[plotting] library is unfolding and should be working tonight

GPy/plotting/gpy_plot/plot_util.py

@@ -27,3 +27,145 @@
 # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 #===============================================================================
+
+import numpy as np
+from scipy import sparse
+
+def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, which_data_ycols, percentiles, **predict_kw):
+    """
+    Make the right decisions for prediction with a model 
+    based on the standard arguments of plotting.
+    
+    This is quite complex and will take a while to understand,
+    so do not change anything in here lightly!!! 
+    """
+    if 'likelihood' not in predict_kw:
+        if plot_raw:
+            from ...likelihoods import Gaussian
+            lik = Gaussian(0) # Make the likelihood not add any noise
+        else:
+            lik = None
+        predict_kw['likelihood'] = lik
+
+    mu, _ = self.predict(Xgrid, **predict_kw)
+    
+    if percentiles is not None:
+        percentiles = self.predict_quantiles(Xgrid, quantiles=percentiles, **predict_kw)
+    else: percentiles = {}
+
+    retmu = np.empty((Xgrid.shape[0], len(ycols)))
+    
+    if plot_raw and apply_link:
+        for i, d in enumerate(ycols):
+            retmu = self.likelihood.gp_link.transf(mu[:, [i]])
+            for perc in percentiles:
+                perc[:, [i]] = self.likelihood.gp_link.transf(perc[:, [i]])
+
+    return mu, percentiles
+
+def update_not_existing_kwargs(to_update, update_from):
+    """
+    This function updates the keyword aguments from update_from in 
+    to_update, only if the keys are not set in to_update.
+    
+    This is used for updated kwargs from the default dicts.
+    """
+    return to_update.update({k:v for k,v in update_from.items() if k not in to_update})
+
+def get_x_y_var(model):
+    """
+    The the data from a model as 
+    X the inputs, 
+    X_variance the variance of the inputs ([default: None])
+    and Y the outputs
+    
+    :returns: (X, X_variance, Y) 
+    """
+    if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs():
+        X = model.X.mean
+        X_variance = model.X.variance
+    else:
+        X = model.X
+        X_variance = None
+    Y = model.Y
+    if sparse.issparse(Y): Y = Y.todense().view(np.ndarray)
+    return X, X_variance, Y
+
+def get_free_dims(model, visible_dims, fixed_dims):
+    """
+    work out what the inputs are for plotting (1D or 2D)
+    
+    The visible dimensions are the dimensions, which are visible.
+    the fixed_dims are the fixed dimensions for this. 
+    
+    The free_dims are then the visible dims without the fixed dims.
+    """
+    if visible_dims is None:
+        visible_dims = np.arange(model.input_dim)
+    assert visible_dims.size <= 2, "Visible inputs cannot be larger than two"
+    if fixed_dims is None:
+        return visible_dims
+    else:
+        return np.setdiff1d(visible_dims, fixed_dims)
+
+def get_fixed_dims(model, fixed_inputs):
+    """
+    Work out the fixed dimensions from the fixed_inputs list of tuples.
+    """
+    if fixed_inputs is None:
+        fixed_inputs = []
+    return np.array([i for i,_ in fixed_inputs])
+
+def get_which_data_ycols(model, which_data_ycols):
+    """
+    Helper to get the data columns to plot.
+    """
+    if which_data_ycols == 'all' or which_data_ycols is None:
+        return np.arange(model.output_dim)
+    return which_data_ycols
+    
+def get_which_data_rows(model, which_data_rows):
+    """
+    Helper to get the data rows to plot.
+    """
+    if which_data_rows == 'all' or which_data_rows is None:
+        return slice(None)
+    return which_data_rows
+
+def x_frame1D(X,plot_limits=None,resolution=None):
+    """
+    Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
+    """
+    assert X.shape[1] ==1, "x_frame1D is defined for one-dimensional inputs"
+    if plot_limits is None:
+        from ...core.parameterization.variational import VariationalPosterior
+        if isinstance(X, VariationalPosterior):
+            xmin,xmax = X.mean.min(0),X.mean.max(0)
+        else:
+            xmin,xmax = X.min(0),X.max(0)
+        xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
+    elif len(plot_limits)==2:
+        xmin, xmax = plot_limits
+    else:
+        raise ValueError("Bad limits for plotting")
+
+    Xnew = np.linspace(xmin,xmax,resolution or 200)[:,None]
+    return Xnew, xmin, xmax
+
+def x_frame2D(X,plot_limits=None,resolution=None):
+    """
+    Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
+    """
+    assert X.shape[1] ==2, "x_frame2D is defined for two-dimensional inputs"
+    if plot_limits is None:
+        xmin,xmax = X.min(0),X.max(0)
+        xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
+    elif len(plot_limits)==2:
+        xmin, xmax = plot_limits
+    else:
+        raise ValueError("Bad limits for plotting")
+
+    resolution = resolution or 50
+    xx, yy = np.mgrid[xmin[0]:xmax[0]:1j*resolution,xmin[1]:xmax[1]:1j*resolution]
+    Xnew = np.vstack((xx.flatten(),yy.flatten())).T
+    return Xnew, xx, yy, xmin, xmax