Source code for flyqma.annotation.mixtures.visualization

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

from ...visualization import *

[docs]def figure(func): """ Decorator for creating axis. """ def wrapper(*args, ax=None, **kwargs): if ax is None: fig, ax = plt.subplots(figsize=(2., 1.25)) func(*args, ax=ax, **kwargs) return wrapper
[docs]def surface_figure(func): """ Decorator for creating joint axis. """ def wrapper(*args, ax=None, **kwargs): if ax is None: fig, ax = plt.subplots(figsize=(2., 2.)) func(*args, ax=ax, **kwargs) return wrapper
[docs]class MixtureVisualization: """ Visualization methods for mixture models. """ @property def summary(self): """ Returns text-based summary of mixture model. """ m = ' :: '.join(['{:0.2f}'.format(x) for x in self.means]) s = ' :: '.join(['{:0.2f}'.format(np.sqrt(x)) for x in self.stds]) w = ' :: '.join(['{:0.2f}'.format(x) for x in self.weights_]) summary = 'Means: {:s}'.format(m) summary += '\nStds: {:s}'.format(s) summary += '\nWeights: {:s}'.format(w) summary += '\nlnL: {:0.2f}'.format(self.log_likelihood) return summary @figure def plot_component_pdf(self, idx, weighted=True, log=True, ax=None, **kwargs): """ Plots PDF for specified component. """ # retrieve pdf for specified component pdf = self.get_component_pdf(idx, weighted=weighted) # plot component pdf if log: ax.plot(, pdf, **kwargs) else: ax.plot(self.scale_factor, pdf/self.scale_factor, **kwargs) self.format_ax(ax, log=log) @figure def plot_pdf(self, log=True, ax=None, **kwargs): """ Plots overall PDF for mixture model. """ if log: ax.plot(, self.pdf, **kwargs) else: ax.plot(self.scale_factor, self.pdf/self.scale_factor, **kwargs) self.format_ax(ax, log=log) @figure def plot(self, log=True, ax=None, pdf_color='k', component_color='r', **kwargs): """ Plots PDF for mixture model as well as each weighted component. """ self.plot_pdf(log=log, ax=ax, color=pdf_color) for i in range(self.n_components): self.plot_component_pdf(i, log=log, ax=ax, color=component_color) @figure def plot_data(self, log=True, ax=None, **kwargs): """ Plot binned values. """ if log: data = self.values else: data = np.exp(self.values) bins = np.linspace(,, num=50) _ = ax.hist(data, bins=bins, density=True, **kwargs) def format_ax(self, ax, log=True): if log: ax.set_xlim(, else: ax.set_xlim(0, self.scale_factor.max()) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False)
[docs]class BivariateVisualization: """ Visualization methods for bivariate mixture models. """ @surface_figure def plot_pdf_surface(self, ax=None, contours=None, **kwargs): ax.imshow(self.pdf, extent=self.extent, **kwargs) if contours is not None: ax.contour(self.pdf, contours, extent=self.extent[[0,1,3,2]], colors='r') self.format_joint_ax(ax) @figure def plot_margin(self, margin=0, invert=False, log=True, pdf_color='k', pdf_linestyle='--', pdf_lw=0.5, component_color='r', component_lw=0.5, min_density=0.01, ax=None): # extract x/y margins if margin == 0: support, pdf = self.get_xmargin(log=log) else: support, pdf = self.get_ymargin(log=log) # define colors for component lines if type(component_color) == str: component_color *= self.n_components # define parameters above = (pdf >= min_density) pdf_kw = dict(color=pdf_color, lw=pdf_lw, linestyle=pdf_linestyle) comp_kw = dict(lw=component_lw) # invert x/y axes (for vertical margin) if invert: ax.plot(pdf[above], support[above], '-', **pdf_kw) for i in range(self.n_components): mpdf = self.get_component_marginal_pdf(i, margin, True) ind = (mpdf >= min_density) ax.plot(mpdf[ind], support[ind], color=component_color[i], **comp_kw) else: ax.plot(support[above], pdf[above], '-', **pdf_kw) for i in range(self.n_components): mpdf = self.get_component_marginal_pdf(i, margin, True) ind = (mpdf >= min_density) ax.plot(support[ind], mpdf[ind], color=component_color[i], **comp_kw) return ax
[docs] def visualize(self, size_ratio=4, figsize=(2, 2), contours=None, **kwargs): """ Visualize joint and marginal distributions. """ # create figure fig = plt.figure(figsize=figsize) ratios = [size_ratio/(1+size_ratio), 1/(1+size_ratio)] gs = GridSpec(2, 2, width_ratios=ratios, height_ratios=ratios[::-1], wspace=0, hspace=0) ax_joint = fig.add_subplot(gs[1, 0]) ax_xmargin = fig.add_subplot(gs[0, 0]) ax_ymargin = fig.add_subplot(gs[1, 1]) ax_xmargin.axis('off') ax_ymargin.axis('off') # plot multivariate pdf surface self.plot_pdf_surface(ax=ax_joint, contours=contours, **kwargs) # plot marginal pdfs self.plot_margin(0, ax=ax_xmargin) self.plot_margin(1, invert=True, ax=ax_ymargin) ax_xmargin.set_xlim(self.lbound, self.ubound) ax_ymargin.set_ylim(self.lbound, self.ubound) return fig
@figure def plot_data(self, ax=None, **kwargs): """ Scatter datapoints on <ax>. """ ax.scatter(*self.values.T, **kwargs) def format_joint_ax(self, ax): ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.invert_yaxis() ax.set_xlabel('ln X') ax.set_ylabel('<ln X> among neighbors') ax.set_xticks(self.tick_positions) ax.set_yticks(self.tick_positions) ax.spines['left'].set_position(('outward', 2)) ax.spines['bottom'].set_position(('outward', 2)) def format_marginal_ax(self, ax): ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.set_xlabel('ln X') @property def tick_positions(self): """ Tick positions. """ lbound = np.ceil(self.supportx.min()) ubound = np.floor(self.supportx.max()) step = (ubound - lbound) // 4 return np.arange(lbound, ubound+1, step)