import os from experiment import Experiment # noinspection PyUnresolvedReferences from soup import Soup from argparse import ArgumentParser import numpy as np import plotly as pl import plotly.graph_objs as go import colorlover as cl import dill from sklearn.manifold.t_sne import TSNE, PCA def build_args(): arg_parser = ArgumentParser() arg_parser.add_argument('-i', '--in_file', nargs=1, type=str) arg_parser.add_argument('-o', '--out_file', nargs='?', default='out', type=str) return arg_parser.parse_args() def build_from_soup_or_exp(soup): particles = soup.historical_particles particle_list = [] for particle in particles.values(): particle_dict = dict( trajectory=[event['weights'] for event in particle], time=[event['time'] for event in particle], action=[event.get('action', None) for event in particle], counterpart=[event.get('counterpart', None) for event in particle] ) if any([x is not None for x in particle_dict['counterpart']]): print('counterpart') particle_list.append(particle_dict) return particle_list def plot_latent_trajectories(soup_or_experiment, filename='latent_trajectory_plot'): assert isinstance(soup_or_experiment, (Experiment, Soup)) bupu = cl.scales['11']['div']['RdYlGn'] data_dict = build_from_soup_or_exp(soup_or_experiment) scale = cl.interp(bupu, len(data_dict)+1) # Map color scale to N bins # Fit the mebedding space transformer = TSNE() for particle_dict in data_dict: array = np.asarray([np.hstack([x.flatten() for x in timestamp]).flatten() for timestamp in particle_dict['trajectory']]) particle_dict['trajectory'] = array transformer.fit(array) # Transform data accordingly and plot it data = [] for p_id, particle_dict in enumerate(data_dict): transformed = transformer._fit(np.asarray(particle_dict['trajectory'])) line_trace = go.Scatter( x=transformed[:, 0], y=transformed[:, 1], text='Hovertext goes here'.format(), line=dict(color=scale[p_id]), # legendgroup='Position -{}'.format(pos), name='Particle - {}'.format(p_id), showlegend=True, # hoverinfo='text', mode='lines') line_start = go.Scatter(mode='markers', x=[transformed[0, 0]], y=[transformed[0, 1]], marker=dict( color='rgb(255, 0, 0)', size=4 ), showlegend=False ) line_end = go.Scatter(mode='markers', x=[transformed[-1, 0]], y=[transformed[-1, 1]], marker=dict( color='rgb(0, 0, 0)', size=4 ), showlegend=False ) data.extend([line_trace, line_start, line_end]) layout = dict(title='{} - Latent Trajectory Movement'.format('Penis'), height=800, width=800, margin=dict(l=0, r=0, t=0, b=0)) # import plotly.io as pio # pio.write_image(fig, filename) fig = go.Figure(data=data, layout=layout) pl.offline.plot(fig, auto_open=True, filename=filename) pass def plot_latent_trajectories_3D(soup_or_experiment, filename='plot'): def norm(val, a=0, b=0.25): return (val - a) / (b - a) data_list = build_from_soup_or_exp(soup_or_experiment) if not data_list: return base_scale = cl.scales['9']['div']['RdYlGn'] # base_scale = cl.scales['9']['qual']['Set1'] scale = cl.interp(base_scale, len(data_list)+1) # Map color scale to N bins # Fit the embedding space transformer = PCA(n_components=2) array = [] for particle_dict in data_list: array.append(particle_dict['trajectory']) transformer.fit(np.vstack(array)) # Transform data accordingly and plot it data = [] for p_id, particle_dict in enumerate(data_list): transformed = transformer.transform(particle_dict['trajectory']) line_trace = go.Scatter3d( x=transformed[:, 0], y=transformed[:, 1], z=np.asarray(particle_dict['time']), text='Particle: {}
It had {} lifes.'.format(p_id, len(particle_dict['trajectory'])), line=dict( color=scale[p_id], width=4 ), # legendgroup='Particle - {}'.format(p_id), name='Particle -{}'.format(p_id), showlegend=False, hoverinfo='text', mode='lines') line_start = go.Scatter3d(mode='markers', x=[transformed[0, 0]], y=[transformed[0, 1]], z=np.asarray(particle_dict['time'][0]), marker=dict( color='rgb(255, 0, 0)', size=4 ), showlegend=False ) line_end = go.Scatter3d(mode='markers', x=[transformed[-1, 0]], y=[transformed[-1, 1]], z=np.asarray(particle_dict['time'][-1]), marker=dict( color='rgb(0, 0, 0)', size=4 ), showlegend=False ) data.extend([line_trace, line_start, line_end]) axis_layout = dict(gridcolor='rgb(255, 255, 255)', gridwidth=3, zerolinecolor='rgb(255, 255, 255)', showbackground=True, backgroundcolor='rgb(230, 230,230)', titlefont=dict( color='black', size=30 ) ) layout = go.Layout(scene=dict( # aspectratio=dict(x=2, y=2, z=2), xaxis=dict(title='Transformed X', **axis_layout), yaxis=dict(title='Transformed Y', **axis_layout), zaxis=dict(title='Epoch', **axis_layout)), # title='{} - Latent Trajectory Movement'.format('Soup'), width=1024, height=1024, margin=dict(l=0, r=0, b=0, t=0) ) fig = go.Figure(data=data, layout=layout) pl.offline.plot(fig, auto_open=True, filename=filename, validate=True) pass def plot_histogram(bars_dict_list, filename='histogram_plot'): # catagorical ryb = cl.scales['10']['div']['RdYlBu'] data = [] for bar_id, bars_dict in bars_dict_list: hist = go.Histogram( histfunc="count", y=bars_dict.get('value', 14), x=bars_dict.get('name', 'gimme a name'), showlegend=False, marker=dict( color=ryb[bar_id] ), ) data.append(hist) layout=dict(title='{} Histogram Plot'.format('Experiment Name Penis'), height=400, width=400, margin=dict(l=0, r=0, t=0, b=0)) fig = go.Figure(data=data, layout=layout) pl.offline.plot(fig, auto_open=True, filename=filename) pass def line_plot(line_dict_list, filename='lineplot'): # lines with standard deviation # Transform data accordingly and plot it data = [] rdylgn = cl.scales['10']['div']['RdYlGn'] rdylgn_background = [scale + (0.4,) for scale in cl.to_numeric(rdylgn)] for line_id, line_dict in enumerate(line_dict_list): name = line_dict.get('name', 'gimme a name') upper_bound = go.Scatter( name='Upper Bound', x=line_dict['x'], y=line_dict['upper_y'], mode='lines', marker=dict(color="#444"), line=dict(width=0), fillcolor=rdylgn_background[line_id], ) trace = go.Scatter( x=line_dict['x'], y=line_dict['main_y'], mode='lines', name=name, line=dict(color=line_id), fillcolor=rdylgn_background[line_id], fill='tonexty') lower_bound = go.Scatter( name='Lower Bound', x=line_dict['x'], y=line_dict['lower_y'], marker=dict(color="#444"), line=dict(width=0), mode='lines') data.extend([upper_bound, trace, lower_bound]) layout=dict(title='{} Line Plot'.format('Experiment Name Penis'), height=800, width=800, margin=dict(l=0, r=0, t=0, b=0)) fig = go.Figure(data=data, layout=layout) pl.offline.plot(fig, auto_open=True, filename=filename) pass def search_and_apply(absolut_file_or_folder, plotting_function, files_to_look_for=[]): if os.path.isdir(absolut_file_or_folder): for sub_file_or_folder in os.scandir(absolut_file_or_folder): search_and_apply(sub_file_or_folder.path, plotting_function, files_to_look_for=files_to_look_for) elif absolut_file_or_folder.endswith('.dill'): file_or_folder = os.path.split(absolut_file_or_folder)[-1] if file_or_folder in files_to_look_for and not os.path.exists('{}.html'.format(absolut_file_or_folder[:-5])): print('Apply Plotting function "{func}" on file "{file}"'.format(func=plotting_function.__name__, file=absolut_file_or_folder) ) with open(absolut_file_or_folder, 'rb') as in_f: exp = dill.load(in_f) try: plotting_function(exp, filename='{}.html'.format(absolut_file_or_folder[:-5])) except ValueError: pass except AttributeError: pass else: # This was either another FilyType or Plot.html alerady exists. pass if __name__ == '__main__': args = build_args() in_file = args.in_file[0] out_file = args.out_file search_and_apply(in_file, plot_latent_trajectories_3D, ["trajectorys.dill", "soup.dill"])