198 lines
6.7 KiB
Python
198 lines
6.7 KiB
Python
import os
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from argparse import ArgumentParser
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import numpy as np
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import plotly as pl
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import plotly.graph_objs as go
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import colorlover as cl
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import dill
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from sklearn.manifold.t_sne import TSNE
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def build_args():
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arg_parser = ArgumentParser()
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arg_parser.add_argument('-i', '--in_file', nargs=1, type=str)
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arg_parser.add_argument('-o', '--out_file', nargs='?', default='out', type=str)
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return arg_parser.parse_args()
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def plot_latent_trajectories(data_dict, filename='latent_trajectory_plot'):
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bupu = cl.scales['9']['seq']['BuPu']
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scale = cl.interp(bupu, len(data_dict)+1) # Map color scale to N bins
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# Fit the mebedding space
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transformer = TSNE()
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for trajectory_id in data_dict:
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transformer.fit(np.asarray(data_dict[trajectory_id]))
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# Transform data accordingly and plot it
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data = []
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for trajectory_id in data_dict:
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transformed = transformer._fit(np.asarray(data_dict[trajectory_id]))
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line_trace = go.Scatter(
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x=transformed[:, 0],
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y=transformed[:, 1],
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text='Hovertext goes here'.format(),
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line=dict(color=scale[trajectory_id]),
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# legendgroup='Position -{}'.format(pos),
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# name='Position -{}'.format(pos),
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showlegend=False,
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# hoverinfo='text',
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mode='lines')
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line_start = go.Scatter(mode='markers', x=[transformed[0, 0]], y=[transformed[0, 1]],
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marker=dict(
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color='rgb(255, 0, 0)',
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size=4
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),
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showlegend=False
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)
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line_end = go.Scatter(mode='markers', x=[transformed[-1, 0]], y=[transformed[-1, 1]],
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marker=dict(
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color='rgb(0, 0, 0)',
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size=4
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),
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showlegend=False
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)
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data.extend([line_trace, line_start, line_end])
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layout = dict(title='{} - Latent Trajectory Movement'.format('Penis'),
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height=800, width=800, margin=dict(l=0, r=0, t=0, b=0))
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# import plotly.io as pio
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# pio.write_image(fig, filename)
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fig = go.Figure(data=data, layout=layout)
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pl.offline.plot(fig, auto_open=True, filename=filename)
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pass
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def plot_latent_trajectories_3D(data_dict, filename='plot'):
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def norm(val, a=0, b=0.25):
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return (val - a) / (b - a)
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bupu = cl.scales['9']['seq']['BuPu']
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scale = cl.interp(bupu, len(data_dict)+1) # Map color scale to N bins
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max_len = max([len(trajectory) for trajectory in data_dict.values()])
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# Fit the mebedding space
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transformer = TSNE()
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for trajectory_id in data_dict:
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transformer.fit(data_dict[trajectory_id])
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# Transform data accordingly and plot it
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data = []
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for trajectory_id in data_dict:
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transformed = transformer._fit(np.asarray(data_dict[trajectory_id]))
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trace = go.Scatter3d(
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x=transformed[:, 0],
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y=transformed[:, 1],
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z=np.arange(transformed.shape[0]),
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text='Hovertext goes here'.format(),
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line=dict(color=scale[trajectory_id]),
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# legendgroup='Position -{}'.format(pos),
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# name='Position -{}'.format(pos),
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showlegend=False,
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# hoverinfo='text',
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mode='lines')
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data.append(trace)
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layout = go.Layout(scene=dict(aspectratio=dict(x=2, y=2, z=1),
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xaxis=dict(tickwidth=1, title='Transformed X'),
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yaxis=dict(tickwidth=1, title='transformed Y'),
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zaxis=dict(tickwidth=1, title='Epoch')),
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title='{} - Latent Trajectory Movement'.format('Penis'),
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width=800, height=800,
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margin=dict(l=0, r=0, b=0, t=0))
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fig = go.Figure(data=data, layout=layout)
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pl.offline.plot(fig, auto_open=True, filename=filename)
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pass
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def plot_histogram(bars_dict_list, filename='histogram_plot'):
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# catagorical
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ryb = cl.scales['10']['div']['RdYlBu']
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data = []
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for bar_id, bars_dict in bars_dict_list:
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hist = go.Histogram(
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histfunc="count",
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y=bars_dict.get('value', 14),
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x=bars_dict.get('name', 'gimme a name'),
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showlegend=False,
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marker=dict(
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color=ryb[bar_id]
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),
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)
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data.append(hist)
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layout=dict(title='{} Histogram Plot'.format('Experiment Name Penis'),
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height=400, width=400, margin=dict(l=0, r=0, t=0, b=0))
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fig = go.Figure(data=data, layout=layout)
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pl.offline.plot(fig, auto_open=True, filename=filename)
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pass
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def line_plot(line_dict_list, filename='lineplot'):
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# lines with standard deviation
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# Transform data accordingly and plot it
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data = []
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rdylgn = cl.scales['10']['div']['RdYlGn']
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rdylgn_background = [scale + (0.4,) for scale in cl.to_numeric(rdylgn)]
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for line_id, line_dict in enumerate(line_dict_list):
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name = line_dict.get('name', 'gimme a name')
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upper_bound = go.Scatter(
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name='Upper Bound',
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x=line_dict['x'],
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y=line_dict['upper_y'],
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mode='lines',
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marker=dict(color="#444"),
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line=dict(width=0),
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fillcolor=rdylgn_background[line_id],
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)
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trace = go.Scatter(
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x=line_dict['x'],
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y=line_dict['main_y'],
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mode='lines',
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name=name,
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line=dict(color=line_id),
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fillcolor=rdylgn_background[line_id],
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fill='tonexty')
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lower_bound = go.Scatter(
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name='Lower Bound',
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x=line_dict['x'],
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y=line_dict['lower_y'],
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marker=dict(color="#444"),
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line=dict(width=0),
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mode='lines')
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data.extend([upper_bound, trace, lower_bound])
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layout=dict(title='{} Line Plot'.format('Experiment Name Penis'),
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height=800, width=800, margin=dict(l=0, r=0, t=0, b=0))
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fig = go.Figure(data=data, layout=layout)
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pl.offline.plot(fig, auto_open=True, filename=filename)
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pass
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if __name__ == '__main__':
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args = build_args()
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in_file = args.in_file[0]
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out_file = args.out_file
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with open(in_file, 'rb') as in_f:
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experiment = dill.load(in_f)
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plot_latent_trajectories_3D(experiment.data_storage)
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print('aha')
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