<reponame>wuffi/ray-optics<gh_stars>100-1000
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright © 2020 <NAME>
"""Single panel, multiplot MPL figure, with support for line and surface plots

    This package is designed to work with data sources in the
    :mod:`~.raytr.analyses` package. This package provides a MPL Figure
    subclass, :class:`~.AnalysisFigure`, that is a container for one or more
    plots. The plots are managed by the other classes in this package:

       - :class:`~.RayFanPlot`: transverse ray aberreations and OPD
       - :class:`~.RayGeoPSF`: spot diagrams and 2D histograms
       - :class:`~.Wavefront`: wavefront map
       - :class:`~.DiffractionPSF`: diffraction Point Spread Function

.. Created on Tue Mar 17 16:21:27 2020

.. codeauthor: <NAME>
"""

import numpy as np
from matplotlib.colors import LogNorm, PowerNorm

from rayoptics.mpl.styledfigure import StyledFigure

from rayoptics.raytr import analyses


class AnalysisFigure(StyledFigure):
    """Containing Figure for single panel plots, supports update_data.

    Attributes:
        subplots: list of plots for this figure
        data_objs: the data sources for the plots. If not specified, the
                   subplots will be queried for the data sources.
        grid: tuple of the # rows and # columns in the figure, defaults to
              (1, 1)
        gs: GridSpec instance, created from *grid*.
        gridspecs: optional, list of subplotspecs, one for each subplot. These
                   are used for detailed control of subplot positions.
        kwargs: passed to Figure base class
    """

    def __init__(self, data_objs=None, subplots=None, grid=None, **kwargs):
        super().__init__(**kwargs)

        self.subplots = subplots if subplots is not None else []
        self.data_objs = data_objs if data_objs is not None else []

        self.grid = grid if grid is not None else (1, 1)
        self.gs = self.add_gridspec(nrows=self.grid[0], ncols=self.grid[1])
        self.gridspecs = []

    def refresh(self, **kwargs):
        """Call update_data() followed by plot(), return self.

        Args:
            kwargs: keyword arguments are passed to update_data

        Returns:
            self (class Figure) so scripting envs will auto display results
        """
        self.update_data(**kwargs)
        self.plot()
        return self

    def update_data(self, build='rebuild', **kwargs):
        if len(self.data_objs) > 0:
            for data_obj in self.data_objs:
                data_obj.update_data(build=build)
        else:
            for subplot in self.subplots:
                subplot.update_data(build=build)

        return self

    def plot(self):
        self.clf()
        nrows, ncols = self.grid
        if len(self.gridspecs) == 0:
            for i, subplot in enumerate(self.subplots, start=1):
                ax = self.add_subplot(nrows, ncols, i)
                subplot.init_axis(ax)
                subplot.plot(ax)
        elif len(self.gridspecs) == len(self.subplots):
            for subplot, gridspec in zip(self.subplots, self.gridspecs):
                ax = self.add_subplot(gridspec)
                subplot.init_axis(ax)
                subplot.plot(ax)

        self.canvas.draw_idle()
        return self


class RayFanPlot():
    """Single axis line plot, supporting data display from multiple RayFans.

    Attributes:
        fan_list: list of (fan, data_type, kwargs)

            - fan: a RayFan instance
            - data_type: 'x', 'y', 'opd' to be extracted from fan
            - kwargs: passed to axis.plot() call

        scale_type: if 'fit', set scale to encompass largest data value
        user_scale_value: max scale to apply if scale_type is 'user'
        title: title, if desired, of this plot panel
        yaxis_ticks_position: 'left' or 'right', default is 'left'
        kwargs: passed to plot call

    """

    def __init__(self, fan_list,
                 user_scale_value=0.1, scale_type='fit',
                 yaxis_ticks_position='left',
                 **kwargs):
        self.fan_list = fan_list

        if 'title' in kwargs:
            self.title = kwargs.pop('title', None)
        self.user_scale_value = user_scale_value
        self.scale_type = scale_type
        self.yaxis_ticks_position = yaxis_ticks_position

        self.update_data()

    def init_axis(self, ax):
        ax.grid(True)
        ax.set_xlim(-1., 1.)
        ax.axvline(0, c=ax.figure._rgb['foreground'], lw=1)
        ax.axhline(0, c=ax.figure._rgb['foreground'], lw=1)
        ax.tick_params(labelbottom=False)
        ax.yaxis.set_ticks_position(self.yaxis_ticks_position)
        if self.title is not None:
            ax.set_title(self.title, fontsize='small')
        return ax

    def refresh(self, build='rebuild'):
        self.update_data(build=build)
        self.plot()

    def update_data(self, build='rebuild'):
        return self

    def plot(self, ax):
        for fan_pkg in self.fan_list:
            fan, data_type, kws = fan_pkg
            if data_type == 'dx':
                dt = 0
            elif data_type == 'dy':
                dt = 1
            elif data_type == 'opd':
                dt = 2
            fan_plot = analyses.select_plot_data(fan.fan, fan.xyfan, dt)
            if 'num_points' in kws:
                num_pts = kws.pop('num_points')
                fan_plot = analyses.smooth_plot_data(*fan_plot,
                                                     num_points=num_pts)

            ax.plot(*fan_plot, **kws)  # x_data, y_data = fan_plot

        if self.scale_type == 'fit':
            y_data = fan_plot[1]
            max_value = max(np.nanmax(y_data), -np.nanmin(y_data))
            ax.set_ylim(-max_value, max_value)
        elif self.user_scale_value is not None:
            us = self.user_scale_value
            ax.set_ylim(-us, us)

        return self


class RayGeoPSF():
    """Single axis spot diagram or 2d histogram.

    Attributes:
        ray_list: a RayList instance
        dsp_type: display type, either 'spot' or 'hist2d'
        scale_type: if 'fit', set scale to encompass largest data value
        user_scale_value: max scale to apply if scale_type is 'user'
        title: title, if desired, of this plot panel
        yaxis_ticks_position: 'left' or 'right', default is 'left'
        kwargs: passed to plot call
    """

    def __init__(self, ray_list,
                 user_scale_value=0.1, scale_type='fit',
                 yaxis_ticks_position='left', dsp_typ='hist2d',
                 **kwargs):
        self.ray_list = ray_list
        self.dsp_typ = dsp_typ

        if 'title' in kwargs:
            self.title = kwargs.pop('title', None)

        if 'norm' in kwargs:
            self.norm = kwargs.pop('norm', None)
        else:
            gamma = kwargs.pop('gamma', 0.5)
            vmax = kwargs.get('vmax') if 'vmax' in kwargs else None
            self.norm = PowerNorm(gamma, vmin=0., vmax=vmax)

        self.plot_kwargs = kwargs

        self.user_scale_value = user_scale_value
        self.scale_type = scale_type
        self.yaxis_ticks_position = yaxis_ticks_position

        self.update_data()

    def init_axis(self, ax):
        if self.dsp_typ == 'spot':
            ax.grid(True)
            linewidth = 0.5
            ax.axvline(0, c=ax.figure._rgb['foreground'], lw=linewidth)
            ax.axhline(0, c=ax.figure._rgb['foreground'], lw=linewidth)

        ax.yaxis.set_ticks_position(self.yaxis_ticks_position)
        if self.title is not None:
            ax.set_title(self.title, fontsize='small')
        return ax

    def refresh(self, build='rebuild'):
        self.update_data(build=build)
        self.plot()

    def update_data(self, build='rebuild'):
        self.ray_list.update_data(build=build)
        return self

    def ray_data_bounds(self):
        x_data = self.ray_list.ray_abr[0]
        y_data = self.ray_list.ray_abr[1]
        min_x = np.nanmin(x_data)
        min_y = np.nanmin(y_data)
        max_x = np.nanmax(x_data)
        max_y = np.nanmax(y_data)
        delta_x = (max_x - min_x)/2
        delta_y = (max_y - min_y)/2
        center_x = (max_x + min_x)/2
        center_y = (max_y + min_y)/2
        return delta_x, delta_y, center_x, center_y

    def plot(self, ax):
        delta_x, delta_y, center_x, center_y = self.ray_data_bounds()
        if self.scale_type == 'fit':
            max_delta = delta_x if delta_x > delta_y else delta_y
            max_value = max_delta
            ax.set_xlim(-max_value, max_value)
            ax.set_ylim(center_y-max_value, center_y+max_value)
            x_edges = np.linspace(-max_value, max_value, num=100)
            y_edges = np.linspace(center_y-max_value, center_y+max_value,
                                  num=100)
            bins = [x_edges, y_edges]
        elif self.scale_type == 'user centered':
            max_value = self.user_scale_value
            ax.set_xlim(-max_value, max_value)
            ax.set_ylim(center_y-max_value, center_y+max_value)
            x_edges = np.linspace(-max_value, max_value, num=100)
            y_edges = np.linspace(center_y-max_value, center_y+max_value,
                                  num=100)
            bins = [x_edges, y_edges]
        elif self.scale_type == 'user':
            max_value = self.user_scale_value
            ax.set_xlim(-max_value, max_value)
            ax.set_ylim(-max_value, max_value)
            edges = np.linspace(-max_value, max_value, num=100)
            bins = edges

        if self.dsp_typ == 'spot':
            ax.scatter(*self.ray_list.ray_abr, **self.plot_kwargs)
        elif self.dsp_typ == 'hist2d':
            x_edges = np.linspace(-max_value, max_value, num=100)
            y_edges = np.linspace(-max_value, max_value, num=100)
            h, xedges, yedges, qmesh = ax.hist2d(*self.ray_list.ray_abr,
                                                 # bins=edges,
                                                 bins=bins,
                                                 norm=self.norm,
                                                 **self.plot_kwargs)
            ax.set_facecolor(qmesh.cmap(0))

        ax.set_aspect('equal')

        return self


class Wavefront():
    """Single axis wavefront map.

    Attributes:
        ray_grid: a RayGrid instance
        do_contours: if True, display contour plot, else plot data grid as an
        image
        scale_type: if 'fit', set scale to encompass largest data value
        user_scale_value: max scale to apply if scale_type is 'user'
        title: title, if desired, of this plot panel
        yaxis_ticks_position: 'left' or 'right', default is 'left'
        cmap: color map for plot, defaults to 'RdBu_r'
        kwargs: passed to plot call
    """

    def __init__(self, ray_grid,
                 do_contours=False, user_scale_value=None,
                 **kwargs):
        self.ray_grid = ray_grid

        if 'title' in kwargs:
            self.title = kwargs.pop('title', None)
        kwargs['cmap'] = kwargs.get('cmap', "RdBu_r")
        self.plot_kwargs = kwargs

        self.do_contours = do_contours
        self.user_scale = user_scale_value

        self.update_data()

    def init_axis(self, ax):
        ax.set_xlim(-1., 1.)
        ax.set_ylim(-1., 1.)
        ax.tick_params(labelbottom=False, labelleft=False)
        if self.title is not None:
            ax.set_title(self.title, fontsize='small')
        return ax

    def refresh(self, build='rebuild'):
        self.update_data(build=build)
        self.plot()

    def update_data(self, build='rebuild'):
        self.ray_grid.update_data(build=build)
        return self

    def plot(self, ax):
        grid = self.ray_grid.grid
        max_value = max(np.nanmax(grid[2]), -np.nanmin(grid[2]))
        max_value = self.user_scale if self.user_scale else max_value

        if self.do_contours:
            hmap = ax.contourf(grid[0],
                               grid[1],
                               grid[2],
                               vmin=-max_value,
                               vmax=max_value,
                               **self.plot_kwargs)
        else:
            # transpose and origin=lower needed to match display orientation
            # of contours
            hmap = ax.imshow(np.transpose(grid[2]),
                             origin='lower',
                             vmin=-max_value,
                             vmax=max_value,
                             extent=[-1., 1., -1., 1.],
                             **self.plot_kwargs
                             )
        ax.figure.colorbar(hmap, ax=ax, use_gridspec=True)
        ax.set_aspect('equal')

        return self


class DiffractionPSF():
    """Point Spread Function (PSF) calculation and display.

    Attributes:
        pupil_grid: a RayGrid instance
        maxdim: the size of the sampling array
        title: title, if desired, of this plot panel
        yaxis_ticks_position: 'left' or 'right', default is 'left'
        cmap: color map for plot, defaults to 'RdBu_r'
        kwargs: passed to plot call
    """

    def __init__(self, pupil_grid, maxdim,
                 yaxis_ticks_position='left', **kwargs):
        self.pupil_grid = pupil_grid
        self.maxdim = maxdim

        if 'title' in kwargs:
            self.title = kwargs.pop('title', None)
        kwargs['cmap'] = kwargs.get('cmap', "RdBu_r")

        if 'norm' in kwargs:
            self.norm = kwargs.pop('norm', None)
        else:
            vmin = kwargs.get('vmin') if 'vmin' in kwargs else None
            vmax = kwargs.get('vmax') if 'vmax' in kwargs else None
            self.norm = LogNorm(vmin=vmin, vmax=vmax)

        self.plot_kwargs = kwargs
        self.yaxis_ticks_position = yaxis_ticks_position

        self.update_data()

    def init_axis(self, ax):
        pupil_grid = self.pupil_grid
        delta_x, delta_xp = analyses.calc_psf_scaling(pupil_grid,
                                                      pupil_grid.num_rays,
                                                      self.maxdim)
        image_scale = self.image_scale = delta_xp * self.maxdim
        ax.set_xlim(-image_scale, image_scale)
        ax.set_ylim(-image_scale, image_scale)
        ax.tick_params(labelbottom=False, labelleft=False)
        ax.yaxis.set_ticks_position(self.yaxis_ticks_position)
        if self.title is not None:
            ax.set_title(self.title, fontsize='small')
        return ax

    def refresh(self, build='rebuild'):
        self.update_data(build=build)
        self.plot()

    def update_data(self, build='rebuild'):
        self.pupil_grid.update_data(build=build)
        ndim = self.pupil_grid.num_rays
        maxdim = self.maxdim
        self.AP = analyses.calc_psf(self.pupil_grid.grid[2], ndim, maxdim)
        return self

    def plot(self, ax):
        image_scale = self.image_scale
        hmap = ax.imshow(self.AP,
                         origin='lower',
                         norm=self.norm,
                         extent=[-image_scale, image_scale,
                                 -image_scale, image_scale],
                         **self.plot_kwargs
                         )
        ax.figure.colorbar(hmap, ax=ax, use_gridspec=True)
        ax.set_aspect('equal')

        return self

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright © 2020 <NAME>
"""Single panel, multiplot MPL figure, with support for line and surface plots

    This package is designed to work with data sources in the
    :mod:`~.raytr.analyses` package. This package provides a MPL Figure
    subclass, :class:`~.AnalysisFigure`, that is a container for one or more
    plots. The plots are managed by the other classes in this package:

       - :class:`~.RayFanPlot`: transverse ray aberreations and OPD
       - :class:`~.RayGeoPSF`: spot diagrams and 2D histograms
       - :class:`~.Wavefront`: wavefront map
       - :class:`~.DiffractionPSF`: diffraction Point Spread Function

.. Created on Tue Mar 17 16:21:27 2020

.. codeauthor: <NAME>
"""

import numpy as np
from matplotlib.colors import LogNorm, PowerNorm

from rayoptics.mpl.styledfigure import StyledFigure

from rayoptics.raytr import analyses


class AnalysisFigure(StyledFigure):
    """Containing Figure for single panel plots, supports update_data.

    Attributes:
        subplots: list of plots for this figure
        data_objs: the data sources for the plots. If not specified, the
                   subplots will be queried for the data sources.
        grid: tuple of the # rows and # columns in the figure, defaults to
              (1, 1)
        gs: GridSpec instance, created from *grid*.
        gridspecs: optional, list of subplotspecs, one for each subplot. These
                   are used for detailed control of subplot positions.
        kwargs: passed to Figure base class
    """

    def __init__(self, data_objs=None, subplots=None, grid=None, **kwargs):
        super().__init__(**kwargs)

        self.subplots = subplots if subplots is not None else []
        self.data_objs = data_objs if data_objs is not None else []

        self.grid = grid if grid is not None else (1, 1)
        self.gs = self.add_gridspec(nrows=self.grid[0], ncols=self.grid[1])
        self.gridspecs = []

    def refresh(self, **kwargs):
        """Call update_data() followed by plot(), return self.

        Args:
            kwargs: keyword arguments are passed to update_data

        Returns:
            self (class Figure) so scripting envs will auto display results
        """
        self.update_data(**kwargs)
        self.plot()
        return self

    def update_data(self, build='rebuild', **kwargs):
        if len(self.data_objs) > 0:
            for data_obj in self.data_objs:
                data_obj.update_data(build=build)
        else:
            for subplot in self.subplots:
                subplot.update_data(build=build)

        return self

    def plot(self):
        self.clf()
        nrows, ncols = self.grid
        if len(self.gridspecs) == 0:
            for i, subplot in enumerate(self.subplots, start=1):
                ax = self.add_subplot(nrows, ncols, i)
                subplot.init_axis(ax)
                subplot.plot(ax)
        elif len(self.gridspecs) == len(self.subplots):
            for subplot, gridspec in zip(self.subplots, self.gridspecs):
                ax = self.add_subplot(gridspec)
                subplot.init_axis(ax)
                subplot.plot(ax)

        self.canvas.draw_idle()
        return self


class RayFanPlot():
    """Single axis line plot, supporting data display from multiple RayFans.

    Attributes:
        fan_list: list of (fan, data_type, kwargs)

            - fan: a RayFan instance
            - data_type: 'x', 'y', 'opd' to be extracted from fan
            - kwargs: passed to axis.plot() call

        scale_type: if 'fit', set scale to encompass largest data value
        user_scale_value: max scale to apply if scale_type is 'user'
        title: title, if desired, of this plot panel
        yaxis_ticks_position: 'left' or 'right', default is 'left'
        kwargs: passed to plot call

    """

    def __init__(self, fan_list,
                 user_scale_value=0.1, scale_type='fit',
                 yaxis_ticks_position='left',
                 **kwargs):
        self.fan_list = fan_list

        if 'title' in kwargs:
            self.title = kwargs.pop('title', None)
        self.user_scale_value = user_scale_value
        self.scale_type = scale_type
        self.yaxis_ticks_position = yaxis_ticks_position

        self.update_data()

    def init_axis(self, ax):
        ax.grid(True)
        ax.set_xlim(-1., 1.)
        ax.axvline(0, c=ax.figure._rgb['foreground'], lw=1)
        ax.axhline(0, c=ax.figure._rgb['foreground'], lw=1)
        ax.tick_params(labelbottom=False)
        ax.yaxis.set_ticks_position(self.yaxis_ticks_position)
        if self.title is not None:
            ax.set_title(self.title, fontsize='small')
        return ax

    def refresh(self, build='rebuild'):
        self.update_data(build=build)
        self.plot()

    def update_data(self, build='rebuild'):
        return self

    def plot(self, ax):
        for fan_pkg in self.fan_list:
            fan, data_type, kws = fan_pkg
            if data_type == 'dx':
                dt = 0
            elif data_type == 'dy':
                dt = 1
            elif data_type == 'opd':
                dt = 2
            fan_plot = analyses.select_plot_data(fan.fan, fan.xyfan, dt)
            if 'num_points' in kws:
                num_pts = kws.pop('num_points')
                fan_plot = analyses.smooth_plot_data(*fan_plot,
                                                     num_points=num_pts)

            ax.plot(*fan_plot, **kws)  # x_data, y_data = fan_plot

        if self.scale_type == 'fit':
            y_data = fan_plot[1]
            max_value = max(np.nanmax(y_data), -np.nanmin(y_data))
            ax.set_ylim(-max_value, max_value)
        elif self.user_scale_value is not None:
            us = self.user_scale_value
            ax.set_ylim(-us, us)

        return self


class RayGeoPSF():
    """Single axis spot diagram or 2d histogram.

    Attributes:
        ray_list: a RayList instance
        dsp_type: display type, either 'spot' or 'hist2d'
        scale_type: if 'fit', set scale to encompass largest data value
        user_scale_value: max scale to apply if scale_type is 'user'
        title: title, if desired, of this plot panel
        yaxis_ticks_position: 'left' or 'right', default is 'left'
        kwargs: passed to plot call
    """

    def __init__(self, ray_list,
                 user_scale_value=0.1, scale_type='fit',
                 yaxis_ticks_position='left', dsp_typ='hist2d',
                 **kwargs):
        self.ray_list = ray_list
        self.dsp_typ = dsp_typ

        if 'title' in kwargs:
            self.title = kwargs.pop('title', None)

        if 'norm' in kwargs:
            self.norm = kwargs.pop('norm', None)
        else:
            gamma = kwargs.pop('gamma', 0.5)
            vmax = kwargs.get('vmax') if 'vmax' in kwargs else None
            self.norm = PowerNorm(gamma, vmin=0., vmax=vmax)

        self.plot_kwargs = kwargs

        self.user_scale_value = user_scale_value
        self.scale_type = scale_type
        self.yaxis_ticks_position = yaxis_ticks_position

        self.update_data()

    def init_axis(self, ax):
        if self.dsp_typ == 'spot':
            ax.grid(True)
            linewidth = 0.5
            ax.axvline(0, c=ax.figure._rgb['foreground'], lw=linewidth)
            ax.axhline(0, c=ax.figure._rgb['foreground'], lw=linewidth)

        ax.yaxis.set_ticks_position(self.yaxis_ticks_position)
        if self.title is not None:
            ax.set_title(self.title, fontsize='small')
        return ax

    def refresh(self, build='rebuild'):
        self.update_data(build=build)
        self.plot()

    def update_data(self, build='rebuild'):
        self.ray_list.update_data(build=build)
        return self

    def ray_data_bounds(self):
        x_data = self.ray_list.ray_abr[0]
        y_data = self.ray_list.ray_abr[1]
        min_x = np.nanmin(x_data)
        min_y = np.nanmin(y_data)
        max_x = np.nanmax(x_data)
        max_y = np.nanmax(y_data)
        delta_x = (max_x - min_x)/2
        delta_y = (max_y - min_y)/2
        center_x = (max_x + min_x)/2
        center_y = (max_y + min_y)/2
        return delta_x, delta_y, center_x, center_y

    def plot(self, ax):
        delta_x, delta_y, center_x, center_y = self.ray_data_bounds()
        if self.scale_type == 'fit':
            max_delta = delta_x if delta_x > delta_y else delta_y
            max_value = max_delta
            ax.set_xlim(-max_value, max_value)
            ax.set_ylim(center_y-max_value, center_y+max_value)
            x_edges = np.linspace(-max_value, max_value, num=100)
            y_edges = np.linspace(center_y-max_value, center_y+max_value,
                                  num=100)
            bins = [x_edges, y_edges]
        elif self.scale_type == 'user centered':
            max_value = self.user_scale_value
            ax.set_xlim(-max_value, max_value)
            ax.set_ylim(center_y-max_value, center_y+max_value)
            x_edges = np.linspace(-max_value, max_value, num=100)
            y_edges = np.linspace(center_y-max_value, center_y+max_value,
                                  num=100)
            bins = [x_edges, y_edges]
        elif self.scale_type == 'user':
            max_value = self.user_scale_value
            ax.set_xlim(-max_value, max_value)
            ax.set_ylim(-max_value, max_value)
            edges = np.linspace(-max_value, max_value, num=100)
            bins = edges

        if self.dsp_typ == 'spot':
            ax.scatter(*self.ray_list.ray_abr, **self.plot_kwargs)
        elif self.dsp_typ == 'hist2d':
            x_edges = np.linspace(-max_value, max_value, num=100)
            y_edges = np.linspace(-max_value, max_value, num=100)
            h, xedges, yedges, qmesh = ax.hist2d(*self.ray_list.ray_abr,
                                                 # bins=edges,
                                                 bins=bins,
                                                 norm=self.norm,
                                                 **self.plot_kwargs)
            ax.set_facecolor(qmesh.cmap(0))

        ax.set_aspect('equal')

        return self


class Wavefront():
    """Single axis wavefront map.

    Attributes:
        ray_grid: a RayGrid instance
        do_contours: if True, display contour plot, else plot data grid as an
        image
        scale_type: if 'fit', set scale to encompass largest data value
        user_scale_value: max scale to apply if scale_type is 'user'
        title: title, if desired, of this plot panel
        yaxis_ticks_position: 'left' or 'right', default is 'left'
        cmap: color map for plot, defaults to 'RdBu_r'
        kwargs: passed to plot call
    """

    def __init__(self, ray_grid,
                 do_contours=False, user_scale_value=None,
                 **kwargs):
        self.ray_grid = ray_grid

        if 'title' in kwargs:
            self.title = kwargs.pop('title', None)
        kwargs['cmap'] = kwargs.get('cmap', "RdBu_r")
        self.plot_kwargs = kwargs

        self.do_contours = do_contours
        self.user_scale = user_scale_value

        self.update_data()

    def init_axis(self, ax):
        ax.set_xlim(-1., 1.)
        ax.set_ylim(-1., 1.)
        ax.tick_params(labelbottom=False, labelleft=False)
        if self.title is not None:
            ax.set_title(self.title, fontsize='small')
        return ax

    def refresh(self, build='rebuild'):
        self.update_data(build=build)
        self.plot()

    def update_data(self, build='rebuild'):
        self.ray_grid.update_data(build=build)
        return self

    def plot(self, ax):
        grid = self.ray_grid.grid
        max_value = max(np.nanmax(grid[2]), -np.nanmin(grid[2]))
        max_value = self.user_scale if self.user_scale else max_value

        if self.do_contours:
            hmap = ax.contourf(grid[0],
                               grid[1],
                               grid[2],
                               vmin=-max_value,
                               vmax=max_value,
                               **self.plot_kwargs)
        else:
            # transpose and origin=lower needed to match display orientation
            # of contours
            hmap = ax.imshow(np.transpose(grid[2]),
                             origin='lower',
                             vmin=-max_value,
                             vmax=max_value,
                             extent=[-1., 1., -1., 1.],
                             **self.plot_kwargs
                             )
        ax.figure.colorbar(hmap, ax=ax, use_gridspec=True)
        ax.set_aspect('equal')

        return self


class DiffractionPSF():
    """Point Spread Function (PSF) calculation and display.

    Attributes:
        pupil_grid: a RayGrid instance
        maxdim: the size of the sampling array
        title: title, if desired, of this plot panel
        yaxis_ticks_position: 'left' or 'right', default is 'left'
        cmap: color map for plot, defaults to 'RdBu_r'
        kwargs: passed to plot call
    """

    def __init__(self, pupil_grid, maxdim,
                 yaxis_ticks_position='left', **kwargs):
        self.pupil_grid = pupil_grid
        self.maxdim = maxdim

        if 'title' in kwargs:
            self.title = kwargs.pop('title', None)
        kwargs['cmap'] = kwargs.get('cmap', "RdBu_r")

        if 'norm' in kwargs:
            self.norm = kwargs.pop('norm', None)
        else:
            vmin = kwargs.get('vmin') if 'vmin' in kwargs else None
            vmax = kwargs.get('vmax') if 'vmax' in kwargs else None
            self.norm = LogNorm(vmin=vmin, vmax=vmax)

        self.plot_kwargs = kwargs
        self.yaxis_ticks_position = yaxis_ticks_position

        self.update_data()

    def init_axis(self, ax):
        pupil_grid = self.pupil_grid
        delta_x, delta_xp = analyses.calc_psf_scaling(pupil_grid,
                                                      pupil_grid.num_rays,
                                                      self.maxdim)
        image_scale = self.image_scale = delta_xp * self.maxdim
        ax.set_xlim(-image_scale, image_scale)
        ax.set_ylim(-image_scale, image_scale)
        ax.tick_params(labelbottom=False, labelleft=False)
        ax.yaxis.set_ticks_position(self.yaxis_ticks_position)
        if self.title is not None:
            ax.set_title(self.title, fontsize='small')
        return ax

    def refresh(self, build='rebuild'):
        self.update_data(build=build)
        self.plot()

    def update_data(self, build='rebuild'):
        self.pupil_grid.update_data(build=build)
        ndim = self.pupil_grid.num_rays
        maxdim = self.maxdim
        self.AP = analyses.calc_psf(self.pupil_grid.grid[2], ndim, maxdim)
        return self

    def plot(self, ax):
        image_scale = self.image_scale
        hmap = ax.imshow(self.AP,
                         origin='lower',
                         norm=self.norm,
                         extent=[-image_scale, image_scale,
                                 -image_scale, image_scale],
                         **self.plot_kwargs
                         )
        ax.figure.colorbar(hmap, ax=ax, use_gridspec=True)
        ax.set_aspect('equal')

        return self
#!/usr/bin/env python3 # -*- coding: utf-8 -*- # Copyright © 2020 <NAME> Single panel, multiplot MPL figure, with support for line and surface plots      This package is designed to work with data sources in the     :mod:`~.raytr.analyses` package. This package provides a MPL Figure     subclass, :class:`~.AnalysisFigure`, that is a container for one or more     plots. The plots are managed by the other classes in this package:         - :class:`~.RayFanPlot`: transverse ray aberreations and OPD        - :class:`~.RayGeoPSF`: spot diagrams and 2D histograms        - :class:`~.Wavefront`: wavefront map        - :class:`~.DiffractionPSF`: diffraction Point Spread Function  .. Created on Tue Mar 17 16:21:27 2020  .. codeauthor: <NAME> Containing Figure for single panel plots, supports update_data.      Attributes:         subplots: list of plots for this figure         data_objs: the data sources for the plots. If not specified, the                    subplots will be queried for the data sources.         grid: tuple of the # rows and # columns in the figure, defaults to               (1, 1)         gs: GridSpec instance, created from *grid*.         gridspecs: optional, list of subplotspecs, one for each subplot. These                    are used for detailed control of subplot positions.         kwargs: passed to Figure base class Call update_data() followed by plot(), return self.          Args:             kwargs: keyword arguments are passed to update_data          Returns:             self (class Figure) so scripting envs will auto display results Single axis line plot, supporting data display from multiple RayFans.      Attributes:         fan_list: list of (fan, data_type, kwargs)              - fan: a RayFan instance             - data_type: 'x', 'y', 'opd' to be extracted from fan             - kwargs: passed to axis.plot() call          scale_type: if 'fit', set scale to encompass largest data value         user_scale_value: max scale to apply if scale_type is 'user'         title: title, if desired, of this plot panel         yaxis_ticks_position: 'left' or 'right', default is 'left'         kwargs: passed to plot call # x_data, y_data = fan_plot Single axis spot diagram or 2d histogram.      Attributes:         ray_list: a RayList instance         dsp_type: display type, either 'spot' or 'hist2d'         scale_type: if 'fit', set scale to encompass largest data value         user_scale_value: max scale to apply if scale_type is 'user'         title: title, if desired, of this plot panel         yaxis_ticks_position: 'left' or 'right', default is 'left'         kwargs: passed to plot call # bins=edges, Single axis wavefront map.      Attributes:         ray_grid: a RayGrid instance         do_contours: if True, display contour plot, else plot data grid as an         image         scale_type: if 'fit', set scale to encompass largest data value         user_scale_value: max scale to apply if scale_type is 'user'         title: title, if desired, of this plot panel         yaxis_ticks_position: 'left' or 'right', default is 'left'         cmap: color map for plot, defaults to 'RdBu_r'         kwargs: passed to plot call # transpose and origin=lower needed to match display orientation # of contours Point Spread Function (PSF) calculation and display.      Attributes:         pupil_grid: a RayGrid instance         maxdim: the size of the sampling array         title: title, if desired, of this plot panel         yaxis_ticks_position: 'left' or 'right', default is 'left'         cmap: color map for plot, defaults to 'RdBu_r'         kwargs: passed to plot call