Python pylab.rc() Examples
The following are 7
code examples of pylab.rc().
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Example #1
| Source File: plot.py From TOPFARM with GNU Affero General Public License v3.0 | 6 votes |
|
def __init__(self, add_inputs, title='', **kwargs):
super(OffshorePlot, self).__init__(**kwargs)
self.fig = plt.figure(num=None, facecolor='w', edgecolor='k') #figsize=(13, 8), dpi=1000
self.shape_plot = self.fig.add_subplot(121)
self.objf_plot = self.fig.add_subplot(122)
self.targname = add_inputs
self.title = title
# Adding automatically the inputs
for i in add_inputs:
self.add(i, Float(0.0, iotype='in'))
#sns.set(style="darkgrid")
#self.pal = sns.dark_palette("skyblue", as_cmap=True)
plt.rc('lines', linewidth=1)
plt.ion()
self.force_execute = True
if not pa('fig').exists():
pa('fig').mkdir()
Example #2
| Source File: analyser.py From spotpy with MIT License | 5 votes |
|
def plot_parametertrace_algorithms(result_lists, algorithmnames, spot_setup,
fig_name='parametertrace_algorithms.png'):
"""Example Plot as seen in the SPOTPY Documentation"""
import matplotlib.pyplot as plt
font = {'family' : 'calibri',
'weight' : 'normal',
'size' : 20}
plt.rc('font', **font)
fig=plt.figure(figsize=(17,5))
subplots=len(result_lists)
parameter = spotpy.parameter.get_parameters_array(spot_setup)
rows=len(parameter['name'])
for j in range(rows):
for i in range(subplots):
ax = plt.subplot(rows,subplots,i+1+j*subplots)
data=result_lists[i]['par'+parameter['name'][j]]
ax.plot(data,'b-')
if i==0:
ax.set_ylabel(parameter['name'][j])
rep = len(data)
if i>0:
ax.yaxis.set_ticks([])
if j==rows-1:
ax.set_xlabel(algorithmnames[i-subplots])
else:
ax.xaxis.set_ticks([])
ax.plot([1]*rep,'r--')
ax.set_xlim(0,rep)
ax.set_ylim(parameter['minbound'][j],parameter['maxbound'][j])
#plt.tight_layout()
fig.savefig(fig_name, bbox_inches='tight')
Example #3
| Source File: analyser.py From spotpy with MIT License | 5 votes |
|
def plot_objectivefunctiontraces(results,evaluation,algorithms,fig_name='Like_trace.png'):
import matplotlib.pyplot as plt
from matplotlib import colors
cnames=list(colors.cnames)
font = {'family' : 'calibri',
'weight' : 'normal',
'size' : 20}
plt.rc('font', **font)
fig=plt.figure(figsize=(16,3))
xticks=[5000,15000]
for i in range(len(results)):
ax = plt.subplot(1,len(results),i+1)
likes=calc_like(results[i],evaluation,spotpy.objectivefunctions.rmse)
ax.plot(likes,'b-')
ax.set_ylim(0,25)
ax.set_xlim(0,len(results[0]))
ax.set_xlabel(algorithms[i])
ax.xaxis.set_ticks(xticks)
if i==0:
ax.set_ylabel('RMSE')
ax.yaxis.set_ticks([0,10,20])
else:
ax.yaxis.set_ticks([])
plt.tight_layout()
fig.savefig(fig_name)
Example #4
| Source File: covc_encdec.py From seq2seq-keyphrase with MIT License | 4 votes |
|
def analyse_(self, inputs, outputs, idx2word, inputs_unk=None, return_attend=False, name=None, display=False):
def cut_zero(sample, idx2word, ppp=None, Lmax=None):
if Lmax is None:
Lmax = self.config['dec_voc_size']
if ppp is None:
if 0 not in sample:
return ['{}'.format(idx2word[w].encode('utf-8'))
if w < Lmax else '{}'.format(idx2word[inputs[w - Lmax]].encode('utf-8'))
for w in sample]
return ['{}'.format(idx2word[w].encode('utf-8'))
if w < Lmax else '{}'.format(idx2word[inputs[w - Lmax]].encode('utf-8'))
for w in sample[:sample.index(0)]]
else:
if 0 not in sample:
return ['{0} ({1:1.1f})'.format(
idx2word[w].encode('utf-8'), p)
if w < Lmax
else '{0} ({1:1.1f})'.format(
idx2word[inputs[w - Lmax]].encode('utf-8'), p)
for w, p in zip(sample, ppp)]
idz = sample.index(0)
return ['{0} ({1:1.1f})'.format(
idx2word[w].encode('utf-8'), p)
if w < Lmax
else '{0} ({1:1.1f})'.format(
idx2word[inputs[w - Lmax]].encode('utf-8'), p)
for w, p in zip(sample[:idz], ppp[:idz])]
if inputs_unk is None:
result, _, ppp = self.generate_(inputs[None, :],
return_attend=return_attend)
else:
result, _, ppp = self.generate_(inputs_unk[None, :],
return_attend=return_attend)
source = '{}'.format(' '.join(cut_zero(inputs.tolist(), idx2word, Lmax=len(idx2word))))
target = '{}'.format(' '.join(cut_zero(outputs.tolist(), idx2word, Lmax=len(idx2word))))
decode = '{}'.format(' '.join(cut_zero(result, idx2word)))
if display:
print(source)
print(target)
print(decode)
idz = result.index(0)
p1, p2 = [np.asarray(p) for p in zip(*ppp)]
print(p1.shape)
import pylab as plt
# plt.rc('text', usetex=True)
# plt.rc('font', family='serif')
visualize_(plt.subplots(), 1 - p1[:idz, :].T, grid=True, name=name)
visualize_(plt.subplots(), 1 - p2[:idz, :].T, name=name)
# visualize_(plt.subplots(), 1 - np.mean(p2[:idz, :], axis=1, keepdims=True).T)
return target == decode
Example #5
| Source File: analyser.py From spotpy with MIT License | 4 votes |
|
def plot_heatmap_griewank(results,algorithms, fig_name='heatmap_griewank.png'):
"""Example Plot as seen in the SPOTPY Documentation"""
import matplotlib.pyplot as plt
from matplotlib import ticker
from matplotlib import cm
font = {'family' : 'calibri',
'weight' : 'normal',
'size' : 20}
plt.rc('font', **font)
subplots=len(results)
xticks=[-40,0,40]
yticks=[-40,0,40]
fig=plt.figure(figsize=(16,6))
N = 2000
x = np.linspace(-50.0, 50.0, N)
y = np.linspace(-50.0, 50.0, N)
x, y = np.meshgrid(x, y)
z=1+ (x**2+y**2)/4000 - np.cos(x/np.sqrt(2))*np.cos(y/np.sqrt(3))
cmap = plt.get_cmap('autumn')
rows=2.0
for i in range(subplots):
amount_row = int(np.ceil(subplots/rows))
ax = plt.subplot(rows, amount_row, i+1)
CS = ax.contourf(x, y, z,locator=ticker.LogLocator(),cmap=cm.rainbow)
ax.plot(results[i]['par0'],results[i]['par1'],'ko',alpha=0.2,markersize=1.9)
ax.xaxis.set_ticks([])
if i==0:
ax.set_ylabel('y')
if i==subplots/rows:
ax.set_ylabel('y')
if i>=subplots/rows:
ax.set_xlabel('x')
ax.xaxis.set_ticks(xticks)
if i!=0 and i!=subplots/rows:
ax.yaxis.set_ticks([])
ax.set_title(algorithms[i])
fig.savefig(fig_name, bbox_inches='tight')
Example #6
| Source File: multiple_files.py From orbkit with GNU Lesser General Public License v3.0 | 4 votes |
|
def plot(self,mo_matrix,symmetry='1',title='All',x_label='index',
y_label='MO coefficients',output_format='png',
plt_dir='Plots',ylim=None,thresh=0.1,x0=0,grid=True,x_grid=None,**kwargs):
'''Plots all molecular orbital coefficients of one self.symmetry.'''
import pylab as plt
from matplotlib.ticker import MultipleLocator
import os
display('Plotting data of self.symmetry %s to %s/' % (symmetry,plt_dir))
if not os.path.exists(plt_dir):
os.makedirs(plt_dir)
if numpy.ndim(mo_matrix) == 2:
mo_matrix = mo_matrix[:,numpy.newaxis,:]
shape = numpy.shape(mo_matrix)
def plot_mo(i):
fig=plt.figure()
plt.rc('xtick', labelsize=16)
plt.rc('ytick', labelsize=16)
ax = plt.subplot(111)
curves=[]
for ij in range(shape[2]):
Y = mo_matrix[:,i,ij]
if x_grid is None:
X = numpy.arange(len(Y))+x0
else:
X = x_grid
if max(numpy.abs(Y)) > thresh:
curves.append(ax.plot(X,Y, '.-' ,linewidth=1.5))
plt.xlabel(x_label, fontsize=16);
plt.ylabel(y_label, fontsize=16);
plt.title('%s: %d.%s'% (title,i+1,symmetry))
plt.ylim(ylim)
plt.tight_layout()
return fig
if output_format == 'pdf':
from matplotlib.backends.backend_pdf import PdfPages
output_fid = '%s.%s.pdf'% (title,symmetry.replace(' ','_'))
display('\t%s' % output_fid)
with PdfPages(os.path.join(plt_dir,output_fid)) as pdf:
for i in range(shape[1]):
fig = plot_mo(i)
pdf.savefig(fig,**kwargs)
plt.close()
elif output_format == 'png':
for i in range(shape[1]):
fig = plot_mo(i)
output_fid = '%d.%s.png' % (i+1,symmetry.replace(' ','_'))
display('\t%s' % output_fid)
fig.savefig(os.path.join(plt_dir, output_fid),format='png',**kwargs)
plt.close()
else:
raise ValueError('output_format `%s` is not supported' % output_format)
Example #7
| Source File: covc_encdec.py From CopyNet with MIT License | 4 votes |
|
def analyse_(self, inputs, outputs, idx2word, inputs_unk=None, return_attend=False, name=None, display=False):
def cut_zero(sample, idx2word, ppp=None, Lmax=None):
if Lmax is None:
Lmax = self.config['dec_voc_size']
if ppp is None:
if 0 not in sample:
return ['{}'.format(idx2word[w].encode('utf-8'))
if w < Lmax else '{}'.format(idx2word[inputs[w - Lmax]].encode('utf-8'))
for w in sample]
return ['{}'.format(idx2word[w].encode('utf-8'))
if w < Lmax else '{}'.format(idx2word[inputs[w - Lmax]].encode('utf-8'))
for w in sample[:sample.index(0)]]
else:
if 0 not in sample:
return ['{0} ({1:1.1f})'.format(
idx2word[w].encode('utf-8'), p)
if w < Lmax
else '{0} ({1:1.1f})'.format(
idx2word[inputs[w - Lmax]].encode('utf-8'), p)
for w, p in zip(sample, ppp)]
idz = sample.index(0)
return ['{0} ({1:1.1f})'.format(
idx2word[w].encode('utf-8'), p)
if w < Lmax
else '{0} ({1:1.1f})'.format(
idx2word[inputs[w - Lmax]].encode('utf-8'), p)
for w, p in zip(sample[:idz], ppp[:idz])]
if inputs_unk is None:
result, _, ppp = self.generate_(inputs[None, :],
return_attend=return_attend)
else:
result, _, ppp = self.generate_(inputs_unk[None, :],
return_attend=return_attend)
source = '{}'.format(' '.join(cut_zero(inputs.tolist(), idx2word, Lmax=len(idx2word))))
target = '{}'.format(' '.join(cut_zero(outputs.tolist(), idx2word, Lmax=len(idx2word))))
decode = '{}'.format(' '.join(cut_zero(result, idx2word)))
if display:
print source
print target
print decode
idz = result.index(0)
p1, p2 = [np.asarray(p) for p in zip(*ppp)]
print p1.shape
import pylab as plt
# plt.rc('text', usetex=True)
# plt.rc('font', family='serif')
visualize_(plt.subplots(), 1 - p1[:idz, :].T, grid=True, name=name)
visualize_(plt.subplots(), 1 - p2[:idz, :].T, name=name)
# visualize_(plt.subplots(), 1 - np.mean(p2[:idz, :], axis=1, keepdims=True).T)
return target == decode
