def tab_install(figsize, install_cap, colors, names, nodes, table_font,
title_font, directory, conversion, style, v_round, fig_format,
kind, unit):
import matplotlib.pyplot as plt
from calliope_graph.graphs import style_check
style = style_check(style)
plt.style.use(style)
install_cap = install_cap * conversion
install_cap = install_cap.round(v_round)
if kind == 'table':
fig, (ax) = plt.subplots(1, figsize=figsize)
table = plt.table(cellText=install_cap.values,
rowColours=colors[install_cap.index],
rowLabels=names[install_cap.index],
colLabels=nodes,
loc='upper center',
rowLoc='center',
colLoc='center',
cellLoc='center')
table.set_fontsize(table_font)
table.scale(1, 2)
plt.box(on=None)
ax = plt.gca()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
elif kind == 'bar':
install_cap.columns = install_cap.columns.to_list()
install_cap.T.plot(kind='bar',
stacked=True,
color=colors[install_cap.index],
figsize=figsize)
plt.legend(bbox_to_anchor=(1.05, 1),
loc='upper left',
borderaxespad=0.,
frameon=True,
labels=names[install_cap.index])
plt.xlabel('Nodes')
plt.ylabel(unit)
else:
raise ValueError(
'/kind/ should be one of the followings: \n 1. /table/ \n 2. /bar/'
)
plt.title('Installed Capacity', fontsize=title_font)
plt.savefig('{}\{}_installed_cap.{}'.format(directory, kind, fig_format),
bbox_inches='tight',
dpi=150)
def cap_f_bar(nodes, fig_format, style, title_font, figsize, directory,
cap_f_inp, colors, names, kind, table_font, v_round):
import matplotlib.pyplot as plt
from calliope_graph.graphs import style_check
cap_f = cap_f_inp.copy()
style = style_check(style)
plt.style.use(style)
colors = colors[cap_f.index]
cap_f.index = names[cap_f.index]
cap_f = cap_f.round(v_round)
if kind == 'bar':
for i in nodes:
cap_f[i].plot(kind='bar',
stacked=True,
color=colors,
figsize=figsize,
legend=False)
plt.title('{} capacity factor'.format(names[i]),
fontsize=title_font)
plt.savefig('{}\{}capacity_factor.{}'.format(
directory, i, fig_format),
bbox_inches='tight',
dpi=150)
plt.show()
elif kind == 'table':
fig, (ax) = plt.subplots(1, figsize=figsize)
table = plt.table(cellText=cap_f.values,
rowColours=colors,
rowLabels=cap_f.index,
colLabels=nodes,
loc='upper center',
rowLoc='center',
colLoc='center',
cellLoc='center')
table.set_fontsize(table_font)
table.scale(1, 2)
plt.box(on=None)
ax = plt.gca()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.title('Capacity Factor', fontsize=title_font)
plt.savefig('{}\system_capacity_factor.{}'.format(
directory, fig_format),
bbox_inches='tight',
dpi=150)
else:
raise ValueError(
'/kind/ should be one of the followings: \n 1. /table/ \n 2. /bar/'
)
def node_disp(nodes, fig_format, unit, conversion, style, date_format,
title_font, production, imports, exports, figsize, demand,
colors, names, rotate, average, sp_techs, sp_nodes, directory,
x_ticks):
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib import gridspec
from calliope_graph.graphs import style_check
from calliope_graph.matrixmaker import prod_imp_exp
style = style_check(style)
plt.style.use(style)
if average == 'weekly':
av = '1w'
elif average == 'daily':
av = '1d'
elif average == 'monthly':
av = '1m'
elif average == 'hourly':
av = '1h'
elif average == 'yearly':
av = '1y'
else:
raise ValueError(
'Incorrect average type.\n Average can be one of the followings: {},{},{},{} and {}'
.format('hourly', 'daily', 'weekly', 'monthly', 'yearly'))
for i in nodes:
data = prod_imp_exp(production, imports, exports, i)
dem = demand[i].resample(av).mean()
data0 = data[0].resample(av).mean()
data1 = data[1].resample(av).mean()
if sp_techs != None and i in sp_nodes:
fig, (axs) = plt.subplots(2, figsize=figsize, sharex=True)
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
axs[1] = plt.subplot(gs[1])
axs[0] = plt.subplot(gs[0], sharex=axs[1])
plt.setp(axs[0].get_xticklabels(), visible=False)
axs[0].margins(x=0)
axs[0].margins(y=0.1)
axs[1].margins(x=0)
axs[1].margins(y=0.1)
axs[0].plot(dem.index,
dem.values * conversion,
'black',
alpha=0.5,
linestyle='-',
label='Demand',
linewidth=1)
# Drawing positivie numbers
axs[0].stackplot(data0.index,
data0.values.T * conversion,
colors=colors[data0.columns],
labels=names[data0.columns])
# Drawing negative numbers
axs[0].stackplot(data1.index,
data1.values.T * conversion,
colors=colors[data1.columns],
labels=names[data1.columns])
axs[0].legend(bbox_to_anchor=(1.05, 1),
loc='upper left',
borderaxespad=0.,
frameon=True)
axs[1].stackplot(data0.index,
data0[sp_techs].values.T * conversion,
colors=colors[sp_techs])
# xticks properties
xfmt = mdates.DateFormatter(date_format)
axs[1].xaxis.set_major_formatter(xfmt)
axs[1].tick_params(axis='x', rotation=rotate)
if x_ticks == 'name':
ticks = date2name(list(data0.index),
list(data0.index.month_name()))
plt.xticks(ticks=ticks[0], labels=ticks[1], rotation=rotate)
axs[1].set_xticks(axs[1].get_xticks()[0:12])
else:
fig, (ax) = plt.subplots(1, figsize=figsize)
ax.margins(x=0)
ax.margins(y=0.1)
# Drawing demand line
plt.plot(dem.index,
dem.values * conversion,
'black',
alpha=0.5,
linestyle='-',
label='Demand',
linewidth=1)
# Drawing positivie numbers
plt.stackplot(data0.index,
data0.values.T * conversion,
colors=colors[data0.columns],
labels=names[data0.columns])
# Drawing negative numbers
plt.stackplot(data1.index,
data1.values.T * conversion,
colors=colors[data1.columns],
labels=names[data1.columns])
# Legend properties
plt.legend(bbox_to_anchor=(1.05, 1),
loc='upper left',
borderaxespad=0.,
frameon=True)
# xticks properties
xfmt = mdates.DateFormatter(date_format)
ax.xaxis.set_major_formatter(xfmt)
plt.xticks(rotation=rotate)
# labels
plt.xlabel('Date')
plt.ylabel(unit)
if x_ticks == 'name':
ticks = date2name(list(data0.index),
list(data0.index.month_name()))
plt.xticks(ticks=ticks[0], labels=ticks[1], rotation=rotate)
ax.set_xticks(ax.get_xticks()[0:12])
# Title
plt.title('{} Dispatch'.format(names[i]), fontsize=title_font)
# saving
fig.savefig('{}\{}_{}_dispatch.{}'.format(directory, i, average,
fig_format),
dpi=fig.dpi,
bbox_inches='tight')
plt.show()
def sys_pie(rational, fig_format, unit, conversion, kind, style, title_font,
production, imports, exports, figsize, colors, names, directory,
table_font, v_round, demand):
import matplotlib.pyplot as plt
from calliope_graph.graphs import style_check
from calliope_graph.matrixmaker import pie_prod
from calliope_graph.matrixmaker import pie_cons
from calliope_graph.matrixmaker import system_matrix
style = style_check(style)
plt.style.use(style)
production = system_matrix(production, demand)
production = production[1]
data = pie_prod(production, kind)
if kind == 'absolute':
data = data * conversion
fig, (ax1, ax2) = plt.subplots(nrows=1,
ncols=2,
figsize=figsize,
gridspec_kw={'width_ratios': [3, 1]})
ax1.pie(data['Production'],
shadow=False,
colors=colors[data.index],
startangle=90)
ax2.patch.set_visible(False)
ax2.get_xaxis().set_visible(False)
ax2.get_yaxis().set_visible(False)
l, b, w, h = ax1.get_position().bounds
ll, bb, ww, hh = ax2.get_position().bounds
ax2.set_position([ll * 0.85, b, w, h])
ax2.axis('off')
if kind == 'share':
tab_label = '%'
else:
tab_label = unit
data = data.round(v_round)
table = ax2.table(cellText=data.values,
rowColours=colors[data.index],
rowLabels=names[data.index],
colLabels=[tab_label],
loc='center',
rowLoc='center',
colLoc='center',
cellLoc='center')
table.auto_set_font_size(False)
table.scale(0.4, 3)
table.set_fontsize(table_font)
fig.suptitle('System', fontsize=title_font, horizontalalignment='left')
plt.show()
fig.savefig('{}\system_{}_pie.{}'.format(directory, kind, fig_format),
dpi=fig.dpi,
bbox_inches='tight')
def nod_pie(nodes, rational, fig_format, unit, conversion, kind, style,
title_font, production, imports, exports, figsize, colors, names,
directory, table_font, v_round):
import matplotlib.pyplot as plt
from calliope_graph.graphs import style_check
from calliope_graph.matrixmaker import pie_prod
from calliope_graph.matrixmaker import pie_cons
style = style_check(style)
plt.style.use(style)
for i in nodes:
if rational == 'production':
data = pie_prod(production[i], kind)
elif rational == 'consumption':
data = pie_cons(production[i], imports[i], exports[i], kind)
#print(data)
else:
raise ValueError(
'rational could be one of the follwoings: \n 1. /production/ \n 2. /consumption/'
)
if kind == 'absolute':
data = data * conversion
fig, (ax1, ax2) = plt.subplots(nrows=1,
ncols=2,
figsize=figsize,
gridspec_kw={'width_ratios': [3, 1]})
ax1.pie(data['Production'],
shadow=False,
colors=colors[data.index],
startangle=90)
ax2.patch.set_visible(False)
ax2.get_xaxis().set_visible(False)
ax2.get_yaxis().set_visible(False)
l, b, w, h = ax1.get_position().bounds
ll, bb, ww, hh = ax2.get_position().bounds
ax2.set_position([ll * 0.85, b, w, h])
ax2.axis('off')
if kind == 'share':
tab_label = '%'
else:
tab_label = unit
data = data.round(v_round)
table = ax2.table(cellText=data.values,
rowColours=colors[data.index],
rowLabels=names[data.index],
colLabels=[tab_label],
loc='center',
rowLoc='center',
colLoc='center',
cellLoc='center')
table.auto_set_font_size(False)
table.scale(0.4, 3)
table.set_fontsize(table_font)
fig.suptitle('{} '.format(names[i]), fontsize=title_font)
plt.show()
fig.savefig('{}\{}_{}_pie.{}'.format(directory, i, kind, fig_format),
dpi=fig.dpi,
bbox_inches='tight')
def sys_disp(rational, fig_format, unit, conversion, style, date_format,
title_font, production, imports, exports, figsize, demand, colors,
names, rotate, average, sp_techs, sp_nodes, directory, x_ticks):
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib import gridspec
from calliope_graph.graphs import style_check
from calliope_graph.matrixmaker import system_matrix
style = style_check(style)
plt.style.use(style)
if average == 'weekly':
av = '1w'
elif average == 'daily':
av = '1d'
elif average == 'monthly':
av = '1m'
elif average == 'hourly':
av = '1h'
elif average == 'yearly':
av = '1y'
else:
raise ValueError(
'Incorrect average type.\n Average can be one of the followings: {},{},{},{} and {}'
.format('hourly', 'daily', 'weekly', 'monthly', 'yearly'))
data = system_matrix(production, demand)
demand = data[0]
if rational == 'techs':
production = data[1]
if sp_nodes:
raise ValueError(
'For /techs/ rational, specific nodes cannot be plotted.')
elif rational == 'nodes':
production = data[2]
if sp_techs:
raise ValueError(
'For /nodes/ rational, specific techs cannot be plotted.')
else:
raise ValueError(
'rational could be one of the followings: \n 1. techs : plotting the graph based on the technologies. \n 2. nodes: Plotting the graph based on the nodes'
)
specific = None
if sp_techs:
specific = sp_techs
elif sp_nodes:
specific = sp_nodes
demand = demand.resample(av).mean()
production = production.resample(av).mean()
if specific:
fig, (axs) = plt.subplots(2, figsize=figsize, sharex=True)
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
axs[1] = plt.subplot(gs[1])
axs[0] = plt.subplot(gs[0], sharex=axs[1])
plt.setp(axs[0].get_xticklabels(), visible=False)
axs[0].margins(x=0)
axs[0].margins(y=0.1)
axs[1].margins(x=0)
axs[1].margins(y=0.1)
axs[0].plot(demand.index,
demand.values * conversion,
'black',
alpha=0.5,
linestyle='-',
label='Demand',
linewidth=0.5)
# Drawing positivie numbers
axs[0].stackplot(production.index,
production.values.T * conversion,
colors=colors[production.columns],
labels=names[production.columns])
# Drawing negative numbers
#axs[0].stackplot(data1.index,data1.values.T*conversion,colors=colors[data1.columns],labels=names[data1.columns])
axs[0].legend(bbox_to_anchor=(1.05, 1),
loc='upper left',
borderaxespad=0.,
frameon=True)
axs[1].stackplot(production.index,
production[specific].values.T * conversion,
colors=colors[specific])
# xticks properties
xfmt = mdates.DateFormatter(date_format)
axs[1].xaxis.set_major_formatter(xfmt)
axs[1].tick_params(axis='x', rotation=rotate)
if x_ticks == 'name':
ticks = date2name(list(production.index),
list(production.index.month_name()))
plt.xticks(ticks=ticks[0], labels=ticks[1], rotation=rotate)
axs[1].set_xticks(axs[1].get_xticks()[0:12])
else:
fig, (ax) = plt.subplots(1, figsize=figsize)
ax.margins(x=0)
ax.margins(y=0.1)
# Drawing demand line
plt.plot(demand.index,
demand.values * conversion,
'black',
alpha=0.5,
linestyle='-',
label='Demand',
linewidth=3)
# Drawing positivie numbers
plt.stackplot(production.index,
production.values.T * conversion,
colors=colors[production.columns],
labels=names[production.columns])
# Drawing negative numbers
#plt.stackplot(data1.index,data1.values.T*conversion,colors=colors[data1.columns],labels=names[data1.columns])
# Legend properties
plt.legend(bbox_to_anchor=(1.05, 1),
loc='upper left',
borderaxespad=0.,
frameon=True)
# xticks properties
xfmt = mdates.DateFormatter(date_format)
ax.xaxis.set_major_formatter(xfmt)
plt.xticks(rotation=rotate)
# labels
plt.xlabel('Date')
if x_ticks == 'name':
ticks = date2name(list(production.index),
list(production.index.month_name()))
plt.xticks(ticks=ticks[0], labels=ticks[1], rotation=rotate)
ax.set_xticks(ax.get_xticks()[0:12])
plt.ylabel(unit)
plt.title('System Dispatch', fontsize=title_font)
fig.savefig('{}\system{}_dispatch.{}'.format(directory, average,
fig_format),
dpi=fig.dpi,
bbox_inches='tight')
plt.show()