plot_storages() and plot_energy_balance() completed and are now a part of the codebase (385d7698) · Commits · energyincities / pyehub

pyehub/outputter.py

+211 −31

Original line number	Diff line number	Diff line
		@@ -358,37 +358,48 @@ def stream_info(results, output_file):
		writer.save()


		def plot_storages(results: dict, to_plot: dict = None, size: tuple = (10, 5)) -> None:
		def plot_storages(results: dict, **kwargs) -> None:
		"""
		Plots various variables related to storages:
		storage state: The level of energy remaining in the storage
		gross charge(charge from stream): Energy sent to storage from stream for charging
		gross discharge(discharge to stream): Energy going into stream after discharging
		net charge: Energy actually reaching storage(after loss of gross charge due to charging efficiency)
		net discharge: Energy actually leaving storage(after loss from this due to discharging efficiency, it becomes gross discharge)
		decay loss(standing loss): Energy dissipating due to standing losses
		storage state: The level of energy remaining in the storage.
		gross charge(charge from stream): Energy sent to storage from stream for charging.
		gross discharge(discharge to stream): Energy going into stream after discharging.
		net charge: Energy actually reaching storage(after loss of gross charge due to charging efficiency).
		net discharge: Energy actually leaving storage(after loss from this due to discharging efficiency, it becomes gross
		discharge).
		decay loss(standing loss): Energy dissipating due to standing losses.
		Note: All of the above are plotted as default. You can change this by passing respective arguments.

		Args:
		results: the 'results' dictionary returned by solve() method
		to_plot: dictionary with following keys and values as boolean indicating whether or not to plot
		the corresponding key (All variables are plotted by default)
		'pl_state': boolean value; whether to plot 'storage state' or not
		'pl_gross_ch': boolean value; whether to plot 'gross charge' or not
		'pl_gross_dch': boolean value; whether to plot 'gross discharge' or not
		'pl_net_ch': boolean value; whether to plot 'net charge' or not
		'pl_net_dch': boolean value; whether to plot 'net discharge' or not
		'pl_decay': boolean value; whether to plot 'decay loss' or not
		size: tuple defining (width, height) of the plot.
		results: dictionary; returned by solve() method.
		pl_state: boolean value; whether to plot 'storage state' or not.
		pl_gross_ch: boolean value; whether to plot 'gross charge' or not.
		pl_gross_dch: boolean value; whether to plot 'gross discharge' or not.
		pl_net_ch: boolean value; whether to plot 'net charge' or not.
		pl_net_dch: boolean value; whether to plot 'net discharge' or not.
		pl_decay: boolean value; whether to plot 'decay loss' or not.
		size: tuple; (width, height) of the plot [default is (10,5)].
		percentage: boolean; y-axis units in % or in kWh [default].
		"""
		solution_section = results['solution'].copy()
		attributes = to_dataframes(solution_section)
		if to_plot is None:
		to_plot = {'pl_state': True, 'pl_gross_ch': True, 'pl_gross_dch': True,
		'pl_net_ch': True, 'pl_net_dch': True, 'pl_decay': True}

		pl_state = kwargs.get('pl_state', True)
		pl_gross_ch = kwargs.get('pl_gross_ch', True)
		pl_gross_dch = kwargs.get('pl_gross_dch', True)
		pl_net_ch = kwargs.get('pl_net_ch', True)
		pl_net_dch = kwargs.get('pl_net_dch', True)
		pl_decay = kwargs.get('pl_decay', True)
		size = kwargs.get('size', (10, 5))
		percentage = kwargs.get('percentage', False)

		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(0.01, 0.5, 'Storage state (in %)', ha='center', va='center', rotation='vertical')
		if percentage:
		fig.text(0.01, 0.5, 'Storage state [%]', ha='center', va='center', rotation='vertical')
		else:
		fig.text(0.01, 0.5, 'Storage state (kWh)', ha='center', va='center', rotation='vertical')
		fig.text(0.5, 0.97, storage, ha='center', va='center')
		ax = axes[0]
		ax1 = axes[1]
		@@ -413,34 +424,203 @@ def plot_storages(results: dict, to_plot: dict = None, size: tuple = (10, 5)) ->
		'discharge to stream': ser_gross_discharge, 'net discharge': ser_net_discharge, 'standing loss': decay_loss}
		df = pd.DataFrame(data=d)

		if percentage:
		if capacity != 0:
		df = (df.div(capacity)).mul(100)

		if to_plot['pl_state']:
		if pl_state:
		df['storage state'].plot(kind='bar', title=' ', ax=ax, color='deepskyblue', legend=True)
		if to_plot['pl_gross_ch']:
		if pl_gross_ch:
		df['charge from stream'].plot(drawstyle='steps-post', title=' ', color='lightgreen',
		linewidth=3, ax=ax1, legend = True)
		if to_plot['pl_net_ch']:
		if pl_net_ch:
		df['net charge'].plot(drawstyle='steps-post', title=' ', color='green',
		linestyle='--', linewidth=2, ax=ax1, legend=True)
		if to_plot['pl_gross_dch']:
		if pl_gross_dch:
		df['discharge to stream'].plot(drawstyle='steps-post', title=' ', color='orange',
		linewidth=3, ax=ax1, legend=True)
		if to_plot['pl_net_dch']:
		if pl_net_dch:
		df['net discharge'].plot(drawstyle='steps-post', title=' ', color='red',
		linestyle='--', linewidth=2, ax=ax1, legend=True)
		if to_plot['pl_decay']:
		if pl_decay:
		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']:
		if pl_state or pl_gross_ch or pl_net_ch or pl_gross_dch \
		or pl_net_dch or pl_decay:
		plt.legend(loc='best')
		for ax in fig.axes:
		plt.sca(ax)
		plt.xticks(rotation=0)
		plt.show()


		def plot_energy_balance(model, results: dict, **kwargs) -> None:
		"""
		Visualization of energy balance for all the streams, i.e., Plots the energy interactions of all the streams with
		loads, converters, storages, imports and exports. Plots are dashed and marked with shape markers for visibility
		in case of overlap. Adjust the properties of the plot by passing arguments from below.

		Args:
		model: The object of EHubModel class (or any child class thereof); the energy hub model.
		results: dictionary; returned by solve() method.
		size: tuple; (width, height) of the plot [default is (9,5)].
		lw: float; linewidth of the plots [default is 2].
		dl: float; length of the dashes constituting the dashed plots [default is 3].
		"""
		attributes = results['solution'].copy()
		streams_wo_sources = [x for x in model.streams if x not in model.sources]

		size = kwargs.get('size', (9, 5))
		lw = kwargs.get('lw', 2)
		dl = kwargs.get('dl', 3)

		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_neg, 'LOAD', curr_neg)

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

		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_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_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_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_pos, 'Imported', curr_pos)

		# --------------------------------Plot Demands----------------------------------
		x = 1
		y = 1

		mk_c = ['1','2','3','4']
		mk_s = ['*','P']

		mk_index_c = 0
		mk_index_s = 0

		for label, data in dict_data_neg.items():
		if label.endswith('LOAD'):
		ds = ()

		mk = 's'
		ms = 7

		if label.endswith('(C)'):
		ds = (dl, x)
		x += 1

		mk = mk_c[mk_index_c % 4]
		mk_index_c += 1
		ms = 12

		if label.endswith('(S)'):
		ds = (dl, y, 1, y)
		y += 1

		mk = mk_s[mk_index_s % 2]
		mk_index_s += 1
		ms = 7.5

		if label.endswith('Exported'):
		mk = '\|'
		ds = (dl, 1, 1.5, 1, 1.5, 1, 1.5, 1, 1.5, 1, 1.5, 1)
		ms = 12

		ax.plot(data, label=label, linewidth=lw, dashes=ds, marker=mk, markersize=ms)

		handle_demand, label_demand = ax.get_legend_handles_labels()

		# --------------------------------Plot Supplies----------------------------------
		x = 1.2
		y = 1.2

		mk_c = ['1', '2', '3', '4']
		mk_s = ['+', 'x']

		mk_index_c = 0
		mk_index_s = 0

		for label, data in dict_data_pos.items():
		if label.endswith('(C)'):
		ds = (dl, x)
		x += 1

		mk = mk_c[mk_index_c % 4]
		mk_index_c += 1
		ms = 12

		if label.endswith('(S)'):
		ds = (dl, y, 1, y)
		y += 1

		mk = mk_s[mk_index_s % 2]
		mk_index_s += 1
		ms = 12

		if label.endswith('Imported'):
		ds = (dl, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
		mk = '\|'
		ms = 12

		ax.plot(data, label=label, linewidth=lw, dashes=ds, marker=mk, markersize=ms)

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

		# To have 'Demands' and 'Supplies' titles, we need a blank handle
		blank_handle = [plt.plot([], marker="", ls="")[0]]

		handle_supply, label_supply = ax.get_legend_handles_labels()

		# Update handles and labels for 'Supplies' by removing 'Demands' handles and labels from them
		handle_supply = handle_supply[len(handle_demand):]
		label_supply = label_supply[len(label_demand):]

		handles = blank_handle + handle_demand + blank_handle*2 + handle_supply

		labels = ["$\\bf{Demands}$"] + label_demand + [" "] + ["$\\bf{Supplies}$"] + label_supply

		plt.legend(loc='best', bbox_to_anchor=(1, 1), labels=labels, handles=handles)
		plt.show()


		def place_in_dict(t, model, dictionary: dict, key: str, value):
		"""Appends an entry in the list corresponding to key if key exists in dictionary.
		Otherwise creates a single entry in the dictionary as {key: [value]}"""
		if t == model.time[0]:
		dictionary.update({key: [value]})
		else:
		dictionary[key].append(value)

pyehub/plots.py

+4 −109

Original line number	Diff line number	Diff line
		from pyehub.outputter import plot_storages
		from pyehub.outputter import plot_energy_balance
		import pickle
		import pandas as pd
		import matplotlib.pyplot as plt
		@@ -9,114 +10,7 @@ from pyehub.outputter import pretty_print
		# sns.set()


		def plot_energy_balance(model, results: dict, size: tuple = (8, 5), lw: float = 3, dl: float = 3 ) -> None:
		attributes = results['solution'].copy()
		streams_wo_sources = [x for x in model.streams if x not in model.sources]

		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_neg, 'LOAD', curr_neg)

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

		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_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_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_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_pos, 'Imported', curr_pos)

		x = 1
		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 = 1.2
		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',ncol=2)
		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.
		Otherwise creates a single entry in the dictionary as {key: [value]}"""
		if t == model.time[0]:
		dictionary.update({key: [value]})
		else:
		dictionary[key].append(value)



		def main():
		@@ -130,7 +24,8 @@ def main():
		results = model.solve()
		results1=model1.solve()
		# print(results['solution'] == results1['solution'])
		# plot_storages(results)
		plot_storages(results)

		plot_energy_balance(model1, results)
		# pretty_print(results)
		# print(results['solution'] == results1['solution'])