diff --git a/Wrap/Python/fit_monitor.py b/Wrap/Python/fit_monitor.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed4758193a550adcbfd20fe3b0986755c2ae777e
--- /dev/null
+++ b/Wrap/Python/fit_monitor.py
@@ -0,0 +1,250 @@
+# ************************************************************************** #
+"""
+# BornAgain: simulate and fit scattering at grazing incidence
+#
+# @file Wrap/Python/fit_monitor.py
+# @brief Plotter classes for monitoring fit progress.
+#
+# @homepage http://apps.jcns.fz-juelich.de/BornAgain
+# @license GNU General Public License v3 or higher (see COPYING)
+# @copyright Forschungszentrum Juelich GmbH 2019
+# @authors Scientific Computing Group at MLZ (see CITATION, AUTHORS)
+"""
+# ************************************************************************** #
+
+import plot_utils
+
+class Plotter:
+ """
+ Draws fit progress. Base class for simulation-specific classes (PlotterGISAS etc).
+ """
+ def __init__(self,
+ zmin=None,
+ zmax=None,
+ xlabel=None,
+ ylabel=None,
+ units=ba.Axes.DEFAULT,
+ aspect=None):
+
+ self._fig = plt.figure(figsize=(10.25, 7.69))
+ self._fig.canvas.draw()
+ self._zmin = zmin
+ self._zmax = zmax
+ self._xlabel = xlabel
+ self._ylabel = ylabel
+ self._units = units
+ self._aspect = aspect
+
+ def reset(self):
+ self._fig.clf()
+
+ def plot(self):
+ self._fig.tight_layout()
+ plt.pause(0.03)
+
+
+class PlotterGISAS(Plotter):
+ """
+ Draws fit progress, for GISAS simulation.
+ """
+ def __init__(self,
+ zmin=None,
+ zmax=None,
+ xlabel=None,
+ ylabel=None,
+ units=ba.Axes.DEFAULT,
+ aspect=None):
+ Plotter.__init__(self, zmin, zmax, xlabel, ylabel, units, aspect)
+
+ @staticmethod
+ def make_subplot(nplot):
+ plt.subplot(2, 2, nplot)
+ plt.subplots_adjust(wspace=0.2, hspace=0.2)
+
+ def plot(self, fit_objective):
+ Plotter.reset(self)
+
+ real_data = fit_objective.experimentalData()
+ sim_data = fit_objective.simulationResult()
+ diff = fit_objective.absoluteDifference()
+
+ self.make_subplot(1)
+
+ # same limits for both plots
+ arr = real_data.array()
+ zmax = np.amax(arr) if self._zmax is None else self._zmax
+ zmin = zmax*1e-6 if self._zmin is None else self._zmin
+
+ ba.plot_colormap(real_data,
+ title="Experimental data",
+ zmin=zmin,
+ zmax=zmax,
+ units=self._units,
+ xlabel=self._xlabel,
+ ylabel=self._ylabel,
+ zlabel='',
+ aspect=self._aspect)
+
+ self.make_subplot(2)
+ ba.plot_colormap(sim_data,
+ title="Simulated data",
+ zmin=zmin,
+ zmax=zmax,
+ units=self._units,
+ xlabel=self._xlabel,
+ ylabel=self._ylabel,
+ zlabel='',
+ aspect=self._aspect)
+
+ self.make_subplot(3)
+ ba.plot_colormap(diff,
+ title="Difference",
+ zmin=zmin,
+ zmax=zmax,
+ units=self._units,
+ xlabel=self._xlabel,
+ ylabel=self._ylabel,
+ zlabel='',
+ aspect=self._aspect)
+
+ self.make_subplot(4)
+ plt.title('Parameters')
+ plt.axis('off')
+
+ iteration_info = fit_objective.iterationInfo()
+
+ plt.text(
+ 0.01, 0.85,
+ "Iterations " + '{:d}'.format(iteration_info.iterationCount()))
+ plt.text(0.01, 0.75,
+ "Chi2 " + '{:8.4f}'.format(iteration_info.chi2()))
+ index = 0
+ params = iteration_info.parameterMap()
+ for key in params:
+ plt.text(0.01, 0.55 - index*0.1,
+ '{:30.30s}: {:6.3f}'.format(key, params[key]))
+ index = index + 1
+
+ Plotter.plot(self)
+
+
+class PlotterSpecular(Plotter):
+ """
+ Draws fit progress, for specular simulation.
+ """
+ def __init__(self, units=ba.Axes.DEFAULT):
+ Plotter.__init__(self)
+ self.gs = gridspec.GridSpec(1, 2, width_ratios=[2.5, 1], wspace=0)
+ self.units = units
+
+ def __call__(self, fit_objective):
+ self.plot(fit_objective)
+
+ @staticmethod
+ def as_si(val, ndp):
+ """
+ Fancy print of scientific-formatted values
+ :param val: numeric value
+ :param ndp: number of decimal digits to print
+ :return: a string corresponding to the _val_
+ """
+ s = '{x:0.{ndp:d}e}'.format(x=val, ndp=ndp)
+ m, e = s.split('e')
+ return r'{m:s}\times 10^{{{e:d}}}'.format(m=m, e=int(e))
+
+ @staticmethod
+ def trunc_str(token, length):
+ """
+ Truncates token if it is longer than length.
+
+ Example:
+ trunc_str("123456789", 8) returns "123456.."
+
+ trunc_str("123456789", 9) returns "123456789"
+
+ :param token: input string
+ :param length: max non-truncated length
+ :return:
+ """
+ return (token[:length - 2] + '..') if len(token) > length else token
+
+ def plot_table(self, fit_objective):
+
+ iteration_info = fit_objective.iterationInfo()
+
+ trunc_length = 9 # max string field width in the table
+ n_digits = 1 # number of decimal digits to print
+
+ n_iterations = iteration_info.iterationCount(
+ ) # current number of iterations passed
+ rel_dif = fit_objective.relativeDifference().array().max(
+ ) # maximum relative difference
+ fitted_parameters = iteration_info.parameterMap()
+
+ # creating table content
+ labels = ("Parameter", "Value")
+ table_data = [["Iteration", '${:d}$'.format(n_iterations)],
+ [
+ "$d_{r, max}$",
+ '${:s}$'.format(self.as_si(rel_dif, n_digits))
+ ]]
+ for key, value in fitted_parameters.iteritems():
+ table_data.append([
+ '{:s}'.format(self.trunc_str(key, trunc_length)),
+ '${:s}$'.format(self.as_si(value, n_digits))
+ ])
+
+ # creating table
+ axs = plt.subplot(self.gs[1])
+ axs.axis('tight')
+ axs.axis('off')
+ table = plt.table(cellText=table_data,
+ colLabels=labels,
+ cellLoc='center',
+ loc='bottom left',
+ bbox=[0.0, 0.0, 1.0, 1.0])
+
+ def plot_graph(self, fit_objective):
+ # retrieving data from fit suite
+ real_data = fit_objective.experimentalData()
+ sim_data = fit_objective.simulationResult()
+ unc_data = fit_objective.uncertaintyData()
+
+ # data values
+ sim_values = sim_data.array(self.units)
+ real_values = real_data.array(self.units)
+ unc_values = None if unc_data is None else unc_data.array(self.units)
+
+ # default font properties dictionary to use
+ font = {'family': 'serif', 'weight': 'normal', 'size': label_fontsize}
+
+ plt.subplot(self.gs[0])
+ plt.semilogy(sim_data.axis(), sim_values, 'b', real_data.axis(),
+ real_values, 'k--')
+ if unc_values is not None:
+ plt.semilogy(real_data.axis(),
+ real_values - unc_values,
+ 'xkcd:grey',
+ alpha=0.6)
+ plt.semilogy(real_data.axis(),
+ real_values + unc_values,
+ 'xkcd:grey',
+ alpha=0.6)
+ plt.ylim((0.5*np.min(real_values), 5*np.max(real_values)))
+
+ xlabel = get_axes_labels(real_data, self.units)[0]
+ legend = ['BornAgain', 'Data']
+ if unc_values is not None:
+ legend = ['BornAgain', 'Data', r'Data $\pm \sigma$']
+ plt.legend(legend, loc='upper right', prop=font)
+ plt.xlabel(xlabel, fontdict=font)
+ plt.ylabel("Intensity", fontdict=font)
+ plt.title("Specular data fitting", fontdict=font)
+
+ def plot(self, fit_objective):
+ Plotter.reset(self)
+
+ self.plot_graph(fit_objective)
+ self.plot_table(fit_objective)
+
+ Plotter.plot(self)
diff --git a/Wrap/Python/plot_utils.py b/Wrap/Python/plot_utils.py
index a19327dd31162e29775f5b03ccab21356e61d642..b1d0eb674727d0447293500f1e0c98cb8252ce5e 100644
--- a/Wrap/Python/plot_utils.py
+++ b/Wrap/Python/plot_utils.py
@@ -2,14 +2,13 @@
"""
# BornAgain: simulate and fit scattering at grazing incidence
#
-# @file Wrap/Python.plot_utils
+# @file Wrap/Python/plot_utils.py
# @brief Python extensions of the SWIG-generated Python module bornagain.
#
# @homepage http://apps.jcns.fz-juelich.de/BornAgain
# @license GNU General Public License v3 or higher (see COPYING)
# @copyright Forschungszentrum Juelich GmbH 2016
-# @authors Scientific Computing Group at MLZ Garching
-# @authors J. Fisher, M. Ganeva, G. Pospelov, W. Van Herck, J. Wuttke
+# @authors Scientific Computing Group at MLZ (see CITATION, AUTHORS)
"""
# ************************************************************************** #
@@ -48,19 +47,19 @@ def translate_axis_label(label):
"""
label_dict = {
'X [nbins]': r'$X \; $(bins)',
+ 'X [mm]': r'$X \; $(mm)',
+ 'Y [nbins]': r'$Y \; $(bins)',
+ 'Y [mm]': r'$Y \; $(mm)',
'phi_f [rad]': r'$\varphi_f \; $(rad)',
'phi_f [deg]': r'$\varphi_f \; $(deg)',
'alpha_i [rad]': r'$\alpha_i \; $(rad)',
'alpha_i [deg]': r'$\alpha_i \; $(deg)',
- 'X [mm]': r'$X \; $(mm)',
- 'Qx [1/nm]': r'$Q_x \; $(nm$^{-1}$)',
- 'Qy [1/nm]': r'$Q_y \; $(nm$^{-1}$)',
- 'Q [1/nm]': r'$Q \; $(nm$^{-1}$)',
- 'Y [nbins]': r'$Y \; $(bins)',
'alpha_f [rad]': r'$\alpha_f \; $(rad)',
'alpha_f [deg]': r'$\alpha_f \; $(deg)',
- 'Y [mm]': r'$Y \; $(mm)',
+ 'Qx [1/nm]': r'$Q_x \; $(nm$^{-1}$)',
+ 'Qy [1/nm]': r'$Q_y \; $(nm$^{-1}$)',
'Qz [1/nm]': r'$Q_z \; $(nm$^{-1}$)',
+ 'Q [1/nm]': r'$Q \; $(nm$^{-1}$)',
'Position [nm]': r'$Position \; $(nm)'
}
if label in label_dict.keys():
@@ -265,234 +264,3 @@ def plot_simulation_result(result,
plt.tight_layout()
if not postpone_show:
plt.show()
-
-
-class Plotter:
- def __init__(self,
- zmin=None,
- zmax=None,
- xlabel=None,
- ylabel=None,
- units=ba.Axes.DEFAULT,
- aspect=None):
-
- self._fig = plt.figure(figsize=(10.25, 7.69))
- self._fig.canvas.draw()
- self._zmin = zmin
- self._zmax = zmax
- self._xlabel = xlabel
- self._ylabel = ylabel
- self._units = units
- self._aspect = aspect
-
- def reset(self):
- self._fig.clf()
-
- def plot(self):
- self._fig.tight_layout()
- plt.pause(0.03)
-
-
-class PlotterGISAS(Plotter):
- def __init__(self,
- zmin=None,
- zmax=None,
- xlabel=None,
- ylabel=None,
- units=ba.Axes.DEFAULT,
- aspect=None):
- Plotter.__init__(self, zmin, zmax, xlabel, ylabel, units, aspect)
-
- @staticmethod
- def make_subplot(nplot):
- plt.subplot(2, 2, nplot)
- plt.subplots_adjust(wspace=0.2, hspace=0.2)
-
- def plot(self, fit_objective):
- Plotter.reset(self)
-
- real_data = fit_objective.experimentalData()
- sim_data = fit_objective.simulationResult()
- diff = fit_objective.absoluteDifference()
-
- self.make_subplot(1)
-
- # same limits for both plots
- arr = real_data.array()
- zmax = np.amax(arr) if self._zmax is None else self._zmax
- zmin = zmax*1e-6 if self._zmin is None else self._zmin
-
- ba.plot_colormap(real_data,
- title="Experimental data",
- zmin=zmin,
- zmax=zmax,
- units=self._units,
- xlabel=self._xlabel,
- ylabel=self._ylabel,
- zlabel='',
- aspect=self._aspect)
-
- self.make_subplot(2)
- ba.plot_colormap(sim_data,
- title="Simulated data",
- zmin=zmin,
- zmax=zmax,
- units=self._units,
- xlabel=self._xlabel,
- ylabel=self._ylabel,
- zlabel='',
- aspect=self._aspect)
-
- self.make_subplot(3)
- ba.plot_colormap(diff,
- title="Difference",
- zmin=zmin,
- zmax=zmax,
- units=self._units,
- xlabel=self._xlabel,
- ylabel=self._ylabel,
- zlabel='',
- aspect=self._aspect)
-
- self.make_subplot(4)
- plt.title('Parameters')
- plt.axis('off')
-
- iteration_info = fit_objective.iterationInfo()
-
- plt.text(
- 0.01, 0.85,
- "Iterations " + '{:d}'.format(iteration_info.iterationCount()))
- plt.text(0.01, 0.75,
- "Chi2 " + '{:8.4f}'.format(iteration_info.chi2()))
- index = 0
- params = iteration_info.parameterMap()
- for key in params:
- plt.text(0.01, 0.55 - index*0.1,
- '{:30.30s}: {:6.3f}'.format(key, params[key]))
- index = index + 1
-
- Plotter.plot(self)
-
-
-class PlotterSpecular(Plotter):
- """
- Draws fit progress. Intended specifically for observing
- specular data fit.
- """
- def __init__(self, units=ba.Axes.DEFAULT):
- Plotter.__init__(self)
- self.gs = gridspec.GridSpec(1, 2, width_ratios=[2.5, 1], wspace=0)
- self.units = units
-
- def __call__(self, fit_objective):
- self.plot(fit_objective)
-
- @staticmethod
- def as_si(val, ndp):
- """
- Fancy print of scientific-formatted values
- :param val: numeric value
- :param ndp: number of decimal digits to print
- :return: a string corresponding to the _val_
- """
- s = '{x:0.{ndp:d}e}'.format(x=val, ndp=ndp)
- m, e = s.split('e')
- return r'{m:s}\times 10^{{{e:d}}}'.format(m=m, e=int(e))
-
- @staticmethod
- def trunc_str(token, length):
- """
- Truncates token if it is longer than length.
-
- Example:
- trunc_str("123456789", 8) returns "123456.."
-
- trunc_str("123456789", 9) returns "123456789"
-
- :param token: input string
- :param length: max non-truncated length
- :return:
- """
- return (token[:length - 2] + '..') if len(token) > length else token
-
- def plot_table(self, fit_objective):
-
- iteration_info = fit_objective.iterationInfo()
-
- trunc_length = 9 # max string field width in the table
- n_digits = 1 # number of decimal digits to print
-
- n_iterations = iteration_info.iterationCount(
- ) # current number of iterations passed
- rel_dif = fit_objective.relativeDifference().array().max(
- ) # maximum relative difference
- fitted_parameters = iteration_info.parameterMap()
-
- # creating table content
- labels = ("Parameter", "Value")
- table_data = [["Iteration", '${:d}$'.format(n_iterations)],
- [
- "$d_{r, max}$",
- '${:s}$'.format(self.as_si(rel_dif, n_digits))
- ]]
- for key, value in fitted_parameters.iteritems():
- table_data.append([
- '{:s}'.format(self.trunc_str(key, trunc_length)),
- '${:s}$'.format(self.as_si(value, n_digits))
- ])
-
- # creating table
- axs = plt.subplot(self.gs[1])
- axs.axis('tight')
- axs.axis('off')
- table = plt.table(cellText=table_data,
- colLabels=labels,
- cellLoc='center',
- loc='bottom left',
- bbox=[0.0, 0.0, 1.0, 1.0])
-
- def plot_graph(self, fit_objective):
- # retrieving data from fit suite
- real_data = fit_objective.experimentalData()
- sim_data = fit_objective.simulationResult()
- unc_data = fit_objective.uncertaintyData()
-
- # data values
- sim_values = sim_data.array(self.units)
- real_values = real_data.array(self.units)
- unc_values = None if unc_data is None else unc_data.array(self.units)
-
- # default font properties dictionary to use
- font = {'family': 'serif', 'weight': 'normal', 'size': label_fontsize}
-
- plt.subplot(self.gs[0])
- plt.semilogy(sim_data.axis(), sim_values, 'b', real_data.axis(),
- real_values, 'k--')
- if unc_values is not None:
- plt.semilogy(real_data.axis(),
- real_values - unc_values,
- 'xkcd:grey',
- alpha=0.6)
- plt.semilogy(real_data.axis(),
- real_values + unc_values,
- 'xkcd:grey',
- alpha=0.6)
- plt.ylim((0.5*np.min(real_values), 5*np.max(real_values)))
-
- xlabel = get_axes_labels(real_data, self.units)[0]
- legend = ['BornAgain', 'Data']
- if unc_values is not None:
- legend = ['BornAgain', 'Data', r'Data $\pm \sigma$']
- plt.legend(legend, loc='upper right', prop=font)
- plt.xlabel(xlabel, fontdict=font)
- plt.ylabel("Intensity", fontdict=font)
- plt.title("Specular data fitting", fontdict=font)
-
- def plot(self, fit_objective):
- Plotter.reset(self)
-
- self.plot_graph(fit_objective)
- self.plot_table(fit_objective)
-
- Plotter.plot(self)