Noe Brucy
458 lines
16 KiB
Python
458 lines
16 KiB
Python
import matplotlib as mpl
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import matplotlib.pyplot as plt
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from matplotlib.widgets import Slider, Button, RadioButtons, LassoSelector, SpanSelector
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import matplotlib.patches as patches
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from matplotlib.lines import Line2D
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from matplotlib.path import Path
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from scipy.stats import linregress
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from postprocessor import *
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from skimage.draw import line
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class DraggablePoint:
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# http://stackoverflow.com/questions/21654008/matplotlib-drag-overlapping-points-interactively
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lock = None # only one can be animated at a time
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def __init__(self, parent, x=0.1, y=0.1, size=0.1):
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self.parent = parent
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if self.parent.list_points:
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color = "r"
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else:
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color = "g"
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self.point = patches.Ellipse(
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(x, y), size, size * 3, fc=color, alpha=0.5, edgecolor=color
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)
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self.x = x
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self.y = y
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self.parent.ax.add_patch(self.point)
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self.press = None
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self.background = None
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self.connect()
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if self.parent.list_points:
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line_x = [self.parent.list_points[0].x, self.x]
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line_y = [self.parent.list_points[0].y, self.y]
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self.line = Line2D(line_x, line_y, color="k", alpha=0.5)
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self.parent.ax.add_line(self.line)
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def connect(self):
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"connect to all the events we need"
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self.cidpress = self.point.figure.canvas.mpl_connect(
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"button_press_event", self.on_press
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)
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self.cidrelease = self.point.figure.canvas.mpl_connect(
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"button_release_event", self.on_release
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)
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self.cidmotion = self.point.figure.canvas.mpl_connect(
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"motion_notify_event", self.on_motion
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)
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def on_press(self, event):
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if event.inaxes != self.point.axes:
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return
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if DraggablePoint.lock is not None:
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return
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contains, attrd = self.point.contains(event)
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if not contains:
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return
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self.press = (self.point.center), event.xdata, event.ydata
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DraggablePoint.lock = self
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# draw everything but the selected rectangle and store the pixel buffer
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canvas = self.point.figure.canvas
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axes = self.point.axes
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self.point.set_animated(True)
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if self == self.parent.list_points[1]:
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self.line.set_animated(True)
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else:
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self.parent.list_points[1].line.set_animated(True)
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canvas.draw()
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self.background = canvas.copy_from_bbox(self.point.axes.bbox)
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# now redraw just the rectangle
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axes.draw_artist(self.point)
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# and blit just the redrawn area
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canvas.blit(axes.bbox)
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def on_motion(self, event):
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if DraggablePoint.lock is not self:
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return
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if event.inaxes != self.point.axes:
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return
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self.point.center, xpress, ypress = self.press
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dx = event.xdata - xpress
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dy = event.ydata - ypress
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self.point.center = (self.point.center[0] + dx, self.point.center[1] + dy)
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canvas = self.point.figure.canvas
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axes = self.point.axes
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# restore the background region
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canvas.restore_region(self.background)
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# redraw just the current rectangle
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axes.draw_artist(self.point)
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if self == self.parent.list_points[1]:
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axes.draw_artist(self.line)
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else:
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self.parent.list_points[1].line.set_animated(True)
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axes.draw_artist(self.parent.list_points[1].line)
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self.x = self.point.center[0]
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self.y = self.point.center[1]
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if self == self.parent.list_points[1]:
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line_x = [self.parent.list_points[0].x, self.x]
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line_y = [self.parent.list_points[0].y, self.y]
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self.line.set_data(line_x, line_y)
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else:
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line_x = [self.x, self.parent.list_points[1].x]
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line_y = [self.y, self.parent.list_points[1].y]
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self.parent.list_points[1].line.set_data(line_x, line_y)
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# blit just the redrawn area
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canvas.blit(axes.bbox)
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def on_release(self, event):
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"on release we reset the press data"
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if DraggablePoint.lock is not self:
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return
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self.press = None
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DraggablePoint.lock = None
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# turn off the rect animation property and reset the background
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self.point.set_animated(False)
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if self == self.parent.list_points[1]:
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self.line.set_animated(False)
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else:
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self.parent.list_points[1].line.set_animated(False)
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self.background = None
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# redraw the full figure
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self.point.figure.canvas.draw()
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self.x = self.point.center[0]
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self.y = self.point.center[1]
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self.parent.draw()
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def disconnect(self):
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"disconnect all the stored connection ids"
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self.point.figure.canvas.mpl_disconnect(self.cidpress)
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self.point.figure.canvas.mpl_disconnect(self.cidrelease)
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self.point.figure.canvas.mpl_disconnect(self.cidmotion)
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class DraggableLine:
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def __init__(self, parent, ax, xy1, xy2, size):
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self.parent = parent
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self.ax = ax
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self.list_points = []
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self.list_points.append(DraggablePoint(self, xy1[0], xy1[1], size))
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self.list_points.append(DraggablePoint(self, xy2[0], xy2[1], size))
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def draw(self):
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self.parent.draw(update_map=False)
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class InteractiveGUI:
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"""
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This is a matplotlib interactive session to restrain analysis to a specific area
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"""
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def update_rmin(self, val):
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# amp is the current value of the slider
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self.rmin = self.srmin.val
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# update curve
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self.draw()
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# redraw canvas while idle
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plt.draw()
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def update_rmax(self, val):
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# amp is the current value of the slider
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self.rmax = self.srmax.val
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# update curve
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self.draw()
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# redraw canvas while idle
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plt.draw()
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def draw(self, first=False, update_map=True):
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if first or update_map:
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self.fmap = np.copy(self.datamap)
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self.frho = np.copy(self.frho_map)
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self.fcs = np.copy(self.fcs_map)
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self.fmap[np.logical_not(self.mask)] = np.nan
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## Map
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plt.sca(self.ax_fluct)
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if first:
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self.im = plt.imshow(
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self.fmap,
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origin="lower",
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cmap="RdBu_r",
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norm=mpl.colors.LogNorm(),
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vmin=1e-2,
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vmax=1e2,
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)
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plt.title("Fluctuations")
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# cbar = plt.colorbar()
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else:
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self.im.set_data(self.fmap)
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## Gamma
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plt.sca(self.ax_gamma)
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# (a, b, r, _, _) = linregress(self.frho_map[self.mask], self.fcs_map[self.mask])
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if first:
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_, _, _, self.gamma_hist = plt.hist2d(
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self.frho_map[self.mask],
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self.fcs_map[self.mask],
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bins=100,
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norm=mpl.colors.LogNorm(),
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cmap=plt.get_cmap("plasma"),
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)
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plt.xlabel(r"$\log(\rho / \bar{\rho})$")
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plt.ylabel(r"$\log(c_s / \bar{c_s})$")
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# cbar = plt.colorbar()
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else:
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self.gamma_hist.remove()
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_, _, _, self.gamma_hist = plt.hist2d(
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self.frho_map[self.mask],
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self.fcs_map[self.mask],
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bins=100,
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norm=mpl.colors.LogNorm(),
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cmap=plt.get_cmap("plasma"),
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)
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plt.legend()
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lps = self.line_gamma.list_points
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xa, ya, xb, yb = lps[0].x, lps[0].y, lps[1].x, lps[1].y
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a = (yb - ya) / (xb - xa)
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self.ax_gamma.set_title("$\Gamma$ = {:.3g}".format(2 * a + 1))
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## PDF
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if first or update_map:
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plt.sca(self.ax_pdf)
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nb_cells = np.sum(self.mask_flat.flatten())
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if first:
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self.std_nb_cells = nb_cells
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values, self.edges = np.histogram(
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np.log10(self.fmap.flatten()[self.mask_flat]),
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self.pp.pp_params.pdf.nb_bin,
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weights=np.ones(nb_cells) / self.std_nb_cells,
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)
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edges = self.edges
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plt.xlabel(r"$\log(\Sigma / \bar{\Sigma})$")
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plt.ylabel(r"$\mathcal{P}_\Sigma$")
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else:
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values, edges = np.histogram(
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np.log10(self.fmap.flatten()[self.mask_flat]),
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self.edges,
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weights=np.ones(nb_cells) / self.std_nb_cells,
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)
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centers = 0.5 * (edges[1:] + edges[:-1])
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mask_fit = (
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(centers > self.pp.pp_params.pdf.xmin_fit)
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& (centers < self.pp.pp_params.pdf.xmax_fit)
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& (values > 0)
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)
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(a, b, r, _, _) = linregress(centers[mask_fit], np.log10(values[mask_fit]))
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if first:
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plt.step(centers, values, where="mid", alpha=0.3)
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(self.step,) = plt.step(centers, values, where="mid")
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(self.fit,) = plt.plot(
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centers,
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10 ** (a * centers + b),
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"--",
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color="navy",
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label=r"a = {:.3g}, $R^2$ = {:.3g}".format(a, r ** 2),
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)
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plt.yscale("log")
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plt.title("PDF")
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else:
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self.step.set_ydata(values)
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self.fit.set_ydata(10 ** (a * centers + b))
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self.fit.set_label(r"a = {:.3g}, $R^2$ = {:.3g}".format(a, r ** 2))
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plt.legend()
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### PROFILE
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plt.sca(self.ax_profile)
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lps = self.line_profile.list_points
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xa, ya, xb, yb = lps[0].x, lps[0].y, lps[1].x, lps[1].y
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xp, yp = line(int(xa), int(ya), int(xb), int(yb))
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rho_prof = self.frho_map[yp, xp]
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# Position on the line
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# x = np.linspace(0, 1, len(rho_prof))
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x = np.sqrt(np.abs((xp - xa) * (xb - xa) + (yp - ya) * (yb - ya)))
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x = (x - float(x[0])) / self.shape[0] # /(x[-1] - x[0])
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mask_fit_prof = (x >= self.fit_prof_vmin) & (x <= self.fit_prof_vmax)
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try:
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(a, b, r, _, _) = linregress(
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np.log10(x[mask_fit_prof]), rho_prof[mask_fit_prof]
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)
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except ValueError as e:
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print("Warning in linregress : {}".format(e))
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a, b, r = np.nan, np.nan, np.nan
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if first:
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(self.prof,) = plt.semilogx(x, rho_prof)
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plt.xlim([None, None])
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plt.title("Profil")
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(self.fit_prof,) = plt.plot(
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x[mask_fit_prof],
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a * np.log10(x[mask_fit_prof]) + b,
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"--",
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color="navy",
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label=r"a = {:.3g}, $R^2$ = {:.3g}".format(a, r ** 2),
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)
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self.ax_profile.set_xlabel(r"$r$")
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self.ax_profile.set_ylabel(r"$\log(\rho)$")
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else:
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self.prof.set_data(x, rho_prof)
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plt.xlim([None, None])
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self.fit_prof.set_data(x[mask_fit_prof], a * np.log10(x[mask_fit_prof]) + b)
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self.fit_prof.set_label(r"a = {:.3g}, $R^2$ = {:.3g}".format(a, r ** 2))
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plt.legend()
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plt.draw()
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self.unselect_lasso()
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def onselect(self, verts, select_data):
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# update curve
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path = Path(verts)
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self.mask_flat = self.mask.flatten()
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self.mask_flat = self.mask_flat & path.contains_points(select_data)
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self.mask = self.mask_flat.reshape(self.shape)
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self.draw()
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def clicked_select(self, val):
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if not self.lasso:
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self.lasso = LassoSelector(
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self.ax_fluct, partial(self.onselect, select_data=self.points)
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)
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self.lasso_gamma = LassoSelector(
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self.ax_gamma, partial(self.onselect, select_data=self.rhocs)
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)
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self.lasso_button.color = "0.55"
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else:
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self.unselect_lasso()
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def unselect_lasso(self):
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self.lasso = False
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self.lasso_gamma = False
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self.lasso_button.color = "0.85"
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def clicked_reset(self, val):
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self.mask = (self.rr > self.rmin) & (self.rr < self.rmax)
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self.mask_flat = self.mask.flatten()
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self.lasso = False
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self.lasso_button.color = "0.85"
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self.draw()
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def fit_selector(self, vmin, vmax):
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self.fit_prof_vmin = vmin
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self.fit_prof_vmax = vmax
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self.draw()
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def __init__(self, num, path="./", pp_params=None, datamap_key="fluct_coldens_z"):
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"""
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Interactive plotting
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"""
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if pp_params is None:
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pp_params = default_params()
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pp_params.input.nml_filename = "disk.nml"
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pp_params.out.interactive = True
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pp_params.pymses.map_size = 4096
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pp_params.pymses.zoom = 4
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pp_params.pymses.variables = ["rho", "vel", "P"]
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pp_params.disk.enable = True
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pp_params.disk.nb_bin = 200
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pp_params.pdf.nb_bin = 100
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self.fig = plt.figure(figsize=(10, 8))
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self.pp = PostProcessor(path, num, pp_params=pp_params, tag="interactive")
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self.pp.pdf_coldens("z")
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self.pp.pdf_rho("z")
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self.pp.pdf_T("z")
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self.datamap = self.pp.get_value("/maps/" + datamap_key)
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self.frho_map = np.log10(self.pp.get_value("/maps/fluct_rho_z"))
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self.T_map = self.pp.get_value("/maps/T_z")
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self.fcs_map = np.log10(np.sqrt(self.pp.get_value("/maps/fluct_T_z")))
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self.rr = self.pp.get_value("/maps/rr_z")
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self.shape = self.datamap.shape
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x = np.arange(self.shape[0])
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y = np.arange(self.shape[1])
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self.xx, self.yy = np.meshgrid(x, y)
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self.points = np.column_stack((self.xx.flatten(), self.yy.flatten()))
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self.rhocs = np.column_stack((self.frho_map.flatten(), self.fcs_map.flatten()))
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self.rmin = self.pp.pp_params.disk.rmin_pdf
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self.rmax = self.pp.pp_params.disk.rmax_pdf
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self.ax_fluct = plt.axes([0.05, 0.6, 0.4, 0.35])
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self.ax_gamma = plt.axes([0.05, 0.15, 0.4, 0.35])
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self.ax_pdf = plt.axes([0.55, 0.6, 0.4, 0.35])
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self.ax_profile = plt.axes([0.55, 0.15, 0.4, 0.35])
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self.fit_prof_vmin = 1e-2
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self.fit_prof_vmax = 1e-1
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self.fit_profile_selector = SpanSelector(
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self.ax_profile,
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self.fit_selector,
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"horizontal",
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useblit=True,
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span_stays=True,
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)
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self.line_profile = DraggableLine(
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self, self.ax_fluct, [100, 2000], [2000, 2000], 50
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)
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self.line_gamma = DraggableLine(self, self.ax_gamma, [0, 0], [1, 0.3], 0.03)
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self.mask = (self.rr > self.rmin) & (self.rr < self.rmax)
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self.mask_flat = self.mask.flatten()
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ax_rmax = plt.axes([0.1, 0.03, 0.4, 0.02])
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self.srmax = Slider(ax_rmax, r"$r_{max}$", 0, 0.5, valinit=self.rmax)
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ax_rmin = plt.axes([0.1, 0.07, 0.4, 0.02])
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self.srmin = Slider(ax_rmin, r"$r_{min}$", 0, 0.5, valinit=self.rmin)
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ax_lasso = plt.axes([0.6, 0.07, 0.2, 0.02])
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self.lasso = False
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self.lasso_gamma = False
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self.lasso_button = Button(ax_lasso, "Select region")
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self.lasso_button.on_clicked(self.clicked_select)
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ax_reset = plt.axes([0.6, 0.03, 0.2, 0.02])
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self.reset_button = Button(ax_reset, "Reset")
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self.reset_button.on_clicked(self.clicked_reset)
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self.srmin.on_changed(self.update_rmin)
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self.srmax.on_changed(self.update_rmax)
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self.draw(True)
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plt.show()
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