value):

    """append a value in the list corresponding to key if key exists in dictionary.

pyehub/tutorials/test_file.xlsx

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