# coding: utf-8 import sys import os import tables import numpy as np import matplotlib as mpl if os.environ.get('DISPLAY','') == '': print('No display found. Using non-interactive Agg backend') mpl.use('Agg') from matplotlib.patches import Polygon import pylab as P from scipy.stats import linregress import matplotlib.patches as mpatches from matplotlib.collections import PatchCollection from functools import partial from numpy.polynomial.polynomial import polyfit from pp_params import * from postprocessor import Rule, BaseProcessor P.rcParams['image.cmap']='plasma' P.rcParams['savefig.dpi']=400 tex_params= {'text.latex.preamble' : [r'\usepackage{amsmath}']} P.rcParams.update(tex_params) class PlotRule(Rule): def plot(self, arg): return self.process_fn(arg) def is_valid(self, arg): save = self.postproc.save valid = True for dep in self.dependencies: if not arg is None: valid = valid and dep + '_' + str(arg) in save else: valid = valid and dep in save return arg in self.args_ok and valid and self.is_valid_add(arg) class Plotter(BaseProcessor): """ This class loads derived quantities and plot them """ # Axes information _ax_nb = {'x' : 0, 'y' : 1, 'z' : 2} # Number of each axes _axes_h = {'x' :'y', 'y' : 'x', 'z' : 'x'} # Associated horizontal axe _axes_v = {'x' : 'z', 'y' : 'z', 'z' : 'y'} # Associated vertical axe _ax_title = {'x' : r'$x$ (code)', 'y' : r'$y$ (code)', 'z' : r'$z$ (code)'} G = 1. # Gravitational constant def __init__(self, filename=None, path_out='.', num=None, pp_params=Params()): self.pp_params = pp_params # Determining output directory if path_out is None: if filename is None: self.path_out='.' else: self.path_out = os.path.dirname(filename) else: self.path_out = path_out # Find HDF5 file if filename is None: if not pp_params.out.tag == '': tag_name = '_' + pp_params.out.tag else : tag_name = '' self.filename = (self.path_out + '/postproc_' + tag_name + format(num,'05') + '.h5') else: self.filename = filename self.log_id = "[plot {}] ".format(self.pp_params.out.tag) self.def_rules() def plot(self, to_plot_list, args=[None], overwrite=False): """ Plot the data in to_plot_list and save them """ self.process(to_plot_list, args, overwrite, False) def _process_single(self, name, rule, arg, overwrite=False, overwrite_dep=False, just_done=[]): done = self._plot_rule(name, rule, arg, overwrite) return [] def _plot_rule(self, name, rule, arg, overwrite): if not arg is None: name_full = rule.group + '/' + name + '_' + str(arg) else: name_full = rule.group + '/' + name if rule.is_valid(arg): plot_filename = self._find_filename(name_full) if overwrite or not os.path.exists(plot_filename): rule.plot(arg) P.tight_layout(pad=1) P.savefig(plot_filename) P.close() self._log("{} plotted".format(name_full), "SUCCESS") else: self._log("Plot {} is already done, skipping...".format(name_full)) else: self._log("{} is not valid in this context".format(name_full), "ERROR") def _find_filename(self, name_full): if not self.pp_params.out.tag == '': tag_name = '_' + self.pp_params.out.tag else : tag_name = '' if 'num' in self.save.root._v_attrs: num = self.save.root._v_attrs.num return (self.path_out + '/' + name_full + tag_name + '_' + format(num,'05') + self.pp_params.plot.out_ext) else: return self.path_out + '/' + name_full + tag_name + self.pp_params.plot.out_ext def _plot_map(self, name, ax_los, label=None, cmap='plasma', vmin=None, vmax=None, overlays=[]): P.figure() ax_h = self._axes_h[ax_los] ax_v = self._axes_v[ax_los] im_extent = self.save.root.maps._v_attrs.im_extent dmap = self.save.get_node('/maps/{}_{}'.format(name, ax_los)).read() im = P.imshow(dmap, extent=im_extent, origin='lower', cmap=cmap, norm=mpl.colors.LogNorm()) im.set_clim(vmin, vmax) P.locator_params(axis=ax_h, nbins=self.pp_params.plot.ntick) P.locator_params(axis=ax_v, nbins=self.pp_params.plot.ntick) P.xlabel(self._ax_title[ax_h]) P.ylabel(self._ax_title[ax_v]) cbar = P.colorbar(im) if not label is None: cbar.set_label(label) for plot_overlay in overlays: plot_overlay(ax_los) def _overlay_levels(self, ax_los): map_level = self.save.get_node('/maps/{}_{}'.format('levels', ax_los)).read() # Computing linewidths levels_ar = np.arange(np.min(map_level), np.max(map_level) + 1) lw = np.ones(levels_ar.size) * 2 lvl_th = 8 # Level threeshold for reducing linewidths lw[levels_ar >= lvl_th] = lw[levels_ar >= lvl_th]**(lvl_th - levels_ar[levels_ar >= lvl_th]) lw[levels_ar < lvl_th] = 1. cont = P.contour(map_level, extent=self.save.root.maps._v_attrs.im_extent, origin='lower', colors='grey', linewidths=lw, levels=levels_ar) cont.levels = cont.levels + 1 P.clabel(cont, cont.levels[cont.levels < 11], inline=1, fontsize=8., fmt='%1d'); def _overlay_speed(self, ax_los): ax_h = self._axes_h[ax_los] ax_v = self._axes_v[ax_los] dmap_vh = self.save.get_node('/maps/speed_h_{}'.format(ax_los)).read() dmap_vv = self.save.get_node('/maps/speed_v_{}'.format(ax_los)).read() vel_red = self.pp_params.plot.vel_red radius = self.save.root.maps._v_attrs.radius center = self.save.root.maps._v_attrs.center lbox = self.save.root._v_attrs.lbox map_vh_red = dmap_vh[::vel_red,::vel_red] # take only a subset of velocities map_vv_red = dmap_vv[::vel_red,::vel_red] nh = map_vh_red.shape[0] nv = map_vv_red.shape[1] vec_h = (np.arange(nh)*2./nh*radius - radius + center[0] + radius/nh) * lbox vec_v = (np.arange(nv)*2./nv*radius - radius + center[1] + radius/nv) * lbox hh, vv = np.meshgrid(vec_h,vec_v) max_v = np.max(np.sqrt(map_vh_red**2 + map_vv_red**2)) Q = P.quiver(hh, vv, map_vh_red, map_vv_red, units='width', color='grey') P.quiverkey(Q, 0.6, 0.98, max_v, r'$'+str(max_v)[0:4]+'$ (code)', labelpos='E', coordinates='figure') def _plot_radial(self, name, ax_los='z', label=None, xlog=False, ylog=False): P.figure() radial_bins = self.save.get_node('/radial/radial_bins_' + ax_los).read() bin_centers = 0.5 * (radial_bins[1:] + radial_bins[:-1]) mean_bin = self.save.get_node('/radial/{}_{}'.format(name, ax_los)).read() P.grid() P.xlabel(r'$r$') if xlog: P.xscale('log') if ylog: P.yscale('log') P.plot(bin_centers, mean_bin) if not label is None: P.ylabel(label) def _plot_hist(self, name, ax_los='z', label=None, ylog=False): P.figure() pdf = self.save.get_node('/hist/' + name + '_' + ax_los) values, centers = pdf.read() width = centers[1] - centers[0] P.bar(centers, values, width, log=ylog) P.grid() if not label is None: P.xlabel(label) if '/hist/fit_' + name + '_' + ax_los in self.save: slope = pdf.attrs.slope origin = pdf.attrs.origin P.plot(centers, 10**(slope*centers + origin), '--', linewidth=2, color='orange') P.ylim([None, 1.]) def _plot(self, name_x, name_y, xlabel=None, ylabel=None, linearfit=False): P.figure() node_x = self.save.get_node(name_x) node_y = self.save.get_node(name_y) if xlabel is None: if 'label' in node_x: xlabel = name_x._vattrs.label else: xlabel = name_x if ylabel is None: if 'label' in node_y: ylabel = name_y._vattrs.label else: ylabel = name_y x = node_x.read() if node_y._v_attrs.CLASS == 'ARRAY': y = node_y.read() P.plot(x, y, fmt='*') else: y = node_y.mean.read() yerr = node_y.std.read() P.errorbar(x, y, yerr=y, fmt='*') P.xlabel(xlabel) P.ylabel(ylabel) if linearfit: if node_y._v_attrs.CLASS == 'ARRAY': (a, b, rho, _, stderr) = linregress(node_x.read(), node_y.read()) else: c = polyfit(node_x.read(), node_y.mean.read(), 1, w = [1.0 / ty for ty in node_y.std.read()], full=False) b, a = c[0], c[1] P.plot(x, a*y + b, '--', linewidth=1.5) P.grid() def def_rules(self): self.rules = { 'coldens' : PlotRule(self, lambda ax_los: self._plot_map('coldens', ax_los, label=r'$\Sigma$ (code)', vmin=0.01, vmax=100), "Column density", ['/maps/coldens']), 'rho' : PlotRule(self, lambda ax_los: self._plot_map('rho', ax_los, label=r'$\rho$ (code)'), "Density slice", ['/maps/rho']), 'coldens_l' : PlotRule(self, lambda ax_los: self._plot_map('coldens', ax_los, label=r'$\Sigma$ (code)', vmin=0.01, vmax=100, overlays=[self._overlay_levels]), "Column density", ['/maps/coldens', '/maps/levels']), 'rho_v' : PlotRule(self, lambda ax_los: self._plot_map('rho', ax_los, label=r'$\rho$ (code)', overlays=[self._overlay_speed]), "Density slice", ['/maps/rho', '/maps/speed_h', '/maps/speed_v']), 'jeans_ratio' : PlotRule(self, lambda ax_los: self._plot_map('jeans_ratio', ax_los, vmin=0.1, vmax=100, cmap='RdBu_r', overlays=[self._overlay_levels]), "Jeans' lenght divided by the max resolution", dependencies=['/maps/jeans_ratio', '/maps/levels']), 'Q' : PlotRule(self, lambda ax_los: self._plot_map('rho', ax_los, label=r'$Q$', vmin=0.01, vmax=100, cmap='RdBu_r'), "Toomre Q parameter for a Keplerian disk", dependencies=['/maps/Q'], args_ok=['z']) } averageables = ['coldens', 'rho', 'T', 'Q'] for name in averageables: self.rules['rad_' + name] = PlotRule(self, partial(self._plot_radial, 'rad_avg_' + name, label=name, xlog=True, ylog=True), "Azimuthal average of {}".format(name), dependencies=['/radial/radial_bins', '/radial/rad_avg_' + name], args_ok=['z']) self.rules['fluct_' + name] = PlotRule(self, partial(self._plot_map, 'fluct_' + name, vmin=0.01, vmax=100, cmap='RdBu_r', label='{}/avg({})'.format(name, name)), "Fluctuation wrt to average of {}".format(name), dependencies=['/maps/fluct_' + name], args_ok=['z']) self.rules['pdf_' + name] = PlotRule(self, partial(self._plot_hist, 'pdf_' + name, ylog=True, label='{}/avg({})'.format(name, name)), "Probability density function of {} fluctuations".format(name), dependencies=['/hist/pdf_' + name], args_ok=['z']) self.rules.update({ 'kappa_beta' : PlotRule(self, partial(self._plot, '/comp/beta', '/comp/avg_pdf_slope_coldens', linearfit=True), args_ok=[None], dependencies=['/comp/beta', '/comp/avg_pdf_slope_coldens']), 'sink_mass' : PlotRule(self, partial(self._plot, '/series/time', '/series/sinks_mass', linearfit=True), args_ok=[None], dependencies=['/series/time', '/series/sinks_mass']) }) class InteractiveGUI: """ This is a matplotlib interactive session to restrain analysis to a specific area """ def onbuttonrelease(self, event): """Deal with click events""" button = ['left','middle','right'] toolbar = P.get_current_fig_manager().toolbar if toolbar.mode=='zoom rect' and event.inaxes == self.ax_col: print("zooming ") xlim = self.ax_col.get_xlim() ylim = self.ax_col.get_ylim() self.reset_mask() elif self.add_mask and event.inaxes == self.ax_col: self.plot_side() P.draw() def onbuttonpress(self, event): """Deal with click events""" button = ['left','middle','right'] toolbar = P.get_current_fig_manager().toolbar if toolbar.mode!='': print("You clicked on something, but toolbar is in mode {:s}.".format(toolbar.mode)) print(self.add_mask) if self.add_mask and toolbar.mode=='' and event.inaxes == self.ax_col: ix, iy = event.xdata, event.ydata print("Add patch {}, {}".format(ix, iy)) xlim = self.ax_col.get_xlim() ylim = self.ax_col.get_ylim() radius = 0.05 * min(abs(xlim[1] - xlim[0]), abs(ylim[1] - ylim[0])) circle = mpatches.Circle([ix, iy], radius, color='black', alpha=0.1, ec="none") self.circles.append(circle) self.ax_col.add_artist(circle) self.ax_col.draw_artist(circle) self.patch_mask = self.patch_mask | ((self.xx - ix)**2 + (self.yy -iy)**2 < radius**2) #self.plot_side() def onkeypress(self, event): """whenever a key is pressed""" if not event.inaxes: return if event.key == 't': self.add_mask = not self.add_mask print("Add mode is {}".format(self.add_mask)) elif event.key == 'r': self.reset_mask() def plot_side(self): if (self.add_mask): mask = (self.patch_mask & self.mask).flatten() else: mask = self.mask.flatten() self.ax_gamma.clear() P.sca(self.ax_gamma) plot_dcsdrho(self.fluct_maps, mask, tag=self.tag) self.ax_pdf.clear() P.sca(self.ax_pdf) sigma_pdf(self.fluct_maps, mask, tag=self.tag, nb_bin_hist=self.args.pdf_nb_bin) def reset_mask(self): xlim = self.ax_col.get_xlim() ylim = self.ax_col.get_ylim() self.mask = (self.xx >= xlim[0]) & (self.xx <= xlim[1]) & (self.yy >= ylim[0]) & (self.yy <= ylim[1]) self.patch_mask = np.full(self.mask.shape, False) for circle in self.circles: circle.remove() self.circles = [] self.plot_side() P.draw() def __init__(self, args, path, num, maps_disk=None, tag='', set_lim=True) : """ Interactive plotting Parameters ---------- num output number path path of the pipeline output """ self.args = args self.add_mask = False self.circles = [] self.tag = tag path_out = path # Load maps file print("load maps file") name_maps = path + '/maps_disk' + '_' + tag + '_' + format(num,'05') + '.save' if maps_disk is None: if (len(glob.glob(name_maps)) == 0): raise IOError('no pickle file for disk maps {}. Run make_image_disk() first'.format(name_maps)) f = open(name_maps,'r') maps_disk = pickle.load(f) f.close() print("maps file loaded") im_extent = maps_disk['im_extent'] fig = P.figure(); self.ax_col = P.subplot(1, 2, 1) coldens = maps_disk['coldens_z'] im = self.ax_col.imshow(coldens, extent=im_extent, origin='lower', norm=mpl.colors.LogNorm()) if set_lim: im.set_clim(0.01, 100) self.ax_col.set_xlabel(r'$x$') self.ax_col.set_ylabel(r'$y$') self.xx, self.yy, self.fluct_maps = disk_pdf(path, num, maps_disk, tag=self.tag, force=True, put_title=False, interactive=True) coord_flat = zip(self.xx.flatten(), self.yy.flatten()) self.ax_gamma = P.subplot(2, 2, 2) self.ax_pdf = P.subplot(2,2,4) xlim = self.ax_col.get_xlim() ylim = self.ax_col.get_ylim() self.mask = (self.xx >= xlim[0]) & (self.xx <= xlim[1]) & (self.yy >= ylim[0]) & (self.yy <= ylim[1]) self.patch_mask = np.full(self.mask.shape, False) self.plot_side() fig.canvas.mpl_connect('button_release_event', self.onbuttonrelease) fig.canvas.mpl_connect('button_press_event', self.onbuttonpress) fig.canvas.mpl_connect('key_press_event', self.onkeypress) P.tight_layout() P.show()