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)