def plot_2d_OLD(self,
axes=('scale', 'maxdist', 'upfreq', 'lowfreq'),
show_best=0,
skip=None,
savefig=None,
clim=None):
"""
A grid of heatmaps representing the result of the optimization.
:param 'scale','maxdist','upfreq','lowfreq' axes: list of axes to be
represented in the plot. The order will define which parameter will
be placed on the x, y, z or w axe.
:param 0 show_best: number of best correlation values to highlight in
the plot
:param None skip: if passed (as a dictionary), fix a given axe,
e.g.: {'scale': 0.001, 'maxdist': 500}
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
"""
results = self._result_to_array()
plot_2d_optimization_result(
(('scale', 'maxdist', 'upfreq', 'lowfreq'),
([float(i) for i in self.scale_range], [
float(i) for i in self.maxdist_range
], [float(i) for i in self.upfreq_range
], [float(i) for i in self.lowfreq_range]), results),
axes=axes,
dcutoff=self.dcutoff_range,
show_best=show_best,
skip=skip,
savefig=savefig,
clim=clim)
def plot_2d(self, axes=('scale', 'maxdist', 'upfreq', 'lowfreq'),
show_best=0, skip=None, savefig=None):
"""
A grid of heatmaps representing the result of the optimization.
:param 'scale','maxdist','upfreq','lowfreq' axes: list of axes to be
represented in the plot. The order will define which parameter will
be placed on the x, y, z or w axe.
:param 0 show_best: number of best correlation values to highlight in
the plot
:param None skip: if passed (as a dictionary), fix a given axe,
e.g.: {'scale': 0.001, 'maxdist': 500}
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
"""
results = self._result_to_array()
plot_2d_optimization_result((('scale', 'maxdist', 'upfreq', 'lowfreq'),
([float(i) for i in self.scale_range],
[float(i) for i in self.maxdist_range],
[float(i) for i in self.upfreq_range],
[float(i) for i in self.lowfreq_range]),
results), axes=axes, show_best=show_best,
skip=skip, savefig=savefig)
def plot_2d(self, axes=('scale', 'kbending', 'maxdist', 'lowfreq', 'upfreq'),
show_best=0, skip=None, savefig=None,clim=None, cmap='inferno'):
"""
A grid of heatmaps representing the result of the optimization.
:param 'scale','kbending','maxdist','lowfreq','upfreq' axes: list of
axes to be represented in the plot. The order will define which
parameter will be placed on the x, y, z or w axe.
:param 0 show_best: number of best correlation values to highlight in
the plot
:param None skip: if passed (as a dictionary), fix a given axe,
e.g.: {'scale': 0.001, 'maxdist': 500}
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
:param None clim: color scale. If None, the max and min values of the input are used.
:param inferno cmap: matplotlib colormap
"""
# Case in which there is more than 1 distance cutoff (dcutoff)
cut = self.get_best_parameters_dict()['dcutoff']
results = self._result_to_array(float(cut))
plot_2d_optimization_result((('scale', 'kbending', 'maxdist', 'lowfreq', 'upfreq'),
([float(i) for i in self.scale_range],
[float(i) for i in self.kbending_range],
[float(i) for i in self.maxdist_range],
[float(i) for i in self.lowfreq_range],
[float(i) for i in self.upfreq_range]),
results), dcutoff=cut, axes=axes, show_best=show_best,
skip=skip, savefig=savefig,clim=clim, cmap=cmap)
def plot_2d(self, axes=('scale', 'maxdist', 'upfreq', 'lowfreq'),
show_best=0, skip=None):
"""
A grid of heatmaps representing the result of the optimization.
:param 'scale','maxdist','upfreq','lowfreq' axes: tuple of axes to
represent. The order will define which parameter will be placed on the
w, z, y or x axe.
:param 0 show_best: number of best correlation value to identifie.
:param None skip: a dict can be passed here in order to fix a given axe,
e.g.: {'scale': 0.001, 'maxdist': 500}
"""
results = self._result_to_array()
plot_2d_optimization_result((('scale', 'maxdist', 'upfreq', 'lowfreq'),
(self.scale_range, self.maxdist_range,
self.upfreq_range, self.lowfreq_range),
results), axes=axes,
show_best=show_best, skip=skip)
def plot_2d(self, axes=('scale', 'maxdist', 'upfreq', 'lowfreq'),
show_best=0, skip=None):
"""
A grid of heatmaps representing the result of the optimization.
:param 'scale','maxdist','upfreq','lowfreq' axes: list of axes to be
represented in the plot. The order will define which parameter will
be placed on the x, y, z or w axe.
:param 0 show_best: number of best correlation values to highlight in
the plot
:param None skip: if passed (as a dictionary), fix a given axe,
e.g.: {'scale': 0.001, 'maxdist': 500}
"""
results = self._result_to_array()
plot_2d_optimization_result((('scale', 'maxdist', 'upfreq', 'lowfreq'),
(self.scale_range, self.maxdist_range,
self.upfreq_range, self.lowfreq_range),
results), axes=axes,
show_best=show_best, skip=skip)
def plot_2d(self,
axes=('scale', 'maxdist', 'upfreq', 'lowfreq'),
show_best=0,
skip=None):
"""
A grid of heatmaps representing the result of the optimization.
:param 'scale','maxdist','upfreq','lowfreq' axes: tuple of axes to
represent. The order will define which parameter will be placed on the
w, z, y or x axe.
:param 0 show_best: number of best correlation value to identifie.
:param None skip: a dict can be passed here in order to fix a given axe,
e.g.: {'scale': 0.001, 'maxdist': 500}
"""
results = self._result_to_array()
plot_2d_optimization_result(
(('scale', 'maxdist', 'upfreq', 'lowfreq'),
(self.scale_range, self.maxdist_range, self.upfreq_range,
self.lowfreq_range), results),
axes=axes,
show_best=show_best,
skip=skip)
def plot_2d(self,
axes=('scale', 'maxdist', 'upfreq', 'lowfreq'),
show_best=0,
skip=None):
"""
A grid of heatmaps representing the result of the optimization.
:param 'scale','maxdist','upfreq','lowfreq' axes: list of axes to be
represented in the plot. The order will define which parameter will
be placed on the x, y, z or w axe.
:param 0 show_best: number of best correlation values to highlight in
the plot
:param None skip: if passed (as a dictionary), fix a given axe,
e.g.: {'scale': 0.001, 'maxdist': 500}
"""
results = self._result_to_array()
plot_2d_optimization_result(
(('scale', 'maxdist', 'upfreq', 'lowfreq'),
(self.scale_range, self.maxdist_range, self.upfreq_range,
self.lowfreq_range), results),
axes=axes,
show_best=show_best,
skip=skip)
