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')