matthias muntwiler
acea809e4e
based on internal repository c9a2ac8 2019-01-03 16:04:57 +0100 tagged rev-master-2.0.0
199 lines
7.9 KiB
Python
199 lines
7.9 KiB
Python
"""
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@package pmsco.graphics.rfactor
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graphics rendering module for r-factor optimization results.
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this module is under development.
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interface and implementation are subject to change.
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@author Matthias Muntwiler, matthias.muntwiler@psi.ch
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@copyright (c) 2018 by Paul Scherrer Institut @n
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Licensed under the Apache License, Version 2.0 (the "License"); @n
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import logging
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import math
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import numpy as np
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from pmsco.helpers import BraceMessage as BMsg
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logger = logging.getLogger(__name__)
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try:
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from matplotlib.figure import Figure
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from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
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except ImportError:
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Figure = None
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FigureCanvas = None
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logger.warning("error importing matplotlib. graphics rendering disabled.")
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def render_param_rfac(filename, data, param_name, summary=None, canvas=None):
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"""
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render an r-factor versus one model parameter graph.
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the default file format is PNG.
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this function requires the matplotlib module.
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if it is not available, the function raises an error.
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@param filename: path and name of the results file.
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this is used to derive the output file path by adding the parameter name and
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the extension of the graphics file format.
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@param data: numpy-structured array of results (one-dimensional).
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the field names identify the model parameters and optimization control values.
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model parameters can have any name not including a leading underscore and are evaluated as is.
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the names of the special optimization control values begin with an underscore.
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of these, at least _rfac must be provided.
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@param param_name: name of the model parameter to display.
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this must correspond to a field name of the data array.
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@param summary: (dict) the dictionary returned by @ref evaluate_results.
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this is used to mark the optimum value and the error limits.
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if None, these values are not marked in the plot.
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@param canvas: a FigureCanvas class reference from a matplotlib backend.
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if None, the default FigureCanvasAgg is used which produces a bitmap file in PNG format.
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@return (str) path and name of the generated graphics file.
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empty string if an error occurred.
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@raise TypeError if matplotlib is not available.
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"""
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if canvas is None:
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canvas = FigureCanvas
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fig = Figure()
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canvas(fig)
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ax = fig.add_subplot(111)
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ax.scatter(data[param_name], data['_rfac'], c='b', marker='o', s=4.0)
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if summary is not None:
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xval = summary['val'][param_name]
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ymin = summary['vmin']['_rfac']
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ymax = summary['vmax']['_rfac']
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ax.plot((xval, xval), (ymin, ymax), ':k')
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xmin = summary['vmin'][param_name]
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xmax = summary['vmax'][param_name]
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varr = summary['rmin'] + summary['rvar']
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ax.plot((xmin, xmax), (varr, varr), ':k')
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ax.grid(True)
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ax.set_xlabel(param_name)
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ax.set_ylabel('R-factor')
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out_filename = "{0}.{1}.{2}".format(filename, param_name, canvas.get_default_filetype())
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fig.savefig(out_filename)
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return out_filename
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def evaluate_results(data, features=50.):
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"""
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@param data: numpy-structured array of results (one-dimensional).
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the field names identify the model parameters and optimization control values.
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model parameters can have any name not including a leading underscore and are evaluated as is.
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the names of the special optimization control values begin with an underscore.
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of these, at least _rfac must be provided.
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@param features: number of independent features (pieces of information) in the data.
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this quantity can be approximated as the scan range divided by the average width of a feature
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which includes an intrinsic component and the instrumental resolution.
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see Booth et al., Surf. Sci. 387 (1997), 152 for energy scans, and
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Muntwiler et al., Surf. Sci. 472 (2001), 125 for angle scans.
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the default value of 50 is a typical value.
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@return dictionary of evaluation results.
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the dictionary contains scalars and structured arrays as follows.
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the structured arrays have the same data type as the input data and contain exactly one element.
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@arg rmin: (scalar) minimum r-factor.
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@arg rvar: (scalar) one-sigma variation of r-factor.
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@arg imin: (scalar) array index where the minimum is located.
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@arg val: (structured array) estimates of parameter values (parameter value at rmin).
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@arg sig: (structured array) one-sigma error of estimated values.
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@arg vmin: (structured array) minimum value of the parameter.
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@arg vmax: (structured array) maximum value of the parameter.
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"""
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imin = data['_rfac'].argmin()
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rmin = data['_rfac'][imin]
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rvar = rmin * math.sqrt(2. / float(features))
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val = np.zeros(1, dtype=data.dtype)
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sig = np.zeros(1, dtype=data.dtype)
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vmin = np.zeros(1, dtype=data.dtype)
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vmax = np.zeros(1, dtype=data.dtype)
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sel = data['_rfac'] <= rmin + rvar
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for name in data.dtype.names:
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val[name] = data[name][imin]
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vmin[name] = data[name].min()
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vmax[name] = data[name].max()
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if name[0] != '_':
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sig[name] = (data[name][sel].max() - data[name][sel].min()) / 2.
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results = {'rmin': rmin, 'rvar': rvar, 'imin': imin, 'val': val, 'sig': sig, 'vmin': vmin, 'vmax': vmax}
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return results
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def render_results(results_file, data=None):
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"""
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produce a graphics file from optimization results.
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the results can be passed in a file name or numpy array (see parameter section).
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the default file format is PNG.
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this function requires the matplotlib module.
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if it is not available, the function will log a warning message and return gracefully.
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@param results_file: path and name of the result file.
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result files are the ones written by swarm.SwarmPopulation.save_array, for instance.
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the file contains columns of model parameters and optimization control values.
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the first row must contain column names that identify the quantity.
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model parameters can have any name not including a leading underscore and are evaluated as is.
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the names of the special optimization control values begin with an underscore.
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of these, at least _rfac must be provided.
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if the optional data parameter is present,
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this is used only to derive the output file path by adding the extension of the graphics file format.
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@param data: numpy-structured array of results (one-dimensional).
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the field names identify the model parameters and optimization control values.
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model parameters can have any name not including a leading underscore and are evaluated as is.
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the names of the special optimization control values begin with an underscore.
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of these, at least _rfac must be provided.
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if this argument is omitted, the data is loaded from the file referenced by the filename argument.
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@return (list of str) path names of the generated graphics files.
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empty if an error occurred.
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the most common exceptions are caught and add a warning in the log file.
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"""
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if data is None:
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data = np.genfromtxt(results_file, names=True)
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summary = evaluate_results(data)
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out_files = []
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try:
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for name in data.dtype.names:
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if name[0] != '_' and summary['sig'][name] > 0.:
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graph_file = render_param_rfac(results_file, data, name, summary)
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out_files.append(graph_file)
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except (TypeError, AttributeError, IOError) as e:
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logger.warning(BMsg("error rendering scan file {file}: {msg}", file=results_file, msg=str(e)))
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return out_files
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