def test_fig_legend():
fig = plt.figure()
ax = fig.add_subplot(111)
make_legend(['a','b','c'], fig, legend_type=PATCH)
set_fig_to_bw(fig, style=GREYSCALE)
plt.show()
def test_fig_legend():
fig = plt.figure()
ax = fig.add_subplot(111)
make_legend(['a', 'b', 'c'], fig, legend_type=PATCH)
set_fig_to_bw(fig, style=GREYSCALE)
plt.draw()
def test_scatter():
x = np.random.rand(5)
y = np.random.rand(5)
color = 'r'
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x, y, c=color, s=80, marker='x')
set_fig_to_bw(fig)
plt.draw()
def test_scatter():
x = np.random.rand(5)
y = np.random.rand(5)
color = 'r'
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x,y, c=color, s=80, marker='x')
set_fig_to_bw(fig)
plt.draw()
def test_scatter():
x = np.random.rand(5)
y = np.random.rand(5)
# color = ['b','r','k','y','r']
color = 'r'
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x,y, c=color, s=80, marker='x')
set_fig_to_bw(fig)
plt.show()
def test_fill_between():
x = np.linspace(0, 1)
y1 = np.sin(4 * np.pi * x) * np.exp(-5 * x)
y2 = np.cos(4 * np.pi * x) * np.exp(-5 * x)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.fill_between(x, y1, y2)
set_fig_to_bw(fig, style=HATCHING)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.fill_between(x, y1, y2, label='test')
set_fig_to_bw(fig, style=GREYSCALE)
plt.draw()
def test_fill_between():
x = np.linspace(0, 1)
y1 = np.sin(4 * np.pi * x) * np.exp(-5 * x)
y2 = np.cos(4 * np.pi * x) * np.exp(-5 * x)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.fill_between(x, y1, y2)
set_fig_to_bw(fig, style=HATCHING)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.fill_between(x, y1, y2,label='test')
set_fig_to_bw(fig, style=GREYSCALE)
plt.show()
def test_lines():
results = test_utilities.load_eng_trans_data()
lines(results,
outcomes_to_show="total fraction new technologies",
experiments_to_show=np.arange(0,600, 20),
group_by='policy',
grouping_specifiers = 'basic policy'
)
lines(results,
experiments_to_show=np.arange(0,600, 2),
group_by='policy',
density=HIST
)
lines(results,
experiments_to_show=np.arange(0,600, 2),
group_by='policy',
density=KDE
)
lines(results,
experiments_to_show=np.arange(0,600, 2),
group_by='policy',
density=BOXPLOT
)
lines(results,
experiments_to_show=np.arange(0,600, 2),
group_by='policy',
density=VIOLIN
)
lines(results,
group_by='index',
grouping_specifiers = {"blaat": np.arange(1, 100, 2)},
density=KDE,
)
lines(results,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=KDE,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy']
)
lines(results,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=HIST,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy']
)
lines(results,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=BOXPLOT,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy']
)
lines(results,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=VIOLIN,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy']
)
plt.draw()
plt.close('all')
lines(results,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=KDE,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True
)
lines(results,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=HIST,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True
)
lines(results,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=BOXPLOT,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True
)
lines(results,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=VIOLIN,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True
)
plt.draw()
plt.close('all')
set_fig_to_bw(lines(results,
experiments_to_show=np.arange(0,600, 20),
group_by='policy',
density=KDE
)[0])
experiments, outcomes = results
new_outcomes = {}
for key, value in outcomes.items():
new_outcomes[key] = value[0:20, :]
experiments = experiments[0:20]
results = experiments, new_outcomes
#no grouping, with density
set_fig_to_bw(lines(results, density=KDE)[0])
set_fig_to_bw(lines(results, density=HIST)[0])
set_fig_to_bw(lines(results, density=BOXPLOT)[0])
set_fig_to_bw(lines(results, density=VIOLIN)[0])
# grouping and density
set_fig_to_bw(lines(results,
group_by='policy',
density='kde')[0])
# grouping, density as histograms
# grouping and density
set_fig_to_bw(lines(results,
group_by='policy',
density='hist',
legend=False)[0])
plt.draw()
plt.close('all')
def test_envelopes():
results = test_utilities.load_eng_trans_data()
#testing titles
envelopes(results,
density=None,
titles=None)
envelopes(results,
density=None,
titles={})
envelopes(results,
density=None,
titles={'total fraction new technologies': 'a',
'total fraction new technologies': 'b'})
plt.draw()
plt.close('all')
#testing ylabels
envelopes(results,
density=None,
ylabels=None)
envelopes(results,
density=None,
ylabels={})
envelopes(results,
density=None,
ylabels={'total fraction new technologies': 'a'})
plt.draw()
plt.close('all')
#no grouping no density
envelopes(results,
titles=None)
set_fig_to_bw(envelopes(results, density=None)[0])
plt.draw()
plt.close('all')
#no grouping, with density
envelopes(results, density=KDE)
envelopes(results, density=HIST)
envelopes(results, density=BOXPLOT)
envelopes(results, density=VIOLIN)
set_fig_to_bw(envelopes(results, density=VIOLIN)[0])
plt.draw()
plt.close('all')
# grouping and density kde
envelopes(results,
group_by='policy',
density=VIOLIN)
envelopes(results,
group_by='policy',
density=BOXPLOT)
envelopes(results,
group_by='policy',
density=KDE,
grouping_specifiers=['no policy', 'adaptive policy'])
envelopes(results,
group_by='policy',
density=BOXPLOT,
grouping_specifiers=['no policy', 'adaptive policy'])
envelopes(results,
group_by='policy',
density=KDE)
plt.draw()
plt.close('all')
envelopes(results,
group_by='policy',
density=VIOLIN)
envelopes(results,
group_by='policy',
density=BOXPLOT)
envelopes(results,
group_by='policy',
density=KDE)
envelopes(results,
group_by='policy',
density=HIST)
plt.draw()
plt.close('all')
envelopes(results,
group_by='policy',
density=VIOLIN,
log=True)
envelopes(results,
group_by='policy',
density=BOXPLOT,
log=True)
envelopes(results,
group_by='policy',
density=KDE,
log=True)
envelopes(results,
group_by='policy',
density=HIST,
log=True)
plt.draw()
plt.close('all')
# grouping and density hist
envelopes(results,
group_by='policy',
density=HIST)
envelopes(results,
group_by='policy',
density=HIST)
set_fig_to_bw(envelopes(results,
group_by='policy',
density=KDE)[0])
# grouping and density
envelopes(results,
group_by='policy',
density=KDE,
fill=True)
set_fig_to_bw(envelopes(results,
group_by='policy',
density=KDE,
fill=True)[0])
plt.draw()
plt.close('all')
def test_lines():
results = load_results(r'..\data\eng_trans_100.cPickle', zipped=False)
# lines(results,
# outcomes_to_show="total fraction new technologies",
# experiments_to_show=np.arange(0,600, 20),
# group_by='policy',
# grouping_specifiers = 'basic policy'
# )
#
# lines(results,
# experiments_to_show=np.arange(0,600, 2),
# group_by='policy',
# density='hist'
# )
#
# lines(results,
# experiments_to_show=np.arange(0,600, 2),
# group_by='policy',
# density='kde'
# )
#
# lines(results,
# group_by='index',
# grouping_specifiers = {"blaat": np.arange(1, 100, 2)},
# density='kde',
# )
#
# lines(results,
# experiments_to_show=np.arange(0,600, 30),
# group_by='policy',
# density='kde',
# show_envelope=True,
# grouping_specifiers=['no policy', 'adaptive policy']
# )
#
# lines(results,
# experiments_to_show=np.arange(0,600, 30),
# group_by='policy',
# density='kde',
# show_envelope=True,
# log=True,
# grouping_specifiers=['no policy', 'adaptive policy']
# )
set_fig_to_bw(lines(results,
experiments_to_show=np.arange(0,600, 20),
group_by='policy',
density='kde'
)[0])
#
# experiments, outcomes = results
# new_outcomes = {}
# for key, value in outcomes.items():
# new_outcomes[key] = value[0:20, :]
# experiments = experiments[0:20]
# results = experiments, new_outcomes
#
# #no grouping, with density
# set_fig_to_bw(lines(results, density='kde')[0])
# set_fig_to_bw(lines(results, density='hist')[0])
#
# # grouping and density
# set_fig_to_bw(lines(results,
# group_by='policy',
# density='kde',
# log=True)[0])
#
# # grouping, density as histograms
# # grouping and density
# set_fig_to_bw(lines(results,
# group_by='policy',
# density='hist',
# legend=False)[0])
plt.show()
# create the time dimension including 2006 as a starting year
time = np.arange(0, new_outcomes['avg. price'].shape[1]) + 2006
time = np.tile(time, (new_outcomes['avg. price'].shape[0], 1))
new_outcomes["TIME"] = time
results = (experiments, new_outcomes)
# create a lines plot on top of an envelope
fig, axes_dict = lines(
results,
density='kde',
outcomes_to_show=[
'total capacity', 'total generation', 'avg. price',
'fraction non-fossil'
],
show_envelope=True,
experiments_to_show=np.random.randint(0,
new_outcomes['avg. price'].shape[0],
(5, )),
titles=None,
)
# use the returned axes dict to modify the ylim on one of the outcomes
axes_dict['fraction non-fossil'].set_ylim(ymin=0, ymax=1)
axes_dict['fraction non-fossil_density'].set_ylim(ymin=0, ymax=1)
# transform the figure to black and white
set_fig_to_bw(fig)
plt.savefig("./pictures/jotke_envelopes.png", dpi=75)
# create the time dimension including 2006 as a starting year
time = np.arange(0, new_outcomes['avg. price'].shape[1])+2006
time = np.tile(time, (new_outcomes['avg. price'].shape[0],1))
new_outcomes["TIME"] = time
results = (experiments, new_outcomes)
# create a lines plot on top of an envelope
fig, axes_dict = lines(results,
density='kde',
outcomes_to_show=['total capacity',
'total generation',
'avg. price',
'fraction non-fossil'],
show_envelope=True,
experiments_to_show=np.random.randint(0, new_outcomes['avg. price'].shape[0], (5,)),
titles=None,
)
# use the returned axes dict to modify the ylim on one of the outcomes
axes_dict['fraction non-fossil'].set_ylim(ymin=0, ymax=1)
axes_dict['fraction non-fossil_density'].set_ylim(ymin=0, ymax=1)
# transform the figure to black and white
set_fig_to_bw(fig)
plt.savefig("./pictures/jotke_envelopes.png", dpi=75)
def test_envelopes():
results = test_utilities.load_eng_trans_data()
#testing titles
envelopes(results, density=None, titles=None)
envelopes(results, density=None, titles={})
envelopes(results,
density=None,
titles={
'total fraction new technologies': 'a',
'total fraction new technologies': 'b'
})
plt.draw()
plt.close('all')
#testing ylabels
envelopes(results, density=None, ylabels=None)
envelopes(results, density=None, ylabels={})
envelopes(results,
density=None,
ylabels={'total fraction new technologies': 'a'})
plt.draw()
plt.close('all')
#no grouping no density
envelopes(results, titles=None)
set_fig_to_bw(envelopes(results, density=None)[0])
plt.draw()
plt.close('all')
#no grouping, with density
envelopes(results, density=KDE)
envelopes(results, density=HIST)
envelopes(results, density=BOXPLOT)
envelopes(results, density=VIOLIN)
set_fig_to_bw(envelopes(results, density=VIOLIN)[0])
plt.draw()
plt.close('all')
# grouping and density kde
envelopes(results, group_by='policy', density=VIOLIN)
envelopes(results, group_by='policy', density=BOXPLOT)
envelopes(results,
group_by='policy',
density=KDE,
grouping_specifiers=['no policy', 'adaptive policy'])
envelopes(results,
group_by='policy',
density=BOXPLOT,
grouping_specifiers=['no policy', 'adaptive policy'])
envelopes(results, group_by='policy', density=KDE)
plt.draw()
plt.close('all')
envelopes(results, group_by='policy', density=VIOLIN)
envelopes(results, group_by='policy', density=BOXPLOT)
envelopes(results, group_by='policy', density=KDE)
envelopes(results, group_by='policy', density=HIST)
plt.draw()
plt.close('all')
envelopes(results, group_by='policy', density=VIOLIN, log=True)
envelopes(results, group_by='policy', density=BOXPLOT, log=True)
envelopes(results, group_by='policy', density=KDE, log=True)
envelopes(results, group_by='policy', density=HIST, log=True)
plt.draw()
plt.close('all')
# grouping and density hist
envelopes(results, group_by='policy', density=HIST)
envelopes(results, group_by='policy', density=HIST)
set_fig_to_bw(envelopes(results, group_by='policy', density=KDE)[0])
# grouping and density
envelopes(results, group_by='policy', density=KDE, fill=True)
set_fig_to_bw(
envelopes(results, group_by='policy', density=KDE, fill=True)[0])
plt.draw()
plt.close('all')
def test_lines():
results = test_utilities.load_eng_trans_data()
lines(results,
outcomes_to_show="total fraction new technologies",
experiments_to_show=np.arange(0, 600, 20),
group_by='policy',
grouping_specifiers='basic policy')
lines(results,
experiments_to_show=np.arange(0, 600, 2),
group_by='policy',
density=HIST)
lines(results,
experiments_to_show=np.arange(0, 600, 2),
group_by='policy',
density=KDE)
lines(results,
experiments_to_show=np.arange(0, 600, 2),
group_by='policy',
density=BOXPLOT)
lines(results,
experiments_to_show=np.arange(0, 600, 2),
group_by='policy',
density=VIOLIN)
lines(
results,
group_by='index',
grouping_specifiers={"blaat": np.arange(1, 100, 2)},
density=KDE,
)
lines(results,
experiments_to_show=np.arange(0, 600, 30),
group_by='policy',
density=KDE,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'])
lines(results,
experiments_to_show=np.arange(0, 600, 30),
group_by='policy',
density=HIST,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'])
lines(results,
experiments_to_show=np.arange(0, 600, 30),
group_by='policy',
density=BOXPLOT,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'])
lines(results,
experiments_to_show=np.arange(0, 600, 30),
group_by='policy',
density=VIOLIN,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'])
plt.draw()
plt.close('all')
lines(results,
experiments_to_show=np.arange(0, 600, 30),
group_by='policy',
density=KDE,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True)
lines(results,
experiments_to_show=np.arange(0, 600, 30),
group_by='policy',
density=HIST,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True)
lines(results,
experiments_to_show=np.arange(0, 600, 30),
group_by='policy',
density=BOXPLOT,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True)
lines(results,
experiments_to_show=np.arange(0, 600, 30),
group_by='policy',
density=VIOLIN,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True)
plt.draw()
plt.close('all')
set_fig_to_bw(
lines(results,
experiments_to_show=np.arange(0, 600, 20),
group_by='policy',
density=KDE)[0])
experiments, outcomes = results
new_outcomes = {}
for key, value in outcomes.items():
new_outcomes[key] = value[0:20, :]
experiments = experiments[0:20]
results = experiments, new_outcomes
#no grouping, with density
set_fig_to_bw(lines(results, density=KDE)[0])
set_fig_to_bw(lines(results, density=HIST)[0])
set_fig_to_bw(lines(results, density=BOXPLOT)[0])
set_fig_to_bw(lines(results, density=VIOLIN)[0])
# grouping and density
set_fig_to_bw(lines(results, group_by='policy', density='kde')[0])
# grouping, density as histograms
# grouping and density
set_fig_to_bw(
lines(results, group_by='policy', density='hist', legend=False)[0])
plt.draw()
plt.close('all')
