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https://github.com/SheffieldML/GPy.git
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[plotting] cleanup first commit, this cleans the plotting library and adds plotting tests
This commit is contained in:
mzwiessele
parent
fee2f3f727
commit
b9bfd0fc6d
10 changed files with 200 additions and 690 deletions
254
GPy/core/gp.py
254
GPy/core/gp.py
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@ -477,260 +477,6 @@ class GP(Model):
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Ysim = self.likelihood.samples(fsim, Y_metadata=Y_metadata)
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return Ysim
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def plot_f(self, plot_limits=None, which_data_rows='all',
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which_data_ycols='all', fixed_inputs=[],
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levels=20, samples=0, fignum=None, ax=None, resolution=None,
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plot_raw=True,
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linecol=None,fillcol=None, Y_metadata=None, data_symbol='kx',
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apply_link=False):
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"""
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Plot the GP's view of the world, where the data is normalized and before applying a likelihood.
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This is a call to plot with plot_raw=True.
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Data will not be plotted in this, as the GP's view of the world
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may live in another space, or units then the data.
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Can plot only part of the data and part of the posterior functions
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using which_data_rowsm which_data_ycols.
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:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
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:type plot_limits: np.array
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:param which_data_rows: which of the training data to plot (default all)
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:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
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:param which_data_ycols: when the data has several columns (independant outputs), only plot these
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:type which_data_ycols: 'all' or a list of integers
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:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
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:type fixed_inputs: a list of tuples
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:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
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:type resolution: int
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:param levels: number of levels to plot in a contour plot.
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:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
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:type levels: int
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:param samples: the number of a posteriori samples to plot
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:type samples: int
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:param fignum: figure to plot on.
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:type fignum: figure number
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:param ax: axes to plot on.
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:type ax: axes handle
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:param linecol: color of line to plot [Tango.colorsHex['darkBlue']]
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:type linecol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
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:param fillcol: color of fill [Tango.colorsHex['lightBlue']]
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:type fillcol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
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:param Y_metadata: additional data associated with Y which may be needed
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:type Y_metadata: dict
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:param data_symbol: symbol as used matplotlib, by default this is a black cross ('kx')
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:type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
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:param apply_link: if there is a link function of the likelihood, plot the link(f*) rather than f*
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:type apply_link: boolean
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"""
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from ..plotting.matplot_dep import models_plots
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kw = {}
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if linecol is not None:
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kw['linecol'] = linecol
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if fillcol is not None:
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kw['fillcol'] = fillcol
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return models_plots.plot_fit(self, plot_limits, which_data_rows,
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which_data_ycols, fixed_inputs,
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levels, samples, fignum, ax, resolution,
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plot_raw=plot_raw, Y_metadata=Y_metadata,
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data_symbol=data_symbol, apply_link=apply_link, **kw)
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def plot(self, plot_limits=None, which_data_rows='all',
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which_data_ycols='all', fixed_inputs=[],
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levels=20, samples=0, fignum=None, ax=None, resolution=None,
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plot_raw=False, linecol=None,fillcol=None, Y_metadata=None,
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data_symbol='kx', predict_kw=None, plot_training_data=True, samples_y=0, apply_link=False):
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"""
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Plot the posterior of the GP.
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- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
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- In two dimsensions, a contour-plot shows the mean predicted function
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- In higher dimensions, use fixed_inputs to plot the GP with some of the inputs fixed.
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Can plot only part of the data and part of the posterior functions
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using which_data_rowsm which_data_ycols.
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:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
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:type plot_limits: np.array
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:param which_data_rows: which of the training data to plot (default all)
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:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
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:param which_data_ycols: when the data has several columns (independant outputs), only plot these
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:type which_data_ycols: 'all' or a list of integers
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:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
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:type fixed_inputs: a list of tuples
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:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
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:type resolution: int
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:param levels: number of levels to plot in a contour plot.
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:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
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:type levels: int
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:param samples: the number of a posteriori samples to plot, p(f*|y)
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:type samples: int
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:param fignum: figure to plot on.
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:type fignum: figure number
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:param ax: axes to plot on.
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:type ax: axes handle
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:param linecol: color of line to plot [Tango.colorsHex['darkBlue']]
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:type linecol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
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:param fillcol: color of fill [Tango.colorsHex['lightBlue']]
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:type fillcol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
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:param Y_metadata: additional data associated with Y which may be needed
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:type Y_metadata: dict
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:param data_symbol: symbol as used matplotlib, by default this is a black cross ('kx')
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:type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
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:param plot_training_data: whether or not to plot the training points
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:type plot_training_data: boolean
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:param samples_y: the number of a posteriori samples to plot, p(y*|y)
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:type samples_y: int
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:param apply_link: if there is a link function of the likelihood, plot the link(f*) rather than f*, when plotting posterior samples f
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:type apply_link: boolean
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"""
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from ..plotting.matplot_dep import models_plots
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kw = {}
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if linecol is not None:
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kw['linecol'] = linecol
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if fillcol is not None:
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kw['fillcol'] = fillcol
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return models_plots.plot_fit(self, plot_limits, which_data_rows,
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which_data_ycols, fixed_inputs,
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levels, samples, fignum, ax, resolution,
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plot_raw=plot_raw, Y_metadata=Y_metadata,
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data_symbol=data_symbol, predict_kw=predict_kw,
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plot_training_data=plot_training_data, samples_y=samples_y, apply_link=apply_link, **kw)
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def plot_density(self, levels=20, plot_limits=None, fignum=None, ax=None,
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fixed_inputs=[], plot_raw=False, edgecolor='none', facecolor='#3465a4',
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predict_kw=None,Y_metadata=None,
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apply_link=False, resolution=200, **patch_kw):
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"""
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Plot the posterior density of the GP.
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- In one dimension, the function is plotted with a shaded gradient, visualizing the density of the posterior.
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- Only implemented for one dimension, for higher dimensions use `plot`.
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:param levels: number of levels to plot in the density plot. This is a number between 1 and 100. 1 corresponds to the normal plot_fit.
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:type levels: int
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:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
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:type plot_limits: np.array
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:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
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:type fixed_inputs: a list of tuples
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:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
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:type resolution: int
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:param edgecolor: color of line to plot [Tango.colorsHex['darkBlue']]
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:type edgecolor: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
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:param facecolor: color of fill [Tango.colorsHex['lightBlue']]
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:type facecolor: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
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:param Y_metadata: additional data associated with Y which may be needed
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:type Y_metadata: dict
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:param apply_link: if there is a link function of the likelihood, plot the link(f*) rather than f*, when plotting posterior samples f
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:type apply_link: boolean
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:param resolution: resolution of interpolation (how many points to interpolate of the posterior).
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:type resolution: int
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:param: patch_kw: the keyword arguments for the patchcollection fill.
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"""
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from ..plotting.matplot_dep import models_plots
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return models_plots.plot_density(self, levels, plot_limits, fignum, ax,
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fixed_inputs, plot_raw=plot_raw,
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Y_metadata=Y_metadata,
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predict_kw=predict_kw,
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apply_link=apply_link,
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edgecolor=edgecolor, facecolor=facecolor,
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**patch_kw)
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def plot_data(self, which_data_rows='all',
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which_data_ycols='all', visible_dims=None,
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fignum=None, ax=None, data_symbol='kx'):
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"""
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Plot the training data
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- For higher dimensions than two, use fixed_inputs to plot the data points with some of the inputs fixed.
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Can plot only part of the data
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using which_data_rows and which_data_ycols.
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:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
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:type plot_limits: np.array
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:param which_data_rows: which of the training data to plot (default all)
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:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
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:param which_data_ycols: when the data has several columns (independant outputs), only plot these
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:type which_data_ycols: 'all' or a list of integers
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:param visible_dims: an array specifying the input dimensions to plot (maximum two)
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:type visible_dims: a numpy array
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:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
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:type resolution: int
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:param levels: number of levels to plot in a contour plot.
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:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
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:type levels: int
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:param samples: the number of a posteriori samples to plot, p(f*|y)
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:type samples: int
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:param fignum: figure to plot on.
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:type fignum: figure number
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:param ax: axes to plot on.
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:type ax: axes handle
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:param linecol: color of line to plot [Tango.colorsHex['darkBlue']]
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:type linecol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
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:param fillcol: color of fill [Tango.colorsHex['lightBlue']]
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:type fillcol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
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:param data_symbol: symbol as used matplotlib, by default this is a black cross ('kx')
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:type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
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"""
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from ..plotting.matplot_dep import models_plots
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kw = {}
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return models_plots.plot_data(self, which_data_rows,
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which_data_ycols, visible_dims,
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fignum, ax, data_symbol, **kw)
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def plot_errorbars_trainset(self, which_data_rows='all',
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which_data_ycols='all', fixed_inputs=[], fignum=None, ax=None,
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linecol=None, data_symbol='kx', predict_kw=None, plot_training_data=True,lw=None):
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"""
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Plot the posterior error bars corresponding to the training data
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- For higher dimensions than two, use fixed_inputs to plot the data points with some of the inputs fixed.
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Can plot only part of the data
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using which_data_rows and which_data_ycols.
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:param which_data_rows: which of the training data to plot (default all)
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:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
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:param which_data_ycols: when the data has several columns (independant outputs), only plot these
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:type which_data_rows: 'all' or a list of integers
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:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
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:type fixed_inputs: a list of tuples
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:param fignum: figure to plot on.
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:type fignum: figure number
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:param ax: axes to plot on.
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:type ax: axes handle
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:param plot_training_data: whether or not to plot the training points
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:type plot_training_data: boolean
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"""
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from ..plotting.matplot_dep import models_plots
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kw = {}
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if lw is not None:
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kw['lw'] = lw
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return models_plots.plot_errorbars_trainset(self, which_data_rows, which_data_ycols, fixed_inputs,
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fignum, ax, linecol, data_symbol,
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predict_kw, plot_training_data, **kw)
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def plot_magnification(self, labels=None, which_indices=None,
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resolution=50, ax=None, marker='o', s=40,
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fignum=None, legend=True,
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plot_limits=None,
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aspect='auto', updates=False, plot_inducing=True, kern=None, **kwargs):
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import sys
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from ..plotting.matplot_dep import dim_reduction_plots
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return dim_reduction_plots.plot_magnification(self, labels, which_indices,
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resolution, ax, marker, s,
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fignum, plot_inducing, legend,
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plot_limits, aspect, updates, **kwargs)
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def input_sensitivity(self, summarize=True):
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"""
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Returns the sensitivity for each dimension of this model
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@ -28,3 +28,6 @@ working = True
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[cython]
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working = True
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[plotting]
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library = matplotlib
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@ -13,7 +13,7 @@ from functools import wraps
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def put_clean(dct, name, func):
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if name in dct:
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dct['_clean_{}'.format(name)] = dct[name]
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#dct['_clean_{}'.format(name)] = dct[name]
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dct[name] = func(dct[name])
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class KernCallsViaSlicerMeta(ParametersChangedMeta):
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@ -2,11 +2,30 @@
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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try:
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import matplotlib
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from . import matplot_dep
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from ..util.config import config
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lib = config.get('plotting', 'library')
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if lib == 'matplotlib':
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import matplotlib
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from . import matplot_dep as plotting_library
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except (ImportError, NameError):
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# Matplotlib not available
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import warnings
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warnings.warn(ImportWarning("Matplotlib not available, install newest version of Matplotlib for plotting"))
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#sys.modules['matplotlib'] =
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#sys.modules[__name__+'.matplot_dep'] = ImportWarning("Matplotlib not available, install newest version of Matplotlib for plotting")
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warnings.warn(ImportWarning("{} not available, install newest version of {} for plotting").format(lib, lib))
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config.set('plotting', 'library', 'none')
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if config.get('plotting', 'library') is not 'none':
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# Inject the plots into classes here:
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# Already converted to new style:
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from . import gpy_plot
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from ..core import GP
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GP.plot_data = gpy_plot.data_plots.plot_data
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# Still to convert to new style:
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GP.plot = plotting_library.models_plots.plot_fit
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GP.plot_f = plotting_library.models_plots.plot_fit_f
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GP.plot_density = plotting_library.models_plots.plot_density
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GP.plot_errorbars_trainset = plotting_library.models_plots.plot_errorbars_trainset
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GP.plot_magnification = plotting_library.dim_reduction_plots.plot_magnification
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@ -1,6 +1,54 @@
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# Copyright (c) 2014, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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from matplotlib import pyplot as plt
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from . import defaults
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def get_new_canvas(kwargs):
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"""
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Return a canvas, kwargupdate for matplotlib. This just a
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dictionary for the collection and we add the an axis to kwarg.
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This method does two things, it creates an empty canvas
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and updates the kwargs (deletes the unnecessary kwargs)
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for further usage in normal plotting.
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in matplotlib this means it deletes references to ax, as
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plotting is done on the axis itself and is not a kwarg.
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"""
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if 'ax' in kwargs:
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ax = kwargs.pop('ax')
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elif 'num' in kwargs and 'figsize' in kwargs:
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ax = plt.figure(num=kwargs.pop('num'), figsize=kwargs.pop('figsize')).add_subplot(111)
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elif 'num' in kwargs:
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ax = plt.figure(num=kwargs.pop('num')).add_subplot(111)
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elif 'figsize' in kwargs:
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ax = plt.figure(figsize=kwargs.pop('figsize')).add_subplot(111)
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else:
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ax = plt.figure().add_subplot(111)
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# Add ax to kwargs to add all subsequent plots to this axis:
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#kwargs['ax'] = ax
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return ax, kwargs
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def show_canvas(canvas):
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try:
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canvas.figure.canvas.draw()
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canvas.figure.tight_layout()
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except:
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pass
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return canvas
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def scatter(ax, *args, **kwargs):
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ax.scatter(*args, **kwargs)
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def plot(ax, *args, **kwargs):
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ax.plot(*args, **kwargs)
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def imshow(ax, *args, **kwargs):
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ax.imshow(*args, **kwargs)
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from . import base_plots
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from . import models_plots
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from . import priors_plots
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|
|
@ -11,8 +59,9 @@ from . import mapping_plots
|
|||
from . import Tango
|
||||
from . import visualize
|
||||
from . import latent_space_visualizations
|
||||
from . import netpbmfile
|
||||
from . import inference_plots
|
||||
from . import maps
|
||||
from . import img_plots
|
||||
from .ssgplvm import SSGPLVM_plot
|
||||
from .ssgplvm import SSGPLVM_plot
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,11 +1,11 @@
|
|||
# #Copyright (c) 2012, GPy authors (see AUTHORS.txt).
|
||||
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
||||
from matplotlib import pyplot as pb
|
||||
from matplotlib import pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
def ax_default(fignum, ax):
|
||||
if ax is None:
|
||||
fig = pb.figure(fignum)
|
||||
fig = plt.figure(fignum)
|
||||
ax = fig.add_subplot(111)
|
||||
else:
|
||||
fig = ax.figure
|
||||
|
|
@ -35,12 +35,14 @@ def gpplot(x, mu, lower, upper, edgecol='#3300FF', fillcol='#33CCFF', ax=None, f
|
|||
plots.append(axes.fill(np.hstack((x,x[::-1])),np.hstack((upper,lower[::-1])),color=fillcol,**kwargs))
|
||||
|
||||
#this is the edge:
|
||||
plots.append(meanplot(x, upper,color=edgecol,linewidth=0.2,ax=axes))
|
||||
plots.append(meanplot(x, lower,color=edgecol,linewidth=0.2,ax=axes))
|
||||
plots.append(meanplot(x, upper,color=edgecol, linewidth=0.2, ax=axes))
|
||||
plots.append(meanplot(x, lower,color=edgecol, linewidth=0.2, ax=axes))
|
||||
|
||||
return plots
|
||||
|
||||
def plot_gradient_fill(ax, x, percentiles, **kwargs):
|
||||
def gradient_fill(x, percentiles, ax=None, fignum=None, **kwargs):
|
||||
_, ax = ax_default(fignum, ax)
|
||||
|
||||
plots = []
|
||||
|
||||
#here's the box
|
||||
|
|
@ -150,19 +152,19 @@ def gperrors(x, mu, lower, upper, edgecol=None, ax=None, fignum=None, **kwargs):
|
|||
|
||||
|
||||
def removeRightTicks(ax=None):
|
||||
ax = ax or pb.gca()
|
||||
ax = ax or plt.gca()
|
||||
for i, line in enumerate(ax.get_yticklines()):
|
||||
if i%2 == 1: # odd indices
|
||||
line.set_visible(False)
|
||||
|
||||
def removeUpperTicks(ax=None):
|
||||
ax = ax or pb.gca()
|
||||
ax = ax or plt.gca()
|
||||
for i, line in enumerate(ax.get_xticklines()):
|
||||
if i%2 == 1: # odd indices
|
||||
line.set_visible(False)
|
||||
|
||||
def fewerXticks(ax=None,divideby=2):
|
||||
ax = ax or pb.gca()
|
||||
ax = ax or plt.gca()
|
||||
ax.set_xticks(ax.get_xticks()[::divideby])
|
||||
|
||||
def align_subplots(N,M,xlim=None, ylim=None):
|
||||
|
|
@ -171,33 +173,33 @@ def align_subplots(N,M,xlim=None, ylim=None):
|
|||
if xlim is None:
|
||||
xlim = [np.inf,-np.inf]
|
||||
for i in range(N*M):
|
||||
pb.subplot(N,M,i+1)
|
||||
xlim[0] = min(xlim[0],pb.xlim()[0])
|
||||
xlim[1] = max(xlim[1],pb.xlim()[1])
|
||||
plt.subplot(N,M,i+1)
|
||||
xlim[0] = min(xlim[0],plt.xlim()[0])
|
||||
xlim[1] = max(xlim[1],plt.xlim()[1])
|
||||
if ylim is None:
|
||||
ylim = [np.inf,-np.inf]
|
||||
for i in range(N*M):
|
||||
pb.subplot(N,M,i+1)
|
||||
ylim[0] = min(ylim[0],pb.ylim()[0])
|
||||
ylim[1] = max(ylim[1],pb.ylim()[1])
|
||||
plt.subplot(N,M,i+1)
|
||||
ylim[0] = min(ylim[0],plt.ylim()[0])
|
||||
ylim[1] = max(ylim[1],plt.ylim()[1])
|
||||
|
||||
for i in range(N*M):
|
||||
pb.subplot(N,M,i+1)
|
||||
pb.xlim(xlim)
|
||||
pb.ylim(ylim)
|
||||
plt.subplot(N,M,i+1)
|
||||
plt.xlim(xlim)
|
||||
plt.ylim(ylim)
|
||||
if (i)%M:
|
||||
pb.yticks([])
|
||||
plt.yticks([])
|
||||
else:
|
||||
removeRightTicks()
|
||||
if i<(M*(N-1)):
|
||||
pb.xticks([])
|
||||
plt.xticks([])
|
||||
else:
|
||||
removeUpperTicks()
|
||||
|
||||
def align_subplot_array(axes,xlim=None, ylim=None):
|
||||
"""
|
||||
Make all of the axes in the array hae the same limits, turn off unnecessary ticks
|
||||
use pb.subplots() to get an array of axes
|
||||
use plt.subplots() to get an array of axes
|
||||
"""
|
||||
#find sensible xlim,ylim
|
||||
if xlim is None:
|
||||
|
|
|
|||
|
|
@ -9,12 +9,13 @@ from ...models.sparse_gp_coregionalized_regression import SparseGPCoregionalized
|
|||
from scipy import sparse
|
||||
from ...core.parameterization.variational import VariationalPosterior
|
||||
from matplotlib import pyplot as plt
|
||||
from .base_plots import plot_gradient_fill
|
||||
from .base_plots import gradient_fill
|
||||
from functools import wraps
|
||||
|
||||
|
||||
def plot_data(model, which_data_rows='all',
|
||||
def plot_data(self, which_data_rows='all',
|
||||
which_data_ycols='all', visible_dims=None,
|
||||
fignum=None, ax=None, data_symbol='kx',mew=1.5):
|
||||
fignum=None, ax=None, data_symbol='kx',mew=1.5,**kwargs):
|
||||
"""
|
||||
Plot the training data
|
||||
- For higher dimensions than two, use fixed_inputs to plot the data points with some of the inputs fixed.
|
||||
|
|
@ -23,7 +24,7 @@ def plot_data(model, which_data_rows='all',
|
|||
using which_data_rows and which_data_ycols.
|
||||
|
||||
:param which_data_rows: which of the training data to plot (default all)
|
||||
:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
|
||||
:type which_data_rows: 'all' or a slice object to slice self.X, self.Y
|
||||
:param which_data_ycols: when the data has several columns (independant outputs), only plot these
|
||||
:type which_data_rows: 'all' or a list of integers
|
||||
:param visible_dims: an array specifying the input dimensions to plot (maximum two)
|
||||
|
|
@ -37,23 +38,23 @@ def plot_data(model, which_data_rows='all',
|
|||
if which_data_rows == 'all':
|
||||
which_data_rows = slice(None)
|
||||
if which_data_ycols == 'all':
|
||||
which_data_ycols = np.arange(model.output_dim)
|
||||
which_data_ycols = np.arange(self.output_dim)
|
||||
|
||||
if ax is None:
|
||||
fig = plt.figure(num=fignum)
|
||||
ax = fig.add_subplot(111)
|
||||
|
||||
if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs():
|
||||
X = model.X.mean
|
||||
X_variance = model.X.variance
|
||||
if hasattr(self, 'has_uncertain_inputs') and self.has_uncertain_inputs():
|
||||
X = self.X.mean
|
||||
X_variance = self.X.variance
|
||||
else:
|
||||
X = model.X
|
||||
X = self.X
|
||||
X_variance = None
|
||||
Y = model.Y
|
||||
Y = self.Y
|
||||
|
||||
#work out what the inputs are for plotting (1D or 2D)
|
||||
if visible_dims is None:
|
||||
visible_dims = np.arange(model.input_dim)
|
||||
visible_dims = np.arange(self.input_dim)
|
||||
assert visible_dims.size <= 2, "Visible inputs cannot be larger than two"
|
||||
free_dims = visible_dims
|
||||
plots = {}
|
||||
|
|
@ -80,7 +81,7 @@ def plot_data(model, which_data_rows='all',
|
|||
return plots
|
||||
|
||||
|
||||
def plot_fit(model, plot_limits=None, which_data_rows='all',
|
||||
def plot_fit(self, plot_limits=None, which_data_rows='all',
|
||||
which_data_ycols='all', fixed_inputs=[],
|
||||
levels=20, samples=0, fignum=None, ax=None, resolution=None,
|
||||
plot_raw=False,
|
||||
|
|
@ -98,7 +99,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
|
|||
:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
|
||||
:type plot_limits: np.array
|
||||
:param which_data_rows: which of the training data to plot (default all)
|
||||
:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
|
||||
:type which_data_rows: 'all' or a slice object to slice self.X, self.Y
|
||||
:param which_data_ycols: when the data has several columns (independant outputs), only plot these
|
||||
:type which_data_rows: 'all' or a list of integers
|
||||
:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
|
||||
|
|
@ -134,98 +135,98 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
|
|||
if which_data_rows == 'all':
|
||||
which_data_rows = slice(None)
|
||||
if which_data_ycols == 'all':
|
||||
which_data_ycols = np.arange(model.output_dim)
|
||||
which_data_ycols = np.arange(self.output_dim)
|
||||
#if len(which_data_ycols)==0:
|
||||
#raise ValueError('No data selected for plotting')
|
||||
if ax is None:
|
||||
fig = plt.figure(num=fignum)
|
||||
ax = fig.add_subplot(111)
|
||||
|
||||
if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs():
|
||||
X = model.X.mean
|
||||
X_variance = model.X.variance
|
||||
if hasattr(self, 'has_uncertain_inputs') and self.has_uncertain_inputs():
|
||||
X = self.X.mean
|
||||
X_variance = self.X.variance
|
||||
else:
|
||||
X = model.X
|
||||
Y = model.Y
|
||||
X = self.X
|
||||
Y = self.Y
|
||||
if sparse.issparse(Y): Y = Y.todense().view(np.ndarray)
|
||||
|
||||
if hasattr(model, 'Z'): Z = model.Z
|
||||
if hasattr(self, 'Z'): Z = self.Z
|
||||
|
||||
if predict_kw is None:
|
||||
predict_kw = {}
|
||||
|
||||
#work out what the inputs are for plotting (1D or 2D)
|
||||
fixed_dims = np.array([i for i,v in fixed_inputs])
|
||||
free_dims = np.setdiff1d(np.arange(model.input_dim),fixed_dims)
|
||||
free_dims = np.setdiff1d(np.arange(self.input_dim),fixed_dims)
|
||||
plots = {}
|
||||
#one dimensional plotting
|
||||
if len(free_dims) == 1:
|
||||
|
||||
#define the frame on which to plot
|
||||
Xnew, xmin, xmax = x_frame1D(X[:,free_dims], plot_limits=plot_limits, resolution=resolution or 200)
|
||||
Xgrid = np.empty((Xnew.shape[0],model.input_dim))
|
||||
Xgrid = np.empty((Xnew.shape[0],self.input_dim))
|
||||
Xgrid[:,free_dims] = Xnew
|
||||
for i,v in fixed_inputs:
|
||||
Xgrid[:,i] = v
|
||||
|
||||
#make a prediction on the frame and plot it
|
||||
if plot_raw:
|
||||
m, v = model._raw_predict(Xgrid, **predict_kw)
|
||||
m, v = self._raw_predict(Xgrid, **predict_kw)
|
||||
if apply_link:
|
||||
lower = model.likelihood.gp_link.transf(m - 2*np.sqrt(v))
|
||||
upper = model.likelihood.gp_link.transf(m + 2*np.sqrt(v))
|
||||
lower = self.likelihood.gp_link.transf(m - 2*np.sqrt(v))
|
||||
upper = self.likelihood.gp_link.transf(m + 2*np.sqrt(v))
|
||||
#Once transformed this is now the median of the function
|
||||
m = model.likelihood.gp_link.transf(m)
|
||||
m = self.likelihood.gp_link.transf(m)
|
||||
else:
|
||||
lower = m - 2*np.sqrt(v)
|
||||
upper = m + 2*np.sqrt(v)
|
||||
else:
|
||||
if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression):
|
||||
if isinstance(self,GPCoregionalizedRegression) or isinstance(self,SparseGPCoregionalizedRegression):
|
||||
extra_data = Xgrid[:,-1:].astype(np.int)
|
||||
if Y_metadata is None:
|
||||
Y_metadata = {'output_index': extra_data}
|
||||
else:
|
||||
Y_metadata['output_index'] = extra_data
|
||||
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata, **predict_kw)
|
||||
fmu, fv = model._raw_predict(Xgrid, full_cov=False, **predict_kw)
|
||||
lower, upper = model.likelihood.predictive_quantiles(fmu, fv, (2.5, 97.5), Y_metadata=Y_metadata)
|
||||
m, v = self.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata, **predict_kw)
|
||||
fmu, fv = self._raw_predict(Xgrid, full_cov=False, **predict_kw)
|
||||
lower, upper = self.likelihood.predictive_quantiles(fmu, fv, (2.5, 97.5), Y_metadata=Y_metadata)
|
||||
|
||||
|
||||
for d in which_data_ycols:
|
||||
plots['gpplot'] = gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], ax=ax, edgecol=linecol, fillcol=fillcol)
|
||||
#if not plot_raw: plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], data_symbol, mew=1.5)
|
||||
if not plot_raw and plot_training_data:
|
||||
plots['dataplot'] = plot_data(model=model, which_data_rows=which_data_rows,
|
||||
plots['dataplot'] = plot_data(self=self, which_data_rows=which_data_rows,
|
||||
visible_dims=free_dims, data_symbol=data_symbol, mew=1.5, ax=ax, fignum=fignum)
|
||||
|
||||
|
||||
#optionally plot some samples
|
||||
if samples: #NOTE not tested with fixed_inputs
|
||||
Fsim = model.posterior_samples_f(Xgrid, samples)
|
||||
Fsim = self.posterior_samples_f(Xgrid, samples)
|
||||
if apply_link:
|
||||
Fsim = model.likelihood.gp_link.transf(Fsim)
|
||||
Fsim = self.likelihood.gp_link.transf(Fsim)
|
||||
for fi in Fsim.T:
|
||||
plots['posterior_samples'] = ax.plot(Xnew, fi[:,None], '#3300FF', linewidth=0.25)
|
||||
#ax.plot(Xnew, fi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
|
||||
|
||||
if samples_y: #NOTE not tested with fixed_inputs
|
||||
Ysim = model.posterior_samples(Xgrid, samples_y, Y_metadata=Y_metadata)
|
||||
Ysim = self.posterior_samples(Xgrid, samples_y, Y_metadata=Y_metadata)
|
||||
for yi in Ysim.T:
|
||||
plots['posterior_samples_y'] = ax.scatter(Xnew, yi[:,None], s=5, c=Tango.colorsHex['darkBlue'], marker='o', alpha=0.5)
|
||||
#ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
|
||||
|
||||
|
||||
#add error bars for uncertain (if input uncertainty is being modelled)
|
||||
if hasattr(model,"has_uncertain_inputs") and model.has_uncertain_inputs() and plot_uncertain_inputs:
|
||||
if hasattr(self,"has_uncertain_inputs") and self.has_uncertain_inputs() and plot_uncertain_inputs:
|
||||
if plot_raw:
|
||||
#add error bars for uncertain (if input uncertainty is being modelled), for plot_f
|
||||
#Hack to plot error bars on latent function, rather than on the data
|
||||
vs = model.X.mean.values.copy()
|
||||
vs = self.X.mean.values.copy()
|
||||
for i,v in fixed_inputs:
|
||||
vs[:,i] = v
|
||||
m_X, _ = model._raw_predict(vs)
|
||||
m_X, _ = self._raw_predict(vs)
|
||||
if apply_link:
|
||||
m_X = model.likelihood.gp_link.transf(m_X)
|
||||
m_X = self.likelihood.gp_link.transf(m_X)
|
||||
plots['xerrorbar'] = ax.errorbar(X[which_data_rows, free_dims].flatten(), m_X[which_data_rows, which_data_ycols].flatten(),
|
||||
xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()),
|
||||
ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
|
||||
|
|
@ -243,9 +244,9 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
|
|||
pass
|
||||
|
||||
#add inducing inputs (if a sparse model is used)
|
||||
if hasattr(model,"Z"):
|
||||
#Zu = model.Z[:,free_dims] * model._Xscale[:,free_dims] + model._Xoffset[:,free_dims]
|
||||
if isinstance(model,SparseGPCoregionalizedRegression):
|
||||
if hasattr(self,"Z"):
|
||||
#Zu = self.Z[:,free_dims] * self._Xscale[:,free_dims] + self._Xoffset[:,free_dims]
|
||||
if isinstance(self,SparseGPCoregionalizedRegression):
|
||||
Z = Z[Z[:,-1] == Y_metadata['output_index'],:]
|
||||
Zu = Z[:,free_dims]
|
||||
z_height = ax.get_ylim()[0]
|
||||
|
|
@ -259,7 +260,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
|
|||
#define the frame for plotting on
|
||||
resolution = resolution or 50
|
||||
Xnew, _, _, xmin, xmax = x_frame2D(X[:,free_dims], plot_limits, resolution)
|
||||
Xgrid = np.empty((Xnew.shape[0],model.input_dim))
|
||||
Xgrid = np.empty((Xnew.shape[0],self.input_dim))
|
||||
Xgrid[:,free_dims] = Xnew
|
||||
for i,v in fixed_inputs:
|
||||
Xgrid[:,i] = v
|
||||
|
|
@ -267,15 +268,15 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
|
|||
|
||||
#predict on the frame and plot
|
||||
if plot_raw:
|
||||
m, _ = model._raw_predict(Xgrid, **predict_kw)
|
||||
m, _ = self._raw_predict(Xgrid, **predict_kw)
|
||||
else:
|
||||
if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression):
|
||||
if isinstance(self,GPCoregionalizedRegression) or isinstance(self,SparseGPCoregionalizedRegression):
|
||||
extra_data = Xgrid[:,-1:].astype(np.int)
|
||||
if Y_metadata is None:
|
||||
Y_metadata = {'output_index': extra_data}
|
||||
else:
|
||||
Y_metadata['output_index'] = extra_data
|
||||
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata, **predict_kw)
|
||||
m, v = self.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata, **predict_kw)
|
||||
for d in which_data_ycols:
|
||||
m_d = m[:,d].reshape(resolution, resolution).T
|
||||
plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=plt.cm.jet)
|
||||
|
|
@ -290,9 +291,9 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
|
|||
if samples:
|
||||
warnings.warn("Samples are rather difficult to plot for 2D inputs...")
|
||||
|
||||
#add inducing inputs (if a sparse model is used)
|
||||
if hasattr(model,"Z"):
|
||||
#Zu = model.Z[:,free_dims] * model._Xscale[:,free_dims] + model._Xoffset[:,free_dims]
|
||||
#add inducing inputs (if a sparse self is used)
|
||||
if hasattr(self,"Z"):
|
||||
#Zu = self.Z[:,free_dims] * self._Xscale[:,free_dims] + self._Xoffset[:,free_dims]
|
||||
Zu = Z[:,free_dims]
|
||||
plots['inducing_inputs'] = ax.plot(Zu[:,0], Zu[:,1], 'wo')
|
||||
|
||||
|
|
@ -300,20 +301,41 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
|
|||
raise NotImplementedError("Cannot define a frame with more than two input dimensions")
|
||||
return plots
|
||||
|
||||
def plot_density(model, levels=20, plot_limits=None, fignum=None, ax=None,
|
||||
def plot_density(self, levels=20, plot_limits=None,
|
||||
fixed_inputs=[], plot_raw=False, edgecolor='none', facecolor='#3465a4',
|
||||
predict_kw=None,Y_metadata=None,
|
||||
apply_link=False, resolution=200, **patch_kwargs):
|
||||
#deal with optional arguments
|
||||
if ax is None:
|
||||
fig = plt.figure(num=fignum)
|
||||
ax = fig.add_subplot(111)
|
||||
"""
|
||||
Plot the posterior density of the GP.
|
||||
- In one dimension, the function is plotted with a shaded gradient, visualizing the density of the posterior.
|
||||
- Only implemented for one dimension, for higher dimensions use `plot`.
|
||||
|
||||
if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs():
|
||||
X = model.X.mean
|
||||
:param levels: number of levels to plot in the density plot. This is a number between 1 and 100. 1 corresponds to the normal plot_fit.
|
||||
:type levels: int
|
||||
:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
|
||||
:type plot_limits: np.array
|
||||
:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
|
||||
:type fixed_inputs: a list of tuples
|
||||
:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
|
||||
:type resolution: int
|
||||
:param edgecolor: color of line to plot [Tango.colorsHex['darkBlue']]
|
||||
:type edgecolor: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
|
||||
:param facecolor: color of fill [Tango.colorsHex['lightBlue']]
|
||||
:type facecolor: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
|
||||
:param Y_metadata: additional data associated with Y which may be needed
|
||||
:type Y_metadata: dict
|
||||
:param apply_link: if there is a link function of the likelihood, plot the link(f*) rather than f*, when plotting posterior samples f
|
||||
:type apply_link: boolean
|
||||
:param resolution: resolution of interpolation (how many points to interpolate of the posterior).
|
||||
:type resolution: int
|
||||
:param: patch_kw: the keyword arguments for the patchcollection fill.
|
||||
"""
|
||||
#deal with optional arguments
|
||||
if hasattr(self, 'has_uncertain_inputs') and self.has_uncertain_inputs():
|
||||
X = self.X.mean
|
||||
else:
|
||||
X = model.X
|
||||
Y = model.Y
|
||||
X = self.X
|
||||
Y = self.Y
|
||||
if sparse.issparse(Y): Y = Y.todense().view(np.ndarray)
|
||||
|
||||
if predict_kw is None:
|
||||
|
|
@ -321,13 +343,13 @@ def plot_density(model, levels=20, plot_limits=None, fignum=None, ax=None,
|
|||
|
||||
#work out what the inputs are for plotting (1D or 2D)
|
||||
fixed_dims = np.array([i for i,v in fixed_inputs])
|
||||
free_dims = np.setdiff1d(np.arange(model.input_dim),fixed_dims)
|
||||
free_dims = np.setdiff1d(np.arange(self.input_dim),fixed_dims)
|
||||
plots = {}
|
||||
#one dimensional plotting
|
||||
if len(free_dims) == 1:
|
||||
#define the frame on which to plot
|
||||
Xnew, xmin, xmax = x_frame1D(X[:,free_dims], plot_limits=plot_limits, resolution=resolution)
|
||||
Xgrid = np.empty((Xnew.shape[0],model.input_dim))
|
||||
Xgrid = np.empty((Xnew.shape[0],self.input_dim))
|
||||
Xgrid[:,free_dims] = Xnew
|
||||
for i,v in fixed_inputs:
|
||||
Xgrid[:,i] = v
|
||||
|
|
@ -340,32 +362,32 @@ def plot_density(model, levels=20, plot_limits=None, fignum=None, ax=None,
|
|||
from ...likelihoods import Gaussian
|
||||
lik = Gaussian(variance=0)
|
||||
else:
|
||||
if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression):
|
||||
if isinstance(self,GPCoregionalizedRegression) or isinstance(self,SparseGPCoregionalizedRegression):
|
||||
extra_data = Xgrid[:,-1:].astype(np.int)
|
||||
if Y_metadata is None:
|
||||
Y_metadata = {'output_index': extra_data}
|
||||
else:
|
||||
Y_metadata['output_index'] = extra_data
|
||||
lik = None
|
||||
percentiles = [i[:, 0] for i in model.predict_quantiles(Xgrid, percs, Y_metadata=Y_metadata, likelihood=lik, **predict_kw)]
|
||||
percentiles = [i[:, 0] for i in self.predict_quantiles(Xgrid, percs, Y_metadata=Y_metadata, likelihood=lik, **predict_kw)]
|
||||
if apply_link:
|
||||
percentiles = model.likelihood.gp_link.transf(percentiles)
|
||||
percentiles = self.likelihood.gp_link.transf(percentiles)
|
||||
|
||||
patch_kwargs['facecolor'] = facecolor
|
||||
patch_kwargs['edgecolor'] = edgecolor
|
||||
plots['density'] = plot_gradient_fill(ax, Xgrid[:, 0], percentiles, **patch_kwargs)
|
||||
plots['density'] = gradient_fill(Xgrid[:, 0], percentiles, **patch_kwargs)
|
||||
else:
|
||||
raise NotImplementedError('Only 1D density plottable.')
|
||||
return plots
|
||||
|
||||
def plot_fit_f(model, *args, **kwargs):
|
||||
"""
|
||||
Plot the GP's view of the world, where the data is normalized and before applying a likelihood.
|
||||
|
||||
All args and kwargs are passed on to models_plots.plot.
|
||||
"""
|
||||
kwargs['plot_raw'] = True
|
||||
plot_fit(model,*args, **kwargs)
|
||||
@wraps(plot_fit)
|
||||
def plot_fit_f(self, plot_limits=None, which_data_rows='all',
|
||||
which_data_ycols='all', fixed_inputs=[],
|
||||
levels=20, samples=0, fignum=None, ax=None, resolution=None,
|
||||
plot_raw=True,
|
||||
linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'], Y_metadata=None, data_symbol='kx',
|
||||
apply_link=False, samples_y=0, plot_uncertain_inputs=True, predict_kw=None, plot_training_data=True):
|
||||
return plot_fit(self, plot_limits, which_data_rows, which_data_ycols, fixed_inputs, levels, samples, fignum, ax, resolution, plot_raw, linecol, fillcol, Y_metadata, data_symbol, apply_link, samples_y, plot_uncertain_inputs, predict_kw, plot_training_data)
|
||||
|
||||
def fixed_inputs(model, non_fixed_inputs, fix_routine='median', as_list=True, X_all=False):
|
||||
"""
|
||||
|
|
@ -465,7 +487,7 @@ def plot_errorbars_trainset(model, which_data_rows='all',
|
|||
for d in which_data_ycols:
|
||||
plots['gperrors'] = gperrors(X, m[:, d], lower[:, d], upper[:, d], edgecol=linecol, ax=ax, fignum=fignum, **kwargs )
|
||||
if plot_training_data:
|
||||
plots['dataplot'] = plot_data(model=model, which_data_rows=which_data_rows,
|
||||
plots['dataplot'] = plot_data(self=model, which_data_rows=which_data_rows,
|
||||
visible_dims=free_dims, data_symbol=data_symbol, mew=1.5, ax=ax, fignum=fignum)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,331 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# netpbmfile.py
|
||||
|
||||
# Copyright (c) 2011-2013, Christoph Gohlke
|
||||
# Copyright (c) 2011-2013, The Regents of the University of California
|
||||
# Produced at the Laboratory for Fluorescence Dynamics.
|
||||
# All rights reserved.
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# * Redistributions of source code must retain the above copyright
|
||||
# notice, this list of conditions and the following disclaimer.
|
||||
# * Redistributions in binary form must reproduce the above copyright
|
||||
# notice, this list of conditions and the following disclaimer in the
|
||||
# documentation and/or other materials provided with the distribution.
|
||||
# * Neither the name of the copyright holders nor the names of any
|
||||
# contributors may be used to endorse or promote products derived
|
||||
# from this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
||||
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
|
||||
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
|
||||
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
|
||||
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
||||
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
|
||||
# CONTRACT, STRICT LIABILITY, 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.
|
||||
|
||||
"""Read and write image data from respectively to Netpbm files.
|
||||
|
||||
This implementation follows the Netpbm format specifications at
|
||||
http://netpbm.sourceforge.net/doc/. No gamma correction is performed.
|
||||
|
||||
The following image formats are supported: PBM (bi-level), PGM (grayscale),
|
||||
PPM (color), PAM (arbitrary), XV thumbnail (RGB332, read-only).
|
||||
|
||||
:Author:
|
||||
`Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/>`_
|
||||
|
||||
:Organization:
|
||||
Laboratory for Fluorescence Dynamics, University of California, Irvine
|
||||
|
||||
:Version: 2013.01.18
|
||||
|
||||
Requirements
|
||||
------------
|
||||
* `CPython 2.7, 3.2 or 3.3 <http://www.python.org>`_
|
||||
* `Numpy 1.7 <http://www.numpy.org>`_
|
||||
* `Matplotlib 1.2 <http://www.matplotlib.org>`_ (optional for plotting)
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> im1 = numpy.array([[0, 1],[65534, 65535]], dtype=numpy.uint16)
|
||||
>>> imsave('_tmp.pgm', im1)
|
||||
>>> im2 = imread('_tmp.pgm')
|
||||
>>> assert numpy.all(im1 == im2)
|
||||
|
||||
"""
|
||||
|
||||
from __future__ import division, print_function
|
||||
|
||||
import sys
|
||||
import re
|
||||
import math
|
||||
from copy import deepcopy
|
||||
|
||||
import numpy
|
||||
|
||||
__version__ = '2013.01.18'
|
||||
__docformat__ = 'restructuredtext en'
|
||||
__all__ = ['imread', 'imsave', 'NetpbmFile']
|
||||
|
||||
|
||||
def imread(filename, *args, **kwargs):
|
||||
"""Return image data from Netpbm file as numpy array.
|
||||
|
||||
`args` and `kwargs` are arguments to NetpbmFile.asarray().
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> image = imread('_tmp.pgm')
|
||||
|
||||
"""
|
||||
try:
|
||||
netpbm = NetpbmFile(filename)
|
||||
image = netpbm.asarray()
|
||||
finally:
|
||||
netpbm.close()
|
||||
return image
|
||||
|
||||
|
||||
def imsave(filename, data, maxval=None, pam=False):
|
||||
"""Write image data to Netpbm file.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> image = numpy.array([[0, 1],[65534, 65535]], dtype=numpy.uint16)
|
||||
>>> imsave('_tmp.pgm', image)
|
||||
|
||||
"""
|
||||
try:
|
||||
netpbm = NetpbmFile(data, maxval=maxval)
|
||||
netpbm.write(filename, pam=pam)
|
||||
finally:
|
||||
netpbm.close()
|
||||
|
||||
|
||||
class NetpbmFile(object):
|
||||
"""Read and write Netpbm PAM, PBM, PGM, PPM, files."""
|
||||
|
||||
_types = {b'P1': b'BLACKANDWHITE', b'P2': b'GRAYSCALE', b'P3': b'RGB',
|
||||
b'P4': b'BLACKANDWHITE', b'P5': b'GRAYSCALE', b'P6': b'RGB',
|
||||
b'P7 332': b'RGB', b'P7': b'RGB_ALPHA'}
|
||||
|
||||
def __init__(self, arg=None, **kwargs):
|
||||
"""Initialize instance from filename, open file, or numpy array."""
|
||||
for attr in ('header', 'magicnum', 'width', 'height', 'maxval',
|
||||
'depth', 'tupltypes', '_filename', '_fh', '_data'):
|
||||
setattr(self, attr, None)
|
||||
if arg is None:
|
||||
self._fromdata([], **kwargs)
|
||||
elif isinstance(arg, basestring):
|
||||
self._fh = open(arg, 'rb')
|
||||
self._filename = arg
|
||||
self._fromfile(self._fh, **kwargs)
|
||||
elif hasattr(arg, 'seek'):
|
||||
self._fromfile(arg, **kwargs)
|
||||
self._fh = arg
|
||||
else:
|
||||
self._fromdata(arg, **kwargs)
|
||||
|
||||
def asarray(self, copy=True, cache=False, **kwargs):
|
||||
"""Return image data from file as numpy array."""
|
||||
data = self._data
|
||||
if data is None:
|
||||
data = self._read_data(self._fh, **kwargs)
|
||||
if cache:
|
||||
self._data = data
|
||||
else:
|
||||
return data
|
||||
return deepcopy(data) if copy else data
|
||||
|
||||
def write(self, arg, **kwargs):
|
||||
"""Write instance to file."""
|
||||
if hasattr(arg, 'seek'):
|
||||
self._tofile(arg, **kwargs)
|
||||
else:
|
||||
with open(arg, 'wb') as fid:
|
||||
self._tofile(fid, **kwargs)
|
||||
|
||||
def close(self):
|
||||
"""Close open file. Future asarray calls might fail."""
|
||||
if self._filename and self._fh:
|
||||
self._fh.close()
|
||||
self._fh = None
|
||||
|
||||
def __del__(self):
|
||||
self.close()
|
||||
|
||||
def _fromfile(self, fh):
|
||||
"""Initialize instance from open file."""
|
||||
fh.seek(0)
|
||||
data = fh.read(4096)
|
||||
if (len(data) < 7) or not (b'0' < data[1:2] < b'8'):
|
||||
raise ValueError("Not a Netpbm file:\n%s" % data[:32])
|
||||
try:
|
||||
self._read_pam_header(data)
|
||||
except Exception:
|
||||
try:
|
||||
self._read_pnm_header(data)
|
||||
except Exception:
|
||||
raise ValueError("Not a Netpbm file:\n%s" % data[:32])
|
||||
|
||||
def _read_pam_header(self, data):
|
||||
"""Read PAM header and initialize instance."""
|
||||
regroups = re.search(
|
||||
b"(^P7[\n\r]+(?:(?:[\n\r]+)|(?:#.*)|"
|
||||
b"(HEIGHT\s+\d+)|(WIDTH\s+\d+)|(DEPTH\s+\d+)|(MAXVAL\s+\d+)|"
|
||||
b"(?:TUPLTYPE\s+\w+))*ENDHDR\n)", data).groups()
|
||||
self.header = regroups[0]
|
||||
self.magicnum = b'P7'
|
||||
for group in regroups[1:]:
|
||||
key, value = group.split()
|
||||
setattr(self, unicode(key).lower(), int(value))
|
||||
matches = re.findall(b"(TUPLTYPE\s+\w+)", self.header)
|
||||
self.tupltypes = [s.split(None, 1)[1] for s in matches]
|
||||
|
||||
def _read_pnm_header(self, data):
|
||||
"""Read PNM header and initialize instance."""
|
||||
bpm = data[1:2] in b"14"
|
||||
regroups = re.search(b"".join((
|
||||
b"(^(P[123456]|P7 332)\s+(?:#.*[\r\n])*",
|
||||
b"\s*(\d+)\s+(?:#.*[\r\n])*",
|
||||
b"\s*(\d+)\s+(?:#.*[\r\n])*" * (not bpm),
|
||||
b"\s*(\d+)\s(?:\s*#.*[\r\n]\s)*)")), data).groups() + (1, ) * bpm
|
||||
self.header = regroups[0]
|
||||
self.magicnum = regroups[1]
|
||||
self.width = int(regroups[2])
|
||||
self.height = int(regroups[3])
|
||||
self.maxval = int(regroups[4])
|
||||
self.depth = 3 if self.magicnum in b"P3P6P7 332" else 1
|
||||
self.tupltypes = [self._types[self.magicnum]]
|
||||
|
||||
def _read_data(self, fh, byteorder='>'):
|
||||
"""Return image data from open file as numpy array."""
|
||||
fh.seek(len(self.header))
|
||||
data = fh.read()
|
||||
dtype = 'u1' if self.maxval < 256 else byteorder + 'u2'
|
||||
depth = 1 if self.magicnum == b"P7 332" else self.depth
|
||||
shape = [-1, self.height, self.width, depth]
|
||||
size = numpy.prod(shape[1:])
|
||||
if self.magicnum in b"P1P2P3":
|
||||
data = numpy.array(data.split(None, size)[:size], dtype)
|
||||
data = data.reshape(shape)
|
||||
elif self.maxval == 1:
|
||||
shape[2] = int(math.ceil(self.width / 8))
|
||||
data = numpy.frombuffer(data, dtype).reshape(shape)
|
||||
data = numpy.unpackbits(data, axis=-2)[:, :, :self.width, :]
|
||||
else:
|
||||
data = numpy.frombuffer(data, dtype)
|
||||
data = data[:size * (data.size // size)].reshape(shape)
|
||||
if data.shape[0] < 2:
|
||||
data = data.reshape(data.shape[1:])
|
||||
if data.shape[-1] < 2:
|
||||
data = data.reshape(data.shape[:-1])
|
||||
if self.magicnum == b"P7 332":
|
||||
rgb332 = numpy.array(list(numpy.ndindex(8, 8, 4)), numpy.uint8)
|
||||
rgb332 *= [36, 36, 85]
|
||||
data = numpy.take(rgb332, data, axis=0)
|
||||
return data
|
||||
|
||||
def _fromdata(self, data, maxval=None):
|
||||
"""Initialize instance from numpy array."""
|
||||
data = numpy.array(data, ndmin=2, copy=True)
|
||||
if data.dtype.kind not in "uib":
|
||||
raise ValueError("not an integer type: %s" % data.dtype)
|
||||
if data.dtype.kind == 'i' and numpy.min(data) < 0:
|
||||
raise ValueError("data out of range: %i" % numpy.min(data))
|
||||
if maxval is None:
|
||||
maxval = numpy.max(data)
|
||||
maxval = 255 if maxval < 256 else 65535
|
||||
if maxval < 0 or maxval > 65535:
|
||||
raise ValueError("data out of range: %i" % maxval)
|
||||
data = data.astype('u1' if maxval < 256 else '>u2')
|
||||
self._data = data
|
||||
if data.ndim > 2 and data.shape[-1] in (3, 4):
|
||||
self.depth = data.shape[-1]
|
||||
self.width = data.shape[-2]
|
||||
self.height = data.shape[-3]
|
||||
self.magicnum = b'P7' if self.depth == 4 else b'P6'
|
||||
else:
|
||||
self.depth = 1
|
||||
self.width = data.shape[-1]
|
||||
self.height = data.shape[-2]
|
||||
self.magicnum = b'P5' if maxval > 1 else b'P4'
|
||||
self.maxval = maxval
|
||||
self.tupltypes = [self._types[self.magicnum]]
|
||||
self.header = self._header()
|
||||
|
||||
def _tofile(self, fh, pam=False):
|
||||
"""Write Netbm file."""
|
||||
fh.seek(0)
|
||||
fh.write(self._header(pam))
|
||||
data = self.asarray(copy=False)
|
||||
if self.maxval == 1:
|
||||
data = numpy.packbits(data, axis=-1)
|
||||
data.tofile(fh)
|
||||
|
||||
def _header(self, pam=False):
|
||||
"""Return file header as byte string."""
|
||||
if pam or self.magicnum == b'P7':
|
||||
header = "\n".join((
|
||||
"P7",
|
||||
"HEIGHT %i" % self.height,
|
||||
"WIDTH %i" % self.width,
|
||||
"DEPTH %i" % self.depth,
|
||||
"MAXVAL %i" % self.maxval,
|
||||
"\n".join("TUPLTYPE %s" % unicode(i) for i in self.tupltypes),
|
||||
"ENDHDR\n"))
|
||||
elif self.maxval == 1:
|
||||
header = "P4 %i %i\n" % (self.width, self.height)
|
||||
elif self.depth == 1:
|
||||
header = "P5 %i %i %i\n" % (self.width, self.height, self.maxval)
|
||||
else:
|
||||
header = "P6 %i %i %i\n" % (self.width, self.height, self.maxval)
|
||||
if sys.version_info[0] > 2:
|
||||
header = bytes(header, 'ascii')
|
||||
return header
|
||||
|
||||
def __str__(self):
|
||||
"""Return information about instance."""
|
||||
return unicode(self.header)
|
||||
|
||||
|
||||
if sys.version_info[0] > 2:
|
||||
basestring = str
|
||||
unicode = lambda x: str(x, 'ascii')
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Show images specified on command line or all images in current directory
|
||||
from glob import glob
|
||||
from matplotlib import pyplot
|
||||
files = sys.argv[1:] if len(sys.argv) > 1 else glob('*.p*m')
|
||||
for fname in files:
|
||||
try:
|
||||
pam = NetpbmFile(fname)
|
||||
img = pam.asarray(copy=False)
|
||||
if False:
|
||||
pam.write('_tmp.pgm.out', pam=True)
|
||||
img2 = imread('_tmp.pgm.out')
|
||||
assert numpy.all(img == img2)
|
||||
imsave('_tmp.pgm.out', img)
|
||||
img2 = imread('_tmp.pgm.out')
|
||||
assert numpy.all(img == img2)
|
||||
pam.close()
|
||||
except ValueError as e:
|
||||
print(fname, e)
|
||||
continue
|
||||
_shape = img.shape
|
||||
if img.ndim > 3 or (img.ndim > 2 and img.shape[-1] not in (3, 4)):
|
||||
img = img[0]
|
||||
cmap = 'gray' if pam.maxval > 1 else 'binary'
|
||||
pyplot.imshow(img, cmap, interpolation='nearest')
|
||||
pyplot.title("%s %s %s %s" % (fname, unicode(pam.magicnum),
|
||||
_shape, img.dtype))
|
||||
pyplot.show()
|
||||
2
setup.py
2
setup.py
|
|
@ -18,7 +18,7 @@
|
|||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# * Neither the name of paramax nor the names of its
|
||||
# * Neither the name of GPy nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@
|
|||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# * Neither the name of paramax nor the names of its
|
||||
# * Neither the name of GPy nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
|
|
@ -32,8 +32,8 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
import matplotlib
|
||||
matplotlib.use('svg')
|
||||
matplotlib.use('agg')
|
||||
|
||||
import nose
|
||||
nose.main('GPy', defaultTest='GPy/testing')
|
||||
nose.main('GPy', defaultTest='GPy/testing/plotting_tests.py')
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue