Remove the dependency on matplotlib

GPy/examples/classification.py

@@ -5,9 +5,13 @@
 """
 Gaussian Processes classification
 """
-import pylab as pb
 import GPy
 
+try:
+    import pylab as pb
+except:
+    pass
+
 default_seed = 10000
 
 def oil(num_inducing=50, max_iters=100, kernel=None, optimize=True, plot=True):

GPy/examples/coreg_example.py

@@ -1,5 +1,8 @@
 import numpy as np
-import pylab as pb
+try:
+    import pylab as pb
+except:
+    pass
 import GPy
 pb.ion()
 pb.close('all')

GPy/examples/non_gaussian.py

@@ -1,7 +1,10 @@
 import GPy
 import numpy as np
-import matplotlib.pyplot as plt
 from GPy.util import datasets
+try:
+    import matplotlib.pyplot as plt
+except:
+    pass
 
 def student_t_approx(optimize=True, plot=True):
     """

GPy/examples/regression.py

@@ -4,7 +4,10 @@
 """
 Gaussian Processes regression examples
 """
-import pylab as pb
+try:
+    import pylab as pb
+except:
+    pass
 import numpy as np
 import GPy
 

GPy/examples/stochastic.py

@@ -1,7 +1,10 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import pylab as pb
+try:
+    import pylab as pb
+except:
+    pass
 import numpy as np
 import GPy
 

GPy/examples/tutorials.py

@@ -6,8 +6,11 @@
 Code of Tutorials
 """
 
-import pylab as pb
-pb.ion()
+try:
+    import pylab as pb
+    pb.ion()
+except:
+    pass
 import numpy as np
 import GPy
 

GPy/kern/_src/rbf.py

@@ -20,8 +20,6 @@ class RBF(Stationary):
     _support_GPU = True
     def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='rbf', useGPU=False):
         super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name, useGPU=useGPU)
-        self.weave_options = {}
-        self.group_spike_prob = False
         self.psicomp = PSICOMP_RBF()
         if self.useGPU:
             self.psicomp = PSICOMP_RBF_GPU()

GPy/kern/_src/trunclinear.py

@@ -3,14 +3,10 @@
 
 
 import numpy as np
-from scipy import weave
 from kern import Kern
-from ...util.linalg import tdot
-from ...util.misc import param_to_array
 from ...core.parameterization import Param
 from ...core.parameterization.transformations import Logexp
 from ...util.caching import Cache_this
-from ...core.parameterization import variational
 from ...util.config import *
 
 class TruncLinear(Kern):

GPy/models/bcgplvm.py

@@ -3,8 +3,6 @@
 
 
 import numpy as np
-import pylab as pb
-import sys, pdb
 from ..core import GP
 from ..models import GPLVM
 from ..mappings import *

GPy/models/gplvm.py

@@ -3,7 +3,6 @@
 
 
 import numpy as np
-import pylab as pb
 from .. import kern
 from ..core import GP, Param
 from ..likelihoods import Gaussian
@@ -55,7 +54,7 @@ class GPLVM(GP):
         #J = np.zeros((X.shape[0],X.shape[1],self.output_dim))
         J = self.jacobian(X)
         for i in range(X.shape[0]):
-            target[i]=np.sqrt(pb.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
+            target[i]=np.sqrt(np.linalg.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
         return target
 
     def plot(self):
@@ -63,6 +62,7 @@ class GPLVM(GP):
         pb.scatter(self.likelihood.Y[:, 0], self.likelihood.Y[:, 1], 40, self.X[:, 0].copy(), linewidth=0, cmap=pb.cm.jet)  # @UndefinedVariable
         Xnew = np.linspace(self.X.min(), self.X.max(), 200)[:, None]
         mu, _ = self.predict(Xnew)
+        import pylab as pb
         pb.plot(mu[:, 0], mu[:, 1], 'k', linewidth=1.5)
 
     def plot_latent(self, labels=None, which_indices=None,

GPy/models/sparse_gplvm.py

@@ -3,13 +3,8 @@
 
 
 import numpy as np
-import pylab as pb
-import sys, pdb
+import sys
 from GPy.models.sparse_gp_regression import SparseGPRegression
-from GPy.models.gplvm import GPLVM
-# from .. import kern
-# from ..core import model
-# from ..util.linalg import pdinv, PCA
 
 class SparseGPLVM(SparseGPRegression):
     """

GPy/plotting/__init__.py

@@ -1,4 +1,7 @@
 # Copyright (c) 2014, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import matplot_dep
+try:
+    import matplot_dep
+except (ImportError, NameError):
+    print 'Fail to load GPy.plotting.matplot_dep.'

GPy/plotting/matplot_dep/base_plots.py

@@ -2,8 +2,11 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
-import Tango
-import pylab as pb
+try:
+    import Tango
+    import pylab as pb
+except:
+    pass
 import numpy as np
 
 def ax_default(fignum, ax):

GPy/plotting/matplot_dep/dim_reduction_plots.py

@@ -1,12 +1,16 @@
-import pylab as pb
+
 import numpy as np
 from latent_space_visualizations.controllers.imshow_controller import ImshowController,ImAnnotateController
 from ...util.misc import param_to_array
 from ...core.parameterization.variational import VariationalPosterior
 from .base_plots import x_frame2D
 import itertools
-import Tango
-from matplotlib.cm import get_cmap
+try:
+    import Tango
+    from matplotlib.cm import get_cmap
+    import pylab as pb
+except:
+    pass
 
 def most_significant_input_dimensions(model, which_indices):
     """

GPy/plotting/matplot_dep/inference_plots.py

@@ -1,8 +1,10 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import pylab as pb
-import sys
+try:
+    import pylab as pb
+except:
+    pass
 #import numpy as np
 #import Tango
 #from base_plots import gpplot, x_frame1D, x_frame2D

GPy/plotting/matplot_dep/mapping_plots.py

@@ -1,9 +1,12 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import pylab as pb
 import numpy as np
-import Tango
+try:
+    import Tango
+    import pylab as pb
+except:
+    pass
 from base_plots import x_frame1D, x_frame2D
 
 

GPy/plotting/matplot_dep/maps.py

@@ -1,13 +1,14 @@
 import numpy as np
-import pylab as pb
-import matplotlib.patches as patches
-from matplotlib.patches import Polygon
-from matplotlib.collections import PatchCollection
-#from matplotlib import cm
+try:
+    import pylab as pb
+    from matplotlib.patches import Polygon
+    from matplotlib.collections import PatchCollection
+    #from matplotlib import cm
+    pb.ion()
+except:
+    pass
 import re
 
-pb.ion()
-
 def plot(shape_records,facecolor='w',edgecolor='k',linewidths=.5, ax=None,xlims=None,ylims=None):
     """
     Plot the geometry of a shapefile

GPy/plotting/matplot_dep/models_plots.py

@@ -1,9 +1,12 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import pylab as pb
+try:
+    import Tango
+    import pylab as pb
+except:
+    pass
 import numpy as np
-import Tango
 from base_plots import gpplot, x_frame1D, x_frame2D
 from ...util.misc import param_to_array
 from ...models.gp_coregionalized_regression import GPCoregionalizedRegression

GPy/plotting/matplot_dep/priors_plots.py

@@ -3,7 +3,10 @@
 
 
 import numpy as np
-import pylab as pb
+try:
+    import pylab as pb
+except:
+    pass
 
 
 def univariate_plot(prior):

GPy/plotting/matplot_dep/ssgplvm.py

@@ -6,7 +6,6 @@ import pylab
 
 from ...models import SSGPLVM
 from img_plots import plot_2D_images
-from ...util.misc import param_to_array
 
 class SSGPLVM_plot(object):
     def __init__(self,model, imgsize):

GPy/util/datasets.py

@@ -2,7 +2,6 @@ import csv
 import os
 import copy
 import numpy as np
-import pylab as pb
 import GPy
 import scipy.io
 import cPickle as pickle
@@ -346,6 +345,7 @@ def football_data(season='1314', data_set='football_data'):
     data_resources[data_set_season]['files'] = [files]
     if not data_available(data_set_season):
         download_data(data_set_season)
+    import pylab as pb
     for file in reversed(files):
         filename = os.path.join(data_path, data_set_season, file)
         # rewrite files removing blank rows.

GPy/util/pca.py

@@ -5,8 +5,11 @@ Created on 10 Sep 2012
 @copyright: Max Zwiessele 2012
 '''
 import numpy
-import pylab
-import matplotlib
+try:
+    import pylab
+    import matplotlib
+except:
+    pass
 from numpy.linalg.linalg import LinAlgError
 
 class pca(object):
@@ -88,13 +91,15 @@ class pca(object):
 
     def plot_2d(self, X, labels=None, s=20, marker='o',
                 dimensions=(0, 1), ax=None, colors=None,
-                fignum=None, cmap=matplotlib.cm.jet, # @UndefinedVariable
+                fignum=None, cmap=None, # @UndefinedVariable
                 ** kwargs):
         """
         Plot dimensions `dimensions` with given labels against each other in 
         PC space. Labels can be any sequence of labels of dimensions X.shape[0].
         Labels can be drawn with a subsequent call to legend()
         """
+        if cmap is None:
+            cmap = matplotlib.cm.jet
         if ax is None:
             fig = pylab.figure(fignum)
             ax = fig.add_subplot(111)