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: {}<br> 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"])