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Added default plotting using Matplotlib
If the nexusformat module is being used outside NeXpy, the NXfield and NXgroup plotting methods use a standard Matplotlib plotting window.
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| #!/usr/bin/env python | ||
| # -*- coding: utf-8 -*- | ||
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| #----------------------------------------------------------------------------- | ||
| # Copyright (c) 2013, NeXpy Development Team. | ||
| # | ||
| # Author: Paul Kienzle, Ray Osborn | ||
| # | ||
| # Distributed under the terms of the Modified BSD License. | ||
| # | ||
| # The full license is in the file COPYING, distributed with this software. | ||
| #----------------------------------------------------------------------------- | ||
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| """ | ||
| This module provides a standard Matplotlib plotting option to the NeXus Python | ||
| API | ||
| """ | ||
| import numpy as np | ||
| from nexusformat.nexus import NXfield | ||
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| def _fixaxes(signal, axes): | ||
| """ | ||
| Remove length-one dimensions from plottable data | ||
| """ | ||
| shape = list(signal.shape) | ||
| while 1 in shape: shape.remove(1) | ||
| newaxes = [] | ||
| for axis in axes: | ||
| if axis.size > 1: newaxes.append(axis) | ||
| return signal.nxdata.view().reshape(shape), newaxes | ||
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| def centers(signal, axes): | ||
| """ | ||
| Return the centers of the axes. | ||
| This works regardless if the axes contain bin boundaries or centers. | ||
| """ | ||
| def findc(axis, dimlen): | ||
| if axis.shape[0] == dimlen+1: | ||
| return (axis.nxdata[:-1] + axis.nxdata[1:])/2 | ||
| else: | ||
| assert axis.shape[0] == dimlen | ||
| return axis.nxdata | ||
| return [findc(a,signal.shape[i]) for i,a in enumerate(axes)] | ||
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| def label(field): | ||
| """ | ||
| Return a label for a data field suitable for use on a graph axis. | ||
| """ | ||
| if hasattr(field, 'long_name'): | ||
| return field.long_name | ||
| elif hasattr(field, 'units'): | ||
| return "%s (%s)"%(field.nxname, field.units) | ||
| else: | ||
| return field.nxname | ||
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| class PylabPlotter(object): | ||
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| """ | ||
| Matplotlib plotter class for NeXus data. | ||
| """ | ||
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| def plot(self, data, fmt, xmin, xmax, ymin, ymax, zmin, zmax, **opts): | ||
| """ | ||
| Plot the data entry. | ||
| Raises NeXusError if the data cannot be plotted. | ||
| """ | ||
| try: | ||
| import matplotlib.pyplot as plt | ||
| except ImportError: | ||
| raise NeXusError("Default plotting package (matplotlib) not available.") | ||
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| over = False | ||
| if "over" in opts.keys(): | ||
| if opts["over"]: | ||
| over = True | ||
| del opts["over"] | ||
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| log = logx = logy = False | ||
| if "log" in opts.keys(): | ||
| if opts["log"]: log = True | ||
| del opts["log"] | ||
| if "logy" in opts.keys(): | ||
| if opts["logy"]: logy = True | ||
| del opts["logy"] | ||
| if "logx" in opts.keys(): | ||
| if opts["logx"]: logx = True | ||
| del opts["logx"] | ||
| if fmt == '': | ||
| fmt = 'o' | ||
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| if over: | ||
| plt.autoscale(enable=False) | ||
| else: | ||
| plt.autoscale(enable=True) | ||
| plt.clf() | ||
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| signal = data.nxsignal | ||
| axes = data.nxaxes | ||
| errors = data.nxerrors | ||
| title = data.nxtitle | ||
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| # Provide a new view of the data if there is a dimension of length 1 | ||
| if 1 in signal.shape: | ||
| data, axes = _fixaxes(signal, axes) | ||
| else: | ||
| data = signal.nxdata | ||
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| # Find the centers of the bins for histogrammed data | ||
| axis_data = centers(data, axes) | ||
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| #One-dimensional Plot | ||
| if len(data.shape) == 1: | ||
| if hasattr(signal, 'units'): | ||
| if not errors and signal.units == 'counts': | ||
| errors = NXfield(np.sqrt(data)) | ||
| if errors: | ||
| ebars = errors.nxdata | ||
| plt.errorbar(axis_data[0], data, ebars, fmt=fmt, **opts) | ||
| else: | ||
| plt.plot(axis_data[0], data, fmt, **opts) | ||
| if not over: | ||
| ax = plt.gca() | ||
| xlo, xhi = ax.set_xlim(auto=True) | ||
| ylo, yhi = ax.set_ylim(auto=True) | ||
| if xmin: xlo = xmin | ||
| if xmax: xhi = xmax | ||
| ax.set_xlim(xlo, xhi) | ||
| if ymin: ylo = ymin | ||
| if ymax: yhi = ymax | ||
| ax.set_ylim(ylo, yhi) | ||
| if logx: ax.set_xscale('symlog') | ||
| if log or logy: ax.set_yscale('symlog') | ||
| plt.xlabel(label(axes[0])) | ||
| plt.ylabel(label(signal)) | ||
| plt.title(title) | ||
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| #Two dimensional plot | ||
| else: | ||
| from matplotlib.image import NonUniformImage | ||
| from matplotlib.colors import LogNorm, Normalize | ||
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| if len(data.shape) > 2: | ||
| slab = [slice(None), slice(None)] | ||
| for _dim in data.shape[2:]: | ||
| slab.append(0) | ||
| data = data[slab].view().reshape(data.shape[:2]) | ||
| print "Warning: Only the top 2D slice of the data is plotted" | ||
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| x = axis_data[1] | ||
| y = axis_data[0] | ||
| if not zmin: | ||
| zmin = np.nanmin(data) | ||
| if not zmax: | ||
| zmax = np.nanmax(data) | ||
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| if log: | ||
| zmin = max(zmin, 0.01) | ||
| zmax = max(zmax, 0.01) | ||
| opts["norm"] = LogNorm(zmin, zmax) | ||
| else: | ||
| opts["norm"] = Normalize(zmin, zmax) | ||
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| ax = plt.gca() | ||
| extent = (x[0],x[-1],y[0],y[-1]) | ||
| im = NonUniformImage(ax, extent=extent, origin=None, **opts) | ||
| im.set_data(x, y, data) | ||
| im.get_cmap().set_bad('k', 1.0) | ||
| ax.images.append(im) | ||
| xlo, xhi = ax.set_xlim(x[0], x[-1]) | ||
| ylo, yhi = ax.set_ylim(y[0], y[-1]) | ||
| if xmin: | ||
| xlo = xmin | ||
| else: | ||
| xlo = x[0] | ||
| if xmax: | ||
| xhi = xmax | ||
| else: | ||
| xhi = x[-1] | ||
| if ymin: | ||
| yhi = ymin | ||
| else: | ||
| yhi = y[0] | ||
| if ymax: | ||
| yhi = ymax | ||
| else: | ||
| yhi = y[-1] | ||
| ax.set_xlim(xlo, xhi) | ||
| ax.set_ylim(ylo, yhi) | ||
| plt.xlabel(label(axes[0])) | ||
| plt.ylabel(label(axes[1])) | ||
| plt.title(title) | ||
| plt.colorbar(im) | ||
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| plt.gcf().canvas.draw_idle() | ||
| plt.ion() | ||
| plt.show() | ||
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| plotview = PylabPlotter() | ||
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