Commit 83672fbc authored by Carlos GO's avatar Carlos GO
Browse files

line colors

parent ffb38ec2
......@@ -6,12 +6,24 @@ import pandas as pd
plt.style.use('fivethirtyeight')
labels = {'orig': 'original',
fivethirtyeight = [c['color'] for c in list(plt.rcParams['axes.prop_cycle'])]
color_wheel = fivethirtyeight
labels = {'orig2': r'$\beta=1$',
'gamma_b0_b0': r'$\beta=0$',
'gamma_002': r'$\beta=1$',
'lin2': 'energy-mut',
'gamma_emut': 'energy-mut',
'norm': 'gauss-fitness',
'nosel3': 'no-selecion',
'gamma_gauss': 'gauss-fitness',
'nosel3': r'$\beta=0$',
'unp': 'unpaired-objective',
'beta01': 'beta=0.1'}
'beta01': r'$\beta=0.1$',
'beta001': r'$\beta=0.01$',
'beta05': r'$\beta=0.05$',
'unp10': 'unpaired fitness'}
gc_labels = {g: g[::-1] for g in ['01', '03', '05', '07', '09']}
def maternal_plot(exps, feature='multi', mode='mutations'):
for e in exps:
......@@ -22,29 +34,61 @@ def maternal_plot(exps, feature='multi', mode='mutations'):
plt.legend()
plt.tight_layout()
plt.show()
pass
pass
def generation_window(feat, window_size=20):
binned = [np.mean(feat[i:i+window_size]) for i in np.arange(0, len(feat), window_size)]
return binned
def subfigs(exps, dim1, dim2, mode='mutations', feature='multi'):
count = 1
modes = ['mutations', 'generation']
features = ['multi', 'energy', 'entropy']
ax = plt.subplot(111)
for mode in modes:
for feature in features:
plt.subplot(len(modes), len(features), count)
count += 1
for e in exps:
df = pd.read_csv(f"Data/{e}_df_{mode}_mean.csv")
for i, e in enumerate(exps):
gc = e.split('_')[-1]
df = pd.read_csv(f"betas/{e}_df_{mode}_mean.csv")
if mode == 'generation':
plt.plot(df[feature][:1000], label=f'{labels[e]}')
xx = generation_window(df[feature][:1000])
# plt.plot(xx, label=f'{labels[e[:-3]]}')
plt.plot(xx, label=f'{e}')
# plt.plot(xx, label=gc_labels[gc])
locs = np.linspace(0, len(xx), 3)
print(locs)
plt.xticks(locs, np.arange(0, 1001, 500))
else:
plt.plot(df[feature], label=f'{labels[e]}')
# plt.plot(df[feature], label=f'{labels[e[:-3]]}')
plt.plot(df[feature], label=f'{e}')
# plt.plot(df[feature], label=gc_labels[gc])
plt.xticks([0, 50], [0, 50])
plt.ylabel(feature)
plt.xlabel(mode)
plt.legend()
# plt.legend(bbox_to_anchor=(-1.5,-.2), ncol=5, prop={'size': 7})
g = exps[0][-2:][::-1]
# plt.suptitle(f"GC: {g}")
plt.suptitle(f"GC: 90")
plt.legend(prop={'size': 8} )
plt.tight_layout()
# plt.savefig(f'gamma_{g}.pdf', format='pdf')
plt.savefig("betas_09_trans.pdf", format="pdf")
plt.show()
if __name__ == "__main__":
exps = [ 'beta01', 'unp', 'nosel3', 'norm', 'lin2']
# maternal_plot(exps, feature='entropy')
# exps = [ 'beta01', 'orig2', 'nosel3', 'norm']
# exps = ['nosel3', 'norm', 'lin2', 'orig2']
# exps = ['gamma_b0_b0_01', 'gamma_b0_b0_03', 'gamma_b0_b0_05', 'gamma_b0_b0_07', 'gamma_b0_b0_09']
# exps = ['gamma_gauss_01', 'gamma_gauss_03', 'gamma_gauss_05', 'gamma_gauss_07', 'gamma_gauss_09']
# exps = ['gamma_002_01', 'gamma_002_03', 'gamma_002_05', 'gamma_002_07', 'gamma_002_09']
exps = [f'exp_bs09_{b}' for b in ['001', '002', '003', '004', '005']]
subfigs(exps, 2, 3)
# for g in ['01', '03', '05', '07', '09']:
# exps = [f'gamma_002_{g}', f'gamma_b0_b0_{g}',
# f'gamma_gauss_{g}', f'gamma_emut_{g}']
# subfigs(exps, 2, 3)
# maternal_plot(exps, feature='entropy')
# maternal_plot(exps, feature='entropy')
# subfigs(exps, 2, 3)
pass
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