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fixed a plotting bug

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James Hensman 2015-08-13 10:04:06 +01:00
parent 90df33af50
commit c8b54ff887
1 changed files with 5 additions and 8 deletions
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13
GPy/plotting/matplot_dep/models_plots.py
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@ -1,17 +1,14 @@
# Copyright (c) 2012-2015, GPy authors (see AUTHORS.txt). # Copyright (c) 2012-2015, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt) # Licensed under the BSD 3-clause license (see LICENSE.txt)
try:
# import Tango
import pylab as pb
except:
pass
import numpy as np import numpy as np
from . import Tango
from base_plots import gpplot, x_frame1D, x_frame2D from base_plots import gpplot, x_frame1D, x_frame2D
from ...models.gp_coregionalized_regression import GPCoregionalizedRegression from ...models.gp_coregionalized_regression import GPCoregionalizedRegression
from ...models.sparse_gp_coregionalized_regression import SparseGPCoregionalizedRegression from ...models.sparse_gp_coregionalized_regression import SparseGPCoregionalizedRegression
from scipy import sparse from scipy import sparse
from ...core.parameterization.variational import VariationalPosterior from ...core.parameterization.variational import VariationalPosterior
from matplotlib import pyplot as plt
def plot_fit(model, plot_limits=None, which_data_rows='all', def plot_fit(model, plot_limits=None, which_data_rows='all',
which_data_ycols='all', fixed_inputs=[], which_data_ycols='all', fixed_inputs=[],
@ -64,7 +61,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#if len(which_data_ycols)==0: #if len(which_data_ycols)==0:
#raise ValueError('No data selected for plotting') #raise ValueError('No data selected for plotting')
if ax is None: if ax is None:
fig = pb.figure(num=fignum) fig = plt.figure(num=fignum)
ax = fig.add_subplot(111) ax = fig.add_subplot(111)
if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs(): if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs():
@ -197,8 +194,8 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata, **predict_kw) m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata, **predict_kw)
for d in which_data_ycols: for d in which_data_ycols:
m_d = m[:,d].reshape(resolution, resolution).T m_d = m[:,d].reshape(resolution, resolution).T
plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet) plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=plt.cm.jet)
if not plot_raw: plots['dataplot'] = ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.) if not plot_raw: plots['dataplot'] = ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=plt.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
#set the limits of the plot to some sensible values #set the limits of the plot to some sensible values
ax.set_xlim(xmin[0], xmax[0]) ax.set_xlim(xmin[0], xmax[0])