def mpl_thumbnails(usetex=False):
"""
Make png thumbnails
"""
plt.rcdefaults()
plt.rc('font', family='serif')
plt.rc('xtick', labelsize='x-small')
plt.rc('ytick', labelsize='x-small')
plt.rc('text', usetex=usetex)
plt.rc('savefig', format='pdf', bbox='tight')
plt.rc('savefig', format='png', bbox='tight')
plt.rc('figure', figsize=(4,3))
def mpl_thumbnails(usetex=False):
"""
Make png thumbnails
"""
plt.rcdefaults()
plt.rc('font', family='serif')
plt.rc('xtick', labelsize='x-small')
plt.rc('ytick', labelsize='x-small')
plt.rc('text', usetex=usetex)
plt.rc('savefig', format='pdf', bbox='tight')
plt.rc('savefig', format='png', bbox='tight')
plt.rc('figure', figsize=(4, 3))
def mpl_span_columns(usetex=False):
"""
Set matplotlib to make pretty plots for publishing a full-page figure
"""
plt.rcdefaults()
plt.rc('font', family='serif', size=12.0, style='normal')
plt.rc('figure', figsize=(7, 5.25))
plt.rc('axes', titlesize=12, labelsize=10)
plt.rc('legend', fontsize=8, numpoints=1, scatterpoints=1)
plt.rc('xtick', labelsize='x-small')
plt.rc('ytick', labelsize='x-small')
plt.rc('text', usetex=usetex)
plt.rc('savefig', format='pdf', bbox='tight')
def mpl_span_columns(usetex=False):
"""
Set matplotlib to make pretty plots for publishing a full-page figure
"""
plt.rcdefaults()
plt.rc('font', family='serif', size=12.0, style='normal')
plt.rc('figure', figsize=(7, 5.25))
plt.rc('axes', titlesize=12, labelsize=10)
plt.rc('legend', fontsize=8, numpoints=1, scatterpoints=1)
plt.rc('xtick', labelsize='x-small')
plt.rc('ytick', labelsize='x-small')
plt.rc('text', usetex=usetex)
plt.rc('savefig', format='pdf', bbox='tight')
def mpl_slides(usetex=False):
"""
Set matplotlibrc to make pretty slides
"""
plt.rcdefaults()
plt.rc('font', family='serif', size=24)
# The default PowerPoint page setup
plt.rc('figure', figsize=(7,5.5))
plt.rc('axes', titlesize=24, labelsize=20, linewidth=3)
plt.rc('legend', fontsize=18, numpoints=1, scatterpoints=1)
plt.rc('xtick', labelsize='small')
plt.rc('ytick', labelsize='small')
plt.rc('text', usetex=usetex)
plt.rc('lines', linewidth=5)
plt.rc('savefig', format='pdf', bbox='tight')
def mpl_slides(usetex=False):
"""
Set matplotlibrc to make pretty slides
"""
plt.rcdefaults()
plt.rc('font', family='serif', size=24)
# The default PowerPoint page setup
plt.rc('figure', figsize=(7, 5.5))
plt.rc('axes', titlesize=24, labelsize=20, linewidth=3)
plt.rc('legend', fontsize=18, numpoints=1, scatterpoints=1)
plt.rc('xtick', labelsize='small')
plt.rc('ytick', labelsize='small')
plt.rc('text', usetex=usetex)
plt.rc('lines', linewidth=5)
plt.rc('savefig', format='pdf', bbox='tight')
import numpy as np
from glob import glob
from matplotlib import pylab as plt
plt.rcdefaults()
from baselines.bench import load_results
def plot(experiment_path,
save_dir="/tmp/",
save_name="results",
limit_steps=None):
fig = plt.figure(figsize=(20, 10))
if len(glob(os.path.join(experiment_path, "train/*monitor*"))) != 0:
exps = glob(experiment_path)
print(exps)
# Get data
df = load_results(os.path.join(experiment_path, "train"))
roll = 5
rdf = df.rolling(5)
df['steps'] = df['l'].cumsum() / 1000000
# Plots
ax = plt.subplot(1, 1, 1)
df.rolling(roll).mean().plot('steps',
'r',
style='-',
ax=ax,
def hor_hist():
import matplotlib.pylab as plt
import numpy as np
from matplotlib import rc
import sys
plt.rcdefaults()
fig, ax = plt.subplots()
f = open("{}".format(sys.argv[1]), "r")
ft = f.readlines()
f.close()
if len(ft) == 0:
print("ERROR IN THE INPUT FILE {}".format(sys.argv[1]))
quit()
ft1 = ft[0].split()
print(ft1)
print("ENTER THE COLUMN FOR X AXIS")
xcol = input()
xcol = xcol - 1
xtest = "{}".format(ft1[int(xcol)]).isdigit()
if xtest != "True":
cc = "x"
print("ENTER THE COLUMN FOR Y AXIS")
ycol = input()
ycol = ycol - 1
ytest = "{}".format(ft1[int(ycol)]).isdigit()
if ytest != "True":
cc = "y"
if "{}".format(xtest) != "True" and "{}".format(ytest) != "True":
print("ERROR: BOTH COLUMNS ARE CHARACTER TYPES")
quit()
print(cc)
k = 0
x = []
y = []
xlabel = []
ylabel = []
while k < len(ft):
ft1 = ft[k].split()
t1 = ft1[int(xcol)]
t2 = ft1[int(ycol)]
if cc == "x":
xlabel.append(t1)
x.append((k + 1))
y.append(int(t2))
if cc == "y":
x.append(int(t1))
ylabel.append(t2)
y.append((k + 1))
k = k + 1
ax.barh(y, x, align='center')
if cc == "x":
ax.set_xticks(x)
ax.set_xticklabels(xlabel)
if cc == "y":
ax.set_yticks(y)
ax.set_yticklabels(ylabel, fontweight='bold')
ax.set_xlabel('MUTANTS', fontweight='bold')
rc('font', weight='bold')
ax.set_title('Distribution of mutants across the taxonomy',
fontweight='bold')
plt.show()
# 显示colorbar
cbar = fig.colorbar(surf, ax=ax,shrink=0.5, aspect=10, spacing = 'uniform') #
cbar.set_label('微伏',fontproperties = ChineseFont2,fontsize = 30)
cbar.set_ticks(np.arange(-150,200,50))
cbar.ax.tick_params(labelsize=20)
cbar.set_ticklabels(tuple([str(ticklabels) for ticklabels in np.arange(-150,200,50)]))
plt.show()
# save
filename_fig = file_path + '_' + title + '_' + ConditionName[i] + '.jpg'
savefig(filename_fig)
#%%
pylab.rcdefaults()
params={'axes.titlesize': 50,
'axes.labelsize': 40,
'axes.grid' : 'True',
'axes.prop_cycle': cycler('color',['cyan', 'indigo', 'seagreen',
'yellow', 'blue','navy','turquoise', 'darkorange',
'cornflowerblue', 'teal']),
'xtick.labelsize':20,
'ytick.labelsize':20,
'lines.linewidth' :4,
'lines.markersize':15,
'legend.fontsize':30,
import numpy as np
from glob import glob
from matplotlib import pylab as plt; plt.rcdefaults()
from baselines.bench import load_results
def plot(experiment_path, save_dir="/tmp/", save_name="results", limit_steps=None):
fig = plt.figure(figsize=(20, 10))
if len(glob(os.path.join(experiment_path, "train/*monitor*"))) != 0:
exps = glob(experiment_path)
print(exps)
# Get data
df = load_results(os.path.join(experiment_path, "train"))
roll = 5
rdf = df.rolling(roll)
df['steps'] = df['l'].cumsum()
if 'rrr' in df:
df = df[df['lives'] == 0]
df['r'] = df['rrr']
ax = plt.subplot(1, 1, 1)
df.rolling(roll).mean().plot('steps', 'r', style='-', ax=ax, legend=False)
rdf.max().plot('steps', 'r', style='-', ax=ax, legend=False, color="#28B463", alpha=0.65)
rdf.min().plot('steps', 'r', style='-', ax=ax, legend=False, color="#F39C12", alpha=0.65)
# X axis
gap = 1
data = get_data(test)
tree = get_tree(test)
bench(test,data)
save_data(test,data)
datestr = time.strftime("%Y-%m-%d-%H-%M-%S")
pickle.dump(results,file('bench-results-%s.pickle' % (datestr),'w'))
pickle.dump(results,file('bench-results.pickle','w'))
else:
results = pickle.load(file('bench-results.pickle','r'))
mat = scipy.io.loadmat('bench-tree.mat')['R'][0]
# matlab indices are: [dim,depth,func]
fgcolor = np.array([20,50,100])/255.
bgcolor = 0.9 + 0.1*fgcolor
plt.rcdefaults()
plt.rcParams['figure.subplot.right']='0.9'
plt.rcParams['figure.subplot.left']='0.2'
plt.rcParams['figure.subplot.top']='0.8'
plt.rcParams['ytick.major.pad']='10'
plt.rcParams['ytick.color']=fgcolor
plt.rcParams['xtick.color']=fgcolor
plt.rcParams['ytick.major.size']=4
plt.rcParams['xtick.major.size']=4
plt.rc('lines', linewidth=2)
plt.rc('text', color=fgcolor)
plt.rc('axes', linewidth=2, facecolor=bgcolor, edgecolor=fgcolor, labelcolor=fgcolor)
plt.show()
plotfuncs = [1,3]
labels = ['Insert','Search']
