Source code for lenstronomy.Plots.plot_util

import numpy as np
import math
from corner import quantile
import matplotlib.pyplot as plt

from lenstronomy.Util.package_util import exporter
export, __all__ = exporter()


[docs]@export def sqrt(inputArray, scale_min=None, scale_max=None): """Performs sqrt scaling of the input numpy array. @type inputArray: numpy array @param inputArray: image data array @type scale_min: float @param scale_min: minimum data value @type scale_max: float @param scale_max: maximum data value @rtype: numpy array @return: image data array """ imageData = np.array(inputArray, copy=True) if scale_min is None: scale_min = imageData.min() if scale_max is None: scale_max = imageData.max() imageData = imageData.clip(min=scale_min, max=scale_max) imageData = imageData - scale_min indices = np.where(imageData < 0) imageData[indices] = 0.0 imageData = np.sqrt(imageData) imageData = imageData / math.sqrt(scale_max - scale_min) return imageData
[docs]@export def text_description(ax, d, text, color='w', backgroundcolor='k', flipped=False, font_size=15): c_vertical = 1/15. #+ font_size / d / 10.**2 c_horizontal = 1./30 if flipped: ax.text(d - d * c_horizontal, d - d * c_vertical, text, color=color, fontsize=font_size, backgroundcolor=backgroundcolor) else: ax.text(d * c_horizontal, d - d * c_vertical, text, color=color, fontsize=font_size, backgroundcolor=backgroundcolor)
[docs]@export def scale_bar(ax, d, dist=1., text='1"', color='w', font_size=15, flipped=False): if flipped: p0 = d - d / 15. - dist p1 = d / 15. ax.plot([p0, p0 + dist], [p1, p1], linewidth=2, color=color) ax.text(p0 + dist / 2., p1 + 0.01 * d, text, fontsize=font_size, color=color, ha='center') else: p0 = d / 15. ax.plot([p0, p0 + dist], [p0, p0], linewidth=2, color=color) ax.text(p0 + dist / 2., p0 + 0.01 * d, text, fontsize=font_size, color=color, ha='center')
[docs]@export def coordinate_arrows(ax, d, coords, color='w', font_size=15, arrow_size=0.05): d0 = d / 8. p0 = d / 15. pt = d / 9. deltaPix = coords.pixel_width ra0, dec0 = coords.map_pix2coord((d - d0) / deltaPix, d0 / deltaPix) xx_, yy_ = coords.map_coord2pix(ra0, dec0) xx_ra, yy_ra = coords.map_coord2pix(ra0 + p0, dec0) xx_dec, yy_dec = coords.map_coord2pix(ra0, dec0 + p0) xx_ra_t, yy_ra_t = coords.map_coord2pix(ra0 + pt, dec0) xx_dec_t, yy_dec_t = coords.map_coord2pix(ra0, dec0 + pt) ax.arrow(xx_ * deltaPix, yy_ * deltaPix, (xx_ra - xx_) * deltaPix, (yy_ra - yy_) * deltaPix, head_width=arrow_size * d, head_length=arrow_size * d, fc=color, ec=color, linewidth=1) ax.text(xx_ra_t * deltaPix, yy_ra_t * deltaPix, "E", color=color, fontsize=font_size, ha='center') ax.arrow(xx_ * deltaPix, yy_ * deltaPix, (xx_dec - xx_) * deltaPix, (yy_dec - yy_) * deltaPix, head_width=arrow_size * d, head_length=arrow_size * d, fc =color, ec=color, linewidth=1) ax.text(xx_dec_t * deltaPix, yy_dec_t * deltaPix, "N", color=color, fontsize=font_size, ha='center')
[docs]@export def plot_line_set_list(ax, coords, line_set_list_x, line_set_list_y, origin=None, color='g', flipped_x=False): """ :param ax: matplotlib.axis instance :param coords: Coordinates() class instance :param origin: [x0, y0], lower left pixel coordinate in the frame of the pixels :param line_set_list_x: list of numpy arrays corresponding of different disconnected regions of the line (e.g. caustic or critical curve) :param line_set_list_y: list of numpy arrays corresponding of different disconnected regions of the line (e.g. caustic or critical curve) :param color: string with matplotlib color :param flipped_x: bool, if True, flips x-axis :return: plot with line sets on matplotlib axis """ if origin is None: origin = [0, 0] pixel_width = coords.pixel_width pixel_width_x = pixel_width if flipped_x: pixel_width_x = -pixel_width for i in range(len(line_set_list_x)): x_c, y_c = coords.map_coord2pix(line_set_list_x[i], line_set_list_y[i]) ax.plot((x_c + 0.5) * pixel_width_x + origin[0], (y_c + 0.5) * pixel_width + origin[1], ',', color=color) return ax
[docs]@export def plot_line_set(ax, coords, line_set_list_x, line_set_list_y, origin=None, color='g', flipped_x=False): """ plotting a line set on a matplotlib instance where the coordinates are defined in pixel units with the lower left corner (defined as origin) is by default (0, 0). The coordinates are moved by 0.5 pixels to be placed in the center of the pixel in accordance with the matplotlib.matshow() routine. :param ax: matplotlib.axis instance :param coords: Coordinates() class instance :param origin: [x0, y0], lower left pixel coordinate in the frame of the pixels :param line_set_list_x: numpy arrays corresponding of different disconnected regions of the line (e.g. caustic or critical curve) :param line_set_list_y: numpy arrays corresponding of different disconnected regions of the line (e.g. caustic or critical curve) :param color: string with matplotlib color :param flipped_x: bool, if True, flips x-axis :return: plot with line sets on matplotlib axis in pixel coordinates """ if origin is None: origin = [0, 0] pixel_width = coords.pixel_width pixel_width_x = pixel_width if flipped_x: pixel_width_x = -pixel_width x_c, y_c = coords.map_coord2pix(line_set_list_x, line_set_list_y) ax.plot((x_c + 0.5) * pixel_width_x + origin[0], (y_c + 0.5) * pixel_width + origin[1], ',', color=color) return ax
[docs]@export def image_position_plot(ax, coords, ra_image, dec_image, color='w', image_name_list=None): """ :param ax: :param coords: :param kwargs_else: :return: """ deltaPix = coords.pixel_width if len(ra_image) > 0: if len(ra_image[0]) > 0: x_image, y_image = coords.map_coord2pix(ra_image[0], dec_image[0]) if image_name_list is None: image_name_list = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L'] for i in range(len(x_image)): x_ = (x_image[i] + 0.5) * deltaPix y_ = (y_image[i] + 0.5) * deltaPix ax.plot(x_, y_, 'or') ax.text(x_, y_, image_name_list[i], fontsize=20, color=color) return ax
[docs]@export def source_position_plot(ax, coords, ra_pos, dec_pos): """ :param ax: :param coords: :param kwargs_source: :return: """ deltaPix = coords.pixel_width if len(ra_pos) > 0: for ra, dec in zip(ra_pos, dec_pos): x_source, y_source = coords.map_coord2pix(ra, dec) ax.plot((x_source + 0.5) * deltaPix, (y_source + 0.5) * deltaPix, '*', markersize=10) return ax
[docs]@export def result_string(x, weights=None, title_fmt=".2f", label=None): """ :param x: marginalized 1-d posterior :param weights: weights of posteriors (optional) :param title_fmt: format to what digit the results are presented :param label: string of parameter label (optional) :return: string with mean \pm quartile """ q_16, q_50, q_84 = quantile(x, [0.16, 0.5, 0.84], weights=weights) q_m, q_p = q_50 - q_16, q_84 - q_50 # Format the quantile display. fmt = "{{0:{0}}}".format(title_fmt).format title = r"${{{0}}}_{{-{1}}}^{{+{2}}}$" title = title.format(fmt(q_50), fmt(q_m), fmt(q_p)) if label is not None: title = "{0} = {1}".format(label, title) return title
[docs]@export def cmap_conf(cmap_string): """ configures matplotlib color map :param cmap_string: string of cmap name, or cmap instance :return: cmap instance with setting for bad pixels and values below the threshold """ if isinstance(cmap_string, str): cmap = plt.get_cmap(cmap_string) else: cmap = cmap_string cmap.set_bad(color='k', alpha=1.) cmap.set_under('k') return cmap